bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel method of skin graft fixation using adhesive hydrofiber foam
Authors: Yen Ya-Hui , Wu Szu-Hsien , Chen Ying-Chen , Chen Yi-Wen , Li Wan-Yu , Hsieh Chia-Nan , Lee Ru-Ping , Huang Chieh-Chi , Hsu Honda 
Published: April 2022

Objective:Conventional skin graft fixation uses a tie-over bolus dressing with splint fixation. However, splints are highly uncomfortable and contribute considerably to medical waste. Previous study has shown positive results using hydrofiber for skin graft fixation. The aim of this study was to assess the effectiveness of using adhesive hydrofiber foam for skin graft fixation.Method:In this retrospective study, patients reconstructed with split-thickness skin graft that was fixated only with adhesive hydrofiber foam from April 2017 until April 2019 were included.Results:A total of 44 patients took part, of whom 32 were male and 12 female, with a mean age of 56±19 years. The mean operative time was 77.5±91 minutes. The average defect size was 42±37cm2. The mean skin graft take was 97±5%. The mean length of hospital admission after skin grafting until discharge was 8.5±9.2 days. Excluding those patients undergoing other procedures at the same time as the skin graft gave a total of 34 patients. Their mean operative time was 32±20 minutes, and mean length of hospital stay after skin grafting was 4.0±4.7 days.Conclusion:Adhesive hydrofiber foam for skin graft fixation was technically very easy to apply, resulting in a waterproof, non-bulky, secure dressing. Splints were not required. Patients were allowed to mobilise. This method resulted in increased patient comfort and decreased medical waste. From these findings, we believe that this is an extremely simple and effective method of skin graft fixation.
Modified application of vacuum-assisted closure in thoracic surgery patients
Authors: Alreshaid Farouk , Aljehani Yasser 
Published: April 2022

Objective: Surgical site infection (SSI), ranging from superficial, deep and to organ space, is one of the major predictors for morbidity and mortality in patients undergoing thoracic surgery. Care to accelerate SSI healing is taken to shorten hospital stay and reduce costs. The deep application of vacuum-assisted closure (VAC) in thoracic patients is not well established in the literature. In this study, the deep application and safety of VAC therapy in patients with various thoracic pathologies was evaluated. Method: A retrospective chart review of all patients who were admitted to the thoracic surgery service between July 2014 and July 2018 and who developed deep SSI was carried out. Results: A total of 12 patients were included, and their demographic data analysed. There were various thoracic pathologies complicated with postoperative deep SSI treated with VAC. The duration of VAC application ranged from 4–40 days with an average hospital stay of 37.6 days. All patients showed clinical, radiological and microbiological improvement rather than developing complications except for one case of mortality due to septicaemia. Conclusion: In this study, partial intrapleural VAC therapy was safe for use in patients who underwent thoracic surgery, regardless of the underling pathology, with caution (i.e., with continued monitoring of the patient's tolerance to the treatment). The overall hospital stay may be reduced with the use of VAC. It also decreased perioperative morbidity, secondary to wound infection.
Relationship between the 3D Footprint of an Agricultural Tire and Drawbar Pull Using an Artificial Neural Network
Authors: Cutini Maurizio , Costa Corrado , Brambilla Massimo , Bisaglia Carlo 
Published: April 2022

HighlightsImprovement of tractor traction provides better field efficiency.Drawbar pull increased with tire footprint length.Drawbar pull decreased with increasing tire footprint volume and depth.3D footprint parameters, which the 2D footprint do not contain, affected the drawbar pull significantly.The ANN highlighted the relation adequately.Abstract.Improving the traction of an agricultural tractor on the field increases its working efficiency and capacity. Heavy work, like plowing, entails high levels of tire slip, which is directly related to power loss when the transmission of drawbar pull is required. Accordingly, it is possible to hypothesize that a tire with a higher traction capability could increase the working efficiency of the machine. The natural evolution for measuring the geometrical parameters of tires has led to the consideration of three-dimensional (3D) footprints since the distribution of the vertical stresses at the soil–tire interface may be highly non-uniform. In this study, the data acquired from 3D footprints of 10 sets of tires underwent processing along with the data from drawbar tests carried out with the same tires on soil terrain at different slip ratios. Subsequently, artificial intelligence multivariate methods based on artificial neural networks allowed traction prediction and verified the importance that the acquired geometrical parameters have on the measured drawbar pull. The study confirmed the correlation of the geometrical parameters of the 3D tire footprint with the drawbar pull and the results of the artificial intelligence modelling underlined the impact of these acquisitions. However, further work that considers various lug geometries is required to extend the generalizability of the studied methodology. Keywords: Field efficiency, Phenolic resin, Traction, Tractor.
Recognizing Potassium Deficiency Symptoms in Soybean with ANN on FPGA
Authors: Sartin Maicon Aparecido , da Silva Alexandre Cesar Rodrigues , Kappes Claudinei 
Published: April 2022

HighlightsIdentifying the deficiency of potassium macronutrients in the soybean crop using artificial neural network (ANN).Image processing based on ANN using a reconfigurable device.Processing and representation of data in neurons are in floating point.Abstract. Precision agriculture aims to improve the production of field crops using different techniques to manage the planting stages, such as the monitoring of field crops by images, fertilization control, nutrient analysis of the soil, and pest and weed control. By investigating field images, a plant leaf can be used to identify the lack of nutrients or the presence of diseases. This study developed a system that identifies the macronutrient deficiency of potassium in soybean crops by analyzing leaves. The methodology of this study was developed using different abstractions (Matlab and FPGA) to obtain consolidated results and facilitate low-level abstraction. The main contribution of this study is developing a multilayer artificial neural network system for a reconfigurable device. The developed system was applied in the image segmentation to determine potassium deficiency using soybean leaves and compared with a high-level abstraction system. The results of the reconfigurable device show that the mean hit percentages are 92%, 96%, and 95% in the leaf, trefoil, and field, respectively. The mean square error values were in the range of 10-2 and the quality factor was between 8.5 and 9.0. Keywords: Artificial Neural Networks, Digital image processing, Potassium deficiency, Reconfigurable device, Soybean.
Heat Treatment for Disinfecting Egg Transport Tools
Authors: Chai Lilong , Zhao Yang , Xin Hongwei , Richardson Brad 
Published: April 2022

HighlightsDisinfecting shell egg transport tools before their reuse is an integral biosecurity step.Avian influenza viruses are heat labile and it can be disinfected with high temperatures.Lab tests proved that egg transport flats and pallets can be heated to 54°C to 60°C in 7 to 9 h.Egg farm tests identified main factors for consideration in designing heat treatment system.Abstract.Disinfecting shell egg transport tools such as egg flats and pallets before their reuse has become an integral biosecurity step after the 2015 highly pathogenic avian influenza (HPAI) outbreaks in the United States. Lab-scale tests and field verification were conducted to investigate dynamic temperature changes and distributions inside stacks of egg pallet and flat at a surrounding air temperature of 54°C to 60°C (130ºF to 140ºF) in this study. The heat treatment tests were conducted in the lab first, and then verified in a newly built heat treatment room [12 m (L) × 4 m (W) × 3 m (H)] on a commercial laying hen farm in Iowa, the largest egg production state in the United States. Iowa lost more than 30 million hens during 2015 outbreak of HPAI. The lab tests proved that air circulation with fans could accelerate the heat exchange between air and stacks; blowing air from top and bottom at the same time plus arranging a 2.5 cm (1 in.) space between stacks could reduce the heat treatment time for the target temperature zone (i.e., 54ºC to 60ºC) from 26 to 60 h (without air circulation) to 7 to 9 h. For commercial egg farm tests, heat treatment results show that the room air and egg pallet stacks temperature reached the target zone (54ºC to 60ºC) in 30 min and 4 h after starting the heater, respectively. However, the temperature of egg flat stacks failed to reach the targeted zone after 8 h heating, because it had larger volume and heat transfer resistance as compared to egg pallet stacks. Further experiments such as extending heating period and installing mixing fans to accelerate heat exchange between the room air and the stacks are suggested for improving heat treatment system design on commercial farms. Keywords: Avian influenza, Disinfection, Egg flat and pallet, Egg production.
Evaluating Uniformity of Center Pivot Irrigation Systems in Western Oklahoma
Authors: Handa Divya , Masasi Blessing , Frazier Scott , Taghvaeian Saleh , Warren Jason , Moriasi Daniel N. 
Published: April 2022

Highlights63% of the 24 center pivots tested in western Oklahoma had acceptable uniformity.On average, 7% of pumped water was lost before reaching the soil surface.Irrigation nonuniformity can have major, yet variable effects on water fluxes.Abstract. Nonuniform application of water through center pivots can lead to under- or over-irrigation at parts of the field, which can lead to water stress and yield reductions or loss of limited water resources and leaching of applied chemicals, respectively. In this study, irrigation uniformity tests were performed to determine the application uniformity and conveyance efficiency of 24 center pivots in western Oklahoma. The average coefficient of uniformity and low quarter distribution uniformity of the tested systems were 78% and 69%, respectively. These values indicate an overall borderline acceptable application uniformity. The average water conveyance efficiency was 93%, indicating that 7% of pumped water was lost before reaching the soil surface. Measured ratios of collected water in each collector to the average collected water for corresponding center pivots were used to simulate the effects of observed nonuniformity on water fluxes for a 10-year period. Under-irrigation (e.g., caused by clogged nozzles) led to considerable reduction in crop transpiration at the simulation location in the Panhandle, while the decreasing effects were more notable for deep percolation for the location in west-central Oklahoma. Over-irrigation (e.g., caused by leaks or missing nozzles) resulted in substantially larger deep percolations at both locations. Keywords: Catch-can, Conveyance efficiency, Water fluxes.
Agricultural Harvester Sound Classification using Convolutional Neural Networks and Spectrograms
Authors: Khorasani Nioosha E , Thomas Gabriel , Balocco Simone , Mann Danny 
Published: April 2022

HighlightsAutomatic classification of harvester sounds.Final classification obtained using three convolutional neural networks.The results of the networks were combined via stacking and voting to achieve 100% accuracy.Abstract. The use of deep learning in agricultural tasks has recently become popular. Deep learning networks have been used for analyzing images of crops, identifying paddy areas, distinguishing sick plants from healthy ones, to name a few applications. Besides visual systems, sound analysis of agricultural machinery is a time-sensitive task that can also be incorporated in decision making and can be done with the help of deep learning models. We propose a method to generate spectrogram images from the sound of a harvester and classify them into three working modes in real-time. We used three convolutional neural networks and use the outputs of these networks as inputs to a stacking ensemble method to improve the accuracy of the system. To achieve 100% classification accuracy, a final decision is made by voting based on several consecutive classifications made by the stacking step. We were able to perform classifications in less than 1 s which was the standard to be considered as a safe time for the harvester. Keywords: Convolutional neural networks, Deep learning, Spectrograms, Stacking, Voting.
Analysis of Broken Rice Kernels Using an Android Application
Authors: Salish Karthik , Gamboa José Alfredo , Ambrose Kingsly 
Published: April 2022

HighlightsAn android application was developed to analyze broken rice kernels.Maximum length and aspect ratio were good indicators to categorize broken rice kernels.The developed algorithm had a precision of 95.9% and an accuracy of 98.0%.Abstract. The morphological characteristics of grain kernels play an important role in identifying the quality of rice. Manual sorting and inspection of rice kernels is a laborious process and susceptible to human errors. Mechanical separators such as indented cylindrical separators have also been used to separate broken kernels. In recent times, computer vision through image analysis has been applied to automate these processes, however, this necessitates image acquisition and processing devices. This article focuses on the development and use of an android application to determine the physical quality of rice kernels by quantifying broken grains using image processing and analysis techniques. The algorithm for the application includes several steps within image processing such as: image acquisition, preprocessing, segmentation, morphological transformation, and feature extraction. This quality inspection system was evaluated for medium-grain white rice. Experimental results showed a maximum average error of 2.8% in the prediction of broken kernels. This application can be used by primary producers and traders for analyzing the quality of rice. Keywords: Android application, Computer vision, Image processing, Rice quality.
Design and Performance Test of Rotary Grate Concave Threshing and Separating Unit of Head-Feeding Combine Harvester
Authors: Zhang Zhengzhong , Xie Fangping , Tian Liquan , Liu Zhenghuai , Looh George Ashwehmbom , Wang Xiushan 
Published: April 2022

HighlightsThis research was aimed at assessing the performance of a head-feeding combine harvester.The rotary grate concave threshing and separating unit were effective for improving and preventing the threshing cylinder from being jammed and improving working efficiency.More broken grains were obtained at a faster speed of concave grate.The speed of feeding chain was significant on the performance parameters that were evaluated.Abstract. It is easy for the threshing cylinder to get blocked in the head-feeding combine harvester with the fixed grate concave threshing and separating unit to affect the working efficiency when high yield rice is harvested. The rotary grate concave threshing and separating unit was designed to solve the above problem. The rotary grate concave surface revolved in the same direction along the cylinder arc of threshing drum. A test-bench of the rotary grate concave threshing and separating unit was developed. The rotational speeds of cylinder (RC), the speed of the concave grate (SG), and the speed of feeding chain (SF) were regarded as experimental factors, and the grain loss rate, broken grain rate, and impurities rate were selected as indices. Central composite design combined with response surface methodology and multi-objective optimization method were employed to conduct experiments and explore the interaction effects of factors on indices. The optimal operating parameters were nonlinearly optimized and determined to be RC of 547.6 rpm, SG of 0.93 m·s-1, and SF of 1.06 m·s-1. Under these conditions, the predicted values of the grain loss rate, broken grain rate, and impurities rate were 1.95%, 0.29%, and 0.58%, respectively. Verification experiments indicated that the observed values were in agreement with the predicted values. Results suggested that the rotary grate concave threshing and separating unit developed in this study were effective for preventing the threshing drum from being blocked, and there was a good potential for performance improvement of head-feeding combine harvester. Keywords: Central composite design, Cylinder speed, Feeding chain speed, Head-feeding combine harvester, Response surface methodology, Rotary grate concave, Threshing and separating unit.
A Paddy Field Segmentation Method Combining Attention Mechanism and Adaptive Feature Fusion
Authors: Deng Hong , Yang Yingting , Liu Zhaopeng , Liu Xing , Huang Dakang , Liu Muhua , Chen Xiongfei 
Published: April 2022

HighlightsA paddy field segmentation network model, SA-DeepLabv3+, for hilly areas in southern China is proposed.SA-DeepLabv3+ uses attention mechanism and adaptive spatial feature fusion algorithm.The deep model was trained end-to-end using a few annotated image datasets.SA-DeepLabv3+ has good segmentation results for the four common types of paddy fields.Abstract. The hilly areas of southern China have complex feature distributions, and the boundaries of cultivated land are irregular. This kind of environment causes several image segmentation problems such as low accuracy of UAV low-altitude remote sensing arable land image interpretation. This paper proposes a SA-DeepLabv3+ paddy field segmentation model that combines attention mechanism and adaptive spatial feature fusion algorithm based on DeepLabv3+ semantic segmentation model. The sampling areas include Yangwan Village, Duchang County, Jiangxi Province, China, Keli Village, Xinjian County, Nanchang City and Chengxin Farm, Nanchang City. A high-resolution image dataset was found by capturing low-altitude remote sensing images of paddy fields with DJI UAVs, and the dataset images are categorised into four types of paddy fields. By analysing the image features of the dataset, the feature map is remapped by incorporating scSE attention mechanism module in the backbone network of the DeepLabv3+ encoder to enhance the paddy field feature representation. The ASFF algorithm is applied in the decoder, which obtains an adaptive weight coefficient to improve the problem of inadequate fusion of multi-scale features in the upsampling. By comparing the segmentation results of models with different ASPP dilation rates, the optimal set of dilation rates is determined to improve the model's ability to extract fine features of paddy fields. Finally, the performance of this model for paddy fields segmentation is verified by comparison experiments of different models. The data show that the mean values of PA, MIoU, Recall, and F1 score of the SA-DeepLabv3+ paddy field segmentation model are 0.955, 0.875, 0.865, and 0.908, respectively, which are improved by 0.019, 0.032, 0.008, and 0.005, and also has a higher segmentation performance compared with the typical segmentation models of UNet, SegNet, and PSPNet. The results show that the SA-DeepLabv3+ paddy field segmentation model with high accuracy and robustness, which provides an important basis for further acquiring high-precision paddy field boundary positioning information and constructing high-precision maps of several paddy fields in larger areas, and plays a positive role in promoting efficient and accurate information management of paddy fields. Keywords: Attention mechanism, ASFF, DeepLabv3+ network, Paddy field.
Improving Milk Value Chains: A Case Study for Qualitative-Economic Feasibility of Decentralized Solar Milk Pasteurization and Chilling Processes
Authors: Khan Khawar Saeed , Munir Anjum , Raza Ali , Latif Yasir , Hensel Oliver 
Published: April 2022

HighlightsSolar-based milk pasteurization enables decentralized maintenance of milk quality, particularly in remote areas of developing countries.Study for milk quality processed with solar technologies.Payback period calculation for indigenous machines.Comparative prices analysis of available commercially packed milk with solar process milk.Abstract.Milk adulteration is common and inevitable which pledges milk quality after processing and lasts in whole supply chain, particularly in least developed countries (LDCs) like Pakistan. The dairy farmers must sell raw milk due to inadequate farm-gate milk processing facilities leading to economic and quality compromises for producers and consumers, respectively. The present study pursues the milk quality and techno-economic analysis of the processed milk (pasteurized and chilled) with indigenously developed Solar Milk Chiller (SMC) and Solar Milk Pasteurizer (SMP) compared to the existing milk value chain.. The processed milk contains fat (5.4%), solid-not-fat (9.1%), salts (0.7%), protein (3.9%), lactose (4.2%), total solids (14.5%), pH (6.85), density (1.031 kg/L), and freezing point (-0.532°C). The results of sensory evaluation using a 9-point hedonic scale showed overall likeness towards solar processed milk in terms of taste, color, aroma, and freshness. The processed milk costs USD 0.003 per liter with extended shelf life and superior in quality compared to locally available open (non-packaged) and packaged milk. With an estimated operational lifespan of 10 years, the payback periods for SMC and SMP have been found to be 1.3 to 4.5 and 1.1 to 2.7 years, respectively, depending upon the alternate source for equivalent energy generation. Keywords: Milk adulteration, On-farm solar milk processing, Solar milk chiller, Solar milk pasteurizer.