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In this paper, the characteristics of absolute value memristors are verified through the circuit implementation and construction of a chaotic system with a conditional symmetric fractional-order memristor. The dynamic behavior of fractional-order memristor systems is explored using fractional-order calculus theory and the Adomian Decomposition Method (ADM). Concurrently, the investigation probes into the existence of coexisting symmetric attractors, multiple coexisting bifurcation diagrams, and Lyapunov exponent spectra (LEs) utilizing system parameters as variables. Additionally, the system demonstrates an intriguing phenomenon known as offset boosting, where the embedding of an offset can adjust the position and size of the system's attractors. To ensure the practical applicability of these findings, a fractional-order sliding mode synchronization control scheme, inspired by integer-order sliding mode theory, is designed. The rationality and feasibility of this scheme are validated through a theoretical analysis and numerical simulation. [ABSTRACT FROM AUTHOR]

Background: Pyroptosis plays an important role in the pathological process of ischemic stroke (IS). However, the exact mechanism of pyroptosis remains unclear. This paper aims to reveal the key molecular markers associated with pyroptosis in IS. Methods: We used random forest learning, gene set variation analysis, and Pearson correlation analysis to screen for biomarkers associated with pyroptosis in IS. Middle cerebral artery occlusion/reperfusion (MCAO/R) and oxygen and glucose deprivation/reoxygenation (OGD/R) models were constructed in vitro and in vivo. Cells were transfected with an Annexin A3 silencing (si-ANXA3) plasmid to observe the effects of ANXA3 on OGD/R + lipopolysaccharides (LPS)-induced pyroptosis. qRT‒PCR and western blotting were used to detect the expression of potential biomarkers and pyroptotic pathways. Results: Samples from a total of 170 IS patients and 109 healthy individuals were obtained from 5 gene expression omnibus databases. Thirty important genes were analyzed by random forest learning from the differentially expressed genes. Then, we investigated the relationship between the above genes and the pyroptosis score, obtaining three potential biomarkers (ANXA3, ANKRD22, ADM). ANXA3 and ADM were upregulated in the MCAO/R model, and the fold difference in ANXA3 expression was greater. Pyroptosis-related factors (NLRP3, NLRC4, AIM2, GSDMD-N, caspase-8, pro-caspase-1, cleaved caspase-1, IL-1β, and IL-18) were upregulated in the MCAO/R model. Silencing ANXA3 alleviated the expression of pyroptosis-related factors (NLRC4, AIM2, GSDMD-N, caspase-8, pro-caspase-1, cleaved caspase-1, and IL-18) induced by OGD/R + LPS or MCAO/R. Conclusion: This study identified ANXA3 as a possible pyroptosis-related gene marker in IS through bioinformatics and experiments. ANXA3 could inhibit pyroptosis through the NLRC4/AIM2 axis. [ABSTRACT FROM AUTHOR]

This paper investigates the squeezing action of hybridized nanofluid flow that takes place in the mechanism of truck brakes, dampers, polymer manufacturing, power transportation, oiling structure, and food production. The modeling technique relied on a set of partial differential equations to direct the fluid, taking into consideration external factors like the magnetized force and the time-dependent source of heat and thermal radiation. The hybridized nanofluid consists of copper and aluminum oxide nanoparticles that are dispersed in the machine oil. Entropic thermodynamic analysis is also examined to evaluate its role in the thermal examination of the system. The optimal homotopy asymptotic and Adomian decomposition methods were used to solve the problem. The study examined the changes in the rate of entropy formation and the characteristics of fluid velocities, heat transference rate, and performance based on the kind and concentration of nanoparticles and external thermal impacts. The results are presented in many key components, including a notable 30% increase in heat conductivity when using a combination of nanoparticles. The use of hybridized nanofluids manages to reduce surface frictional force, whereas the employment of a combination of particles results in an increase in friction owing to the heightened viscosity of the mixture. [ABSTRACT FROM AUTHOR]

The present paper aims to predict the optimal design parameters of a T-shaped fin subjected to natural convection using constructal theory. Both analytical and computational investigations are performed to determine its thermal performance for a wide range of thermo-physical and geometric parameters. Power law type temperature-dependent heat transfer coefficient is assumed for natural convection. For analytical solution Adomian decomposition method is adopted to evaluate the temperature distribution in both the stem and flange part of the fin. A numerical scheme called finite difference method is implemented to validate the analytical results. The temperature distribution obtained from the computational analysis using ANSYS FLUENT 14.0 is lower than that obtained from the analytical calculations. Moreover, the actual heat transfer rate is also compared between the two methods which reveal that the computational analysis gives higher value as compared to the analytical results. Further, optimization study has also been carried out in a generalized way so that either the fin volume or the heat transfer rate can be taken as a constraint. It is found that with the increase in length ratio, the optimum effectiveness is obtained at a higher value of aspect ratio whereas the optimum efficiency is obtained at a lower value of it. Though, a decrease in thickness ratio predicts a higher value of actual dimensionless heat transfer rate. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study was made to assess the effectiveness of the Modular Approach as an Alternative Delivery Mode (ADM) in Teaching Physical Education. It tried to gain some perspectives to improve the implementation of the new curriculum. The researcher identified the teachers’ and students’ engagement, participation, and experiences of the new learning modality in this time of the Covid-19 pandemic. Also, it dealt with the role of school administrators, parents, and other stakeholders and how they took part in the implementation of the program. In undertaking the study, the quantitative method of research was employed with the questionnaire as the principal instrument. The survey / questionnaire contained the following themes: (1) the level of participation of students in using the modular learning delivery in this new normal context; (2) the challenges both the teachers and the students encountered in teaching the Physical Education subject using the modules; and (3) the suggested responses for solutions to faced problems and issues regarding the implementation and participation in the use of the learning modules. The teacher respondents were randomly selected from the different public Secondary Schools in the District of Magsaysay, Occidental Mindoro composed of forty (40) male and female Physical Education teachers from Grades 7-12 who are taking the modular learning approach. An assessment of problems that were gained in the use of the modules revealed information on the factors affecting its integration that could be used as the basis for a proposed perspective for the development of a curriculum or program related to the use of the modular approach learning approach. [ABSTRACT FROM AUTHOR]

Current wargaming techniques are effective training and research instruments for military scenarios with fixed tools and boundaries on the problem. Control cells composed of officiants adjudicating and evaluating moves enforce these boundaries. Real-world crises, however, unfold in several dimensions in a chaotic context, a condition requiring decision-making under deep uncertainty. In this article, we assess how pedagogical exercises can be designed to effectively capture this level of complexity and describe a new framework for developing deeply immersive exercises. We propose a method for designing crisis environments that are dynamic, deep, and decentralized (3D). These obviate the need for a control cell and enhance the usefulness of exercises in preparing military and policy practitioners by better replicating real-world decision-making dynamics. This paper presents the application of this 3D method, which integrates findings from wargame and negotiation simulation design into immersive crisis exercises. We share observations from the research, design, and execution of "Red Horizon," an immersive crisis exercise held three times at Harvard University with senior civilian and military participants from multiple countries. It further explores connections to contemporary trends in international relations scholarship. [ABSTRACT FROM AUTHOR]

Algorithmic explainability has become one of the key topics of the last decade of the discourse about automated decision making (AMD, machine-made decisions). Within this discourse, an important subfield deals with the explainability of machine-made decisions or outputs that affect a person's legal position or have legal implications in general – in short, the algorithmic legal decisions. These could be decisions or recommendations taken or given by software which support judges, governmental agencies, or private actors. These could involve, for example, the automatic refusal of an online credit application or e-recruiting practices without any human intervention, or a prediction about one's likelihood of recidivism. This article is a contribution to this discourse, and it claims, that as explainability has become a prominent issue in hundreds of ethical codes, policy papers and scholarly writings, so it has become a 'semantically overloaded' concept. It has acquired such a broad meaning, which overlaps with so many other ethical issues and values, that it is worth narrowing down and clarifying its meaning. This study suggests that this concept should be used only for individual automated decisions, especially when made by software based on machine learning, i.e. 'black box-like' systems. If the term explainability is only applied to this area, it allows us to draw parallels between legal decisions and machine decisions, thus recognising the subject as a problem of legal reasoning, and, in part, linguistics. The second claim of this article is, that algorithmic legal decisions should follow the pattern of legal reasoning, translating the machine outputs to a form, where the decision is explained as applications of norms to a factual situation. Therefore, as the norms and the facts should be translated to data for the algorithm, so the data outputs should be back-translated to a proper legal justification. [ABSTRACT FROM AUTHOR]

Adrenomedullin has emerged as a promising biomarker in the field of viral diseases. Numerous studies have demonstrated its potential in assessing disease severity, predicting clinical outcomes, and monitoring treatment response. Adrenomedullin (AM) is a multifaceted peptide implicated in vasodilation, hormone secretion, antimicrobial defense, cellular growth, angiogenesis, and, importantly, chronic pain. AM and related peptides interface with cytoskeletal proteins within neuronal contexts, influencing microtubule dynamics. AM has primarily been utilized in diagnosing diseases of bacterial origin, including sepsis. Nevertheless, there are reports suggesting its utility in diseases of viral origin, and this is the focus of the present study. Furthermore, adrenomedullin has been shown to be elevated in various viral infections, suggesting its role in immune response modulation. Furthermore, AM may contribute to neuronal dysfunction through mechanisms involving immune and inflammatory responses, apoptosis, and disruptions in calcium homeostasis. This review aims to consolidate current knowledge regarding AM and its potential implications in viral diseases, elucidating its diverse roles in neurological pathophysiology. This review highlights the growing importance of adrenomedullin as a biomarker in viral diseases and the need for further functional studies to understand the underlying mechanisms involved. [ABSTRACT FROM AUTHOR]

Background: Algorithmic decision-making (ADM) utilises algorithms to collect and process data and develop models to make or support decisions. Advances in artificial intelligence (AI) have led to the development of support systems that can be superior to medical professionals without AI support in certain tasks. However, whether patients can benefit from this remains unclear. The aim of this systematic review is to assess the current evidence on patient-relevant benefits and harms, such as improved survival rates and reduced treatment-related complications, when healthcare professionals use ADM systems (developed using or working with AI) compared to healthcare professionals without AI-related ADM (standard care)—regardless of the clinical issues. Methods: Following the PRISMA statement, MEDLINE and PubMed (via PubMed), Embase (via Elsevier) and IEEE Xplore will be searched using English free text terms in title/abstract, Medical Subject Headings (MeSH) terms and Embase Subject Headings (Emtree fields). Additional studies will be identified by contacting authors of included studies and through reference lists of included studies. Grey literature searches will be conducted in Google Scholar. Risk of bias will be assessed by using Cochrane's RoB 2 for randomised trials and ROBINS-I for non-randomised trials. Transparent reporting of the included studies will be assessed using the CONSORT-AI extension statement. Two researchers will screen, assess and extract from the studies independently, with a third in case of conflicts that cannot be resolved by discussion. Discussion: It is expected that there will be a substantial shortage of suitable studies that compare healthcare professionals with and without ADM systems concerning patient-relevant endpoints. This can be attributed to the prioritisation of technical quality criteria and, in some cases, clinical parameters over patient-relevant endpoints in the development of study designs. Furthermore, it is anticipated that a significant portion of the identified studies will exhibit relatively poor methodological quality and provide only limited generalisable results. Systematic review registration: This study is registered within PROSPERO (CRD42023412156). [ABSTRACT FROM AUTHOR]

The concept of „automated decision making” (ADM) is currently a key vector reshaping industries in the era of technology-based decision making, and the use of artificial intelligence (AI) in this process is a hot topic worldwide with the adoption of generative AI tools like ChatGPT. ADM enables companies to use technology for tasks that were not long ago the domain of human judgment. AI is essential in financial services, such as credit worthiness assessment, automated investment advice (often called robo-advice) or insurance. The regulation of activities related to ADM is contained in various acts and regulations, including the Sale of Goods Directive 2019/771, the Digital Contracts Directives 2019/770 and the EU General Regulation on Data Protection. However, these directives do not contain special rules for new technologies such as Artificial Intelligence (AI). Although recently, the EU has moved forward with regulations on product liability laws affecting digital products and services, with the EU Data Act, the EU Cyber Resilience Act and the highly publicized EU AI Act - regulations with a transition period of two years for compliance, the speed with which in practice the ADM tools are developed and trained make the legislative efforts slow and incomprehensible. In light of this issue, this article explores the relationship between AI, ADM and discusses whether existing EU consumer law is equipped to deal with situations where AI systems are either used for internal purposes by companies or offered to consumers as the main subject of the contract. The paper will reveal a number of gaps in current EU consumer law and briefly discuss future legislation. This article also reviews the efficiency of key EU consumer law directives for the conclusion of contracts with consumers through the use of AI, with recommendations for clarifications and additions to improve the use of ADM.

The present manuscript proposes a computational approach to efficiently tackle a class of two-point boundary value problems that features third-order nonlinear ordinary differential equations. Specifically, this approach is based upon a combination of the shooting method with a modification of the renowned Adomian decomposition method. The approach starts by transforming the governing BVP into two appropriate initial-value problems, and thereafter, solves the resulting IVPs recurrently. In addition, the application of this method to varied test models remains feasible—of course, this is supported by the competing Runge–Kutta method, among others, and reported through comparison plots and tables. [ABSTRACT FROM AUTHOR]

Background: Distal lower extremity reconstruction is challenging. This study aims to propose a protocol for the treatment of traumatic soft tissue defects. The key concept is to combine the surgical armamentarium of the reconstructive surgeon with the advantages provided by hyperbaric oxygen therapy. Methods: This retrospective study analyzed data of 57 patients affected with unilateral or bilateral lower extremity trauma distal to the knee and involving soft tissues with no indication of immediate reconstruction between 2010 and 2021. Before the reconstructive procedure, all the patients underwent a stick swab procedure for the collection of microbiological samples and debridement. Patients were divided into two treatment groups and only one group underwent a combined therapeutic procedure with hyperbaric oxygen therapy. Negative pressure wound therapy (NPWT) was employed only if deemed necessary according to the defect's depth and wound exudate. Surgical techniques, outcomes, and complications were discussed. Results: All patients achieved a complete recovery with no major complications and only minor complications observed. The study group treated with HBOT had a lower complication rate and lower percentages of minimal and partial graft loss compared with the same complications observed in the control group. No patients experienced HBOT-related complications. Significant reductions in the time to complete healing and the time from reconstruction to healing were found (p = 0.002 and p < 0.00001, respectively). Conclusions: A lower complication rate was observed in the group treated with HBOT. The administration of HBOT prior to soft tissue reconstruction significantly reduced the time to complete healing and the time interval from skin grafting to healing. However, prospective studies and randomized trials with larger cohorts should be designed to investigate the efficacy of HBOT for the treatment of lower extremity injuries with extensive soft tissue defects. [ABSTRACT FROM AUTHOR]

The investigation detailed in this paper explores the behavior of a slippery nanofluid flowing over a permeable stretched sheet under the influence of magnetohydrodynamic forces, considering factors like thermal radiation, viscous dissipation, and convective boundary conditions. The analysis systematically establishes principles for conserving mass, heat, momentum, and nanoparticle concentration, deriving a set of nonlinear ordinary differential equations from the governing partial differential equations. To tackle these challenges, the Adomian decomposition approach is utilized as a central element of the solution methodology. Graphical representations are employed to depict the solutions across various physical parameters. Despite the significance of nanofluids in both industrial and scientific contexts, there exists a noticeable research gap concerning the combined impacts of thermal radiation, viscous dissipation, and convective boundary conditions on heat and mass transfer, especially when coupled with a permeable linear rough stretched sheet. This study aims to fill this void by offering quantitative insights into these intricate phenomena, thereby enhancing our understanding of nanofluid dynamics and their practical implications. The findings indicate that increasing slip velocity and magnetic parameters reduce the boundary layer thickness of the velocity profile but increase it for the temperature profile, suggesting a nuanced interplay between slip velocity and magnetic effects on velocity boundary layers, while the temperature boundary layer exhibits distinct thermal dynamics within the system.

Abstract Background Pyroptosis plays an important role in the pathological process of ischemic stroke (IS). However, the exact mechanism of pyroptosis remains unclear. This paper aims to reveal the key molecular markers associated with pyroptosis in IS. Methods We used random forest learning, gene set variation analysis, and Pearson correlation analysis to screen for biomarkers associated with pyroptosis in IS. Middle cerebral artery occlusion/reperfusion (MCAO/R) and oxygen and glucose deprivation/reoxygenation (OGD/R) models were constructed in vitro and in vivo. Cells were transfected with an Annexin A3 silencing (si-ANXA3) plasmid to observe the effects of ANXA3 on OGD/R + lipopolysaccharides (LPS)-induced pyroptosis. qRT‒PCR and western blotting were used to detect the expression of potential biomarkers and pyroptotic pathways. Results Samples from a total of 170 IS patients and 109 healthy individuals were obtained from 5 gene expression omnibus databases. Thirty important genes were analyzed by random forest learning from the differentially expressed genes. Then, we investigated the relationship between the above genes and the pyroptosis score, obtaining three potential biomarkers (ANXA3, ANKRD22, ADM). ANXA3 and ADM were upregulated in the MCAO/R model, and the fold difference in ANXA3 expression was greater. Pyroptosis-related factors (NLRP3, NLRC4, AIM2, GSDMD-N, caspase-8, pro-caspase-1, cleaved caspase-1, IL-1β, and IL-18) were upregulated in the MCAO/R model. Silencing ANXA3 alleviated the expression of pyroptosis-related factors (NLRC4, AIM2, GSDMD-N, caspase-8, pro-caspase-1, cleaved caspase-1, and IL-18) induced by OGD/R + LPS or MCAO/R. Conclusion This study identified ANXA3 as a possible pyroptosis-related gene marker in IS through bioinformatics and experiments. ANXA3 could inhibit pyroptosis through the NLRC4/AIM2 axis.

Background: In pursuit of diagnostic tools capable of targeting distinct stages of Alzheimer's disease (AD), this study explores the potential of electroencephalography (EEG) combined with machine learning (ML) algorithms to identify patients with mild or moderate AD (ADM) and advanced AD (ADA). Objective: This study aims to assess the classification accuracy of six classical ML algorithms using a dataset of 668 patients from multiple hospitals. Methods: The dataset comprised measurements obtained from 668 patients, distributed among control, ADM, and ADA groups, collected from five distinct hospitals between 2011 and 2022. For classification purposes, six classical ML algorithms were employed: support vector machine, Bayesian linear discriminant analysis, decision tree, Gaussian Naïve Bayes, K-nearest neighbor and random forest. Results: The RF algorithm exhibited outstanding performance, achieving a remarkable balanced accuracy of 93.55% for ADA classification and 93.25% for ADM classification. The consistent reliability in distinguishing ADA and ADM patients underscores the potential of the EEG-based approach for AD diagnosis. Conclusions: By leveraging a dataset sourced from multiple hospitals and encompassing a substantial patient cohort, coupled with the straightforwardness of the implemented models, it is feasible to attain notably robust results in AD classification. [ABSTRACT FROM AUTHOR]

In this paper, the characteristics of absolute value memristors are verified through the circuit implementation and construction of a chaotic system with a conditional symmetric fractional-order memristor. The dynamic behavior of fractional-order memristor systems is explored using fractional-order calculus theory and the Adomian Decomposition Method (ADM). Concurrently, the investigation probes into the existence of coexisting symmetric attractors, multiple coexisting bifurcation diagrams, and Lyapunov exponent spectra (LEs) utilizing system parameters as variables. Additionally, the system demonstrates an intriguing phenomenon known as offset boosting, where the embedding of an offset can adjust the position and size of the system’s attractors. To ensure the practical applicability of these findings, a fractional-order sliding mode synchronization control scheme, inspired by integer-order sliding mode theory, is designed. The rationality and feasibility of this scheme are validated through a theoretical analysis and numerical simulation.

The vast majority of human pancreatic ductal adenocarcinomas (PDACs) harbor TP53 mutations, underscoring p53’s critical role in PDAC suppression. PDAC can arise when pancreatic acinar cells undergo acinar-to-ductal metaplasia (ADM), giving rise to premalignant pancreatic intraepithelial neoplasias (PanINs), which finally progress to PDAC. The occurrence of TP53 mutations in late-stage PanINs has led to the idea that p53 acts to suppress malignant transformation of PanINs to PDAC. However, the cellular basis for p53 action during PDAC development has not been explored in detail. Here, we leverage a hyperactive p53 variant—p5353,54—which we previously showed is a more robust PDAC suppressor than wild-type p53, to elucidate how p53 acts at the cellular level to dampen PDAC development. Using both inflammation-induced and KRASG12D-driven PDAC models, we find that p5353,54 both limits ADM accumulation and suppresses PanIN cell proliferation and does so more effectively than wild-type p53. Moreover, p5353,54 suppresses KRAS signaling in PanINs and limits effects on the extracellular matrix (ECM) remodeling. While p5353,54 has highlighted these functions, we find that pancreata in wild-type p53 mice similarly show less ADM, as well as reduced PanIN cell proliferation, KRAS signaling, and ECM remodeling relative to Trp53-null mice. We find further that p53 enhances chromatin accessibility at sites controlled by acinar cell identity transcription factors. These findings reveal that p53 acts at multiple stages to suppress PDAC, both by limiting metaplastic transformation of acini and by dampening KRAS signaling in PanINs, thus providing key new understanding of p53 function in PDAC. [ABSTRACT FROM AUTHOR]

In recent years, reversible gates have a great impact on optical nanotechnology, quantum and DNA computing. In the optical field, reversible gates like the Fredkin gate, Feynman gate, Toffoli gate, and Peres gate are very demanding due to their low power consumption. In this article, a novel design of Feynman gate using Add/Drop Multiplexer and Reflective Semiconductor Optical Amplifier (RSOA) is proposed. Frequency encoding scheme and dibit-based logic are incorporated in the proposed design. The Frequency encoding technique decreases the probability of bit error in long-range transmission. Due to the high gain and low noise property of RSOA, the proposed design can perform operations like computation and data processing at ultra-high speed with low noise. To verify the operation of the proposed design, we have used MATLAB Simulink (R2018a) software. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we apply the homotopy perturbation method (HPM), modified homotopy perturbation method (MHPM), variational iteration method (VIM), Adomian decomposition method (ADM), and modified Adomian decomposition method (MADM) to solve nonlinear mixed Volterra-Fredholm integral equations and its system. We investigate the approximate solution of this equation and its system via proposed methods. The validity and efficiency of these methods are demonstrated through various numerical examples that illustrate the efficiency, accuracy, and simplicity of the proposed methods. Moreover, the convergence and uniqueness of the solution of the suggested methods are confirmed and compared with the exact solutions.

Qualitative theory, together with approximate solutions to a dynamic system, are investigated. The proposed mathematical model is composed of protected, susceptible, infected and treated classes. The adopted model expresses the mechanism of disease due to Typhoid fever. A modified type Caputo-Fabrizio fractional derivative (CFFD) is considered for the intended results. With the help of fixed point theory, some sufficient conditions for the existence of approximate solutions are developed. Also, to compute an approximate solution with respect to each compartment, we utilize the Laplace Transform and the Adomian decomposition method (ADM). A graphical presentation corresponding to some fundamental data is given. [ABSTRACT FROM AUTHOR]

The use of acellular dermal matrix (ADM) implants has enhanced breast reconstruction. ADM is a biotechnologically designed human tissue of bovine or porcine origin in which tissue processing removes cellular antigens. In this case report, we describe the use of ADM in one-stage prepectoral breast reconstruction. Skin-reduction breast reconstruction with a prepectoral implant was performed. We created a combined dermal pocket using the inferior dermal flap, sutured with a patch of acellular dermal matrix to continue its extension until the upper pole, to cover the implant. This technique offers single-stage immediate reconstruction, with a decreased requirement for ADM and increased use of vascularized tissue and implant support. Additionally, in the pre-pectoral space, decreased pain postoperatively and less anatomic disruption is offered. [ABSTRACT FROM AUTHOR]

In this article, we present the fuzzy Adomian decomposition method (ADM) and fuzzy modified Laplace decomposition method (MLDM) to obtain the solutions of fuzzy fractional Navier–Stokes equations in a tube under fuzzy fractional derivatives. We have looked at the turbulent flow of a viscous fluid in a tube, where the velocity field is a function of only one spatial coordinate, in addition to time being one of the dependent variables. Furthermore, we investigate the fuzzy Elzaki transform, and the fuzzy Elzaki decomposition method (EDM) applied to solving fuzzy linear-nonlinear Schrodinger differential equations. The proposed method worked perfectly without any need for linearization or discretization. Finally, we compared the fuzzy reduced differential transform method (RDTM) and fuzzy homotopy perturbation method (HPM) to solving fuzzy heat-like and wave-like equations with variable coefficients. The RDTM and HPM solutions are simpler than other already existing methods. Several examples are provided to illustrate the methods that have been offered. The results obtained using the scheme presented here agree well with the analytical solutions and the numerical results presented elsewhere. These studies are important in the context of the development of the theory of fuzzy partial differential equations. [ABSTRACT FROM AUTHOR]

Using optical add–drop multiplexer/remover multiplexer (OADM), it is possible to add or remove wavelengths and change or route them through the various nodes and networks. At this moment, key problems in add–drop multiplexer (ADM) are the bandwidth, modulation format, and reuse wavelength. In this article, the Optisystem software simulation is used as a platform to design, test, and verify the method applied to the current work; the OADM is proposed based on the metro network to get distribution between nodes over a transmission link; OADM analysis was presented with four channels (193.1, 193.2, 193.3, and 193.4 THz) at total bandwidth of 1.6 Tb/s, none-return-to-zero (NRZ), and return to zero coding types. Experiment one shows that the average output power is −17.997 dBm, the average drop power is −17.997 dBm, and the average add power is −18.338 dBm, the average gain is −0.0429 dB, the average noise figure is 0 dB, the average power input signal is 10.679 dBm, the average of power output signal is 10.633 dBm, and the average output optical signal-to-noise ratio (OSNR) is 0 dB, However, the second experiment shows that the average output power is −24.238 dBm, the average drop power is −24.288 dBm, and the average add power is −24.753 dBm, the average gain is −0.0417 dB, the average noise figure is 0 dB, average power input signal is 7.691 dBm, average of power output signal is 7.677 dBm, and the average output OSNR 0 dB. The system supports four input channels, four add channels, four output channels, and four drop channels. The results are acceptable after three spans of Solitons fiber with 600 km length, 200 km for each span. Nonetheless, it is believed that it is well justified to adopt these schemes in the current optical network with a low cost for overall expenditure. [ABSTRACT FROM AUTHOR]