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Selenium (Se), is an essential trace element in the diet of fish and aquatic animals needed for growth, thyroid hormone metabolism, antioxidant activity, and immunity. Thus, this study has evaluated the dietary selenium yeast (Se-yeast) impact on the growth performance, antioxidant capacity, and gut bacterial community of zebrafish, Danio rerio. The fish with an average weight of 0.247 g ± 0.005 were distributed in 12 glass aquariums. 30 fish per aquarium in three replicates per group were exposed to different dietary Se-yeast concentrations: 0.3 mg/kg (control), 0 mg/kg, 3 mg/kg, and 30 mg/kg for three weeks. The result indicated a higher body weight gain and specific growth rate in the 3 mg/kg group in all groups. Se significantly accumulated in the liver, muscle, gill, and gut. The antioxidant genes superoxide dismutase 1 (SOD1), catalase (CAT), and glutathione peroxidase 1 (GPx1), and the gut marker genes: solute carrier family 15 member 1b (slc15a1b), fatty acid binding protein 2 (fabp2), cytochrome P450 family 8 subfamily B member 1 (cyp8b1), and cytochrome P450 family 3 subfamily A, polypeptide 65 (cyp3a65) were significantly expressed in the gut tissue. The 16 s rRNA sequencing showed a significant difference at phyla Proteobacteria, Fusobacteria, Firmicutes, and Actinobacteria and Cetobacterium, Aeromonas, mycobacterium, and Lactobacillus at the genus level. It was concluded that dietary Se 3 mg/kg can promote growth performance and antioxidant status, furthermore, dietary Se yeast has a significant impact on the gut microbiota of D. rerio.

The hyper yield of peroxidase (POX) was investigated for a novel native Aspergillus niger strain identified by 18S RNA analysis. A. niger strains sequences were submitted to GenBank; IDs allotted were MN611114.1 (BMB 17) and MN559756.1 (BMB-18). The identified Aspergillus strains in combination showed enhanced (POX) activity (601.5 U/mL) by solid-state fermentation in comparison to their individual activities. POX was purified by ammonium sulfate, and size exclusion gel chromatography exhibited a 7.83-fold increase in POX concentration (13.3 U/mg) in comparison to BMB17 and BMB 18 (11.8 & 7.6 U/mg respectively). The best POX activity was obtained with pH 6.5, 37 °C, and 5 days of incubation. Using guaiacol as substrate, POX showed maximum activity (Vmax) of 537 U/mL with a corresponding Michaelis constant (Km) value of 126 µM. Calcium chloride worked as a POX activator at 300 & 400 mM. Zinc sulfate (500 mM), EDTA (5 mM); ethanol, propanol, and acetonitrile (50%) inhibited (18-30%) POX. Urea (1M), and copper sulfate (500 mM) strongly inhibited POX up to 40%. Polysorbate-80 (1%) slightly reduced the POX by 10% to 15%. BMB17+18-induced promising dye decolorization (88-98%) against all vat dyes, methylene blue, and phenol red.

With growing urbanization, water contamination has become a problem. The water quality is assessed using physicochemical parameters and requires manual collection. Moreover, physicochemical parameters are insufficient for water quality monitoring as heavy rainfalls and abundance of air pollutants cause water pollution. Thus, considering natural factors as influencing parameters and the latest technology for easy and global coverage for sampling, water quality monitoring is modified. This study investigates Rawal watershed with (a) physicochemical, (b) air pollutants like nitrogen dioxide (NO2), and (c) meteorological variables like wind speed for June 2018 to September 2022. Correlation and regression analysis are performed. The results show negative correlations for NO2 with total dissolved solids (TDS) (ranging, 0.51–0.85), turbidity (range, 0.53–0.65), pH (range, 0.5–0.75), and dissolved oxygen (DO) (range, 0.5–0.82), and positive correlation with electric conductivity (EC) (range, 0.54–0.85). The regression analysis with LightGBM, multi-layer perceptron (MLP), and support vector machine (SVM) is applied with air pollutants, and meteorological parameters taken as independent variables giving root-mean-square error (RMSE) (ranging, 0.015–0.18). MLP gave an RMSE of 0.18 and 0.003 for TDS and pH, respectively. SVM performed well for DO, turbidity, and EC with RMSE ranging from 0.015 to 0.027. Moreover, floods on August 2022 are taken as a case study. HIGHLIGHTS Impact assessment of air pollutants on physicochemical parameters.; Meteorological features can have a moderate impact on water quality, i.e., wind speed with chl-α, EC, DO, and TDS, and air temperature with DO and TDS in August and September.; Machine learning approaches, i.e., LightGBM, MLP, and SVM, are applied for the analysis.; Floods can have a negative impact on water quality introducing an excess of pollutants and nutrients in water.;

The present experimental study investigates the thermal and hydraulic performance of Ethylene Glycol (EG)-based ZnO nanofluids (NFs) in circular minichannel test sections, each of 330 mm in length and 1.0–2.0 mm inner diameters. The experiments were conducted under steady-state constant heat flux and laminar flow conditions. The stable ZnO/EG-based NFs were synthesized using a standard two-step method in varying nanoparticles (NPs) loadings (0.012–0.048 wt%). The morphological characteristics, crystal structure, and specific surface area (SSA) showed that the NPs were sized in nm, possessing excellent crystal structure and enhanced surface area. Thermal conductivity (TC) and viscosity (VC) of the NFs were examined in the 20–60 °C temperature range. Both TC and VC possessed an increasing trend with the rise in concentration of the NPs. However, with the temperature rise, TC increased while the VC decreased and vice versa. The highest enhancements in TC and VC were 14.38 % and 15.22 %, respectively, at 40 °C and 0.048 wt% of NPs loading. The highest enrichment recorded in the local and average heat transfer coefficient (HTC) were 14.80 % and 13.48% in a minichannel with 1.0 mm inner diameter, respectively. It was directly proportional to the NPs loading and volume flow rate of the NFs. The friction factor was also directly proportional to the test section's inner cross-sectional area, while the pressure gradient showed an inverse behavior. An inverse relationship was recorded for the volume flow rate of the NFs and vice versa. Maximum friction factor and the pressure drop for all three minichannel test sections were recorded as 34.58 % and 32.16 %, respectively. The well-known Shah correlation predicted the local and average HTC within ±15.0 %, while the friction factor and the pressure gradient were well predicted by the Darcy correlation within the ±10.0 % range.

The purpose of current research work was to investigate the effect of mutagenesis on endoglucanase B activity of indigenous strain of Aspergillus niger and its heterologous expression studies in the pET28a+ vector. The physical and chemical mutagens were employed to incorporate mutations in A. niger. For determination of mutations, mRNA was isolated followed by cDNA synthesis and cellulase gene was amplified, purified and sequenced both from native and mutant A. niger. On comparison of gene sequences, it was observed that 5 nucleotide base pairs have been replaced in the mutant cellulase. The mutant recombinant enzyme showed 4.5 times higher activity (428.5 µmol/mL/min) as compared to activity of native enzyme (94 µmol/mL/min). The mutant gene was further investigated using Phyre2 and I-Tesser tools which exhibited 71% structural homology with Endoglucanase B of Thermoascus aurantiacus. The root mean square deviation (RMSD), root mean square fluctuation (RMSF), solvent accessible surface area (SASA), radius of gyration (Rg) and hydrogen bonds analysis were carried at 35°C and 50°C to explore the integrity of structure of recombinant mutant endoglucanase B which corresponded to its optimal temperature. Hydrogen bonds analysis showed more stability of recombinant mutant endoglucanase B as compared to native enzyme. Both native and mutant endoglucanase B genes were expressed in pET 28a+ and purified with nickel affinity chromatography. Theoretical masses determined through ExPaSy Protparam were found 38.7 and 38.5 kDa for native and mutant enzymes, respectively. The optimal pH and temperature values for the mutant were 5.0 and 50°C while for native these were found 4.0 and 35°C, respectively. On reacting with carboxy methyl cellulose (CMC) as substrate, the mutant enzyme exhibited less Km (0.452 mg/mL) and more Vmax (50.25 µmol/ml/min) as compared to native having 0.534 mg/mL as Km and 38.76 µmol/ml/min as Vmax. Among metal ions, Mg2+ showed maximum inducing effect (200%) on cellulase activity at 50 mM concentration followed by Ca2+ (140%) at 100 mM concentration. Hence, expression of a recombinant mutant cellulase from A. niger significantly enhanced its cellulytic potential which could be employed for further industrial applications at pilot scale.

Objective: To compare laparoscopic needle-assisted extra-corporeal hernia repair regarding recurrence in children with intracorporeal repair. Study Design: Retrospective longitudinal study. Department of Paediatric Surgery, Pak Emirates Military Hospital, Rawalpindi Pakistan, from Jul 2019 to Dec 2020. Methodology: Retrospectively, all patients who underwent laparoscopic herniotomy were searched through hospital records.Ninety-seven patients matched the inclusion criteria out of 148 laparoscopic surgeries. All patients were diagnosed with inguinal hernia based on history and clinical examination. Fifty-one patients underwent laparoscopic needle-assisted repair (LNAR) with extra-corporeal knotting, whereas 46 underwent repair with intracorporeal knotting of the deep inguinal ring. Pneumoperitoneum was created in all cases by open method. All patients were followed up for six months outdoors for recurrence. Results: The age of patients ranged from 1-12 years; the mean age of study patients was 5.29±3.17 years. Regarding the inguinal hernia site, 45(46.4%) had a right-sided hernia, and 52(53.6%) had a left-sided hernia. All operations were completed laparoscopically. There were no intraoperative complications. A total of 2(3.53%) patients developed recurrence, one from each group, 2.17% in Group-A and 1.96% in Group-B, with no statistically significant difference between the two groups after six months of follow-up (p-value=0.942). Conclusion: Extra-corporeal knotting in children with inguinal hernia is an effective, quick and safe technique. It is comparable to the standard intracorporeal knotting technique in terms of recurrence.

Objectives: To find the frequency, aetiologies, treatment options and outcomes in neonates with persistent pulmonary hypertension of the new-born and to identify risk factors associated with severe pulmonary hypertension. Study Design: Prospective longitudinal study. Place and Duration of Study: Neonatal Intensive Care Unit, Fatima Memorial Hospital, Lahore Pakistan, from Apr to Oct 2020. Methodology: All neonates admitted to the Neonatal Unit with signs and symptoms suggestive of PPHN and confirmed on echocardiography were included in the study. Multiple maternal and neonatal risk factors leading to PPHN were identified. Results: A total of 70 neonates with persistent pulmonary hypertension were identified. The all-cause mortality rate was 18.6% (13 of 70). Pneumonia was the primary cause of pulmonary hypertension (64.3%). Pneumonia was the primary cause of pulmonary hypertension (64.3%). Out of 72, 46(65.7%) were male and 24(34.3%) were female. The mean gestational age was 35.38±3.39 weeks. On binary logistic regression analysis, severe pulmonary hypertension showed a significant association with maternal septic risk factors (OR 0.26, p=0.03, emergency section (OR 3.69, p=0.05). Conclusions: The persistent pulmonary hypertension of newborns in the current study was higher than in Western countries. Pneumonia and sepsis are the primary aetiologies of developing PPHN. Maternal septic risk factors and emergency section are associated with an increased risk of severe pulmonary hypertension.

Precise estimation of crop yield is crucial for ensuring food security, managing the supply chain, optimally utilizing resources, promoting economic growth, enhancing climate resilience, controlling losses, and mitigating risks in the agricultural industry. Accurate yield prediction depends upon several interactive factors, including crop genotype, climate conditions, soil fertility, sowing & irrigation plan, and crop management practices. For this purpose, remote sensing data and machine learning (ML) algorithms are emerging as indispensable tools that can significantly increase farm productivity while using minimal resources and reducing environmental impact. In this context, the study presents a framework for wheat grain yield prediction using three regression techniques including Random Forest, Xtreme Gradient Boosting (XGB) regression, and Least Absolute Shrinkage & Selection Operator (LASSO) regression. Various aspects of the three models are investigated and results are compared to explore the optimal technique. Drone-based multispectral sensors are employed to acquire data from three wheat experimental fields with three different sowing dates (SD1, SD2, SD3), and the effect of the seeding plan on crop yield is examined. The prediction performance of models is assessed at different growth stages of the crop using several evaluation metrics. The results show that LASSO achieved the highest performance in April with the coefficient of determination (R2) of 0.93 and mean absolute error (MAE) of 21.72. The average annual predicted yield is 260.54 g/m2, 201.64 g/m2, and 47.29 g/m2 in the wheat field with SD1, SD2, and SD3 respectively. This study can help farmers and agronomists to make informed decisions about crop management activities such as planting & harvest plans, and resource handling.

Objective: To document the causes and clinical significance of direct Coomb’s test distal convoluted tubule in neonatal Jaundice at a tertiary care setup. Study Design: Cross-sectional study. Place and Duration of the Study: Fatima Memorial Hospital, Lahore Pakistan, from Nov 2019 to Oct 2020. Methodology: All babies born to “O” or Rh-negative blood group mothers were included. In addition, data including demographics, mother’s blood group, baby’s blood group, direct Coomb’s test results, bilirubin values, and need for treatment was recorded. Results: The study included 989 neonates. Of all, 489(49.4%) were male, the mean birth weight was 2.80±0.75 kg, and the mean gestation was 36±4.3 weeks. ABO mismatch was present in 452(45.7%) cases and Rh mismatch in 123(12.4%). Of all, 58(5.9%) had positive direct Coomb’s test. Amongst the positive direct Coomb’s test patients, 44(75.8%) babies needed treatment, of which 40(90.9%) received phototherapy, and 4(6.8%) needed Intensive phototherapy (360 degrees). No baby needed an exchange transfusion. Conclusion: In most cases, positive direct Coomb’s test was due to ABO blood group incompatibility. In addition, early phototherapy can control bilirubin rise in most cases, so few would need aggressive or intensive treatment. Keywords: ABO mismatch, Direct Coomb’s test, Neonatal jaundice, Rh mismatch.

Metal-oxide based nanofluids have grabbed many researchers' attention because of their better dispersion stability and enhanced thermophysical properties for potential heat transfer applications. This work presents an experimental investigation of well-dispersed ZnO nanoparticles' dispersion stability and thermophysical properties in deionized water. Aqueous ZnO nanofluids with different mass concentrations (0.012, 0.024, 0.036, and 0.048%) of nanoparticles were synthesized using a typical two-step method with and without using surfactants. Sodium hexametaphosphate and acetylacetone were used as stabilizing agents in this study. Under optimal operating conditions and surfactants' concentration, acetylacetone-based nanofluids were found stable for more than 60 days. Viscosity and thermal conductivity of nanofluids have been investigated in the 20–60 °C temperature range. Both viscosity and thermal conductivity of nanofluids increased with increasing nanoparticle loading, while with an increase in temperature, viscosity decreased, in contrast to an increase in nanofluids' thermal conductivity. At a fixed temperature and concentration of nanoparticles, maximum enhancement in viscosity and thermal conductivity was recorded as 16.75% and 23.70%, respectively, for sodium hexametaphosphate stabilized nanofluids. The existing well-known theoretical models failed to predict the nanofluids' viscosity and thermal conductivity; however, the proposed new correlations well projected the experimental findings.

Objective: To determine the diagnostic accuracy of Barium contrast enema for diagnosis of Hirsch-sprung disease, taking histopathology as the gold standard. Study Design: Cross-sectional validation study. Place and Duration of Study: Department of Paediatric Surgery, Pak Emirates Military Hospital, Rawalpindi Pakistan, from Nov 2019 to May 2021. Methodology: A total of 251 patients with symptoms of Hirsch-sprung disease were included. All patients were assessed with a Barium enema and full-thickness rectal biopsy. Barium enema was reported for the presence of contrast in the rectum after 24 hours, the presence or absence of transitional zone and a Recto Sigmoid Index less than 1. Full-thickness rectal biopsy was taken 2 cm above the dentate line, and histopathology was performed to see the ganglion cells in Meissner`s and Myenteric plexus. Results: Histo-pathologically confirmed cases were 120(true positive), where 12(false positive) had no Hirsch-sprung disease on histopathology. In Barium enema negative patients, 113 were true negatives while 06 were false negatives. Overall sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of Barium contrast enema for diagnosis of Hirsch-sprung disease were 95.24%, 90.40%, 90.91%, 94.96% and 92.83%, respectively. Conclusion: We concluded that the diagnostic accuracy of Barium contrast enema for diagnosis of Hirsch-sprung disease is quite high, taking histopathology as the gold standard.

The advancement of nanotechnology has demonstrated the ability of metal-oxide-based nanofluids (NFs) to produce high heat flux in microscale thermal applications. Convective heat transfer (HTC) and flow characteristics (pressure drop (ΔP) and friction factor (f)) of aqueous ZnO NFs' within a circular mini tube (Di = 1.0 mm, L = 330 mm) were analyzed. Experiments were carried out under steady-state and varying flow rates using 0.012–0.048 wt % of NFs and sodium hexametaphosphate (SHMP) and acetylacetone (ACAC) as surfactants (SFs). Laminar flow and constant wall heat flux conditions were used to assess NFs heat transfer properties, ΔP and f. The viscosity (VC) and thermal conductivity (TC) of NFs exhibited a strong dependence on the operating temperature and NFs concentration. VC and TC increased by increasing the NFs concentration and decreased by increasing the operating temperature. Maximum VC and TC enhancement of 16.75% and 23.70% were achieved for SHMP-stabilised NFs, respectively. The average HTC increased by increasing NFs loading and flow rate, with HTCmax of 17.0% noticed for ACAC-stabilised NFs. The ΔPmax and fmax were 16.0% and 12.0%, respectively. Experimental and theoretical results showed a maximum deviation of ±7.0% and ±4.0%, respectively.

Background and objectiveAcute kidney injury (AKI) was observed in sick neonates and was associated with poor outcomes. Our cohort represents the neonatal characteristics of those diagnosed with AKI using Kidney Disease: Improved Global Outcome (KDIGO) guidelines.MethodologyA cohort study was conducted in the NICU of FMH from June 2019 to May 2021. Data were collected on a proforma. All continuous variables were not normally distributed and expressed as the median and interquartile range. Categorical variables were analyzed by proportional differences with the Pearson chi-square test or Fisher's exact tests. A multinomial logistic regression model was used to explore the independent risk factors for AKI. Time to the event (death) and the cohort's survival curves were plotted using the Cox proportional hazard model.ResultsAKI occurred in 473 (37.6%) neonates. The risk factors of AKI were outborn birth [adjusted odds ratio (AOR): 3.987, 95% confidence interval (CI): 2.564–6.200, p: 0.000], birth asphyxia (AOR: 3.567, 95% CI: 2.093–6.080, p: 0.000), inotropic agent (AOR: 2.060, 95% CI: 1.436–2.957, p: 0.000), antenatal steroids (AOR: 1.721, 95% CI: 1.213–2.443, p: 0.002), central lines (AOR: 1.630, 95% CI: 1.155–2.298, p: 0.005) and intraventricular hemorrhage (IVH)/intracranial hemorrhage/disseminated intravascular coagulopathy (AOR: 1.580, 95% CI: 1.119–2.231, p: 0.009). AKI significantly increases the duration of stay and mortality rates by 16.5% vs. 3.9% in neonates with normal renal function (p

Objective: To find the types and frequency of prelacteal use, factors influencing their use and its impact on breastfeeding. Study Design: Cross-sectional study. Place and Duration of Study: Fatima Memorial Hospital, Lahore, Pakistan from Jul to Dec 2019. Methodology: In the study, mothers and female attendants of newborn babies were interviewed following a specific questionnaire. The given answers were recorded and captured data about socio-demographics, knowledge, attitude and actual practices of mothers and families related to use of prelacteals. The results were analysed using SPSS 22. Results: We interviewed 1601 mothers and female attendants of neonates. The mean age was 32.3 ± 8.2 years. Most of the females were married (1211, 75.6%) and resided in Lahore (1281, 80%). About one third, 581 (36.3%) were either illiterate or had inadequate schooling. Two-thirds (1000, 62.5%) of women wanted to give some prelacteal feed soon after birth. Honey was the most the commonest choice (in 722, 45.1%), seconded by ghutti (107, 6.7%). Most females (1467, 91.6%) wanted to give colostrum, and 1552 (96.9%) would breastfeed the baby. 1221 (75.2%) wanted to start mother-feed immediately or within 2 hours of birth. Conclusion: The use of prelacteal is common in Pakistan culture. A consistent effort is needed to counter the unsafe prelacteal use.

This experimental study is focused on heat transfer performance and pressure drop characteristics of ZnO/DIW-based nanofluids (NFs) in horizontal mini tubes of different (1.0–2.0 mm) diameters. Different mass concentrations (0.012–0.048 wt %) of nanoparticles (NPs) were tested with varying fluid flow rates (12–24 ml/min) of NFs. The thermal conductivity (TC) and viscosity (VC) of stable NFs were tested at 20–60 ᵒC, at a fixed temperature (40 °C), and concentration of NPs (0.048 wt%) the maximum rise was 18.27% and 20.31%, respectively. The local and average heat transfer coefficients (HTCs) and the pressure gradient were noticed to be directly proportional to volume flow rate of NFs and the mass concentration of NPs. However, an inverse trend was noticed with the test section's diameter. At 0.048 wt % of NPs and 24.0 ml/min flow rate of NFs, the maximum rise in local and average HTCs and pressure gradient was 17.11–11.61% and 13.05–9.79%, and 29.19–12.25%, respectively, in a tube's diameter of 1.0–2.0 mm. The friction factor increased with NP's loading while the same reduced with the fluid flow rate. The corresponding maximum change in the friction factor was 28.85–12.72% for the tubes with 1.0–2.0 mm diameters, respectively, at a 12.0 ml/min flow rate of NFs. The comparison of experimental findings for the HTCs, pressure gradients and friction factors with the standard Shah and Darcy's correlations showed that the observations are in good agreement with the predicted ones.

Monitoring of aflatoxin levels in milk is often complicated in developing countries due to the dominance of informal markets channeling milk in raw form. Farmer’s awareness and voluntary participation in aflatoxin mitigation can be critical in such scenarios. Therefore, the present study was conducted to understand the perceptions of dairy farmers about aflatoxins and link it with aflatoxin mitigation programs on milk in Pakistan. Information was collected from 450 peri-urban dairy farmers in seven cities using questionnaires. Majority (77.9%) of the farmers were aware of the negative impact of moldy feed on animal health. However, only 40.6% of the farmers were aware of the transferability of the toxins from moldy feed to milk. The farmers had almost no awareness of aflatoxins as 95% never heard of the term. After receiving an onsite briefing on effects of the toxin on animal and human health, and its transferability to milk, 98.3% farmers showed willingness to buy aflatoxin-safe feedstuffs, while 88.5% showed willingness to control aflatoxin in milk. Around half of the farmers considered aflatoxin control programs as affordable. On average, farmers agreed to pay 10.1% higher price for aflatoxin certified oilseed cakes. Availability of feedstuffs certified of low aflatoxin content was suggested by 22% of the participants as the critical step in reducing aflatoxins in milk. Other important suggestions included; subsidy on quality feeds (18%), raising awareness (18%), and legislation and monitoring (16%). The present results suggest that the current practice of milk monitoring in the country can yield desirable results only if it is coupled with feed certification programs ensuing availability of aflatoxin-safe feeds. Further, awareness can positively impact participation of producers in aflatoxin control programs. In this regard, awareness about effects of aflatoxins on animal health was found to be a more powerful trigger of voluntary control compared with the awareness of the toxin’s transferability to milk.

Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning models, including long short-term memory (LSTM) and bidirectional LSTM, and machine learning models, including decision tree and random forest regression, were utilized to forecast the levels of NO2, SO2, and CO in the atmosphere. Our results demonstrate that implementing green energy transportation systems in urban centers of developing countries can enhance air quality by approximately 98%. Notably, the bidirectional LSTM model outperformed others in predicting NO2 and SO2 concentrations, while the LSTM model excelled in forecasting CO concentration. These results offer valuable insights into predicting air pollution levels and guiding green energy policies to mitigate the adverse health effects of air pollution.

There is an exponential rise in the use of smartphones in government and private institutions due to business dependencies such as communication, virtual meetings, and access to global information. These smartphones are an attractive target for cybercriminals and are one of the leading causes of cyber espionage and sabotage. A large number of sophisticated malware attacks as well as advanced persistent threats (APTs) have been launched on smartphone users. These attacks are becoming significantly more complex, sophisticated, persistent, and undetected for extended periods. Traditionally, devices are targeted by exploiting a vulnerability in the operating system (OS) or device sensors. Nevertheless, there is a rise in APTs, side-channel attacks, sensor-based attacks, and attacks launched through the Google Play Store. Previous research contributions have lacked contemporary threats, and some have proven ineffective against the latest variants of the mobile operating system. In this paper, we conducted an extensive survey of papers over the last 15 years (2009–2023), covering vulnerabilities, contemporary threats, and corresponding defenses. The research highlights APTs, classifies malware variants, defines how sensors are exploited, visualizes multiple ways that side-channel attacks are launched, and provides a comprehensive list of malware families that spread through the Google Play Store. In addition, the research provides details on threat defense solutions, such as malware detection tools and techniques presented in the last decade. Finally, it highlights open issues and identifies the research gap that needs to be addressed to meet the challenges of next-generation smartphones.

Delonix regia (common name: Flame tree) pods, an inexpensive lignocellulosic waste matrix, were successfully used to produce value-added bioethanol. Initially, the potentiality of D. regia pods as a lignocellulosic biomass was assessed by Fourier-transform infrared spectroscopy (FTIR), which revealed the presence of several functional groups belonging to cellulose, hemicellulose, and lignin, implying that D. regia pods could serve as an excellent lignocellulosic biomass. Response Surface Methodology (RSM) and Central Composite Design (CCD) were used to optimize pretreatment conditions of incubation time (10–70 min), H2SO4 concentration (0.5–3%), amount of substrate (0.02–0.22 g), and temperature (45–100 °C). Then, RSM-suggested 30 trials of pretreatment conditions experimented in the laboratory, and a trial using 0.16 g substrate, 3% H2SO4, 70 min incubation at 90 °C, yielded the highest amount of glucose (0.296 mg·mL−1), and xylose (0.477 mg·mL−1). Subsequently, the same trial conditions were chosen in the downstream process, and pretreated D. regia pods were subjected to enzymatic hydrolysis with 5 mL of indigenously produced cellulase enzyme (74 filter per unit [FPU]) at 50 °C for 72 h to augment the yield of fermentable sugars, yielding up to 55.57 mg·mL−1 of glucose. Finally, the released sugars were fermented to ethanol by Saccharomyces cerevisiae, yielding a maximum of 7.771% ethanol after 72 h of incubation at 30 °C. Conclusively, this study entails the successful valorization of D. regia pods for bioethanol production.

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. In this paper, a fog computing-based smart farming framework is proposed that utilizes UAVs to gather data from IoT sensors deployed in farms and offloads it at fog sites deployed at the network edge. The framework adopts the concept of a charging token, where upon completing a trip, UAVs receive tokens from the fog node. These tokens can later be redeemed to charge the UAVs for their subsequent trips. An intrusion detection system is deployed at the fog nodes that utilize machine learning models to classify UAV behavior as malicious or benign. In the case of malicious classification, the fog node reduces the tokens, resulting in the UAV not being able to charge fully for the duration of the trip. Thus, such UAVs are automatically eliminated from the UAV pool. The results show a 99.7% accuracy in detecting intrusions. Moreover, due to token-based elimination, the system is able to conserve energy. The evaluation of CPU and memory usage benchmarks indicates that the system is capable of efficiently collecting smart-farm data, even in the presence of attacks.

Aim: Colorectal cancer is a kind of solid tumor and third most common of cancer which leads to death. It is a heterogeneous disease characterized by genetic and epigenetic aberrations. The present study was aimed to reveal the mutation at TP53 in Peshawar Pakistan. Material and methods: For this purpose, a total of 50 blood samples in EDTA tubes was collected from Peshawar with complete information of patients using questionnaire. The polymerase chain reaction was performed. Results: The results of the present study have revealed that the ratio of colorectal cancer in males is higher 62% than females (38%). Histopathological results have indicated that moderately differentiated adenocarcinoma is 25 (50%) followed by poorly differentiated adenocarcinoma 15 (30%), Well-differentiated adenocarcinoma 8 (16%), and Metastatic adenocarcinoma 2 (4%) in 50 colorectal cancer patients. The nucleotide sequence of the human TP53 gene of colorectal cancer patients was aligned with the human wild-type TP53 database sequence with NC_191170 (NCBI) using CLUSTALW. After BLASTING, the mutation was found in exon 5 and exon 7 of TP53, while no mutation was found in exon 8 of TP53. That patient in which point mutation was found in exon 5 and 7 respectively showed less survival as compared to that of colorectal cancer patients having no point mutation in the TP53 gene. Conclusion: The present study concluded that point mutations were found in exons 5&7 of the TP53 gene and patients having TP53 gene mutations shown less survival rate compared to colorectal cancer patients having no mutations.

Abstract Fusarium verticillioides is an important fungal pathogen of maize, causing stalk rot and severely affecting crop production. The aim of this study was to characterize the protective effects of formulations based on Jacaranda mimosifolia leaf extracts against F. verticillioides in maize. We compared different seed treatments comprising J. mimosifolia extracts, chemical fungicide (mefenoxam) and salicylic acid to modulate the defense system of maize host plants. Both aqueous and methanolic leaf extracts of J. mimosifolia (1.2% w/v) resulted in 96–97% inhibition of mycelial growth of F. verticillioides. While a full-dose (1.2%) extract of J. mimosifolia provided significant protective effects on maize plants compared to the inoculated control, a half-dose (0.6% w/v) application of J. mimosifolia in combination with half-strength mefenoxam was the most effective treatment in reducing stalk rot disease in pot and field experiments. The same seed treatment significantly upregulated the expression of genes in the leaves encoding chitinase, glucanase, lipid transfer protein, and pathogenesis-related proteins PR-1, PR-5 and PR-10, 72 h after inoculation. This treatment also induced the activities of peroxidase, polyphenol oxidase, protease, acid invertase, chitinase and phenylalanine ammonia lyase. We conclude that seed pre-treatment with J. mimosifolia extract with half-strength chemical mefenoxam is a promising approach for the management of stalk rot in maize.

In recent years, the interdisciplinary field of quantum computing has rapidly developed and garnered substantial interest from both academia and industry due to its ability to process information in fundamentally different ways, leading to hitherto unattainable computational capabilities. However, despite its potential, the full extent of quantum computing’s impact on healthcare remains largely unexplored. This survey paper presents the first systematic analysis of the various capabilities of quantum computing in enhancing healthcare systems, with a focus on its potential to revolutionize compute-intensive healthcare tasks such as drug discovery, personalized medicine, DNA sequencing, medical imaging, and operational optimization. Through a comprehensive analysis of existing literature, we have developed taxonomies across different dimensions, including background and enabling technologies, applications, requirements, architectures, security, open issues, and future research directions, providing a panoramic view of the quantum computing paradigm for healthcare. Our survey aims to aid both new and experienced researchers in quantum computing and healthcare by helping them understand the current research landscape, identifying potential opportunities and challenges, and making informed decisions when designing new architectures and applications for quantum computing in healthcare.

The large family of enzymes, known as polyphenols oxidases, includes laccase. Due to the inclusion of a copper atom in their catalytic core, laccases are frequently referred to as multi-copper oxidases. Laccases are versatile enzymes that can catalyze the oxidation of a wide range of phenolic and non-phenolic substances. In the current study, a local strain of Aspergillus niger was used for solid-state fermentation to produce fungal laccase, as well as purify and optimize laccase. The enzyme profile, which was acquired using guaiacol to measure enzyme activity, showed that after five days of incubation, wheat straw provided the highest level of laccase activity, or 2.551 U/mL. A technique called response surface methodology (RSM) was used to examine the effects of various conditions on the production of enzymes. The RSM results demonstrated that after five days of incubation, the enzyme activity was at its highest at 45 °C, pH 5.5, and 30% moisture level, inoculated with 2 mL mycelium. Through ammonium sulphate precipitation and dialysis, the enzyme was purified. Additionally, column chromatography was used to further purify laccase. The next step was enzyme characterization to evaluate how temperature and pH affected enzyme activity. At 45 °C and pH 5.5, the isolated enzyme produced its highest level of activity. The findings of the current study showed that A. niger is capable of producing laccase in an economical and environmentally friendly way. Due to its unique oxidative and catalytic features, this enzyme has received a lot of attention recently.

With the global energy crisis, increasing demand, and a national-level emphasis on electric vehicles (EVs), numerous innovations are being witnessed throughout the EV industry. EVs are equipped with sensors that maintain a sustainable environment for the betterment of society and enhance human sustainability. However, at the same time, as is the case for any new digital technology, they are susceptible to threats to security and privacy. Recent incidents demonstrate that these sensors have been misused for car and energy theft, financial fraud, data compromise, and have caused severe health and safety problems, amongst other things. To the best of our knowledge, this paper provides a first systematic analysis of EV sustainability, digital technologies that enhance sustainability, their potential cybersecurity threats, and corresponding defense. Firstly, three robust taxonomies have been presented to identify the dangers that can affect long-term sustainability domains, including (1) life and well-being, (2) safe environment, and (3) innovation and development. Second, this research measures the impact of cybersecurity threats on EVs and correspondingly to their sustainability goals. Third, it details the extent to which specific security controls can mitigate these threats, thereby allowing for a smooth transition toward secure and sustainable future smart cities.

Water quality deterioration is a serious problem with the increase in the urbanization rate. However, water quality monitoring uses grab sampling of physico-chemical parameters and a water quality index method to assess water quality. Both processes are lengthy and expensive. These traditional indices are biased towards the physico-chemical parameters because samples are only collected from certain sampling points. These limitations make the current water quality index method unsuitable for any water body in the world. Thus, we develop an enhanced water quality index method based on a semi-supervised machine learning technique to determine water quality. This method follows five steps: (i) parameter selection, (ii) sub-index calculation, (iii) weight assignment, (iv) aggregation of sub-indices and (v) classification. Physico-chemical, air, meteorological and hydrological, topographical parameters are acquired for the stream network of the Rawal watershed. Min-max normalization is used to obtain sub-indices, and weights are assigned with tree-based techniques, i.e., LightGBM, Random Forest, CatBoost, AdaBoost and XGBoost. As a result, the proposed technique removes the uncertainties in the traditional indexing with a 100% classification rate, removing the necessity of including all parameters for classification. Electric conductivity, secchi disk depth, dissolved oxygen, lithology and geology are amongst the high weighting parameters of using LightGBM and CatBoost with 99.1% and 99.3% accuracy, respectively. In fact, seasonal variations are observed for the classified stream network with a shift from 55:45% (January) to 10:90% (December) ratio for the medium to bad class. This verifies the validity of the proposed method that will contribute to water management planning globally.

The internet of things (IoT) is prone to various types of denial of service (DoS) attacks due to their resource-constrained nature. Extensive research efforts have been dedicated to securing these systems, but various vulnerabilities remain. Notably, it is challenging to maintain the confidentiality, integrity, and availability of mobile ad hoc networks due to limited connectivity and dynamic topology. As critical infrastructure including smart grids, industrial control, and intelligent transportation systems is reliant on WSNs and IoT, research efforts that forensically investigate and analyze the cybercrimes in IoT and WSNs are imperative. When a security failure occurs, the causes, vulnerabilities, and facts behind the failure need to be revealed and examined to improve the security of these systems. This research forensically investigates the performance of the ad hoc IoT networks using the ad hoc on-demand distance vector (AODV) routing protocol under the blackhole attack, which is a type of denial of service attack detrimental to IoT networks. This work also examines the traffic patterns in the network and nodes to assess the attack damage and conducts vulnerability analysis of the protocol to carry out digital forensic (DF) investigations. It further reconstructs the networks under different modes and parameters to verify the analysis and provide suggestions to design roubust routing protocols.

Abstract Background In recent decades, parasitism has remained one of the major problems of dairy industry in Pakistan. In this context, the most common tick species prevailing is Hyalomma, Boophilus, Haemaphysalis, and Rhipicephalus. Result High prevalence was found in cow 20.83% as compared to buffaloes. Over all prevalence of ticks was found as 11.3% whereas no ticks were found from LRS (livestock research station), and Malpur showed higher prevalence (8.67%). Breed wise tick prevalence showed that crossbred cattle was heavily infested (39.53%) while pure bred indigenous cattle was mildly infested (3.03%). Three tick genera found were Hyalomma with high prevalence rate 6.63% and Rhipicephalus and Boophilus with low prevalence rate 1.53% while management system of LRS in Islamabad was found satisfactory with no tick prevalence and rural areas of Islamabad was not satisfactory with 23% of tick prevalence ration. It was concluded from the study that ticks were present in the crossbred cattles of pre-urban areas specially Malpur region of Islamabad with low management system, so there is a need of proper monitoring and management in the pre-urban areas. Conclusion Key steps should be taken for good managements regarding large animals while special care must be taken for small animals. Proper spray must be carried out for the removal of pathogenic species.

Smart cities are envisioned to facilitate the well-being of the society through efficient management of the Internet of Things resources and the data produced by these resources. However, the enormous number of such devices would result in unprecedented growth in data, creating capacity issues related to the acquisition, transfer from one location to another, storage, and finally the analysis. The traditional networks are not sufficient to support the transfer of this huge amount of data, proving to be costly both in terms of delay and energy consumption. Alternative means of data transfers are thus required to support this big data produced by smart cities. In this paper, we have proposed an efficient data-transfer framework based on volunteer vehicles whereby we employ vehicles to carry data in the direction of the destination. The framework promotes self-belonging, social awareness, and energy conservation through urban computing encouraging participation by citizens. The proposed framework can also facilitate the research community to benchmark their own route selection algorithms easily. Further, we performed an extensive evaluation of the proposed framework based on realistic models of vehicles, routes, data-spots, data chunks to be transmitted and the energy consumed. Our results show the efficacy of the proposed data transfer framework as the energy consumed through vehicles is significantly less than that consumed by transmission over the Internet thereby reducing the carbon footprint. The results also offer several insights into the optimal configuration of a vehicular data transfer network based on analysis of delay, energy consumption, and data-spot utilization.

Software-defined networking (SDN) offers a novel paradigm for effective network management by decoupling the control plane from the data plane thereby allowing a high level of manageability and programmability. However, the notion of a centralized controller becomes a bottleneck by opening up a host of vulnerabilities to various types of attacks. One of the most harmful, stealthy, and easy to launch attacks against networked systems is the link flooding attack (LFA). In this paper, we demonstrate the vulnerability of the SDN control layer to LFA and how the attack strategy differs when targeting traditional networks which primarily involves attacking the links directly. In LFA, the attacker employs bots to surreptitiously send low rate legitimate traffic on the control channel which ultimately results in disconnecting control plane from the data plane. Mitigating LFA on the control channel remains a challenge in the network security paradigm with the use of network traffic filtering only. To address this challenge, we propose CyberPulse, a novel effective countermeasure, underpinning a machine learning-based classifier to alleviate LFA in SDN. CyberPulse performs network surveillance by classifying network traffic using deep learning techniques and is implemented as an extension module in the Floodlight controller. CyberPulse was evaluated for its accuracy, false positive rate, and effectiveness as compared to competing approaches on realistic networks generated using Mininet. The results show that CyberPulse can classify malicious flows with high accuracy and mitigate them effectively.

Electric vehicles (EVs) are becoming popular due to their efficiency, eco-friendliness, and the increasing cost of fossil fuel. EVs support a variety of apps because they house powerful processors and allow for increased connectivity. This makes them an attractive target of stealthy cryptomining malware. Recent incidents demonstrate that both the EV and its communication model are vulnerable to cryptojacking attacks. The goal of this research is to explore the extent to which cryptojacking impacts EVs in terms of recharging and cost. We assert that while cryptojacking provides a financial advantage to attackers, it can severely degrade efficiency and cause battery loss. In this paper we present a simulation model for connected EVs, the cryptomining software, and the road infrastructure. A novel framework is proposed that incorporates these models and allows an objective quantification of the extent of this economic damage and the advantage to the attacker. Our results indicate that batteries of infected cars drain more quickly than those of normal cars, forcing them to return more frequently to the charging station for a recharge. When just 10% of EVs are infected we observed 70.6% more refueling requests. Moreover, if the hacker infects a charging station then he can make a USD 436.4 profit per day from just 32 infected EVs. Overall, our results demonstrate that cryptojackers injected into EVs indirectly provide a financial advantage to the charging stations at the cost of an increased energy strain on society.

Environmental concerns have urged a search for eco-friendly refrigerants in the refrigeration industry to overcome ozone depletion and global warming problems. Therefore, current research emphasizes frictional pressure drop during flow boiling of environment-friendly refrigerants (GWP

The convective and conductive heat transfer between the solar collector and working fluids make photothermal performance limited, and result in a higher rate of heat loss from the surface of the conventional absorber to the surroundings. Direct absorption solar collectors (DASC) are a favorable alternative for their improved photothermal performance. In this study, a simulation based on the performance of a nanostructured solar collector has been carried out using TRNSYS. The connective and conductive heat transfer from direct solar collectors were improved by using nanofluids and three different nanostructured materials, CuO, GO, and ZnO, in this study. The analysis determines the outlet temperature of the working fluids that passed through the direct solar collector. The TRNSYS model consists of a direct solar collector and weather model for Lahore city, the simulations were performed for the whole year for 1,440 h. The stability of these nanostructured materials in the water was investigated by using a UV‐Vis spectrophotometer. Various performance parameters of direct solar collectors were determined, such as variation in outlet collector temperature and heat transfer rates. The numerical model is validated with experimental results. A maximum outlet temperature of 63°C was observed for GO-based nanofluids. The simulation results show that for the whole year, nanofluids improved the performance of direct solar collectors. Significant improvements in the heat transfer rate of 23.52, 21.11, and 15.09% were observed for the nanofluids based on nanostructures of CuO, ZnO, and GO respectively, as compared to water. These nanostructured energy materials are beneficial in solar-driven applications like solar desalination, solar water, and space heating.

Chordee is a condition in which the penile head has a curvature. It can be towards ventral or dorsal aspect. Dorsal chordee is very rare. In many normal males mild degrees of curvature can exist. Causes of dorsal chordee can be diverse but urethral duplication as its etiology is even rarer. We report a case of six year old boy who presented with severe dorsal chordee with associated double urethra and cleft of the glans dorsally without hypospadia.

Annular mini-channel Stirling heater are replacing conventional tubular heater in modern free piston Stirling engines due to reliability and manufacturing issues. Heat transfer characteristics of a new mini channel heater with variable cross section are analyzed under oscillating flow. The effect of oscillating speed and dimensionless fluid displacement (Aₒ) on heat transfer rate is evaluated. The variation of operating speed from 150 to 1200 rpm enhanced heating power requirement from 191 to 581 W for wall temperature in 473–773 K range. However, hot space gas temperature is decreased by 66–162 K under same conditions. The change in Aₒ from 10.98 to 15.09 resulted in 13.2% enhancement in heat transfer at 300 rpm whereas maximum enhancement of 27.6% was noticed at 1200 rpm with maximum hot space temperature drop of 9.5 K. Based on the data, an empirical correlation for heat transfer in annular mini channel Stirling heater is proposed, the correlation covers kinetic Reynolds number (Reω) in 0.48–1.94 range.

Aflatoxin M1 contamination of milk in Pakistan, like many developing countries, is poorly understood. The present study was therefore conducted to determine AFM1 contamination of milk and its contributory factors in Pakistan. We sampled milk and feedstuffs from 450 peri-urban dairy farms in seven major cities following a cross-sectional study design. Analysis of milk using ELISA revealed high contamination with an overall average of 3164.5 ng of AFM1/L, and significant differences (p < 0.001) between cities. The milk sampled from Gilgit, in northern hilly areas, had an average AFM1 level of 92.5 ng/L. Milk from other cities had 3529.7 ng/L average contamination, with only 5.7% samples qualifying the maximum tolerable limit of 500 ng of AFM1/L. Heavy mean aflatoxin contamination was found in bakery waste (724.6 μg/kg), and cottonseed cake (600.8 μg/kg). Rest of the other feedstuffs had moderate to low mean aflatoxin contamination, ranging from 66.0 μg/kg in maize stover to 3.4 μg/kg in wheat bran. The mean aflatoxin level in commercial dairy concentrates was 32.7 µg/kg. About 80% of the total aflatoxin intake of dairy animals was contributed by cottonseed cake alone due to its high aflatoxin contamination and proportion in dairy rations. On-farm storage time of oilseed cakes varied (p < 0.01) in different cities but was not associated with aflatoxin contamination. The exceptionally high AFM1 contamination suggests that milk from peri-urban dairy farms is a serious public health threat in Pakistan. This situation can be mitigated by reducing aflatoxin contamination in cottonseed cake and promoting the use of commercial concentrates and other feedstuffs with low contamination.

Jacaranda mimosifolia trees are grown in frost-free regions globally. The aim of this study was to evaluate the methanol crude extract and various fractions of increasing polarity of J. mimosifolia leaves for bioactive metabolites, as well as antimicrobial, antioxidant and anticancer activities. The anti-inflammatory potential of the various fractions of J. mimosifolia leaf extract was studied via the lipoxygenase (LOX) inhibitory assay. Methanol crude extract (ME), derived fractions extracted with chloroform (CF) and ethyl acetate (EAF), and residual aqueous extract (AE) of dried J. mimosifolia leaves were assayed for polyphenolic compounds, their antioxidant, antimicrobial and lipoxygenase (LOX) inhibitory activities, and anticancer properties. Polyphenolic compounds were determined via HPLC while phytochemicals (total phenolics, flavonoids, tannins and ortho-diphenol contents), antioxidant activities (DPPH, hydrogen peroxideperoxide, hydroxyl and superoxide radical anions) and LOX were measured via spectrophotometry. Methanol extracts and various fractions were evaluated for antibacterial activities against Bacillus subtilis, Klebsiella pneumonia, Pseudomonas aeruginosa and Staphylococcus aureus. Antifungal potential of the fractions was tested against three species: Aspergillus flavus, Aspergillus fumigatus and Fusarium oxysporum. The highest values for total phenolic content (TPC), total flavonoid content (TFC), flavonols, tannins and ortho-diphenols were in the ME, followed by CF > EAF > AE. ME also had the highest antioxidant activity with EC50 values 48±1.3, 45±2.4, 42±1.3 and 46±1.3 μg/mL based on the DPPH, hydrogen peroxide, hydroxyl radical and superoxide radical assays, respectively. TPC and TFC showed a significant, strong and positive correlation with the values for each of these antioxidant activities. ME exhibited anti-inflammatory potential based on its LOX inhibitory activity (IC50 = 1.3 μg/mL). ME also had the maximum antibacterial and antifungal potential, followed by EAF > CF > AE. Furthermore, ME showed the strongest cytotoxic effect (EC50 = 10.7 and 17.3 μg/mL) against human hormone-dependent prostate carcinoma (LnCaP) and human lung carcinoma (LU-1) cell lines, respectively. Bioactive compounds present in leaf methanol extracts of J. mimosifolia were identified using gas chromatography-mass spectrometry (GC-MS). Fifteen compounds were identified including phenolic and alcoholic compounds, as well as fatty acids. Our results suggest that J. mimosifolia leaves are a good source of natural products with antioxidant, anti-inflammatory and anti-cancer properties for potential therapeutic, nutraceutical and functional food applications.

IntroductionBMI is a tool to measure maternal nutritional status. Maternal malnutrition is frequently reported health problem especially during child bearing age and effects neonatal birth weight.AimTo determine relationship between prepregnancy maternal BMI and neonatal birth weight.Methods and materialProspective, cross sectional study conducted in Fatima Memorial Hospital, Lahore, Pakistan over a period of 1 year including 2766 mother-neonate pairs. All full term, live born neonates of both gender in early neonatal period (ResultsData analysis of 2766 mother-neonates pairs showed that there were 32.9% overweight and 16.5% obese mothers. More than two third of all overweight and obese mothers were of age group between 26-35 years. Diabetes mellitus, hypertension, medical illness, uterine malformations and caesarean mode of delivery were more prevalent in obese mothers as 22.8%, 10.1%, 13.2%, 2.6% and 75.4% respectively. Mean birth weight, length and OFC increased with increasing maternal BMI. Comparing for normal weight mothers, underweight mothers were at increased risk of low birth weight (p< 0.01) and low risk of macrosomic neonates (pConclusionsOur study highlights that direct relationship exists between maternal BMI and neonatal birth weight.

Kabul was a bridge between Indian Mughal Empire and Central Asia, the ancestral homeland of the founder of the Mughal Empire. Kabul, the capital of Afghanistan, carries about 3,500 years old historical records mentioning Kabul with different names like, Kubha, Gandahara, Kabura, Ortospana, Kapul, Zavul, and Zabul etc. Many great warriors and conquerors from Central Asia used Kabul as their route to India. It was also known as a gateway to India and Central Asia. Kabul became the foundation stone for the Mughal Empire in India. During the Mughal era Kabul entered into a new phase and with the invasion of Babur the area got the position of the capital of the Mughals. The early Mughal rulers paid much attention to the affairs of Kabul, because their existence to a greater extent was dependent on their strong hold over Kabul. The research work is focused on development of Kabul under Mughal kings particularly Babur, Humayun, Akbar, Shah Jahan and Aurangzeb. The later Mughal kings after Aurangzeb were not able to end political disturbances in Kabul which not only displeased the people of Kabul but also encouraged the neighbouring powers to invade the valley. Historical and analytical methods are used in this research and Pashtu, Dari, Urdu and English sources have been utilized to gauge development of Kabul during that point in time. No research work has been carried out on this aspect of Kabul so far. The analysis of the development of Kabul under Mughals brings to limelight the geostrategic and politico-economic worth of Kabul as an important Caravanserai on the trade route between Central and South Asia.

Computational fluid dynamics (CFD) analyses for the performance improvement of a closed-conduit ultraviolet (UV) reactor were performed by changing the lamp positions from symmetric to asymmetric. The asymmetric lamp positioning can be useful for UV reactor design and optimization. This goal was achieved by incorporating the two performance factors, namely reduction equivalent dose (RED) and system dose performance. Four cases were carried out for asymmetric lamp positioning within the UV reactor chamber and each case consisted of four UV lamps that were simulated once symmetrically and four times asymmetrically. The results of the four asymmetric cases were compared with the symmetric one. Moreover, these results were evaluated by using CFD simulations of a closed-conduit UV reactor. The fluence rate model, UVCalc3D was employed to validate the simulations results. The simulation results provide detailed information about the dose distribution, pathogen track modeling and RED. The RED value was increased by approximately 15% by using UVCalc3D fluence rate model. Additionally, the asymmetric lamp positioning of the UV lamps had more than 50% of the pathogens received a better and a higher UV dose than in the symmetric case. Consequently, the system dose performance was improved by asymmetric lamp positioning. It was concluded that the performance parameters (higher RED and system dose performance) were improved by using asymmetric lamp positioning.

The Internet of Things (IoT) is being deployed for a plethora of use-case scenarios. In any deployment, a number of configuration choices are available that achieve the mission goal. However, IoT security incidents have demonstrated that different configurations are vulnerable to varied risk levels. We propose the IoTRiskAnalyzer framework to formally and quantitatively analyze these risks using probabilistic model checking. IoTRiskAnalyzer takes vulnerability scores, candidate IoT configurations, and attacker's capabilities as inputs. It then generates the system and threat models to compute attack likelihood and attacker cost for each configuration. Evaluation indicates that IoTRiskAnalyzer is efficient and automatically prioritizes the input configurations on the basis of risk exposure.

At present, biodiesel is gaining tremendous attention due to its eco-friendly nature and is possible substitute for diesel fuel. Biodiesel as renewable energy source can be produced from edible and non-edible feedstock. Non-edible resources are preferred to circumvent for food competition. In the present study FAME was produced from Ricinus communis L. oil by transesterification with methanol and ethanol in the presence of potassium hydroxide. The practical optimal condition for the production of biodiesel from castor bean was found to be: methanol/oil molar ratio, 6:1; temperature, 60 °C; time, 45 min; catalyst concentration 0.32 g. Quality assessment of biodiesel showed comparable results with ASTM standards. The values of specific gravity (SG) were 0.5, kinematic viscosity 2.45 cSt, acid values 0.13 mg KOH/g, carbon residue 0.03%, flash point 119 °C, fire point 125 °C, cloud point −10 °C and pour point −20 °C of Ricinus FAME, respectively. Based on our data, it is suggested that to overcome prevailing energy crisis this non-edible plant is useful for production of biodiesel, which is an alternate to fossil fuel and may be used alone or in blend with HSD in engine combustion.

Agro-based materials are primarily composed of complex polysaccharides that strengthen microbial growth for the production of industrially relevant value-added products. Therefore, in the present study, solid state fermentation (SSF) was carried out using agro-based waste materials (apple pomace, peanut shell and a mixture of both apple pomace and peanut shell with 50:50 ratio) as carriers/support for SSF to enhance citric acid production from single and co-culture consortia of Aspergillus ornatus and Alternaria alternata. During initial screening trial it was observed that growth media supplemented with apple pomace under SSF process of co-culture consortia presenting the preeminent 0.46 ± 0.42 mg/mL of citric acid. On partial optimization co-culture showed the maximum citric acid yield (2.644 ± 0.99 mg/mL) in the presence of arginine as a nutritional ingredient at 30 °C in an apple pomace based medium at 50% moisture content with pH of 5 and substrate concentration (25 g) after 48th of solid state fermentation. In conclusion, a suitable addition of fermentative substrate to the SSF medium increased fungal growth, sugar utilization and citric acid production when used in lower concentrations.

The present study was aimed to investigate the culture influence on pectin lyase production potential of fungal strain Aspergillus oryzae. The enzyme profile of A. oryzae showed highest activity of pectin lyase after 3rd day of incubation on lemon peel waste under solid state fermentation conditions. To induce the pectin lyase synthesis capability of A. oryzae at optimal level various culture variables including physical and nutritional parameters were optimized by adopting classical optimization technique. Therefore, through fermentation process optimization the production of pectin lyase was substantially induced up to the level of 875 U/mL, when fermentation medium of lemon peel waste inoculated with 5 mL spore suspension of A. oryzae. The optimal fermentation conditions for maximum pectin lyase yield were as: optimum pH 5, 70% moisture level and incubated at 40 °C in addition with 1% sterile glucose solution as readily available carbon source and 0.2% yeast extract as an inexpensive nitrogen supplement (1%). The results obtained in current investigation so far demonstrated that culture conditions have great influence on the pectin lyase production potential of A. oryzae.

From the last several years, in serious consideration of the worldwide economic and environmental pollution issues there has been increasing research interest in the value of bio-sourced lignocellulosic biomass. Agro-industrial biomass comprised on lignocellulosic waste is an inexpensive, renewable, abundant and provides a unique natural resource for large-scale and cost-effective bio-energy collection. To expand the range of natural bio-resources the rapidly evolving tools of biotechnology can lower the conversion costs and also enhance target yield of the product of interest. In this background green biotechnology presents a promising approach to convert most of the solid agricultural wastes particularly lignocellulosic materials into liquid bio based energy-fuels. In fact, major advances have already been achieved to competitively position cellulosic ethanol with corn ethanol. The present summarized review work begins with an overview on the physico-chemical features and composition of agro-industrial biomass. The information is also given on the multi-step processing technologies of agro-industrial biomass to fuel ethanol followed by a brief summary of future considerations.

Lactic acid is one of the most important organic acid which is being extensively used around the globe in a range of industrial and biotechnological applications. From its very old history to date, many methods have been introduced to improve the optimization of lactic acid to get highest yields of the product of industrial interests. In serious consideration of the worldwide economic and lactic acid consumption issues there has been increasing research interest in the value of materials with natural origin, which are cheap, abundant and easily available all around the year. Recent trends showed that lactic acid production through fermentation is advantageous over chemical due to the environmental concerns of the modern world. The eco-friendly processing and fermentable capability of many of the agricultural and agro-industrial based raw materials or by-products respectively makes them attractive candidates in fermentation biotechnology to produce a value-added product with multiple applications. In fact, major advances have already been achieved in recent years in order to get pure lactic acid with optimal yield. The present review work is summarized on the multi-step processing technologies to produce lactic acid from different substances as a starting material potentially from various agro-industrial based biomasses. The information is also given on a purification through schematic representation of the product of quality interests.

Advance Persistent Threats (APTs) are a continuous hacking process during which the perpetrator changes signatures and uses different malware to launch an attack. For these reasons most of the time APTs remain undetected by the conventional IDSs. Ironically a large amount of data is available regarding APTs in literature and online repositories. However, due to high adaptivity and large volume of data, analyzing information about APT incidents is challenging for security analysts. Several security models have been proposed for analysis and understanding of the APTs. In this regard, two recent approaches: Cyber Kill Chain (CKC) and Pyramid of Pain (POP) are noteworthy. CKC is an attacker model while POP is a defender model. If these approaches are combined into a suitable defense framework, then these can be used as an early warning system against APTs. The contributions of this paper are two-fold. The first is development of CKC and POP’s standalone ontologies, identifying relationships between these and developing a common ontology of APTs. Secondly, we propose a novel framework “APTs Analysis and Classification System – A2CS” which uses semantic rules for automatic analysis of APTs such as identification of their missing artifacts and inferencing of the Tactics, Techniques and Procedures being employed.

The Internet of Things (IoT) has spurred the interaction of a multitude of smart physical objects with the existing cyber world. These connected “things” leverage heterogeneous protocols, diverse capabilities and complex environmental interdependencies, which have reshaped their risk profiles through introduction of novel threat vectors. In this paper, we present a formal framework to model and analyze the security risks linked with generic IoT systems. The approach uses existing and widely-accepted Web Ontology Language (OWL) based ontologies, by extending them with IoT-specific concepts and populating them with IoT instances. Risk assessment, quantification and selection of viable mitigation techniques is carried out automatically with the help of rule-based constraints and queries applied over OWL knowledgebase. The practicality and effectiveness of the approach is verified through implementation and evaluation over realistic IoT systems.