Traditional Processing Methods Research Articles

Deep Spiking Residual Shrinkage Network for Bearing Fault Diagnosis.

Bearing fault diagnosis of electrical equipment has been a popular research area in recent years because there are often some faults during continuous operation in production due to the harsh working environment. However, the traditional fault signal processing methods rely on highly expert experience, and some parameters are difficult to be optimized by machine-learning methods. Thus, the satisfactory recognition accuracy of fault diagnosis cannot be achieved in the above methods. In this article, a new model based on the spiking neural network (SNN) is proposed, which is called deep the spiking residual shrinkage network (DSRSN) for bearing fault diagnosis. In the model, attention mechanisms and soft thresholding are introduced to improve the recognition rate under a high-level noise background. The higher recognition accuracy is obtained in the proposed model which is tested on the fault signal dataset under different noise intensities. Meanwhile, the training time is about treble as fast as the training time of the artificial neural network, which is reflecting the high efficiency of SNN.

A Comprehensive Review of Cured Meat Products in the Irish Market: Opportunities for Reformulation and Processing.

Cured meat products constitute one of the meat categories commonly consumed in Ireland and has been part of the Irish cuisine and diet for many years. Ham, gammon, and bacon are some of the products that involve curing as part of the traditional processing methods. Common among these products are high levels of salt and the addition of nitrites. These products undergo processing treatments to create variety, preserve shelf-life, and develop their unique quality and safety characteristics. However, consumers are becoming more conscious of the level of processing involved in these products, and the effects of some components and ingredients might be perceived as unhealthy. Meat product developers have been exploring ways to reduce the amount of ingredients such as salt, saturated fat, and chemical preservatives (e.g., nitrites), which are linked to health concerns. This is a challenging task as these ingredients play an important techno-functional role in the products' quality, safety, and identity. While innovative processing techniques are being introduced and progress has been made in reformulation and packaging technologies, much is still unknown, especially regarding the applicability of many of the proposed interventions to a wide range of meat products and their sustainability at the industrial scale.

Braiding drive data processing through rote learning

It is difficult for traditional data processing methods to make full use of the potential of braiding driven data, unable to quickly collect and preprocess data, and difficult to ensure the accuracy of data. Rote learning (RL) is part of the research field of artificial intelligence, which aims to enable computers to learn autonomously, just like humans. This allows understanding of relationships and patterns between data and helps computers process information quickly. In order to solve the problems of poor data integrity, slow data processing efficiency and poor information sharing in traditional data processing, and further optimize the braiding driven data processing technology, this paper combined RL with braiding driven data. Through the method of mechanical learning, the potential of weaving driving data is fully exerted, so that it can better cope with nonlinear relations and high-dimensional features. It used the effective method provided by the RL to process the braiding drive data, collect the data, and preprocessed the collected data to ensure the correctness of the data. It extracted the features of the data, which was convenient to classify the data according to its attributes. At the same time, this paper verified it by the steps of feature extraction, model training and data analysis. In order to test whether braiding drive data processing by RL can effectively solve the problems existing in traditional drive data technology, this paper tested the performance of compiled drive data processing, and the analysis results were as follows. The data integrity rate of braiding drive data was as low as 81%, which was much higher than that of traditional drive data processing. The recognition ability of data acquisition and matching was much higher than that of traditional drive data processing. Compared with the traditional drive data processing, the information sharing has been greatly improved. In terms of data processing efficiency, it is also much higher than the traditional drive data processing. It can be seen that the method of braiding drive data processing through RL effectively improves the accuracy of data processing. It strengthens the identification ability of data collection and matching, improves the sharing of information, enables users to obtain data and analyze it faster, and also improves the processing efficiency of data.

Effects of traditional processing techniques on nutritional quality and sensory acceptability of value-added products made from cowpea (Vigna unguiculata L. walp.) produced in Ethiopia

Aims: The aim of this study was to ascertain how different traditional processing methods affect the nutritive values and sensory acceptability of two cowpea varieties (Bole and Kenketi) growing in Ethiopia. Materials and Methods: A factorial design with two factors was used: cowpea varieties (Bole and Kanketi) and four processing methods (boiling, roasting, dehulling after roasting, and fermentation). Results: Moisture content, crude protein, total ash, crude fiber, crude fat, utilizable carbohydrate, and total energy of the raw cowpeas were 8.45%, 28.80%, 5.04%, 4.71%, 1.91%, 51.12%, 336.89 Kcal/100g and 8.91%, 25.32%, 4.71%, 6.60%, 2.12%, 52.34%, 329.72 Kcal/100g, for Bole and Kanketi varieties, respectively. After processing the values of these parameters were 8.00%, 27.44%, 4.81%, 3.61%, 1.75%, 54.39%, 343.03 Kcal/100g and 8.53%, 24.04%, 4.51%, 5.07%, 1.93%, 55.91%, 337.21 Kcal/100g, for Bole and Kanketi varieties, respectively. Fe, Zn and Ca, contents of raw cowpea variety were 15.65, 6.17 and 43.36 mg/100g, for Bole variety and 13.32, 4.99 and 41.91 mg/100g, respectively, for Kanketi variety. After processing, the contents were 13.46, 5.04 and 34.34 mg/100g for Bole variety whereas for Kanketi variety they were 11.65, 4.08 and 33.40 mg/100g, respectively. The anti-nutritional factors; tannin, and phytic acid were 28.43 and 80.37 mg/100g in the raw Bole variety while for Kanketi variety they were 31.23 and 127.99 mg/100g, respectively. After processing, these parameters were reduced to 16.75 and 50.37 mg/100g, respectively, for Bole whereas for Kanketi variety they were 18.42 and 80.05 mg/100g, respectively. The sensory acceptability scores of cowpea food products showed significant (p<0.05) differences for most of the quality parameters as affected by different processing methods. The products processed by boiling, roasting, dehulling after roasting and fermentation showed acceptability scores that ranged from 5.97 to 6.68 for appearance, 5.83 to 6.58 for color, 5.30 to 6.57 for flavor, 4.57 to 6.43 for taste, 5.12 to 6.55 for mouthfeel and 5.36 to 6.55 for overall acceptability in scale of 7 points. The results indicated that such processing methods are helpful in improving the nutritional quality of cowpea through the reduction of antinutritional factors. Conclusion: Therefore, different processing methods significantly affect the sensory quality of processed cowpea food products and useful for improving the nutritional quality with respect to crude protein, crude fat, crude fibre, total ash, utilizable carbohydrate and mineral bioavailability through reduction of anti-nutritional factors. Keywords: Antinutritional factors, Cowpea, Nutritional composition, Traditional processing.

Automated wound care by employing a reliable U-Net architecture combined with ResNet feature encoders for monitoring chronic wounds.

Quality of life is greatly affected by chronic wounds. It requires more intensive care than acute wounds. Schedule follow-up appointments with their doctor to track healing. Good wound treatment promotes healing and fewer problems. Wound care requires precise and reliable wound measurement to optimize patient treatment and outcomes according to evidence-based best practices. Images are used to objectively assess wound state by quantifying key healing parameters. Nevertheless, the robust segmentation of wound images is complex because of the high diversity of wound types and imaging conditions. This study proposes and evaluates a novel hybrid model developed for wound segmentation in medical images. The model combines advanced deep learning techniques with traditional image processing methods to improve the accuracy and reliability of wound segmentation. The main objective is to overcome the limitations of existing segmentation methods (UNet) by leveraging the combined advantages of both paradigms. In our investigation, we introduced a hybrid model architecture, wherein a ResNet34 is utilized as the encoder, and a UNet is employed as the decoder. The combination of ResNet34's deep representation learning and UNet's efficient feature extraction yields notable benefits. The architectural design successfully integrated high-level and low-level features, enabling the generation of segmentation maps with high precision and accuracy. Following the implementation of our model to the actual data, we were able to determine the following values for the Intersection over Union (IOU), Dice score, and accuracy: 0.973, 0.986, and 0.9736, respectively. According to the achieved results, the proposed method is more precise and accurate than the current state-of-the-art.

Automated quality inspection of baby corn using image processing and deep learning

The food industry typically relies heavily on manual operations with high proficiency and skills. According to the quality inspection process, a baby corn with black marks or blemishes is considered a defect or unqualified class which should be discarded. Quality inspection and sorting of agricultural products like baby corn are labor-intensive and time-consuming. The main goal of this work is to develop an automated quality inspection framework to differentiate between ‘pass’ and ‘fail’ categories based on baby corn images. A traditional image processing method using a threshold principle is compared with relatively more advanced deep learning models. Particularly, Convolutional neural networks, specific sub-types of deep learning models, were implemented. Thorough experiments on choices of network architectures and their hyperparameters were conducted and compared. A Shapley additive explanations (SHAP) framework was further utilized for network interpretation purposes. The EfficientNetB5 networks with relatively larger input sizes yielded up to 99.06% accuracy as the best performance against 95.28% obtained from traditional image processing. Incorporating a region of interest identification, several model experiments, data application on baby corn images, and the SHAP framework are our main contributions. Our proposed quality inspection system to automatically differentiate baby corn images provides a potential pipeline to further support the agricultural production process.

Studi Etnobotani Tumbuhan Obat Masyarakat Bumi Pajo

This research aims to study the use of medicinal plants in the community as one of the community's healing traditions in Bumi Pajo Village, Donggo District, Bima Regency as well as the traditional medicinal plant processing methods used by the people of Bumi Pajo Village, Donggo District, Bima Regency. As a type of qualitative research, this research has four informants: village heads, community leaders, religious leaders, and medicine makers. Data collection methods use observation, interviews and documentation. Data analysis consists of three stages: data reduction, data display, and drawing conclusions. This study found that the use and management of traditional medicinal plants in Bumi Pajo Village has been passed down from generation to generation. Medicinal plants are processed in various ways, such as boiling, pounding, steaming, pounding, grinding, grating, and kneading in water. Recommendations from this research are (1) The use of medicinal plants must be maintained and taught to the next generation (2) In the process of consuming plants medication by the people of Bumi Pajo Village, care needs to be taken in determining the amount of medication that can be used to prevent or treat disease (3) This is necessary because it is hoped that this will enable researchers to study more factors related to the use of traditional medicine, especially relating to local communities in certain areas.

Impact of traditional processing conditions and methods on the aflatoxin content of cooked maize

Impact of traditional processing conditions and methods on the aflatoxin content of cooked maize

MBPPE: A Modular Batch Processing Platform for Electroencephalography

To ensure the accuracy and reliability of subsequent analysis, research on electroencephalogram (EEG) signals typically requires preliminary processing of large datasets to eliminate noise and artifacts. Traditional batch processing methods require substantial hardware resources while lacking flexible automated workflows and user-friendly interactions. To address these challenges, we have implemented a modular batch processing platform for EEG (MBPPE) that offers both local execution and private deployment options to meet the demands of efficient signal processing from individuals to laboratories. We modularize the processing methods and organize them into pluggable multi-task batch processes, providing asynchronous processing solutions. In addition, we extend user functions by introducing plugins and promoting collaborative interaction through data sharing, access control, and comment communication. Simultaneously, interactive features are integrated into the visualization design, enabling users to process and analyze data more intuitively and naturally. Currently, the platform integrates several commonly used data preprocessing and analysis techniques, providing a novel solution for batch processing of EEG signals.

Estimation of Solar Irradiance Using a Neural Network Based on the Combination of Sky Camera Images and Meteorological Data

In recent years, with the growing proliferation of photovoltaics (PV), accurate nowcasting of PV power has emerged as a challenge. Global horizontal irradiance (GHI), which is a key factor influencing PV power, is known to be highly variable as it is determined by short-term meteorological phenomena, particularly cloud movement. Deep learning and computer vision techniques applied to all-sky imagery are demonstrated to be highly accurate nowcasting methods, as they encode crucial information about the sky’s state. While these methods utilize deep neural network models, such as Convolutional Neural Networks (CNN), and attain high levels of accuracy, the training of image-based deep learning models demands significant computational resources. In this work, we present a computationally economical estimation technique, based on a deep learning model. We utilize both all-sky imagery and meteorological data, however, information on the sky’s state is encoded as a feature vector extracted using traditional image processing methods. We introduce six all-sky image features utilizing detailed knowledge of meteorological and physical phenomena, significantly decreasing the amount of input data and model complexity. We investigate the accuracy of the determined global and diffuse radiation for different combinations of meteorological parameters. The model is evaluated using two years of measurements from an on-site all-sky camera and an adjacent meteorological station. Our findings demonstrate that the model provides comparable accuracy to CNN-based methods, yet at a significantly lower computational cost.

Design and Machining of a Spherical Shell Rotor for a Magnetically Levitated Momentum Ball

Ball-shell rotors with non-standard shapes, non-uniform conductive coatings, and eccentric masses machined by conventional processes constrain the improvement of levitation and torque accuracy of magnetically levitated momentum balls. This paper focuses on the machining method of multilayer ball-shell rotors to develop a ball-shell rotor with a standard shape and uniform conductive coating, which can improve the levitation and torque accuracy of magnetically levitated momentum balls. In this paper, a machining method for multi-coated ball-shell rotors is proposed. The machining process combining hemispherical surface and workpiece is adopted, and the whole sphere is assembled by threading, which effectively reduces the machining error. The influence of the cutting depth and feed rate of the tool on the cutting force of the ball shell was analyzed through the cutting force model. The effect of cutting force on the deformation of the ball shell was analyzed by the finite element method. The superiority of the machining method was verified by measuring the dimensions of the ball shell with a coordinate measuring machine. Compared with the traditional machining process and assembly method, the proposed ball-shell rotor machining method effectively improves the dimensional accuracy, reduces the center of mass to center of mass deviation, and ensures the levitation accuracy and output torque accuracy of the magnetically levitated momentum ball. Measurement results show that the diameter values of the pure iron ball shell are between 98.694 and 98.707 mm with a machining error of ±0.007 mm, and the diameter values of the spray-painted ball shell are between 99.490 and 99.510 mm with a machining error of ±0.01 mm. The machining static equilibrium of the pure iron ball shell and the spray-painted ball shell is good by the static equilibrium test method.

Indigo production goes green: a review on opportunities and challenges of fermentative production.

Indigo is a widely used dye in various industries, such as textile, cosmetics, and food. However, traditional methods of indigo extraction and processing are associated with environmental and economic challenges. Fermentative production of indigo using microbial strains has emerged as a promising alternative that offers sustainability and cost-effectiveness. This review article provides a critical overview of microbial diversity, metabolic pathways, fermentation strategies, and genetic engineering approaches for fermentative indigo production. The advantages and limitations of different indigo production systems and a critique of the current understanding of indigo biosynthesis are discussed. Finally, the potential application of indigo in other sectors is also discussed. Overall, fermentative production of indigo offers a sustainable and bio-based alternative to synthetic methods and has the potential to contribute to the development of sustainable and circular biomanufacturing.

The application of mirabilite in traditional Chinese medicine and its chemical constituents, processing methods, pharmacology, toxicology and clinical research.

Purpose: This study reviews the use of mirabilite in traditional Chinese medicine and various preparations by describing its chemical composition, processing methods, pharmacology, toxicology, and clinical research progress. Methods: The applications and processing methods of mirabilite are searched in traditional and modern Chinese medical writings, and the articles on chemical composition, pharmacological effects, toxicology, and clinical studies of mirabilite and its combinations in PubMed and China Knowledge Network are reviewed, sorted, and analyzed. Results: The main chemical component of mirabilite is sodium sulfate decahydrate (Na2SO4·10H2O), followed by small amounts of sodium chloride, magnesium sulfate, calcium sulfate, and other inorganic salts. This study systematically organizes the history of the medicinal use of mirabilite in China for more than 2,000years. This mineral has been used by nine Chinese ethnic groups (Han, Dai, Kazakh, Manchu, Mongolian, Tujia, Wei, Yi, and Tibetan) in a large number of prescription preparations. The Pharmacopoeia of the People's Republic of China (2020 edition) records stated that mirabilite can be used for abdominal distension, abdominal pain, constipation, intestinal carbuncle, external treatment of breast carbuncle, hemorrhoids, and other diseases. The traditional processing methods of mirabilite in China include refining, boiling, sautéing, filtration after hot water blistering, and firing. Since the Ming Dynasty, processing by radish has become the mainstream prepared method of mirabilite. Mirabilite can exhibit anti-inflammatory detumescence effects by inhibiting AMS, LPS, IL-6, IL-10, TNF-α, and NO levels and attenuating the upregulation of TNF-α and NF-κB genes. It can promote cell proliferation and wound healing by increasing the production of cytokines TGFβ1 and VEGF-A and gastrointestinal motility by increasing the release of vasoactive intestinal peptide, substance P, and motilin. It can increase the expression of low-density lipoprotein receptor and AKT phosphorylation in the liver by up-regulating bile acid synthesis genes; reduce TRB3 expression in the liver, FGF15 co-receptor KLB expression, and FGF15 production in the ileum, and JNK signal transduction; and increase the transcription of CYP7A1 to achieve a cholesterol-lowering effect. Mirabilite also has a variety of pharmacological effects, such as regulating intestinal flora, anti-muscle paralysis, anti-colon cancer, promoting water discharge, and analgesic. Only a few toxicological studies on mirabilite are available. External application of mirabilite can cause local skin to be flushed or itchy, and its oral administration is toxic to neuromuscular cells. The sulfur ions of its metabolites can also be toxic to the human body. At present, no pharmacokinetic study has been conducted on mirabilite as a single drug. This mineral has been widely used in the clinical treatment of inflammation, edema, wound healing, digestive system diseases, infusion extravasation, hemorrhoids, skin diseases, breast accumulation, muscle paralysis, intestinal preparation before microscopic examination, and other diseases and symptoms. Conclusion: Mirabilite has good application prospects in traditional Chinese medicine and ethnomedicine. In-depth research on its processing methods, active ingredients, quality control, pharmacokinetics, pharmacological and toxicological mechanisms, and standardized clinical application is needed. This paper provides a reference for the application and research of mirabilite in the future.

NOVEL THERMOPLASTIC ELASTOMERS BASED ON PVDF/SKF-32: EFFECT OF COMPOSITION ON TECHNOLOGICAL AND PERFORMANCE CHARACTERISTICS

Thermoplastic elastomers (TPE) based on SKF-32 fluorine rubber and polyvinylidene fluoride, PVDF (polyvinylidene fluoride content ranges from 30 to 70% relative to rubber) were investigated. The mixes have been produced by using a Brabender-type rotary micromixer. Such thermoplastic elastomers can be an alternative to traditional oil & fuel resistant butadiene-nitrile-based rubbers and can be used in manufacture of sleeves, hoses and tubes for pumping aggressive liquids and gases, wires/cables insulation, fuel storage tanks, valves, etc. Traditional methods of plastics processing can be used to manufacture this materials. It was established that the crystallinity degree increases with the rise of PVDF content. That fact is confirmed by the IR-Fourier spectroscopy data and differential scanning calorimetry. It was shown that the hardness and strength of TPEs increases with the rise of PVDF content. The tensile stress for TPEs containing 50% or more of PVDF are higher than those for conventional oil & fuel resistant rubber IRP 1078. The obtained TPEs possess high resistance against AI-92 grade gasoline, 80% sulfuric acid, 50% sodium hydroxide, and I-40 oil. They also retain their physical and mechanical properties even after three cycles of reprocessing. The TPE with the 1:1 thermoplastic : elastomer ratio, has been vulcanized using a peroxide vulcanizing system at varying concentrations. The dynamic vulcanization is confirmed by a decrease in the swelling degree of the obtained materials in solvents with an increase in the content of the vulcanizing agent. Peroxide dynamic vulcanization has not lead to a significant increase in physical and mechanical parameters, however, it resulted in an increase in chemical resistance of against aggressive solvents.

A comparative analysis of traditional meat processing methods

This comparative analysis examines traditional meat preservation techniques, including drying, curing, fermentation, smoking, fat preservation, air drying, fermented fish sauce, pickling and traditional sausage making. Rooted in different cultures, these methods use specific processes and ingredients to preserve meat, enhance flavour and extend shelf life. Some different variations and adaptations showcase techniques such as marinating, braising, and incorporating herbs or probiotics. Studies highlight advances in reducing salt and nitrite levels during pickling, improving brine handling methods, and optimizing smoking techniques for safety. Factors affecting preservation include the microbial community in fermented cured meats, temperature control during smoking and moisture content during air drying. Recent research findings observed that fermentation-enhanced sausage flavour and certain microorganisms declined histamine levels in fish sauce. New technologies and traditional methods combined facilitate conservation, nutrition, and the sensory quality of meat.

Research on the design of automatic image processing function for intelligent face management system

Abstract Around the background of rapid development of intelligent technology, an efficient image processing system oriented to the smart management of human faces is focused on. The system is mainly developed towards high speed, high definition, high integration and reliability. The article investigates a novel automatic image processing algorithm, covering three key modules: automatic exposure control, color interpolation and chromaticity space conversion. The algorithm can process the output image of CMOS sensor in Bayer format in real time and adjust the image parameters to obtain a high-quality image. In terms of face recognition performance, the algorithm has a significant advantage in recognition speed compared with other algorithms, and the average recognition accuracy reaches 94.258%. In the practical application of ID card portrait processing, the image shows a more uniform grayscale distribution in the range of 5 to 255 after automatic color adjustment, and the color quality is significantly improved. Meanwhile, in the portrait enhancement experiments, the images obtained with this image automatic processing algorithm outperform the traditional ID card image processing methods regarding information entropy, mutual information, standard deviation and peak signal-to-noise ratio (PSNR).

Variations in micronutrient concentrations and retentions in fufu made from yellow-fleshed cassava as a function of genotype and processing methods.

The biofortification of staple foods such as cassava is one of the technological breakthroughs in the nutritional improvement of foods. Fufu is one of the fermented cassava products produced and consumed in major West African countries, including Sierra Leone, and the majority of the processes involved in its production have direct and indirect effects on its properties. This study looked at how the concentration and retention of micronutrients in yellow-fleshed cassava fufu varied depending on genotype and processing method. Six yellow-fleshed cassava root genotypes (TMS-070557, TMS-011371, TMS-011412, TMS-011663, TMS-083724, TMS-083774) and one white (TME 419 as a control) were processed into fufu using both conventional (oven and sun-dried) and traditional (bowl and river) methods. The Statistical Analysis System (SAS) version 9.4 was used to analyze data using means, percentages, analysis of variance and means separated by least significant differences (LSD). In the modified traditional river method, raw and cooked fufu samples had significantly higher β-carotene concentrations and true retention (TR) percentages (11.06 g/g (46.77%) and 4.54 g/g (16.94%), respectively) than other genotypes (p < 0.0001). Modified traditional fufu processing methods increased total β-carotene concentrations, while raw roots showed a significant decrease in total carotenoid and β-carotene concentrations, regardless of genotype or processing method. Sun-drying was the most effective method, with significantly higher concentrations and TR percentages of iron (10.01 mg/kg, 18.02%) and zinc (11.49 mg/kg, 40.64%) in raw and cooked fufu samples. Genotype TMS-083724 outperformed both conventional fufu processing methods, displaying a significant total carotenoid concentration and true retention percentage. Finally, this study found that the concentrations and percentages of TR of micronutrients varied depending on the processing method and genotype. It is recommended that a modified traditional river fufu processing method be further developed and improved in order to maximize provitamin A carotenoids, concentrations, and percentage TR.

Processing and characterization of high-performance thermoplastic composites manufactured by laser-assisted automated fiber placement in-situ consolidation and hot-press

Due to the short dwell time of processing temperature and pressure, the quality of the composite manufactured by AFP was not equal to the traditional processing method like autoclave or hot-press. Deeply understanding the mechanism of differences was conducive to improving the performance of the composite manufactured by AFP. In this study, the properties of high-performance carbon fiber reinforced polyphenylene sulfide (CF/PPS) thermoplastic composites manufactured by laser-assisted automated fiber placement (LAFP) in-situ consolidation and hot-press was characterized and compared. The temperature history of LAFP in situ consolidation indicated that the heating and cooling process occurred during a few seconds. And the crystallinity was only 21.6% for the laminate from LATP, while the void content was 2.75%. Due to the low crystallinity, high void content and low interlaminar bonding, the ILSS of the laminate from LAFP in situ consolidation was 34.9% lower than that of hot-press. However, the mode Ⅰ fracture toughness was 103.7% higher than hot-press.

DETERMINANTS OF VALUE ADDITION AMONG SMALLHOLDER SWEET POTATO FARMING HOUSEHOLDS IN EBONYI STATE, NIGERIA

Smallholder farmers play a crucial role in sweet potato production, but limited value addition hinders their income generation and market participation. This study explored the factors that smallholder farmers in Ebonyi State considered when adding value to their sweet potato production. Using a multi-stage sampling technique, primary data was gathered from 400 small-scale sweet potato growers out of the state's 380,000 growers. Interview schedules were the instrument utilized for data collection. Information was gathered on the sweet potato processing method as well as the socioeconomic traits of the growers. Statistics from the descriptive and probit regression models were used to analyze the data. Findings revealed that the traditional method of processing sweet potatoes was predominant in the area. At a 1% probability level, the probit regression coefficient of educational attainment and farmers’ association membership was significant and positively correlated with the addition of value to sweet potatoes. Additionally, at a 5% probability level, access to credit was substantial and positively associated with the addition of value to the sweet potato. At 1% and 5% probability levels, respectively, coefficients of gender and farming status were significant and inversely associated with the addition of value to sweet potatoes. The study concludes that the most common processing methods used by sweet potato farmers are traditional processing methods. Therefore, strategies to educate farmers about the value of forming farmer groups are needed, as most training on value addition and access to knowledge about market opportunities can be easily shared through farmers' groups.

GACNet: Defect Detection with Graph Attention Mechanism Convolutional Network Model

Convolutional Neural Networks (CNNs) are classic models for image classification. In modern industrial and manufacturing fields, automated defect detection and classification often rely on CNNs and their variant networks. However, due to the diversity and complexity of defects, traditional image processing methods often struggle to perform this task effectively. Graph Neural Networks (GNN), as effective tools for handling graph data, can capture relationships and local structures among nodes in a graph. This is valuable for describing the distribution and interconnections of defects in images. This paper introduces a deep learning framework that combines the advantages of both CNNs and GNNs, along with a method for transforming 2D images into graph data. In defect classification tasks, this framework outperforms ResNet-50 with pre-trained weights, achieving 4.07% higher precision.