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"Amin, Mohammad"
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Mitigating spread of contamination in meat supply chain management using deep learning
Industry 4.0 recommends a paradigm shift from traditional manufacturing to automated industrial practices, especially in different parts of supply chain management. Besides, the Sustainable Development Goal (SDG) 12 underscores the urgency of ensuring a sustainable supply chain with novel technologies including Artificial Intelligence to decrease food loss, which has the potential of mitigating food waste. These new technologies can increase productivity, especially in perishable products of the supply chain by reducing expenses, increasing the accuracy of operations, accelerating processes, and decreasing the carbon footprint of food. Artificial intelligence techniques such as deep learning can be utilized in various sections of meat supply chain management––where highly perishable products like spoiled meat need to be separated from wholesome ones to prevent cross-contamination with food-borne pathogens. Therefore, to automate this process and prevent meat spoilage and/or improve meat shelf life which is crucial to consumer meat preferences and sustainable consumption, a classification model was trained by the DCNN and PSO algorithms with 100% accuracy, which discerns wholesome meat from spoiled ones.
Journal Article
Phytosomes as Innovative Delivery Systems for Phytochemicals: A Comprehensive Review of Literature
Nowadays, medicinal herbs and their phytochemicals have emerged as a great therapeutic option for many disorders. However, poor bioavailability and selectivity might limit their clinical application. Therefore, bioavailability is considered a notable challenge to improve bio-efficacy in transporting dietary phytochemicals. Different methods have been proposed for generating effective carrier systems to enhance the bioavailability of phytochemicals. Among them, nano-vesicles have been introduced as promising candidates for the delivery of insoluble phytochemicals. Due to the easy preparation of the bilayer vesicles and their adaptability, they have been widely used and approved by the scientific literature. The first part of the review is focused on introducing phytosome technology as well as its applications, with emphasis on principles of formulations and characterization. The second part provides a wide overview of biological activities of commercial and non-commercial phytosomes, divided by systems and related pathologies. These results confirm the greater effectiveness of phytosomes, both in terms of biological activity or reduced dosage, highlighting curcumin and silymarin as the most formulated compounds. Finally, we describe the promising clinical and experimental findings regarding the applications of phytosomes. The conclusion of this study encourages the researchers to transfer their knowledge from laboratories to market, for a further development of these products.
Journal Article
Quantum Boltzmann Machine
Inspired by the success of Boltzmann machines based on classical Boltzmann distribution, we propose a new machine-learning approach based on quantum Boltzmann distribution of a quantum Hamiltonian. Because of the noncommutative nature of quantum mechanics, the training process of the quantum Boltzmann machine (QBM) can become nontrivial. We circumvent the problem by introducing bounds on the quantum probabilities. This allows us to train the QBM efficiently by sampling. We show examples of QBM training with and without the bound, using exact diagonalization, and compare the results with classical Boltzmann training. We also discuss the possibility of using quantum annealing processors for QBM training and application.
Journal Article
Quantum critical dynamics in a 5,000-qubit programmable spin glass
Experiments on disordered alloys
1
–
3
suggest that spin glasses can be brought into low-energy states faster by annealing quantum fluctuations than by conventional thermal annealing. Owing to the importance of spin glasses as a paradigmatic computational testbed, reproducing this phenomenon in a programmable system has remained a central challenge in quantum optimization
4
–
13
. Here we achieve this goal by realizing quantum-critical spin-glass dynamics on thousands of qubits with a superconducting quantum annealer. We first demonstrate quantitative agreement between quantum annealing and time evolution of the Schrödinger equation in small spin glasses. We then measure dynamics in three-dimensional spin glasses on thousands of qubits, for which classical simulation of many-body quantum dynamics is intractable. We extract critical exponents that clearly distinguish quantum annealing from the slower stochastic dynamics of analogous Monte Carlo algorithms, providing both theoretical and experimental support for large-scale quantum simulation and a scaling advantage in energy optimization.
Using a quantum annealing processor to study three-dimensional spin glasses demonstrates an accurate large-scale quantum simulation of critical dynamics and a scaling advantage over analogous classical methods for energy optimization.
Journal Article
Detection and location of EEG events using deep learning visual inspection
The electroencephalogram (EEG) is a major diagnostic tool that provides detailed insight into the electrical activity of the brain. This signal contains a number of distinctive waveform patterns that reflect the subject’s health state in relation to sleep, neurological disorders, memory functions, and more. In this regard, sleep spindles and K-complexes are two major waveform patterns of interest to specialists, who visually inspect the recordings to identify these events. The literature typically follows a traditional approach that examines the time-varying signal to identify features representing the events of interest. Even though most of these methods target individual event types, their reported performance results leave significant room for improvement. The research presented here adopts a novel approach to visually inspect the waveform, similar to how specialists work, to develop a single model that can detect and determine the location of both sleep spindles and K-complexes. The model then produces bounding boxes that accurately delineate the location of these events within the image. Several object detection algorithms (i.e., Faster R-CNN, YOLOv4, and YOLOX) and multiple backbone CNN architectures were evaluated under a wide range of conditions, revealing their true representative performance. The results show exceptional precision (>95% mAP@50) in detecting sleep spindles and K-complexes, albeit with less consistency across backbones and thresholds for the latter.
Journal Article
The efficient chitosan–polythiophene–graphene oxide bionanocomposite with enhanced antibacterial activity, dye adsorption ability, mechanical and thermal properties
Water pollution is the most serious environmental issues due to toxic impurity such as dye and pathogenic microorganisms. The main goal of the present study is to produce a novel ternary chitosan–polythiophene–graphene oxide (CS–PTh–GO) bionanocomposites using the intercalation of GO into CS through solution mixing process followed by the in-situ polymerization of thiophene for removal of dye and killing microorganisms from an aqueous solution. The fabricated CS–PTh–GOs were characteristically examined via FTIR, XRD, SEM, TEM, TGA, tensile analysis and subsequently applied for adsorption of cationic dyes such as methylene blue (MB) in the dark or under light and killing the growth of Gram-positive and Gram-negative microorganisms. The data revealed that presence of PTh–GO enhanced the surface roughness, tensile strength, thermal stability, adsorption characteristics and antibacterial activity. The CS–PTh–GO showed 97% dye removal of MB in 50 min. Ultimately, the CS–PTh–GO bionanocomposites analysis against the growth of
Staphylococcus aureus
, and
Escherichia coli
manifesting a minimum inhibitory concentration (MIC) of 5 µg/mL, respectively. Thus, the CS–PTh–GO bionanocomposite has the potential to use as an efficient adaptable antimicrobial and dye absorbent of organic dyes in industrial wastewater.
Journal Article
Geyser Inspired Algorithm: A New Geological-inspired Meta-heuristic for Real-parameter and Constrained Engineering Optimization
Over the past years, many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world problems. This study presents a new optimization method based on an unusual geological phenomenon in nature, named Geyser inspired Algorithm (GEA). The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization process. The efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005, CEC 2014, CEC 2017, and real-parameter benchmark functions. Moreover, GEA has been applied to several real-parameter engineering optimization problems to evaluate its effectiveness. In addition, to demonstrate the applicability and robustness of GEA, a comprehensive investigation is performed for a fair comparison with other standard optimization methods. The results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms, including ABC, BBO, PSO, and RCGA. Note that the source code of the GEA is publicly available at
https://www.optim-app.com/projects/gea
.
Journal Article
The Pivotal Roles of the Epithelial Membrane Protein Family in Cancer Invasiveness and Metastasis
The members of the family of epithelial membrane proteins (EMPs), EMP1, EMP2, and EMP3, possess four putative transmembrane domain structures and are composed of approximately 160 amino acid residues. EMPs are encoded by the growth arrest-specific 3 (GAS3)/peripheral myelin protein 22 kDa (PMP22) gene family. The GAS3/PMP22 family members play roles in cell migration, growth, and differentiation. Evidence indicates an association of these molecules with cancer progression and metastasis. Each EMP has pro- and anti-metastatic functions that are likely involved in the complex mechanisms of cancer progression. We have recently demonstrated that the upregulation of EMP1 expression facilitates cancer cell migration and invasion through the activation of a small GTPase, Rac1. The inoculation of prostate cancer cells overexpressing EMP1 into nude mice leads to metastasis to the lymph nodes and lungs, indicating that EMP1 contributes to metastasis. Pro-metastatic properties of EMP2 and EMP3 have also been proposed. Thus, targeting EMPs may provide new insights into their clinical utility. Here, we highlight the important aspects of EMPs in cancer biology, particularly invasiveness and metastasis, and describe recent therapeutic approaches.
Journal Article
The cheetah optimizer: a nature-inspired metaheuristic algorithm for large-scale optimization problems
Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at
https://www.optim-app.com/projects/co
.
Journal Article