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Tannery wastewater (TWW) is of serious environmental concern to pollution control authorities, because it contains highly toxic, recalcitrant organic and inorganic pollutants. The nature and characteristics of recalcitrant organic pollutants (ROPs) are not fully explored to date. Hence, the purpose of this study was to characterize and identify the ROPs present in the treated TWW. Gas chromatography–mass spectrometry data analysis showed the presence of a variety of ROPs in the treated TWW. Results unfolded that benzyl chloride, butyl octyl phthalate, 2,6-dihydroxybenzoic acid 3TMS, dibutyl phthalate, benzyl alcohol, benzyl butyl phthalate, 4-chloro-3-methyl phenol, phthalic acid, 2′6′-dihydroxyacetophenone, diisobutyl phthalate, 4-biphenyltrimethylsiloxane, di-(-2ethy hexyl)phthalate, 1,2-benzenedicarboxylic acid, dibenzyl phthalate, and nonylphenol were present in the treated TWW. Due to endocrine disrupting nature and aquatic toxicity, the U.S. Environmental Protection Agency classified many of these as “priority pollutants” and restricted their use in leather industries. In addition, the physicochemical analysis of the treated TWW also showed very high BOD, COD, and TDS values along with high Cr and Pb content beyond the permissible limits for industrial discharge. Furthermore, phytotoxicity assessment unfolds the inhibitory effects of TWW on the seed germination, seedling growth parameters, and α-amylase activity in Phaseolus aureus L. This indicates that the TWW discharged even after secondary treatment into the environment has very high pollution parameters and may cause a variety of serious health threats in living beings upon exposure. Overall, the results reported in this study will be helpful for the proper treatment and management of TWW to combat the environmental threats.

Atmospheric conditions induced by suspended particles such as fog, smog, rain, haze etc., severely affect the scene appearance and computer vision applications. In general, existing defogging algorithms use various constraints for fog removal. The efficiency of these algorithms depends on the accurate estimation of the depth models and the perfection of these models solely relies on pre-calculated coefficients through the training data. However, the depth model developed on the basis of these pre-calculated coefficients for dehazing may provide better accuracy for some kind of images but not equally well for every type of images. Therefore, training data-independent based depth model is required for a perfect haze removal algorithm. In this paper, an effective haze removal algorithm is reported for removing fog or haze from a single image. The proposed algorithm utilizes the atmospheric scattering model in fog removal. Apart from this, linearity in the depth model is achieved by the ratio of difference and sum of the intensity and saturation values of the input image. Besides, the proposed method also take care the well-known problems of edge preservation, white region handling and colour fidelity. Experimental results show that the proposed model is more efficient in comparison to the existing haze removal algorithms in terms of qualitative and quantitative analysis.

The daily advancing usage of Internet in India helps facilitate growing prospects for shopping online. Online shopping is an advancing sphere of technology. The explosion of online shopping has provided ground for comprehensive research targeted at luring and engaging customers from both the technology-oriented and consumer’s view. Behaviour of consumer is regarded as an applied discipline since mostly decisions are considerably effected by human behaviour or anticipated actions. Most companies today employ the Internet as a means to cut marketing costs, consequently lowering the cost of their services or products with a view of remaining in the lead in a greatly competitive market. Additionally companies deploy the Internet to transfer, communicate and circulate information, to market a product, to obtain feedback as well as to make satisfaction surveys with the consumers. The consumer employs the Internet to purchase a product online, as well as to compare rates, features of a product and after sale service facilities provided after buying the product from a specific website. The current work aims at exploring possible dependent and independent variables that affect customer’s attitude towards E-shopping behavior in NCR. The work is based on a pragmatic research study.

As of late, Automatic speech recognition has advanced on account of instruments, for example, natural language processing, and deep learning, among others. It is a framework or put in another way, a gadget that changes a raw signal into computer comprehensible text. The genuine creation of speech is comprised of changes in air pressure that outcomes in pressure wave that our ear and cerebrum comprehend. The vocal tract is utilized to deliver a human speech, which is adjusted by teeth, tongue, and lips. Speech recognition alludes to a machine's ability to perceive human speech and transform it into a computer comprehensible text. Speech recognition is a magnificent illustration of good interaction between humans and computers. In this paper, we introduce the process to extricate the feature from the signal utilizing Mel-frequency cepstral coefficients. Mel-frequency cepstral coefficients are a genuinely far wide and proficient methodology for feature extraction from a sound file. This technique improved the speech recognition process and removes the distortion in the voice. In this manuscript we applied the Mel-frequency filtration process to improve speech and remove the background noise. the Therefore, the proposed methodology gives better performance in the automated speech recognition system.

Context: Automated detection of COVID-19 in real time can greatly help clinicians to handle increasing number of cases for preliminary screening. Deep CNN models trained with sufficiently large datasets may become best candidates to meet the purpose. Aims: This study aims for automated detection and classification of COVID-19 and viral pneumonia diseases by applying deep CNN model using chest X-ray images. The proposed model performs multiclass classification to meet the purpose. Settings and Design: The proposed model is built on top of VGG16 architecture with pretrained ImageNet weights. The model was fine-tuned using additional custom layers to deliver better performance specific to the target. Subjects and Methods: A total of 15,153 samples are used in this work. These samples include chest X-ray images of COVID-19, viral pneumonia, and normal cases. The entire dataset was split into train and test sets, with a ratio of 80:20 before training the model. To enhance important image features, image preprocessing and augmentation were applied before feeding the image batches to the model. Statistical Analysis Used: Performance of the model is evaluated through accuracy, precision, recall, and F1 score performance metrics. The results produced by the model are also compared with other recent leading studies. Results: The proposed model has achieved a classification accuracy of 98% with 98% precision, 96% recall, and 97% F1 score on the test dataset for multiclass classification. The area under receiver operating characteristic curve score was 0.99 for all three cases of multiclass classification. Conclusions: The proposed classification model may be highly useful for the preliminary diagnosis of COVID-19 and viral pneumonia cases, especially during heavy workloads and large quantities.

Thermal inactivation of normal and starved cells of Salmonella Enteritidis on chicken skin previously exposed to different concentrations of acidified sodium chlorite (ASC) or tri-sodium phosphate (TSP) was investigated. Inoculated skin was pretreated with different concentration of ASC or TSP, packaged in bags, and then immersed in a circulating water bath at 60 to 68 °C. The recovery medium was Hektoen enteric agar. D -values, determined by linear regression, for normal cells on chicken skin, were 2.79, 1.17 and 0.53 min whereas D -values for starved cells were 4.15, 1.83 and 0.66 at 60, 64 and 68 °C, respectively. z -values for normal cells were 3.54 and for starved cells were 2.29. Pretreatment of Salmonella Enteritidis cells with 0 to 200 ppm of ASC or 0 to 1.0 % TSP resulted in lower D -values at all temperatures. Sensory results indicated no significance differences for control and treatments. Thus, results of this study indicated that pretreatment of chicken skin with ASC or TSP increased sensitivity of Salmonella Enteritidis to heat without affecting organoleptic quality of chicken meat.

Accelerometers play a pivotal role in spacecraft navigation, particularly in rendezvous and docking missions, by estimating incremental velocity with precision. This paper presents the on-board performance of an inertial-grade three-axes MEMS accelerometer package that measured the linear acceleration of the spacecraft over its background structural vibration input. Utilizing Hybrid Micro Circuits (HMC) technology for packaging, each axis integrates two in-house fabricated MEMS sensor chips, catering to both fine and coarse range measurements. The paper discusses on the observation of an onboard bias in the output of accelerometer and the reasons for the same and proposes mitigation strategies for future missions. Additionally, the feasibility of leveraging MEMS accelerometers for spacecraft platform vibration measurement is explored. The accelerometer practically achieved exceptional inertial performance with ± 2 µg Noise Equivalent Acceleration (NEA) with a ± 15 mg operating range and an overall accuracy of 10 µg for the fine range sensor. For the coarse range sensor, ± 30 µg NEA is attained with a ± 500 mg operating range and an overall accuracy of 100 µg. This onboard experiment affirms the capability of MEMS accelerometers to assist rendezvous and docking applications in future.

Water is critical for life, but many people do not have access to clean and safe drinking water and die because of waterborne diseases. The analysis of drinking water for the presence of indicator microorganisms is key to determining microbiological quality and public health safety. However, drinking water-related illness outbreaks are still occurring worldwide. Moreover, different indicator microorganisms are being used in different countries as a tool for the microbiological examination of drinking water. Therefore, it becomes very important to understand the potentials and limitations of indicator microorganisms before implementing the guidelines and regulations designed by various regulatory agencies. This review provides updated information on traditional and alternative indicator microorganisms with merits and demerits in view of their role in managing the waterborne health risks as well as conventional and molecular methods proposed for monitoring of indicator and pathogenic microorganisms in the water environment. Further, the World Health Organization (WHO) water safety plan is emphasized in order to develop the better approaches designed to meet the requirements of safe drinking water supply for all mankind, which is one of the major challenges of the 21st century.

The ill-posed character of haze or fog makes it difficult to remove from a single image. While most existing methods rely on a transmission map refined through depth estimation and assume a constant scattering coefficient, this assumption limits their effectiveness. In this paper, we propose an enhanced transmission map that incorporates spatially varying scattering information inherent in hazy images. To improve linearity, the model utilizes the ratio of the difference between intensity and saturation to their sum. Our approach also addresses critical issues such as edge preservation and color fidelity. In terms of qualitative as well as quantitative analysis, experimental outcomes show that the suggested framework is more effective than the currently used haze removal techniques.

Purpose>This study aims to examine the lay notions of happiness and determine the factors that influence one’s experience of happiness.Design/methodology/approach>This study used a qualitative technique to understand better how happiness is conceptualised. This study uses a purposive sample to select a diverse and representative sample (N = 357). Participants responded to an open-ended questionnaire designed to elucidate their understanding of happiness. The data is analysed using grounded theory and a bottom-up approach.Findings>Happiness is defined as a harmonious state where the individual’s physiological and psychological needs are satisfied in the past, present and future, leading them to live a meaningful and contented life. However, several factors may affect an individual’s level of happiness. Family and friends; health and wellness; personal and professional successes; recreation and personal traits all contributed to the feeling of happiness. On the other hand, factors impeding happiness include unfavourable surroundings, work and play impediments, strained relationships and undesirable behavioural characteristics. The authors compare and contrast these findings to the current empirical literature and hypotheses.Originality/value>Despite the substantial study, no uniform definition of happiness exists. The existing body of knowledge is dominated by western viewpoints, which are not necessarily congruent with their eastern counterparts. This study presents a thorough and culturally unique understanding of happiness. This understanding would enable academics, policymakers and educators to develop successful policies that promote happiness. Additionally, this study aid future researchers to develop new measures that enable cross-regional and cross-national comparisons of happiness dynamics