Search

Integrating Tiny Machine Learning (TinyML) with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level. Constrained devices efficiently implement a Binary Neural Network (BNN) for road feature extraction, utilizing quantization and compression through a pruning strategy. The modifications resulted in a 28-fold decrease in memory usage and a 25% enhancement in inference speed while only experiencing a 2.5% decrease in accuracy. It showcases its superiority over conventional detection algorithms in different road image scenarios. Although constrained by computer resources and training datasets, our results indicate opportunities for future research, demonstrating that quantization and focused optimization can significantly improve machine learning models' accuracy and operational efficiency. ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device: Advanced machine learning in edge computing. The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing, suggesting ways to improve smart city frameworks in road network assessment, traffic management, and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks. [ABSTRACT FROM AUTHOR]

This study proposes a novel image segmentation algorithm for estimating the average size of densely packed grains, particles, cells, powders, bubbles, and aggregates. The algorithm comprises two sub-algorithms: valley edge detection and grain size estimation. The valley edge detection sub-algorithm identifies weak edges among grains using four directions. In contrast, the grain size estimation sub-algorithm calculates the average size of the grains based on the detected edge density. The algorithm can recognize grains with circular, elliptical, or other regular shapes without explicitly delineating each grain, making it ideal for complex and densely packed grain images. The algorithm was tested using various samples, including aggregate particles, tomatoes, chicken/duck eggs, turtle eggs, soybeans, lawn seeds, rapeseeds, and other seeds, and the testing results were satisfactory. The proposed algorithm is 200-500 times faster than ordinary grain image segmentation algorithms. It is suitable for online applications that require real-time image processing of densely packed and detailed grain images. [ABSTRACT FROM AUTHOR]

The importance of cybersecurity in contemporary society cannot be inflated, given the substantial impact of networks on various aspects of daily life. Traditional cybersecurity measures, such as anti-virus software and firewalls, safeguard networks against potential threats. In network security, using Intrusion Detection Systems (IDSs) is vital for effectively monitoring the various software and hardware components inside a given network. However, they may encounter difficulties when it comes to detecting solitary attacks. Machine Learning (ML) models are implemented in intrusion detection widely because of the high accuracy. The present work aims to assess the performance of machine learning algorithms in the context of intrusion detection, providing valuable insights into their efficacy and potential for enhancing cybersecurity measures. The main objective is to compare the performance of the well-known ML models using the UNSW-NB15 dataset. The performance of the models is discussed in detail with a comparison using evaluation metrics and computational performance. [ABSTRACT FROM AUTHOR]

Biotic and abiotic stresses severely affect agriculture by affecting crop productivity, soil fertility, and health. These stresses may have significant financial repercussions, necessitating a practical, cost-effective, and ecologically friendly approach to lessen their negative impacts on plants. Several agrochemicals, such as fertilizers, pesticides, and insecticides, are used to improve plant health and protection; however, these chemical supplements have serious implications for human health. Plants being sessile cannot move or escape to avoid stress. Therefore, they have evolved to develop highly beneficial interactions with endophytes. The targeted use of beneficial plant endophytes and their role in combating biotic and abiotic stresses are gaining attention. Therefore, it is important to experimentally validate these interactions and determine how they affect plant fitness. This review highlights research that sheds light on how endophytes help plants tolerate biotic and abiotic stresses through plant–symbiont and plant–microbiota interactions. There is a great need to focus research efforts on this vital area to achieve a system-level understanding of plant–microbe interactions that occur naturally. [ABSTRACT FROM AUTHOR]

The paved cracks are one of the major concerns for scientists and engineers in road maintenance and damage evaluation study. Digital image processing applications have been applied in road surface inspection, classification, and decomposition of paved roads. This paper has tested and proposed the process to evaluate the road cracks and their possible solution as we know that the key issues for analysis are enhancement and segmentation of image along with edge detection to attain the promising results we have gained and discussed under the heading of simulations for the experimental and numerical of crack detection. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. The method is based upon one of the histogram modification techniques, which is coupled with the segmentation method and the crack edge detection. At last, three feature methods are used, namely, Harris, MSERF, and SURF, to wind up our research. [ABSTRACT FROM AUTHOR]

The paved cracks are one of the major concerns for scientists and engineers in road maintenance and damage evaluation study. Digital image processing applications have been applied in road surface inspection, classification, and decomposition of paved roads. This paper has tested and proposed the process to evaluate the road cracks and their possible solution as we know that the key issues for analysis are enhancement and segmentation of image along with edge detection to attain the promising results we have gained and discussed under the heading of simulations for the experimental and numerical of crack detection. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. The method is based upon one of the histogram modification techniques, which is coupled with the segmentation method and the crack edge detection. At last, three feature methods are used, namely, Harris, MSERF, and SURF, to wind up our research. [ABSTRACT FROM AUTHOR]

In today's technology era, the field of digital image processing is growing in popularity and developing demand. When the input is given to the system, after the processing a variation is performed and the output is taken from the system. This research is about a visualized image of road pavement that was first given as an input to obtain an output using gray-level image enhancing techniques. In this research, the image enhancement is performed through different visualized enhancement images, which is the backbone of this research. Initially, it was cogitating as a piece of cake but once we start implementing the technique then we realize that it was not that easy and it needs a lot of knowledge, and the variation of research to overcome complicated problems correlate with this research. The complicated assignments were a variation of light from day to night, visibility of the cracks, homogeneous background, etc. The most complex issue is the visibility and recognition of the road cracks that are complex to classify using different algorithms in obtained images. For deep research purposes, we must identify and categorize many properties of the input images, such as intensity, color adjustment, and conversion for the sake of better image enhancement. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. This depicts the entire outcome of road pavement images to assess the characteristics and repercussions of alteration. To more precisely check road crack detection, image enhancement techniques such as image negative, logarithmic transformations, gamma corrections, and others are applied. Every single specified technique, such as gamma and constant values, is proposed with a general mathematical implementation. Piecewise linear contrast widening subclasses are also studied to more comprehensively discover and assess road cracks. Every technique mentioned above has specific features and a unique image enhancement. Each method has a different and unique feature, so none in all the ways can be implemented for all kinds of gray-level image enhancement. Because in some cases, it will be necessary to implement gamma corrections for better enhancement, while in other areas, it will be necessary to implement logarithmic transformations for the sake of good results. The logarithmic transformation is suitable for such kind of conversion in which logarithm is taken and the image goes under process, but for the same image, the other enhancement like the negative image does not suit. A comparison will be made at the end of this study for different algorithms. We aim to compare and contrast the benefit of one technique over another, and the researcher will implement that technique for better accuracy and result. The proposed framework for image enhancement to classify the road surface cracks is beneficial in discriminating among different types. It also gives us insight into selecting the appropriate technique for the image enhancement of the road cracks according to their kinds. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19) primarily spreads through imbalanced social distancing practices. Automatic analysis is possible through deep learning-based methods to understand and control COVID-19. Healthcare analysis and prediction are best made in the situation of a pandemic such as COVID-19. This analysis can be used to classify the COVID-19 and non-COVID-19 groups and social distancing measures with good estimation by preventing immense dissemination. Postpreventive measures require parallel reinforcement to analyse current, upcoming, and uncertain situations of COVID-19 prevalence, which are effectively handled by implementing multicriteria decision-making methods. Herein, we estimate and measure the social distance by deep learning technique usage (You Only Look Once, Version 3 is a real-time object detection algorithm) in the proposed model for the analytic network process. The multicriteria decision making increases the evaluation of the risk factors. The modification of the pandemic model increases the application of social distancing and preventive measures. This model will alert us when the number of people exceeds in some area from the experimented barrier. RISTECB simulation is used in the preventive measures of the social distance among the sample population to see the initiators, infectors, suspicious, expirer, survivor, and transmitters. Postpreventive criteria used those results to set the barriers that are the critical points for prevention in uncertain situations. Therefore, this paper aimed to develop a framework, including social distancing and distance estimation, by using deep learning-based techniques through multicriteria decision-making methods such as the analytical network process. For simulation for statistical information of inclusive information of preventive measures and postpreventive measures, an automatic resonant transfer learning-based practice is used. General proportional analyses illustrate that the projected model helps in postpandemic COVID-19 preventive measures by amalgamating multiple techniques. [ABSTRACT FROM AUTHOR]

Mixed script identification is a hindrance for automated natural language processing systems. Mixing cursive scripts of different languages is a challenge because NLP methods like POS tagging and word sense disambiguation suffer from noisy text. This study tackles the challenge of mixed script identification for mixed-code dataset consisting of Roman Urdu, Hindi, Saraiki, Bengali, and English. The language identification model is trained using word vectorization and RNN variants. Moreover, through experimental investigation, different architectures are optimized for the task associated with Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Experimentation achieved the highest accuracy of 90.17 for Bi-GRU, applying learned word class features along with embedding with GloVe. Moreover, this study addresses the issues related to multilingual environments, such as Roman words merged with English characters, generative spellings, and phonetic typing. [ABSTRACT FROM AUTHOR]

In the present study, preparation and characterization of an adsorbent made from the chemically modified Putranjiva roxburghii seeds and its applicability toward removing Ni(II) abatement from water and electroplating wastewater have been thoroughly investigated. For process optimization, various physicochemical factors affecting the adsorption process were examined. The equilibrium was achieved in 60 min for the experimental conditions accompanying the Ni(II) concentration of 50 mg L−1 with the adsorbent dose of 0.2 g in 20 mL solution. The practical utility and cost-efficiency of the adsorbent have been analyzed. The present adsorbent was proved to be efficient for the decontamination of laboratory prepared polluted water and real wastewater (electroplating wastewater of the lock industry) containing Ni(II) ions. The additional advantages of facile synthesis, low-cost, and reusability of the present adsorbent make it a novel. [ABSTRACT FROM AUTHOR]

The methanolic extracts, fractions and sub-fractions of Adenium obesum (Forssk.) Roem. & Schult., Ixora coccinea Linn. and Aegle marmelos Linn. were screened for their free radical scavenging ability by using ascorbic acid and butylated hydroxytoulene (BHT) as standard antioxidants and evaluated through DPPH free radical scavenging assay. I. coccinea exhibited strongest inhibition, followed by A. obseum, and A. marmelos. The methanolic extracts of I. coccinea leaves proved to be the most potent antioxidant among these extracts with IC50 0.00315 ± 0.0001 mg/mL. Upon the purification of the active fractions twenty-six (1-26) pure chemical constituents were isolated and examined for their antioxidant inhibition ability, out of which 5-O-caffeoyl quinic acid (19) isolated from the active fraction of I. coccinea flower found to be potent antioxidant with IC50 value 0.0467 ± 0.0018 mg/mL as compared to standard ascorbic acid (0.0491 ± 0.0009) and BHT (0.0407 ± 0.003). [ABSTRACT FROM AUTHOR]

In the present work, a new sorbent was successfully prepared by chemically modifying pods ofAlbizia lebbeck(AL) by Fenton’s reagent, followed by sodium silicate. Sorption studies were carried out by batch process. The optimum pH was found to be 6. Equilibrium isotherm data were analysed by non-linear curve fitting analysis, to fit Langmuir, Freundlich and Temkin isotherm models. Based on the Langmuir isotherm model, maximum monolayer sorption capacity (qm) was found to be 21.22 mg.g−1at 50°C. Breakthrough and exhaustive capacities were found to be 10 and 50 mg.g−1, respectively. Desorption study showed 95% recovery of Cd(II) ions. [ABSTRACT FROM PUBLISHER]

The present work explored the adsorption properties ofTerminalia arjunafruit powder for the removal of Pb(II) from aqueous solution. Effect of concentration, contact time, pH, adsorbent dose, and temperature were studied using batch process to optimize conditions for maximum adsorption of Pb(II). Kinetic data showed that the pseudo-second-order model was the best fit for the experimental data over a wide range of concentrations. Thermodynamic parameters (∆H°and ∆G°) suggested endothermic and spontaneous nature of adsorption. Mean free energies at different temperatures were in between 10.1 and 14.3 kJ mol−1indicating chemical nature of adsorption process. Equilibrium isotherm data were analyzed using Langmuir, Freundlich, Temkin and Dubinin–Radushkevich (D–R) isotherms models at 30, 40, and 50°C. The breakthrough and exhaustive capacities were found to be 5 and 15 mg g−1, respectively. Desorption studies were carried out by column method and 84% of Pb(II) ions were recovered when 0.05 N HCl was used. A single-stage batch adsorption design was proposed to predict the amount of adsorbent required for 98% removal of Pb(II) from different volumes of water. [ABSTRACT FROM PUBLISHER]

The fruit of Aegle marmelos Linn. is a most edible part of the tree, ripe, unripe and dry fruit is recommended for wide variety of disorders. Lack of pharmacological evaluation limits the use to nutritional value. Dry and ripe fruit of A. marmelos was investigated as crude extract and its butanol fraction in isolated working male Wistar rat heart. Aortic out flow, coronary effluent, cardiac output, dP/dt(max), dP/dt(min), systolic and diastolic pressure, heart rate, and aortic pressure were measured. Preload and afterload was kept constant. These parameters are relevant to cardiac physiology and pathological conditions. All the concentrations of crude extract caused concentration-dependent effects. The crude extract decreased coronary effluent at 100.0 and 300.0 mg/mL, increased the dP/dt(max) at 300.0 mg/mL, systolic pressure and aortic pressure were observed maximum at 100.0 mg/mL. Whereas, aortic out flow, dP/dt(min) diastolic pressure and cardiac output and heart rate remained unaffected. The butanol fraction increased the aortic outflow, dP/dt(max), aortic pressure at 10.0 and 30.0 mg/mL maximally. Whereas, dP/dt(min), diastolic pressure and cardiac output remained unaffected. Coronary effluent at 1.0, 10.0, and 30.0 mg/mL and heart rate at 10.0 and 30.0mg/mL were decreased maximally. It seems that the bioactive compound got concentrated in butanol fraction according to polarity of the solvent. The crude extract and butanol fraction were found to be positive inotropic whereas, butanol fraction showed negative chronotropic effect as well. [ABSTRACT FROM AUTHOR]

Two new esters of dammarane triterpenoids ixorene isovalerate (1), ixorene 3′,8′-dimethyloctanoate (2) and a new ursane-type triterpenoids Ixoroid acid (3) were isolated from the methanolic extract of flowers of Ixora coccinea Linn., along with the three known constituents. The structures of compounds 1 and 3 were elucidated on the basis of extensive 1D,2D NMR studies and mass spectrometry as 17β-dammara-12,20-diene-3β-isovelarate and 3β-hydroxy-18β-urs-12ene-29β-oic acid, respectively, whereas 2 was identified as 17β-dammara-12,20-diene-3β-3′,8′-dimethyloctanoate through 1H NMR and mass spectral data. Compounds 1, 2, 4 and 5 were evaluated for their in vitro cytotoxic activity, which exhibited weak activity against the 3T3, PC3 and HeLa cell lines with the IC50 value >30 μM. Antioxidant results of 1 – 5 revealed that only compound 5 showed antioxidant activity in DPPH radical scavenging inhibition with the IC50 1.31 × 10− 6 ± 0.005 μm mL− 1. Both activities are the first records of these isolated compounds from the flowers of Ixora coccinea Linn. [ABSTRACT FROM AUTHOR]

The author focuses on the Osmani College library which provides educational facilities to the residents of Osmania Colony in Karachi Central, Pakistan and equipped with basic reading facilities for students and faculty members. She notes the College and its library's determination to provide quality education and to develop intelligent individuals in the country. Information on library collections, reference and readers services, as well as the facilities in the said library is also offered.

A biography of librarian Muhammad Adil Usmani is presented. He was born on January 2, 1932 at Rashidmai, Kara District, Allahabad, India. He was married to Noor Jehan with whom he had five daughters and three sons. He finished his Bachelor of Arts degree at the Allahabad University and his Master of Arts in Library Science at Rutgers, The States University in New Jersey. He had extensive teaching experience and several publications. Usmani died of heart failure on October 6, 2009 in Jeddah, Saudi Arabia.

The pharmaceutical science demand for sustainable and selective electrochemical sensors which exhibit ultrasensitive capabilities for the monitoring of different drugs. In an attempt to build a useful electrochemical sensor, we describe a most efficient method for the fabrication of NiO/ZnO nanocomposite through aqueous chemical growth method. The successfully synthesized NiO/ZnO nanocomposite is successfully employed to modify a glassy carbon electrode in order to build a sensitive and reliable electrochemical sensor for the detection of carbamazepine (CBZ), an anticonvulsant drug. The morphological texture, functionalities and crystalline structure of prepared nanocomposite were determined via FTIR, XRD, EDX, TEM, and SEM analysis. In order to examine the charge transfer kinetics, the cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were used to exploit the electrochemical properties of the synthesized nanocomposite. The NiO/ZnO nanocomposite exhibited excellent electron transfer kinetics and less resistive behavior than the individual NiO and ZnO nanoparticles. The differential pulse voltammetry and cyclic voltammetry tools were used for the fluent determination of CBZ. Certain parameters were optimized to develop an effective method including optimum scan rate 60 mV/s, potential range from 0.4 to 1.4 V and BRB as supporting electrolyte with pH 3. The developed sensor showed exceptional response for CBZ under the linear dynamic range from 5 to 100 μM. The limit of detection of proposed NiO/ZnO sensor for the CBZ was calculated to be 0.08 μM. The analytical approach of prepared electrochemical sensor was investigated in different pharmaceutical formulation with acceptable percent recoveries ranging from 96.7 to 98.6%. [Display omitted] • Facile Synthesis of NiO/ZnO Nanocomposite. • Nano sensor. • Fast detection of Carbamazepine. • Monitoring of drugs for quality control. [ABSTRACT FROM AUTHOR]