Search Results (154,712)

A single-photon avalanche diode (SPAD) is a photon-counting sensor renowned for its exceptional single-photon sensitivity. One significant feature of SPADs is their non-linear response to light, making them ideal for high-dynamic range imaging applications. In SPAD imaging, the photon detection mode, which depends on the quenching method employed, is crucial for optimizing image quality and dynamic range. This paper examines the free-running and gating imaging modes, evaluating their impacts on photon capture and saturation limits. Given that the number of incident photons follows a Poisson distribution, we introduce an innovative imaging-quenching model based on statistical mathematics. We designed and fabricated two SPAD imaging sensors using 180 nm CMOS technology. Image processing and evaluation were conducted using a mapping method. Our results show that in low-light conditions, the gating mode surpasses the free-running mode in the signal-to-noise ratio (SNR). However, the free-running mode exhibits a saturation limit of more than an order of magnitude higher than that of the gating mode, demonstrating its superior capability to handle a broader range of light intensities. This paper provides a thorough analysis of the differences between the two imaging methods, incorporating the theoretical mathematical model, circuit characteristics, and computed imaging quality. Full article

This study delves into the impact of industrial intelligence on corporate carbon performance using micro-level data from 1072 listed manufacturing companies in China’s A-share market from 2012 to 2021. Industrial intelligence, through the integration of advanced technologies such as AI, IoT, and big data analytics applied to industrial robots, significantly improves the corporate carbon performance, measured by the carbon intensity and total emissions. Although the total carbon emissions increase due to the output effect, the efficiency optimization effect of industrial intelligence has a greater impact, reducing carbon intensity and emissions. The reduction effect from increased production efficiency outweighs the increase from the output effect. Heterogeneity tests show significant carbon reduction effects of industrial intelligence in industries with heavy and moderate carbon emissions, but an increase in carbon emissions in industries with light carbon emissions. Regional differences also emerge, with more effective carbon reduction in the Yangtze River Delta and Pearl River Delta regions compared to the Beijing-Tianjin-Hebei region. These findings highlight the carbon reduction potential of industrial intelligence across different industries and regions, offering valuable insights for targeted environmental policies and corporate strategies. Full article

We propose a new methodology to transform a time series into an ordered sequence of any entropic and information functionals, providing a novel tool for data analysis. To achieve this, a new algorithm has been designed to optimize the Probability Density Function (PDF) associated with a time signal in the context of non-parametric Kernel Density Estimation (KDE). We illustrate the applicability of this method for anomaly detection in time signals. Specifically, our approach combines a non-parametric kernel density estimator with overlapping windows of various scales. Regarding the parameters involved in the KDE, it is well-known that bandwidth tuning is crucial for the kernel density estimator. To optimize it for time-series data, we introduce an adaptive solution based on Jensen–Shannon divergence, which adjusts the bandwidth for each window length to balance overfitting and underfitting. This solution selects unique bandwidth parameters for each window scale. Furthermore, it is implemented offline, eliminating the need for online optimization for each time-series window. To validate our methodology, we designed a synthetic experiment using a non-stationary signal generated by the composition of two stationary signals and a modulation function that controls the transitions between a normal and an abnormal state, allowing for the arbitrary design of various anomaly transitions. Additionally, we tested the methodology on real scalp-EEG data to detect epileptic crises. The results show our approach effectively detects and characterizes anomaly transitions. The use of overlapping windows at various scales significantly enhances detection ability, allowing for the simultaneous analysis of phenomena at different scales. Full article

Morphological scoring is a common evaluation method for domestic animals. The National Association of Maremmano Breeders (ANAM) has provided a dataset containing the records of 600 horses, four metric measurements (cm) and 24 traits with a continuous evaluation scale, each one with 15 classes. Moreover, a body condition score (BCS) with five classes is included. In this study, factor analysis was conducted to create a small number of informative factors (3) obtained from these traits, and a new BLUP-AM-MT index was established. The New Estimated Breeding Value (NEBV1) of each horse was computed by adding the genetic indexes of the three factors, with each one multiplied using a coefficient indicated by ANAM. The practical feasibility of the NEBV1 was evaluated through Spearman correlations between the rankings of the NEBV1 and the rankings of the BLUP-AM-MT, estimated through the four biometric measures and the morphological score (MS) assigned to each horse by the ANAM judges. The factorial analysis was used to estimate three factors: the “Trunk Dimension”, “Legs” and “Length”. As the explained variance was only 32%, the model was rotated, and the heritability of the three factors were 0.51, 0.05 and 0.41, respectively. After rotation, the estimated correlations between the new NEBV1 and the biometric measures were improved. These results should encourage breeders to adopt a breeding value index that takes into consideration the factors derived from all the variables observed in the morphological evaluation of the Maremmano. In this way, breeders can use it to select the best animals for breeding. Full article

Anaerobic wastewater treatment technology has been intensively and extensively investigated in the industry and scientific research. Inspired by the advantages of multi-stage and multi-phase anaerobic reactor technology (SMPA) in recent years, a three-stage anaerobic reactor (3S-AR) was designed and applied to treat poplar chemical–mechanical pulp wastewater, and various operation parameters, including the volume loading rate (VLR), hydraulic retention time (HRT), ascending velocity, reflux ratio, pH and temperature of the 3S-AR, were optimized to evaluate the reactor’s removal efficiency for poplar wastewater. The properties of anaerobic granular sludge and the composition of wastewater were also characterized to assess microorganism growth and pollutant migration. Results show that the COD removal rate was over 75% with a volume loading rate range of 15–25 gCOD/(L·d) in the 3S-AR; the hydraulic retention time was also found to be an important factor affecting the performance of the 3S-AR reactor. The volume loading rate and degradation efficiency of the 3S-AR reactor are higher than those of the up-flow anaerobic sludge blanket (UASB) reactor. Microorganism separation can be achieved in the 3S-AR, which is conducive to the growth and methanogenesis activity of bacteria, thereby leading to enhanced removal and buffering efficiency. After treatment in the 3S-AR, the main pollutants of poplar wastewater were benzene aromatic acids and long-chain esters, which do no biodegrade easily; in contrast, most of the fatty acid substances with small molecules were completely degraded. Full article

In ion chromatography studies, the matrix effect of other inorganic ions present in the sample is a well-known phenomenon. In this work, the behavior of inorganic and organic ions was studied in a system overloaded with ammonium ions. The ammonium ions came from a solution of ammonium hydroxide in various concentrations (0.25%–1.25%). In this system, which was significantly overloaded with ammonium ions, the behavior of three ions were tested (lithium, tris, and sodium cations). The measurements were performed at different eluent concentrations (6–17 mM), chromatographic column temperatures (25–40 ${}^{\circ }$C), and injected volumes (15–40 $\mathsf{\mu }$L). The retention times of sodium and lithium ions increased with increasing amounts of injected ammonium, while tris remained essentially unchanged, indicating that the resolution of these ions can be influenced by varying the concentration of the matrix. The results suggested that the observed effect was due to a combination of the pH change caused by the injected matrix, the dissociation of tris ions, the dissociation of the carbocylic ion-exchange groups of stationary phase, the change in buffer capacity, and the amount of ammonium ion introduced. It has been shown that in a well-designed experiment, the addition of ammonium hydroxide to the sample at concentrations greater than 1% can improve the efficiency of organic and inorganic cation separation. It was found that 8 mM methanesulfonic acid eluent, 30 ${}^{\circ }$C, 1% ammonium hydroxide matrix concentration, and 25 $\mathsf{\mu }$L injection were optimal for the baseline separation of tris and sodium ions on the high-capacity Dionex CS16 column. These ions could not be separated on this column without the presence of the ammonium matrix. Full article

In this paper, we propose a stochastic primal-dual adaptive method based on an inexact augmented Lagrangian function to solve non-convex programs, referred to as the SPDAM. Different from existing methods, SPDAM incorporates adaptive step size and momentum-based search directions, which improve the convergence rate. At each iteration, an inexact augmented Lagrangian subproblem is solved to update the primal variables. A post-processing step is designed to adjust the primal variables to meet the accuracy requirement, and the adjusted primal variable is used to compute the dual variable. Under appropriate assumptions, we prove that the method converges to the $\epsilon$-KKT point of the primal problem, and a complexity result of SPDAM less than $O\left({\epsilon }^{-\frac{11}{2}}\right)$ is established. This is better than the most famous $O\left({\epsilon }^{-6}\right)$ result. The numerical experimental results validate that this method outperforms several existing methods with fewer iterations and a lower running time. Full article

Abstract: Hydraulic fracturing stands as a pivotal technological approach for enhanced tight gas recovery. This paper investigates the influences of geological and engineering parameters on the vertical extension mechanism of hydraulic fractures. In addition, the feasibility and effectiveness of fracture height prediction method and various fracture height control techniques have been examined. The results indicate that the height of hydraulic fractures decreases with an increase in the thickness of the barrier layers, the stress difference between the barrier and reservoir layers, the difference in tensile strength, and the difference in fracture toughness, whereas it increases with the increasing of difference in elastic modulus between the barrier and reservoir layers. Compred with the difference in Poisson’s ratio, the volume of fracturing fluid, discharge rate, and fluid viscosity have little impactd. The influence of these factors on fracture height, in descending order, is stress difference between barrier and reservoir layers, fracturing fluid viscosity, fracturing discharge, fracturing fluid volume, barrier layer thickness, tensile strength difference between barrier and reservoir layers, elastic modulus difference between barrier and reservoir layers, Poisson’s ratio difference between barrier and reservoir layers. Furthermore, based on typical geomechanic and reservoir parameters of the target area, a fracture height prediction workflow has been developed. Engineering practice has proven the reliability of fracture height prediction method. The results of this study provide theoretical support and guidance for predicting fracture morphology, controlling fracture height in the hydraulic fracturing development of the tight gas reservoir, and optimizing fracturing process design. Full article

The spatial distribution pattern of rural settlements in the Yellow River Basin is scattered and numerous. It is of great significance to study the discrete distribution of rural settlements for achieving high-quality development and promoting rural revitalization strategy. In this paper, we propose an enhanced evaluation model for assessing the spatial distribution dispersion of rural settlements, incorporating the weight of road grade (the road grade refers to the ranking of traffic capacity and importance of a particular type of road, indicating varying levels of time accessibility). We investigate the dispersion characteristics of rural settlements in the Yellow River Basin in 2020, focusing on both county and city scales. Furthermore, we conduct a comprehensive analysis of the spatial differentiation and scale effects of dispersion evaluation outcomes and their driving forces. Our findings reveal the following insights: (1) The road grade significantly influences the dispersion evaluation. When considering road grade in the dispersion calculation, the results align more closely with the actual situation. (2) The dispersion of rural settlements in the Yellow River Basin exhibits a decreasing trend from west to east. Specifically, the dispersion is higher in the upper reaches compared to the middle and lower reaches. Both city and county scales show spatial autocorrelation in dispersion, with a positive spatial correlation observed. High dispersion values cluster in the west, while low values concentrate in the east. Notably, the agglomeration degree is more pronounced at the county scale than at the city scale, highlighting more localized patterns of agglomeration and dispersion. (3) The multiscale geographically weighted regression model emerges as the optimal model for analyzing the driving forces of dispersion. At the city scale, factors such as river density, road density, and rural economy negatively impact dispersion. However, at the county scale, average elevation and rural economy positively affect dispersion, whereas river density, road density, and rural population density have a negative influence. By incorporating the weight of road grade into our evaluation model, we provide a more nuanced understanding of the spatial distribution dispersion of rural settlements in the Yellow River Basin. Our findings offer valuable insights for policymakers and planners seeking to optimize rural settlement patterns and promote sustainable rural development. Full article

In recent years, with the rapidly aging population, alleviating the pressure on medical staff has become a critical issue. To improve the work efficiency of medical staff and reduce the risk of infection, we consider the multi-trip autonomous mobile robot (AMR) routing problem in a stochastic environment. Our goal is to minimize the total expected operating cost and maximize the total service quality for patients, ensuring that each route violates the vehicle capacity and the time window with only a minimal probability. The travel time of AMRs is stochastically affected by the surrounding environment; the demand for each ward is unknown until the AMR reaches the ward, and the service time is linearly related to the actual demand. We developed a population-based tabu search algorithm (PTS) that combines the genetic algorithm with the tabu search algorithm to solve this problem. Extensive numerical experiments were conducted on the modified Solomon instances to demonstrate the efficiency of the PTS algorithm and reveal the impacts of the confidence level on the optimal solution, providing insights for decision-makers to devise delivery schemes that balance operating costs with patient satisfaction. Full article

Wastewater containing a mixture of heavy metals, a byproduct of chemical, petrochemical, and refinery activities driven by urbanization and industrial expansion, poses significant environmental threats. Analyzing such wastewater through adsorbate-adsorbent experiments yields extensive datasets. However, traditional methodologies like the Box–Behnken design (BBD) within the response surface methodology (RSM) struggle with managing large datasets and capturing the complex, nonlinear relationships inherent in such experimental data. To address these challenges, ML techniques have emerged as promising tools for accurately predicting the removal percentage of heavy metals from wastewater. In this study, we utilized tree-based regression models—specifically decision tree regression (DTR), random forest regression (RFR), and extra tree regression (ETR)—to forecast the efficiency of gooseberry seed powder in removing chromium (Cr(VI)) from wastewater. Additionally, we employed an ML-based Nelder–Mead optimization approach to identify the optimal values for key features (initial Cr(VI) concentration, pH, and Indian gooseberry powder dosage) which maximized the Cr(VI) removal percentage. Our experimental results reveal that the ETR model achieved an impressive $R^2$ score of 0.99, demonstrating a low error rate in predicting the Cr(VI) removal percentage. Furthermore, we used DTR-Nelder–Mead, RFR-Nelder–Mead, and ETR-Nelder–Mead optimization approaches on a synthesized dataset of 2000 instances while varying the initial Cr(VI) concentration, pH, and Indian gooseberry powder dosage. The analysis determined that the DTR-Nelder–Mead and RFR-Nelder–Mead approaches yielded the highest Cr(VI) removal percentages of 78.21% and 78.107% at an initial concentration of 95.55 mg/L, respectively, a pH level of four, and an adsorbent dosage of 8 g/L of gooseberry seed powder. Furthermore, the ETR-Nelder–Mead approach obtained the maximum Cr(VI) removal percentage of 85.11% at an initial concentration of 99.25 mg/L, a pH level of 4.97, and an adsorbent dosage of 9.62 g/L of gooseberry seed powder. These results reported an increase in the Cr(VI) removal percentage ranging from 4.66% to 11.56% more than the Cr(VI) removal percentage obtained by experimentation. These findings underscore the efficacy of tree-based regression models and ML-based Nelder–Mead optimization in elucidating chromium removal processes from wastewater, offering valuable insights into effective treatment strategies. Full article

The nonferrous metal mines in China are rapidly depleting due to years of mining, and it has become difficult to identify new mineral resources in the periphery of the old mining area. In order to deal with this situation, advanced technologies and equipment must be deployed. The borehole transient electromagnetic method (TEM) has become a key technology due to its deep investigative capabilities within conductive geological structures. In the present study, in order to meet the exploration needs at depths of less than 3000 m, surface-borehole TEM exploration was used to analyze the characteristics of electromagnetic signals generated by a long wire source and a large loop source, providing essential data for the development of key technologies, such as sensor parameter design and signal gain optimization of the TEM system in the borehole. This study discussed in detail two key technical problems as follows: firstly, the efficient synchronization mechanism between the ground transmitter system and the borehole electromagnetic signal acquisition system ensured the accuracy and timeliness of data acquisition; and secondly, the realization of mass storage technology, which effectively solved the problem of mass storage and real-time transmission of data in a deep borehole environment. The effectiveness of the surface-borehole TEM systems with a long wire source and a large loop source was verified by tests in real mines. The surface-borehole electromagnetic signal acquisition system developed in this study effectively collected electromagnetic signals in the borehole, and the results accurately reflected the stratigraphic information of mineral resources in the study area. This study can pave a new technical path for the exploration of deep and peripheral areas of non-ferrous metal mines and provide valuable experience and insights for mineral resource exploration in similarly complex geological environments. Full article

Configuring green power transmissions for heavy-industry marines is treated as a crucial request in an era of global energy and pollution crises. Following up on this hotspot trend, this paper examines the effectiveness of a modified optimization-based energy management strategy (OpEMS) for a dual proton exchange membrane fuel cells (dPEMFCs)-battery-ultra-capacitors (UCs)-driven hybrid electric vessels (HEVs). At first, the summed power of the dual PEMFCs is defined by using the equivalent consumption minimum strategy (ECMS). Accordingly, a map search engine (MSE) is proposed to appropriately split power for each FC stack and maximize its total efficiency. The remaining power is then distributed to each battery and UC using an adaptive co-state, timely determined based on the state of charge (SOC) of each device. Due to the strict constraint of the energy storage devices’ (ESDs) SOC, one fine-corrected layer is suggested to enhance the SOC regulations. With the comparative simulations with a specific rule-based EMS and other approaches for splitting power to each PEMFC unit, the effectiveness of the proposed topology is eventually verified with the highest efficiency, approximately about 0.505, and well-regulated ESDs’ SOCs are obtained. Full article

Resource-intensive processes currently hamper the discovery of bioactive peptides (BAPs) from food by-products. To streamline this process, in silico approaches present a promising alternative. This study presents a novel computational workflow to predict peptide release, bioactivity, and bioavailability, significantly accelerating BAP discovery. The computational flowchart has been designed to identify and optimize critical enzymes involved in protein hydrolysis but also incorporates multi-enzyme screening. This feature is crucial for identifying the most effective enzyme combinations that yield the highest abundance of BAPs across different bioactive classes (anticancer, antidiabetic, antihypertensive, anti-inflammatory, and antimicrobial). Our process can be modulated to extract diverse BAP types efficiently from the same source. Here, we show the potentiality of our method for the identification of diverse types of BAPs from by-products generated from Solanum lycopersicum, the widely cultivated tomato plant, whose industrial processing generates a huge amount of waste, especially tomato peel. In particular, we optimized tomato by-products for bioactive peptide production by selecting cultivars like Line27859 and integrating large-scale gene expression. By integrating these advanced methods, we can maximize the value of by-products, contributing to a more circular and eco-friendly production process while advancing the development of valuable bioactive compounds. Full article

The optical properties of an off-axis integrated cavity system are influenced by both structural deformation and thermal deformation. In this paper, the finite element simulation and analysis software COMSOL multiphysics was used to numerically simulate the optical system. By coupling geometric optics, solid mechanics, and solid heat transfer and conducting parametric temperature scanning, a multiphysics simulation of the off-axis integrated cavity optical sensing system was achieved. The effects of different temperature conditions on the stress field, displacement field, and optical mirrors were analyzed, and changes in optical properties were assessed using ray trajectories and point diagrams. Additionally, optical simulation software was used to simulate and optimize the experimental optical path, obtaining the distribution of light spots on the detector surface. This provides a theoretical basis for the subsequent optimization of the off-axis integrated cavity optical system. Full article