Multiple Parameters Research Articles

Time Parametrized Motion Planning

Time can be treated as a free parameter to isotropically stretch the tangent space. A trajectory, which matches the boundary conditions on its configuration, is adjusted so that velocity conditions are met. The modified trajectory is found by substitution, without the computational cost of re-integrating the velocity function. This concept is extended to stretch the tangent space anisotropically. This method of time parametrization especially applies to Geometric Control, where the Pontryagin Maximum Principle minimizes some cost function and matches the boundary configuration constraints but not the velocity constraints. The optimal trajectory is modified by the parametrization so that the cost function is minimized if the stretching is stopped at any time. This is a theoretical contribution, using a wheeled robot example to illustrate the modification of an optimal velocity under multiple parametrizations.

A characterization method for reprocessed spent nuclear fuel through neutron and gamma-ray multiplicity counting

It is difficult to measure neutrons and gamma rays coming directly from uranium and plutonium atoms in pyroprocessed ingot, which is of great interest to nuclear safeguards. Most neutrons of the ingot are emitted from 244Cm isotope and gamma rays from its fission products. Therefore, it is necessary to identify spent fuel characteristics such as initial enrichment, burnup and cooling time from measurement and estimate plutonium mass from calibration curve or burnup calculations.In this study, we explored the possibility of characterizing pyroprocessed ingots using neutron and gamma-ray multiplicity counting. We particularly focused on finding the possible correlation between neutron and gamma-ray multiplicity properties (singles, doubles, and triples) and spent fuel properties for U/TRU/RE preprocessed ingot. We examined neutron and gamma-ray correlation for spent fuel, using the Cascading Gamma-ray Multiplicity with Fission (CGMF) and the Fission Reaction Event Yield Algorithm (FREYA) library in MCNP6.2. Due to high spontaneous fission rate, neutron and gamma-ray multiplicity parameters did not strongly correlate with multiplication factor from the point model but was proportional to 244Cm mass. The results based on the CGMF and FREYA fission library showed the similar trends for neutron and gamma-ray cases. The sum of two multiplicity data yielded best linearity with the 244Cm effective mass. Although total neutron counting (S) alone was sufficient for effectively characterizing spent fuel due to its strong linearity, this feasibility study confirmed that, in principle, it will be more effective to calculate the sum of neutron and gamma-ray multiplicity properties to directly estimate the mass of 244Cm, rather than relying on the conventional method of calculating the multiplication factor.

A polynomial regression model based educational software tool to interpret the internal combustion engine characteristics

Technical education requires regular upgrades in pedagogical methodologies to keep up student’s skill on par with ever demanding job market. This paves the way for creating newer e-learning concepts for classroom to replace or supplement established teaching protocols. In line with this motive, this study deals with the development of an educational software tool to understand the traits of an internal combustion engine. The core of this software tool consists of polynomial regression equations, which in turn was arrived from statistical models using real world experimental data. A MATLAB-based GUI allows the operator to effortlessly interact with the software tool. Upon installation, the software requires the user to define input variables for it to automatically compute data and represent the output data in both visual and tabulated form. The advantage of three-dimensional surface plots for visual representation allows for understating the interactive effect of multiple input parameters on any given output parameter. Overall, average relative error for the model is less than 6%, thus exhibiting a good statistical fit.

Critical Review on Water Quality Analysis using IoT and Machine Learning Models

Water quality and its management are the most precise concerns confronting humanity globally. This article evaluates the various sensors used for water quality monitoring and focuses on the water quality index considering the multiple physical, chemical, and biological parameters. A Review of Internet of Things (IoT) research for water quality monitoring and analysis, sensors used for water quality can help remote monitoring of the water quality parameters using various IoT-based sensors that convey the assembled estimations utilizing Low-Power Wide Area Network innovations. Overall, the IoT system was 95% accurate in measuring pH, Turbidity, TDS, and Temperature, while the traditional method was only 85% accurate. Also, this study reviewed the different A.I. techniques used to assess water quality, including conventional machine learning techniques, Support Vector Machines, Deep Neural Networks, and K-nearest neighbors. Compared to traditional methods, machine learning and deep learning can significantly increase the accuracy of measurements of groundwater quality. However, various variables, such as the caliber of the training data, the water quality metrics' complexity, and the monitoring frequency, will affect the accuracy. The geographical information system (GIS.) is used for spatial data analysis and managing water resources. The quality of its data is also reviewed in the paper. Based on these analyses, the study has forecasted the future sensors, Geospatial Technology, and machine learning techniques for water quality monitoring and analysis.

The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models

Thompson sampling (TS) has been known for its outstanding empirical performance supported by theoretical guarantees across various reward models in the classical stochastic multi-armed bandit problems. Nonetheless, its optimality is often restricted to specific priors due to the common observation that TS is fairly insensitive to the choice of the prior when it comes to asymptotic regret bounds. However, when the model contains multiple parameters, the optimality of TS highly depends on the choice of priors, which casts doubt on the generalizability of previous findings to other models. To address this gap, this study explores the impact of selecting noninformative priors, offering insights into the performance of TS when dealing with new models that lack theoretical understanding. We first extend the regret analysis of TS to the model of uniform distributions with unknown supports, which would be the simplest non-regular model. Our findings reveal that changing noninformative priors can significantly affect the expected regret, aligning with previously known results in other multiparameter bandit models. Although the uniform prior is shown to be optimal, we highlight the inherent limitation of its optimality, which is limited to specific parameterizations and emphasizes the significance of the invariance property of priors. In light of this limitation, we propose a slightly modified TS-based policy, called TS with Truncation (TS-T), which can achieve the asymptotic optimality for the Gaussian models and the uniform models by using the reference prior and the Jeffreys prior that are invariant under one-to-one reparameterizations. This policy provides an alternative approach to achieving optimality by employing fine-tuned truncation, which would be much easier than hunting for optimal priors in practice.

Detecting wire breaks in prestressed concrete pipes: an easy-to-install distributed fibre acoustic sensing approach

The escalating water stress resulting from drought conditions in certain global regions underscores the imperative to minimize water losses, particularly within drinking water supply networks. One way to achieve this is by improving pipe monitoring systems to allow the early detection of possible structural collapse of the pipes. One type of pipe widely used in water mains is the prestressed concrete pipe, whose main cause of structural failure is the breakage of prestressing wires. This research paper analyses the ability of an easy-to-install distributed acoustic sensing (DAS) monitoring system using fibre optics to identify and locate the acoustic signal produced by the wire breaks in prestressed concrete pipes to make early detection of possible structural failures. For this purpose, a large experimental pipeline stretch was built (approximately 1 m in diameter and 40 m long) where wire breaks were simulated. Several variables were studied: the origin of the signal (to distinguish wire breaks from events of a similar nature), the location of the event in the pipe, the presence of background noise, the internal water pressure, the length of the prestressed wire not subject to bonding with the concrete and the presence of water in the pipe. The results showed that the DAS system could detect almost all events. In addition, two of the multiple parameters measured in the signals, the zero-crossing rate and the short-time energy, made it possible to precisely determine the signal’s origin and the event’s location. Another parameter measured, the duration of the signal in this case, made it possible to differentiate whether the events had occurred when the pipe was empty or full of water. These and other results in this paper present a highly promising perspective on using this DAS system in water main monitoring.

A Multimodal Bio‐Sensing and Imaging Platform (MSIP) for Real‐Time Intraoperative Margin Detection in Cancer Surgery

AbstractIn cancer surgery, the precise characterisation of tissue margins is of paramount importance to ensure effective resection. However, conventional methods have inherent limitations in terms of both speed and accuracy, often necessitating labour‐intensive postsurgical tissue analyses. To overcome these challenges and enhance the precision of cancer surgery, we introduced an innovative Multimodal Sensing and Imaging Platform (MSIP) equipped with advanced pH sensing capabilities. The MSIP represents the synergistic fusion of a miniaturised, flexible wire‐based electrochemical pH sensor with a sensitivity of 62.4 mV per pH for real‐time functional imaging. Simultaneously, it incorporates a high‐speed confocal endomicroscope utilizing a 2.6 mm fibre bundle to achieve subcellular resolution in morphological imaging. The pH sensor enables continuous monitoring of dynamic changes in the tumour microenvironment, offering valuable insights. Confocal endomicroscopy provides high‐resolution, real‐time imaging of tumour morphology with an impressive 2.2 μm resolution. This integration not only enhances the precision of cancer surgery but also fosters a comprehensive understanding of the biological and molecular characteristics of tumours. By consolidating multiple diagnostic parameters into a unified platform, MSIP has emerged as a transformative technology poised to revolutionise our approach to cancer surgery.

Dual-parameter stretchable, transferable mesh piezoresistive sensor for electronic skin detection of strain and temperature changes

Abstract Wearable sensors integrating multiple functions have great potential in artificial intelligence and flexible electronics at this stage and can perceive various external stimuli with high sensitivity and accuracy, such as strain, stress, and temperature. However, because multiple parameters do affect each other and reduce the sensing performance, making a single device that can detect multiple functions simultaneously is a huge challenge. In this paper, a strain-temperature dual-parameter sensor is developed with a planar structure design and used poly(3,4-ethylenedioxythiophene): poly(styrenesulfonic acid) and multi-walled carbon nanotubes polymerization materials to prepare a micron-sized film. The influence of two-dimensional structures on sensing performance is explored through simulation, and a structure with large deformation is selected to improve the strain detection range. The sensor can detect static and dynamic strain signals, and can maintain good linearity and response speed below 100 ms within a large strain range of 20%. In addition, the sensor also exhibits good temperature detection capability, with a temperature sensitivity of 18.2 μV K−1 and the ability to detect static and dynamic temperature changes with long-term stability. Finally, the sensor is tested in some actual scenarios, reflecting that the sensor manufactured has the dual-detection ability, showing sensitive strain monitoring and temperature perception decoupled between the dual signals. The sensor is realized with circuit board acquisition and wireless communication, combining multi-channel applications. Our research provides a feasible method for constructing multi-parameter human-computer interaction sensors.

Effects of mosquito age and batch size on knockdown and mortality of laboratory-reared Anopheles dirus, Anopheles minimus, and wild-caught Anopheles harrisoni (Diptera: Culicidae) exposed to transfluthrin using WHO tube and CDC bottle bioassays.

WHO tube and CDC bottle bioassays are currently available for insecticide resistance monitoring and malaria transmission research. Multiple parameters including mosquito density, age, and nutritional status may affect the readout in these bioassays' tests. This study aims to assess the effects of experimental factors on knockdown and mortality measurements in dominant malaria vectors in Thailand following exposure to sublethal and lethal doses of transfluthrin. The effects of (i) 3 different mosquito batch sizes (5, 10, and 20 individuals) and (ii) 2 age groups (3-5 and 20-23 days old) on outcomes measured using the WHO tube (14.7 µg/cm2) and CDC bottle bioassay discriminating concentration (0.006 µg/cm2) against 2 laboratory strains: Anopheles dirus Peyton & Harrison and Anopheles minimus Theobald (species A) and wild-caught Anopheles harrisoni Harbach & Manguin (species C). Our results showed higher knockdown at 1-h exposure using WHO tube and CDC bottle bioassays containing 20 individuals compared to batches containing 10 and 5 individuals. Older mosquitoes showed greater susceptibility than younger test population, especially for An. mininus. Our study supports WHO recommendations for using 3- to 5-day-old mosquitoes. It also validates Praulin et al. (2022) proposal to divide the cohort into smaller batches with more test replicates when it is not practicable to test 25 mosquitoes per replicate.

Lactate Albumin Ratio as Predictor of 28 Days Mortality in Septic Shock Patients and its Correlation to SOFA Score for Assessment of Organ Dysfunction: A Prospective Observational Study

Background: Sepsis and septic shock are leading cause of mortality in ICU patients. Prognostication and early intervention forms most integral part of management of septic shock. There many validated score to assess the critical state of septic shock patients. APACHE2 and SOFA scores are widely used world over. Multiple parameters used in the score and complexity involved have lead researchers to search for more convenient and simple markers. Lactate albumin Ratio (L/A ratio) turns out to be one such marker, combining two easily available parameters and abbreviating fallacies associated with both parameters when assessed individually. We aimed to assess L/A ratio as predictor of all cause 28 days’ mortality in septic shock patients and correlated L/A ratio to the SOFA score to assess organ dysfunction.
 Methods: It was a prospective observational study. 130 patients admitted in the state of septic shock were studied. L/A ratio was obtained at 24 hours after admission in the ICU. SOFA score was also calculated at 24 hours. ROC curve was plotted for L/A ratio to evaluate diagnostic accuracy, as predictor of 28 days’ mortality. Multivariable analysis of L/A ratio with other significant parameters was performed. Spearman rank correlation coefficient was used for correlation of L/A ratio with SOFA score.
 Results: L/A ratio at 24 hours (AUC 0.99; 95% CI: 0.95 to 0.99), p value =0.0001, was significant predictor of mortality at 28 days, at cut off point of >1.15. Significant positive correlation was found between L/A ratio and SOFA score at 24 hours, with correlation coefficient of 0.828, p value=0.0001.
 Conclusion: L/A ratio was found to be independent predictor of 28 days’ Mortality in Septic Shock Patients. Also strong correlation was obtained of L/A ratio to SOFA score to assess organ dysfunction in septic shock patients.

A Comparative Study of Embedded Wall Displacements Using Small-Strain Hardening Soil Model

Traditional analysis of embedded earth-retaining walls relies on simplistic lateral earth pressure theory methods, which do not allow for direct computation of wall displacements. Contemporary numerical models rely on the Mohr–Coulomb model, which generally falls short of accurate wall displacement prediction. The advanced constitutive small-strain hardening soil model (SS-HSM), effectively captures complex nonlinear soil behavior. However, its application is currently limited, as SS-HSM requires multiple input parameters, rendering numerical modeling a challenging and time-consuming task. This study presents an extensive numerical investigation, where wall displacements from numerical models are compared to empirical findings from a large and reliable database. A novel automated computational scheme is created for model generation and advanced data analysis is undertaken for this objective. The main findings indicate that the SS-HSM can provide realistic predictions of wall displacements. Ultimately, a range of input parameters for the utilization of SS-HSM in earth-retaining wall analysis is established, providing a good starting point for engineers and researchers seeking to model more complex scenarios of embedded walls with the SS-HSM.

Control strategies for inverted pendulum: A comparative analysis of linear, nonlinear, and artificial intelligence approaches.

An inverted pendulum is a challenging underactuated system characterized by nonlinear behavior. Defining an effective control strategy for such a system is challenging. This paper presents an overview of the IP control system augmented by a comparative analysis of multiple control strategies. Linear techniques such as linear quadratic regulators (LQR) and progressing to nonlinear methods such as Sliding Mode Control (SMC) and back-stepping (BS), as well as artificial intelligence (AI) methods such as Fuzzy Logic Controllers (FLC) and SMC based Neural Networks (SMCNN). These strategies are studied and analyzed based on multiple parameters. Nonlinear techniques and AI-based approaches play key roles in mitigating IP nonlinearity and stabilizing its unbalanced form. The aforementioned algorithms are simulated and compared by conducting a comprehensive literature study. The results demonstrate that the SMCNN controller outperforms the LQR, SMC, FLC, and BS in terms of settling time, overshoot, and steady-state error. Furthermore, SMCNN exhibit superior performance for IP systems, albeit with a complexity trade-off compared to other techniques. This comparative analysis sheds light on the complexity involved in controlling the IP while also providing insights into the optimal performance achieved by the SMCNN controller and the potential of neural network for inverted pendulum stabilization.

Ground air pollutants explanation based on multiple visibility graph of complex network by temporal community division.

In air pollution studies, the correlation analysis of environmental variables has usually been challenged by parametric diversity. Such variable variations are not only from the extrinsic meteorological conditions and industrial activities but also from the interactive influences between the multiple parameters. A promising solution has been motivated by the recent development of visibility graph (VG) on multi-variable data analysis, especially for the characterization of pollutants' correlation in the temporal domain, the multiple visibility graph (MVG) for nonlinear multivariate time series analysis has been verified effectively in different realistic scenarios. To comprehensively study the correlation between pollutant data and season, in this work, we propose a multi-layer complex network with a community division strategy based on the joint analysis of the atmospheric pollutants. Compared to the single-layer-based complex networks, our proposed method can integrate multiple different atmospheric pollutants for analysis, and combine them with multivariate time series data to obtain higher temporary community division for ground air pollutants interpretation. Substantial experiments have shown that this method effectively utilizes air pollution data from multiple representative indicators. By mining community information in the data, it successfully achieves reasonable and strong interpretive analysis of air pollution data.

Recent Research in Wire Cut Electrical Discharge Machining Process

A product's shape and size are developed through the manufacturing process, which is essential to all sectors. With its unique thermal machining technique, Wire Cut Electrical Discharge Machining (WEDM), items with sharp edges and varied hardness that prove challenging to produce using conventional machining methods can be precisely machined. Utilizing the widely used non-contact material removal technique, the practical technology of the WEDM process is based on the typical EDM sparking phenomenon. When the process was first introduced, WEDM has developed from a crude way to make tools and dies to the best way to produce micro-scale parts with the highest level of surface finish quality and dimensional accuracy. This paper reviews the extensive amount of research done from the EDM process to the development of the WEDM. It reports on the WEDM research that involves optimizing the process parameters and examining the impact of various factors on productivity and machining performance. The impact of multiple WEDM process input parameters, including wire speed, peak current, pulse on and off times, and peak on material removal rate (MRR), surface roughness (Ra), and micro structural analysis, on various process output responses is reviewed in this study.

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

Exploring Cooperative Mechanisms in the Chinese Agricultural Value Chain: A Game Model Analysis Based on Leading Enterprises and Small Farmers

In the context of agricultural modernization in China, this paper examines the micro-level perspective of agricultural value chains. Drawing from three primary models of agricultural value chain cooperation—namely, “leading enterprises + small farmers”, “leading enterprises + cooperatives + small farmers”, and “corporate integration”—it establishes four game models: the decentralized decision-making game model, the two revenue-sharing game models, and the centralized decision-making game model. It systematically analyzes the cooperation mechanisms between leading enterprises and small farmers in upstream production links of the agricultural value chain, aiming to improve the cooperation strategy between leading enterprises and small farmers, elevate the status of small farmers in the agricultural value chain, promote increased income for farmers, and strengthen the agricultural value chain. The research findings are as follows: Firstly, the traditional contract of “leading enterprise + smallholder farmers” is incomplete, which makes it difficult to avoid opportunism and moral hazard that may arise between the two parties. By comparing multiple parameter values, it is found that this model is at a lower level of agricultural value chain development. Secondly, the model of “leading enterprise + cooperative + smallholder farmers” improves the tightness and stability of cooperation between leading enterprises and smallholder farmers. This model explains to some extent the operability of smallholder farmers sharing the value of the agricultural value chain. Compared with various parameter values, this model is at a medium level between other models. Finally, the “corporate integration” model is a fully vertical integration model. Compared with various parameter values, this model is at an advanced stage of agricultural value chain development. Therefore, agricultural value chains will ultimately develop toward the direction of corporate integration. This study has positive practical significance for enhancing the status and claim rights of small farmers, promoting increased income for farmers, enhancing the consistency of values between leading enterprises and small farmers, strengthening the stability of the agricultural value chain, and ultimately achieving common prosperity and agricultural modernization in China.

Cardiopulmonary Exercise Testing in Evaluating Transthyretin Amyloidosis.

Cardiopulmonary exercise testing (CPET) has an established role in the assessment of patients with heart failure. However, data are lacking in patients with transthyretin (ATTR) amyloidosis. To use CPET to characterize the spectrum of functional phenotypes in patients with ATTR amyloidosis and assess their association with the cardiac amyloid burden as well as the association between CPET parameters and prognosis. This single-center study evaluated patients diagnosed with ATTR amyloidosis from May 2019 to September 2022 who underwent CPET at the National Amyloidosis Centre. Of 1045 patients approached, 506 were included and completed the study. Patients were excluded if they had an absolute contraindication to CPET or declined participation. The mean (SD) follow-up period was 22.4 (11.6) months. Comparison of CPET parameters across disease phenotypes (ATTR with cardiomyopathy [ATTR-CM], polyneuropathy, or both [ATTR-mixed]), differences in CPET parameters based on degree of amyloid infiltration (as measured by cardiovascular magnetic resonance [CMR] with extracellular volume mapping), and association between CPET parameters and prognosis. Among the 506 patients with ATTR amyloidosis included in this study, the mean (SD) age was 73.5 (10.2) years, and 457 participants (90.3%) were male. Impairment in functional capacity was highly prevalent. Functional impairment in ATTR-CM and ATTR-mixed phenotypes (peak mean [SD] oxygen consumption [VO2], 14.5 [4.3] mL/kg/min and 15.7 [6.2] mL/kg/min, respectively) was observed alongside impairment in the oxygen pulse, with ventilatory efficiency highest in ATTR-CM (mean [SD] ventilatory efficiency/volume of carbon dioxide expired slope, 38.1 [8.6]). Chronotropic incompetence and exercise oscillatory ventilation (EOV) were highly prevalent across all phenotypes, with both the prevalence and severity being higher than in heart failure from different etiologies. Worsening of amyloid burden on CMR was associated with decline in multiple CPET parameters, although chronotropic response and EOV remained abnormal irrespective of amyloid burden. On multivariable Cox regression analysis, peak VO2 and peak systolic blood pressure (SBP) were independently associated with prognosis (peak VO2: hazard ratio, 0.89 [95% CI, 0.81-0.99; P = .03]; peak SBP: hazard ratio, 0.98 [95% CI, 0.97-0.99; P < .001]). In this study, ATTR amyloidosis was characterized by distinct patterns of functional impairment between all disease phenotypes. A high prevalence of chronotropic incompetence, EOV, and ventilatory inefficiency were characteristic of this population. CPET parameters were associated with amyloid burden by CMR and with peak VO2, and SBP, which have been shown to be independent predictors of mortality. These findings suggest that CPET may be useful in characterizing distinct patterns of functional impairment across the spectrum of amyloid infiltration and predicting outcomes, and potentially offers a more comprehensive method of evaluating functional capacity for future prospective studies.

A novel method for multi-pollutant monitoring in water supply systems using chemical machine vision.

Drinking water is vital for human health and life, but detecting multiple contaminants in it is challenging. Traditional testing methods are both time-consuming and labor-intensive, lacking the ability to capture abrupt changes in water quality over brief intervals. This paper proposes a direct analysis and rapid detection method of three indicators of arsenic, cadmium, and selenium in complex drinking water systems by combining a novel long-path spectral imager with machine learning models. Our technique can obtain multiple parameters in about 1s. The experiment involved setting up samples from various drinking water backgrounds and mixed groups, totaling 9360 injections. A raw visible light source ranging from 380 to 780nm was utilized, uniformly dispersing light into the sample cell through a filter. The residual beam was captured by a high-definition camera, forming a distinctive spectrum. Three deep learning models-ResNet-50, SqueezeNet V1.1, and GoogLeNet Inception V1-were employed. Datasets were divided into training, validation, and test sets in a 6:2:2 ratio, and prediction performance across different datasets was assessed using the coefficient of determination and root mean square error. The experimental results show that a well-trained machine learning model can extract a lot of feature image information and quickly predict multi-dimensional drinking water indicators with almost no preprocessing. The model's prediction performance is stable under different background drinking water systems. The method is accurate, efficient, and real-time and can be widely used in actual water supply systems. This study can improve the efficiency of water quality monitoring and treatment in water supply systems, and the method's potential for environmental monitoring, food safety, industrial testing, and other fields can be further explored in the future.

A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration

The monitoring of anthropogenic CO2 emissions, which increase the atmospheric CO2 concentration, plays the most important role in the management of emission reduction and control. With the massive increase in satellite-based observation data related to carbon emissions, a data-driven machine learning method has great prospects for predicting anthropogenic CO2 emissions. Training samples, which are used to model predictions of anthropogenic CO2 emissions through machine learning algorithms, play a key role in obtaining accurate predictions for the spatial heterogeneity of anthropogenic CO2 emissions. We propose an approach for predicting anthropogenic CO2 emissions using the training datasets derived from the clustering of the atmospheric CO2 concentration and the segmentation of emissions to resolve the issue of the spatial heterogeneity of anthropogenic CO2 emissions in machine learning modeling. We assessed machine learning algorithms based on decision trees and gradient boosting (GBDT), including LightGBM, XGBoost, and CatBoost. We used multiple parameters related to anthropogenic CO2-emitting activities as predictor variables and emission inventory data from 2019 to 2021, and we compared and verified the accuracy and effectiveness of different prediction models based on the different sampling methods of training datasets combined with machine learning algorithms. As a result, the anthropogenic CO2 emissions predicted by CatBoost modeling from the training dataset derived from the clustering analysis and segmentation method demonstrated optimal prediction accuracy and performance for revealing anthropogenic CO2 emissions. Based on a machine learning algorithm using observation data, this approach for predicting anthropogenic CO2 emissions could help us quickly obtain up-to-date information on anthropogenic CO2 emissions as one of the emission monitoring tools.

Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis.

Due to the increasing interest in the use of artificial intelligence (AI) algorithms in hepatocellular carcinoma detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI and to compare them with clinicians' performance. A search in PubMed and Scopus was performed in January 2024 to find studies that evaluated and/or validated an AI algorithm for the detection of HCC. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the modality of imaging and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST) reporting guidelines. Out of 3177 studies screened, 44 eligible studies were included. The pooled sensitivity and specificity for internally validated AI algorithms were 84% (95% CI: 81,87) and 92% (95% CI: 90,94), respectively. Externally validated AI algorithms had a pooled sensitivity of 85% (95% CI: 78,89) and specificity of 84% (95% CI: 72,91). When clinicians were internally validated, their pooled sensitivity was 70% (95% CI: 60,78), while their pooled specificity was 85% (95% CI: 77,90). This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by screening images and highlighting regions of interest, thus improving workflow.