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22 pages, 1104 KiB  
Review
The Plethora of Microbes with Anti-Inflammatory Activities
Int. J. Mol. Sci. 2024, 25(5), 2980; https://doi.org/10.3390/ijms25052980 (registering DOI) - 04 Mar 2024
Abstract
Inflammation, which has important functions in human defense systems and in maintaining the dynamic homeostasis of the body, has become a major risk factor for the progression of many chronic diseases. Although the applied medical products alleviate the general status, they still exert [...] Read more.
Inflammation, which has important functions in human defense systems and in maintaining the dynamic homeostasis of the body, has become a major risk factor for the progression of many chronic diseases. Although the applied medical products alleviate the general status, they still exert adverse effects in the long term. For this reason, the solution should be sought in more harmless and affordable agents. Microorganisms offer a wide range of active substances with anti-inflammatory properties. They confer important advantages such as their renewable and inexhaustible nature. This review aims to provide the most recent updates on microorganisms of different types and genera, being carriers of anti-inflammatory activity. Full article
(This article belongs to the Special Issue Natural Bioactives and Inflammation)
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17 pages, 1277 KiB  
Article
Analysis of Road Infrastructure and Traffic Factors Influencing Crash Frequency: Insights from Generalised Poisson Models
Infrastructures 2024, 9(3), 47; https://doi.org/10.3390/infrastructures9030047 (registering DOI) - 04 Mar 2024
Abstract
This research utilises statistical modelling to explore the impact of roadway infrastructure elements, primarily those related to cross-section design, on crash occurrences in urban areas. Cross-section design is an important step in the roadway geometric design process as it influences key operational characteristics [...] Read more.
This research utilises statistical modelling to explore the impact of roadway infrastructure elements, primarily those related to cross-section design, on crash occurrences in urban areas. Cross-section design is an important step in the roadway geometric design process as it influences key operational characteristics like capacity, cost, safety, and overall functionality of the transport system entity. Evaluating the influence of cross-section design on these factors is relatively straightforward, except for its impact on safety, especially in urban areas. The safety aspect has resulted in inconsistent findings in the existing literature, indicating a need for further investigation. Negative binomial (NB) models are typically employed for such investigations, given their ability to account for over-dispersion in crash data. However, the low sample mean and under-dispersion occasionally exhibited by crash data can restrict their applicability. The generalised Poisson (GP) models have been proposed as a potential alternative to NB models. This research applies GP models for developing crash prediction models for urban road segments. Simultaneously, NB models are also developed to enable a comparative assessment between the two modelling frameworks. A six-year dataset encompassing crash counts, traffic volume, and cross-section design data reveals a significant association between crash frequency and infrastructure design variables. Specifically, lane width, number of lanes, road separation, on-street parking, and posted speed limit are significant predictors of crash frequencies. Comparative analysis with NB models shows that GP models outperform in cases of low sample mean crash types and yield similar results for others. Overall, this study provides valuable insights into the relationship between road infrastructure design and crash frequency in urban environments and offers a statistical approach for predicting crash frequency that maintains a balance between interpretability and predictive power, making it more viable for practitioners and road authorities to apply in real-world road safety scenarios. Full article
(This article belongs to the Special Issue Road Systems and Engineering)
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27 pages, 83136 KiB  
Article
Frequency-Dependent Contrast Enhancement for Conductive and Non-Conductive Materials in Electrical Impedance Tomography
Appl. Sci. 2024, 14(5), 2141; https://doi.org/10.3390/app14052141 (registering DOI) - 04 Mar 2024
Abstract
This research investigates the critical role of frequency selection in Electrical Impedance Tomography (EIT), a non-invasive imaging technique that reconstructs internal conductivity distributions through injected electrical currents. Empirical frequency selection is paramount to maximizing the fidelity and specificity of EIT images. The study [...] Read more.
This research investigates the critical role of frequency selection in Electrical Impedance Tomography (EIT), a non-invasive imaging technique that reconstructs internal conductivity distributions through injected electrical currents. Empirical frequency selection is paramount to maximizing the fidelity and specificity of EIT images. The study explores the impact of distinct frequency ranges—low, medium, and high—on image contrast and clarity, particularly focusing on differentiating conductive materials from non-conductive materials. The findings reveal distinct empirical frequency bands for enhancing the respective contrasts: 15–38 kHz for conductive materials (copper) and 45–75 kHz for non-conductive materials (acrylic resin). These insights shed light on the frequency-dependent nature of material contrast in EIT images, guiding the selection of empirical operating ranges for various target materials. This research paves the way for improved sensitivity and broader applicability of EIT in diverse areas. Full article
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3 pages, 185 KiB  
Editorial
Non-Covalent Interactions in Coordination Chemistry
Inorganics 2024, 12(3), 79; https://doi.org/10.3390/inorganics12030079 (registering DOI) - 04 Mar 2024
Abstract
Non-covalent interactions [...] Full article
(This article belongs to the Special Issue Non-covalent Interactions in Coordination Chemistry)
23 pages, 6168 KiB  
Article
Analyzing the Mechanical and Durability Characteristics of Steel Slag-Infused Asphalt Concrete in Roadway Construction
Buildings 2024, 14(3), 679; https://doi.org/10.3390/buildings14030679 (registering DOI) - 04 Mar 2024
Abstract
Steel slag is a solid byproduct of the steelmaking process, widely generated in the metallurgical industry. Due to its alkaline nature and excellent adhesive properties with asphalt, it represents a potential road construction material with outstanding road performance, making it well-suited for utilization [...] Read more.
Steel slag is a solid byproduct of the steelmaking process, widely generated in the metallurgical industry. Due to its alkaline nature and excellent adhesive properties with asphalt, it represents a potential road construction material with outstanding road performance, making it well-suited for utilization in highway construction. This paper conducts a systematic analysis of the physical and chemical properties of steel slag, specifically South Steel Electric Furnace slag, and compares it with natural basalt and limestone aggregates. The aim is to establish a foundation for the application of steel slag in asphalt mixtures. Building upon this foundation, we carry out proportioning design for AC-13C and SMA-13 steel slag asphalt mixtures, followed by a comprehensive study of their high-temperature stability, low-temperature stability, water stability, and fatigue performance. Our research reveals variations in the chemical composition of different steel slags, with CaO, SiO2, and Fe2O3 being the primary components. The content of harmful elements varies depending on the steelmaking raw materials and additives used. Notably, the optimum asphalt-to-aggregate ratios for AC-13C and SMA-13 significantly surpass the specified requirements. The freeze–thaw splitting strength ratio and residual stability of steel slag AC-13C and SMA-13 asphalt mixtures exceed the specified requirements, with AC-13C demonstrating the highest water stability, boasting a freeze–thaw splitting strength ratio of 94.07%, and a residual stability of 93.8%. In terms of fatigue characteristics, SMA-13 exhibits a longer fatigue life than AC-13C, indicating superior fatigue performance for steel slag SMA-13. Steel slag enhances the abrasion resistance and rutting resistance of asphalt pavement surface layers, fully meeting the performance requirements for high-grade road surface layers. Full article
(This article belongs to the Special Issue Innovation in Pavement Materials: 2nd Edition)
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16 pages, 2747 KiB  
Review
Aging and Adiposity—Focus on Biological Females at Midlife and Beyond
Int. J. Mol. Sci. 2024, 25(5), 2972; https://doi.org/10.3390/ijms25052972 (registering DOI) - 04 Mar 2024
Abstract
Menopause is a physiological phase of life of aging women, and more than 1 billion women worldwide will be in menopause by 2025. The processes of global senescence parallel stages of reproductive aging and occur alongside aging-related changes in the body. Alterations in [...] Read more.
Menopause is a physiological phase of life of aging women, and more than 1 billion women worldwide will be in menopause by 2025. The processes of global senescence parallel stages of reproductive aging and occur alongside aging-related changes in the body. Alterations in the endocrine pathways accompany and often predate the physiologic changes of aging, and interactions of these processes are increasingly being recognized as contributory to the progression of senescence. Our goal for this review is to examine, in aging women, the complex interplay between the endocrinology of menopause transition and post-menopause, and the metabolic transition, the hallmark being an increasing tendency towards central adiposity that begins in tandem with reproductive aging and is often exacerbated post menopause. For the purpose of this review, our choice of the terms ‘female’ and ‘woman’ refer to genetic females. Full article
(This article belongs to the Section Molecular Biology)
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19 pages, 3213 KiB  
Article
An Empirical Evaluation of a Novel Ensemble Deep Neural Network Model and Explainable AI for Accurate Segmentation and Classification of Ovarian Tumors Using CT Images
Diagnostics 2024, 14(5), 543; https://doi.org/10.3390/diagnostics14050543 (registering DOI) - 04 Mar 2024
Abstract
Ovarian cancer is one of the leading causes of death worldwide among the female population. Early diagnosis is crucial for patient treatment. In this work, our main objective is to accurately detect and classify ovarian cancer. To achieve this, two datasets are considered: [...] Read more.
Ovarian cancer is one of the leading causes of death worldwide among the female population. Early diagnosis is crucial for patient treatment. In this work, our main objective is to accurately detect and classify ovarian cancer. To achieve this, two datasets are considered: CT scan images of patients with cancer and those without, and biomarker (clinical parameters) data from all patients. We propose an ensemble deep neural network model and an ensemble machine learning model for the automatic binary classification of ovarian CT scan images and biomarker data. The proposed model incorporates four convolutional neural network models: VGG16, ResNet 152, Inception V3, and DenseNet 101, with transformers applied for feature extraction. These extracted features are fed into our proposed ensemble multi-layer perceptron model for classification. Preprocessing and CNN tuning techniques such as hyperparameter optimization, data augmentation, and fine-tuning are utilized during model training. Our ensemble model outperforms single classifiers and machine learning algorithms, achieving a mean accuracy of 98.96%, a precision of 97.44%, and an F1-score of 98.7%. We compared these results with those obtained using features extracted by the UNet model, followed by classification with our ensemble model. The transformer demonstrated superior performance in feature extraction over the UNet, with a mean Dice score and mean Jaccard score of 0.98 and 0.97, respectively, and standard deviations of 0.04 and 0.06 for benign tumors and 0.99 and 0.98 with standard deviations of 0.01 for malignant tumors. For the biomarker data, the combination of five machine learning models—KNN, logistic regression, SVM, decision tree, and random forest—resulted in an improved accuracy of 92.8% compared to single classifiers. Full article
(This article belongs to the Special Issue Generative AI and Deep Learning in Medical Diagnostics)
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12 pages, 3618 KiB  
Article
Optimization of a Circular Planar Spiral Wireless Power Transfer Coil Using a Genetic Algorithm
Electronics 2024, 13(5), 978; https://doi.org/10.3390/electronics13050978 (registering DOI) - 04 Mar 2024
Abstract
Circular planar spiral coils are the most important parts of wireless power transfer systems. This paper presents the optimization of wireless power transfer coils used for wireless power transfer, which is a problem when designing wireless power transfer systems. A single transmitter coil [...] Read more.
Circular planar spiral coils are the most important parts of wireless power transfer systems. This paper presents the optimization of wireless power transfer coils used for wireless power transfer, which is a problem when designing wireless power transfer systems. A single transmitter coil transfers power to a single receiving side. The performance of the wireless power transfer system depends greatly on the size and shape of the wireless power transfer system. Therefore, the optimization of the coils is of the utmost importance. The main optimization parameter was the coupling coefficient between the transmitter and the receiver coil in the horizontally aligned and misaligned position. A genetic evolutionary algorithm was used to optimize the coil, according to the developed cost function. The algorithm was implemented using the MATLAB programming language. The constraints regarding the design of the coils are also presented for the problem to be analyzed correctly. The results obtained using the genetic algorithm were first verified using FEM simulations. The optimized coils were later fabricated and measured to confirm the theory. Full article
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12 pages, 248 KiB  
Article
The Impacts of Background Music on the Effects of Loving-Kindness Meditation on Positive Emotions
Behav. Sci. 2024, 14(3), 204; https://doi.org/10.3390/bs14030204 (registering DOI) - 04 Mar 2024
Abstract
Loving-kindness meditation (LKM) has been widely used in promoting mental health, with positive emotions as an important mechanism. The current study explored the impact of background music on the effects and difficulties of LKM practice. Two hundred participants were randomly divided into six [...] Read more.
Loving-kindness meditation (LKM) has been widely used in promoting mental health, with positive emotions as an important mechanism. The current study explored the impact of background music on the effects and difficulties of LKM practice. Two hundred participants were randomly divided into six groups, wherein LKM plus music with harmony only, LKM plus music with harmony and melody, and LKM without music were presented in a different order during the intermediate three days of a five-day LKM intervention. Participants reported three types of positive emotions (pro-social, low-arousal, and medium-arousal positive emotions) and the difficulties during meditation (lack of concentration and lack of pro-social attitudes) after each of three sessions. The results of MANOVA indicated that compared to the session without music, incorporating music could evoke more low-arousal positive emotions and pro-social positive emotions without altering the difficulties. However, the results did not reveal significant differences in the effects of music with harmony and music with harmony and melody on both emotions and difficulties. Additionally, practice effects may have influenced the generation of medium-arousal positive emotions and the difficulty of concentration, but the results were inconsistent across groups. Our findings suggest potential benefits for practitioners of LKM in incorporating music during the meditation process, and the directions for future research were further discussed. Full article
11 pages, 704 KiB  
Article
Effects of Myofascial Release Technique along with Cognitive Behavior Therapy in University Students with Chronic Neck Pain and Forward Head Posture: A Randomized Clinical Trial
Behav. Sci. 2024, 14(3), 205; https://doi.org/10.3390/bs14030205 (registering DOI) - 04 Mar 2024
Abstract
The purpose of this randomized controlled trial was to evaluate the effectiveness of the Myofascial Release Technique (MRT) along with Cognitive Behavioral Therapy (CBT) on pain, craniovertebral angle (CVA), and neck disability in university students with chronic neck pain and forward head posture. [...] Read more.
The purpose of this randomized controlled trial was to evaluate the effectiveness of the Myofascial Release Technique (MRT) along with Cognitive Behavioral Therapy (CBT) on pain, craniovertebral angle (CVA), and neck disability in university students with chronic neck pain and forward head posture. A total of sixty-six eligible participants with chronic neck pain and forward head posture were randomized into the Myofascial Release Therapy (MRT) group (n = 33) and MRT and Cognitive Behavior Therapy (CBT) group (n = 33). Clinical outcomes included neck pain measured using the numerical pain rating scale, neck disability measured through the neck disability index, and forward head posture measured through the cranial vertebral angle. The outcomes were assessed at baseline and the four and eight weeks after the intervention. Both groups showed significant improvement in pain intensity, CVA, and neck disability after the intervention. However, the CBT group demonstrated greater improvements than the MRT group. The difference in outcomes between the groups was statistically significant. Myofascial Release Therapy combined with CBT is an effective treatment method for patients with chronic neck pain and forward head posture. Full article
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17 pages, 4706 KiB  
Article
Improvement of Electrical Conductivity of In Situ Iodine-Doped Polypyrrole Film Using Atmospheric Pressure Plasma Reactor with Capillary Electrodes
Nanomaterials 2024, 14(5), 468; https://doi.org/10.3390/nano14050468 (registering DOI) - 04 Mar 2024
Abstract
To improve the electrical conductivity of polypyrrole (PPy) nanostructure film through in situ iodine (I2) doping, this study proposes an atmospheric pressure plasma reactor (APPR) where heated I2 dopant vapor is fed through capillary electrodes that serve as electrodes for [...] Read more.
To improve the electrical conductivity of polypyrrole (PPy) nanostructure film through in situ iodine (I2) doping, this study proposes an atmospheric pressure plasma reactor (APPR) where heated I2 dopant vapor is fed through capillary electrodes that serve as electrodes for discharge ignition. A large amount of the heated I2 vapor introduced into the reactor separately from a monomer gas can be effectively activated by an intense plasma via capillary electrodes. In particular, intensive plasma is obtained by properly adjusting the bluff body position in the APPR. Based on the ICCD and OES results, the I2 vapor injected through the capillary nozzle electrode is observed to form I2 charge species. The formed I2 species could directly participate in growing in situ I2-doped PPy films. Thus, in situ I2-doped PPy nanostructure films grown using the proposed APPR exhibit higher thicknesses of 15.3 μm and good electrical conductivities, compared to the corresponding non-doped films. Full article
(This article belongs to the Special Issue Synthesis of Nanostructures in Gas-Discharge Plasma)
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16 pages, 7857 KiB  
Article
From Sparse to Dense Representations in Open Channel Flow Images with Convolutional Neural Networks
Inventions 2024, 9(2), 27; https://doi.org/10.3390/inventions9020027 (registering DOI) - 04 Mar 2024
Abstract
Convolutional neural networks (CNN) have been widely adopted in fluid dynamics investigations over the past few years due to their ability to extract and process fluid flow field characteristics. Both in sparse-grid simulations and sensor-based experimental data, the establishment of a dense flow [...] Read more.
Convolutional neural networks (CNN) have been widely adopted in fluid dynamics investigations over the past few years due to their ability to extract and process fluid flow field characteristics. Both in sparse-grid simulations and sensor-based experimental data, the establishment of a dense flow field that embeds all spatial and temporal flow information is an open question, especially in the case of turbulent flows. In this paper, a deep learning (DL) method based on computational CNN layers is presented, focusing on reconstructing turbulent open channel flow fields of various resolutions. Starting from couples of images with low/high resolution, we train our DL model to efficiently reconstruct the velocity field of consecutive low-resolution data, which comes from a sparse-grid Direct Numerical Simulation (DNS), and focus on obtaining the accuracy of a respective dense-grid DNS. The reconstruction is assessed on the peak signal-to-noise ratio (PSNR), which is found to be high even in cases where the ground truth input is scaled down to 25 times. Full article
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12 pages, 1630 KiB  
Article
Impact of Pre-Liver Transplant Treatments on the Imaging Accuracy of HCC Staging and Their Influence on Outcomes
Cancers 2024, 16(5), 1043; https://doi.org/10.3390/cancers16051043 (registering DOI) - 04 Mar 2024
Abstract
The outcome of liver transplantation (LT) for hepatocarcinoma (HCC) is strongly influenced by HCC staging, which is based on radiological examinations in a pre-LT setting; concordance between pre-LT radiological and definitive pathological staging remains controversial. To address this issue, we retrospectively analyzed our [...] Read more.
The outcome of liver transplantation (LT) for hepatocarcinoma (HCC) is strongly influenced by HCC staging, which is based on radiological examinations in a pre-LT setting; concordance between pre-LT radiological and definitive pathological staging remains controversial. To address this issue, we retrospectively analyzed our LT series to assess concordance between radiology and pathology and to explore the factors associated with poor concordance and outcomes. We included all LTs with an HCC diagnosis performed between 2013 and 2018. Concordance (Co group) was defined as a comparable tumor burden in preoperative imaging and post-transplant pathology; otherwise, non-concordance was diagnosed (nCo group). Concordance between radiology and pathology was observed in 32/134 patients (Co group, 24%). The number and diameter of the nodules were higher when nCo was diagnosed, as was the number of pre-LT treatments. Although concordance did not affect survival, more than three pre-LT treatments led to a lower disease-free survival. Patients who met the Milan Criteria (Milan-in patients) were more likely to receive ≥three prior treatments, leading to a lower survival in multi-treated Milan-in patients than in other Milan-in patients. In conclusion, the concordance rate between the pre-LT imaging and histopathological results was low in patients with a high number of nodules. Multiple bridging therapies reduce the accuracy of pre-LT imaging in predicting HCC stages and negatively affect outcomes after LT. Full article
(This article belongs to the Special Issue New Insights on Therapy in Hepatocellular Carcinoma)
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21 pages, 5992 KiB  
Article
Identification and Analysis of the MIR399 Gene Family in Grapevine Reveal Their Potential Functions in Abiotic Stress
Int. J. Mol. Sci. 2024, 25(5), 2979; https://doi.org/10.3390/ijms25052979 (registering DOI) - 04 Mar 2024
Abstract
MiR399 plays an important role in plant growth and development. The objective of the present study was to elucidate the evolutionary characteristics of the MIR399 gene family in grapevine and investigate its role in stress response. To comprehensively investigate the functions of miR399 [...] Read more.
MiR399 plays an important role in plant growth and development. The objective of the present study was to elucidate the evolutionary characteristics of the MIR399 gene family in grapevine and investigate its role in stress response. To comprehensively investigate the functions of miR399 in grapevine, nine members of the Vvi-MIR399 family were identified based on the genome, using a miRBase database search, located on four chromosomes (Chr 2, Chr 10, Chr 15, and Chr 16). The lengths of the Vvi-miR399 precursor sequences ranged from 82 to 122 nt and they formed stable stem–loop structures, indicating that they could produce microRNAs (miRNAs). Furthermore, our results suggested that the 2 to 20 nt region of miR399 mature sequences were relatively conserved among family members. Phylogenetic analysis revealed that the Vvi-MIR399 members of dicots (Arabidopsis, tomato, and sweet orange) and monocots (rice and grapevine) could be divided into three clades, and most of the Vvi-MIR399s were closely related to sweet orange in dicots. Promoter analysis of Vvi-MIR399s showed that the majority of the predicted cis-elements were related to stress response. A total of 66.7% (6/9) of the Vvi-MIR399 promoters harbored drought, GA, and SA response elements, and 44.4% (4/9) of the Vvi-MIRR399 promoters also presented elements involved in ABA and MeJA response. The expression trend of Vvi-MIR399s was consistent in different tissues, with the lowest expression level in mature and young fruits and the highest expression level in stems and young leaves. However, nine Vvi-MIR399s and four target genes showed different expression patterns when exposed to low light, high light, heat, cold, drought, and salt stress. Interestingly, a putative target of Vvi-MIR399 targeted multiple genes; for example, seven Vvi-MIR399s simultaneously targeted VIT_213s0067g03280.1. Furthermore, overexpression of Vvi_MIR399e and Vvi_MIR399f in Arabidopsis enhanced tolerance to drought compared with wild-type (WT). In contrast, the survival rate of Vvi_MIR399d-overexpressed plants were zero after drought stress. In conclusion, Vvi-MIR399e and Vvi-MIR399f, which are related to drought tolerance in grapevine, provide candidate genes for future drought resistance breeding. Full article
(This article belongs to the Special Issue The Role of Non-coding RNA in Plant Response to Stress)
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5 pages, 171 KiB  
Editorial
Soft Tissue Sarcomas: Treatment and Management
Cancers 2024, 16(5), 1042; https://doi.org/10.3390/cancers16051042 (registering DOI) - 04 Mar 2024
Abstract
Due to the rarity and heterogeneity of soft tissue sarcoma (STS), investigating new treatments for this condition has been challenging [...] Full article
(This article belongs to the Topic Soft Tissue Sarcomas: Treatment and Management)
16 pages, 3898 KiB  
Article
2-DOF Woven Tube Plane Surface Soft Actuator Using Extensional Pneumatic Artificial Muscle
by and
Hardware 2024, 2(1), 50-65; https://doi.org/10.3390/hardware2010003 (registering DOI) - 04 Mar 2024
Abstract
Soft actuators, designed for fragile item conveyance and navigation in complex environments, have garnered recent attention. This study proposes a cost-effective soft actuator, created by weaving tubes into twill patterns, capable of transportation and movement. The actuator achieves this by inducing traveling waves [...] Read more.
Soft actuators, designed for fragile item conveyance and navigation in complex environments, have garnered recent attention. This study proposes a cost-effective soft actuator, created by weaving tubes into twill patterns, capable of transportation and movement. The actuator achieves this by inducing traveling waves on its upper and lower surfaces through sequential pressurization of tubes. Notably, its fabrication does not require specialized molds, contributing to cost efficiency. The single actuator generates traveling waves with two degrees of freedom. Conventional silicone tube-based actuators demonstrate slow transport speeds (3.5 mm/s). To address this, this study replaced silicone tubes with pneumatic artificial muscles, enhancing overall body deformation and actuator speed. Experiments involving both extensional and contractional artificial muscles demonstrated that soft actuators with extensional artificial muscles significantly improved transportation and movement speed to 8.0 mm/s. Full article
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19 pages, 6240 KiB  
Article
Genome-Wide Identification and Expression Analysis of Salt-Tolerance-Associated NAC Family Genes in Cyclocarya paliurus
Forests 2024, 15(3), 479; https://doi.org/10.3390/f15030479 (registering DOI) - 04 Mar 2024
Abstract
Soil salinity affects approximately 20% of the world’s arable land, presenting a significant challenge for studying the mechanisms by which plants adapt to saline environments. Cyclocarya paliurus, an invaluable research model due to its ecological and medicinal significance, is primarily concentrated in [...] Read more.
Soil salinity affects approximately 20% of the world’s arable land, presenting a significant challenge for studying the mechanisms by which plants adapt to saline environments. Cyclocarya paliurus, an invaluable research model due to its ecological and medicinal significance, is primarily concentrated in central and southern China. Nevertheless, Cyclocarya paliurus faces challenges from environmental factors such as soil salinization, which adversely impacts its growth, subsequently affecting the yield and quality of its bioactive compounds. The NAC gene family, a critical group of plant-specific transcription factors, plays pivotal roles in responding to abiotic stresses. However, there has not yet been any studies on NAC genes under salt stress in Cyclocarya paliurus. In this study, we identified 132 NAC genes within the Cyclocarya paliurus genome. Our analysis of the conserved structures and gene organization revealed a high degree of conservation in the proteins of the CpNAC gene family. Cis-element analysis unveiled the participation of these genes in a variety of biological processes, including light responses, phytohormone responses, cell cycle responses, and abiotic stress responses. Under salt stress conditions, the expression of 35 CpNAC genes changed significantly, indicating a response to salt treatment. Furthermore, we provided additional evidence for the identification of the NAC gene family and revealed their potential positive regulatory role in signal transduction by conducting a transcriptional activation activity analysis of CpNAC132(D) and CpNAC040, which are homologous to Arabidopsis thaliana NAC062/91 and NAC103, respectively. This research not only advances our comprehension of the salt stress adaptation in Cyclocarya paliurus but also provides robust support for future investigations into plant responses to environmental stress and the cultivation of salt-tolerant crops. Full article
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20 pages, 6919 KiB  
Article
Short-Term Energy Consumption Prediction of Large Public Buildings Combined with Data Feature Engineering and Bilstm-Attention
Appl. Sci. 2024, 14(5), 2137; https://doi.org/10.3390/app14052137 (registering DOI) - 04 Mar 2024
Abstract
Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large public buildings often pose challenges in improving prediction [...] Read more.
Accurate building energy consumption prediction is a crucial condition for the sustainable development of building energy management systems. However, the highly nonlinear nature of data and complex influencing factors in the energy consumption of large public buildings often pose challenges in improving prediction accuracy. In this study, we propose a combined prediction model that combines signal decomposition, feature screening, and deep learning. First, we employ the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose energy consumption data. Next, we propose the Maximum Mutual Information Coefficient (MIC)-Fast Correlation Based Filter (FCBF) combined feature screening method for feature selection on the decomposed components. Finally, the selected input features and corresponding components are fed into the Bi-directional Long Short-Term Memory Attention Mechanism (BiLSTMAM) model for prediction, and the aggregated results yield the energy consumption forecast. The proposed approach is validated using energy consumption data from a large public building in Shaanxi Province, China. Compared with the other five comparison methods, the RMSE reduction of the CEEMDAN-MIC-FCBF-BiLSTMAM model proposed in this study ranged from 57.23% to 82.49%. Experimental results demonstrate that the combination of CEEMDAN, MIC-FCBF, and BiLSTMAM modeling markedly improves the accuracy of energy consumption predictions in buildings, offering a potent method for optimizing energy management and promoting sustainability in large-scale facilities. Full article
(This article belongs to the Section Computing and Artificial Intelligence)
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23 pages, 583 KiB  
Review
Prevalence of Emergent Dolutegravir Resistance Mutations in People Living with HIV: A Rapid Scoping Review
Viruses 2024, 16(3), 399; https://doi.org/10.3390/v16030399 (registering DOI) - 04 Mar 2024
Abstract
Background: Dolutegravir (DTG) is a cornerstone of global antiretroviral (ARV) therapy (ART) due to its high efficacy and favorable tolerability. However, limited data exist regarding the risk of emergent integrase strand transfer inhibitor (INSTI) drug-resistance mutations (DRMs) in individuals receiving DTG-containing ART. Methods: [...] Read more.
Background: Dolutegravir (DTG) is a cornerstone of global antiretroviral (ARV) therapy (ART) due to its high efficacy and favorable tolerability. However, limited data exist regarding the risk of emergent integrase strand transfer inhibitor (INSTI) drug-resistance mutations (DRMs) in individuals receiving DTG-containing ART. Methods: We performed a PubMed search using the term “Dolutegravir”, last updated 18 December 2023, to estimate the prevalence of VF with emergent INSTI DRMs in people living with HIV (PLWH) without previous VF on an INSTI who received DTG-containing ART. Results: Of 2131 retrieved records, 43 clinical trials, 39 cohorts, and 6 cross-sectional studies provided data across 6 clinical scenarios based on ART history, virological status, and co-administered ARVs: (1) ART-naïve PLWH receiving DTG plus two NRTIs; (2) ART-naïve PLWH receiving DTG plus lamivudine; (3) ART-experienced PLWH with VF on a previous regimen receiving DTG plus two NRTIs; (4) ART-experienced PLWH with virological suppression receiving DTG plus two NRTIs; (5) ART-experienced PLWH with virological suppression receiving DTG and a second ARV; and (6) ART-experienced PLWH with virological suppression receiving DTG monotherapy. The median proportion of PLWH in clinical trials with emergent INSTI DRMs was 1.5% for scenario 3 and 3.4% for scenario 6. In the remaining four trial scenarios, VF prevalence with emergent INSTI DRMs was ≤0.1%. Data from cohort studies minimally influenced prevalence estimates from clinical trials, whereas cross-sectional studies yielded prevalence data lacking denominator details. Conclusions: In clinical trials, the prevalence of VF with emergent INSTI DRMs in PLWH receiving DTG-containing regimens has been low. Novel approaches are required to assess VF prevalence with emergent INSTI DRMs in PLWH receiving DTG in real-world settings. Full article
(This article belongs to the Section Human Virology and Viral Diseases)
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42 pages, 9567 KiB  
Article
An Interdisciplinary Approach to Enhancing Cyber Threat Prediction Utilizing Forensic Cyberpsychology and Digital Forensics
Forensic Sci. 2024, 4(1), 110-151; https://doi.org/10.3390/forensicsci4010008 (registering DOI) - 04 Mar 2024
Abstract
The Cyber Forensics Behavioral Analysis (CFBA) model merges Cyber Behavioral Sciences and Digital Forensics to improve the prediction and effectiveness of cyber threats from Autonomous System Numbers (ASNs). Traditional cybersecurity strategies, focused mainly on technical aspects, must be revised for the complex cyber [...] Read more.
The Cyber Forensics Behavioral Analysis (CFBA) model merges Cyber Behavioral Sciences and Digital Forensics to improve the prediction and effectiveness of cyber threats from Autonomous System Numbers (ASNs). Traditional cybersecurity strategies, focused mainly on technical aspects, must be revised for the complex cyber threat landscape. This research proposes an approach combining technical expertise with cybercriminal behavior insights. The study utilizes a mixed-methods approach and integrates various disciplines, including digital forensics, cybersecurity, computer science, and forensic psychology. Central to the model are four key concepts: forensic cyberpsychology, digital forensics, predictive modeling, and the Cyber Behavioral Analysis Metric (CBAM) and Score (CBS) for evaluating ASNs. The CFBA model addresses initial challenges in traditional cyber defense methods and emphasizes the need for an interdisciplinary, comprehensive approach. This research offers practical tools and frameworks for accurately predicting cyber threats, advocating for ongoing collaboration in the ever-evolving field of cybersecurity. Full article
(This article belongs to the Special Issue Human and Technical Drivers of Cybercrime)
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21 pages, 6834 KiB  
Article
The Numerical Analysis of Textile Reinforced Concrete Shells: Basic Principles
Appl. Sci. 2024, 14(5), 2140; https://doi.org/10.3390/app14052140 (registering DOI) - 04 Mar 2024
Abstract
In the case of solid slabs made from reinforced concrete that are usually subjected to bending, large areas of the structure are stressed well below their load-bearing capacity or remain stress-free. Contrary to this are shell structures, which can bridge large spans with [...] Read more.
In the case of solid slabs made from reinforced concrete that are usually subjected to bending, large areas of the structure are stressed well below their load-bearing capacity or remain stress-free. Contrary to this are shell structures, which can bridge large spans with little material if designed well. To improve the efficiency of ceiling slabs, we want to utilize the shell load-bearing behaviour on a smaller scale by dissolving the solid interior accordingly. In order to be able to study a wide range of such constructions virtually, a parametric multi-objective simulation environment is to be developed in an ongoing research project, the basic analysis approaches of which are presented in this paper. In addition to the basic workflow and the programs used, the material models for TRC material compared and their calibration are described on the basis of tests on textile reinforced concrete (TRC) samples. Various material models were implemented within the commercially available software RFEM (Version 5.19). Laboratory tests on two different geometry solutions of TRC structures served to verify the models. The structures were selected in a way that differentiates between the bending and membrane actions to indicate the application fields for various approaches in the numerical modelling of TRC structures. Full article
(This article belongs to the Section Civil Engineering)
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12 pages, 1099 KiB  
Article
A Cross-Sectional Study on the Role of a Lab Test Screening Program in Defining Cardiovascular Disease Risk Prevalence
J. Pers. Med. 2024, 14(3), 284; https://doi.org/10.3390/jpm14030284 (registering DOI) - 04 Mar 2024
Abstract
Recent epidemiologic studies carried out in Romania confirmed an ascending trend for cardiovascular disease (CVD) risk factor prevalence such as diabetes mellitus (DM), obesity and dyslipidemia. The aim of this study is to describe the CVD risk factor profile and preventative behavior in [...] Read more.
Recent epidemiologic studies carried out in Romania confirmed an ascending trend for cardiovascular disease (CVD) risk factor prevalence such as diabetes mellitus (DM), obesity and dyslipidemia. The aim of this study is to describe the CVD risk factor profile and preventative behavior in a representative sample of the general adult population of an Eastern Romanian urban area. More than 70% of the studied population had a body mass index (BMI) above the normal range for their age, with 36.7% of the subjects residing in obesity and severe obesity clusters. For overweight and obese subjects, the number of comorbidities (CVD, arterial hypertension and DM type 2) was higher than in the population with normal weight (44% vs. 31%, 22% vs. 14% and 18% vs. 10%, respectively). The prevalence of high blood pressure was almost double that reported in previous Romanian studies (69.3% vs. 36.6%) and higher than expected, based on self-reported known CVD diagnoses (37.5%). There was a visible difference between the results obtained for quantifiable CVD risk factors and self-reported lifestyle ones. Routine blood test monitoring may be an easy and inexpensive tool to guide educational and medical interventions to address modifiable CV risk factors in the adult population in order to prevent the fatal consequences of cardiovascular disease. Full article
(This article belongs to the Section Epidemiology)
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33 pages, 5115 KiB  
Article
The Regime-Switching Structural Default Risk Model
Risks 2024, 12(3), 48; https://doi.org/10.3390/risks12030048 (registering DOI) - 04 Mar 2024
Abstract
We develop the regime-switching default risk (RSDR) model as a generalization of Merton’s default risk (MDR) model. The RSDR model supports an expanded range of asset probability density functions. First, we show using simulation that the RSDR model incorporates [...] Read more.
We develop the regime-switching default risk (RSDR) model as a generalization of Merton’s default risk (MDR) model. The RSDR model supports an expanded range of asset probability density functions. First, we show using simulation that the RSDR model incorporates sudden changes in asset values faster than the MDR model. Second, we empirically implement the RSDR, MDR and an extension of the MDR model with changes in drift parameters, using maximum likelihood estimation. Focusing on the period before and after corporate rating downgrades used primarily for investment advice, we find that the RSDR model uses changes in equity mean returns and volatility to produce higher estimated default probabilities, faster, than both benchmark models. Full article
(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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