Exploiting Genotypic Variability among Cotton Cultivars for Potassium Use Efficiency

Crop responses to potassium in Pakistan are sporadic. Furthermore farmers are reluctant to use K fertilizer, depleting K in soil. Cultivation of efficient K-utilization genotypes may be a promising alternate strategy. Therefore, an experiment was conducted in the greenhouse of the Institute of Soil and Environmental Sciences, University of Agriculture, Faisalabad to study the differential growth response and K-utilization efficiency among cotton genotypes. We evaluated growth response and potassium utilization efficiency of 7 cotton cultivars grown under adequate (3.0 mM) and deficient (0.3 mM) K supply in hydroponics. Cultivars were grown for 4-5 weeks to study growth physiological parameters relating tolerance against K deficiency. Cultivars differed significantly in biomass production, shoot K concentration, uptake and use efficiency at both levels of K supply. Shoot and root biomass production was significantly decreased due to K deficiency stress. Reduction in shoot dry matter varied significantly among cultivars and efficient cultivars showed minimum reduction in shoot dry matter due to K deficiency. The result indicated significant genetic differences in K utilization efficiency among cotton cultivars which can be exploited for breeding efficient cultivars to be grown under low K soils especially in low input sustainable agriculture Keywords: Cotton, potassium, genetic variations, nutrient use efficienc

A secure food supply chain solution: blockchain and IoT-enabled container to enhance the efficiency of shipment for strawberry supply chain

The supply chain systems in the food industry are complex, including manufacturers, dealers, and customers located in different areas. Currently, there is a lack of transparency in the distribution and transaction processes of online food trade. The global food supply chain industry has enormous hurdles because of this problem, as well as a lack of trust among individuals in the sector and a reluctance to share information. This study aims to develop a blockchain-based strawberry supply chain (SSC) framework to create a transparent and secure system for tracking the movement of strawberries from the farm to the consumer. Using Ethereum smart contracts, the proposed solution monitors participant interactions, triggers events, and logs transactions to promote transparency and informed decision-making. The smart contracts also govern interactions between vendors and consumers, such as monitoring the status of Internet of Things (IoT) containers for food supply chains and notifying consumers. The proposed framework can be extended to other supply chain industries in the future to increase transparency and immutability

IMPACT OF FIRMS RELATIONSHIP WITH PAST AND EXISTING SUPPLIERS ON FUTURE SUPPLIER SELECTION DECISIONS: A FOCUS GROUP STUDY

Supplier selection is a critical process that directly affects overall firms’ performance. There are various factors that come into play when firms are establishing supplier selection criteria. One of the key factors that directly influence firms’ decision towards establishing supplier selection criteria is their relationships with current and previous suppliers. In this study, we qualitatively explore the impact of few decision biases that generates from firms’ relationship with existing or previous suppliers on firms’ supplier selection criteria for new suppliers. Focus group interviews were used as a tool to collect qualitative data. The data was analyzed, and results revealed that the selection criteria for new supplier is affected by reference point bias in decision making based on failed relationship between firm and a supplier. Keywords: Decision making; Supplier selection; Firms’ relationships; Selection criteri

Financing Decisions and the Role of CSR in Donation-Based Crowdfunding - Evidence from Pakistan and Indonesia

Donation-based crowdfunding and corporate social responsibility (CSR) activities have potential symbiotic ramifications to raise funds, but campaigners are confronted with challenges and competition to accomplish their charitable target. For instance, CSR activities could warrant the possibility of using crowdfunding to raise money. On the other hand, a company\u27s CSR objectives can be achieved by using crowdfunding to micro-fund various social initiatives. Current research investigates the relationship between fundraisers in donation-based crowdfunding activities, which become potential CSR activities. Exclusively, the study analyzes the correlation among the value raised at the end of fundraising activity, the amounts targeted by the fundraiser, and CSR-Type activities on the project\u27s success in donation-based crowdfunding. Based on this, a research taxonomy has been established for a comparative analysis between Pakistan and Indonesia. Secondary data is collected from donation-based platforms and analyzed through Ordinary Least Square (OLS) regression and the models are validated using a robustness check. The outcomes show that a higher value raised (V) correlates more positively with project success in Pakistan (164) as compared with Indonesia (122). The Target fund (T) has a significant and negative association with the project\u27s success in the Pakistani market, however, the significant and negative effect on the project’s success in the Indonesian market. Lastly, CSR-related activities such as education, environment, community, and health have a positive relationship with project success in Pakistan, except for the product which has a negative, however significant relationship. In contrast, for Indonesia, CSR-type activities such as education, environment, community, product, and health have a positive and significant relationship with the project\u27s success. This study contributes to the donation-based crowdfunding literature to develop a vivid understanding of different CSR activities and their impact on the project\u27s success. The current study is one of the first to examine the significance of CSR activities and will enrich the body of knowledge regarding crowdfunding in diverse economies

A Review - Colorectal Cancer, Prevalence, along with Screening, Diagnosis, and Novel Therapies

Colorectal cancer is considering a communal health problem and in the whole world, its number is third in all cancers that were diagnosed. It causes a significant burden in terms of sickness and death with the estimation of seven lakhs annual deaths. In many countries of the world western way of life is rapidly adopted that is a well-debated factor for colorectal cancer and in term of primary preventive measures, it could be besieged. Comparatively slow advancement of this cancer allows severe reduction of occurrence and death rate with the help of secondary prevention. These facts motivate primary care physicians to play a key role in health plans that improve prevention and rapid diagnosis. In ancient years, the targeted therapies with combinational treatment have proven to be very effective for specific colorectal cancer patients. These therapies are epidermal growth factor, receptor inhibitor, and growth factor. As the advancements in clinic and science have visible that give new treatment options for metastatic colorectal cancer, the five-year existence rate is still fourteen percent low. But in other subtypes of colorectal cancer, the results may not be successful and not highly explored. We can reduce side effects and make the treatment effect by using alternative therapies instead of traditional therapies such as anticancer drugs, probiotics, etc. Herein, some major topics related to CRC in recent literature have been reviewed, to acknowledge its malignancy, risk, and defensive factors, along with the screening methodologies. Moreover, we also debate over preventive as well as screening strategies to fight against CRC

A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and mortality rate of this disease pose severe global health issues for women. Identifying the disease’s influence is the only practical way to lessen it immediately. Numerous research works have developed automated methods using different medical imaging to identify BC. Still, the precision of each strategy differs based on the available resources, the issue’s nature, and the dataset being used. We proposed a novel deep bottleneck convolutional neural network with a quantum optimization algorithm for breast cancer classification and diagnosis from mammogram images. Two novel deep architectures named three-residual blocks bottleneck and four-residual blocks bottle have been proposed with parallel and single paths. Bayesian Optimization (BO) has been employed to initialize hyperparameter values and train the architectures on the selected dataset. Deep features are extracted from the global average pool layer of both models. After that, a kernel-based canonical correlation analysis and entropy technique is proposed for the extracted deep features fusion. The fused feature set is further refined using an optimization technique named quantum generalized normal distribution optimization. The selected features are finally classified using several neural network classifiers, such as bi-layered and wide-neural networks. The experimental process was conducted on a publicly available mammogram imaging dataset named INbreast, and a maximum accuracy of 96.5% was obtained. Moreover, for the proposed method, the sensitivity rate is 96.45, the precision rate is 96.5, the F1 score value is 96.64, the MCC value is 92.97%, and the Kappa value is 92.97%, respectively. The proposed architectures are further utilized for the diagnosis process of infected regions. In addition, a detailed comparison has been conducted with a few recent techniques showing the proposed framework’s higher accuracy and precision rate

A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model

With the current advancement in the Internet, there has been a growing demand for building intelligent and smart systems that can efficiently address the detection of health-related problems on social media, such as the detection of depression and anxiety. These types of systems, which are mainly dependent on machine learning techniques, must be able to deal with obtaining the semantic and syntactic meaning of texts posted by users on social media. The data generated by users on social media contains unstructured and unpredictable content. Several systems based on machine learning and social media platforms have recently been introduced to identify health-related problems. However, the text representation and deep learning techniques employed provide only limited information and knowledge about the different texts posted by users. This is owing to a lack of long-term dependencies between each word in the entire text and a lack of proper exploitation of recent deep learning schemes. In this paper, we propose a novel framework to efficiently and effectively identify depression and anxiety-related posts while maintaining the contextual and semantic meaning of the words used in the whole corpus when applying bidirectional encoder representations from transformers (BERT). In addition, we propose a knowledge distillation technique, which is a recent technique for transferring knowledge from a large pretrained model (BERT) to a smaller model to boost performance and accuracy. We also devised our own data collection framework from Reddit and Twitter, which are the most common social media sites. Finally, we employed word2vec and BERT with Bi-LSTM to effectively analyze and detect depression and anxiety signs from social media posts. Our system surpasses other state-of-the-art methods and achieves an accuracy of 98% using the knowledge distillation technique