A Green Computing Business Aggregation Strategy for Low Earth Orbit Satellite Networks.

This paper proposes a green computing strategy for low Earth orbit (LEO) satellite networks (LSNs), addressing energy efficiency and delay optimization in dynamic and energy-constrained environments. By integrating a Markov Decision Process (MDP) with a Double Deep Q-Network (Double DQN) and introducing the Energy-Delay Ratio (EDR) metric, this study effectively quantifies and balances energy savings with delay costs. Simulations demonstrate significant energy savings, with reductions of up to 47.

Synthesis of a Bimetallic-Doped Phytate-Melamine Composite as an Efficient Additive for Epoxy Resins with High Fire Safety.

The issue of hazardous smoke and toxic gases released from epoxy resins (EP), which often causes casualties in real fires, has limited its application. Therefore, we have developed a novel flame retardant based on a bimetallic-doped phytate-melamine (BPM) structure with Zn and Fe ions incorporated into the polymer matrix using a straightforward solution-based synthetic method. The combustion performance of the composite was evaluated using a cone calorimeter test, which showed that the peak heat release, total heat release, and total smoke production were reduced by 50%, 31.

Design of Two Compact Wideband Monopoles Through Loading with Linear Approximated Lumped Components.

In this paper, two ultra-wideband monopoles in a colinear structure are presented for application in remote terrestrial communication systems. The antennas consist of a loaded monopole with a hat and an elevated loaded monopole located in the upper position. All lumped loads are modeled as linear frequency-dependent components to approximate the practical component property for achieving ultra-wideband characteristics, since the constant value property of a component is only present in a relatively narrow band.

Unveiling the enigma of blind box impulse buying curiosity: The moderating role of price consciousness.

This study explores the causes of curiosity-driven impulse buying in blind boxes using the Stimulus-Organism-Response (S-O-R) model and adaptation level theory. It examines how store environment and product factors contribute to customer curiosity, incorporating price consciousness into the overall framework. Insights from an online survey of 306 Chinese respondents indicate that environmental factors and specific product characteristics positively influence consumer curiosity, while price consciousness has a negative moderate effect.

Task offloading optimization in mobile edge computing based on a deep reinforcement learning algorithm using density clustering and ensemble learning.

Mobile edge computing offloads compute-intensive tasks generated on mobile wireless devices (WD) to edge servers (ES), which provides mobile users with low-latency computing services. Opportunistic computing offloading is effective to enhance computing performance in dynamic edge network environments; however, careless offloading of tasks to ESs can lead to WDs preempting network computing resources with limited bandwidth, thereby resulting in inefficient allocation of computing resources. To address these challenges, this paper proposes the density clustering and ensemble learning training-based deep reinforcement learning (DCEDRL) method for task offloading decision-making in mobile edge computing (MEC).

Spectrophotometric determination of phosphate using sodium molybdate and its field application to the simultaneous measurement with ammonium in seawater.

Phosphate (PO) and ammonium (NH), two major components of the phosphorus and nitrogen cycles, respectively, play vital roles in aquatic ecosystems. Very few studies have reported methods for the simultaneous determination of PO and NH in seawater. Additionally, when ammonium molybdate solution was employed as a chromogenic reagent for the simultaneous determination of both PO and NH in a single analysis system, there was a possibility of cross-contamination.

Modeling diabetes progression with risk factors: A case study in China.

Background: Approximately 537 million adults worldwide have diabetes, more than 90 % of which is type 2 diabetes mellitus (T2DM). China has the largest number of people living with diabetes. Understanding the epidemiological mechanism can guide diabetes surveillance and control.

3D printing of continuous cotton thread reinforced poly (lactic acid).

This paper purposed to prepare poly (lactic acid)/continuous cotton thread (PLA /CCT) filaments by using prepreg method, and investigated the properties of PLA/CCT filament and their 3D printed composites. Firstly, a prepreg device was home-made to immerse CCT with PLA melts. The effects of the dragging speed and tensioning equipment on the quality of PLA/CCT filament was investigated.

Carbon-based light addressable potential aptasensor based on the synergy of C-MXene@rGO and OPD@NGQDs for low-density lipoprotein detection.

A novel carbon-based light-addressable potentiometric aptasensor (C-LAPS) was constructed for detection low-density lipoprotein (LDL) in serum. Carboxylated TiC MXene @reduced graphene oxide (C-MXene@rGO) was used as interface and o-phenylenediamine functionalized nitrogen-doped graphene quantum dots (OPD@NGQDs) as the photoelectric conversion element. The photosensitive layers composed of OPD@NGQDs/C-MXene@rGO exhibit superior photoelectric conversion efficiency and excellent biocompatibility, which contribute to an improved response signal.

Microstructures of Directionally Solidified NbTiFe Alloy and Its Hydrogen Permeation Properties in the Presence of HS.

Currently, the main limitations of Pd-coated Nb-TiFe dual-phase alloys include insufficient hydrogen permeability, susceptibility to hydrogen embrittlement (HE), and poor tolerance of HS poisoning. To address these issues, this study proposes a series of improvements. First, a novel NbTiFe alloy composed of a well-aligned Nb-TiFe eutectic was successfully prepared using directional solidification (DS) technology.

AMBWO: An Augmented Multi-Strategy Beluga Whale Optimization for Numerical Optimization Problems.

Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy beluga optimization (AMBWO).

Research on Intelligent Monitoring and Concentration Prediction for Penicillin Fermentation Process.

In the biopharmaceutical industry, accurately predicting penicillin concentration during fermentation is key to boosting production efficiency and quality assurance. This study leverages the PenSim simulation data set and applies various machine learning and deep learning techniques to forecast penicillin fermentation concentration. Initially, through correlation analysis, nine feature variables with significant impacts on penicillin concentration were screened, and the data underwent preprocessing and standardization.

Machine Learning in Solid-State Hydrogen Storage Materials: Challenges and Perspectives.

Machine learning (ML) has emerged as a pioneering tool in advancing the research application of high-performance solid-state hydrogen storage materials (HSMs). This review summarizes the state-of-the-art research of ML in resolving crucial issues such as low hydrogen storage capacity and unfavorable de-/hydrogenation cycling conditions. First, the datasets, feature descriptors, and prevalent ML models tailored for HSMs are described.

Dataset for identifying paracentral acute middle maculopathy lesions in spectral-domain optical coherence tomography images.

This paper introduces a comprehensive dataset of spectral-domain optical coherence tomography (SD-OCT) images of human eyes affected by paracentral acute middle maculopathy (PAMM). Acquired with an SD-OCT device (Optovue, Fremont, California, USA), the dataset includes 133 OCT images of lesions. Each image is paired with a corresponding YOLO label in TXT format, representing manually annotated lesion regions of PAMM, created with the assistance of ophthalmologists.

A Review of Methodologies for the Rapid Analysis and Quantification of Gases in Solid Media.

Gas sensors are essential measurement devices that have found extensive applications across various fields, including industry, agriculture, ecological and environmental monitoring, military operations, and biomedical research. Numerous sensing methods based on a diverse range of principles-including optics, electrochemistry, and semiconductors-have been used in the development and manufacture of gas sensing technologies. However, the measurement of certain gases remains challenging when using current sensing techniques and sensors; this is particularly true for the gases that are present in solid media.

Multi-Sensor Fusion for Wheel-Inertial-Visual Systems Using a Fuzzification-Assisted Iterated Error State Kalman Filter.

This paper investigates the odometry drift problem in differential-drive indoor mobile robots and proposes a multi-sensor fusion approach utilizing a Fuzzy Inference System (FIS) within a Wheel-Inertial-Visual Odometry (WIVO) framework to optimize the 6-DoF localization of the robot in unstructured scenes. The structure and principles of the multi-sensor fusion system are developed, incorporating an Iterated Error State Kalman Filter (IESKF) for enhanced accuracy. An FIS is integrated with the IESKF to address the limitations of traditional fixed covariance matrices in process and observation noise, which fail to adapt effectively to complex kinematic characteristics and visual observation challenges such as varying lighting conditions and unstructured scenes in dynamic environments.

Switchable Pancharatnam-Berry Phases in Heterogeneously Integrated THz Metasurfaces.

The Pancharatnam-Berry (PB) phase has revolutionized the design of metasurfaces, offering a straightforward and robust method for controlling wavefronts of electromagnetic waves. However, traditional metasurfaces have fixed PB phases determined by the orientation of their individual elements. In this study, an innovative structural design and integration scheme is proposed that utilizes vanadium dioxide, a phase-change material, to achieve thermally controlled dynamic PB phase control within the metasurface.

Future of efficient power generation: Innovative systems combining chemical looping gasification and solid oxide fuel cell.

As urbanization accelerates, the substantial increase in municipal solid waste (MSW) presents significant challenges for effectively managing and converting this waste into renewable energy. This paper explores an innovative system that integrates chemical looping gasification (CLG) and solid oxide fuel cell (SOFC) technologies to achieve efficient power generation. The SOFC system operates at 3 bar and 900 °C, with a power generation capacity of 9.

Smooth and safe stop: Fixed-time fault tolerant control for heavy legged robot with active identification on tolerance capability.

Heavy-legged robots (HLRs), integral to optimizing efficiency in manufacturing and transportation, rely on advanced active servo fault diagnosis and fault-tolerant control (FTC) mechanisms. This study presents an FTC framework with active fault status identification, fault tolerance capability assessment, and model uncertainty handling. A key contribution is the introduction of an active servo fault state estimator (ASFSE), which enables real-time monitoring of servo status by comparing residual differences between servo and controller outputs.

Compensatory behavior for mental workload induced by freeway destination signs.

Objective: Exit ramps are accident-prone areas of freeways. One of the reasons for this is the information overload induced by destination signs, which makes them challenging to recognize and may even result in tension or mistakes. This study examined the cognitive workload that destination signs place on drivers and the compensatory behavior they use to counteract the additional workload.

Adaptive Multicore Dual-Path Fusion Multimodel Extraction of Heterogeneous Features for FAIMS Spectral Analysis.

With the increasing application scenarios and detection needs of high-field asymmetric waveform ion mobility spectrometry (FAIMS) analysis, deep learning-assisted spectral analysis has become an important method to improve the analytical effect and work efficiency. However, a single model has limitations in generalizing to different types of tasks, and a model trained from one batch of spectral data is difficult to achieve good results on another task with large differences. To address this problem, this study proposes an adaptive multicore dual-path fusion multimodel extraction of heterogeneous features for FAIMS spectral analysis model in conjunction with FAIMS small-sample data analysis scenarios.

A perfect ultra-wideband solar absorber with a multilayer stacked structure of Ti-SiO-GaAs: structure and outstanding characteristics.

In this paper, we introduce an entirely new solar absorber design-a multi-layer periodic stacked structure. Through coupling effects, this design has perfect ultra-wideband absorption characteristics. The absorber structure is composed of four absorption units with varying cycle lengths, which are cyclically stacked on the surface of the refractory metal Cr.

Efficient Targeted Regulation of the Interfaces and Bulk in Inverted Perovskite Solar Cells With a [closo-BH]-based Derivative.

The performance and stability of inverted perovskite solar cells (PSCs) is adversely affected by the recombination loss, ion migration, and residual stress arising from issues within the bulk and at the cathode interface. Using simple post-treatment with a novel solution-processable derivative of the dodecahydro-closo-dodecaborate anion ([closo-BH])-(TBA)[BH(OCHCH)OH] (TBAB)-it is simultaneously address these issues. In inverted PSCs, the cationic and anionic components of TBAB uniquely self-separate by positioning themselves precisely to perform their specific modification functions.

Robust modelling of arterial blood pressure reconstruction from photoplethysmography.

Blood pressure is a crucial indicator of cardiovascular disease, and arterial blood pressure (ABP) waveforms contain information that reflects the cardiovascular status. We propose a novel deep-learning method that converts photoplethysmogram (PPG) signals into ABP waveforms. We used [Formula: see text]-Net as a feature extractor and designed a Bi-block to capture individualised time information in encoder feature extraction.