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Biological wastewater treatment plants (BWWTPs) based on the activated sludge (AS) process have dramatically improved worldwide water sanitation despite increased urbanization and industrialization. However, current AS-based operations are considered economically and environmentally unsustainable. In this Perspective, we discuss our current understanding of microbial populations and their metabolic transformations in AS-based BWWTPs in view of developing more sustainable processes in the future. In particular, much has been learned over the course of the past 25 years about specialized microorganisms, which could be more comprehensively leveraged to recover energy and/or nutrients from wastewater streams. To achieve this, we propose a bottom-up design approach, focused around the concept of a wastewater biorefinery column, which would rely on the engineering of distinct ecological niches into a BWWTP in order to guarantee the targeted enrichment of specific organismal groups which in turn will allow the harvest of high-value resources from wastewater. This concept could be seen as a possible grand challenge to microbial ecologists and engineers alike at the centenary of the discovery of the AS process.

Fifth-generation(5G) and beyond communication systems, assisted by Intelligent Reflecting Surfaces (IRS), often encounter hindrances such as unreliable connections, high energy usage, and prolonged latency. Channel estimation in IRS-aided systems is challenging in vehicular communication systems with roadside IRS units and fast-moving users. This paper proposes an efficient and low-complex channel estimation strategy for high-speed vehicular mmWave communication systems equipped with roadside IRS. The method consists of two stages, sensing and prediction, which aim to improve efficiency and accuracy under dynamic channel conditions. In the sensing phase, an initial assessment of channel characteristics is estimated by exploiting the sparse nature of the channel. We use the Compressive Sampling Matching Pursuit (CoSaMP) algorithm for accurate estimation with reduced computational complexity. The prediction stage consists of real-time tracking and prediction of the Angle of Arrival (AoA) and the Angle of Departure (AoD) using the Extended Kalman Filter (EKF). This ensures more accurate dynamic channel estimation based on predicted array response vectors without increasing the pilot overhead. Simulation results demonstrate that our proposed approach can offer precise channel estimation with significantly reduced training overhead.

The study focused on analysing the eutrophication indicators of the Krishnagiri Reservoir Project (KRP) using the Water Quality Analysis Simulation Program (WASP). The reservoir was divided into six segments to simulate the indicators, and field measurements from 2015 to 2018 were used to calibrate the model while 2019 to 2022 were used to validate it. The results showed that the model predictions were in good agreement with the measured values, indicating the reliability of the model. The study assessed the impact of nutrient loads on dissolved oxygen, nitrate, phosphate, and chlorophyll-a. Four nutrient loading scenarios were simulated, and the most effective scenario (Scenario 4) involved a 75% reduction in nutrient load, which increased the reaeration rate by 1.47% and decreased chlorophyll-a concentrations by 88%. The study concluded that maintaining nitrates below 2.5 mg/L and phosphates below 0.75 mg/L could help restore the KRP reservoir's trophic status from hypereutrophic to mesotrophic. Overall, the study demonstrated the use of the WASP model in developing nutrient loading scenarios to manage reservoir water quality effectively. The findings could help policymakers and managers make informed decisions about reducing nutrient loads and restoring the trophic status of eutrophicated reservoirs. HIGHLIGHTS The study focused on the eutrophication management of the Krishnagiri Reservoir Project (KRP) using the Water Quality Analysis Stimulation Program (WASP) model with different nutrient load scenarios.; WASP is used to simulate the eutrophication indicators such as dissolved oxygen, chlorophyll-a, nitrate, and phosphate concentrations in the KRP reservoir.; Calibrated Eutro-model was used to develop scenarios for eutrophication management.;

Abstract The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.

Background: Arsenic is viewed as a bizarre and frightful element in human history. Recent studies showed that even low levels of arsenic can cause neurological damage. Phyllanthus amarus is an important medicinal herb proved to have neuro protective effect in experimental animals. This pre-clinical study used an animal model of arsenic toxicity and screened the different neuroprotective mechanisms of Phyllanthus amarus ethanolic extract. Purpose: To assess the effect of Phyllanthus amarus ethanolic extract on various biochemical parameter's in the brain of arsenic administered Wistar albino rats. Methodology: The neuroprotective activity of P. amarus against arsenic induced toxicity was studied in Wistar albino rats. The experimental animals (n=24) were divided into four groups of 6 rats each. Rats in group I received distilled water, whereas rats in groups II, III, and IV received arsenic as sodium arsenite salt (40 mg/kg b.w) in drinking water daily for 28 days. In addition, rats from groups III, and IV also received P. amarus ethanolic extract 100 and 200 mg/kg b.w, by intragastric route, once daily for a period of 28 days. At the end of the experiment rats were subjected to various neurobehavioral tests such as Elevated plus maze, Light dark arena, Forced swim test and Tail suspension test. After 24 h of the last treatment, the animals were euthanized and brain tissue was assayed for various biochemical parameters. Results: ANOVA and Post hoc testing of behavioral and biochemical data (Dunnett's t-test and Student unpaired T test) showed co-administration of P. amarus ethanolic extract significantly reversed anxiety and depression-like behaviors in experimental animals. P. amarus ethanolic extract effectively reduced acetyl cholinesterase activity in the brain, and it also increased the levels of dopamine and serotonin in the brains of arsenic-treated animals as compared to only arsenic treated group. P. amarus ethanolic extract significantly decreased lipid peroxidase and increased brain catalase and super oxidase dismutase activities. Conclusion: Based on the pharmacological, and biochemical analysis the plant extracts of P. amarus have shown protective effects against arsenic induced neurotoxicity.

Abstract With the increasing level of IoT applications, computation offloading is now undoubtedly vital because of the IoT devices limitation of processing capability and energy. Computation offloading involves moving data from IoT devices to another processing layer with higher processing capability. However, the size of data offloaded is directly proportional to the delay incurred by the offloading. Therefore, introducing data reduction technique to reduce the offloadable data minimizes delay resulting from the offloading method. In this paper, two main strategies are proposed to address the enormous data volume that result to computation offloading delay. First, IoT Canonical Polyadic Decomposition for Deep Learning Algorithm is proposed. The main purpose of this strategy is to downsize the IoT offloadable data. In the study, the Kaggle-cat-and-dog dataset was used to evaluate the impact of the proposed data compression. The proposed method downsizes the data significantly and can reduce the delay due to network traffic. Secondly, Rank Accuracy Estimation Model is proposed for determining the Rank-1 value. The result of the proposed method proves that the proposed methods are better in terms of data compression compared to distributed deep learning layers. This method can be applied in smart city, vehicular networks, and telemedicine etc.

Aim: Using atenolol as a model active pharmaceutical and galactomannan from the seeds of Trigonella foenumgraecum as a mucoadhesive polymer, an effort was made to develop a gastroretentive bioadhesive floating delivery system to improve the residence time in the stomach. Materials and Methods: Different formulations were made with Hydroxypropyl Methyl Cellulose (HPMC) and galactomannan. Galactomannan was chosen because of its exceptional swelling properties in aqueous environments. In this investigation, citric acid and sodium bicarbonate were utilized as gas-producing components. Pre- and postcompression evaluations were performed on the manufactured tablets. The floating properties as well as the atenolol release pattern were investigated. Results: The extracted polymer was characterized using various techniques and found to be similar to literature. The results from GC analysis showed a residual acetone content of 0.127 ppm, indicating that the extracted polymer is safer to use in the formulation of gastroretentive tablets. The physicochemical properties of the manufactured tablets were satisfactory in terms of swelling index, release characteristics, and buoyancy pattern. All of the manufactured batches had adequate in vitro buoyancy. The gastroretentive tablet exhibited axial and radial swelling throughout the in vitro buoyancy test. According to the testing, the formulation remained buoyant for approximately 8 to 12 hr. Conclusion: According to the results of the evaluation, the manufactured tablet has a pleasing look, is heat stable, and is therapeutically efficient. F3 was determined to be the optimal formulation based on the data collected. [ABSTRACT FROM AUTHOR]

Rosai–Dorfman disease (RDD) with spinal cord involvement is a rare clinical entity. We report a case of RDD with paravertebral and intraspinal epidural involvement in a 24-year-old male Bangladeshi patient who presented with progressive bilateral lower limb weakness for 20 days duration associated with spasticity and muscle spasm. MRI demonstrated an enhancing paravertebral soft tissue lesion extending from C7 through T4 with intraspinal epidural extension encasing the spinal cord with focal cord oedema. Histopathology of the paraspinal-epidural lesion reported a finding consistent with RDD. The patient was initiated on high-dose steroids. Follow up after 2 months demonstrated symptomatic improvement as the patient was able to move on the crutch and repeated MRI showed lesion regression. Keywords: Rosai–Dorfman disease, Tuberculosis, Lymphoma, Spinal cord

New methods and apparatuses for information security have evolved as a result of the rapid expansion of optical information processing. Security is one of the major issues in digital image transmission because it can deliver very secret information to any corresponding agency such as the military, biomedical, and security agencies. Previously, various techniques are proposed to perform optical image encryption techniques using different transformation and pixel-level techniques. Each work has its advantages and disadvantages in terms of computational complexity, security level, flexibility, quality, and so on. To overcome the security issues present in the previous works, a novel optical image encryption standard is proposed in this paper. This work uses information hiding followed by image encryption using Gyrator Transform (GT) using mean gradient key-based block swapping techniques. The main advantage of this work is that the key generation is dynamic and it depends upon the pixel intensity of 8 × 8 blocks. Secret information hiding is performed in the Discrete Cosine Transform (DCT) domain to protect the data against noise attacks. To analyze the performance, various evaluation metrics are used to measure the quality of the decrypted image under various distortions such as cropping and rotation. The robustness of information hiding is analyzed using a noise attack on the received image. This work achieved 45.6 dB of Peak Signal-to-Noise Ratio (PSNR) and 0.965 of Structural Similarity Index (SSIM), which is the best when compared to the conventional image encryption standards.

Automation of the various steps of radiotherapy is gaining importance nowadays. Predicting the amount of radiation dose received by the tumor and nearby organs is one among them. Many deep learning architectures that predict the 3D dose distribution images from corresponding CT and contour images have been proposed in the literature. However, a detailed investigation has yet to be done on the significance of input images (CT and contour) in predicting dose distributions. This study introduces a novel two‐stage deep learning framework using a transfer learning technique for the same domain. The main objective of this approach is to accurately extract valuable information from the CT and contour images to determine the amount of radiation dose received by the organs. Training and testing are performed on the public dataset—OpenKBP, consisting of 340 oropharyngeal cancer patient data. The model performance is evaluated using the metrics dose score and DVH score. The proposed model outperforms the single‐stage deep learning models by 0.34% for the DVH score and 0.14% for the dose score. While comparing the mean dose difference between the predicted and actual dose values for each organ, the proposed model shows better performance in almost all cases. The results imply that medical professionals can utilize the predicted dose distributions to aid the optimization process in the treatment planning systems. [ABSTRACT FROM AUTHOR]

The present work aimed to identify the effects of knowledge, attitudes, and practices (KAP) of poultry handlers on the prevalence of Campylobacter along the poultry production chain in Peninsular Malaysia. A total of 1230 microbiological samples were collected from five companies in different Malaysian states. The sampling points involved the entire poultry supply chain from the farm to the retailer. The collected samples were subjected to bacteriological isolation and morphological identification for microbiological analyses. All Campylobacter-positive samples were further confirmed with molecular identification by using a polymerase chain reaction. Campylobacter prevalence in poultry farms, processing plants, and retailers were identified. For the KAP survey, 300 respondents answered a questionnaire evaluating their levels of KAP regarding food safety while handling live birds and subsequently poultry products after slaughtering. Overall, the mean KAP scores of the workers at farms, processing plants, and retail outlets were assessed. The survey data indicated that even though the overall KAP levels of the poultry workers were excellent, the bacterial prevalence of Campylobacter was still high. In conclusion, the KAP of poultry handlers had insignificant effects on the prevalence of Campylobacter along the poultry production chain in Peninsular Malaysia. [ABSTRACT FROM AUTHOR]

Chemical synthesis of water soluble conducting polymer composite poly(vinyl alcohol-cysteine) [PVAC] was carried out in oxalic acid medium using ammonium persulfate. The composite was characterized by UV, FTIR and SEM–EDX. The corrosion inhibition performance of PVAC on mild steel in molar hydrochloric acid solution was studied by weight loss and electrochemical methods. A maximum inhibition efficiency of 94% was observed in the presence of 0.6 wt% of the inhibitor. The influence of inhibitor concentration, solution temperature, and immersion time on the corrosion of mild steel has been investigated. Polarization measurements showed the mixed type inhibitive nature of the polymer composite. The results obtained from the different methods are in good agreement. The various kinetic and thermodynamic parameters of metal dissolution and composite adsorption processes were evaluated from the weight loss methods in order to elaborate the adsorption mechanism. Adsorption of inhibitor obeyed El-Awady adsorption isotherm.

The objective of the present work is to improve the output waveform of three level inverters used in high-power applications, where the switching frequency is very low. This is achieved by maintaining the synchronization, half-wave symmetry, quarter-wave symmetry, and three-phase symmetry in the pnisewidth modulation (PWM) waveforms. The principles of achieving synchronization and symmetries in terms of space vectors for three level inverters are presented. A novel synchronized space vector pulsewidth modulation (SVPWM) algorithms is proposed and verified experimentally. The experimental waveforms of the inverter output voltage and motor no load current for different operating conditions of the drive are presented. The performance measure in terms of the weighted total harmonic distortion (THD) of the line voltage is computed for the linear modulation region of the drive for the proposed algorithm and compared with that of synchronized SVPWM and synchronized sine-triangle pulsewidth modulation (SPWM) technique. The comparative results show that consideration of synchronization and symmetry results in improved THD. Another significant feature of the proposed algorithm is that the symmetry and synchronization leads to self-balancing of the direct current (dc) bus capacitor voltages over every one third cycle of the fundamental. Index Terms--Harmonic distortion, induction motor drives, pulsewidth modulated inverters, pulsewidth modulation.

Current source inverter (CSI) fed drives are employed in high power applications. The conventional CSI drives suffer from drawbacks such as harmonic resonance, unstable operation at low speed ranges, and torque pulsation. This paper presents a novel CSI drive which overcomes all these drawbacks and results in sinusoidal motor voltage and current even with CSI switching at fundamental frequency. The proposed CSI drive uses a three-level inverter as an active filter across motor terminals replacing the bulky ac capacitors used in the conventional drive. A sensorless vector controlled CSI drive based on proposed configuration is developed. The simulation and experimental results are presented. Experimental results show that the proposed drive has stable operation even at low speeds. Comparative results show that the proposed CSI drive has improved torque ripple compared to the conventional configuration. Index Terms--Active filters, current control, current source inverters (CSI), harmonic distortion, induction motor drives, multi-level inverters, pulsewidth modulation (PWM).

Salmonella are widely found in the poultry industry, which subsequently may pose a risk to animal and human health. The aim of this review is to highlight strategies for the prevention and control of Salmonella at each stage in the poultry production chain by monitoring risks from the farm to the retailer. Among the primary approaches for control of Salmonella at the farm level includes the administration of synthetic and natural compounds to live chickens (vaccination and antibiotic), litter management as well as fortification of feed and acidification of drinking water. In the poultry processing plant, multiple hurdle technology and different chilling conditions to reduce Salmonella were discussed. In the retail level, an effective monitoring program to control Salmonella contamination by good manufacturing practices and hazard analysis and critical control points has been reviewed. Overall, we conclude that these approaches play a role in reducing the dissemination of Salmonella in the poultry industry. However, there is no published data related to logistic scheduling of poultry processing. [ABSTRACT FROM AUTHOR]

• Encoding complexity in fractal image compression is reduced by adopting a prediction method. • Relative geometric transformation is determined using a gradient. • Affine transformation is predicted using Cayley Table. • Score value calculation reduces the number of mean square error calculation. Encoding complexity reduction is an active area of research in the field of fractal image compression. In this proposed method, affine transformation is predicted to reduce the computational complexity and score value is calculated to further minimise the number of computations in the exhaustive process of encoding. In this hybrid wavelet- fractal image compression, relative geometric transformation of range and domain blocks is determined using a reference matrix as G R and G D respectively. Then affine transformation is predicted as the geometric composition of G R and G D , using Cayley table. Score value is defined individually for domain and range blocks. It is the absolute value of maximum change in the intensity in the respective block. An important merit of this proposed method is that the encoding time is highly reduced in comparison to other recent fractal coding techniques, with a better value of PSNR. This method is 641 times faster than Standard fractal image compression method (SFIC). Cayley table is utilized for the first time in fractal image compression. This lossy compression technique can be used in multimedia applications to handle the situation of limited storage space and bandwidth and also to transmit enormous amount of data with acceptable value of PSNR. As the medical images are huge in size a good lossy compression technique is required to store them in medical archives in an economical manner. Since this proposed method provides a good trade-off between compression ratio and the quality of reconstructed image, it is a better solution for the storage of medical images in an efficient manner. [ABSTRACT FROM AUTHOR]

Background: Endotracheal intubation may produce haemodynamic fluctuations which may be deleterious in patients with cardiovascular and neurological disorders. This is further worsened in patients with difficult airway. This study was conducted to compare haemodynamic fluctuations produced when intubation was done using Airtraq and fibreoptic bronchoscopy which are used in patients with anticipated difficult airway. Methodology: Prospective randomized study. Eighty patients, ASA PS 1 and 2 undergoing elective surgery were randomized into two groups – Group A and Group F. After routine induction and muscle relaxation, orotracheal intubation was done using Airtraq in Group A and fibreoptic bronchoscope in Group F. Heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP) were recorded at regular intervals. Duration and number of attempts for intubation were also noted Results: There was no significant difference in the average of variation in HR (p=0.384), SBP (p=0.179), DBP (p=0.746) and MAP (p=0.057) from the baseline between the two groups. Duration of intubation in FOB group (mean value of 56.98 s) was more than Airtraq® video laryngoscope group (mean value of 37.38 s) which was statistically significant. Conclusion: Use of fibreoptic bronchoscope offer no added advantage over Airtraq video laryngoscope in terms of haemodynamic response for intubation in difficult airway situation such as cervical spine injury. [ABSTRACT FROM AUTHOR]

With the development of controlled converter circuits and the rapid growth of independent electric power station, the self-excited induction generator (SEIG) is increasingly being used for different small power generation systems. This paper investigates the analysis and implementation of a control scheme for maintaining a single phase load RMS voltage and frequency constant, fed by self-excited induction generator under variable speed and load. The proposed control is useful for isolated small power applications such as uncontrolled small hydro turbine or wind turbine. Experimental results confirm good performance and the effectiveness of the proposed control. [ABSTRACT FROM AUTHOR]