This study was designed to assess the health impacts related to noninvasive carbon monoxide saturation (SPCO %) in the blood of respondents. For this purpose, 150 respondents from the labour community of Hattar Industrial Estate (testing site) and 100 respondents from Sultan Pur (control site) were selected. To achieve this objective, a Rad-57 Pulse CO-Oximeter was used for noninvasive carboxyhemoglobin measurement. Carbon monoxide saturation (SPCO%) in the blood of respondents from Hattar Industrial Estate, Haripur, Pakistan has been compared with the WHO’s standard concentration of SPCO% (5%). High saturation of carbon monoxide (carboxyhemoglobin SPCO) in the blood of respondents and disease association have been interpreted in graphs formed on the basis of statistical analysis in terms of frequencies, using statistical software (SPSS), based on demographic entries as well as exposure time of the employees in the processing, food and steel industries. The highest SPCO% measured was 17% in the steel industry and the lowest measured level was 4.2%. Frequencies and percentages of respiratory inflammation, dermatosis, asthma, breathing issues and eye inflammation among respondents were 29%, 35%, 16.7%, 23.5% and 9%, respectively. Prevalence of disease in three different groups of respondents (from three testing sites) was also analyzed on the basis of exposure time (hrs.) to carbon monoxide emissions. Prevalence of disease among the exposed and non-exposed groups was analyzed and showed comparatively lower disease prevalence in the group of respondents who were not exposed to high carbon monoxide emissions. The data of the current study was also subjected to statistical modelling to find the health risk of air pollutants (carbon monoxide) on population health by calculating attributable risk (AR) or attributable proportion (AP). Results indicated that attributable risk of carbon monoxide exposure for respiratory diseases, dermatosis and eye inflammation were 61.12%, 65.77% and 24.95% respectively. Findings of statistical modelling indicated that dermatosis and respiratory diseases were more prevalent in laborers of industrial units than those at control site.

Urea is a crucial nutrient for plant growth, but because of its substantial losses due to nitrification, ammonification, and subsurface leaching, there is currently a push to reduce these losses. Urea is frequently uploaded and trapped in gelatin. In this research, the improvement of urea uploading and encapsulation efficiency is investigated using wood ash made from plant biomass (Pinus roxburghii). The 8 g w/v of gelatin was mixed with various concentrations of wood ash (from 4 to 16 g w/w), urea (from 4 to 24 g w/w), and glutaraldehyde (from 0.5 to 3 mL g⁻¹) to prepare various formulations of slow-release fertilizer (SRF). According to this study, adding wood ash to gelatin increases its ability to upload and encapsulate urea. The urea on its surface and the metal in wood ash both considerably contribute to the compositional alterations in gelatin in SRFs, which were demonstrated by IR spectroscopy. Visualization from photographs revealed that the homogenous dispersion of wood ash improved structural compatibility. The water content of the SRF formulation showed that wood ash can reduce water absorption by changing how hydrophobic gelatin is. Wood ash improves the gelatin’s ability to reduce the rapid release of urea over time, according to testing of cumulative urea release from SRF. The optimal combinations for achieving the maximum 53.43% of urea uploading were 2.44 g of urea, 2.47 mL of glutaraldehyde, and 1.50 g of wood ash, according to the Box–Behnken model. The gelatin-based SRF that had been amended with wood ash was applied to the Mentha spicata plant, and the plant’s healthy development and higher chlorophyll content revealed its agronomic potential. This study has a significant contribution to the development of an affordable and more effective wood ash-modified gelatin-based SRF.

This research was carried out to analyze variations in indoor and outdoor ozone concentrations and their health impact on local communities of megacities in Pakistan. For indoor ozone measurements, industrial units of an economic zone, Hattar Industrial Estate, Haripur, KPK, Pakistan, were selected. For outdoor ozone measurements, maximum and minimum peaks from different selected stations of three megacities (Islamabad, Abbottabad, and Haripur Hattar) in Pakistan were analyzed for paired comparisons. The tropospheric ozone levels were measured with the help of a portable SKY 2000-WH-O₃ meter from December 2018 to November 2019. According to the findings of this investigation, the indoor ozone concentrations at Hattar Industrial Estate exceeded the permissible limit devised by the WHO. The highest concentration (0.37 ppm) was recorded in the month of May in the food industry, while the lowest concentration (0.00 ppm) was recorded in the cooling area of the steel industry in the month of December. For outdoor ozone concentrations, the maximum concentration (0.23 ppm) was detected in Islamabad in the month of March 2019, whereas the rest of year showed comparatively lower concentrations. In Haripur, the maximum concentration (0.22 ppm) was detected in the month of February 2019 and a minimum concentration (0.11 ppm) was found in the month of November 2019. In Abbottabad, the maximum concentration (0.21 ppm) was detected in the month of March 2019 and the minimum concentration was 0.082 ppm. Increasing tropospheric ozone levels might be harmful for local communities and industrial laborers in the winter season because of the foggy weather. In the Abbottabad and Hattar regions, since COVID infection is indirectly related to low temperature and high emission of gases may compromise the respiratory systems of humans. The results of the present study were shared with industrialists to set precautions for ambient air quality and support the adoption of low emission techniques in industries for the safety of labour and nearby residents.

The rise of methicillin-resistant Staphylococcus epidermidis (MRSE) makes it difficult to treat infections that increase morbidity and mortality rates in various parts of the world. The study’s objectives include identifying the clinical prevalence, antibiogram profile, and Gompertz growth kinetics of MRSE treated with synthetically created nanoparticles of rosin obtained from Pinus roxburghii. A total of 64 of 200 clinical isolates of S. epidermidis (32% of the total) displayed sensitivity (40.62%) and resistance (59.37%) to seven different antibiotic classes. The most sensitive patterns of antibiotic resistance were seen in 20 (78.95%) and 24 (94.74%) isolates of MRSE against piperacillin/tazobactam and cephradine, respectively. Fosfomycine was found to be the most effective antibiotic against MRSE in 34 (89.47%) isolates, followed by amoxicillin. Successfully produced, described, and used against MRSE were rosin maleic anhydride nanoparticles with a size range of 250 nm to 350 nm. Five different concentrations of 25, 50, 75, 100, and 150 mg mL⁻¹ rosin maleic anhydride nanoparticles were investigated to treat MRSE resistance. According to Gompertz growth kinetics, the maximal growth response was 32.54% higher and the lag phase was also 10.26% longer compared to the control when the amount of rosin maleic anhydride nanoparticles was increased in the MRSE. Following the application of rosin maleic anhydride nanoparticles, the growth period is extended from 6 to 8 h. A potential mechanism for cell disintegration and distortion is put forth. This investigation came to the conclusion that rosin maleic anhydride nanoparticles better interfere with the surface of MRSE and demonstrated a preferred bacteriostatic action.

Distributed denial of service (DDoS) attack is the most common attack that obstructs a network and makes it unavailable for a legitimate user. We proposed a deep neural network (DNN) model for the detection of DDoS attacks in the Software-Defined Networking (SDN) paradigm. SDN centralizes the control plane and separates it from the data plane. It simplifies a network and eliminates vendor specification of a device. Because of this open nature and centralized control, SDN can easily become a victim of DDoS attacks. We proposed a supervised Developed Deep Neural Network (DDNN) model that can classify the DDoS attack traffic and legitimate traffic. Our Developed Deep Neural Network (DDNN) model takes a large number of feature values as compared to previously proposed Machine Learning (ML) models. The proposed DNN model scans the data to find the correlated features and delivers high-quality results. The model enhances the security of SDN and has better accuracy as compared to previously proposed models. We choose the latest state-of-the-art dataset which consists of many novel attacks and overcomes all the shortcomings and limitations of the existing datasets. Our model results in a high accuracy rate of 99.76% with a low false-positive rate and 0.065% low loss rate. The accuracy increases to 99.80% as we increase the number of epochs to 100 rounds. Our proposed model classifies anomalous and normal traffic more accurately as compared to the previously proposed models. It can handle a huge amount of structured and unstructured data and can easily solve complex problems.