Energy Efficiency and Econometric Analysis of Organic Kiwifruit (Actinidia Deliciosa A. Chev.) Production

The energy exchange ratio of cultivation and different parameter values of input affecting the organic production of kiwifruit in the mid-hill Himalayan region of India during 2017 and 2018 was determined. The experimental trial was divided into 7 organic treatment i.e. T1 to T3 was sole application on equivalence 100 per cent Dairy manure (DM), Vermicompost (VC) and Poultry manure, T4 to T7 was a compound application of 50: 50 DM: PM, DM: VC and VC: PM and T7 in which DM = PM = VC applied on N equivalence. Five foliar sprays of organic formulation were applied in each of the treatment. The Energy efficiency and econometric analysis of organic kiwifruit production were examined. The highest energy inputs unit per hectare was utilized by T1 out of which over 86 per cent were from organic manure inputs and provided 26401.02 MJ/ha. The highest yield per hectare, as well as the output energy were observed in the treatment T5. Whereas the highest energy ratio, energy productivity, and specific energy were recorded under T2. Likewise, the highest productivity ratio and benefit-cost ratio were recorded under T7 which was followed by T2. From a farming point of view, the T2 gave the superior result because it has provided optimum amount output along with maximum returns. Bangladesh J. Bot. 50(4): 1051-1057, 2021 (December)

Fault enhancement using probabilistic neural networks and Laplacian of a Gaussian filter: a case study in the Great South Basin, New Zealand.

Fault identification is critical in defining the structural framework for both exploration and reservoir characterization studies. Interpreters routinely use edge-sensitive attributes such as coherence to accelerate the manual picking process, where the actual choice of a particular edge-sensitive attribute varies with the seismic data quality and with the reflectivity response of the faulted geologic formations. CMY color blending provides an effective way to combine the information content of two or three edge-sensitive attributes when more than one attribute is sensitive to faults. We evaluate whether combining the information content of more than three attributes using probabilistic neural networks (PNN) provides any additional uplift. We employ a training data consisting of manually picked faults on a coarse grid of 3D seismic lines, and then we employ an exhaustive search PNN to identify the optimal set of attributes to create a fault probability volume for a 3D survey acquired over the Great South Basin, New Zealand. We construct a suite of candidate attributes using our understanding of the attribute response to faults seen in the data and examples extracted from the published literature to use the list as the analyzed attributes. Using a subset of picked faults as training data, we evaluate which suite of attributes and hyperparameters exhibit the highest validation on the remaining training data. When used together, we find that volume aberrancy magnitude, GLCM homogeneity, GLCM entropy, Sobel filter similarity, and envelope best predict the faults for this dataset. The PNN supervised classification creates a seismic image volume that exhibits fault probabilities providing a simple combination of multiple seismic attributes. We also find that applying a directional Laplacian of a Gaussian and skeletonization filters to the PNN fault volumes provides a superior result to simple CMY blending techniques.

Predicting Malignant Cancer Using Machine Learning

One of the primary concerns that is also a demanding issue within the realm of medical specialism is the detection and removal of tumours. Because visualisation approaches had the drawback of being adversarial, doctors relied heavily on MRI images to provide a superior result. Pre-processing, tumour segmentation, and tumour operations are the three stages in which tumour image processing takes place. Following the acquisition of the source image, the original image is converted to grayscale. Additionally, a noise removal filter and a median filter for quality development are provided, followed by an exploration stage that yields hits orgasmic identical images. Finally, the watershed algorithm is used to complete the segmentation. This proposed methodology is useful in automatically organising reports in a short amount of time, and exploration has resulted in the removal of many less tumour parameters. Keywords: MRI Imaging, Segmentation, Watershed Algorithm.

An optimized Parkinson's disorder identification through evolutionary fast learning network

PurposeParkinson's disease (PD) is a well-known complex neurodegenerative disease. Typically, its identification is based on motor disorders, while the computer estimation of its main symptoms with computational machine learning (ML) has a high exposure which is supported by researches conducted. Nevertheless, ML approaches required first to refine their parameters and then to work with the best model generated. This process often requires an expert user to oversee the performance of the algorithm. Therefore, an attention is required towards new approaches for better forecasting accuracy.Design/methodology/approachTo provide an available identification model for Parkinson disease as an auxiliary function for clinicians, the authors suggest a new evolutionary classification model. The core of the prediction model is a fast learning network (FLN) optimized by a genetic algorithm (GA). To get a better subset of features and parameters, a new coding architecture is introduced to improve GA for obtaining an optimal FLN model.FindingsThe proposed model is intensively evaluated through a series of experiments based on Speech and HandPD benchmark datasets. The very popular wrappers induction models such as support vector machine (SVM), K-nearest neighbors (KNN) have been tested in the same condition. The results support that the proposed model can achieve the best performances in terms of accuracy and g-mean.Originality/valueA novel efficient PD detection model is proposed, which is called A-W-FLN. The A-W-FLN utilizes FLN as the base classifier; in order to take its higher generalization ability, and identification capability is also embedded to discover the most suitable feature model in the detection process. Moreover, the proposed method automatically optimizes the FLN's architecture to a smaller number of hidden nodes and solid connecting weights. This helps the network to train on complex PD datasets with non-linear features and yields superior result.

In Vitro Effects of Different Combinations of Phytohormones on Callus Induction from Different Explants of Cabbage Brassica Oleracea Var. Capitata L. Seedlings

The leading purpose of this work is the development of efficient culture conditions to induce calli from cabbage (Brassica oleracea var. capitata L.) under in vitro conditions. The mature seeds were surface sterilized with combinations of different concentrations of ethanol and NaOCl in different time durations and were germinated on MS basal medium. The results revealed that the best sterilization method of cabbage seeds was by using 70% ethanol for one minute, followed by 15 min in 2% (NaOCl). Seedlings were used as donor sources for hypocotyls, cotyledon leaves, true leaves, and shoot tip explants. These explants were cultured on different combinations of cytokinins (TDZ, BAP, Ad) and auxins (IAA, NAA, 2, 4-D) then implanted in Murashige and Skoog (MS) media. 4 weeks after culturing, a significant difference was found among the explants in response to plant hormones. The maximum percentage of callus induction (100%) was using the combinations of 1 BAP + 1 2, 4-D, 1 BAP + 1 NAA, and 1 BAP + 2 2,4-D mg. l-1. In addition, explants responses varied and the hypocotyls showed a superior result (85.71 %) as compared to other explants. For callus fresh weight, the combination of 0.22 TDZ + 79.9 Ad mg. l-1 had a significant effect, causing the highest fresh weight (0.2745g), while control treatment gave the lowest mean of 0.0066 g. Data showed that cotyledon explants were significantly superior in giving highest callus fresh weight with the mean of 0.1723 g. On the other hand, hypocotyl explants gave the lowest mean, reaching 0.1542 g.

Relation classification via BERT with piecewise convolution and focal loss

Recent relation extraction models’ architecture are evolved from the shallow neural networks to natural language model, such as convolutional neural networks or recurrent neural networks to Bert. However, these methods did not consider the semantic information in the sequence or the distance dependence problem, the internal semantic information may contain the useful knowledge which can help relation classification. Focus on these problems, this paper proposed a BERT-based relation classification method. Compare with the existing Bert-based architecture, the proposed model can obtain the internal semantic information between entity pair and solve the distance semantic dependence better. The pre-trained BERT model after fine tuning is used in this paper to abstract the semantic representation of sequence, then adopt the piecewise convolution to obtain semantic information which influence the extraction results. Compare with the existing methods, the proposed method can achieve a better accuracy on relational extraction task because of the internal semantic information extracted in the sequence. While, the generalization ability is still a problem that cannot be ignored, and the numbers of the relationships are difference between different categories. In this paper, the focal loss function is adopted to solve this problem by assigning a heavy weight to less number or hard classify categories. Finally, comparing with the existing methods, the F1 metric of the proposed method can reach a superior result 89.95% on the SemEval-2010 Task 8 dataset.

Quality of root canal treatment performed by undergraduate students at the Wits Oral Health Centre

In 2012, the School of Oral Health Sciences at the University of the Witwatersrand modified its undergraduateendodontic curriculum which led to a need to assess the impact of curriculum changes on root canal treatmentoutcomes. This study was an audit of root canal treatment performed by undergraduate BDS students using postoperative radiographs, and compared the results between different undergraduate clinical years of study.Postoperative periapical radiographs of patients treated by undergraduate students were examined to assess length, density and taper. Two independent investigators were first calibrated, and thereafter assessed 299 endodontic cases that were performed by third, fourth and fifth year students during the 2013-2015 period at the Wits Oral Health Centre. 68.9%, 73.6% and 70.9% were found for adequate length, acceptable density and acceptable taper of root filling respectively. The most acceptable length, density and taper results were seen in patients treated by final year students, while the lowest results were observed in the fourth year student cohort. There was a tendency for third year students to overfill due to over-instrumentation of anterior teeth.The change in the curriculum has been justified, though room for improvement exists. The superior result found in the 5th year student cohort was most likely due to their relative experience, and the use of rotary instrumentation and dental operating microscopes. Endodontic teaching should further emphasize the importance of length control during endodontic treatment and more stringent steps may be necessary during patient allocation and clinical supervision of fourth year dental students.

IgG Food Antibody Guided Elimination-Rotation Diet was more Effective Than FODMAP Diet and Control Diet in the Treatment Of Women with Mixed IBS – Results From an Open Label Study

Background Irritable bowel syndrome (IBS) is a chronic disease with recurrent abdominal pain, disturbed bowel emptying and change in stool consistency. We compared the effectiveness of dietary treatment of three different diet plans (G1-FM-low FODMAP diet, G2-IP IgG based elimination-rotation-diet, and as control group, G3-K control diet recommended by an attending gastroenterologist) in treating patients diagnosed with mixed irritable bowel syndrome. Methods 73 female patients diagnosed with mixed form of irritable bowel syndrome (IBS-M) were enrolled in the study. The diet of each patient in group 1 (G1-FM) and 2 (G2-IP) was determined individually during a meeting with dietitian. Patients from group 3 (G3-K) received nutrition advice from a gastroenterologist. Results Significant differences in reduction of IBS symptoms were found between the groups. IBS symptoms as well as comorbid symptoms significantly improved or disappeared completely in the G2-IP group, while in the G1-FM group, some of the IBS symptoms significantly improved. In group G3-K no significant improvement was seen.Conclusion Based on the results of this open study it was found that the different dietary intervention in the treatment of patients with IBS-M was unlikely effective. The G2-IP IgG based elimination-rotation-diet demonstrating a significant overall superior result compared to the others. Trial registration The clinical trial was retrospective registered at ClinicalTrials.gov, March 12, 2020, ClinicalTrials.gov ID: NCT04307368.

Role of Osteogenesis of MC3T3-E1 Induced by Demineralized Tooth Block with Composite Acids: An In Vitro Study

More and more research had focused on the osteogenesis of demineralized dentin in clinic, especially when the first application of deminerized dentin in 2008. The study tried to compare the osteogenetic ability of the demineralized dentin block, which were processed two different regents by VacuaSonic system. The extracted human permanent teeth were demineralized by two different methods. Then the MC3T3-E1 cells were invited to culture on the surface of these demineralized dentin blocks (DDB). The cell attachment, proliferation and differentiation were tested. Adhesion of MC3T3-E1 on DDM was observed using scanning electron microscopy and confocal test, the Alizarin Red S, ALP activity, and the protein of BMP-2/-7 and OCN were employed to confirm the level of cell differentiation. The P value was set at 0.05. The microfilaments established a good contact and formed a network in Group A. The Group A had more full cytoskeleton and actin stretched more obviously than Group C, the number of cells on three scaffolds were difference (p < 0 05). The MTT results showed no cytotoxicity in all experiment groups, and Group C had a significant difference in cell proliferation than other groups (p < 0 05) except for day 1. While when related to the cell differentiation, Group A showed a similar result with Group C, but in Alizarin Red S, Group A had a superior result (p < 0 05). The tooth dentin scaffold processed with composite acids in Group A presents the superiority in osteoconduction and preferable osteogenesis ability, which could be an alternative method to process the tooth scaffold.

Phenylethyl Resorcinol Loaded in Liposomal Cream Formulation for Cosmeceutical Application

Phenylethyl Resorcinol (PR) is a cosmeceutical skin lightening agent and the purpose of this study was to enhance its stability by using liposomal cream formulation which increases local efficacy and safety. Liposome formulation was prepared by modified ethanol injection method, and it contained soy phosphatidyl choline (SPC), cholesterol (CHO), Tween 80 (TW80) and deoxycholic acid (DA) mixed with 2% PR. The physicochemical properties, skin permeation as well as cellular study were evaluated in order to obtain the optimized formulation. The optimized liposome formulation composed of SPC:TW80:DA (84:16:2.5) and exhibited vesicle size, polydispersity index (PDI) and zeta potential of 286.4±8.04 nm, 0.317±0.03 and -39.20±3.85 mV, respectively. Entrapment efficiency (EE) of liposome formulation was 93.55±0.05%. The vesicle was spherical in shape and showed good physicochemical stability for 4 months. The skin permeation study demonstrated that liposome with a negative charge could result in a high PR skin deposition value of 1732.76±216.24 µg/cm2 after 24 h. Cellular study showed that liposome formulation could inhibit melanin content in B16 melanoma cells and enhance cell viability in HaCaT keratinocyte cells. The optimized PR liposome was incorporated in cream and investigated physicochemical properties, stability and skin permeation. Liposomal PR cream showed a good stability and a superior result than PR cream in skin permeation parameters, as well as in tyrosinase inhibition.