Acoustic detection of machinery defaults from in-duct measurements is of practical importance in many areas, such as the health assessment of turbines in ventilation systems or engine testing in the surface and air transport sectors. This approach is, however, impeded by the low signal-to-noise ratio (SNR) observed in such environments. In this study, it is proposed to exploit the slow sound effect of Sonic Black Hole (SBH) ducted silencers to enhance the sensing of incident pulse acoustic signals with low SNR. It is found from transfer matrix and finite element modelling that fully opened SBH silencers with perforated skin interfaces are able to substantially enhance an incident pulse amplitude while channeling an air flow. We demonstrate that the graded depths of the SBH cavities provide rainbow spectral decomposition and amplification of the incident pulse frequency components, provided that impedance matching, slow sound, and critically coupled conditions are met. In-duct experiments showed the ability of a 3D printed SBH silencer to simultaneously enhance acoustic sensing and fully trap the pulse spectral components in the SBH cavities in the presence of a low-speed flow. This study opens up new avenues for the development of dual-purpose silencers designed for acoustic monitoring and noise control in duct systems without obstructing the air flow. Full article

Thus, this review aims to deepen the understanding of buffalo farming in the Amazon, presenting the quality and nutritional value of buffalo meat and liver. This information serves as a subsidy to improve practices related to the breeding system, nutrition, health and sustainability associated with aquatic buffaloes. For this, a review of the databases was carried out using the descriptors “nutritional value of buffalo meat”, “nutritional value of buffalo liver” and “buffalo breeding in the Amazon”. Thus, the consumption of foods derived from aquatic buffaloes has important nutritional value for human consumption. In view of this, it is possible to conclude that the nutrition of these animals is influenced by the biodiversity of the Amazon, giving unique characteristics to its products, also highlighting the importance of carrying out research that aims to value the potential use of this species and strengthen the economy of the region. Full article

Severe fever with thrombocytopenia syndrome (SFTS) is a potentially fatal tick-borne zoonosis caused by SFTS virus (SFTSV). In addition to tick bites, animal-to-human transmission of SFTSV has been reported, but little is known about feline SFTSV infection. In this study, we analyzed data on 187 cats with suspected SFTS to identify biomarkers for SFTS diagnosis and clinical outcome. Body weight, red and white blood cell and platelet counts, and serum aspartate aminotransferase and total bilirubin levels were useful for SFTS diagnosis, whereas alanine aminotransferase, aspartate aminotransferase and serum SFTSV RNA levels were associated with clinical outcome. We developed a scoring model to predict SFTSV infection. In addition, we performed a phylogenetic analysis to reveal the relationship between disease severity and viral strain. This study provides comprehensive information on feline SFTS and could contribute to the protection of cat owners, community members, and veterinarians from the risk of cat-transmitted SFTSV infection. Full article

The recent advancements in autonomous driving come with the associated cybersecurity issue of compromising networks of autonomous vehicles (AVs), motivating the use of AI models for detecting anomalies on these networks. In this context, the usage of explainable AI (XAI) for explaining the behavior of these anomaly detection AI models is crucial. This work introduces a comprehensive framework to assess black-box XAI techniques for anomaly detection within AVs, facilitating the examination of both global and local XAI methods to elucidate the decisions made by XAI techniques that explain the behavior of AI models classifying anomalous AV behavior. By considering six evaluation metrics (descriptive accuracy, sparsity, stability, efficiency, robustness, and completeness), the framework evaluates two well-known black-box XAI techniques, SHAP and LIME, involving applying XAI techniques to identify primary features crucial for anomaly classification, followed by extensive experiments assessing SHAP and LIME across the six metrics using two prevalent autonomous driving datasets, VeReMi and Sensor. This study advances the deployment of black-box XAI methods for real-world anomaly detection in autonomous driving systems, contributing valuable insights into the strengths and limitations of current black-box XAI methods within this critical domain. Full article

Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from the CoSpectroCam sensor (CSC, licensed to AgriWatch BV, Enschede, The Netherlands) mounted on an unmanned aerial vehicle (UAV) in Iran. The model included nine bands from Landsat-8/9, 11 bands from Sentinel-2, and 1252 bands from the CSC (covering the wavelength range between 420 and 850 nm). The relative feature importance and band sensitivity to SM variations were analyzed. In addition, four indices, including the perpendicular index (PI), ratio index (RI), difference index (DI), and normalized difference index (NDI) were calculated from the different bands of the datasets, and their sensitivity to SM was evaluated. The results showed that the PI exhibited the highest sensitivity to SM changes in all datasets among the four indices considered. Comparisons of the performance of the datasets in SM estimation emphasized the superior performance of the UAV hyperspectral data (R² = 0.87), while the Sentinel-2 and Landsat-8/9 data showed lower accuracy (R² = 0.49 and 0.66, respectively). The robust performance of the CSC data is likely due to its superior spatial and spectral resolution as well as the application of preprocessing techniques such as noise reduction and smoothing filters. The lower accuracy of the multispectral data from Sentinel-2 and Landsat-8/9 can also be attributed to their relatively coarse spatial resolution compared to the CSC, which leads to pixel non-uniformities and impurities. Therefore, employing the CSC on a UAV proves to be a valuable technology, providing an effective link between satellite observations and ground measurements. Full article

Due to the inhomogeneity and anisotropy of mine rock bodies, ionic rare earth ore bodies exhibit varying pore structures at different depths. This research focuses on an ionic rare earth mine in Fujian Province, where in situ ore samples rather than remodeled soil samples were studied. Samples from the fully weathered layer at depths of 1 m, 12 m, and 21 m, both before and after leaching, were collected for onsite analysis. Microscopic pore characteristics were evaluated using scanning electron microscopy, and digital image processing was utilized to study the evolution of the pore scale, distribution, and shape in rare earth ore samples at various depths pre- and post-leaching. The results indicate an increase in the ore body’s porosity with the depth of the ore samples both before and after leaching. The variation in pore scale is predominantly dictated by the ratio of macropore and large pores. Pre-leaching, the middle ore sample showcased the highest uniformity, with the upper part being the most irregular. Post-leaching, the highest uniformity was observed in the lower ore samples, with the upper part remaining irregular. Pre-leaching, as depth increased, the pore distribution in ore samples became more dispersed, with decreasing orderliness. Post-leaching, the orderliness was most improved in upper ore samples, while middle ore samples became the least orderly. Additionally, before leaching, pore-shape roughness increased with depth; after leaching, the pore shape became more rounded as depth increased, simplifying the pore-shape structure of the ore samples both before and after leaching. Full article

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell’s representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data’s underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback–Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods. Full article

This paper presents a current-mode VCSEL driver (CMVD) implemented using 180 nm CMOS technology for application in short-range LiDAR sensors, in which current-steering logic is suggested to deliver modulation currents from 0.1 to 10 mA_pp and a bias current of 0.1 mA simultaneously to the VCSEL diode. For the simulations, the VCSEL diode is modeled with a 1.6 V forward-bias voltage and a 50 Ω series resistor. The post-layout simulations of the proposed CMVD clearly demonstrate large output pulses and eye-diagrams. Measurements of the CMVD demonstrate large output pulses, confirming the simulation results. The chip consumes a maximum of 11 mW from a 3.3 V supply, and the core occupies an area of 0.1 mm². Full article

This paper presents a comprehensive state-of-the-art review of magnetic negative stiffness (MNS) devices in the realm of vibration isolation systems, spanning from foundational theoretical models to practical engineering applications. The emergence of MNS technology represents a significant advancement in the field of vibration isolation, introducing a method capable of achieving near-zero stiffness to effectively attenuate low-frequency vibration. Through a systematic exploration of the evolution of vibration isolation methodologies—encompassing passive, active, and hybrid techniques—this article elucidates the underlying principles of quasi-zero stiffness (QZS) and investigates various configurations of MNS isolators, such as the linear spring, bending beam, level spring-link, and cam-roller designs. Our comprehensive analysis extends to the optimization and application of these isolators across diverse engineering domains, highlighting their pivotal role in enhancing the isolation efficiency against low-frequency vibrations. By integrating experimental validations with theoretical insights, this study underscores the transformative potential of MNS devices in redefining vibration isolation capabilities, particularly in expanding the isolation frequency band while preserving the load-bearing capacities. As the authors of this review, not only are the current advancements within MNS device research cataloged but also future trajectories are projected, advocating for continued innovation and tailored designs to fully exploit the advantages of MNS technology in specialized vibration isolation scenarios. Full article

With the surge in population growth, the demand for electricity has escalated, necessitating efficient solutions to enhance the reliability and security of electrical systems. Smart grids, functioning as self-sufficient systems, offer a promising avenue by facilitating bi-directional communication between producers and consumers. Ensuring the stability and predictability of smart grid operations is paramount to evaluating their efficacy and usability. Machine learning emerges as a crucial tool for decision-making amidst fluctuating consumer demand and power supplies, thereby bolstering the stability and reliability of smart grids. This study explores the performance of various machine learning classifiers in predicting the stability of smart grid systems. Utilizing a smart grid dataset obtained from the University of California’s machine learning repository, classifiers such as logistic regression (LR), XGBoost, linear support vector machine (Linear SVM), and SVM with radial basis function (SVM-RBF) were evaluated. Evaluation metrics, including accuracy, precision, recall, and F1 score, were employed to assess classifier performance. The results demonstrate high accuracy across all models, with the Deep Neural Network (DNN) model achieving the highest accuracy of 99.5%. Additionally, LR, linear SVM, and SVM-RBF exhibited comparable accuracy levels of 98.9%, highlighting their efficacy in smart grid stability prediction. These findings underscore the utility of machine learning techniques in enhancing the reliability and efficiency of smart grid systems. Full article

Few-shot learning, especially few-shot image classification (FSIC), endeavors to recognize new categories using only a handful of labeled images by transferring knowledge from a model trained on base categories. Despite numerous efforts to address the challenge of deficient transferability caused by the distribution shift between the base and new classes, the fundamental principles remain a subject of debate. In this paper, we elucidate why a decline in performance occurs and what information is transferred during the testing phase, examining it from a frequency spectrum perspective. Specifically, we adopt causality on the frequency space for FSIC. With our causal assumption, non-causal frequencies (e.g., background knowledge) act as confounders between causal frequencies (e.g., object information) and predictions. Our experimental results reveal that different frequency components represent distinct semantics, and non-causal frequencies adversely affect transferability, resulting in suboptimal performance. Subsequently, we suggest a straightforward but potent approach, namely the FrequencySpectrumMask (FRSM), to weight the frequency and mitigate the impact of non-causal frequencies. Extensive experiments demonstrate that the proposed FRSM method significantly enhanced the transferability of the FSIC model across nine testing datasets. Full article

Extreme weather poses huge challenges for animals that must adapt to wide variations in environmental temperature and, in many cases, it can lead to the local extirpation of populations or even the extinction of an entire species. Previous studies have found that one element of amphibian adaptation to environmental stress involves changes in mitochondrial gene expression at low temperatures. However, to date, comparative studies of gene expression in organisms living at extreme temperatures have focused mainly on nuclear genes. This study sequenced the complete mitochondrial genomes of five Asian hylid frog species: Dryophytes japonicus, D. immaculata, Hyla annectans, H. chinensis and H. zhaopingensis. It compared the phylogenetic relationships within the Hylidae family and explored the association between mitochondrial gene expression and evolutionary adaptations to cold stress. The present results showed that in D. immaculata, transcript levels of 12 out of 13 mitochondria genes were significantly reduced under cold exposure (p < 0.05); hence, we put forward the conjecture that D. immaculata adapts by entering a hibernation state at low temperature. In H. annectans, the transcripts of 10 genes (ND1, ND2, ND3, ND4, ND4L, ND5, ND6, COX1, COX2 and ATP8) were significantly reduced in response to cold exposure, and five mitochondrial genes in H. chinensis (ND1, ND2, ND3, ND4L and ATP6) also showed significantly reduced expression and transcript levels under cold conditions. By contrast, transcript levels of ND2 and ATP6 in H. zhaopingensis were significantly increased at low temperatures, possibly related to the narrow distribution of this species primarily at low latitudes. Indeed, H. zhaopingensis has little ability to adapt to low temperature (4 °C), or maybe to enter into hibernation, and it shows metabolic disorder in the cold. The present study demonstrates that the regulatory trend of mitochondrial gene expression in amphibians is correlated with their ability to adapt to variable climates in extreme environments. These results can predict which species are more likely to undergo extirpation or extinction with climate change and, thereby, provide new ideas for the study of species extinction in highly variable winter climates. Full article

This paper explores the applicability of cryogenic permanent magnet motor stator materials for LNG pumps. First, this study selected four kinds of silicon steel sheets for motor stators tested at room temperature and ultra-low temperature and obtained the magnetization characteristics and loss characteristics of the four silicon steel sheets at room temperature and ultra-low temperature. Then, through a comparative analysis of experimental data, the applicability of silicon steel sheet material in an ultra-low-temperature environment was verified. Finally, the improved methods of the basic iron loss model of silicon steel sheets and the basic iron loss model of motors were proposed, and the accuracy and feasibility of the improved models were verified. Full article

Heterogeneous image change detection is a very practical and challenging task because the data in the original image have a large distribution difference and the labeled samples of the remote sensing image are usually very few. In this study, we focus on solving the issue of comparing heterogeneous images without supervision. This paper first designs a self-paced multi-scale joint feature mapper (SMJFM) for the mapping of heterogeneous data to similar feature spaces for comparison and incorporates a self-paced learning strategy to weaken the mapper’s capture of non-consistent information. Then, the difference information in the output of the mapper is evaluated from two perspectives, namely noise robustness and detail preservation effectiveness; then, the change detection problem is modeled as a multi-objective optimization problem. We decompose this multi-objective optimization problem into several scalar optimization subproblems with different weights, and use particle swarm optimization to optimize these subproblems. Finally, the robust evaluation strategy is used to fuse the multi-scale change information to obtain a high-precision binary change map. Compared with previous methods, the proposed SMJFM framework has the following three main advantages: First, the unsupervised design alleviates the dilemma of few labels in remote sensing images. Secondly, the introduction of self-paced learning enhances SMJFM’s capture of the unchanged region mapping relationship between heterogeneous images. Finally, the multi-scale change information fusion strategy enhances the robustness of the framework to outliers in the original data. Full article

Sample pretreatment is a key step for qualitative and quantitative analysis of trace substances in complex samples. Cis-dihydroxyl (cis-diol) group-containing substances exist widely in biological samples and can be selectively bound by boronate affinity adsorbents. Based on this, in this article, we proposed a simple method for the preparation of novel spherical three-dimensionally ordered macropore (3DOM) materials based on a combination of the boronate affinity technique and colloidal crystal template method. The prepared 3DOM materials were characterized using Fourier transform–infrared spectroscopy, scanning electron microscopy, X-ray photoelectron spectroscopy, and thermo-gravimetric analysis, and results showed that they possessed the characteristics of a high specific surface area, high porosity, and more boronic acid recognition sites. The adsorption performance evaluation results showed that the maximum adsorption capacity of the boron affinity 3DOMs on ovalbumin (OVA) could reach to 438.79 mg/g. Kinetic and isothermal adsorption experiments indicated that the boronate affinity 3DOM material exhibited a high affinity and selectivity towards OVA and adenosine. Sodium dodecyl sulfate–polyacrylamide gel electrophoresis analysis of the proteins in egg whites was conducted and proved that the glycoprotein in the egg whites could be separated and enriched with a good performance. Therefore, a novel boronate affinity 3DOM material a with highly ordered and interconnected pore structure was prepared and could be applied in the separation and enrichment of molecules with cis-diol groups from complex samples with a good selectivity, efficiency, and high throughput. Full article

(1) Background: This study assessed the efficacy of hydroxychloride sources of zinc (Zn), manganese (Mn), and copper (Cu) compared with organic sources in the rearing diets of Lohmann brown pullets, focusing on pullet performance, tibia quality, egg production, and eggshell quality. (2) Methods: A total of 120 birds (six replications and 10 birds each) received diets with Mn, Zn, and Cu from organic or hydroxychloride sources during the rearing phase. After the onset of lay, birds were fed diets containing oxide/sulfate sources up to 50 weeks of age. (3) Results: no significant differences were observed in growth performance and tibia quality during the rearing phase (p > 0.05). From 18 to 24 weeks of age, no carryover effect on egg production performance was observed. However, from 25–50 weeks, pullets fed hydroxychloride sources showed lower feed intake and egg mass compared to the organic group (p < 0.05), whereas egg production and eggshell quality remained similar between groups (p > 0.05). (4) Conclusions: These findings suggest the potential of hydroxychloride sources in rearing diets without compromising overall growth in the pullet phase and feed efficiency in the laying cycle. Full article

Extrusion-based 3D Construction Printing (3DCP) involves developing novel material mixtures that incorporate local geo-materials. Given that clay minerals and silt are major causes of soil variability, this study focuses on the fine fraction of soil to facilitate purpose-oriented design, classification, and standardization. We begin with an overview of current research in the field and general information about clays. Subsequently, we establish an evaluation methodology, examining various clay–sand mix ratios, along with locally sourced material to gain general insights into the material’s clay-dependent macro-printability characteristics. The findings are then correlated and discussed in relation to the microcharacteristics of the clays, emphasizing the significance of both intraparticle and interparticle swelling for strength and cohesiveness. Factors such as swelling ability, and charge, which may be reflected by pH, are pivotal for strength; while the quantity of clay and its interparticle swelling ability, denoted by the plasticity index (PI), delineate cohesiveness, which is essential for pumpability and extrudability. Furthermore, the presence of organic material and other minerals is observed to have a significant impact on these properties. Full article

Amorphous phase separation (APS) is ubiquitously found in a large number of glass systems, because the glass can be regarded as solid with a heterogeneous structure at the nanoscale. However, little attention has been paid to the big challenges in utilizing APS in searching novel amorphous glass from above to below, which highlights the meticulous microstructure tunability of glass. Correspondingly, we develop a novel SiO₂-Al₂O₃-P₂O₅-Li₂O-ZrO₂ glass with APS (SAPLZ APS) which has robust crystallization resistance via the APS engineering. A comparative study is conducted to reveal the APS–crystallization property relationship. It can be found that the introduced APS can substantially impede the precipitated crystal growth in the studied glass system. Considering detailed glassy structure and microstructure, a diffusion barrier around each Li-rich droplet is created by the presence of P⁵⁺ concentration surrounding the Li-rich region. Meanwhile, due to the increase in Q⁴ at the expense of Q³, the polymerization degree in the Si-rich amorphous area can be enhanced, further increasing its viscosity and raising the kinetic barrier of Si-related crystal growth. These findings provide a new manner to develop new glass with superior anti-crystallization performance. Full article

Males and females exhibit intrinsic differences in the structure and function of the heart, while the prevalence and severity of cardiovascular disease vary in the two sexes. However, the mechanisms of this sex-based dimorphism are yet to be elucidated. Sex chromosomes and sex hormones are the main contributors to sex-based differences in cardiac physiology and pathophysiology. In recent years, the advances in induced pluripotent stem cell-derived cardiac models and multi-omic approaches have enabled a more comprehensive understanding of the sex-specific differences in the human heart. Here, we provide an overview of the roles of these two factors throughout cardiac development and explore the sex hormone signaling pathways involved. We will also discuss how the employment of stem cell-based cardiac models and single-cell RNA sequencing help us further investigate sex differences in healthy and diseased hearts. Full article

To tackle the variability of distributed renewable energy (DRE) and the timing differences in load demand, this paper perfects the integrated layout of “source−load−storage” energy control in virtual power plants (VPPs). Introducing a comprehensive control approach for VPPs of varying ownerships, and encompassing load aggregators (LAs), a robust and cost−efficient operation strategy is proposed for VPP clusters. Initially, the influence of real−time electricity prices on cluster energy utilization is taken into account. Flexible shared electricity prices are formulated cluster−wide, based on the buying and selling data reported by each VPP, and are distributed equitably across the cluster. Following this, a flexible supply and demand response mechanism is established. With the goal of minimizing operational costs, this strategy responds to demand (DR) on the end−user side, instituting shifts and reductions in electricity and heat loads based on electricity and heat load forecasting data. On the supply side, optimization strategies are developed for gas turbines, residual heat boilers, and ground−source heat pumps to restrict power output, thus achieving economical and low−carbon cluster operations. Finally, the efficacy of the proposed optimization strategy is demonstrated through tackling numerous scenario comparisons. The results showcase that the proposed strategy diminishes operational costs and carbon emissions within the cluster by 11.7% and 5.29%, respectively, correlating to the unoptimized scenario. Full article

Multiple myeloma and monoclonal gammopathy of undetermined significance are plasma cell dyscrasias characterized by monoclonal proliferation of pathological plasma cells with uncontrolled production of immunoglobulins. Autoimmune pathologies are conditions in which T and B lymphocytes develop a tendency to activate towards self-antigens in the absence of exogenous triggers. The aim of our review is to show the possible correlations between the two pathological aspects. Molecular studies have shown how different cytokines that either cause inflammation or control the immune system play a part in the growth of immunotolerance conditions that make it easier for the development of neoplastic malignancies. Uncontrolled immune activation resulting in chronic inflammation is also known to be at the basis of the evolution toward neoplastic pathologies, as well as multiple myeloma. Another point is the impact that myeloma-specific therapies have on the course of concomitant autoimmune diseases. Indeed, cases have been observed of patients suffering from multiple myeloma treated with daratumumab and bortezomib who also benefited from their autoimmune condition or patients under treatment with immunomodulators in which there has been an arising or worsening of autoimmunity conditions. The role of bone marrow transplantation in the course of concomitant autoimmune diseases remains under analysis. Full article

This paper delves into the realm of cryptographic analysis by employing mixed-integer linear programming (MILP), a powerful tool for automated cryptanalysis. Building on this foundation, we apply the division property method alongside MILP to conduct a comprehensive cryptanalysis of the IIoTBC (industrial Internet of Things block cipher) algorithm, a critical cipher in the security landscape of industrial IoT systems. Our investigation into IIoTBC System A has led to identifying a 14-round integral distinguisher, further extended to a 22-round key recovery. This significant finding underscores the cipher’s susceptibility to sophisticated cryptanalytic attacks and demonstrates the profound impact of combining the division property method with MILP in revealing hidden cipher weaknesses. In the case of IIoTBC System B, our innovative approach has uncovered a full-round distinguisher. We provide theoretical validation for this distinguisher and uncover a pivotal structural issue in the System B algorithm, specifically the non-diffusion of its third branch. This discovery sheds light on inherent security challenges within System B and points to areas for potential enhancement in its design. Our research, through its methodical examination and analysis of the IIoTBC algorithm, contributes substantially to the field of cryptographic security, especially concerning industrial IoT applications. By uncovering and analyzing the vulnerabilities within IIoTBC, we enhance the understanding of cipher robustness and pave the way for advancements in securing industrial IoT communications. Full article