Network Structure Research Articles

Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.

Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

Substitutional/positional disorder of biguanide and guanylurea in the structure of a decavanadate complex [(Bg)(HV10O285-)]0.4[(HGU+)(V10O286-)]0.6(H2Met2+)2(H3O+)·8H2O.

A hydrated salt of decavanadate containing diprotonated metforminium(2+) (H2Met2+), hydronium (H3O+) and either neutral biguanide (Bg) or monoprotonated guanylurea (HGU+) exhibits a previously seen complex charge-stabilized hydrogen-bonded network [Chatkon et al. (2022). Acta Cryst. B78, 798-808]. Charge balance is achieved in two ways through substitutional disorder: a 0.6 occupied HGU+ cation is paired with a V10O286- anion, and a 0.4 occupied neutral Bg molecule is paired with a HV10O285- anion, with the remaining charge in both cases balanced by two H2Met2+ dications and one H3O+ monocation. Bg/HGU+ moieties exhibit bifurcated N-H...O hydrogen bonding to the H3O+ cation and are substitutionally/positionally disordered along with the H3O+ cation about an inversion center. The HGU+ V10O286- synthon seen in the previous study occurs again. Bg exhibits bifurcated hydrogen bonding from two amino groups to two rows of cluster O atoms running diagonally across the equatorial plane of the HV10O285- anion with a return hydrogen bond from the cluster H atom to the imino N atom of the Bg. Thus, a Bg...cluster synthon similar to the HGU+...cluster synthon previously reported is found. The disordered moieties occupy spaces with excess volume in the 3-D network structure. Interestingly, when the crystallographic unit cell of the current compound, whose X-ray data was collected at 100 K, is compared with that of a previous compound exhibiting the same supramolecular framework, unit-cell parameter c does not shorten as a and b expectantly do because of the lower data collection temperature. The lack of contraction on unit-cell parameter c is possibly due to the supramolecular structure.

Interface engineering of La0.6Sr0.4Co0.2Fe0.8O3−δ/Gd0.1Ce0.9O1.95 heterostructure oxygen electrode for solid oxide electrolysis cells with enhanced CO2 electrolysis performance

For CO2 electrolysis in solid oxide electrolysis cells (SOECs), oxygen evolution reaction in the oxygen electrode limits the electrolysis process due to its slower reaction kinetics. Composite oxygen electrodes with good uniformity and interfacial connectivity are expected to promote the charge carriers’ transport effectively and thus increase the electrocatalytic performance of the cell. In this work, interface engineering is applied to promote the conventional La0.6Sr0.4Co0.2Fe0.8O3−δ-Gd0.1Ce0.9O1.95 (LSCF-GDC) composite oxygen electrode to form a heterostructure via co-synthesis method. The results prove that LSCF co-synthesized with 40 wt.%GDC (LSCF/40GDC) exhibits higher conductivity and faster oxygen exchange kinetics and bulk diffusion processes. The nickel/yttria stabilized zirconia (Ni-YSZ) supported cell with LSCF/40GDC oxygen electrode could approach an impressive CO2 electrolysis current density of 2.6 A·cm−2 at 1.6 V and 800 °C. Meanwhile the cell can operate at 1.5 V and 750 °C with electrolysis current density of 1.3 A·cm−2 for more than 100 h without apparent degradation. This heterostructure enhances the interfacial bonding strength between LSCF and GDC phases, thereby providing a continuous network structure for electron and ion transport. Such an interface engineering via co-synthesis method offers a straightforward and convenient modification for oxygen electrodes, providing an alternative route for advanced materials development of CO2 electrolysis.

Multifunctional Nano‐Conductive Hydrogels With High Mechanical Strength, Toughness and Fatigue Resistance as Self‐Powered Wearable Sensors and Deep Learning‐Assisted Recognition System

AbstractHigh mechanical strength, toughness, and fatigue resistance are essential to improve the reliability of conductive hydrogels for self‐powered sensing. However, achieving mutually exclusive properties simultaneously remains challenging. Hence, a novel directed interlocking strategy based on topological network structure and mechanical training is proposed to construct tough hydrogels by optimizing the network structure and modulating the orientation of molecular chains. Combining Zn2+ crosslinked cellulose nanofibers (CNFs) and a polyacrylamide‐poly(vinyl alcohol) double‐network, the unique interlocked‐network structure exhibits an enhanced toughening effect due to hydrogen bonding and metal‐ligand interactions. The aligned nanocrystalline domains achieved by training further contribute to an increase in the toughness and fatigue thresholds. This innovative approach synergistically enhances the mechanical properties of the nano‐conductive hydrogel, achieving a maximum tensile strength of 4.98 MPa and a toughness of 48 MJ m−3. Notably, the CNFs template with anchored polyaniline, when oriented through mechanical training, forms a unique directional conductive pathway, which significantly enhances the power output performance. Besides, a motion recognition system based on a self‐powered sensing device is designed with the assistance of deep learning techniques to accurately identify human motion behaviors. This work showcases a potentially transformative flexible electronic material for self‐powered sensing systems and intelligent recognition systems.

Spectral Bipartition via Gap Cut on DNA Sequences

Deoxyribonucleic Acid (DNA) and graph partitioning are two distinct fields of study which can be linked in the structure of biological networks. Graph partitioning has been extensively studied but not its application in the biological field. This research explored on the application of spectral graph partitioning in DNA splicing, aiming to simulate the cleavage of DNA by performing spectral bipartition on DNA sequences in a DNA splicing system. This research incorporates Fiedler theory and algebraic graph theory, which are commonly utilized in network analysis and the analysis of graph connectivity. Some DNA sequences of even length are selected and expressed in graphical representations. The adjacency matrix, Laplacian matrix, and degree matrix are computed from the graphs, as well as the Fiedler value and Fiedler vector associated with the graphs. Gap cut is used as a method of spectral bipartition which produces two partitions of DNA sequence of unequal lengths. The generalizations of gap cut on DNA sequences of even length are provided as lemmas and theorem.

Deep learning to improve the sensitivity of Di-Higgs searches in the 4b channel

The study of di-Higgs events, both resonant and non-resonant, plays a crucial role in understanding the fundamental interactions of the Higgs boson. In this work we consider di-Higgs events decaying into four b-quarks and propose to improve the experimental sensitivity by utilizing a novel machine learning algorithm known as Symmetry Preserving Attention Network (Spa-Net) — a neural network structure whose architecture is designed to incorporate the inherent symmetries in particle reconstruction tasks. We demonstrate that the Spa-Net can enhance the experimental reach over baseline methods such as the cut-based and the Dense Neural Network-based analyses. At the Large Hadron Collider, with a 14-TeV center-of-mass energy and an integrated luminosity of 300 fb−1, the Spa-Net allows us to establish 95% C.L. upper limits in resonant production cross-sections that are 10% to 45% stronger than baseline methods. For non-resonant di-Higgs production, Spa-Net enables us to constrain the self-coupling that is 9% more stringent than the baseline method.

Rewiring cattle movements to limit infection spread

Cattle tracing databases have become major resources for representing demographic processes of livestock and assessing potential risk of infections spreading by trade. The herds registered in these databases are nodes of a network of commercial movements, which can be altered to lower the risk of disease transmission. In this study, we develop an algorithm aimed at reducing the number of infected animals and herds, by rewiring specific movements responsible for trade flows from high- to low-prevalence herds. The algorithm is coupled with a generic computational model based on the French cattle movement tracing database (BDNI), and used to describe different scenarios for the spread of infection within and between herds from a recent outbreak (epidemic) or a five-year-old outbreak (endemic). Results show that rewiring successfully contains infections to a limited number of herds, especially if the outbreak is recent and the estimation of disease prevalence frequent, while the respective impact of the parameters of the algorithm depend on the infection parameters. Allowing any animal movement from high to low-prevalence herds reduces the effectiveness of the algorithm in epidemic settings, while frequent and fine-grained prevalence assessments improve the impact of the algorithm in endemic settings. Our approach focusing on a few commercial movements is expected to lead to substantial improvements in the control of a targeted disease, although changes in the network structure should be monitored for potential vulnerabilities to other diseases. This general algorithm could be applied to any network of controlled individual movements liable to spread disease.

Intricate DG and EV Planning Impact Assessment with Seasonal Variation in a Three-Phase Distribution System

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network.

Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure

One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs.

Three-Dimensional Nanoscale Imaging of SiO2 Nanofiller in Styrene-Butadiene Rubber with High-Resolution and High-Sensitivity Ptychographic X-ray Computed Tomography

Abstract SiO2 aggregates in styrene-butadiene rubber (SBR) were observed using ptychographic X-ray computed tomography (PXCT). The rubber composites were illuminated with X-rays focused by total reflection focusing mirrors, and the ptychographic diffraction patterns were collected using a CITIUS detector in the range of −75° to +75° angle of incidence. The projection images of the rubber composites were reconstructed with a two-dimensional resolution of 76 nm, and no significant structural changes were observed during the PXCT measurements. A three-dimensional image of the rubber composite was reconstructed with an isotropic resolution of 98 nm. Segmentation of SiO2 from the SBR, based on a histogram analysis of the phase shift, revealed a fragmented network structure of interconnected SiO2 aggregates.

Mechanical properties and micro-mechanism of seawater cementitious materials reinforced by in-situ polymerization

Understanding the potential mechanism of in-situ polymerization of acrylamide (AM) for modifying seawater cementitious materials is crucial for designing high-strength and durable marine concrete. Herein, the acrylamide (AM) in-situ polymerization was investigated for its effects on the hydration behavior, micro-morphology, and pore structure of cementitious materials mixed with seawater and freshwater through a series of elaborately designed microscopic characterization methods. The results reveal that the hydration process of cementitious materials proceeds simultaneously with in-situ polymerization. However, compared with freshwater mixtures, seawater provides a large number of metal ions and SO42- ions, which can cross-link with the generated polyacrylamide (PAM) during in-situ polymerization to form a three-dimensional network structure. The synergistic effect of the hydration, in-situ polymerization, and cross-linking processes of cementitious materials can improve the pore structure of seawater-mixed paste, enhance erosion resistance, and improve the stability and toughness of microstructure. These findings were further confirmed by comparing infrared spectroscopy results, hydration products, pore size, and micro-morphology analysis as well as flexural performance tests. This is of great significance to guide the design of novel materials in marine infrastructure.

Digital strategic collaborations in agriculture: a novel asset for local identity enhancement toward Agrifood 5.0

PurposeThe aim of this research is to elucidate the correlation between open innovation, digital strategies and networking in enhancing agricultural enterprises within the new perspective of Agrifood 5.0. As such, it contributes to making businesses more competitive, especially in the Italian agricultural sector, where small and medium-sized enterprises are highly fragmented. Numerous studies have asserted that the competitiveness of actors operating within a specific territory is closely linked to local identity and image enhancement. Agricultural organizations are undergoing a profound transformation, with technological assets emerging as catalysts for new synergies. Advanced technologies such as robotics, the Internet of Things (IoT) and automation (AI) are emerging as differentiating elements capable of further advancing the agricultural sector, transitioning it from Agrifood 4.0 to Agrifood 5.0. The empirical analysis of the research shows a positive correlation between a collaborative attitude and a propensity for innovation. Indeed, the data demonstrated that digital strategies and open innovation positively influence competitiveness in agricultural SMEs.Design/methodology/approachThe methodology employed in this study is mixed, incorporating both qualitative and quantitative approaches. The quantitative aspect involves analysis of the dataset from the Italian Statistical Institute (ISTAT) through logistic regression, while the qualitative component entails analysis of semi-structured interviews conducted with a sample of 174 agricultural cooperatives in southern Italian regions (Campania). This approach allows for a comprehensive understanding of the research topic, capturing both numerical trends and nuanced insights from interviews.FindingsAfter analyzing the data from the 7th General Census of Agriculture conducted by ISTAT, a clear understanding of the sector has emerged, revealing several potential research avenues. It is evident that innovation in the agricultural sector is often driven by the largest and best-capitalized production entities, primarily located in Italy. Conversely, smaller agricultural entities can benefit from networking as new technological assets act as catalysts for new synergies, innovation and competitiveness.Practical implicationsEnhancing the relational contribution within the network and humanizing a fragmented sector are crucial elements for promoting open innovation. Network structuring facilitates the transmission of managerial knowledge, contributing to an overall increase in the intellectual and relational capital of the agricultural sector. These factors, combined with open innovation, enhance the competitiveness of individual firms and elevate the brand of the entire sector, creating a conducive environment for transitioning toward Agrifood 5.0. This transition is characterized by increased interconnection, continuous innovation and overall prosperity. Specific studies on this topic are lacking in Italy, particularly in the southern regions. Therefore, this contribution focuses on investigating the Campania region.Originality/valueThe novelty of this study lies in its investigation of the relationship between agricultural enterprises and innovation in the context of enterprises networking strategies (i.e. associationism and/or cooperation), promoting competitiveness. The limitations of this study are related to the dimension of the sample selected and its relationship with other productive sectors.

Automatic Architecture Design for Distributed Quantum Computing

Abstract In distributed quantum computing (DQC), quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections. Meanwhile, quantum software tries best to reduce the required number of remote quantum gates between chips. However, this "hardware first, software follows" methodology may not fully exploit the potential of DQC. Inspired by classical software-hardware co-design, this paper explores the design space of application-specific DQC architectures. More specifically, we propose AutoArch, an automated quantum chip network (QCN) structure design tool. With qubits grouping followed by a customized QCN design, AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms. Experimental results show that the DQC architecture generated by AutoArch can outperform other general QCN architectures when executing target quantum algorithms.

Spatiotemporal interactions between jaguars (Panthera onca) and their potential prey in Amazonian islands

AbstractAlthough large carnivores usually prefer large prey, in some situations, they may shift their predation patterns towards smaller but abundant prey. The jaguar (Panthera onca) is a large carnivore capable of changing its diet according to prey and habitat availability. Here, we assessed the temporal and spatiotemporal interactions between jaguars and their prey in the Maracá‐Jipioca Islands (Northeastern Amazon, Brazil) through camera traps. We assessed overlapping activity patterns and tested for spatiotemporal segregation/avoidance between jaguars and nine potential prey species. We used a time‐to‐encounter approach, which consists in calculating the minimum time between prey and jaguar's detections, and vice versa, for each record of preys' species at a specific camera trap station, which translates into aggregation or avoidance behaviors. We found that these insular jaguars are more active in daylight periods when most of their prey are active and in locations used by species that cannot become nocturnal to avoid predators due to morphology constraints. Four prey species (great egret, white‐tailed deer, muscovy duck, and black‐and‐white tegu) presented moderate activity overlapping with jaguars. Agoutis and white‐tailed deer seek to spatiotemporally segregate from jaguars, although jaguars did not show spatiotemporal aggregation with any of the evaluated prey. Understanding the spatiotemporal dynamics is essential to establish the islands' trophic network composition and structure. This is fundamental information to efficiently allocate efforts for reducing costs and maximizing benefits in managing this population aiming to protect and conserve it, and consequently, the related ecosystems.

Emodin repairs interstitial cells of Cajal damaged by cholelithiasis in the gallbladder

BackgroundHypercholesterolemia induces cholelithiasis and dysfunction of gallbladder motility. Interstitial cells of Cajal (ICCs) contribute to gallbladder motility. Emodin modulates the contractility of the gallbladder muscle; however, the underlying mechanism is unknown.AimThis study aimed to explore the effects of emodin on gallbladder ICCs with cholelithiasis in a guinea pig model.MethodsAnimals were randomly divided into a healthy control group and three study groups. All study groups received a high-cholesterol diet (HCD) for 8 weeks. Subsequently, they were randomly assigned to either the HCD group or one of the emodin treatment groups lasting 4 or 8 weeks. Total cholesterol (TC) and triglycerides (TG) were measured to determine changes in serum lipid levels. Immunohistochemistry was performed to detect the morphology and number of ICCs. TUNEL assays were performed to detect ICC apoptosis. Transmission electron microscopy was employed to observe ICC structure. Western blotting and real-time polymerase chain reaction were used to detect changes in stem cell factor (SCF)/c-kit pathway expression.ResultsSerum TC and TG were higher in all study groups. In cases of cholelithiasis, the SCF/c-kit pathway was downregulated, the number of gallbladder ICCs decreased, apoptosis increased, and the ICC network structure was damaged. After emodin treatment, the SCF/c-kit pathway was upregulated, the number of gallbladder ICCs increased, apoptosis decreased, and the ICC network structure recovered.ConclusionCholelithiasis downregulates the SCF/c-kit pathway and damages gallbladder ICCs. Emodin upregulates the SCF/c-kit pathway and increases gallbladder ICCs, contributing to recovery from gallbladder motility disorders.\

The Mechanism of Bacterial Endotoxin Invasion Pathways in Porcine Reproductive and Respiratory Syndrome Virus-Positive Porcine Endometrial Epithelial Cells

Porcine reproductive and respiratory syndrome virus (PRRSV) causes abortions, stillbirths, and dummy pregnancies. Previous studies found that PRRSV can promote secondary bacterial infections and elevate bacterial endotoxin levels, further increasing the abortion rate in sows. However, the pathways by which bacterial endotoxins invade the bodies of PRRSV(+) sows and aggravate their clinical symptoms are unknown. In this study, we established a model of PRRSV and lipopolysaccharide (LPS) working together on porcine endometrial epithelial cells (PEECs). We speculate that PRRSV and LPS affect PEECs through viral protein interaction with cytokines and cytokine receptors, natural killer cell-mediated cytotoxicity, and regulation of actin cytoskeleton signaling pathways by analyzing seq-RNA. The PRRSV proteins act on inflammatory factors and their receptors to activate chemokines-5 (CCL5), chemokines-4 (CCL4), and chemokines-8 (CCL8) mRNA expression, causing severe inflammatory reactions. In addition, the elevation of MEK1/2 factors and the integrins acting on NK cells promote the upregulation of VAV1/Tiam1, RAC, and IRSp53, leading to increased expression of Arp2/3 and F-actin in PEECs in the PRRSV + LPS(+) groups. However, the highly expressed cell microfilaments and cytoskeleton disrupt the original network structure, causing changes in the original physiological function of the PEECs. In summary, the PRRSV protein interacts with cytokines and cytokine receptors of PEECs, thereby enhancing virus-mediated chemokine factors and their receptor activity, accelerating bacterial endotoxin entry into the body and the invasion of cells. They destroy the cytoskeletal structure of the cells and increase damage to uterine tissue.

Social Network Density Mediates the Association Between Problematic Social Media Use and Depressive Symptoms

Previous studies have indicated that problematic social media use (PSMU) is positively related to depressive symptoms. However, the potential mechanisms underlying this relationship are not well understood. For example, prior research found that depressive symptoms are related to one’s social network structure, such as network size and density. Therefore, we examined whether one’s social network size and density mediate the relationship between PSMU and depressive symptoms. We conducted an in-person survey to collect measures of PSMU, social network size and density, and depressive symptoms. Our analysis showed that there was a positive relationship between PSMU and depressive symptoms, which was mediated by social network density but not size. Specifically, the greater one’s PSMU, the less dense their social network, and the greater their depressive symptoms. Our research suggests that PSMU may affect how individuals maintain their social connections with others, which may affect mental health. Implications were discussed.

Responses of diazotrophic network structure and community diversity to alfalfa-maize intercropping are soil property-dependent

IntroductionIntercropping and soil properties both affect soil diazotrophic communities. However, the specific effects that alfalfa-maize intercropping has on diazotrophic networks and community diversity under different soil properties remain unclear.MethodsIn this study, we investigated the soil diazotrophic communities of two crop systems, alfalfa monoculture (AA) and alfalfa-maize intercropping (A/M), in two sites with similar climates but different soil properties (poor vs. average).Results and discussionThe diazotrophic network complexity and community diversity were higher at the site with poor soil than at the site with average soil (p < 0.05). Community structure also varied significantly between the sites with poor and average soil (p < 0.05). This divergence was mainly due to the differences in soil nitrogen, phosphorus, and organic carbon contents between the two sites. At the site with poor soil, the A/M system had lower diazotrophic diversity, lower network complexity and greater competition between diazotrophs than the AA system (p < 0.05) because intercropping intensified the soil phosphorus limitation under poor soil conditions. However, in the average soil, it was the A/M system that had an altered diazotrophic structure, with an increased abundance of 11 bacterial genera and a decreased abundance of three bacterial genera (p < 0.05).ConclusionOur results indicated that the effects of alfalfa-maize intercropping on diazotrophic communities were soil property-dependent.

Metal-organic framework-derived hierarchical Ag/Ti3C2Tx/Co/C nanocomposite for enhanced electromagnetic wave absorption

The microwave absorption (MA) performance of 2D Ti3C2Tx is significantly restricted due to its lack of magnetic loss capacity and mismatched electromagnetic impedance. The Ag/Ti3C2Tx/Co/C (ATCC) structure, based on a unique 2D layered Ti3C2Tx, has shown exceptional electromagnetic wave absorption (EWA) properties. The addition of a silver nanoparticles enhances the polarization effect and conductance loss of Ti3C2Tx. By incorporating ZIF-67 with varying Co content, nanocomposite heterostructures and interfaces are formed in Ag/Ti3C2Tx. Moreover, the carbon source of organic ligand and the 3D network structure of Ti3C2Tx provide additional electronic transition channels and improve conductive loss. This modification results in non-uniform interfaces between Ti3C2Tx and metal nanoparticles, leading to electron accumulation and the formation of a micro-capacitance structure that generates interface polarization. Eddy current loss and magnetic loss from metal nanoparticles further enhance EWA properties. This study envisions a novel strategy for developing efficient Ti3C2Tx-based microwave absorbers modified with metal nanoparticles.

Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation

AbstractLow‐light object detection is needed for ensuring security, enabling surveillance, and enhancing safety in diverse applications, including autonomous vehicles, surveillance systems, and search and rescue operations. A comprehensive study on low‐light object detection is presented using state‐of‐the‐art you only look once (YOLO) models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8, aiming to enhance detection performance under challenging low‐light conditions. The ExDark dataset is a dataset that consists of adequate low‐light images, modified to simulate realistic low‐light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low‐light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. The experimental results show that YOLOv8 consistently outperforms baseline models, achieving significant improvements in accuracy and robustness in low‐light scenarios. The deep learning algorithm that acquired the best score, YOLOv8s, had a mean average precision score of 0.5513. This work contributes to the field of low‐light object detection, offering promising solutions for real‐world applications like nighttime surveillance and autonomous navigation in low‐light conditions, addressing the growing demand for advanced low‐light object detection.