An Automated Methodology For A Comprehensive Definition Of The Supply Chain Using Generic Ontological Components

Today, worldwide business communities are in the era of the Supply Chains. A Supply Chain is a collection of several independent enterprises that partner together to achieve specific goals. These enterprises may plan, source, produce, deliver, or transport materials to satisfy an immediate or projected market demand, and may provide the after sales support, warranty services, and returns. Each enterprise in the Supply Chain has roles and elements. The roles include supplier, customer, or carrier and the elements include functional units, processes, information, information resources, materials, objects, decisions, practices, and performance measures. Each enterprise, individually, manages these elements in addition to their flows, their interdependencies, and their complex interactions. Since a Supply Chain brings several enterprises together to complement each other to achieve a unified goal, the elements in each enterprise have to complement each other and have to be managed together as one unit to achieve the unified goal efficiently. Moreover, since there are a large number of elements to be defined and managed in a single enterprise, then the number of elements to be defined and managed when considering the whole Supply Chain is massive. The supply chain community is using the Supply Chain Operations Reference model (SCOR model) to define their supply chains. However, the SCOR model methodology is limited in defining the supply chain. The SCOR model defines the supply chain in terms of processes, performance metrics, and best practices. In fact, the supply chain community, SCOR users in particular, exerts massive effort to render an adequate supply chain definition that includes the other elements besides the elements covered in the SCOR model. Also, the SCOR model is delivered to the user in a document, which puts a tremendous burden on the user to use the model and makes it difficult to share the definition within the enterprise or across the supply chain. This research is directed towards overcoming the limitations and shortcomings of the current supply chain definition methodology. This research proposes a methodology and a tool that will enable an automated and comprehensive definition of the Supply Chain at any level of details. The proposed comprehensive definition methodology captures all the constituent parts of the Supply Chain at four different levels which are, the supply chain level, the enterprise level, the elements level, and the interaction level. At the Supply Chain level, the various enterprises that constitute the supply chain are defined. At the enterprise level, the enterprise elements are identified. At the enterprises\u27 elements level, each element in the enterprise is explicitly defined. At the interaction level, the flows, interdependence, and interactions that exist between and within the other three levels are identified and defined. The methodology utilized several modeling techniques to generate generic explicit views and models that represents the four levels. The developed views and models were transformed to a series of questions and answers, where the questions correspond to what a view provides and the answers are the knowledge captured and generated from the view. The questions and answers were integrated to render a generic multi-view of the supply chain. The methodology and the multi-view were implemented in an ontology-based tool. The ontology includes sets of generic supply chain ontological components that represent the supply chain elements and a set of automated procedures that can be utilized to define a specific supply chain. A specific supply chain can be defined by re-using the generic components and customizing them to the supply chain specifics. The ontology-based tool was developed to function in the supply chain dynamic, information intensive, geographically dispersed, and heterogeneous environment. To that end, the tool was developed to be generic, sharable, automated, customizable, extensible, and scalable

Analytical and numerical water quality model for a sinusoidally varying pollutant discharge concentration

Analytical solution has been obtained for one-dimensional advection-diffusion equation which includes terms of decay and increasing sources by using Laplace transformation. Also numerical solution has been obtained by using explicit finite difference scheme. In this study the boundary condition applied at x = 0 describes a sinusoidal variation in pollutant concentration. The analytical solution obtained produces results that are exact for any location at any time. Impact of different parameters controlling the pollutant dispersion along the river at any time has been studied separately with figures help. This publication proved mathematically the fact that the high concentration of pollutant can be reduced by releasing fresh water discharges from Delta Barrage in the Nile River. For a real situation, our simple model can give decision support for planning restrictions to be imposed on cultivating and urban practices

PREPARATION AND EVALUATION OF RAPIDLY DISSOLVING TABLETS OF RALOXIFENE HYDROCHLORIDE BY TERNARY SYSTEM FORMATION

Objectives: Enhancing the dissolution rate of raloxifene hydrochloride for the preparation of rapidly disintegrating tablets with subsequent rapid dissolution.Methods: Binary and ternary solid dispersions (SDs) with different carriers were prepared at various drug: carrier ratios including cremophor RH 40, polyvinylpyrrolidone (PVP K30), poloxamer 407 and gelucire 44/14 as carriers and were evaluated by drug content, In-vitro dissolution studies, Differential scanning calorimetry (DSC) and Fourier transform infrared spectroscopy (FTIR) analysis analysis. The most efficient solid dispersion was selected for preparation of rapidly dissolving tablets.Results: SDs showed enhanced dissolution rate compared to the unprocessed drug. Differential scanning calorimetry revealed that enhancement in drug dissolution was mainly due to a change in its crystalline structure. FTIR studies revealed no interaction between the drug and excipients. The dissolution pattern of the drug from the prepared tablet depended on the components of the tablets with those containing a combination of super-disintegrants (crospovidone and croscarmellose) in the presence of citric acid as channeling agent and pH modifier being the best.Conclusion: The study presented a system capable of increasing the dissolution rate of raloxifene with successful incorporation in rapidly disintegrating tablets with subsequent fast dissolution. 3

COMPARATIVE ANALYSIS OF DEEP LEARNING AND TRADITIONAL OPTIMIZATION ALGORITHMS FOR ADAPTIVE BEAMFORMING IN MIMO SYSTEMS

This study provides a comprehensive methodological contribution through rigorous comparative analysis between deep learning approaches and traditional optimization algorithms for adaptive beamforming in MIMO systems. Traditional optimization methods face significant challenges in dynamic environments due to computational complexity and convergence issues. Through systematic experimentation with standardized datasets (DeepMIMO, 3GPP TR 38.901, and IEEE MIMO Data Challenge), we evaluate performance using statistically validated metrics including signal-to-interference-plus-noise ratio, bit error rate, computational efficiency, and adaptation speed. Our findings reveal that deep learning approaches achieve significantly faster convergence (23.7%, p < 0.01) and higher SINR (18.5%, p < 0.01) in dynamic channel conditions, while traditional algorithms maintain superior performance in steady-state scenarios. Traditional methods outperform deep learning by 12.3% (p < 0.01) in terms of BER in low-SNR environments. Computational complexity analysis shows traditional methods scale as O(N³) with MIMO size N, while deep learning maintains O(N) inference complexity. The main contribution of this work is a novel adaptive selection framework with mathematically proven optimality bounds that dynamically switches between methodologies based on current channel conditions, achieving 15.3% higher average SINR (p < 0.01) in mixed scenarios compared to fixed algorithms

Effects of chemical structure, solvent and solution pH on the visible spectra of some new methine cyanine dyes

Some new dimethine and bis-dimethine, cyanine dyes derived from benzo[2,3-b; 2`,3`-b`]bis-pyrazolo[4,5-b]-l,4-(oxa-, thia-, and pyra-)-zine-6,12-dione were synthesized. Effect of chemical structure on the electronic visible absorption spectra of all the synthesized cyanine dyes was investigated in 95% ethanol solution. Effects of solvent and/or solution pH on the electronic visible absorption spectra of some selected synthesized cyanine dyes were also examined in pure solvents having different polarities and/or in aqueous universal buffer solutions, respectively. Structural confirmations were carried out through elemental analysis, mass spectroscopy, IR and 1H NMR spectra

Using Social Media Sites and Its Relation with Social Isolation and Selfishness in Youth: A Predictive Study

This study aimed at highlighting the correlation between social isolation and selfishness in youth and the intensive use of social media in a sample of university students, and revealing the possibility of predicting the level of social isolation and selfishness among young people through their use of social media. The research sample consisted of 600 male and female students. The descriptive method was applied using a scale of Social isolation and Selfishness questionnaire, as well as some questions that measure exposure, its intensity, times and special places of social networking sites. The researchers applied methodological steps in constructing the scale and questionnaire from previous studies and refereeing it by experts. The main findings include: 1) there is a positive correlation between the intensive use of social media sites and social isolation and selfishness in the research sample; 2) the heavy use of social networks negatively contributes to predicting the level of selfishness and social isolation in the future among young people; 3) the larger proportion of subjects (almost 67.3%) reported that social media networks had a negative effect on their relation with reality; and 4) Facebook leads the list of Respondents’ preferred social media, followed by WhatsApp

Remediation of pollution in a river by releasing clean water using the solution of advection-diffusion equation in two dimensions

Analytical and numerical solutions are obtained for two-dimensional advection-diffusion equation, using Laplace transformation technique and explicit finite difference method for the pollutant concentration in a river or in shallow aquifer with time-dependent dispersion coefficients. We take two cases, first case: concentration of increasing nature (mixed type or third type) is considered at the origin and initially the domain is solute free. Second case: the river’s water is polluted initially (at time t = 0 ) while at the origin, at time t \u3e 0, the source of pollution is removed by releasing fresh water. We have proved mathematically the fact that the high concentration of pollutant can be reduced by releasing adequate discharges from barrage in a river. Both the dispersion coefficients, the velocity components and first order decay term are considered exponentially decreasing function of time. The different effects of the parameters controlling the pollutant dispersion along the river at any time are studied separately with the help of figures. The parameters that have a role in removing or reducing concentration of pollutant along the river have been studied in detail. When comparing the analytical solution with the numerical solution, we found a very good agreement between them. For a real situation, our simple model can provide decision support for planning restrictions to be imposed on farming and urban practices

Subsynchronous resonance oscillations mitigation via fuzzy controlled novel braking resistor model

Subsynchronous resonance (SSR) torsional torque oscillations is a problem of a great concern in the power engineering community. SSR causes torsional torque oscillations with ever-increasing magnitudes occurring in the machine shaft sections causing a premature fatigue life expenditure of the shaft metal. In this paper, dynamic braking switching strategy designed through fuzzy logic control theory and implemented via novel braking resistor model, namely chopper rectifier controlled braking resistor for tempering SSR torsional torque oscillations of a large turbo-generator. The proposed mitigation scheme has been tested on the IEEE second benchmark model for SSR studies. Comparative simulation study via MATLAB/Simulink-based modeling and simulation environment of the test model with and without the suggested mitigation regime should demonstrate its effectuality for mitigation of SSR torsional torque oscillations

Accurate skin cancer diagnosis based on convolutional neural networks

Although melanoma is not the most common type of skin cancer, it is supposed to extend to other areas of the body if not early diagnosed. Melanoma is the deadliest form of skin cancer and accounts for about 75% of deaths associated with skin cancer. The present study introduces an automated technique for skin cancer prediction, detection, and diagnosis including trending noninvasive and nonionizing techniques that combines deep learning methods to diagnose melanoma with high accuracy. Computer-aided diagnosis (CAD) using medical images is utilized to distinguish benign and malignant tumors, which can assist physicians in early identification of symptoms, thus lowering the mortality rate. The CAD system consists of four phases; detection of the region of interest (RoI), using data augmentation techniques, processing RoI using convolutional neural network (CNN) to extract the most important features, and finally the extracted CNN features are input to a support vector machine (SVM) classifier to decode the two classes benign (B) and malignant (M). Two datasets, ISIC and CPTAC-CM, were utilized to train the CNNs. GoogleNet, ResNet-50, AlexNet, and VGG19 were investigated and compared. The accuracy of the proposed CAD system has reached 99.8% for ISIC database and 99.9% for CPTAC-CM database

Determination Of Energy Gap Of The Iron-Based Oxypnictide And Laofege Superconductors Using Specific Heat Capacity

The most prominent indicators of superconductivity are the superconducting transition temperature (Tc) and the superconducting energy gap (Δ). These indicators associated with electronic state of temperature dependence of resistivity and specific heat measurements, respectively. The specific heat is a bulk measurement that reflects the behavior of the entire sample response. Here, we introduce a model that examines the transition characteristic to a normal/superconducting state at a critical temperature of the electron and phonon contributions of specific heat. Three basic postulates were adopted. First is that the transition of the system from normal to superconducting state, which allows phonons to bind electrons to form Cooper pairs, requiring a change in energy differences appearing in a specific heat behavior. Second, specific heat has different contributions, changing differently at Tc. This change is possibly a result of the physical function on such contributions. The third postulate is that phonon behavior can manifest superconductive property, particularly in the coexisting state. Based on the suggested superconducting transition model, which was constructed depending on the superconductive behavior of specific heat in accordance with above postulates, energy scales were obtained at normal state for iron-based oxypnictides. The pseudogap 2Δ was 14.26 meV for the SmO0.80F0.20FeAs compound, which was determined from the far-infrared reflectance spectra based on the phonon state at room temperature