A Comparative Analysis of RAD and Agile Technique for Management of Computing Graduation Projects

Computing students face the problem with time and quality of the work while managing their graduation/senior projects. Rapid Application Development (RAD) model is based on continual user involvement for the process of requirement gathering via prototyping. After each iteration, the developers can validate the requirements that are completed in the iteration. Managing a project with RAD is easier but not flexible. On the other hand, Agile project management techniques focus on flexibility, agility, teamwork and quality based on user stories. Continual user involvement is avoided, which requires extensive maintenance time for fixing iteration and release of the story points. This also makes it necessary to provide onsite training to the users of the application. This research provides the pros and cons of RAD and Agile project management techniques, to help students in deciding the best approach for managing their graduation projects. For the evaluation of these techniques, similar case studies were given to the senior project students (having similar CGPAs) for building similar functionality-based applications. The two projects “Life Organizer” developed and managed using RAD and “Smart Patient Assistant (SPA)” developed and managed through Agile methodology were evaluated against the quality assurance criteria for senior projects. The study found that the project developed with RAD methodology performed 13.33% better in providing extensive and elaborated documentation than the students following the Agile technique. On the other hand, SPA-Agile based project, due to teamwork had 2.5% better implementation than Life Organizer-RAD based project.     

Cascaded neural network based small array synthesis with robustness to noise

A cascaded neural network approach has been presented in this paper to estimate the excitation for the desired field distribution using a radial basis function neural network (RBFNN). The article has employed an electromagnetic design example consisting of 5 × 5 and 6 × 6 planar antenna array of isotropic sources with inter element‐distance of 0.5λ to show the adaptation of the neural network model in estimating the desired output. A neural network is trained using a dataset of suitable excitation voltages and its corresponding radiation patterns, which proves to be efficient in predicting the excitation voltages required to generate the desired pattern. A set of techniques based on a cascaded neural network is adopted for pattern synthesis using magnitude and phase, magnitude only, and template‐based input data. The robustness of the method has also been tested by considering noise with different SNR levels. The results found in each case have a close fit with the desired pattern.     

XAI-driven model for crop recommender system for use in precision agriculture

Agriculture serves as the predominant driver of a country's economy, constituting the largest share of the nation's manpower. Most farmers are facing a problem in choosing the most appropriate crop that can yield better based on the environmental conditions and make profits for them. As a consequence of this, there will be a notable decline in their overall productivity. Precision agriculture has effectively resolved the issues encountered by farmers. Today's farmers may benefit from what's known as precision agriculture. This method takes into account local climate, soil type, and past crop yields to determine which varieties will provide the best results. The explainable artificial intelligence (XAI) technique is used with radial basis functions neural network and spider monkey optimization to classify suitable crops based on the underlying soil and environmental conditions. The XAI technology would provide assets in better transparency of the prediction model on deciding the most suitable crops for their farms, taking into account a variety of geographical and operational criteria. The proposed model is assessed using standard metrics like precision, recall, accuracy, and F1-score. In contrast to other cutting-edge approaches discussed in this study, the model has shown fair performance with approximately 12% better accuracy than the other models considered in the current study. Similarly, precision has improvised by 10%, recall by 11%, and F1-score by 10%.     

Optical Signal Processing Using Tunable Delay Elements Based on Slow Light

Tunable optical delay lines have many applications for high-performance optical switching and signal processing. Slow light has emerged as an enabling technology for achieving continuously tunable optical delays. Delay reconfigurability opens up a whole new field of nonlinear signal processing using slow light. In this paper, the authors review recent advances in slow-light-based optical signal processing, with a focus on the data fidelity after traversing the slow light elements. The concept of slow-light-induced data pattern dependence is introduced and is shown to be the main signal degrading effect. We then propose and experimentally demonstrate phase-preserving slow light by delaying 10 Gb/s differential phase-shift keying (DPSK) signals with reduced DPSK pattern dependence. Spectrally efficient slow light using advanced multilevel phase-modulated formats is further described. With this technique, doubled bit-rate signals can be transmitted through a bandwidth-limited slow light element. We finally show several novel slow-light-based signal processing modules. Unique features such as multichannel operation, variable bit-rate capability, and simultaneous multiple functions are highlighted.     

A family of integration algorithms for constitutive equations in finite deformation elasto-viscoplasticity

A two parameter family of incrementally objective integration schemes is proposed for the analysis of a broad range of unified rate-dependent viscoplastic constitutive models in large deformation problems. A similar scheme can be applied to rate-independent solids as well. These algorithms are a generalization of the mid-point integration rule. Full linearization of the principle of virtual work is performed in an updated Lagrangian framework together with a calculation of the consistent linearized moduli. Some details of the finite element implementation are given for plane strain and axisymmetric problems. The method is compared with other objective integration schemes and is tested with several examples where large strains and rotations occur.     

Novel hybrid models of ANFIS and metaheuristic optimizations (SCE and ABC) for prediction of compressive strength of concrete using rebound hammer field test

In this study, we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System (ANFIS) optimized by Shuffled Complex Evolution (SCE) on the one hand and ANFIS with Artificial Bee Colony (ABC) on the other hand. These were used to predict compressive strength (Cs) of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory. Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway, Vietnam were considered. The dataset was randomly divided into a 70:30 ratio, for training (70%) and testing (30%) of the hybrid models. Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics: Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that both of the novel models depict close agreement between experimental and predicted results. However, the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs. Thus, the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.     

Investigating the effect of material and weave design on comfort properties of bilayer-woven fabrics

The paper focuses on the development of a bilayer-woven fabric and investigating the effect of weave design and material type on its comfort properties. Face layer was plain woven with cotton yarn, while two different weave designs (2/2 and 3/1 twill) and four different materials (cotton, polyester, micropolyester and nylon) were used for the back layer. The comfort properties of fabric, including air permeability (AP), thermal resistance, water vapour resistance and overall moisture management capacity, were determined. It was found that both the layers of fabric as a whole contribute to the comfort properties of bilayer fabric. The highest AP was exhibited by fabrics having both layers of cotton, while 3/1 twill samples have a lower value of thermal resistance as compared to the 2/2 twill samples. The results further showed that micro polyester woven in 3/1 twill weave exhibits better comfort properties.     

Detection of ROS and translocation of ERP-57 in apoptotic induced human neuroblastoma (SH-SY5Y) cells

Several toxic compounds are known to induce apoptosis in mammalian cell lines. The human neuroblastomacells (SH-SY5Y) were exposed to the phosphatase inhibiting toxin okadaic acid (OA) or hydrogen peroxide (H2O2)to induce apoptosis as well as generate reactive oxygen species (ROS). Mitoxantrone (MXT) was used as a positivecontrol for apoptosis. The SH-SY5Y cells were transfected with eukaryotic expression plasmid pHyPer-dMito encodingmitochondrial-targeted fluorescent or pHyPer-dCito encoding cytoplasmic-targeted fluorescent sensor for hydrogenperoxide (HyPer). The ERp57, also called GRP58 (Glucose-regulated protein 58), is a stress protein induced inconditions like glucose starvation and viral infection. Recently ERp57 was shown to translocate from the endoplasmaticreticulum to the cell surface in anthracycline-induced apoptotic cells. ERp57 co-translocation together with calreticulinhas been suggested to be crucial for recognizing tumor cells to induce immunogenic cell death. ERp57 translocationafter exposure to okadaic acid was studied using immunofluorescence and confocal microscopy. These studies indicatedthat okadaic acid has induced the translocation of ERp57 to the cellular membrane.     

Seed micromorphology and its taxonomic evidence in subfamily Alsinoideae (Caryophyllaceae)

Seed micromorphology of 13 species, belonging to four genera of subfamily Alsinoideae (Caryophyllaceae) were investigated with scanning electron microscopy (SEM), in order to assess their diagnostic significance at generic level and provide additional evidence on species delimitation, as well as correct identification and phylogenetic position. Genera and species of subfamily Alsinoideae exhibit great variation in ultrastructure and a high diversity of novel micromorphological characters were observed. Variation in seed shape, color, hilum, anticlinal wall, epidermal cell, cell surface, margins, and quantitative characters as length and width were studied in detail, compared, illustrated, and their taxonomic significant were discussed. Seed shapes of the species were classified as reniform, round, angular, subcircular, subreniform, and elliptical pyriform, with sub‐central, central, basal, and nearly basal hilum. Wavy, irregular, tetragonal, and elongated epidermal cells structure has been observed as an exomorphological character. The present findings show that the micromorphology of subfamily Alsinoideae provides taxonomic information and is helpful to distinguish different species. The results also explained that SEM morphology of seeds provide important data about affinity among taxa and give potential characters in delimitation of members of subfamily Alsinoideae at generic and species level. A principal component analysis allowed to highlight the most outsiders among seed micromorphology with a possible explanation. Taxonomic keys were developed based on micromorphological characters to delimit the species and useful for their quick identification within subfamily Alsinoideae.