Breast cancer (BC) is the third leading cause of deaths in women globally. In general, histopathology images are recommended for early diagnosis and detailed analysis for BC. Thus, state-of-the-art ...classification models are required for the early prediction of BC using histopathology images. This study aims to develop an accurate and computationally feasible classification model named Biopsy Microscopic Image Cancer Network (BMIC_Net) to classify BC into eight distinct subtypes through deep learning (DL) and hierarchical classification approach. For experiments, the publicly available dataset BreakHis is used and splitted into training and testing set. Furthermore, data augmentation was performed on training set only and 4096 result-oriented features were extracted through DL. In order to improve the classification performance, feature reduction schemes were experimented to elicit the most discriminative feature subset. Finally, six machine-learning algorithms were analyzed to acquire the best results. The experimental results revealed that BMIC_Net outperformed existing baseline models by obtaining the highest accuracy of 95.48% for first-level classifier and 94.62% and 92.45% for second-level classifiers. Thus, this model can be deployed on a normal desktop machine in any healthcare center of less privileged areas in under-developing countries to serve as second opinion for breast cancer classification.
Breast cancer is a common and fatal disease among women worldwide. Therefore, the early and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of patients with this ...disease. Several studies have developed automated techniques using different medical imaging modalities to predict breast cancer development. However, few review studies are available to recapitulate the existing literature on breast cancer classification. These studies provide an overview of the classification, segmentation, or grading of many cancer types, including breast cancer, by using traditional machine learning approaches through hand-engineered features. This review focuses on breast cancer classification by using medical imaging multimodalities through state-of-the-art artificial deep neural network approaches. It is anticipated to maximize the procedural decision analysis in five aspects, such as types of imaging modalities, datasets and their categories, pre-processing techniques, types of deep neural network, and performance metrics used for breast cancer classification. Forty-nine journal and conference publications from eight academic repositories were methodically selected and carefully reviewed from the perspective of the five aforementioned aspects. In addition, this study provided quantitative, qualitative, and critical analyses of the five aspects. This review showed that mammograms and histopathologic images were mostly used to classify breast cancer. Moreover, about 55% of the selected studies used public datasets, and the remaining used exclusive datasets. Several studies employed augmentation, scaling, and image normalization pre-processing techniques to minimize inconsistencies in breast cancer images. Several types of shallow and deep neural network architecture were employed to classify breast cancer using images. The convolutional neural network was utilized frequently to construct an effective breast cancer classification model. Some of the selected studies employed a pre-trained network or developed new deep neural networks to classify breast cancer. Most of the selected studies used accuracy and area-under-the-curve metrics followed by sensitivity, precision, and F-measure metrics to evaluate the performance of the developed breast cancer classification models. Finally, this review presented 10 open research challenges for future scholars who are interested to develop breast cancer classification models through various imaging modalities. This review could serve as a valuable resource for beginners on medical image classification and for advanced scientists focusing on deep learning-based breast cancer classification through different medical imaging modalities.
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CEKLJ, EMUNI, FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture ...industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one. This radical change has shaken existing farming techniques and produced the latest prospects in a series of challenges. This comprehensive review article enlightens the potential of the IoT in the advancement of agriculture and the challenges faced when combining these advanced technologies with conventional agricultural systems. A brief analysis of these advanced technologies with sensors is presented in advanced agricultural applications. Numerous sensors that can be implemented for specific agricultural practices require best management practices (e.g., land preparation, irrigation systems, insect, and disease management). This review includes the integration of all suitable techniques, from sowing to harvesting, packaging, transportation, and advanced technologies available for farmers throughout the cropping system. Besides, this review article highlights the utilization of other tools such as unmanned aerial vehicles (UAVs) for crop monitoring and other beneficiary measures, such as optimizing crop yields. In addition, advanced programs based on the IoT are also discussed. Finally, based on our comprehensive review, we identified advanced prospects regarding the IoT, which are essential tools for sustainable agriculture.
The field of entrepreneurship is considered essential for the economy, and many researchers around the world have studied it from diverse perspectives. The outcomes of this research are not yet ...consensual. Today, it is gaining attention and consensus due to the increasing pressure of sustainable development, so there is a need for academic research to examine this field by incorporating sustainability value creation practices and the efforts of current entrepreneurs towards said goal, especially in the case of the agricultural sector. Accordingly, this study aims to address the problem of what drives students to incorporate sustainable agriculture in their entrepreneurial ventures. Moreover, we aim to determine whether the value placed in the triple bottom line affects the intention to establish sustainable businesses. The study is based on five antecedents of the Theory of Planned Behavior (TBP) and was designed to explore the mechanism underlying the intention to promote sustainable entrepreneurship in agriculture. The primary objective was to collect and analyze the data using the partial least square structural equation model (PLS-SEM) to test the determinants. The results revealed that the indicators of a favorable sustainable attitude, supportive subjective norms, control behaviors, adequate opportunity recognitions, and encouraging the triple bottom line had strong influences on the intention of promoting sustainability in entrepreneurship. Besides, the attitudes, subjective norms, opportunity recognition, and sustainability values can also predict students’ significant positive intentions toward sustainable agriculture in entrepreneurship. The research findings contribute to the literature by providing an empirical basis for the formulation of policies to encourage students to start an agribusiness, thereby improving the effectiveness of entrepreneurship education development programs and bridging the gap between sustainable entrepreneurial intentions and actions. Therefore, the insight into the determinants of sustainable entrepreneurship can be an essential step toward designing a practical and durable policy mechanism for the implementation of the triple bottom line when developing entrepreneurial agriculture education programs.
This article investigates acculturation stress among Pakistani students who are studying in Chinese universities, located in five provinces where international students are concentrated, with a ...mix-method approach. 203 students among 260 questionnaire recipients responded the online survey. When using the ASSIS (Acculturation Stress Scale for International Students) as instrument, the Principal Component Analysis Method and SPSS 20.0, we found that Pakistani students are under acculturative stress, 68.53%, 10.97% and 9.15% of them perceived discrimination, home sickness and perceived hate, and 5.25%, 3.11% and 2.58% of them fear, culture shock and guilt respectively. The qualitative segment of the study is consisted of 20 Pakistani students studying in 4 universities located in Wuhan city of Hubei capital enquiring through semi-structured interviews. The findings illustrate that Pakistani students in China are expressing their major concerns on culture shock, homesickness, food and language barriers while disconfirm ASSIS findings like perceived discrimination, hate, fear and guilt as factors responsible for acculturative stress. The study suggested that pre-departure orientation lectures about host country's cultural values and campus environment, and on-campus extra-curricular, cultural activities and maximum social interaction with local students can effectively acculturate students in new cultural setting, and can lower their acculturative stress.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Nanoparticle morphology is expected to play a significant role in the stability, aggregation behavior, and ultimate fate of engineered nanomaterials in natural aquatic environments. The aggregation ...kinetics of ellipsoidal and spherical titanium dioxide (TiO2) nanoparticles (NPs) under different surfactant loadings, pH values, and ionic strengths were investigated in this study. The stability results revealed that alteration of surface charge was the stability determining factor. Among five different surfactants investigated, sodium citrate and Suwannee river fulvic acid (SRFA) were the most effective stabilizers. It was observed that both types of NPs were more stable in monovalent salts (NaCl and NaNO3) as compared with divalent salts (Ca(NO3)2 and CaCl2). The aggregation of spherical TiO2 NPs demonstrated a strong dependency on the ionic strength regardless of the presence of mono or divalent salts; while the ellipsoids exhibited a lower dependency on the ionic strength but was more stable. This work acts as a benchmark study toward understanding the ultimate fate of stabilized NPs in natural environments that are rich in Ca(CO3)2, NaNO3, NaCl, and CaCl2 along with natural organic matters.
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IJS, KILJ, NUK, PNG, UL, UM
In recent years, the changing climate has become a major global concern, and it poses a higher threat to the agricultural sector around the world. Consequently, this study examines the impact of ...changing climate and technological progress on soybean yield in the 13 major provinces of China, and considers the role of agricultural credit, farming size, public investment, and power of agricultural machinery from 2000 to 2020. Fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are applied to assess the long-run effect, while Dumitrescu and Hurlin's (2012) causality test is used to explore the short-run causalities among the studied variables. The results revealed that an increase in the annual mean temperature negatively and significantly affects soybean yield, while precipitation expressively helps augment soybean yield. Furthermore, technological factors such as chemical fertilizers accelerate soybean yield significantly, whereas pesticides negatively influence soybean yield. In addition, farming size, public investment, and power of agricultural machinery contribute remarkably to soybean yield. The causality results endorse that chemical fertilizers, pesticides used, agricultural credit, public investment, and power of agricultural machinery have bidirectional causality links with soybean yield. This study suggests several fruitful policy implications for sustainable soybean production in China.
Breast cancer (BrC) is the leading cause of abnormal death in women. Mammograms and histopathology (Hp) biopsy images are generally recommended for early diagnosis of BrC because Hp image-based ...diagnosis enables doctors to make cancer diagnostic decisions more confidently than with mammograms. Several studies have used Hp images to classify BrC. However, the performance of classification models is compromised due to the higher misclassification rate. Therefore, this study aimed to develop a reliable, accurate, and computationally cost-effective ensembled BrC classification network (EBrC-Net) model with three misclassification algorithms to diagnose breast malignancy in early stages using Hp images. The proposed EBrC-Net model is based on the deep convolutional neural network approach. For experiments, the publicly available BreakHis dataset was used and split into training, validation, and testing sets. In addition, image augmentation was adopted for the training set only, and features were extracted through the well-trained EBrC-Net. Thereafter, the extracted features were further evaluated by six machine learning classifiers, of which two best performing classifiers (i.e., softmax and k-nearest neighbour kNN) were selected on the basis of five performance metric evaluation results. Furthermore, three misclassification reduction (McR) algorithms were developed and implemented in cascaded manner to reduce the false predictions of the softmax and kNN classifiers. After the implementation of the McR algorithms, experiments showed that the kNN results were much better and reliable than the softmax. The proposed BrC classification model achieved accuracy, specificity, and sensitivity rates of 97.74%, 100%, and 97.01%, respectively. Moreover, the performance of proposed BrC classification model was compared with that of state-of-the-art baseline models. Findings showed that the proposed EBrC-Net classification model, coupled with the proposed McR algorithms, achieved the best results in comparison with the baseline classification models. The proposed EBrC-Net model and the McR algorithms are a reliable source for doctors aiming for second opinion in making early diagnostic decisions for BrC using Hp images.
The current study examines the long-run effects of climatic factors on wheat production in China's top three wheat-producing provinces (Hebei, Henan, and Shandong). The data set consists of ...observations from 1992 to 2020 on which several techniques, namely, fully modified OLS (FMOLS), dynamic OLS (DOLS), and canonical co-integrating regression (CCR) estimators, and Granger causality, are applied. The results reveal that climatic factors, such as temperature and rainfall, negatively influenced wheat production in Henan Province. This means that Henan Province is more vulnerable to climate change. In contrast, it is observed that climatic conditions (via temperature and rainfall) positively contributed to wheat production in Hebei Province. Moreover, temperature negatively influenced wheat production in Shandong Province, while rainfall contributed positively to wheat production. Further, the results of Granger causality reveal that climatic factors and other determinants significantly influenced wheat production in the selected provinces.
Soybean (Glycine max) is an important legume that is used to fulfill the need of protein and oil of large number of population across the world. There are large numbers of soybean germplasm present ...in the USDA germplasm resources. Finding and understanding genetically diverse germplasm is a top priority for crop improvement programs. The current study used 20 functional EST-SSR and 80 SSR markers to characterize 96 soybean accessions from diverse geographic backgrounds. Ninety-six of the 100 markers were polymorphic, with 262 alleles (average 2.79 per locus). The molecular markers had an average polymorphic information content (PIC) value of 0.44, with 28 markers ≥ 0.50. The average major allele frequency was 0.57. The observed heterozygosity of the population ranged from 0-0.184 (average 0.02), while the expected heterozygosity ranged from 0.20-0.73 (average 0.51). The lower value for observed heterozygosity than expected heterozygosity suggests the likelihood of a population structure among the germplasm. The phylogenetic analysis and principal coordinate analysis (PCoA) divided the total population into two major groups (G1 and G2), with G1 comprising most of the USA lines and the Australian and Brazilian lines. Furthermore, the phylogenetic analysis and PCoA divided the USA lines into three major clusters without any specific differentiation, supported by the model-based STRUCTURE analysis. Analysis of molecular variance (AMOVA) showed 94% variation among individuals in the total population, with 2% among the populations. For the USA lines, 93% of the variation occurred among individuals, with only 2% among lines from different US states. Pairwise population distance indicated more similarity between the lines from continental America and Australia (189.371) than Asia (199.518). Overall, the 96 soybean lines had a high degree of genetic diversity.
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