13780 research outputs found
Sort by
Fresh Produce Supply Chain Network Design and Management Using Swarm Intelligence: A Case Study of Egypt
No full textPurpose: The objective of this work is to fulfil a strategic requirement in Egypt’s agriculture industry by establishing a fresh produce supply chain network (SCN) that manages the collection, processing, packaging, and distribution of products. Design/methodology/approach: A cost minimization dynamic facility location-allocation (FLA) problem is modeled and solved using a hybrid binary particle swarm optimization (BPSO) algorithm, to strategically locate a network of food aggregation hubs across the country for the collection, consolidation, and distribution of products. The hub FLA decision is then complemented with optimal fleet sizing, transportation scheduling, and routing decisions, by solving the split-delivery vehicle routing problem (SDVRP) using a hybrid ant-colony optimization (ACO) algorithm, considering positioning loading constraints, and shelf-lives of products. Findings: Two national fresh produce SCN configurations were obtained; one that minimizes the total cost of the network, and the other minimizes the number of aggregation hubs. Results showed a strong correlation between the locations and capacities of the hubs, and the locations of supply points and densely populated demand areas. The hybrid ACO algorithm was further utilized to optimize the fleet sizing, routing and scheduling decisions for one of the obtained hubs. Practical implications: Establishment of the SCN can reduce the proportion of wasted product during transit, and improve the quality of the delivered products. In addition, accounting for product spoilage has a significant effect on network design, and collection and distribution decisions. Social implications: Establishment of the SCN will improve the exposure of small farmers to wider markets, and hence their return and standard of living, and potentially reduce the prices for the final customer. Originality/value: This study is the first attempt to establish an efficient fresh produce supply chain network in Egypt. In addition, the proposed solution approach considered a multitude of problem characteristics, simultaneously for the first time
Gas chromatography/mass spectrometry-based metabolite profiling of chia and quinoa seeds in comparison with wheat and oat
No full textIntroduction: With an increasing interest in healthy and affordable cereal intake, efforts are made toward exploiting underutilized cereals with high nutritional values. Objectives: The current study aims to explore the metabolome diversity in 14 cultivars of chia and quinoa collected from Germany, Austria, and Egypt, compared with wheat and oat as major cereals. Material and Methods: The samples were analyzed using gas chromatography–mass spectrometry (GC-MS). Multivariate data analysis (MVA) was employed for sample classification and markers characterization. Results: A total of 114 metabolites were quantified (sugars, alcohols, organic and amino acids/nitrogenous compounds, fatty acids/esters), but the inorganic and phenolic acids were only identified. Fatty acids were the major class followed by amino acids in quinoa and chia. Chia and oats were richer in sucrose. Quinoa encompassed higher amino acids. Quinoa and chia were rich in essential amino acids. Higher levels of unsaturated fatty acids especially omega 6 and omega 9 were detected in quinoa versus omega 3 in chia compared with oat and wheat, whereas ω6/ω3 fatty acid ratio of chia was the lowest. To the best of our knowledge, this is the first comprehensive metabolite profiling of these pseudo cereals. Conclusion: Quinoa and chia, especially red chia, are more nutritionally valuable compared with oat and wheat because of their compositional profile of free amino acids, organic acids, and essential fatty acids, besides their low ω6/ω3 fatty acid ratio. Such results pose them as inexpensive alternative to animal proteins and encourage their inclusion in infant formulas
Photocatalytic Nanocomposite Based on Titanate Nanotubes Decorated with Plasmonic Nanoparticles for Enhanced Broad-Spectrum Antibacterial Activity
No full textInfections resulting from microorganisms pose an ongoing global public health challenge, necessitating the constant development of novel antimicrobial approaches. Utilizing photocatalytic materials to generate reactive oxygen species (ROS) presents an appealing strategy for combating microbial threats. In alignment with this perspective, sodium titanate nanotubes were prepared by scalable hydrothermal method using TiO2 and NaOH. Ag, Au, and Ag/Au-modified titanate nanotubes (TNTs) were prepared by a cost-effective and simple ion-exchange method. All samples were characterized by XRD, FT-IR, HRTEM, and DLS techniques. HRTEM images indicated that the tubular structure was preserved in all TNTs even after the replacement of Na+ with Ag+ and/or Au3+ ions. The antibacterial activity in dark and sunlight conditions was evaluated using different bacterial strains, Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. The results showed that while a low bacterial count (∼log 5 cells per well) was used for inoculation, the TNTs exhibited no antibacterial activity against the three bacterial strains, regardless of whether they were tested under light or dark conditions. However, the plasmonic nanoparticle-decorated TNTs showed remarkable activity in the dark. Additionally, Ag/Au-TNTs demonstrated significantly higher activity in the dark compared with either Ag-TNTs or Au-TNTs alone. Notably, under dark conditions, the Au/Ag-TNTs achieved log reductions of up to 4.5 for P. aeruginosa, 5 for S. aureus, and 3.7 for E. coli. However, when exposed to sunlight, Au/Ag-TNTs resulted in a complete reduction (log reduction ∼9) for P. aeruginosa and E. coli. The combination of two plasmonic nanoparticles (Ag/Au) decorated on the surface of TNTs showed synergetic bactericidal activity under both dark and light conditions. Ag/Au-TNTs could be explored to design surfaces that are responsive to visible light and exhibit antimicrobial properties
Enhancing Predictive Maintenance Hyperparameter Optimization and Adopted Strategies
No full textMaintenance operations constitute a substantial cost element within the manufacturing sector, typically representing 15% to 60% of the plant conversion budget. The optimization of these operations is paramount in reducing costs and avoiding the traditional Run to Failure methodology. This research paper investigates machine learning techniques, specifically Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, to enhance the efficacy of predictive maintenance strategies. Utilizing C-MAPSS, NCMAPSS, and the NASA battery dataset, our investigation focuses on predicting machinery\u27s Remaining Useful Life (RUL) and the State of Health (SOH) of lithium-ion batteries.Our findings not only demonstrate the effectiveness of models employing a Parallel CNN-LSTM architecture, further optimized through Genetic Algorithms (GA), but also highlight their potential to significantly enhance prediction accuracy compared to conventional models. For instance, an optimized Parallel CNN-LSTM model applied to the C-MAPSS dataset achieved a Test Root Mean Squared Error (RMSE) of 14.98 and an RMSE of 0.047 on the NASA battery dataset. These results underscore the potential of integrating CNNs with LSTMs to improve predictive maintenance outcomes, thereby reducing maintenance frequency and associated costs while enhancing machine and plant availability. This research opens up exciting possibilities for a more efficient and cost-effective future in the manufacturing sector, offering a promising outlook.This paper\u27s contributions to the academic and practical domains are twofold: It not only illustrates the effective application of machine learning in predictive maintenance, a topic of immediate relevance, but also offers a viable approach to cost reduction and efficiency improvement in manufacturing operations. These practical implications highlight the immediate benefits that can be derived from our research, making it highly applicable in real-world scenarios
Surface Engineering of Stainless-Steel 316L and 304L Electrodes for Hydrogen Production in Alkaline and Saline Water
No full textAlthough green hydrogen is a promising future energy carrier, mass production of green hydrogen via water electrolysis is still contingent upon identifying cost-effective, scalable, and durable electrocatalysts. This study demonstrates a facile, controllable, and scalable process to modify stainless steel’s morphology and chemical composition (SS) via anodic oxidation (anodization). Anodization was performed in an ethylene glycol/H2SO4/NH4F/MeOH-based bath to enhance the surface roughness, while the presence and absence of Cr, Fe, and Ni metal ions in the anodization bath were used to engineer the surface chemical composition. An effective cyclic voltammetry (CV)-based electrochemical activation step was implemented to enhance the electrocatalytic activity of the SS electrodes. The X-ray photoelectron spectroscopy (XPS) analysis confirmed the successful surface oxide reduction and Cr6+ removal. The anodization bath containing Ni and Fe nitrates resulted in highly rough surfaces with electrochemical active surface areas (ECSA) of 8.4 and 9.8 cm-2 with a high degree of homogeneity for both SS 316L and SS 304L alloys, respectively. Upon their use as hydrogen evolution catalysts in both alkaline (1 M KOH) and neutral (0.5 M NaCl) aqueous electrolytes, the anodized SS 316L electrode, at the best conditions, exhibited an overpotential of 256 mV at 10 mA/cm2 with a decrease in the overpotential and Tafel slope values of 112 and 32 mV/dec, respectively, compared to that of the as-received SS 316L in alkaline water. The hydrogen evolution reaction (HER) was found to follow the Volmer-Heyrovsky mechanism in both alkaline and neutral water, with a difference in the adsorbed hydrogen binding energy, causing a dramatic increase in the overpotential in neutral water compared to the alkaline water
Decoding the Role of CYP450 Enzymes in Metabolism and Disease: A Comprehensive Review
No full textCytochrome P450 (CYP450) is a group of enzymes that play an essential role in Phase I metabolism, with 57 functional genes classified into 18 families in the human genome, of which the CYP1, CYP2, and CYP3 families are prominent. Beyond drug metabolism, CYP enzymes metabolize endogenous compounds such as lipids, proteins, and hormones to maintain physiological homeostasis. Thus, dysregulation of CYP450 enzymes can lead to different endocrine disorders. Moreover, CYP450 enzymes significantly contribute to fatty acid metabolism, cholesterol synthesis, and bile acid biosynthesis, impacting cellular physiology and disease pathogenesis. Their diverse functions emphasize their therapeutic potential in managing hypercholesterolemia and neurodegenerative diseases. Additionally, CYP450 enzymes are implicated in the onset and development of illnesses such as cancer, influencing chemotherapy outcomes. Assessment of CYP450 enzyme expression and activity aids in evaluating liver health state and differentiating between liver diseases, guiding therapeutic decisions, and optimizing drug efficacy. Understanding the roles of CYP450 enzymes and the clinical effect of their genetic polymorphisms is crucial for developing personalized therapeutic strategies and enhancing drug responses in diverse patient populations
Same Story, Different Narratives: The Influence of Refugee Concerned Organizations on Egyptian Media
Get PDFIt has been documented in the literature that media usually portrays refugees negatively, which affects how the public reacts to them. Consequently, organizations catering to refugees, referred to in this thesis as Refugee Concerned Organizations (RCOs), try to influence the media aiming to influence the public. So, they produce prepacked information and employ different communication tools. Using the agenda building theory, this research investigated the influence of communication tools used by RCOs on Egyptian media focusing on Facebook and news websites. Qualitative content analysis was conducted for 615 Facebook posts and 111 News reports and supplemented by semi-structured interviews with Communication Professionals from four RCOs. The findings suggest that the RCOs general representation of refugees in Egypt is positive and that they cover refugees’ stories frequently. However, they tend to focus more on showcasing their work and assistance to refugees. Regarding the media, the findings indicate that reporting on refugee issues is more extensive during emergencies and that the focus is usually on refugees’ numbers and the Egyptian government’s efforts. The main two sources of information for the media are the Egyptian government and the UNHCR. Moreover, the findings showed that the issues covered by the media were not always the same issues highlighted by RCOs. However, the substantive and affective attributes used by both showed more similarities. As such, it can be argued that the communication tools of RCOs had a minimal influence on the first level of agenda building but an influence on the second level was observed
Activity Descriptors as Tools for Designing Highly Efficient Electrocatalysts for Water Splitting
Get PDFThere is an uprising urge to design efficient, active, and durable catalysts to achieve sustainable hydrogen production. The unambiguity of which material should be used pushed the scientific community to devote enormous efforts in exploring different elements with different ratios on trial and error bases. However, the bridge between the theoretical calculation and the experiments introduced a new realm of designing efficient catalysts by introducing the concept of activity descriptors. In the first part of the thesis, the ability to convert waste stainless steel (SS) 316L meshes into highly efficient and durable oxygen evolution reaction (OER) catalysts is demonstrated. The activity of the resulted electrocatalysts is in the order anodized SS annealed in oxygen (ASS-O2) \u3e anodized SS annealed in hydrogen (ASS-H2) \u3e anodized SS annealed in air (ASS-Air). The ASS-O2 showed an impressive low overpotential (η) of 280 mV at 10 mA/cm2, which is 120 mV less than that of the as-received SS (SS-AR), with a low Tafel slope of 63 mV dec−1 in 1 M KOH. These findings have also been asserted by the estimated electrochemical active surface area, electrochemical impedance spectroscopy analysis, Mott−Schottky analysis, and the calculated turnover frequency, affirming the superiority of the ASS-O2 electrocatalyst over the ASS- H2 and ASS-Air counterparts. The high activity of the ASS-O2 electrocatalyst can be ascribed to the surface composition that is rich in Fe3+ and Ni2+ as revealed by the X-ray photoelectron spectroscopy analysis. The simple method of anodization and thermal annealing in O2 at moderate conditions (450 °C for 1 h) lead to the formation of a SS mesh -based OER electrocatalyst with activity exceeding that of the state-of-the-art IrO2/RuO2 and other complex modified SS catalysts. These results were also confirmed via density functional theory calculations, which unveiled the OER reaction mechanism and elucidated the d-band center as an activity descriptor in different SS samples with different oxygen content. The presence of oxygen moved the d-band center closer to the Fermi level in the case of ASS-O2, explaining its superior activity. While for the second part of the thesis, a one-step hydrothermal synthesis method is demonstrated for the fabrication of flower-shaped spinel CoFe2O4 nanosheets on Ni foam at various pHs with different cation distribution. The XPS and Raman analyses revealed the cation distribution of Co and Fe as the main factor determining the catalytic activity of the material, which has been confirmed both experimentally and computationally. The catalyst with the largest δ showed η as low as 66 mV at -10 mA cm-2 with exceptional stability for 44 hours of continuous electrolysis in 1 M KOH. Our study demonstrates cation distribution as a catalytic activity descriptor of spinels for HER
Bitcoin and Stock Market Return Correlation: A Sectoral Analysis of the S&P500
Get PDFThis thesis studies the correlation between Bitcoin returns and stock market price returns for the S&P500 US stock market index by dividing the index into 11 sectors based on the industry and checking for correlation between Bitcoin returns and the returns of the stocks in each sector individually. The GARCH model is used due to its ability to account for the empirical phenomenon of volatility clustering that is usually present in financial time series datasets such as those used in this thesis. The results show statistically significant positive correlation between Bitcoin returns and stock market returns for all 11 sectors, indicating Bitcoin does not act as a hedge against any of the sectors of the S&P500 when each sector is considered individually. The results of this paper are of value to both policymakers and investors as this segregation of the S&P500 to determine correlation with cryptocurrency returns has not been attempted in previous research. The results would specifically be of benefit to investors who are overweight in one of the 11 sectors of the S&P500
A Comprehensive Analysis of the Integration of ESD Competencies in a Primary Four Curriculum in Egypt: A Case Study
Get PDFThis qualitative study examines the integration of Education for Sustainable Development (ESD) competencies within the Primary Four Science Curriculum in Egypt under the Education Reform Project EDU 2.0. ESD competencies are viewed as essential for empowering citizens to address real-world challenges. Utilizing a qualitative research design, the study employs document and content analysis of the teacher guide of the Techbook to investigate the incorporation of these competencies. Additionally, interviews are conducted with science teachers to gain insights into the application of the curriculum design. Interviews with curriculum developers directly involved in the curriculum development process with the Egyptian Ministry of Education and Technical Education (MOETE) are also conducted. Furthermore, insights are gathered through an interview with Dr. Tarek Shawki, the former Minister of Education and Technical Education of Egypt, who initiated and led EDU 2.0. Dr. Shawki shares perspectives on the contextual factors, objectives, and philosophy underlying the curriculum design, as well as the challenges encountered in its implementation. This study contributes to the understanding of how ESD competencies are integrated into the curriculum and sheds light on the practical challenges and implications of integration, highlighting the pivotal role of teachers in the educational process