Aggregate Efficiency Research Articles

Fault diagnosis method for elevator carriages based on temporal generative federated distillation

Addressing the ‘data silo’ issue among different elevator operating units and the temporal correlations in elevator vibration signals, a novel small-sample fault diagnosis method for elevator carriages based on temporal generative federated distillation is proposed. This method incorporates a temporal generative adversarial network into Federated Distillation via Generative Learning (FedGen). FedGen combines federated learning, knowledge distillation, and generative models to enhance model aggregation efficiency while mitigating data heterogeneity. However, the original generative model struggles to maintain dynamic correlations between signals when extracting temporal features. Therefore, an improved Time Series Generative Adversarial Networks (TimeGAN) model is introduced, substituting the initial logarithmic loss function with a least squares error function, thereby enhancing training stability and data quality. This approach eliminates the need for proxy datasets in knowledge distillation, avoiding the loss of temporal information during central server feature extraction. Simulation results demonstrate that this method enables data sharing while protecting data privacy, and enhances model generalization capabilities.

Conjugated oligo (phenylene vinylene) covalently linked porphyrin for sonodynamic therapy

AbstractSonodynamic therapy (SDT) is garnering considerable attention as a promising treatment for deep‐seated tumors because of its strong tissue penetration ability, non‐invasiveness, and controllability. However, the SDT efficiency of traditional sonosensitizers including porphyrins and their derivatives are limited due to their poor water dissolubility, high aggregation, and low reactive oxygen species (ROS) production efficiency. Consequently, it is crucial to develop novel sonosensitizers with high yields of ROS, outstanding water solubility, and good biocompatibility. Herein, we constructed a new platform for SDT based on unimolecular porphyrin derivatives OPV‐C3‐TPP. The probe OPV‐C3‐TPP was synthesized by covalently linking conjugated oligomers (OPV) with 5, 10, 15, 20‐tetra (4‐aminophenyl) porphyrin (TAPP). The introduction of OPV greatly improves the water solubility of the porphyrins and reduces the self‐aggregation of the porphyrins. In addition, OPV‐C3‐TPP has good intramolecular energy transfer efficiency, thus enhancing the yield of ROS. The experimental results show that OPV‐C3‐TPP exhibits excellent ROS generation capacity under ultrasound (US) irradiation, which leads to apoptosis and necrosis of tumor cells. In vivo tumor growth is also significantly inhibited in the OPV‐C3‐TPP + US group, exhibiting better SDT effects than TAPP. Therefore, the unimolecular OPV‐C3‐TPP can be used as a potential sonosensitizer, providing a promising SDT for deep‐tissue tumors.

A Hybrid Strategy Two‐Dimensional Concrete Aggregate Filling Algorithm

ABSTRACTAccurate and highly efficient construction of numerical concrete models with different gradations is important to analyze the acoustic properties of ultrasonic waves in concrete. The random aggregates‐based method is a classic approach to meso‐structural modeling of concrete and is particularly suitable for generating models with low‐density aggregates. However, when facing the simulation requirements of concrete models with large volumes, small particle sizes, and high aggregate content, traditional random aggregates‐based methods encounter challenges in satisfying aggregate packing density and modeling efficiency. In order to improve the efficiency of aggregate placement in modeling concrete meso‐models, we proposed a hybrid‐strategy point cloud placement algorithm that combines the re‐placement and sedimentation strategies. Then, the algorithm's effectiveness in improving the modeling efficiency is verified by reconstruction experiments on random aggregate models with different aggregate grades. Finally, the model's reliability and validity is further confirmed by simulation analysis of the ultrasonic attenuation characteristics of the model. The results show that the algorithm can rapidly generate more than 75% aggregate packing density concrete specimens and significantly outperforms similar algorithms regarding aggregate placement efficiency and model generation time. In addition, the model generated by the proposed algorithm can accurately simulate the attenuation characteristics of ultrasonic waves in concrete.

Comprehensive Analysis of Sulfated Flavonoids in Eclipta prostrata for Quality Evaluation

Eclipta prostrata (Asteraceae) is employed as a hemostatic agent in many traditional medicines, owing to its sulfated flavonoid content. In this study, we obtained crude drug samples from three provinces collected in different years and analyzed their sulfated flavonoid contents using liquid chromatography–mass spectrometry (LC–MS) for quality evaluation. Because sulfated flavonoids are unstable and difficult to isolate from extracts, this study first synthesized a variety of sulfated flavonoids and accumulated spectral data in order to identify the compounds in E. prostrata. The LC–MS analysis of six crude drug samples revealed the presence of luteolin 7-sulfate, apigenin 7-sulfate, diosmetin 7-sulfate, and diosmetin 3′-sulfate. The samples without luteolin 3′-sulfate featured high apigenin 7-sulfate content. Although the samples were collected from the same locality, their compositions differed depending on the year of collection. Further, they were classified according to three patterns: (1) samples with luteolin 7-sulfate as the main component, (2) samples with apigenin 7-sulfate as the main component, and (3) samples with relatively high diosmetin sulfate content. Luteolin 7-sulfate typically exhibits relatively high erythrocyte aggregation efficiency and fibrinogen aggregation rate. These results demonstrate that the analysis of sulfated flavonoids is beneficial for the quality evaluation of E. prostrata for hemostatic applications.

Evaluation of the Aggregation Efficiency Modeling at Colder Atmospheric Temperatures in Comparison to Satellite Observations

Abstract Aggregation efficiency in the upper troposphere is highly uncertain because of the lack of laboratory experiments and aircraft measurements, especially at atmospheric temperatures below −30°C. Aggregation is physically broken down into collision and sticking. In this study, theory-based parameterizations for the collision efficiency and sticking efficiency are newly implemented into a double-moment bulk cloud microphysics scheme. Satellite observations of the global ice cloud distribution are used to evaluate the aggregation efficiency modeling. Sensitivity experiments of 9-day global simulations using a high-resolution climate model show that the use of collision efficiency parameterization causes a slight increase in the cloud ice amount above the freezing level over the tropics to midlatitudes and that the use of our new sticking efficiency parameterization causes a significant increase in the cloud ice amount and a slight decrease in the snow amount particularly in the upper troposphere over the tropics. The increase/decrease in the cloud ice/snow amount in the upper troposphere over the tropics is consistent with the vertical profile of radar echoes. Moreover, the ice fraction of the cloud optical thickness is still underestimated worldwide. Finally, the cloud radiative forcing increases over the tropics to reduce the bias in the radiation budget. These results indicate that our new aggregation efficiency modeling reasonably functions even at atmospheric temperatures below −30°C; however, further improvements in the ice cloud modeling are needed. Significance Statement Long-standing biases in the radiative budget in climate models indicate the existence of a missing mechanism to realistically represent the ice cloud growth in the upper troposphere. This study focuses on aggregation efficiency, which has been assumed to be a tuning parameter to optimize the global radiative budget. Therefore, this study employs a theory-based parameterization to calculate the aggregation efficiency. According to the parameterization, aggregation efficiency in high clouds varies by the growth stage of the individual ice particles. As a result, small ice crystals are likely to grow more slowly, and the lifetime of cirrus clouds is prolonged to enhance cloud radiative forcing, particularly over the tropics. These results are promising for reducing the biases observed in climate models.

MixFuse: An iterative mix-attention transformer for multi-modal image fusion

Multi-modal image fusion plays a crucial role in various visual systems. However, existing methods typically involve a multi-stage pipeline, i.e., feature extraction, integration, and reconstruction, which limits the effectiveness and efficiency of feature interaction and aggregation. In this paper, we propose MixFuse, a compact multi-modal image fusion framework based on Transformers. It smoothly unifies the process of feature extraction and integration, As its core, the Mix Attention Transformer Block (MATB) integrates the Cross-Attention Transformer Module (CATM) and the Self-Attention Transformer Module (SATM). The CATM introduces a symmetrical cross-attention mechanism to identify modality-specific and general features, filtering out irrelevant and redundant information. Meanwhile, the SATM is designed to refine the combined features via a self-attention mechanism, enhancing the internal consistency and proper preservation of the features. This successive cross and self-attention modules work together to enhance the generation of more accurate and refined feature maps, which are essential for later reconstruction. Extensive evaluation of MixFuse on five public datasets shows its superior performance and adaptability over state-of-the-art methods. The code and model will be released at https://github.com/Bitlijinfu/MixFuse.

Comparative analysis of aggregate and sectoral time-varying market efficiency in the Russian stock market during the COVID-19 outbreak and the Russia–Ukraine conflict (RUC)

PurposeThe primary purpose of this research is to identify and compare the multifractal behavior of different sectors during these crises and analyze their implications on market efficiency.Design/methodology/approachWe used multifractal detrended fluctuation analysis (MF-DFA) to analyze stock returns from various sectors of the Moscow Stock Exchange (MOEX) in between two significant periods. The COVID-19 pandemic (January 1, 2020, to December 31, 2021) and the Russia–Ukraine conflict (RUC) (January 1, 2022, to June 30, 2023). This method witnesses multifractality in financial time series data and tests the persistency and efficiency levels of each sector to provide meaningful insights.FindingsResults showcased persistent multifractal behavior across all sectors in between the COVID-19 pandemic and the RUC, spotting heightened arbitrage opportunities in the MOEX. The pandemic reported a greater speculative behavior, with the telecommunication and oil and gas sectors exhibiting reduced efficiency, recommending abnormal return potential. In contrast, financials and metals and mining sectors displayed increased efficiency, witnessing strong economic performance. Findings may enhance understanding of market dynamics during crises and provide strategic insights for the MOEX’s investors.Practical implicationsUnderstanding the multifractal properties and efficiency of different sectors during crisis periods is of paramount importance for investors and policymakers. The identified arbitrage opportunities and efficiency variations can aid investors in optimizing their investment strategies during such critical market conditions. Policymakers can also leverage these insights to implement measures that bolster economic stability and development during crisis periods.Originality/valueThis research contributes to the existing body of knowledge by providing a comprehensive analysis of multifractal properties and efficiency in the context of the MOEX during two major crises. The application of MF-DFA to sectoral stock returns during these events adds originality to the study. The findings offer valuable implications for practitioners, researchers and policymakers seeking to navigate financial markets during turbulent times and enhance overall market resilience.

Vertical Profile Climatology of Polarimetric Radar Variables and Retrieved Microphysical Parameters in Synoptic and Lake Effect Snowstorms

AbstractThis study derives polarimetric radar vertical profiles and microphysical retrievals for 25 Synoptic Snow (SS) and 23 Lake Effect Snow (LES) cases using the Range‐Defined Quasi‐Vertical Profiles (RD‐QVP), Columnar Vertical Profiles (CVP), and Process‐oriented Vertical Profiles (POVP) methods. For all vertical profile techniques, SS cases exhibit a near‐linear increase in reflectivity from −30 to 0°C whereas ZDR and Kdp locally peak in the dendritic growth layer. LES cases universally exhibit negative ZDR, rather high Z, negligible Kdp, and near‐unity ρhv. Ground measurements from the past OWLeS campaign provide direct evidence that conical graupel may strongly affect these polarimetric measurements in LES bands. Aggregation efficiencies for SS cases are estimated by optimizing the theoretical number concentration (Nt) and mean volume diameter (Dm) steady‐state vertical profiles against radar‐retrieved profiles derived from 20 of the 25 synoptic storm RD‐QVPs. The median estimated aggregation efficiency is approximately 0.15 with a relatively narrow interquartile range that spans from 0.1 to just over 0.2. Values of optimized aggregation efficiencies are nearly independent of the assumed gamma distribution shape parameter. These results are used to derive temperature‐dependent, climatological steady‐state relations for vertical profiles of Nt, Dm, and liquid‐equivalent snowfall rates. These results can be used in numerical weather prediction model aggregation parameterizations and can also provide climatologically representative vertical profiles of radar and microphysical quantities.

Smooth vetch covering alters soil aggregate microbial metabolic limitations in citrus orchards

Green manure covering alters the supply-demand relationship between soil resources and microorganisms by improving soil structure and increasing carbon inputs. However, it remain unclear how soil microorganism respond to the imbalance between resources and demands at the aggregate scale. The present study analyzed the stoichiometric ratios of organic carbon, nitrogen, and phosphorus nutrients, microbial extracellular enzyme activities, and biomass in large macroaggregates (LMA, 2–8 mm), small macroaggregates (SMA, 0.25–2 mm), and microaggregates (MIA, < 0.25 mm) under smooth vetch covering. The relationship between microbial carbon use efficiency and nutrient restriction in soil aggregates was elucidated and the alteration was revealed in microbial metabolic pathways and carbon sequestration functions within soil aggregates under green manure covering. The results showed that: (1) Smooth vetch covering significantly increased the C: N resource imbalance (ln(MBC: MBN)/ln(SOC: TN)) in soil aggregates from 0.50 to 0.69, and thus increased the nitrogen requirement of soil aggregates. However, the N:P resource imbalance (ln(MBN: MBP)/ln(TN: TP)) decreased from 2.25 to 1.78, especially in microaggregates. (2) To mitigate the limitation imposed by nitrogen availability at the aggregate level, microorganisms have ramped up the activity of C- (46.72 %–98.64 %), N- (2.32 %–121.00 %), and P- acquiring enzymes (119.11 %–187.78 %). Nevertheless, the spatial heterogeneity inherent in soil aggregates, characterized by varying particle sizes, has led to a pivotal shift in microbial nutrient limitation. As soil aggregate size diminishes, the primary constraint on microbial activity transitions from nitrogen limitation to phosphorus limitation, reflecting the dynamic interplay between soil structure and nutrient availability. (3) In addition, smooth vetch covering increased the microbial carbon use efficiency of soil aggregates, and it increased with the increase of the particle size of soil aggregates: LMA (178.80 %) > SMA (147.23 %) > MIA (9.99 %). Microorganisms allocated more energy to generate biomass instead of obtaining limiting nutrient elements. These results indicate that smooth vetch covering could increase the organic carbon content of soil aggregates. In addition, in order to adapt to nutrient imbalance, soil aggregates microorganisms choose a “egoistic” metabolic pathway, which alleviates nutrient limitation and assimilated more energy and nutrients into their own biomass and reduces the release of organic carbon. It is evident that integrating Vicia villosa Roth var. glabresens Koch as a ‘green manure’ in orchards serves a dual purpose: it mitigates the environmental impact of chemical fertilizers while simultaneously promoting soil carbon sequestration. This approach offers a robust theoretical framework for achieving the harmonious integration of ecological sustainability and economic viability in economic forestry, such as citrus orchards.

Catalyst-free regeneration of plasma-activated water via ultrasonic cavitation: Removing aggregation concealment of antibiotic-resistant bacteria with enhanced wastewater sustainability

Aggregation is a crucial factor in bacterial biofilm formation, and comprehending its properties is vital for managing waterborne antibiotic-resistant bacteria. In this study, we examined Methicillin-resistant Staphylococcus aureus (MRSA) cell aggregation under varying conditions and assessed the inactivation efficiency of a novel disinfection method, micro-nano bubbles plasma-activated water via ultrasonic stirring cavitation (MPAW-US), on aggregated MRSA cells. Aggregation efficiency increased over time and at low salt concentrations but diminished at higher concentrations. Elevated MRSA cell aggregation in actual water samples represented significant real-life biohazard risks. Unlike conventional disinfection, MPAW-US treatment exhibited minimal change in the inactivation rate constant despite protective outer layers. Enhanced inactivation efficiency results from the synergistic effects of increased intracellular oxidative stress damage and extracellular substance disruption, triggered by ultrasound-activated micro-nano bubbles that improve PAW reactivity and applicability. This approach neither induced MRSA cross-resistance to unfavorable conditions nor increased toxicity or regrowth potential of aggregative MRSA, utilizing ATP levels as potential regrowth capability indicators. Ultimately, this energy-efficient disinfection technology functions effectively across diverse temperature ranges, showcasing exceptional sterilization and nutritional bean sprout production after cyclic filtering, thereby promoting wastewater sustainability amidst carbon emission concerns.

Exploring Scalability of BFT Blockchain Protocols through Network Simulations

Novel Byzantine fault-tolerant (BFT) state machine replication protocols improve scalability for their practical use in distributed ledger technology, where hundreds of replicas must reach consensus. Evaluating the performance of BFT protocol implementations requires careful evaluation. We propose a new methodology using scalable network simulations to predict BFT protocol performance. Our simulation architecture allows for the integration of existing BFT implementations without modification or re-implementation, offering a cost-effective alternative to large-scale cloud experiments. We validate our method by comparing simulation results with real-world cloud deployments, showing that simulations can accurately predict performance at larger scales when network limitations dominate. In our study, we applied this methodology to assess the performance of several “blockchain-generation” BFT protocols, including HotStuff, Kauri, Narwhal &amp; Tusk, and Bullshark, under realistic network conditions (with constrained 25 Mbit/s bandwidth) and induced faults. Kauri emerges as the top performer, achieving 6742 operations per second (op/s) with 128 replicas, outperforming BullShark (2318 op/s) and Tusk (1952 op/s). HotStuff, using secp256k1 and BLS signatures, reaches 494 op/s and 707 op/s, respectively, demonstrating the efficiency of BLS-signature aggregation for saving bandwidth. This study demonstrates that state-of-the-art asynchronous BFT protocols can achieve competitive throughput in large-scale, real-world scenarios.

Aggregation of Albumins under Reductive Radical Stress.

The reactions of radicals with human serum albumin (HSA) under reductive stress conditions were studied using pulse radiolysis and photochemical methods. It was proved that irradiation of HSA solutions under reductive stress conditions results in the formation of stable protein aggregates. HSA aggregates induced by ionizing radiation are characterized by unique emission, different from the UV emission of non-irradiated solutions. The comparison of transient absorption spectra and the reactivity of hydrated electrons (eaq-) with amino acids or HSA suggests that electron attachment to disulfide bonds is responsible for the transient spectrum recorded in the case of albumin solutions. The reactions of eaq- and CO2•- with HSA lead to the formation of the same products. Recombination of sulfur-centered radicals plays a crucial role in the generation of HSA nanoparticles, which are stabilized by intermolecular disulfide bonds. The process of creating disulfide bridges under the influence of ionizing radiation is a promising method for the synthesis of biocompatible protein nanostructures for medical applications. Our Raman spectroscopy studies indicate strong modification of disulfide bonds and confirm the aggregation of albumins as well. Low-temperature measurements indicate the possibility of electron tunneling through the HSA protein structure to specific CyS-SCy bridges. The current study showed that the efficiency of HSA aggregation depends on two main factors: dose rate (number of pulses per unit time in the case of pulse radiolysis) and the temperature of the irradiated solution.

Digital Twin and federated learning enabled cyberthreat detection system for IoT networks

The widespread deployment of Internet of Things (IoT) devices across various smart city applications presents significant security challenges, increased by the rapidly evolving landscape of cyber threats. Traditional security solutions, including those using Federated Learning with federated averaging, suffer from inefficiencies due to random node selection and partial data sampling, which can hinder the detection of comprehensive network-wide attacks. This paper introduces a novel Cyberthreat Detection System for IoT networks that leverages Digital Twin technology and an optimized Federated Learning approach. Our hypothesis implies integrating Digital Twin models within an IoT security framework to improve real-time cyberthreat detection capabilities. We implement a 'Adaptive Thresholding with Early Stopping method' based methodology in Federated Learning to systematically train and aggregate local models based on predefined training rounds, thereby ensuring that all local models contribute to the global model until a target accuracy is achieved. This method significantly improves the detection of zero-day attacks by reducing dependency on random selections and partial data. The system architecture features Digital Twins of IoT medical infrastructure components—such as radiology, intensive care, and outpatient care—positioned at the network edge to minimize latency and bandwidth usage. Comparative evaluations of our model against traditional federated averaging methods demonstrate superior performance, with enhancements in model aggregation efficiency evidenced by higher F1 scores and reduced CPU usage. Specifically, our distributed digital twin environment at the edge layer shows 14% and 33% latency reductions compared to fog and cloud-based implementations, respectively. This study highlights the potential of Digital Twin and advanced Federated Learning methodologies to secure IoT networks against evolving and growing cyber threats.

Dual effects of additional food supply and threshold control on the dynamics of a Leslie–Gower model with pest herd behavior

Bemisia tabaci is a major agricultural pest and recognized by the United Nations Food and Agriculture Organization (FAO) as the second largest pest in the world. This paper aims to study the management of Bemisia tabaci from theoretical aspects. Firstly, considering the omnivorous nature of natural enemy species and pest herd behavior, a Leslie–Gower type pest-natural enemy model with additional food supply and non-differentiable rational uptake function is put forward, and the effects of additional food supply and aggregation efficiency on the dynamical behavior of the system are analyzed. Secondly, for the slowness of pest control by natural enemy species, a threshold control strategy is adopted to prevent the spread of Bemisia tabaci by spraying a certain intensity of insecticides and releasing a certain number of natural enemies. The complex dynamics of the system induced by the threshold control are discussed such as the existence of predator-extinction periodic solution, the existence and stability of coexisting order-1 periodic solution. Thirdly, from the perspective of cost control, an optimization problem is constructed based on the existence of the order-1 periodic solution, and the optimal control threshold, control strength and control frequency are determined. At last, to illustrate the theoretical results more intuitively, the numerical simulations in MATLAB program are presented to illustrate the effectiveness and practical significance of the control measures. The results of this study provide a reference for the scientific and reasonable management of Bemisia tabaci and is helpful to realize the sustainable development of agricultural production.

Study on quality of treated wastewater for urban agriculture use in Addis Ababa, Ethiopia

Non-conventional water supplied from Bole Lemi Industrial Park (BLIP) in Addis Ababa, Ethiopia is treated to remove contaminants, but never has been analyzed for its quality for urban agriculture (UA) use. The objective of this study is to analyze the quality of treated wastewater (TWW) using treated wastewater quality index (TWWQI). Treated wastewater samples were taken at the influent and effluent of the wastewater treatment plant located within BLIP. The physico-chemical properties of the water samples from BLIP were analyzed at the third-party laboratory. The result showed that the aggregate TWWQI value falls under the category of very poor water for urban irrigation agriculture (UIA) use. This very poor water quality grade attributes 85 % to the presence of heavy metals, 4 % to nutrients load, 8 % to saline condition contributors and 4 % to miscellaneous contaminants. Discrete analyses of the indices for heavy metals, nutrient loads, saline condition contributors and miscellaneous contaminants shifted the water quality from very poor to very polluted, excellent, poor and good for UIA use respectively. Though the aggregate TWWQI is of very poor category for UIA use, the treatment plants of BLIP exhibited contaminants removal efficiencies of between 30 and 100 % with aggregate removal efficiency of 58 %. Results also revealed that there were contaminants that exhibited higher effluent values than influent after treatment indicating the failure of BLIP’s treatment plants to remove these contaminants. In conclusion, treated wastewater from BLIP, falls under very poor water category for use in irrigation agriculture where Cr+3, Cl− and TDS have contributed most in that order. Therefore, BLIP should upgrade and optimize its treatment plants to increase the removal efficiency for the respective contaminants. Moreover, BLIP should enhance the capacity of the experts through training and continually monitor the quality of the water to protect the environment and ensure its contribution to the building of a resilient city.

Optimization method of dynamic reconfiguration in virtual power plants

With the continuous expansion of distributed energy resources (DERs), virtual power plants (VPPs) have emerged as an efficient solution for their aggregation and management. This study proposes a dynamic reconfiguration method for VPPs, addressing the problem of optimally aggregating DERs within a VPP. To enhance DER participation, an innovative membership rewards mechanism is introduced. Furthermore, we develop a method for DER feature extraction and classification to deeply understand their performance. We also assess DERs' willingness to participate, which is the precondition for effective aggregation. The core of our method is an optimized strategy for multi-stage dynamic planning of VPPs, designed to accommodate the dynamic evolution of DERs and electricity markets conditions, and formulated as a two-layer model to offer optimal solutions for DERs aggregation. Numerical simulations validate the superiority of our method in economic benefits and operational efficiency of VPP, highlighting our contributions to enhance the feasibility and efficiency of VPP aggregation through multi-stage planning, dynamic adaptability, and strategic incentives.

The relationship between conctere composition and structural stability in case of fire

A series of disasters caused by fire have made engineers aware that understanding the consequences of fire is essential, as human lives depend on it, and it can also cause significant material damage. The structural integrity of structures during and after a fire, including their stability, can be better ensured if we accurately understand the effects of fire. Of course, the effects of fire do not always lead to significant damage, but in many cases they do. Fire protection is intended, on the one hand, to ensure that buildings remain stable under fire for a certain period, which is necessary to enable occupants to escape safely and, on the other hand, to minimize damage to building structures. The concrete composition, i.e., the structure's microstructure, significantly influences the stability of reinforced concrete structures in the event of fire or heat. In the case of concrete, in addition to changes in the internal material structure in the event of fire, spalling has also appeared recently, largely due to the denser concrete structure. In summary, the higher strength of concrete and the concomitant change in the test strength will lead to spalling, which will have a major impact on the stability of structures under fire. Both small-element (fiber efficiency, aggregate efficiency) and large-element tests have been carried out to verify this. The small-element tests were carried out in an electric furnace to monitor possible concrete surface delamination and strength loss. The small-element tests can be used to select concrete compositions for the large-element tests. Based on their experimental results, an increase in the fire resistance limit of a structural element can be achieved by reducing the delamination of the concrete surface when plastic fibers are added to the concrete. However, if a further increase in the fire resistance limit is required, a change in the type of cement or a modification of the supplementary material may be necessary to increase the residual strength value. Overall, the microstructure of concrete has a significant impact on the stability of structures in the event of a fire.

An Energy Efficient and Scalable WSN with Enhanced Data Aggregation Accuracy

This paper introduces a method that combines the K-means clustering genetic algorithm (GA) and Lempel-Ziv-Welch (LZW) compression techniques to enhance the efficiency of data aggregation in wireless sensor networks (WSNs). The main goal of this research is to reduce energy consumption, improve network scalability, and enhance data aggregation accuracy. Additionally, the GA technique is employed to optimize the cluster formation process by selecting the cluster heads, while LZW compresses aggregated data to reduce transmission overhead. To further optimize network traffic, scheduling mechanisms are introduced that contribute to packets being transmitted from sensors to cluster heads. The findings of this study will contribute to advancing packet scheduling mechanisms for data aggregation in WSNs in order to reduce the number of packets from sensors to cluster heads. Simulation results confirm the system's effectiveness compared to other compression methods and non-compression scenarios relied upon in LEACH, M-LEACH, multi-hop LEACH, and sLEACH approaches.

Improving the sensing sensitivity of silver nanoparticle-based colorimetric biosensors from the point of salt.

The sensing sensitivity was improved for silver nanoparticles (AgNPs)-based colorimetric biosensors by using the most suitable salt to induce AgNPs aggregation. As for the salt composed of low-affinity anion and monovalent cation, the cation-dependent charge screening effect was the driving force for AgNPs aggregation. Apart from the charge screening effect, both the bridging of multivalent cation to the surface ligand of AgNP and the interaction between anion and Ag contributed to inducing AgNPs aggregation. Considering the higher aggregation efficiency of AgNPs resulted in a narrower sensing range, salt composed of low-affinity anion and monovalent cation was recommended for AgNPs-based colorimetric analysis, which was confirmed by fourfold higher sensitivity of DNA-21 detection using NaF than NaCl. This work inspires further thinking on improving the sensing performanceof metal nanomaterials-based sensors from the point of colloidal surface science.

On the provision of excludable public goods: General taxes or user prices?

AbstractThis paper compares some of the most common ways of financing the provision of publicly provided impure public goods/services in a unified dynamic general equilibrium framework with heterogeneous income groups. The focus is on the effects of user prices on individual incentives and how the latter shape aggregate outcomes and distribution. We show that a market‐like mechanism of user prices not only improves individual incentives and enhances aggregate efficiency, but it is also Pareto improving. However, such a mechanism is not beneficial or simply collapses when social externalities are strong, when the government adopts a mixed system that combines user prices with heavy subsidization of user costs, and when there is a minimum amount of the good that everybody needs.