bluuebunny/crossref_metadata_2025 huggingface dataset:
Out-of-Distribution Node Detection Based on Graph Heat Kernel Diffusion
Authors: Li Fangfang , Wang Yangshuai , Du Xinyu , Li Xiaohua , Yu Ge
Published: September 2024

Over the past few years, there has been a surge in research attention towards tasks involving graph data, largely due to the impressive performance demonstrated by graph neural networks (GNNs) in handling such information. Currently, out-of-distribution (OOD) detection in graphs is a hot research topic. The goal of graph OOD detection is to identify nodes or new graphs that differ from the training data distribution, primarily in terms of attributes and structures. OOD detection is crucial for enhancing the stability, security, and robustness of models. In various applications, such as biological networks and financial fraud, graph OOD detection can help models identify anomalies or unforeseen situations, thereby enabling appropriate responses. In node-level OOD detection, existing models typically only consider first-order neighbors. This paper introduces graph diffusion to the OOD detection task for the first time, proposing the HOOD model, a graph diffusion-based OOD node detection algorithm. Specifically, the original graph is processed through graph diffusion to obtain a new graph that can directly capture high-order neighbor information, overcoming the limitation that message passing must go through first-order neighbors. The new graph is then sparsified using a top-k approach. Based on entropy information, regularization is employed to ensure the uncertainty of OOD nodes, thereby giving these nodes higher scores and enabling the model to effectively detect OOD nodes while ensuring the accuracy of in-distribution node classification. Experimental results demonstrate that the HOOD model outperforms existing methods in both node classification and OOD detection tasks on multiple benchmarks, highlighting its robustness and effectiveness.
Distributed Observer for Linear Systems with Multirate Sampled Outputs Involving Multiple Delays
Authors: Galicia-Galicia Laura-Adriana , Hernández-González Omar , Garcia-Beltran Carlos Daniel , Valencia-Palomo Guillermo , Guerrero-Sánchez María-Eusebia
Published: September 2024

This paper focuses on the design of a continuous distributed observer for linear systems under multirate sampled output measurements involving multiple delays. It is mathematically proved that the continuous distributed observer can achieve estimation in a sensor network environment, where output measurements from each sensor are available at different sampling instants, whether these times are periodic or aperiodic, and despite the presence of multiple time-varying delays. Each sampled and delayed measurement represents a node of the network, necessitating a dedicated observer for each node, which has access to only part of the system’s output and communicates with its neighbors according to a given network graph. The exponential convergence of the error dynamics is ensured by Lyapunov stability analysis, which accounts for the influence of the sampled and delayed measurements at each node. To demonstrate the effectiveness of the proposed observer, simulation tests were conducted on the tracking control of chasing satellites in low Earth orbit (LEO), encompassing both small and large sampling rates and delays. The continuous distributed observer with sampled output measurements exhibited convergence in scenarios with different sampling intervals, even in the presence of time-varying delays, achieving asymptotic omniscience, as demonstrated in the convergence analysis.
Prediction of Shale Gas Well Productivity Based on a Cuckoo-Optimized Neural Network
Authors: Peng Yuanyuan , Chen Zhiwei , Xie Linxuan , Wang Yumeng , Zhang Xianlin , Chen Nuo , Hu Yueming
Published: September 2024

Current shale gas well production capacity predictions primarily rely on analytical and numerical simulation methods, which necessitate extensive calculations and manual parameter tuning and produce lowly accurate predictions. Although employing neural networks yields highly accurate predictions, they can easily fall into local optima. This paper suggests a new way to use Cuckoo Search (CS)-optimized neural networks to make shale gas well production capacity predictions more accurate and to solve the problem of local optima. It aims to assist engineers in devising more effective development plans and production strategies, optimizing resource allocation, and reducing risk. The method first analyzes the factors influencing the production capacity of shale gas wells in a block located in western China through correlation coefficients. It identifies the main factors affecting the gas test absolute open flow as organic carbon content, small-layer passage rate, fracture pressure, acid volume, pump-in fluid volume, brittle mineral content in the rock, and rock density. Subsequently, we used the CS algorithm to conduct the global training of the neural network, avoiding the problem of local optima, and established a neural network model for predicting shale gas well production capacity optimized by the CS algorithm. A comparative analysis with other relevant methods demonstrates that the CS-optimized neural network model can accurately predict production capacity, enabling a more rational and effective exploitation of shale gas resources, which lower development costs and increase the economic returns of oil and gas fields. Compared to numerical simulation, SVM, and BP neural network algorithms, the CS-optimized BP neural network (CS-BP) exhibits significantly lower prediction error. Its correlation coefficient between predicted and actual values reaches as high as 0.9924. Verification experiments conducted on another shale gas well also demonstrate that, in comparison to the BP neural network algorithm, CS-BP offers superior prediction performance, with model validation showing a prediction error of only 0.05. This study can facilitate more rational and efficient exploitation of shale gas resources, reduce development costs, and enhance the economic benefits of oil and gas fields.
Optimization of MOSFET Copper Clip to Enhance Thermal Management Using Kriging Surrogate Model and Genetic Algorithm
Authors: Cheon Yubin , Jung Jaehyun , Ki Daeyeon , Khalid Salman , Kim Heung Soo
Published: September 2024

Metal–oxide–semiconductor field-effect transistors (MOSFETs) are critical in power electronic modules due to their high-power density and rapid switching capabilities. Therefore, effective thermal management is crucial for ensuring reliability and superior performance. This study used finite element analysis (FEA) to evaluate the electro-thermal behavior of MOSFETs with copper clip bonding, showing a significant improvement over aluminum wire bonding. The aluminum wire model reached a maximum temperature of 102.8 °C, while the copper clip reduced this to 74.6 °C. To further optimize the thermal performance, Latin Hypercube Sampling (LHS) generated diverse design points. The FEA results were used to select the Kriging regression model, chosen for its superior accuracy (MSE = 0.036, R2 = 0.997, adjusted R2 = 0.997). The Kriging model was integrated with a Genetic Algorithm (GA), further reducing the maximum temperature to 71.5 °C, a 4.20% improvement over the original copper clip design and a 43.8% reduction compared to aluminum wire bonding. This integration of Kriging and the GA to the MOSFET copper clip package led to a significant improvement in the heat dissipation and overall thermal performance of the MOSFET package, while also reducing the computational power requirements, providing a reliable and efficient solution for the optimization of MOSFET copper clip packages.
A Novel Pareto-Optimal Algorithm for Flow Shop Scheduling Problem
Authors: Shahsavari-Pour Nasser , Heydari Azim , Fekih Afef , Asadi Hamed
Published: September 2024

Minimizing job waiting time for completing related operations is a critical objective in industries such as chemical and food production, where efficient planning and production scheduling are paramount. Addressing the complex nature of flow shop scheduling problems, which pose significant challenges in the manufacturing process due to the vast solution space, this research employs a novel multiobjective genetic algorithm called distance from ideal point in genetic algorithm (DIPGA) to identify Pareto-optimal solutions. The effectiveness of the proposed algorithm is benchmarked against other powerful methods, namely, NSGA, MOGA, NSGA-II, WBGA, PAES, GWO, PSO, and ACO, using analysis of variance (ANOVA). The results demonstrate that the new approach significantly improves decision-making by evaluating a broader range of solutions, offering faster convergence and higher efficiency for large-scale scheduling problems with numerous jobs. This innovative method provides a comprehensive listing of Pareto-optimal solutions for minimizing makespan and total waiting time, showcasing its superiority in addressing highly complex problems.
Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices
Authors: Gupta Rangan , Pierdzioch Christian
Published: September 2024

Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample period from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability but scarce evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregressive (HAR)-RV model. This lack of systematic evidence of out-of-sample forecasting gains is corroborated by extensive robustness checks, including an in-depth study of the quantiles of the distributions of the RVs and subsample periods that account for increases in the total spillovers among the RVs. We also study an extended model that features the RVs of energy commodities and precious metals, but our conclusions remain unaffected. Besides offering important lessons for future research, our results are interesting for financial market participants, who rely on accurate forecasts of RVs when solving portfolio optimization and derivatives pricing problems, and policymakers, who need accurate forecasts of RVs when designing policies to mitigate the potential adverse effects of a rise in the RVs of agricultural commodity prices and the concomitant economic and political uncertainty.
Launching Point Estimation Using Inverse First-Order Pitch Programming
Authors: Cho Sungjin , Song Taek Lyul , Choi Hyeok-Jin
Published: September 2024

This paper presents an estimation of the launching points of unmanned aerial vehicles (UAVs) equipped with boosters. When UAVs are detected and tracked by sensors such as radars, the fast identification of vehicle launching points is needed for air traffic control and defense systems. When UAVs in a boosting phase are controlled by first-order pitch programming, this paper leverages inverse first-order pitch programming to analytically solve launching points. Furthermore, a two-point robust measurement selection (T-RMS) scheme is developed to reduce errors such as random noise and bias by utilizing multiple moving average filters. The proposed work is verified by various simulation results.
Nonparametric Test for Logistic Regression with Application to Italian Enterprises’ Propensity for Innovation
Authors: Bonnini Stefano , Borghesi Michela
Published: September 2024

In this work, a nonparametric method is proposed to jointly test the significance of the regression coefficient estimates in a logistic regression model and identify which explanatory variables are effective in predicting the binary response. The motivating example is related to the factors affecting the propensity of Italian Small Medium Enterprises (SMEs) to innovate. The explanatory variables of the model represent firms’ characteristics, such as size and age, and the possible effect of the sector of economic activity is taken into account by including a set of binary variables as control factors. The dependent variable indicates whether a company, in the period under study, introduced at least one product or process innovation. Therefore, it is also dichotomous, and the logistic regression model is appropriate for representing the relationship between explanatory variables and dependent variable. Specifically, the logit transformation of the firm’s propensity to innovate, i.e., the probability that a company randomly chosen from the population of Italian SMEs has introduced an innovation or, equivalently, the proportion of innovative companies among the Italian SMEs, is expressed as a linear function of the predictors (explanatory and control variables). The proposed test is based on the permutation approach and satisfies important statistical properties, proved in a simulation study. The test is more flexible and robust than the classic parametric approach, and is preferable to typical stepwise regression procedures for the selection of a parsimonious and effective model.
On the Temporal Evolution of Key Hemofilter Parameters—In Vitro Study under Co-Current Flow
Authors: Karabelas Anastasios J. , Moschona Alexandra , Merenidis Konstantinos
Published: September 2024

Effective permeability KP, the ultrafiltration coefficient (KUF), the sieving coefficient (SC), and the loss/permeation of proteins (primarily albumin) are key parameters/specifications characterizing hemofilter (HF) performance. However, there are uncertainties regarding their determination. This work aims (a) to demonstrate that the co-current flow (of blood and dialysate) can lead to beneficial unidirectional filtration (from blood/plasma to dialysate) under a fairly uniform local trans-membrane pressure (TMP), unlike the presently employed counter-current flow; (b) to study the temporal evolution of key HF performance parameters under co-current flow, particularly during the important early stage of hemocatharsis (HC). Experiments with human plasma and BSA solutions in co-current flow mode (for which a fluid mechanical model is developed) show a fairly uniform local/axial TMP, which also improves the local/axial uniformity of protein membrane fouling, particularly under (currently favored) high convective flux operation. Due to incipient membrane fouling, a significant temporal variability/decline in the effective KP is observed, and, in turn, of other parameters (i.e., the Kuf, SC, and permeation/mass flux Mm for albumin and total proteins). A satisfactory correlation of the albumin/protein mass flux Mm with permeability KP is obtained, indicating strong inter-dependence. In conclusion, co-current flow, allowing for a fair local TMP axial uniformity, enables the acquisition of accurate/representative data on the evolution of HF parameters, facilitating their interpretation and correlation. The new results provide a basis for exploring the clinical application of the co-current flow.
Advancements in Metal Processing Additive Technologies: Selective Laser Melting (SLM)
Authors: Soni Neetesh , Renna Gilda , Leo Paola
Published: September 2024

Nowadays, the use of metal processing additive technologies is a rapidly growing field in the manufacturing industry. These technologies, such as metal 3D printing (also known as additive manufacturing) and laser cladding, allow for the production of complex geometries and intricate designs that would be impossible with traditional manufacturing methods. They also offer the ability to create parts with customized properties, such as improved strength, wear resistance, and corrosion resistance. In other words, these technologies have the potential to revolutionize the way we design and produce products, reducing costs and increasing efficiency to improve product quality and functionality. One of the significant advantages of these metal processing additive technologies is a reduction in waste and environmental impact. However, there are also some challenges associated with these technologies. One of the main challenges is the cost of equipment and materials, which can be prohibitively expensive for small businesses and individuals. Additionally, the quality of parts produced with these technologies can be affected by factors such as printing speed, temperature, and post-processing methods. This review article aims to contribute to a deep understanding of the processing, properties, and applications of ferrous and non-ferrous alloys in the context of SLM to assist readers in obtaining high-quality AM components. Simultaneously, it emphasizes the importance of further research, optimization, and cost-effective approaches to promote the broader adoption of SLM technology in the industry.
Superconductivity in ZrB12 under High Pressure
Authors: Zhang Zexiao , Zheng Xu , Luo Hanshan , Gao Chan , Xue Xiaowei , Zhu Jingcheng , Li Ruobin , Jin Changqing , Yu Xiaohui
Published: September 2024

Transition metal borides have emerged as pivotal players in various fields. In addition to their exceptional properties such as high hardness, a high melting point, and corrosion resistance, certain compounds exhibit remarkable characteristics including superconductivity, magnetism, electrical conductivity, and catalytic activity. Among these compounds, ZrB12 has garnered significant attention due to its unique physicochemical properties. However, previous research on ZrB12 has predominantly focused on its mechanical behavior while overlooking the electron-electron interactions of the superconducting state. In this paper, resistance characterization of ZrB12 under high-pressure conditions was conducted to further investigate its superconductivity. Our research findings indicate that ZrB12 maintains its superconductivity within a pressure range of 0 to 1.5 GPa and is classified as a type 2 superconductor. Additionally, the results confirm the anisotropic nature of ZrB12’s superconductivity. As the pressure increases, the superconducting transition temperature undergoes a gradual decrease. Remarkably, ZrB12 exhibits metallic behavior under pressures up to 31.4 GPa. The observed decline in superconductivity in ZrB12 can be ascribed to the intensified influence of Zr’s movement on phonon dispersion, ultimately leading to a reduction in carrier concentration.
In Situ Microstructural Evolution and Precipitate Analysis of High-Nickel Shipbuilding Steel Using High-Temperature Confocal Laser-Scanning Microscopy
Authors: Sun Guojin , Zhu Shengzhi , Li Zhenggui , Wang Qi
Published: September 2024

This study investigates the microstructural evolution and mechanical properties of high-nickel shipbuilding steel during thermal processing using high-temperature confocal laser-scanning microscopy (HTCLSM). An in situ observation of the heating and holding processes reveals critical insights into phase transformations, grain-growth behavior, and the formation of precipitates. The experimental results demonstrate that austenitization begins at approximately 700 °C, with significant grain-boundary nucleation. At 900 °C, the formation of black precipitates was observed, and their persistence up to temperatures exceeding 1000 °C was confirmed. Scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) analyses identified these precipitates as chromium carbides (Cr7C3), which significantly contribute to the material’s strength. A comprehensive analysis using transmission electron microscopy (TEM) confirmed the presence and distribution of Cr7C3 within the grains and along grain boundaries. These findings provide a deeper understanding of the microstructural dynamics in high-nickel steels, guiding the optimization of heat-treatment processes to enhance mechanical properties for maritime applications.
Study of the Thermomechanical Behavior of Single-Crystal and Polycrystal Copper
Authors: Kunda Sudip , Schmelzer Noah J. , Pedgaonkar Akhilesh , Rees Jack E. , Dunham Samuel D. , Lieou Charles K. C. , Langbaum Justin C. M. , Bronkhorst Curt A.
Published: September 2024

This research paper presents an experimental, theoretical, and numerical study of the thermomechanical behavior of single-crystal and polycrystal copper under uniaxial stress compression loading at varying rates of deformation. The thermomechanical theory is based on a thermodynamically consistent framework for single-crystal face-centered cubic metals, and assumes that all plastic power is partitioned between stored energy due to dislocation structure evolution (configurational) and thermal (kinetic vibrational) energy. An expression for the Taylor–Quinney factor is proposed, which is a simple function of effective temperature and is allowed by second-law restrictions. This single-crystal model is used for the study of single- and polycrystal copper. New polycrystal thermomechanical experimental results are presented at varying strain rates. The temperature evolution on the surface of the polycrystal samples is measured using mounted thermocouples. Thermomechanical numerical single- and polycrystal simulations were performed for all experimental conditions ranging between 10−3 and 5 × 103 s−1. A Taylor homogenization model is used to represent polycrystal behavior. The numerical simulations of all conditions compare reasonable well with experimental results for both stress and temperature evolution. Given our lack of understanding of the mechanisms responsible for the coupling of dislocation glide and atomic vibration, this implies that the proposed theory is a reasonably accurate approximation of the single-crystal thermomechanics.
Surface Reaction-Diffusion-Coupled Simulation of Ni–Fe–Cr Alloy under FLiNaK Molten Salt
Authors: Cho Maehyun , Tonks Michael R. , Chang Kunok
Published: September 2024

A molten salt reactor is one of the fourth-generation reactors and is considered to be a feasible replacement for current reactors due to their many advantages. However, there are a number of issues that remain; one of which is the corrosion of the materials. Corrosion problems in molten salt reactors have been reported since The Molten Salt Reactor Experiment at Oak Ridge National Laboratory in the 1960s. There have been many attempts to mitigate the corrosion problem, but a fundamental solution has not yet been achived. In this study, surface reaction-diffusion-coupled simulations were performed to simulate the corrosion of a Ni–Cr–Fe material, a prototype of Hastelloy N, which is being promoted as a structural material for molten salt reactors in F–Li–Na–K eutectic salts. This surface reaction-diffusion-coupled simulation framework was developed to study which corrosion reactions are dominant in molten salt environment corrosion where a large number of oxidation–reduction reactions exist, the correlation between composition of alloy and corrosion rate, and the effect of Cr depletion on corrosion.
Realization of Friction Stir Welding of Aluminum Alloy AA5754 Using a Ceramic Tool
Authors: Sprigode Toni , Gester Andreas , Wagner Guntram , Degenhardt Ulrich
Published: September 2024

When engaging in the friction stir welding of aluminum/aluminum joints, the conventional use of tools made of hard metal and steel involves a complex and costly production process. These tools experience wear over welding distances and require frequent replacement to ensure the consistency of the welded seams. The exploration of silicon nitrite as a tool material emerges as a promising alternative in this scenario. The heightened hardness of non-oxide ceramics anticipates a diminished wear rate compared to traditional welding materials, translating into an extended operational lifespan. Nevertheless, the adoption of ceramics introduces challenges initially perceived as detrimental to friction stir welding. The inherent brittleness of silicon nitrite makes it susceptible to breakage under specific loads, and thermal stresses within the component can lead to failure. To mitigate these vulnerabilities, a ceramic material with high thermal shock resistance and a low proportion of sintering additives was used. Employing these accurately designed tools friction stir welding (FSW) was performed on sheets of AA5754, followed by a comprehensive examination of their microstructural and mechanical properties. It was demonstrated that a joint efficiency of 88% can be achieved, and that an increase in hardness within the stir zone occurred as a consequence of grain refinement. Furthermore, the Portevin–Le Chatelier effect, which is characteristic of this alloy, was influenced by the FSW process.
Effects of CTAB (Cetyltrimethylammonium Bromide) and Betaine as Corrosion Inhibitors on the Galvanic Corrosion of Cu Coupled with Au on Print Circuit Board in Etching Solution
Authors: Shin HeeKwon , Oh SeKwon
Published: September 2024

This study investigates the suppression of galvanic corrosion between copper and gold using cetyltrimethylammonium bromide (CTAB) and betaine as inhibitors. When copper is electrically connected to gold in PCB etching solutions, the substantial difference in their electrochemical potentials leads to the accelerated corrosion of copper, posing severe reliability risks. To mitigate this, we systematically investigated the galvanic corrosion inhibition properties of CTAB and betaine. Through comprehensive electrochemical analyses, it was found that the galvanic corrosion current density of copper, initially at 3.26 mA/cm2, decreased significantly to 0.251 mA/cm2 with 0.9 mM CTAB, indicating an inhibition efficiency of 92.3%. Furthermore, betaine, at a concentration of 0.1 mM, demonstrated an even higher inhibition efficiency, reducing the corrosion current to 0.03 mA/cm2, achieving a 99.1% inhibition rate. These findings provide strong evidence that CTAB and betaine are highly effective in suppressing galvanic corrosion in copper–gold systems, thereby enhancing the long-term performance and reliability of PCBs in electronic applications.
Waveguide-Enhanced Nanoplasmonic Biosensor for Ultrasensitive and Rapid DNA Detection
Authors: Barshilia Devesh , Komaram Akhil Chandrakanth , Chau Lai-Kwan , Chang Guo-En
Published: September 2024

DNA is fundamental for storing and transmitting genetic information. Analyzing DNA or RNA base sequences enables the identification of genetic disorders, monitoring gene expression, and detecting pathogens. Traditional detection techniques like polymerase chain reaction (PCR) and next-generation sequencing (NGS) have limitations, including complexity, high cost, and the need for advanced computational skills. Therefore, there is a significant demand for enzyme-free and amplification-free strategies for rapid, low-cost, and sensitive DNA detection. DNA biosensors, especially those utilizing plasmonic nanomaterials, offer a promising solution. This study introduces a novel DNA-functionalized waveguide-enhanced nanoplasmonic optofluidic biosensor using a nanogold-linked sorbent assay for enzyme-free and amplification-free DNA detection. Integrating plasmonic gold nanoparticles (AuNPs) with a glass planar waveguide (WG) and a microfluidic channel, fabricated through cost-effective, vacuum-free methods, the biosensor achieves specific detection of complementary target DNA sequences. Utilizing a sandwich architecture, AuNPs labeled with detection DNA probes enhance sensitivity by altering evanescent wave distribution and inducing plasmon resonance modes. The biosensor demonstrated exceptional performance in DNA detection, achieving a limit of detection (LOD) of 33.1 fg/mL (4.36 fM) with a rapid response time of approximately 8 min. This ultrasensitive, rapid, and cost-effective biosensor exhibits minimal background nonspecific adsorption, making it highly suitable for clinical applications and early disease diagnosis. The innovative design and fabrication processes offer significant advantages for mass production, presenting a viable tool for precise disease diagnostics and improved clinical outcomes.
Scan-Rate-Dependent Ion Current Rectification in Bipolar Interfacial Nanopores
Authors: Zhang Xiaoling , Wang Yunjiao , Zheng Jiahui , Yang Chen , Wang Deqiang
Published: September 2024

This study presents a theoretical investigation into the voltammetric behavior of bipolar interfacial nanopores due to the effect of potential scan rate (1–1000 V/s). Finite element method (FEM) is utilized to explore the current–voltage (I–V) properties of bipolar interfacial nanopores at different bulk salt concentrations. The results demonstrate a strong impact of the scan rate on the I–V response of bipolar interfacial nanopores, particularly at relatively low concentrations. Hysteresis loops are observed in bipolar interfacial nanopores under specific scan rates and potential ranges and divided by a cross-point potential that remains unaffected by the scan rate employed. This indicates that the current in bipolar interfacial nanopores is not just reliant on the bias potential that is imposed but also on the previous conditions within the nanopore, exhibiting history-dependent or memory effects. This scan-rate-dependent current–voltage response is found to be significantly influenced by the length of the nanopore (membrane thickness). Thicker membranes exhibit a more pronounced scan-rate-dependent phenomenon, as the mass transfer of ionic species is slower relative to the potential scan rate. Additionally, unlike conventional bipolar nanopores, the ion current passing through bipolar interfacial nanopores is minimally affected by the membrane thickness, making it easier to detect.
Infective Endocarditis—Predictors of In-Hospital Mortality, 17 Years, Single-Center Experience in Bulgaria
Authors: Dobreva-Yatseva Bistra , Nikolov Fedya , Raycheva Ralitsa , Tokmakova Mariya
Published: September 2024

Despite enormous developments in medicine, infective endocarditis (IE) remains an ongoing issue for physicians due to increased morbidity and persistently high mortality. Our goal was to assess clinical outcomes in patients with IE and identify determinants of in-hospital mortality. Material and methods: The analysis was retrospective, single-centered, and comprised 270 patients diagnosed with IE from 2005 to 2021 (median age 65 (51–74), male 177 (65.6%). Native IE (NVIE) was observed in 180 (66.7%), prosthetic IE (PVIE) in 88 (33.6%), and cardiac device-related IE (CDRIE) in 2 (0.7%), with non-survivors having much higher rates. Healthcare-associated IE (HAIE) was 72 (26.7%), Staphylococci were the most prevalent pathogen, and the proportion of Gram-negative bacteria (GNB) non-HACEK was significantly greater in non-survivors than survivors (11 (15%) vs. 9 (4.5%), p = 0.004). Overall, 54 (20%) patients underwent early surgery, with a significant difference between dead and alive patients (3 (4.5%) vs. 51 (25.1%, p = 0.000). The overall in-hospital mortality rate was 24.8% (67). Logistic regression was conducted on the total sample (n = 270) for the period 2005–2021, as well as the sub-periods 2005–2015 (n = 119) and 2016–2021 (n = 151), to identify any differences in the trend of IE. For the overall group, the presence of septic shock (OR-83.1; 95% CI (17.0–405.2), p = 0.000) and acute heart failure (OR—24.6; 95% CI (9.2–65.0), p = 0.000) increased the risk of mortality. Early surgery (OR-0.03, 95% CI (0.01–0.16), p = 0.000) and a low Charlson comorbidity index (OR-0.85, 95% CI (0.74–0.98, p = 0.026) also lower this risk. Between 2005 and 2015, the presence of septic shock (OR 76.5, 95% CI 7.11–823.4, p = 0.000), acute heart failure (OR-11.5, 95% CI 2.9–46.3, p = 0.001), and chronic heart failure (OR-1.3, 95% CI 1.1–1.8, p = 0.022) enhanced the likelihood of a fatal outcome. Low Charlson index comorbidity (CCI) lowered the risk (OR-0.7, 95% CI 0.5–0.95, p = 0.026). For the period 2016–2021, the variable with the major influence for the model is the failure to perform early surgery in indicated patients (OR-240, 95% CI 23.2—2483, p = 0.000) followed by a complication of acute heart failure (OR-72.2, 95% CI 7.5–693.6. p = 0.000), septic shock (OR-17.4, 95% CI 2.0–150.8, p = 0.010), previous stroke (OR-9.2, 95% CI 1.4–59.4, p = 0.020) and low ejection fraction (OR-1.1, 95% CI 1.0–1.2, p = 0.004). Conclusions: Knowing the predictors of mortality would change the therapeutic approach to be more aggressive, improving the short- and long-term prognosis of IE patients.
An Overview of the Recent Advances in Antimicrobial Resistance
Authors: Oliveira Manuela , Antunes Wilson , Mota Salete , Madureira-Carvalho Áurea , Dinis-Oliveira Ricardo Jorge , Dias da Silva Diana
Published: September 2024

Antimicrobial resistance (AMR), frequently considered a major global public health threat, requires a comprehensive understanding of its emergence, mechanisms, advances, and implications. AMR’s epidemiological landscape is characterized by its widespread prevalence and constantly evolving patterns, with multidrug-resistant organisms (MDROs) creating new challenges every day. The most common mechanisms underlying AMR (i.e., genetic mutations, horizontal gene transfer, and selective pressure) contribute to the emergence and dissemination of new resistant strains. Therefore, mitigation strategies (e.g., antibiotic stewardship programs—ASPs—and infection prevention and control strategies—IPCs) emphasize the importance of responsible antimicrobial use and surveillance. A One Health approach (i.e., the interconnectedness of human, animal, and environmental health) highlights the necessity for interdisciplinary collaboration and holistic strategies in combating AMR. Advancements in novel therapeutics (e.g., alternative antimicrobial agents and vaccines) offer promising avenues in addressing AMR challenges. Policy interventions at the international and national levels also promote ASPs aiming to regulate antimicrobial use. Despite all of the observed progress, AMR remains a pressing concern, demanding sustained efforts to address emerging threats and promote antimicrobial sustainability. Future research must prioritize innovative approaches and address the complex socioecological dynamics underlying AMR. This manuscript is a comprehensive resource for researchers, policymakers, and healthcare professionals seeking to navigate the complex AMR landscape and develop effective strategies for its mitigation.
Renal Actinomycosis in Humans—A Narrative Review
Authors: Giannakodimos Ilias , Ziogou Afroditi , Giannakodimos Alexios , Mitakidi Evangelia , Kaltsas Aris , Kratiras Zisis , Chrisofos Michael
Published: September 2024

Actinomycosis of the kidney is extremely rare, with only a few cases reported in the literature. This rare entity usually presents with common clinical manifestations and non-specific imaging findings, thus rendering its diagnosis extremely challenging. According to case reports published in the literature, fever was present in the majority of cases (56.25%). Several risk factors have been related to the development of renal actinomycosis, including a history of urogenital surgery, urolithiasis, or urogenital cancer. Initial diagnostic investigation consists of abdominal ultrasonography (15 patients, 46.88%) and abdominal computed tomography (23 patients, 48.15%). Regarding therapeutic approach, 5 patients (16.67%) received only antibiotic treatment, 5 patients (16.67%) underwent surgery, and 20 patients (66.67%) received both antibiotic and surgical treatment. Accurate diagnosis relies on the clinician’s high index of suspicion and is ultimately confirmed through histological examination or cultures, obtained either preoperatively via biopsies or postoperatively after surgical removal of the infected kidney. To our knowledge, this is the first narrative review of the literature that collects knowledge concerning infection of UUT induced by dysbiosis of actinomycosis species. The aim of this narrative review was to systematically search the literature on primary renal actinomycosis, highlighting the diagnostic approach and treatment strategies for the management of this rare entity.
Changes in Cyanobacterial Phytoplankton Communities in Lake-Water Mesocosms Treated with Either Glucose or Hydrogen Peroxide
Authors: Linz David , Partridge Charlyn G. , Hassett Michael C. , Sienkiewicz Nathan , Tyrrell Katie , Henderson Aimèe , Tardani Renee , Lu Jingrang , Steinman Alan D. , Vesper Stephen
Published: September 2024

When cyanobacterial phytoplankton form harmful cyanobacterial blooms (HCBs), the toxins they produce threaten freshwater ecosystems. Hydrogen peroxide is often used to control HCBs, but it is broadly toxic and dangerous to handle. Previously, we demonstrated that glucose addition to lake water could suppress the abundance of cyanobacteria. In this study, glucose was compared to hydrogen peroxide for the treatment of cyanobacterial phytoplankton communities. The six-week study was conducted in the large mesocosms facility at Grand Valley State University’s Annis Water Resources Institute in Michigan. To 1000 L of Muskegon Lake water, glucose was added at either 150 mg or 30 mg glucose/L. Hydrogen peroxide was added at 3 mg/L to two 1000 L mesocosms. And two mesocosms were left untreated as controls. Triplicate 100 mL samples were collected weekly from each mesocosm, which were then filtered and frozen at −80 °C for 16S rRNA amplicon sequencing. The 16S rRNA amplicon sequencing results revealed that hydrogen peroxide treatment quickly reduced the relative abundance of the cyanobacteria compared to the control mesocosms, but the cyanobacteria population returned over the course of the 6-week study. On the other hand, both glucose concentrations caused a rapid proliferation of multiple low abundance proteobacterial and bacteroidotal taxa resulting in notable increases in taxonomic richness over the duration of the study and reducing the relative abundance of cyanobacteria. Although hydrogen peroxide quickly suppressed the cyanobacteria, the population later returned to near starting levels. The glucose suppressed the cyanobacterial phytoplankton apparently by promoting competitive heterotrophic bacteria.
A First Principles Study of Lithium Adsorption in Nanoporous Graphene
Authors: Barabanova Liudmyla , Buldum Alper
Published: September 2024

Recently, nanoporous graphene has attracted great interest in the scientific community. It possesses nano-sized holes; thus, it has a highly accessible surface area for lithium adsorption for energy storage applications. Defective graphene has been extensively studied. However, the lithium adsorption mechanism of nanoporous graphene is not clearly understood yet. Here, we present theoretical investigations on the lithium-ion adsorption mechanism in nanoporous graphene. We perform ab initio electronic structure calculations based on density functional theory. Lithium adsorption in a graphene nanopore is studied and adsorption sites are determined. We also study different lithium-ion distributions in graphene nanopores to determine the best lithium–nanoporous graphene structures for lithium-ion batteries. We show that lithium ions can be adsorbed in a graphene nanopore, even in a single layer of graphene. It is also shown that adding more nanopores to multilayer nanoporous graphene can result in higher Li storage capacity for new-generation lithium-ion batteries.
Recent Progress on Layered Sn and Pb-Based Mono Chalcogenides: Synthesis, Structure, Optical, and Thermoelectric Properties and Related Applications
Authors: Rahman Safwan , Sharme Razia Khan , Terrones Mauricio , Rana Mukti M.
Published: September 2024

The research on two-dimensional materials has gained significant traction due to their potential for thermoelectric, optical, and other properties. The development of two-dimensional (2D) nanostructured-based TE generators and photodetectors has shown promising results. Over the years, researchers have played a crucial role in advancing this field, enhancing the properties of 2D materials through techniques such as doping, alloying, and various growth methods. Among these materials, black phosphorus, transition metal dichalcogenides, graphene, and IVA-VIA compounds stand out for their remarkable electronic, mechanical, and optical properties. This study presents a comprehensive review of the progress in the field, focusing on IVA-VIA compounds and their applications in TE and photodetector technologies. We summarize recent advancements in enhancing these materials’ TE and optical properties and provide an overview of various synthesis techniques for their fabrication. Additionally, we highlight their potential applications as photodetectors in the infrared spectrum. This comprehensive review aims to equip researchers with a deep understanding of the TE and optical properties of 2DMs and their potential applications and to inspire further advancements in this field of research.
Improved Electromagnetic Interference Shielding Efficiency of PVDF/rGO/AgNW Composites via Low-Pressure Compression Molding and AgNW-Backfilling Strategy
Authors: Li Zhunzhun , Li Yaqun , Mao Zhusong , Mei Xingyu , Zhang Qimei
Published: September 2024

Silver nanowires (AgNWs) have excellent electrical conductivity and nano-sized effects and have been widely used as a high-performance electromagnetic shielding material. However, silver nanowires have poor mechanical properties and are prone to fracture during the preparation of composite materials. In this study, PVDF/rGO/AgNW composites with a segregated structure were prepared using low-pressure compression molding and the AgNW-backfilling process. The low-pressure compression of the composite significantly improves its electromagnetic shielding performance because the low-pressure process can maintain the AgNWs’ integrity. The backfilled AgNWs played a vital role in increasing the path of electromagnetic wave propagation and the absorption of electromagnetic waves. The backfilled amount of AgNWs was only 1 wt%, which increased the composite material’s conductivity by one order of magnitude. The total electromagnetic interference shielding (SET) of the composite materials increased by 23.3% from 24.88 dB to 30.67 dB. The absorption contribution (SEA/SET) increased from 84.2% to 92.8%, significantly improving the electromagnetic interference shielding and the absorption contribution of the AgNWs in the composites. This was attributed to the backfilling of the porous structure by the AgNWs, which promoted multiple reflections and enhanced the absorption contribution.
Recent Progress on Stability of Layered Double Hydroxide-Based Catalysts for Oxygen Evolution Reaction
Authors: He Lielie , Zhou Yangen , Wang Mengran , Li Simin , Lai Yanqing
Published: September 2024

Water electrolysis is regarded as one of the most viable technologies for the generation of green hydrogen. Nevertheless, the anodic oxygen evolution reaction (OER) constitutes a substantial obstacle to the large-scale deployment of this technology, due to the considerable overpotential resulting from the retardation kinetics associated with the OER. The development of low-cost, high-activity, and long-lasting OER catalysts has emerged as a pivotal research area. Layered double hydroxides (LDHs) have garnered significant attention due to their suitability for use with base metals, which are cost-effective and exhibit enhanced activity. However, the current performance of LDHs OER catalysts is still far from meeting the demands of industrial applications, particularly in terms of their long-term stability. In this review, we provide an overview of the causes for the deactivation of LDHs OER catalysts and present an analysis of the various mechanisms employed to improve the stability of these catalysts, including the synthesis of LDH ultrathin nanosheets, adjustment of components and doping, dissolution and redeposition, defect creation and corrosion, and utilization of advanced carbon materials.
In Situ Rapid Hierarchical Growth of Ag Nanodendrites in Phytantriol: Influence on Polydispersity and Optical Characteristics
Authors: Alnaim Nisrin
Published: September 2024

Silver nanodendrites (AgNDs) were effectively synthesized utilizing a phytantriol template at ambient temperature without electrodeposition. In comparison to AgNDs, the phytantriol-templated silver nanodendrites (P/AgNDs) exhibited a smoother structure with the well-ordered growth of smaller particles around 51 nm. Moreover, the P/AgNDs exhibited uniform elemental distribution, forming chemical bonds with functional groups, crystallite size around 42 nm, and high transmittance around 95%. These experimental findings indicate that room temperature-based hierarchical P/AgNDs have considerable potential for diverse applications, specifically in sensing.
1 × 2 Graphene Surface Plasmon Waveguide Beam Splitter Based on Self-Imaging
Authors: Lu Liu , Xu Peng , Zhang Liang , Le Jia , Chen Daifen
Published: September 2024

Based on the principle of self-imaging, a 1 × 2 graphene waveguide beam splitter is proposed in this work, which can split the graphene surface plasmons excited by far-infrared light. The multimode interference process in the graphene waveguide is analyzed by guided-mode propagation analysis (MPA), and then the imaging position is calculated. The simulation results show that the incident beam can be obviously divided into two parts by the self-imaging of the graphene surface plasmon. In addition, the influences of the excited light wavelength, Fermi level, dielectric environment on the transmission efficiency are studied, which provide a reference for the research of graphene waveguide related devices.
Resonant Metasurfaces with Van Der Waals Hyperbolic Nanoantennas and Extreme Light Confinement
Authors: Babicheva Viktoriia E.
Published: September 2024

This work reports on a metasurface based on optical nanoantennas made of van der Waals material hexagonal boron nitride. The optical nanoantenna made of hyperbolic material was shown to support strong localized resonant modes stemming from the propagating high-k waves in the hyperbolic material. An analytical approach was used to determine the mode profile and type of cuboid nanoantenna resonances. An electric quadrupolar mode was demonstrated to be associated with a resonant magnetic response of the nanoantenna, which resembles the induction of resonant magnetic modes in high-refractive-index nanoantennas. The analytical model accurately predicts the modes of cuboid nanoantennas due to the strong boundary reflections of the high-k waves, a capability that does not extend to plasmonic or high-refractive-index nanoantennas, where the imperfect reflection and leakage of the mode from the cavity complicate the analysis. In the reported metasurface, excitations of the multipolar resonant modes are accompanied by directional scattering and a decrease in the metasurface reflectance to zero, which is manifested as the resonant Kerker effect. Van der Waals nanoantennas are envisioned to support localized resonances and can become an important functional element of metasurfaces and transdimensional photonic components. By designing efficient subwavelength scatterers with high-quality-factor resonances, this work demonstrates that this type of nanoantenna made of naturally occurring hyperbolic material is a viable substitute for plasmonic and all-dielectric nanoantennas in developing ultra-compact photonic components.