Fuzzy-HLSTM (Hierarchical Long Short-Term Memory) for Agricultural Based Information Mining

This research proposes a machine learning approach using fuzzy logic to build an information retrieval system for the next crop rotation. In case-based reasoning systems, case representation is critical, and thus, researchers have thoroughly investigated textual, attribute-value pair, and ontological representations. As big databases result in slow case retrieval, this research suggests a fast case retrieval strategy based on an associated representation, so that, cases are interrelated in both either similar or dissimilar cases. As soon as a new case is recorded, it is compared to prior data to find a relative match. The proposed method is worked on the number of cases and retrieval accuracy between the related case representation and conventional approaches. Hierarchical Long Short-Term Memory (HLSTM) is used to evaluate the efficiency, similarity of the models, and fuzzy rules are applied to predict the environmental condition and soil quality during a particular time of the year. Based on the results, the proposed approaches allows for rapid case retrieval with high accuracy.     

A robust face hallucination technique based on adaptive learning method

Position-patch based approaches have been proposed for single-image face hallucination. This paper models the face hallucination problem as a coefficient recovery problem with respect to an adaptive training set for improved noise robustness. The image-adaptive training set is constructed by corrupting a local training set of position-patches by adding specific amounts of noise depending on the input image noise level. In this proposed method, image denoising and super-resolution are simultaneously carried out to obtain superior results. Though the principle is general and can be extended to most super-resolution algorithms, we discuss this in context of existing locality-constrained representation (LcR) approach in order to compare their performances. It can be demonstrated that the proposed approach can quantitatively and qualitatively yield better results in high noisy environments.

     

Stress analysis of multi-layer electronic and mechanical systems (MEMS) under fatigue and impact loading conditions

Detailed micro electronic and mechanical systems (MEMS) for a mobile microprocessor complex shape were modeled using Finite Element (FE) processing. Fatigue and impact conditions were performed on the Ball Grid Array (BGA) Integrated Circuit (IC) using Abaqus\CAE finite element analysis software. The main objective of this research is to make sure that BGA products can endure the roughness of the daily usage, where a portable electronic product is habitually coupled with potential damage of functional failure when the device falls.     

Drivers of microbial community composition in mesophilic and thermophilic temperature-phased anaerobic digestion pre-treatment reactors

Temperature-phased anaerobic digestion (TPAD) is an emerging technology that facilitates improved performance and pathogen destruction in anaerobic sewage sludge digestion by optimising conditions for 1) hydrolytic and acidogenic organisms in a first-stage/pre-treatment reactor and then 2) methogenic populations in a second stage reactor. Pre-treatment reactors are typically operated at 55–65 °C and as such select for thermophilic bacterial communities. However, details of key microbial populations in hydrolytic communities and links to functionality are very limited. In this study, experimental thermophilic pre-treatment (TP) and control mesophilic pre-treatment (MP) reactors were operated as first-stages of TPAD systems treating activated sludge for 340 days. The TP system was operated sequentially at 50, 60 and 65 °C, while the MP rector was held at 35 °C for the entire period. The composition of microbial communities associated with the MP and TP pre-treatment reactors was characterised weekly using terminal-restriction fragment length polymorphism (T-RFLP) supported by clone library sequencing of 16S rRNA gene amplicons. The outcomes of this approach were confirmed using 454 pyrosequencing of gene amplicons and fluorescence in-situ hybridisation (FISH). TP associated bacterial communities were dominated by populations affiliated to the Firmicutes, Thermotogae, Proteobacteria and Chloroflexi. In particular there was a progression from Thermotogae to Lutispora and Coprothermobacter and diversity decreased as temperature and hydrolysis performance increased. While change in the composition of TP associated bacterial communities was attributable to temperature, that of MP associated bacterial communities was related to the composition of the incoming feed. This study determined processes driving the dynamics of key microbial populations that are correlated with an enhanced hydrolytic functionality of the TPAD system.     

Cover Image,Volume 66, Issue 10

The cover image, by Bruna A. Bregadiolli et al., is based on the Research Article Towards the synthesis of poly(azafulleroid)s: main chain fullerene oligomers for organic photovoltaic devices, DOI: 10.1002/pi.5419 .

     

Structural and functional characterization of glutaraldehyde-polymerized bovine hemoglobin and its isolated fractions

Glutaraldehyde-polymerized bovine hemoglobin (PolyHbBv, trade name Oxyglobin), is a non-site-specific modified hemoglobin-based oxygen-carrying solution, developed for use in veterinary medicine. PolyHbBv was fractionated into four distinct tetrameric and multiple polytetrameric forms ranging in molecular mass (87.2-502.3 kDa) using size exclusion chromatography (SEC) and verified by laser light scattering. We evaluated the structural modification occurring in the fractionated mixture of PolyHbBv and assessed the functionality and redox stability of each fraction in relation to the mixture as a whole. Intramolecular cross-linking evaluation as performed by MALDI-MS and SEC under dissociating conditions revealed no-site-specific tetramer stabilization within the fractions; Intermolecular cross-linking was highly correlated with lysine and histidine modification as determined by amino acid composition analysis. While native unmodified hemoglobin, HbBv, PolyHbBv, and PolyHbBv fractions (F1-F4) revealed significant methionine oxidation, modification, or both, the critical betaMet55 located in the functionally plastic domains (alpha1-beta1 interface) of HbBv was unaltered. Moreover, neither of the two betaCys93 located in the highly plastic alpha1-beta2 interface were modified in PolyHbBv or in F1-F4. Our structural analysis also revealed that the reported loss in sensitivity to chloride in PolyHbBv could not be attributed to direct alteration of chloride ion binding amino acids. Structural modification imparted by glutaraldehyde resulted in nearly identical functional characteristics of PolyHbBv and its fractions with regard to oxygen equilibrium, ligand binding, and autoxidative kinetics.     

A Secure NDN Framework for Internet of Things Enabled Healthcare

Healthcare is a binding domain for the Internet of Things (IoT) to automate healthcare services for sharing and accumulation patient records at anytime from anywhere through the Internet. The current IP-based Internet architecture suffers from latency, mobility, location dependency, and security. The Named Data Networking (NDN) has been projected as a future internet architecture to cope with the limitations of IP-based Internet. However, the NDN infrastructure does not have a secure framework for IoT healthcare information. In this paper, we proposed a secure NDN framework for IoT-enabled Healthcare (IoTEH). In the proposed work, we adopt the services of Identity-Based Signcryption (IBS) cryptography under the security hardness Hyperelliptic Curve Cryptosystem (HCC) to secure the IoTEH information in NDN. The HCC provides the corresponding level of security using minimal computational and communicational resources as compared to bilinear pairing and Elliptic Curve Cryptosystem (ECC). For the efficiency of the proposed scheme, we simulated the security of the proposed solution using Automated Validation of Internet Security Protocols and Applications (AVISPA). Besides, we deployed the proposed scheme on the IoTEH in NDN infrastructure and compared it with the recent IBS schemes in terms of computation and communication overheads. The simulation results showed the superiority and improvement of the proposed framework against contemporary related works.     

Prediction of COVID-19 Confirmed Cases Using Gradient Boosting Regression Method

The fast spread of coronavirus disease (COVID-19) caused by SARSCoV-2 has become a pandemic and a serious threat to the world. As of May 30, 2020, this disease had infected more than 6 million people globally, with hundreds of thousands of deaths. Therefore, there is an urgent need to predict confirmed cases so as to analyze the impact of COVID-19 and practice readiness in healthcare systems. This study uses gradient boosting regression (GBR) to build a trained model to predict the daily total confirmed cases of COVID-19. The GBR method can minimize the loss function of the training process and create a single strong learner from weak learners. Experiments are conducted on a dataset of daily confirmed COVID-19 cases from January 22, 2020, to May 30, 2020. The results are evaluated on a set of evaluation performance measures using 10-fold cross-validation to demonstrate the effectiveness of the GBR method. The results reveal that the GBR model achieves 0.00686 root mean square error, the lowest among several comparative models.     

Finite element analysis of optimized brace angle for the diagrid structural system

International Journal of Steel Structures - The Diagrid structural system is considered to be the best structural system for constructing free form structures, but it is also a very effective...     

Anti-Hepatocellular Carcinoma Biomolecules: Molecular Targets Insights

This report explores the available curative molecules directed against hepatocellular carcinoma (HCC). Limited efficiency as well as other drawbacks of existing molecules led to the search for promising potential alternatives. Understanding of the cell signaling mechanisms propelling carcinogenesis and driven by cell proliferation, invasion, and angiogenesis can offer valuable information for the investigation of efficient treatment strategies. The complexity of the mechanisms behind carcinogenesis inspires researchers to explore the ability of various biomolecules to target specific pathways. Natural components occurring mainly in food and medicinal plants, are considered an essential resource for discovering new and promising therapeutic molecules. Novel biomolecules normally have an advantage in terms of biosafety. They are also widely diverse and often possess potent antioxidant, anti-inflammatory, and anti-cancer properties. Based on quantitative structure–activity relationship studies, biomolecules can be used as templates for chemical modifications that improve efficiency, safety, and bioavailability. In this review, we focus on anti-HCC biomolecules that have their molecular targets partially or completely characterized as well as having anti-cancer molecular mechanisms that are fairly described.