Time Cost Research Articles

Problems of the crisis of spatial design of art events in conditions of military aggression (Ukrainian experience)

Rethinking the processes accompanying spatial design from the correlation of the environment to the social, material, and spiritual aspects of its creation produce driving systems, even in the crisis period of wartime. In this regard, the study of the spatial design of art events is an urgent issue that requires a thorough study of the trends in the adaptation of spatial design to the modern demands of the artistic space of Ukraine. The purpose of the scientific work is to study the phenomenon of “spatial design”, as well as to study the problems of the crisis, in particular, the Ukrainian design of the space of the events of the artistic direction in the realities of martial law. To study the issues of the crisis state of the spatial design of Ukrainian artistic events, the method of analysis, the descriptive method of research, and the survey were used. As a result of the scientific research, the real state of the crisis of spatial design of artistic activities in Ukraine was studied and analysed, as well as the focus of the vision and trends introduced in the field of spatial design in the conditions of war were studied and described. The development of digital technologies and their use in the field of design has greatly reduced the material and time costs associated with the mobility of planning and creating a project of artistic space, but in order to create a universal modern project of spatial design of artistic events, the designer needs to understand the context of wartime. That is why the comfort of the atmosphere, where people will feel safe and comfortable, is one of the main requirements for the space of artistic events that take place during military operations. In the scientific work, in addition to studying the issues of the crisis of spatial design of Ukrainian art events, the requests of visitors to art events in Ukraine were investigated using a survey. Based on the answers and the wartime context, the main requirements for the spatial design of artistic events in Ukraine under the conditions of military aggression from Russia were formed.

Ensemble meta-heuristics and Q-learning for staff dissatisfaction constrained surgery scheduling and rescheduling

In this study, we investigate the multi-objective surgery scheduling and rescheduling problems with considering medical staff dissatisfaction and fuzzy surgery time. Rescheduling is activated when emergency patients arrive. First, a multi-objective mathematical model is established for maximizing the average patient satisfaction, and minimizing the fuzzy maximum completion time and total medical cost, simultaneously. Second, five meta-heuristics are employed and improved to solve the concerned problems. Five heuristic rules are developed to improve the diversity and quality of initial solutions. For improving the performance of meta-heuristics, six local search operators are designed and two Q-learning-based strategies are developed to select optimal ones intelligently. Finally, 29 instances with different scales are used to verify the performance of the proposed algorithms. Compared with the basic meta-heuristics, the average performance of the algorithms with the second Q-learning-based strategy is improved by 62.5%, 62.1%, 50%, 70.7%, and 70.7%, respectively. Through the Friedman test, the asymptotic significance values of both metrics (0.034 and 0.000) are less than 0.05, indicating that there is a significant performance gap among five algorithms with the second Q-learning-based strategy. The average rank values of the teaching-learning-based optimization with the second Q-learning strategy are 3.7069 and 2.0690 for two metrics, which are better than the compared ones.

(Joint) Bank Savings, Female Empowerment, and Child Labor in Rural Ethiopia

Abstract This study examines whether the random allocation of single and joint savings accounts to cash crop farmers in rural Ethiopia is associated with increased savings and changes in decision-making authority and control over resources that could ultimately affect child labor and schooling resource allocations. Consistent with posited channels of intrahousehold bargaining models, women from households assigned to the joint saving treatment group show significant gains in autonomy and control of savings resources, broader financial empowerment, and increased labor participation. Positive effects on school participation and attendance are reported for girls, although point estimates are measured imprecisely. In a setting where schooling and child labor are not mutually exclusive, children work more when joint deposit accounts are available. In the absence of impacts on household income, this increase in child labor is explained by complementarities between adult farm labor and child labor in the household production function, which is reinforced by lumpy investments in labor-intensive agricultural inputs that likely increased the opportunity costs of children's time.

Spatio-temporal features based deep learning model for depression detection using two electrodes

Deep learning has made significant contributions to the medical field and has shown great potential in various applications. Its ability to process vast amounts of data and extraction of patterns has enabled breakthroughs in medical research, diagnosis and treatment. The application of deep learning plays a vital role in depression detection. Depression is a neurological disorder characterized by persistent feelings of sadness, hopelessness and a lack of interest. The prevalence of depression is a significant factor contributing to the rise in suicide cases on a global scale. The electroencephalogram (EEG) is a non-invasive technique used to detect depression. It records brain activity using multiple electrodes. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. The present manuscript proposes a deep learning model for depression detection, focusing on two electrodes named FP1 and FP2. The purpose of employing two electrodes is to enhance the system’s portability while reducing data acquisition time and system cost. EEG is spatio-temporal data and possesses inherent spatial and temporal features. The present manuscript proposes a methodology for extracting temporal and spatial features. The temporal feature extraction module extracts temporal features in the time domain and the spatial module extracts spatial features in the spatial domain. This manuscript presents a study on the applicability of two electrodes for depression detection. This research can enhance accessibility, user-friendliness and easier data collection and analysis. The proposed deep learning model is evaluated on two benchmark datasets. It achieves 93.41% classification accuracy, 92.54% precision, 93.23% recall, 93.06% F1 score and 97.80% area under the curve (AUC) for Hospital University Sains Malaysia dataset and for Multi-modal Open Dataset for Mental-disorder Analysis dataset it achieves 79.40% accuracy, 81.18% precision, 67.73% recall, 73.80% F1 score and 85.66% AUC.

Kinematic synergies show good consistency when extracted with a low-cost markerless device and a marker-based motion tracking system

Recently, markerless tracking systems, such as RGB-Depth cameras, have spread to overcome some of the limitations of the gold standard marker-based tracking systems. Although these systems are valuable substitutes for human motion analysis, as they guarantee higher flexibility, faster setup time and lower costs, their tracking accuracy is lower with respect to marker-based systems. Many studies quantified the error made by markerless systems in terms of body segment length estimation, articular angles, and biomechanics, concluding that they are appropriate for many clinical applications related to motion analysis. We propose an innovative approach to compare a markerless tracking system (Kinect V2) with a gold standard marker-based system (Vicon), based on motor control assessment. We quantified kinematic synergies from the tracking data of fifteen participants performing multi-directional upper limb movements. Kinematic synergy analysis decomposes the kinematic data into a reduced set of motor primitives that describe how the central nervous system coordinates motion at spatial and temporal level. Synergies were extracted with the recently released mixed-matrix factorization algorithm. Four synergies were extracted from both marker-based and markerless datasets and synergies were grouped in 6 clusters for each dataset. Cosine similarity in each cluster was ⩾0.60 in both systems, showing good consistency of synergies. Good matching was found between synergies extracted from markerless and from marker-based data, with a cosine similarity between matched synergies ⩾0.60 in 5 out 6 synergies. These results showed that the markerless sensor can be feasible for kinematic synergy analysis for gross movements, as it correctly estimates the number of synergies and in most cases also their spatial and temporal organization.

Bayesian optimization based extreme gradient boosting and GPR time-frequency features for the recognition of moisture damage in asphalt pavement

Moisture damage is one of the major defects in asphalt pavement, and will evolve into potholes in a short time which will affect traffic safety. Ground Penetrating Radar (GPR) is an effective non-destructive testing (NDT) method for detecting moisture damage but its data explanation replies on human experience and subjects to labor intensive. To address this issue, an automatic detection method based on extreme gradient boosting (XGBoost) combined with Bayesian hyper-parameter optimization (BHPO) was proposed to detect moisture damage area from GPR traces. High frequency GPR antenna with 2.3 GHz was used to detect the moisture damage area from simulation, laboratory and field tests, and moisture damage dataset with 7960 traces was collected. Thirty time-frequency parameters were extracted from each GPR trace, normalized to unify the three data source, and then optimized into 12 sensitive parameters by feature importance method. These 12 parameters were used to build the recognition model with XGBoost, and the model tuning parameters were optimized by BHPO. To obtain optimization model, random forest (RF) and artificial neural network (ANN) were also trained with BHPO, and compared with XGBoost model. The results indicate that performance of XGBoost model with BHPO achieves the highest accuracy and lowest time cost both in moisture damage and normal trace classification, the accuracies for moisture damage are XGBoost (96.9%) > ANN (95.6%) > RF (95.4%), respectively, and normal are XGBoost (96.5%) > RF (96.1%) and ANN (96.0%), respectively. On this basis, field tests were conducted by core samples, which verified the correct result of XGBoost model. Our method provides a swift and accurate method to locate subsurface targets directly from GPR signals.

Flexible cost-penalized Bayesian model selection: Developing inclusion paths with an application to diagnosis of heart disease.

We propose a Bayesian model selection approach that allows medical practitioners to select among predictor variables while taking their respective costs into account. Medical procedures almost always incur costs in time and/or money. These costs might exceed their usefulness for modeling the outcome of interest. We develop Bayesian model selection that uses flexible model priors to penalize costly predictors a priori and select a subset of predictors useful relative to their costs. Our approach (i) gives the practitioner control over the magnitude of cost penalization, (ii) enables the prior to scale well with sample size, and (iii) enables the creation of our proposed inclusion path visualization, which can be used to make decisions about individual candidate predictors using both probabilistic and visual tools. We demonstrate the effectiveness of our inclusion path approach and the importance of being able to adjust the magnitude of the prior's cost penalization through a dataset pertaining to heart disease diagnosis in patients at the Cleveland Clinic Foundation, where several candidate predictors with various costs were recorded for patients, and through simulated data.

A new approach in the development of a dental caries vaccine

The genomes of the bacteria Streptococcus mutans and Streptococcus sobrinus, which cause dental caries, currently have been fully sequenced. However, the secondary and tertiary structures of the full-size surface proteins of these microorganisms, by which they attach to the surface of teeth covered with saliva, have not been instrumentally determined at the moment. There are a number of experimental studies on the use of these proteins in the process of developing a dental caries vaccine. However, there is currently no commercially available dental caries vaccine.The aim of the study was to choose an antigen for subsequent molecular modeling of a unique peptide for the development of a dental caries vaccine.To develop an effective and safe dental caries vaccine, it is necessary to perform a number of experiments in silico, preceding experiments in vitro and in vivo. Today, this approach is not only generally recognized, but also allows to significantly reduce the cost of experiments and time at the preclinical and clinical studies. According to our hypothesis, as an antigen for the development of a dental caries vaccine, it is necessary to use a short fragment of the surface protein (a peptide) of Streptococcus mutans and/or Streptococcus sobrinus, whose homology in amino acid sequence is 84.8 %, the spatial structure of which should correspond to the spatial structure of the corresponding fragment in a full-sized protein. In addition, the selected protein fragment, which will be part of the vaccine peptide, must be available to antibodies, i. e. located on the surface of the protein and defined as a B-cell linear and spatial epitope. Also, according to our hypothesis, the vaccine peptide may consist of the most stable fragments of alanine and proline rich regions of the surface protein of Streptococcus mutans and/or Streptococcus sobrinus for mutual stabilization of the spatial structure.

Efficient acid hydrolysis for compound-specific δ15N analysis of amino acids for determining trophic positions

Compound-specific isotope analysis of nitrogen in amino acids (CSIA-AA, δ15NAA) has gained increasing popularity for elucidating energy flow within food chains and determining the trophic positions of various organisms. However, there is a lack of research on the impact of hydrolysis conditions, such as HCl concentration and hydrolysis time, on δ15NAA analysis in biota samples. In this study, we investigated two HCl concentrations (6 M and 12 M) and four hydrolysis times (2 h, 6 h, 12 h, and 24 h) for hydrolyzing and derivatizing AAs in reference materials (Tuna) and biological samples of little egret (n = 4), night heron (n = 4), sharpbelly (n = 4) and Algae (n = 1) using the n-pivaloyl-iso-propyl (NPIP) ester approach. A Dowex cation exchange resin was used to purify amino acids before derivatization. We then determined δ15NAA values using gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS). The results revealed no significant differences (p > 0.05) in δ15NAA values among samples treated with different HCl concentrations or hydrolysis times, particularly for δ15NGlx (range: 21.0–23.5‰) and δ15NPhe (range: 4.3–5.4‰) in Tuna (12 M). Trophic positions (TPs) calculated based on δ15NAA at 2 h (little egret: 2.9 ± 0.1, night heron: 2.8 ± 0.1, sharpbelly: 2.0 ± 0.1 and Algae: 1.3 ± 0.2) were consistent with those at 24 h (3.1 ± 0.1, 2.8 ± 0.1, 2.2 ± 0.1 and 1.1 ± 0.1, respectively), suggesting that a 2-h hydrolysis time and a 6 M HCl concentration are efficient pretreatment conditions for determining δ15NAA and estimating TP. Compared to the currently used hydrolysis conditions (24 h, 6 M), the proposed conditions (2 h, 6 M) accelerated the δ15NAA assay, making it faster, more convenient, and more efficient. Further research is needed to simplify the operational processes and reduce the time costs, enabling more efficient applications of CSIA-AA.

Dynamic Magnetic Field Analysis and Delivery Strategy Optimization for Scanning Magnet in Proton Therapy System

In pencil beam scanning proton therapy, the regulation and stabilization of the scanning magnetic field between two spots should be completed as quickly as possible in order to reduce treatment time. Because of the eddy current effect, the dynamic magnetic field lags behind the excitation current. It is significant to analyze the dynamic field and reduce the field stability time to minimize the delivery time and improve the therapy efficiency. In this paper, dynamic magnetic field simulation is carried out with a full lamination model of the scanning magnet in the Huazhong University of Science and Technology Proton Therapy Facility. In addition, a single lamination model instead of a full lamination model is explored to reduce time cost and memory for lamination of no more than 1-mm thickness. The eddy current diffusion trend and the influence of lamination on the eddy current are investigated. Moreover, the effect of lamination thickness (ranging from 5 to 0.1 mm) and current ramp rate (ranging from 20 to 100 A/ms) on the magnetic field stability time is studied. In addition, the characteristic of magnetic stability time for various spot steps is analyzed. Considering two spot patterns with discrete or clustered spots, an optimized delivery strategy with various scanning dead times according to the step is presented. When the lamination is 1 mm, the scanning time can be reduced by 39.2% for a clustered pattern and 38.4% for a discrete pattern using a genetic algorithm based on the different scanning dead-time strategy instead of the fixed dead-time strategy. With a thinner 0.1-mm lamination, the scanning time can be reduced by 49.8% for the clustered pattern and 43.3% for the discrete pattern, compared to that of the 1-mm lamination.

ABWOA: adaptive boundary whale optimization algorithm for large-scale digital twin network construction

Digital twin network (DTN) as an emerging network paradigm, have garnered growing attention. For large-scale networks, a crucial problem is how to effectively map physical networks onto the infrastructure platform of DTN. To address this issue, we propose a heuristic method of the adaptive boundary whale optimization algorithm (ABWOA) to solve the digital twin network construction problem, improving the efficiency and reducing operational costs of DTN. Extensive comparison experiments are conducted between ABWOA and various algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony, differential evolution algorithm, moth search algorithm and original whale optimization algorithm. The experimental results show that ABWOA is superior to other algorithms in terms of solution quality, convergence speed, and time cost. It can solve the digital twin network construction problem more effectively.

Simulation Model of Precast Concrete Element Delivery on Time and Operational Costs of the Precast Yard in the Board Mill Construction Project

Simulation Model of Precast Concrete Element Delivery on Time and Operational Costs of the Precast Yard in the Board Mill Construction Project

Cost optimization and reliability analysis of fault tolerant system with service interruption and reboot

Due to widespread usage in many real time systems, reliability modeling and cost optimization of fault tolerance system have drawn attention of the practitioners. The fault tolerance in these systems can be provided by the support of maintenance and redundant components that help in smooth operation of the system in spite of failure of some active components. This investigation deals with the performance modeling of a fault-tolerant system consisting of a finite number of active (online) and standby components. During the switching from active to standby, the recovery procedure is performed, which may be imperfect. In case of imperfect recovery, the system reboot takes place. The maintenance of all the components is managed by a repairman (server) which is subject to failure. When the server is interrupted for rendering the service, functioning does not get stopped due to the system switch-over from perfect working to working breakdown mode. The system works even when the server is on working vacation and performs repair jobs of the failed components. The machine repair model based on Markovian process is developed to derive the transient probabilities and other performance indices of the fault tolerant system using Laplace transforms and matrix analytical method. Using the direct search strategy and particle swarm optimization, the cost-benefit analysis is done. The optimal design of the control parameters for the fault-tolerant system are presented by framing a cost-effective ratio function. The model is examined computationally by performing the numerical simulation and cost optimization.

Formation of Repetitive Group Sampling Plan for Life Tests Based on Percentiles using Odd Frechet Inverse Rayleigh Distribution

Objectives: This paper presents the idea of Repetitive Group Sampling Plans using Odd Frechet Inverse Rayleigh Distribution, when the life test is truncated at a pre-specified time. The main objective of the proposed sampling plan is to minimize the sample size because the analogous inspection time and inspection cost will be reduced. Methods: A truncated life test may be conducted to evaluate the smallest sample size to insure certain percentile lifetime of products. The minimum sample size and its ratio for the specified lifetime percentile were derived for the producer’s risk. The operating characteristic values are also obtained for the proposed sampling plan. The corresponding table values are obtained through Monto Carlo Simulation technique. Findings: The results from the sampling plan developed in this research can easily be adaptable in practical situation with small sample sizes and fewer experimental times and hence the plan yields a better result for reliability sampling plan. Novelty: The work designed and developed with the aim that sampling plan in this paper may be helpful for the engineers and statistical plan developers in the field of statistical quality control especially in attribute reliability sampling plan. The tables are generated which is useful for both producer and consumer. This plan can highly recommendable since the products are randomly sampled and also the procedure for sampling involves less experiment time and with minimum sample size. Keywords: Odd Frechet Inverse Rayleigh Distribution, Percentiles, Repetitive Group Sampling Plan (RGS), Producer’s risk

ВЫЯВЛЕНИЕ ПОТЕНЦИАЛЬНЫХ ГОРОДСКИХ ЦЕНТРОВ НА ОСНОВЕ МОДЕЛИРОВАНИЯ ПАССАЖИРОПОТОКОВ (НА ПРИМЕРЕ САНКТ-ПЕТЕРБУРГА)

The article considers the development of methodological foundations and suggests methodological approaches to identify urban areas with signs of subcenters in the implementation of a polycentric model of urban development. It is demonstrated that the application of mathematical modeling of passenger flows contributes to solving the problem of urban areas differentiation according to criteria of transport accessibility, time costs and travel speeds. As part of the study, calculations of passenger flows and passenger turnover were performed on the example of the St. Petersburg agglomeration – using network and pre-network models. Comparing the values of transport demand and transport supply in the nodes of the network made it possible to identify the location of potential urban centers.

P.131 Comparison of interhemispheric and transcortical approaches for resection of colloid cysts of the third ventricle

Background: Colloid cysts are rare benign lesions of the third ventricle that can cause hydrocephalus and intracranial hypertension. Their primary treatment is surgical removal. Although open surgery presents the best opportunity for total resection of the cyst, there is no consensus regarding the optimal choice between the interhemispheric and the transcortical approaches. We aim to compare these two approaches in regard of the radicality of excision, the recurrence rate, and the surgical imprint. Methods: Retrospective cohort study on all patients who underwent surgical resection of colloid cyst between 2003 and 2023 at CHU de Québec. Data on demographics, symptoms, complications, and imaging was gathered. Results: In a cohort of 28 patients (17 interhemispheric, 11 transcortical), the preliminary results demonstrate prolonging operative time (270min. vs. 187min.) and increasing blood loss (193cc vs 100cc) associated with the interhemispheric approach. Despite these results, the hospitalization duration remains similar (p=0.734). However, the interhemispheric approach results in significantly lower surgery-induced encephalomalacia (1.1cc vs. 4.4cc, p=0.006). Conclusions: The interhemispheric approach could lead to potentially lesser consequences due to the reduced volume of encephalomalacia left by the surgical intervention, at the cost of prolonged operating time and higher blood loss.

A soft resource optimization method based on autonomous coordination of unmanned swarms system driven by resilience

The resilience improvement of unmanned swarm systems endows them with enhanced survivability and mission execution capability in complex environments. However, the current methods for improving resilience in unmanned swarm systems face challenges such as high time costs, difficult implementation, and weak anti-interference ability. Therefore, a soft resource optimization method based on the autonomous collaboration of unmanned swarm systems is proposed in this paper. Firstly, a research framework illustrates the complete cycle of resilience improvement processes in distributed unmanned swarm systems. Secondly, a soft resource optimization model is developed based on autonomous collaboration among distributed unmanned swarm systems, providing detailed insights into autonomous collaboration in complex environments. Finally, simulation experiments are conducted to validate the advantages of the proposed soft resource optimization method. The experimental results show that compared with the centralized control, the soft resource optimization method through autonomous coordination not only improves the resilience, but also improves the running speed by 99.8%. In addition, a pre-analysis of resilience threats is also carried out to facilitate proactive implementation of resilience improvement measures for unmanned swarm systems by engineers in the simulation experiment.

#1381 Economic evaluation of an integrated care program versus conventional care for chronic kidney disease patients in rural communities of Thailand

Abstract Background and Aims This study evaluates an integrated care program for chronic kidney disease (CKD), which has demonstrated effectiveness in delaying kidney function decline in the rural population of Thailand. Efficacy of the program was initially established through the ESCORT-1 study, a cluster-randomized controlled trial (doi: 10.1186/s12882-016-0414-4), and further demonstrated the effectiveness by the larger-scale prospective cohort study, ESCORT-2 (doi: 10.1111/nep.13849). The aim of this study is to assess the cost-effectiveness of incorporating this program into Thailand's public health benefit package, with the goal of establishing it as a model for similar rural areas in resource-limited settings. Method Following the Thailand's guideline for health technology assessment, a cost-utility analysis utilizing data from the ESCORT studies, local databases, and literatures. The integrated care program was tailored to primary healthcare system by using preexisting workforces. It involved a collaboration between Multi-Disciplinary Care Teams (MDCT) at district hospitals and Community Care Network (CCN) teams at Sub-District Health Centers (SDHC). The MDCT, comprising general practitioners, CKD nurse managers, pharmacists, a nutritionist, and a physical therapist, provided key patient education on diet, medication adherence, exercise, and avoiding certain medications and herbal remedies. The CCN teams, including community nurses and village health volunteers, conducted home visits for health monitoring and lifestyle guidance. Both MDCT and CCN personnel received training in CKD care based on the NKF-K/DOQI and KDIGO Clinical Practice Guidelines. A decision-analytic Markov model and Monte-Carlo simulation were applied to quantify and compare the lifetime costs and quality-adjusted life years (QALY) outcomes of the intervention and conventional-care from a societal perspective considering both direct and indirect costs. The analysis included extensive sensitivity analyses, incorporating a probabilistic model and scenario-analyses based on patient ages, CKD stages, and varying program efficacy. Additionally, a budget impact analysis from the payer's perspective assessed the financial impact and affordability over a five-year period. Results The integrated care program was found to dominate conventional care. The results indicated an incremental cost-effectiveness of (-)278,000 Thai Baht (∼7,267 EUR) per QALY for the base case analysis. Probabilistic analysis demonstrated consistent outcomes, with the integrated care program being cost-effective at almost 100% of the time during analysis at the 160,000 Thai Baht (∼4,182 EUR) willingness-to-pay threshold (Fig. 1). These results are driven by significant cost savings due to reduced time and overall costs in dialysis before death. The program is most cost-effective when provided early to patients, in terms of both age and CKD stages (Fig. 2). The budget impact analysis showed that introducing the intervention could save the payer 184 billion Thai Baht (4.76 billion EUR) over five years. (The exchange rate; 1 Thai Baht = 38.26 EUR) Conclusion The integrated care program for patients with stage 3-4 CKD is cost-effective and suitable for inclusion in Thailand's health benefit package, mainly due to its significant cost savings and enhanced health outcomes. The program's design, focusing on sustainability and feasibility by utilizing existing workforces, positions it as a viable model for similar economic and healthcare contexts, especially in resource-limited countries.

Extraction of Corn Plant Phenotypic Parameters with Keypoint Detection and Stereo Images

Corn is a global crop that requires the breeding of superior varieties. A crucial aspect of the breeding process is the accurate extraction of phenotypic parameters from corn plants. The existing challenges in phenotypic parameter extraction include low precision, excessive manual involvement, prolonged processing time, and equipment complexity. This study addresses these challenges by opting for binocular cameras as the data acquisition equipment. The proposed stereo corn phenotype extraction algorithm (SCPE) leverages binocular images for phenotypic parameter extraction. The SCPE consists of two modules: the YOLOv7-SlimPose model and the phenotypic parameter extraction module. The YOLOv7-SlimPose model was developed by optimizing the neck component, refining the loss function, and pruning the model based on YOLOv7-Pose. This model can better detect bounding boxes and keypoints with fewer parameters. The phenotypic parameter extraction module can construct the skeleton of the corn plant and extract phenotypic parameters based on the coordinates of the keypoints detected. The results showed the effectiveness of the approach, with the YOLOv7-SlimPose model achieving a keypoint mean average precision (mAP) of 96.8% with 65.1 million parameters and a speed of 0.09 s/item. The phenotypic parameter extraction module processed one corn plant in approximately 0.2 s, resulting in a total time cost of 0.38 s for the entire SCPE algorithm to construct the skeleton and extract the phenotypic parameters. The SCPE algorithm is economical and effective for extracting phenotypic parameters from corn plants, and the skeleton of corn plants can be constructed to evaluate the growth of corn as a reference. This proposal can also serve as a valuable reference for similar functions in other crops such as sorghum, rice, and wheat.

Determination of chlorothalonil in vegetables/fruits using liquid chromatography-tandem mass spectrometry with a discharge adapter interface.

Chlorothalonil (CHT), a broad-spectrum fungicide, has been employed widely to control foliar diseases, whereas with a major metabolite of polar 4-hydroxychlorothalonil (CHT-4-OH), only an acceptable nonpolar CHT residue is allowed by most countries. This study involves the method development for CHT residue in vegetables/fruits using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with a novel modified discharge-adaptor (DA) interface. CHT residue was analyzed using LC-MS/MS with DA interface (LC-DA-MS/MS), developed in our previous works. A DA was placed on the electrospray tip to switch the ionization modes. A modified quick, easy, cheap, effective, rugged, and safe (QuEChERS) method was applied to extract CHT residue of vegetables/fruits efficiently with less sample preparation time and analysis cost. CHT and CHT-4-OH spiked in four different vegetables/fruits were extracted using the modified QuEChERS method. After LC with isocratic elution, CHT and CHT-4-OH were separated within 3min. Using LC-DA-MS/MS, the ion signals of CHT were improved two to three times, and the limit of quantification of 5ng/g and linearity (r2 > 0.99) in the range of 5-200 ng/g were achieved using 10g of vegetables/fruits. The precision and accuracy were within 15% each. The modified QuEChERS and LC-DA-MS/MS were applied to examine eight field-grown vegetables/fruits; 9.5 and 2588.9ng/g of CHT were detected in two vegetables/fruits. LC-DA-MS/MS combined with modified QuEChERS was successfully applied to determine CHT residue <10ng/g in vegetables/fruits and with satisfied validation results. The developed method could reduce both analysis cost and time, attributing to simplifications in modified QuEChERS, isocratic elution, and DA interface in LC-DA-MS/MS.