Alocasia macrorrhiza, an underutilized corm, can play an important role in food security. High moisture content and acrid flavor are two major problems of corms that can be overcome by converting into flour after applying thermal processing which alter the flour properties. Therefore, this study aimed to evaluate physicochemical properties of flours as affected by thermal processing. After milling raw, boiled and roasted corm into flour, they were subjected to proximate and mineral elements analysis. Then granular morphology, particle size, and color of flours were analyzed. Carbohydrate (76.96±0.16%-79.57±0.08%), protein (2.75±0.07%-2.82±0.21%), and moisture contents (9.10±0.03%-11.89±0.52%) showed a significant difference between the samples while fat (0.90±0.03%-0.91±0.01%), ash (3.39±0.11%-3.73±0.02%), and fiber contents (3.76±0.19%-3.91±0.35%) were not significantly affected by thermal processing of corm. Thermal processing increased sodium (1.45 to 2.44-2.99 mg.100 g^-1), potassium (145.27 to 159.85-193.13 mg.100 g^-1), calcium (234.04 to 244.44-269.28 mg.100 g^-1), iron (16.99 to 17.69-18.32 mg.100 g^-1), and zinc (7.26 to 8.91-10.45 mg.100 g^-1) contents of flours. The water activity of flours was ranged from 0.25±0.01 to 0.58±0.01 which was safe from microbial growth. Flours were slightly acidic according to the pH values (5.67±0.02-6.05±0.03). Both raw and processed corms produced gluten free flours. Color analysis results revealed that boiled corm flour was the brightest flour and the highest whiteness index was recorded for raw corm flour followed by the wheat flour. The particle size of flours (1.050-1.527 μm) were not significantly changed by thermal processing of corms. The scanning electron micrographs revealed corm flours have irregular shaped granules and the poor protein network when compared to the wheat flour. In conclusion, processed corm flour can play an important role in human nutrition and boiled corm flour has shown the better physicochemical properties.

The use of metal chelating agents has made it possible to prevent critical biological processes in microbial cells, interrupting the vital metal metabolism of the microorganism, hindering metal absorption and bioavailability for critical reactions. Over the years, nanotechnological development has made a wide range of nanomaterials is available for possible uses in the antibacterial field. This complex field is shaped by antimicrobial nanoparticles, which are also being studied as therapeutic and drug delivery tools. Specifically, a novel drug delivery system based on the beta-cyclodextrin-maltol derivative was designed and manufactured. HEMA (hydroxyethyl methacrylate) monomer/maltol derivative was synthesized and co-polymerized with beta-cyclodextrin acrylic monomer. The 3D porous materials containing hydrophobic cavities were loaded with lomefloxacin, resulting in optimal drug loading efficiency while maintaining iron chelating ability. This system has been extensively characterized using different techniques such as: NMR, IR, SEM and EDX. This unconventional approach was successfully designed as a proof of concept of a dual-action antibacterial nanomaterial with lomefloxacin and iron-depleting properties achieved by maltol derivative.

Recently, the need for thermo-regulating fabric in the textile industry has motivated both researchers and scientists to explore this new type of smart fabric. This study aimed to develop a smart textile using a polyester fabric coated with microencapsulated trimethylolethane (TME) hydrate as phase change material. The TME microcapsules were produced via in-situ polymerization of melamine-urea-formaldehyde (MUF) at varying emulsification time, stirring rate, and TME hydrate concentration. A knife-over-roll coating method was incorporated using polyester resin as binder for the production of the smart fabric. Fourier Transform Infrared Spectroscopy (FT-IR) analysis, Scanning Electron Microscopy (SEM), and Differential Scanning Calorimetry (DSC) were conducted to examine the chemical, morphological, and thermal characteristics of the microcapsules and the smart fabric respectively. Results showed that the highest amount of microencapsulated TME phase change material obtained is 18.883 mg. FT-IR results confirmed the presence of TME hydrate and MUF resin in the microcapsule at 3300, 2870, 1148, and 1390 cm^-1. The SEM results revealed an amorphous and rough surface of microcapsules. Furthermore, the DSC results demonstrated favorable thermal characteristics, measuring the latent heat storage capacities of the microcapsules before and after application to the fabric as 205.1674 J/g and 224.7318 J/g, respectively. Finally, the encapsulation efficiency was calculated as 64.715%, indicating potential fabric thermal storage application.

The rapid advancement of technology has led to the pervasive presence of electronic devices in our lives, enabling convenience and connectivity. Cryptography offers solutions, but vulnerabilities persist due to physical attacks like malware. This led to the emergence of Physical Unclonable Functions (PUFs). PUFs leverage inherent disorder in physical systems to generate unique responses to challenges. Strong PUFs, susceptible to modeling attacks, can be predicted by malicious parties using machine learning and algebraic techniques. Weak PUFs, with minimal challenges, face similar threats if built upon strong PUFs. Despite some weaknesses, PUFs serve as security components in various protocols. Modeling attacks' success depends on suitable models and machine learning algorithms. Logistic Regression and Random Forest Classifier are potent in this context. Deep Learning Techniques, including Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs), exhibit promise, particularly in one-dimensional data scenarios. Experimental results indicate CNN's superiority, achieving precision, recall, and accuracy exceeding 90%, demonstrating its effectiveness in breaking PUF security. This signifies the potential of deep learning techniques in breaking PUF security. In conclusion, the paper highlights the urgent need for improved security measures in the face of evolving technology. It proposes the utilization of deep learning techniques, particularly CNNs, to strengthen the security of PUFs against modeling attacks. The presented findings underscore the critical importance of reevaluating PUF security protocols in the era of ever-advancing technological threats.

In technological advancement, noise reduction has played a key role in every field in our technical world. Recently, one of the remarkable developments in M16 Blackhole image classification has been the exact view of a real digital image. This research paper is mainly focused on the proposed techniques involved in reducing image noise through noise removal algorithms and image processing combined with wavelength formation using biomedical operations and the functioning of the proposed Bio-medical 3D image scanning device, which directly scans when the user wants to see our human body or any body, and then exact body organs and internal parts are viewed exactly as per autonomy on devices. We give alternative solutions to X-Ray film technology and replace it with 3-D image virtual projection and viewing on any personal devices using existing resources that the user or patient wants to see. We are trying to focus on Broken Human bones, internal body organs, and pregnant women's womb baby formation current status with result orientation and accuracy rates of object noise detection with a reduction of more than 90% with valid datasets executed on significant platforms. This research work will help better understand noise reduction with image processing techniques as an approach to artificial intelligence, and it can support current medical applications, including future technologies, and be helpful to the biological research fields. Hence, this work will have the best impact on those who work in Image Quality , Object Detection, Medical, Healthcare, Maps, and space research domains.

Transition Metal Oxide (TMO) nanoparticles have emerged as promising materials for various applications including color imaging, magnetic recording media, soft magnetic materials, heterogeneous catalysis, and different field of biomedical science. Apart from the TMO, Fe₃O₄ nanoparticles hold great promise in a variety of biomedical uses such as drug delivery, cell separation, and MRI imaging. Magnetite (Fe₃0₄) nanoparticles exhibit their potential as antimicrobial agents due to their unique properties and interactions with microorganisms. This study focuses on the synthesis, characterization, and evaluation of the antimicrobial activity of magnetite (Fe₃O₄) nanoparticles prepared using the sol-gel method. The Fe₃O₄ nanoparticles were synthesized through a facile and cost-effective sol-gel route, involving the ferric nitrate and ethanol as precursors. Different characterization techniques, including Energy-Dispersive X-ray Spectroscopy (EDAX), X-ray diffraction (XRD), and UV-VIS NIR spectroscopy were employed to analyze the compositional analysis, crystalline structure, and optical properties of the nanoparticles. The EDAX and XRD analysis confirmed that the synthesized nanoparticles are near to stoichiometry and formation of single-phase magnetite nanoparticles. The obtained bandgap of synthesized nanoparticles is 5.03 eV. Furthermore, the synthesized Fe₃O₄ nanoparticles were evaluated for their antimicrobial efficacy against a panel of including both Gram-positive (e.g., Staphylococcus aureus) and Gram-negative (e.g., Enterobacter aerogenes) bacteria. Investigations into the nanoparticles biocompatibility and long-term effects would be crucial for their safe and effective utilization in real-world applications.

Community water fluoridation (CWF) adjusts fluoride levels in public water supplies to prevent tooth decay and promote dental health, irrespective of socioeconomic status or dental care access. Regular sampling by community water systems (CWS) ensures compliance with regulations and standards. Centers for Disease Control and Prevention (CDC) provide biennial reports for health statistics surveillance by monitoring CWF status in US water systems. It’s important to note that specific policies and practices related to CWF can vary between countries. Therefore, this research applies the spectral clustering method to group and analyse the reception of fluorinated water by CWS between populations of US countries.

The data from the National Water Fluoridation Statistics (2016-2018-2020) reported by the CDC have been considered. The spectral clustering approach identified five clusters of US countries, which represent the different percentages of the population served by CWS receiving fluorinated water. Among the results, one cluster has the lowest value of the percentage (33.3%) and it includes Hawaii, New Jersey, Oregon, Idaho, Montana, Louisiana, New Hampshire, Alaska, and Utah. Conversely, the cluster of countries Ohio, Indiana, Maryland, South Dakota, Georgia, Virginia, North Dakota, Illinois, Minnesota, Kentucky, District of Columbia had the highest percentage (96.1%). These findings reveal relevant variations in the implementation of CWF across different US countries, with some states having a notably lower percentage of their population receiving fluorinated water than others. This could inform policy and public health efforts to improve access to fluoridated water and enhance dental health outcomes in areas with lower coverage.

Human cancellous bone can be regarded as a biphasic porous medium comprising a fluid-saturated elastic structure. When subjected to ultrasonic excitation, the interaction between the structure and the saturating fluid leads to visco-inertial exchanges. These interactions can be effectively described by Johnson's model, which modifies Biot's theory. Biot's theory predicts the propagation of two coupled waves known as the fast P1 and slow P2 waves. In this study, we present a theoretical modeling approach to investigate the propagation of ultrasonic waves in human cancellous bone within the framework of the modified Biot theory. We derive an analytical expression for the transmission coefficient in the frequency domain, which accounts for the physical and mechanical parameters of the system as well as the excitation frequency of the incident wave. To calculate the transmitted signal, we multiply the spectrum of the incident signal with the obtained transmission coefficient in the frequency domain. We then examine and discuss the effects of varying physical and mechanical parameters on the two compression wave modes, namely the transmitted fast P1 and slow P2 waves, as they propagate through a hypothetical sample of fluid-saturated human cancellous bone. By investigating the transmission characteristics of ultrasonic waves in human cancellous bone, this study contributes to our understanding of the behavior of this complex biological material. The obtained results shed light on the influence of different factors on wave propagation, providing valuable insights for various applications, such as medical diagnostics and the design of bone-mimicking materials.

The agriculture sector plays a very important role in increasing population year by year to fulfill their requirements and contributes significantly to the economies of country. One of the main challenges in agriculture is the prevention and early detection of pest attack in crops. Farmers spend a significant amount of time and money in detecting pest and disease, often by looking at plant leaves and analyzing the presence of diseases and pests. Late detection of pest attacks and improper use of pesticides application, which can cause damage to plant and compromise food quality. This problem can be solving through artificial intelligence, machine learning, and accurate image classification system. In recent years, the machine learning has made improvement in the recognition of image and classification. Hence, in this research article, we used convolutional neural network (CNN)- based models, such as Cov2D library and VGG-16, to identify pest attacks. Our experiments involved a personal dataset consisting of 7000 images of pest attacked leaf samples of different position on maize plants, categorized into two classes. The Google Colab environment was used for experimentation and implementation, specially designed for cloud computing and machine learning.

In this work, a biocompatible and fully sustainable, self-poled green energy harvester is designed from the exoskeleton of prawn fish. The prawn shells (PS) are collected from the biowaste of a local seafood processing plant. Shell surfaces are properly cleaned with DI water to remove any loose debris or contaminants. A strong chelating agent Ethylenediaminetetraacetic acid (EDTA), that can effectively bind to metal ions is used to remove the mineral content and metal ions from the shell surface. Any trapped water content on the PS surfaces is dissipated by keeping the sample at room temperature for 24 hours. The PS contains 20%–50% calcium carbonate, 20%–40% protein and 15%–40% chitin, where the chitin nano fiber act as an active piezoelectric element. X-ray diffraction peak obtained at 2θ° =9.24° and 19.4° confirms the presence of crystalline intersheet α-chitin and intrasheet β-chitin that possess piezoelectric properties and contributes inherently to piezoelectricity of PS. The (PS) energy harvester of a very small surface area of “10 mm X 8 mm ” fabricated as silver-prawn shell-silver layer, generates 480 mV open circuit output voltage only by finger taping longitudinally on its surface. Optimizing the electrical load, the piezoelectric generator can generate 470 mV output across 500 kΩ and harvest 441.8 nW of output power at applied mechanical stress only by finger taping at 2 Hz.