623 results about "Food quality" patented technology

The invention discloses a campus smart canteen management platform based on AI and IOT. The campus intelligent canteen management platform comprises a global sensing system, an intelligent decision support system, a credible tracing system, a safety and supervision system and a user experience optimization system. The global sensing system is used for collecting multi-modal data and carrying out data fusion and preprocessing on the multi-modal data; the intelligent decision support system is used for performing multifunctional analysis on the fused and preprocessed multi-modal data to obtain a management decision; the credible traceability system is used for recording digital traceability information, storing reserved food samples and detecting pesticide residues in real time; the safety and supervision system is used for providing food reserved sample management, food quality event monitoring and analysis, supervision record management and parent applet functions; and the user experience optimization system is used for providing health inspection, health monitoring and personalized suggestions, identity verification and payment modes. In this way, the overall level of canteen management is improved.

The invention relates to the technical field of food component analysis, in particular to a food quality component analysis method and system based on a near infrared spectrum technology, and the method comprises the following steps: obtaining a food sample set, and obtaining original near infrared spectrum data based on the food sample set, combining the original near infrared spectrum data to form an original infrared spectrum matrix; preprocessing the original infrared spectrum matrix to generate a processed spectrum matrix; and extracting the characteristic wavelength of the near infrared spectrum information based on the processed spectrum matrix, constructing a food quality quantitative analysis model according to the characteristic wavelength in combination with a partial least squares regression algorithm, and outputting a component content predicted value according to the food quality quantitative analysis model. And generating a quality component analysis report of the to-be-detected food based on the component content predicted value. The method has the advantages that food quality and ingredients can be quickly, nondestructively and accurately detected, and detection precision and accuracy are improved.

The present invention relates to the technical field of food quality detection, and in particular to a quality assessment system and method for Xanthoceras sorbifolia oil based on spectral analysis, comprising a spectral acquisition module, a data preprocessing module, a feature extraction module and a quality assessment module; wherein: the spectral acquisition module is used to collect spectral data of Xanthoceras sorbifolia oil samples, including reflection spectrum and transmission spectrum information; the data preprocessing module is used to preprocess the spectral data collected by the spectral acquisition module; the feature extraction module is used to extract feature data related to the quality of Xanthoceras sorbifolia oil from the preprocessed spectral data; the quality assessment module is used to construct a quality assessment model, assess the quality of Xanthoceras sorbifolia oil samples, and output the quality grade. The present invention provides an efficient, non-destructive and real-time quality assessment system and method for Xanthoceras sorbifolia oil by combining near infrared spectroscopy technology with a support vector machine regression algorithm, which significantly improves the assessment accuracy.

The invention discloses a dietary pattern evaluation method and system based on digital intelligent analysis, and relates to the technical field of dietary pattern evaluation.The method comprises the steps that a mobile terminal is used for synchronously collecting multi-modal data, and the multi-modal data are preprocessed; food pictures are retrieved based on keywords to form a data set, an image recognition model is constructed, the volume and area of food materials are calculated in combination with a 3D reconstruction technology, a food material density database is matched, and automatic estimation of the quality of the food materials is achieved; building a voice interaction model fusing characters and regional voice features, and recognizing food material types and food material quality by using voice data; a dynamic allocation weight is calculated through multi-modal confidence, a graph attention mechanism is utilized to detect and correct data conflicts, and a space-time constraint rule base is combined to align dispersed intake records; and constructing a data visualization chart library. According to the method, the rise of dietary structure analysis from extensive statistics to refined intelligent evaluation is realized, and the problem of error accumulation caused by dependence on a single data source in a traditional method is effectively solved.

The invention discloses a grain quality detection device and method, a control device and a storage medium, and the device comprises a grain sample separation device which is used for dividing a target grain sample into two paths and conveying the target grain sample to a first discharge port and a second discharge port; the appearance quality detection device is used for detecting an appearance detection result; a volume weight detection device for detecting a target volume weight; the internal quality detection device is used for detecting an internal quality detection result; the safety index detection device is used for detecting a safety index detection result; a recovery device; the conveying device is used for sequentially conveying the target grain sample among the first discharge port, the appearance quality detection device and the recovery device and among the second discharge port, the volume weight detection device, the internal quality detection device, the safety index detection device and the recovery device; and the control device is used for determining grade information of the target grain sample according to the appearance detection result, the target volume weight, the internal quality detection result and the safety index detection result. The detection efficiency and the accuracy of the detection result can be improved.

The invention discloses a food detection system and method based on computer vision, and relates to the technical field of food visual detection and intelligent identification, and the system comprises an image collection module which obtains initial food image data from a production line through a preset image collection device, processes the data through an edge detection algorithm to extract contour features, and sends the contour features to an image processing module; meanwhile, a boundary point coordinate extraction process is fused to obtain appearance boundary description of the food; the boundary clustering module is used for grouping boundary points by adopting a clustering algorithm according to the extracted appearance boundary description, carrying out processing under an iterative convergence condition in combination with a distance metric function and cluster center initialization, and judging that the shape is irregular if the grouping number exceeds a preset threshold value, so as to obtain a shape classification label; according to the food detection system and method based on computer vision, the accuracy and adaptability of food quality detection are remarkably improved, and the quality control requirement of a multi-scene production line is met.

The invention discloses an automatic quality detection system for a food production line, and relates to the technical field of food detection.The system collects food surface images in real time through an industrial camera, and an appearance feature data set is generated after preprocessing; using a deep learning model constructed by a convolutional neural network to extract appearance features, forming a food defect data set, calculating an appearance score through an image processing algorithm, and evaluating the appearance score and a surface flaw score threshold value; component content and microbial pollution data are acquired and processed in real time through near infrared spectrum analysis and multi-sensor detection, and component content scores and pollution scores are generated; performing quality judgment by calculating a comprehensive quality score and evaluating the comprehensive quality score with a set comprehensive quality score threshold value; in an abnormal condition, the system triggers an alarm and informs a worker through wireless communication; the whole system realizes automatic, precise and multi-dimensional detection of food quality, and the production detection efficiency and the food safety control capability are remarkably improved.

The invention relates to the technical field of machine vision and food quality detection, in particular to a food defect real-time detection method and system based on image processing. The method specifically comprises the following steps: integrating multi-modal data, and adaptively adjusting a weight coefficient to improve data quality; multi-stage noise reduction is carried out, key frames are extracted, and a key frame verification mechanism is enhanced through a 2FA control variable; constructing a YOLO-BioNet model, and optimizing the detection efficiency and precision in a complex scene through dynamic convolution kernel allocation and a spiking neural network; integrating Kalman filtering, LSTM (Long Short Term Memory) time sequence modeling and a deep reinforcement learning model, analyzing a food surface change track and predicting a defect state; real-time data and historical information are fused, and a dynamic threshold value is calculated through a Bayesian network; performing linkage response between an automatic sorting instruction and a production line; a distributed storage and graph neural network optimization model is adopted, and defect tracing is supported. The method has remarkable advantages in the aspects of real-time performance, detection accuracy and self-adaptive capability.

There is a challenge in creating a seamless food supply chain that aligns with unique characteristics of each food item while considering business values. The present disclosure address the challenges associated with the food supply chain by providing a system and method for evaluating sustainability of a food supply chain network using digital twin. The present discourse describes developing a set of modeling abstractions and a digital twin-based approach to explore ideal supply chains for food, aiming to reduce food wastage, ensure food quality and improve profit margins. In the present disclosure, unique characteristics of fruit supply chains are described which are further applied to food supply chains. Using the characteristics of climacteric fruits, unique requirements that contemporary perishable food supply chains need to consider are described. Furthermore, a digital twin-based approach specifically designed for climacteric fruits is provided to address modeling requirements and enable evidence-based and justification-backed holistic decision-making.

The invention relates to the technical field of food quality detection, in particular to a tea oil authenticity identification method based on an in-situ mass spectrum-machine learning coupling technology. Comprising the following steps: S1, preparing a sample; s2, acquiring mass spectrum data; s3, feature extraction: performing dynamic binning processing on the mass spectrum data, extracting feature peak information, and performing time dimension feature aggregation to form an initial feature matrix; s4, data preprocessing; s5, training a machine learning model; and S6, intelligent identification and visual output: inputting the mass spectrum original data of the tea oil sample to be detected into the trained multi-model integrated system, outputting an identification result through weighted voting or an optimal model strategy, and giving a confidence interval to quantify the reliability of the result.

The invention relates to the field of food safety, in particular to a food quality rapid detection result evaluation method, which comprises the following steps: detecting the optimal temperature duration ratio and the transportation temperature fluctuation value of each key raw material, and determining a corresponding transportation analysis mode according to the optimal temperature duration ratio and the transportation temperature fluctuation value of each key raw material; according to whether abnormal key raw materials exist in the key raw materials used in the abnormal production batches or not, whether adjustment is conducted according to the transportation process of the corresponding abnormal key raw materials or not is determined; if the transportation process is adjusted, a corresponding transportation adjustment strategy is determined according to the abnormal factors of all the abnormal key raw materials; if the transportation scheme of each key raw material is adjusted under the transportation optimization condition, the accuracy of the obtained influence factors of each abnormal key raw material is improved, and the effectiveness of the adjustment measures for the transportation process is further improved.

The invention discloses a food quality safety tracking management system based on big data, and relates to the technical field of food safety, the system can collect various quality data in real time at key nodes of dairy product production and storage, and realize automatic association of batch information. The parameter acquisition module acquires dairy product production data in real time through acquisition equipment installed at a production node, and acquires storage data at a storage node at the same time. After the data is transmitted to the central database, the system preprocesses the data, and finally a complete preservative traceability data set is generated. And associating the quality data of each batch by using the unique identifier, and associating the dairy product batch ID with the preservative batch ID to form a dairy product component association table PCA, and establishing a detailed tracing relationship between different batches. The module enhances the traceability and transparency of the product quality, and is convenient for quickly finding out the problem source when the quality problem occurs.

The utility model relates to the technical field of food detection, and discloses an isolated food quality detection sampling device which comprises a liquid storage tank, a cavity is formed in the liquid storage tank, a plurality of mounting jackets are fixedly connected to the top side of the liquid storage tank, and the mounting jackets are slidably connected to the interiors of the mounting jackets through sliding assemblies. Elastic pieces are arranged at the front end and the rear end of the outer wall of the mounting outer sleeve, mounting holes are formed in the front end and the rear end of the outer wall of the mounting outer sleeve, protrusions are fixedly connected to the outer walls of the elastic pieces, and the elastic pieces are connected with the mounting holes in a clamped mode through the protrusions. According to the sampling device, the sampling needle cylinders can be conveniently and rapidly installed, the multiple sampling needle cylinders are installed on the top side of the liquid storage tank, a sample in the liquid storage tank is sampled multiple times, meanwhile, air is prevented from entering the liquid storage tank to affect the sample in the sampling process, and therefore the working flexibility is improved, and meanwhile the working quality is guaranteed.

The invention discloses a food quality safety traceability system and method based on a block chain technology, and relates to the technical field of block chains, a food feature vector set Es is collected and generated through an Internet of Things device, then a feature extraction vector set Te is generated through data normalization processing and feature extraction, the abnormal degree of the food surface state is further measured, and a food quality safety traceability result is obtained. And obtaining an abnormal state result of the food surface through judgment. And calculating and matching the comprehensive risk index Rs and the food quality risk assessment threshold value SZ to generate a targeted quality assessment scheme, tracing the circulation path based on the block chain, marking and notifying abnormal nodes, and ensuring timely tracing and management and control. According to feedback information of quality safety detection personnel, a calculation model of the comprehensive risk index Rs is dynamically adjusted, and the flexibility and accuracy of risk judgment are improved. The food quality abnormity problem is efficiently tracked and responded, and fine control and dynamic optimization of food quality safety in the whole process of the supply chain are achieved.

The invention relates to an inspection system for quality analysis of a food product, comprising a conveyor for moving it to an inspection area, a lighting device consisting of LEDs that emit a light sequence directed towards the inspection area to light up the product by transmission, a linear camera with at least one line of pixels for collecting a plurality of images in each light sequence of the lighting device, and a focusing member for focusing the beam emitted by the lighting device. Advantageously, the lighting device is activated in a pulsed manner, generating a light sequence with at least two different illuminations, at least one of which is a transmission-pulsed illumination. The lighting device is aligned on the axis formed by the product to be inspected and the linear camera.

The invention belongs to the field of food quality evaluation, and provides a delicate flavor synergistic effect evaluation method based on an electroencephalogram monitoring technology. Psychological or physiological factors such as subjective judgment, forced selection or scoring, individual umami cognition and umami sensitivity difference of sensory officers may affect the accuracy of sensory experiment results; the invention discloses methods such as umami sample selection and strength evaluation, electroencephalogram signal acquisition experiment normal form, quantitative evaluation and statistical analysis, feature extraction and response difference analysis, and the like, and the response mechanism of typical umami compounds in the human brain is explored by taking the response of people to umami signals as a research object and taking electroencephalogram signal detection analysis as a main method. A new theoretical basis is provided for quantitative evaluation based on the human palatable taste intensity, and the method is worthy of wide popularization.

The invention relates to the technical field of electrochemical detection and food quality safety, and particularly discloses an edible oil rancidity degree rapid detection system based on an electrochemical sensor, comprising: a data acquisition module for receiving an original electrochemical response signal and performing format standardization, abnormal data rejection and integrity verification; the data preprocessing module adopts a statistical optimization algorithm to reduce noise, correct baseline drift, generate a unified dimension data set and complete stability verification; the feature analysis module is used for extracting rancidity related feature parameters through statistical correlation analysis, and carrying out fusion calculation after significance verification to obtain feature values representing rancidity; and the evaluation and judgment module inputs the characteristic value into a pre-training model, outputs a quantitative index through multivariate statistical calculation, dynamically sets a safety threshold in combination with the edible oil type, and outputs a rancidity judgment conclusion after comparison. The method depends on statistical data processing and analysis, the detection reliability and accuracy are improved, and the method is suitable for edible oil quality safety rapid screening and quality monitoring.

The invention discloses an intelligent food transportation monitoring method and system based on a multi-dimensional dynamic coupling algorithm, and the method comprises the steps: collecting food transportation data in real time through wireless communication; constructing a digital twinborn model of the food transportation process, and fusing the digital twinborn model and the risk prediction model; simulating a transportation environment change trend to obtain a current multi-dimensional dynamic coupling environment influence factor; obtaining a food heat accumulation amount, and predicting a food quality deterioration probability factor; judging whether the food quality deterioration probability factor exceeds a threshold value or not, and predicting a transportation risk multi-dimensional quantitative index by combining the multi-dimensional dynamic coupling environment influence factor and the food quality deterioration probability factor; generating an optimization strategy, and outputting an intervention scheme; according to the method, the defect of the traditional technology in the aspect of risk prediction of food transportation monitoring is overcome, and through accurate risk prediction and timely intervention measures, the deterioration loss of food in the transportation process is remarkably reduced, the transportation resource allocation is optimized, and the transportation efficiency is improved.

The invention discloses an information processing method and system for improving a food detection effect, and relates to the technical field of food detection.The information processing method comprises the steps that food data are collected, the food data comprise the pH value, the moisture content, the appearance image, spectral data and VOCs concentration data, and the food data are preprocessed; feature extraction is carried out on the food data, alignment and splicing are carried out according to timestamps, an input matrix is obtained and mapped to a high-dimensional space through linear transformation, an embedded vector is generated, position codes are added to the embedded vector, and a time-enhanced embedded vector is obtained. According to the method, position codes are generated from odd and even dimensions of an embedded vector by adopting sine and cosine functions respectively, and the position codes and the embedded vector are added element by element, so that anti-noise embedding of time sequence dynamic characteristics is realized, the error of time sequence alignment is reduced, and the robustness of a food quality degradation path is enhanced.

The invention discloses a taste receptor derived peptide capable of effectively detecting caffeine and a biosensor, and belongs to the technical field of biosensors. Currently, traditional spectrum and chromatography on which caffeine detection depends have the problems of expensive instruments, time-consuming detection, complex procedures and the like, and a sensor based on a taste receptor is also faced with the limitations of high cost, complex preparation process and easy inactivation of the receptor. In order to solve the problem, the taste receptor derived peptide provided by the invention has an amino acid sequence as shown in SEQ ID NO: 4, the biosensor comprises a substrate and a disc interdigital electrode, and a carbon nanotube is firmly attached to the electrode and is connected with the taste receptor derived peptide. The biosensor can specifically detect caffeine in a beverage, has no response to other substances, and has application potential in the fields of food quality control, clinical drug monitoring, public health safety assessment and the like.

The invention relates to the technical field of fruit and vegetable production equipment, and discloses a full-automatic fruit and vegetable food processing and packaging assembly line which comprises a bubble cleaning pool. The first mechanical arm is arranged on the front side of the bubble cleaning pool, a mechanical claw is installed at the output end of the first mechanical arm, and the fruits and vegetables stored in the frame shape are put into the bubble cleaning pool through the mechanical claw; the first elevator is arranged on one side of the bubble cleaning pool, and the input end of the first elevator is located in the bubble cleaning pool. According to the full-automatic fruit and vegetable food processing and packaging assembly line, automation and intellectualization of the fruit and vegetable food processing and packaging assembly line are achieved through innovative design, the efficiency of the links such as cleaning, selecting and packaging is greatly improved, and the production efficiency is improved. According to the method, quality fluctuation and resource waste caused by manual operation are reduced, the food quality stability and safety are ensured, meanwhile, the labor cost is remarkably reduced, and the market competitiveness and sustainable development capacity of enterprises are enhanced.

The invention relates to the field of food safety supervision, in particular to a food quality safety supervision system, which comprises a PDA (Personal Digital Assistant) terminal, comprising an integrated information collector for acquiring a food electronic certificate, an electronic nose for acquiring food smell data, a spectral imager for acquiring a food spectral image and a visual sensor for acquiring a food surface image, the supervision cloud platform is used for matching each big data evaluation model with the corresponding food type through the electronic certificate; and for a single type of food, the big data evaluation model judges the quality grade of the food based on the surface image of the food and re-determines the quality grade of the food based on the smell data and the spectral image, and if the quality grade is unqualified, a food processing suggestion is generated based on the quality grade. Rapid detection is carried out through the PDA terminal, and the quality grade of the food is generated through the supervision cloud platform, so that portable and rapid detection of the food quality and efficient supervision of multi-link detection are realized.

The invention provides a food quality risk early warning method and system, and belongs to the technical field of food detection, and the method comprises the steps: obtaining a visible light image and a near-infrared image of food; based on the visible light image of the food, determining the category and the segmentation mask of the food by adopting an improved multi-scale feature fusion network; performing binary mask filtering on the visible light image and the near-infrared image based on the segmentation mask to obtain image data, calling different food quality detection models according to food categories, and inputting the image data into the food quality detection models to determine the quality risk of the food; wherein the food quality detection model is an improved Transform network, and the improved Transform network comprises an encoder, a feature mapping module and a multi-layer perceptron. The visible light image and the near-infrared imaging are fused, and the detection accuracy is improved by detecting the appearance quality and the internal quality at the same time.

The invention discloses a multi-modal fusion-based roast chicken eating quality grading intelligent judgment method, which comprises the following steps of: performing matrix setting on a roasting process, preparing roast chickens with different quality grades, evaluating sensory attributes and overall scores of the roast chickens, and quantifying different modal data characteristics by adopting multiple quality detection methods. By constructing a process-sensory-multi-modal feature full-link database and combining a random forest for determining optimization and an XGBoost interpretable machine learning model, accurate grading and intelligent prediction of the roast chicken eating quality are realized, and the core problems of high subjectivity, poor single-modal generalization ability, data islands and the like of a traditional method are solved. Quality accurate grading prediction and digital traceability from processing to finished products are realized, and data support is provided for process optimization and market positioning prediction. The method has remarkable practical application value in the aspects of product comprehensive grade prediction and data management, and has prediction potential suitable for multiple scenes.

The invention relates to a home-based care technology for the aged, is used for solving the problems of difficulty in shopping, damage to the body caused by large activity and difficulty in distinguishing food quality in home-based care for the aged, and particularly relates to a home-based care smart cloud system based on the Internet of Things, which comprises a shopping auxiliary module, a health management module and a service evaluation module, according to the invention, the shopping auxiliary module is combined with the health management module to judge the activity amount of the elderly, so that the situation of shopping difficulty caused by non-support of the activity amount of the elderly is avoided; the health management module compares the activity data of the elderly with the optimal activity to judge whether the elderly is suitable for self-shopping, so as to prevent the physical health of the elderly from being damaged due to excessive activity; whether an article is fresh or not is judged through the service evaluation module according to quality assurance information of an article package and color difference comparison, the situation that old people eat deteriorated food to affect body health is avoided, and service scoring evaluation is conducted on stores in a set range in the aspects of pre-stocking and expenditure amount through the service evaluation module.

The invention relates to the field of food detection, in particular to a method for detecting dish food quality, which comprises the following steps: acquiring a package three-dimensional image and a food three-dimensional image of a dish food to be detected, and respectively processing the package three-dimensional image and the food three-dimensional image to obtain a corresponding package contour map and a food contour map; analyzing contour lines of the package contour diagram and the food contour diagram, and calculating the curvature difference of each corresponding point; according to the curvature difference threshold value and the abnormal corresponding point threshold value, judging whether the to-be-detected dish food has an abnormal area or not; performing spectrum detection on the to-be-determined abnormal food sample to obtain the nutrient content, and determining whether the to-be-determined abnormal food goes bad according to the nutrient content; and storing the to-be-determined qualified product, and detecting the residual nutrient content of the to-be-determined qualified product when the rated storage time is up. According to the method, potential abnormal samples are rapidly screened out by analyzing the contour lines of the package and the food contour, the number of samples needing spectrum detection is reduced, and the overall detection efficiency is improved.

The invention discloses a royal jelly freshness detection design matching method based on a structure diffusion model, and relates to the technical field of food quality detection. The invention discloses a royal jelly freshness detection design matching method based on a structure diffusion model. The method comprises the steps of sample acquisition, peptide fragment analysis, peptide fragment reverse target searching, binding energy calculation and detection design matching system construction. According to the method, protein and degradation products thereof related to royal jelly freshness can be quickly identified by virtue of a peptide fragment reverse target searching method and a mass spectrometry and database matching technology, so that the time for searching related protein markers is greatly shortened, and the freshness detection efficiency is remarkably improved; by accurately matching protein sequences and identifying degradation products, it is ensured that screened markers are closely related to the degradation process of royal jelly, and therefore scientificity and accuracy of freshness detection are improved; through utilization of royal jelly proteome information, data analysis efficiency and cost effectiveness are improved.

The invention provides a chicken deterioration marker screening and AICAR prediction method and system based on multi-task learning and transfer learning. The method comprises the following steps: selecting chicken samples with different storage conditions and shelf lives, collecting metabolite data of multiple batches, and carrying out quantitative analysis on the metabolite by utilizing high performance liquid chromatography (HPLC), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and other technologies. Through the multi-task learning model, multiple differential metabolites are deeply mined, and the prediction precision and the calculation efficiency are improved. And providing chicken storage period optimization suggestions according to the AICAR predicted value. The innovative method and system can significantly improve scientificity and accuracy of chicken quality evaluation, have wide application prospects, and are especially suitable for the fields of food quality monitoring, supply chain management and intelligent storage and transportation.

The invention belongs to the technical field of crystalline materials, and particularly relates to a cadmium complex crystal material as well as a preparation method and fluorescent recognition application thereof. According to the invention, Cd < 2 + > is selected as a central metal ion, a flexible polycarboxylic acid ligand 4, 4 '-((5-carboxyl-1, 3-phenylene) bis (oxy)) dibenzoic acid is selected, and two cadmium complexes Cd-1 and Cd-2 with novel structures are synthesized. The prepared Cd-1 and Cd-2 can rapidly detect Fe < 3 + > and Tb < 3 + >, and can be used as a novel fluorescent recognition sensor. The material can be used in the fields of environmental protection, quality inspection and the like, and has important significance in protecting the environment, improving food quality safety and the like.

The invention discloses a food quality safety tracking management system based on big data, and relates to the technical field of food, and the system comprises the steps: collecting the label identification data and storage stacking parameters of to-be-tracked food; extracting a label risk coefficient between a label inconsistency degree and a quality compliance risk based on the historical supervision data, and processing the label risk coefficient and the label identification data to obtain a label risk value; according to the storage stacking parameters of the to-be-tracked food, the stacking abnormal degree value of the to-be-tracked food in the storage process is judged; processing the stacking abnormal degree value and the label identification data to obtain a storage standard interference value; according to the storage specification interference value, the storage specification interference degree of the to-be-tracked food is judged, and first-grade storage specification interference and second-grade storage specification interference are obtained; the method has the effect of improving the efficiency and effect of food quality safety management.