Overcoming the liability of renewal at the Saudi Railway Sector

関西学院大

Overcoming the Liability of Renewal at the Saudi Railway Sector

関西学院大

fixino 1.0

Fixino 1.0 is 3D short film that explores the contrast between a lazy human and an efficient robot in a comedic visual-gag style narrative. Produced at the Rochester Institute of Technology, this 3D short attempts to take the audience on a journey of contrasting spaces and characters. On one side, we have the efficient robot, Fixino and his well-kept operation space. On the other, we have the careless and lazy human, Charlie, and his disorderly environment. I wanted this story to comment on our current world state, as well as the near future, where humans become a liability in the work pipeline as robots’ AI becomes more and more advanced. Already, we see this happening in factories like Elon Musk’s Gigafactory that is responsible for building Tesla cars. It’s a factory where machines (robots) are used to build the machine. I tried to take this core idea and tackle it in a comedic and light-hearted narrative. Another goal I tried to accomplish with this film is using the environment as a visual tool that reflects the distinct contrast between the characters inhabiting this environment, their inherent features and their role in the story. I was interested in taking advantage of the 3D animation format to allow for as many exaggerations as possible in the visual gags between characters and in the aesthetics of the environment design. I also wanted to maximize the freedom of design and control within the lighting and shading that is provided for in CG animation

Versatility of hot-melt extrusion for dosage form design

Recently, Hot Melt Extrusion (HME) has attracted the attention of pharmaceutical companies and scientists as a technique for manufacturing a variety of dosage forms. In this research, HME was applied in different ways to achieve different goals, such as solubility enhancement, taste masking, solid state stability enhancement, and sustained release formulations for oral drug delivery, using one model drug, mefenamic acid. For solubility enhancement and taste masking formulations, Eudragit EPO was blended with MA in different ratios (20, 25, 30, and 40% of drug loads) and processed by a hot melt extruder to produce a solid dispersion system. FT-IR analysis suggested hydrogen bonding between the drug and the carrier up to 25% drug loading. SEM images indicated aggregation of MA at over 30% drug loading. Based on the FT-IR, SEM, and dissolution results for the extrudates, two optimized formulations (20% and 25% drug loads) were selected to formulate orally disintegrating tablets (ODTs). ODTs were successfully prepared with excellent friability and rapid disintegration time, in addition to the desired taste-masking effect. In chapter 3, HME was applied to enhance the solubility of class II drugs by making solid dispersion systems by mixing MA with hydrophilic polymers (polyvinylpyrrolidone, PVP) in different ways as follow: (1) to demonstrate the effect of polyvinylpyrrolidone (PVP) matrices on the release of the poorly water-soluble drug, MA was prepared using the hot-melt extrusion technique, (2) to investigate the effect of PEG as a plasticizer and swelling agent in dissolution studies, (3) to study the influence of MgO as an alkalizer on the modification of the microenvironmental pH of the matrices, and (4) to investigate the combined effect of PEG and MgO on the drug release behavior of the formulations. In addition, we have also studied the ability of HME techniques to produce sustained release formulations for oral drug delivery. Various drug loads of MA and Kollidon® SR as a polymeric carrier were blended and extruded using a twin-screw extruder (16-mm Prism EuroLab, ThermoFisher Scientific) to prepare a solid dispersion system. Thermal analyses were used to confirm thermal stability, miscibility and to select the optimum processing conditions for extrusion. Sustained release tablets were successfully prepared with excellent tablet characteristics of these formulations. The drug release from the 40% drug-loaded extrudate reached 20% within 2 hours and 80% within 12 hours, compared to more than 80% drug release of the corresponding physical mixture, and 100% of the pure drug and formulations with higher drug loads of 60% and 80% within 2 hours. Therefore, the drug release of MA was further retarded by increasing the concentration of this polymer, which indicates Kollidon® SR has a significant effect on MA sustained release formulations

Character-level word encoding deep learning model for combating cyber threats in phishing URL detection

A cyber threat is generally a malicious activity that damages or steals data, or something that disrupts digital life. Such threats include viruses, security breaches, DoS attacks, and data theft. Phishing is a type of cyber threat whereby the attackers mimic a genuine URL or a webpage and steal user data, 21% fall into the phishing category. The novel approach of using the character-level encoding of URLs is introduced. Unlike word-level encoding, the use of character-level encoding decreases the discrete workspace and can be effective even in an energy-constrained environment. The experimental results of comparisons to other state-of-the-art methods demonstrate that the proposed method achieved 98.12% of true positive instances. Moreover, Conclusions: An experimental evaluation was performed to demonstrate the efficiency, and it was observed that the accuracy reached an all-time high of 98.13%. the experiments prove that the proposed method can operate efficiently even in energy-saving modes of phishing detection systems

Automatic neonatal sleep stage classification:A comparative study

Sleep is an essential feature of living beings. For neonates, it is vital for their mental and physical development. Sleep stage cycling is an important parameter to assess neonatal brain and physical development. Therefore, it is crucial to administer newborn's sleep in the neonatal intensive care unit (NICU). Currently, Polysomnography (PSG) is used as a gold standard method for classifying neonatal sleep patterns, but it is expensive and requires a lot of human involvement. Over the last two decades, multiple researchers are working on automatic sleep stage classification algorithms using electroencephalography (EEG), electrocardiography (ECG), and video. In this study, we present a comprehensive review of existing algorithms for neonatal sleep, their limitations and future recommendations. Additionally, a brief comparison of the extracted features, classification algorithms and evaluation parameters is reported in the proposed study

Recent Progress in Lipid Nanoparticles for Cancer Theranostics: Opportunity and Challenges

Cancer is one of the major leading causes of mortality in the world. The implication of nanotherapeutics in cancer has garnered splendid attention owing to their capability to efficiently address various difficulties associated with conventional drug delivery systems such as non-specific biodistribution, poor efficacy, and the possibility of occurrence of multi-drug resistance. Amongst a plethora of nanocarriers for drugs, this review emphasized lipidic nanocarrier systems for delivering anticancer therapeutics because of their biocompatibility, safety, high drug loading and capability to simultaneously carrying imaging agent and ligands as well. Furthermore, to date, the lack of interaction between diagnosis and treatment has hampered the efforts of the nanotherapeutic approach alone to deal with cancer effectively. Therefore, a novel paradigm with concomitant imaging (with contrasting agents), targeting (with biomarkers), and anticancer agent being delivered in one lipidic nanocarrier system (as cancer theranostics) seems to be very promising in overcoming various hurdles in effective cancer treatment. The major obstacles that are supposed to be addressed by employing lipidic theranostic nanomedicine include nanomedicine reach to tumor cells, drug internalization in cancer cells for therapeutic intervention, off-site drug distribution, and uptake via the host immune system. A comprehensive account of recent research updates in the field of lipidic nanocarrier loaded with therapeutic and diagnostic agents is covered in the present article. Nevertheless, there are notable hurdles in the clinical translation of the lipidic theranostic nanomedicines, which are also highlighted in the present review along with plausible countermeasures.Peer reviewedFinal Published versio

A Lightweight Deep Learning-Based Model for Tomato Leaf Disease Classification

© 2023 Tech Science Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Tomato leaf diseases significantly impact crop production, necessitating early detection for sustainable farming. Deep Learning (DL) has recently shown excellent results in identifying and classifying tomato leaf diseases. However, current DL methods often require substantial computational resources, hindering their application on resource-constrained devices. We propose the Deep Tomato Detection Network (DTomatoDNet), a lightweight DL-based framework comprising 19 learnable layers for efficient tomato leaf disease classification to overcome this. The Convn kernels used in the proposed (DTomatoDNet) framework is 1 × 1, which reduces the number of parameters and helps in more detailed and descriptive feature extraction for classification. The proposed DTomatoDNet model is trained from scratch to determine the classification success rate. 10,000 tomato leaf images (1000 images per class) from the publicly accessible dataset, covering one healthy category and nine disease categories, are utilized in training the proposed DTomatoDNet approach. More specifically, we classified tomato leaf images into Target Spot (TS), Early Blight (EB), Late Blight (LB), Bacterial Spot (BS), Leaf Mold (LM), Tomato Yellow Leaf Curl Virus (YLCV), Septoria Leaf Spot (SLS), Spider Mites (SM), Tomato Mosaic Virus (MV), and Tomato Healthy (H). The proposed DTomatoDNet approach obtains a classification accuracy of 99.34%, demonstrating excellent accuracy in differentiating between tomato diseases. The model could be used on mobile platforms because it is lightweight and designed with fewer layers. Tomato farmers can utilize the proposed DTomatoDNet methodology to detect disease more quickly and easily once it has been integrated into mobile platforms by developing a mobile application.Peer reviewe