A NOVEL SIMPLE AND HIGHLY SECURE METHOD FOR DATA ENCRYPTION-DECRYPTION

In the course of the past 30 years, data has become pivotal to all aspects of human life. Data generated, captured, and replicated are increasing in size and expanding applications. The proliferation of fast wireless networks has encouraged data storage within the cloud. So, protecting data from attackers has become urgent to maintain its security and confidentiality, need for security and privacy technologies, systems, and processes to address it. This research paper proposes a simple and highly secure encryption decryption (SHSED) algorithm that can be used for cloud computing-based applications. It achieves the Shannon’s concept of diffusion and confusion by the involvement of logical operations, such as XORing, addition, and subtraction in addition to byte shifting. It is also characterized by the flexibility in the secret key length and the number of rounds. Experimental results have demonstrated powerful security level and a clear improvement in the encryption execution time measurements and security strength as compared with cryptosystems widely used in cloud computing

Using Deep Learning to Classify Corn Diseases

Abstract: A corn crop typically refers to a large-scale cultivation of corn (also known as maize) for commercial purposes such as food production, animal feed, and industrial uses. Corn is one of the most widely grown crops in the world, and it is a major staple food for many cultures. Corn crops are grown in various regions of the world with different climates, soil types, and farming practices. In the United States, for example, the Midwest is known as the "Corn Belt" due to its extensive corn production. Corn crops can be grown using a variety of methods, including conventional tillage, no-till farming, and the use of genetically modified crops. Due to its importance, there is a need for discovering the corn diseases and treating them. The aim of this study is to propose a deep learning model for the classification of corn leaf diseases based on Convolutional Neural Network. A model for classifying maize diseases was developed using a dataset for classifying Corn diseases that contains 4 classes of disease. The dataset was collected from Keggel website. The proposed model was trained, validated, and tested. The F1-score (99.83%), Recall (99.83%), precision (99.83%), Accuracy (99.83%)

Biocompatible polymeric microparticles produced by a simple biomimetic approach

The use of superhydrophobic surfaces to produce polymeric particles proves to be biologically friendly since it entails the pipetting and subsequent cross-linking of polymeric solutions under mild experimental conditions. Moreover, it renders encapsulation efficiencies of ∼100%. However, the obtained particles are 1 to 2 mm in size, hindering to a large extent their application in clinical trials. Improving on this technique, we propose the fabrication of polymeric microparticles by spraying a hydrogel precursor over superhydrophobic surfaces followed by photo-cross-linking. The particles were produced from methacrylamide chitosan (MA-CH) and characterized in terms of their size and morphology. As demonstrated by optical and fluorescence microscopy, spraying followed by photo-cross-linking led, for the first time, to the production of spherical particles with diameters on the order of micrometers, nominal sizes not attainable by pipetting. Particles such as these are suitable for medical applications such as drug delivery and tissue engineering.We thank Ivo Aroso and Ana Isabel Neto for their valuable support with FTIR and compression experiments, respectively. A.M.S.C. thanks FCT for financial support through grant BIM/PTDC/CTM-BPC/112774/2009_02. M.A.-M. thanks CONACyT (Mexico) for financial support through post-doc grant no. 203732. N.M.O. thanks FCT for financial support through Ph.D. scholarship no. SFRH/BD/73172/2010. This work was funded by the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. REGPOT-CT2012-316331-POLARIS, by FEDER through the Competitive Factors Operation Program-COMPETE, and by national funds through FCT - Fundacao para a Ciencia e a Tecnologia in the scope of project PTDC/CTM-BIO/1814/2012

Natural carriers for application in tuberculosis treatment

Tuberculosis remains the leading cause of preventable deaths worldwide and unsuccessful therapy is mainly due to non-compliance with very prolonged treatments, often associated with severe side-effects. Overcoming this problem demands the introduction of drug carriers releasing the antimicrobial agents in a targeted and sustained manner, allowing reduction in frequency and dosing numbers. Nano and microparticles have taken the forefront of this approach, providing the means for the desired improvement of therapeutic schedules. Natural polymers are strong candidates as matrix forming materials, usually exhibiting biocompatibility, biodegradability, low cost and some technological advantages as compared with synthetic counterparts. In this review, natural particulate carriers developed for tuberculosis therapy are presented, mainly focusing on the use of polysaccharides and lipids. Their effectiveness is discussed taking into account their composition. Finally, considerations on the general potential of natural materials for this application, as well as key factors still to be addressed, are discussed

Chronic Pulmonary Aspergillosis Complicating Bronchial Atresia

Bronchial atresia is a rare pulmonary developmental anomaly characterized by the presence of a focal obliteration of a segmental or lobar bronchial lumen. The lung distal to the atretic bronchus is typically emphysematous along with the presence of mucus filled ectatic bronchi (mucoceles). BA is usually asymptomatic but pulmonary infections can rarely develop in the emphysematous lung distal to the atretic bronchus. We present a unique case of chronic pulmonary aspergillosis (CPA) in a patient with BA with no evidence of immune dysfunction. The patient was treated initially with voriconazole and subsequently underwent surgical excision of the involved area. On follow-up, she has done extremely well with no evidence for recurrence. In summary, we describe the first case of chronic pulmonary aspergillosis in an immunocompetent patient with bronchial atresia

Benchmarking pavement practices in data-scarce regions–case of Saudi Arabia

This study aims at employing the benchmarking strategy for assessing the state of the practice of the pavement industry in regions that have limited access to or lack the prerequisites for effective pavement practices. The research specifically seeks to propose a benchmark framework that identifies major categories and sub-categories necessary for effective evaluation of the state of practice of the pavement industry in data-scarce regions. The benchmark framework is developed and demonstrated for the case of Kingdom of Saudi Arabia (KSA), where pavement practices in KSA are benchmarked against those of select Gulf Cooperative Council (GCC) countries. For this purpose, data are collected based on a thorough review of literature and relevant industry documentation, as well as based on interviews with project stakeholders. Commonly adopted practices in North America, Europe, and Australia are used as a blueprint for identifying performance gaps and devising recommendations for improvement. Findings from this study would aid highway agencies and stakeholders in performing internal evaluation of their current state-of-practice and adapt the proposed framework to meet their local needs and constraints. Additionally, the paper provides a much-needed compilation of pavement-related data for the GCC countries—a data-scarce region. © 2019 Informa UK Limited, trading as Taylor & Francis Group

Developing an Expert System to Diagnose Tomato Diseases

There is no doubt that tomato diseases are one of the important reasons that destroy the tomato plant and its crops. This leads to clear damage to these plants and they become inedible. Discovering these diseases after a good step for proper and correct treatment. Determining the treatment with high accuracy depends on the method used in the diagnosis. Correctly, expert systems can greatly help to avoid damage to these plants. The expert system diagnoses tomato disease correctly to facilitate farmers to find the correct treatment based on the appropriate diagnosis. Objectives: An expert system has been established based on CLIPS to diagnose tomato plant diseas

AI in HRM: Revolutionizing Recruitment, Performance Management, and Employee Engagement

Artificial Intelligence (AI) is rapidly transforming Human Resource Management (HRM) by enhancing the efficiency and effectiveness of key functions such as recruitment, performance management, and employee engagement. This paper explores the integration of AI technologies in HRM, focusing on their potential to revolutionize these critical areas. In recruitment, AI-driven tools streamline candidate sourcing, screening, and selection processes, leading to more accurate and unbiased hiring decisions. Performance management is similarly transformed, with AI enabling continuous, data-driven feedback and personalized development plans that align with organizational goals. Furthermore, AI facilitates more engaging and tailored employee experiences by leveraging predictive analytics to anticipate and address employee needs. Despite these advancements, the paper also addresses the ethical considerations and challenges associated with AI in HRM, including data privacy concerns and the risk of over-reliance on automated systems. Through a comprehensive analysis, this paper aims to provide insights into how AI can be strategically leveraged to optimize HRM practices, while highlighting the need for a balanced approach that integrates human judgment with technological innovation