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In this study, ALOS-2 PALSAR L-band dual-pol (HH and HV) synthetic aperture radar (SAR) images were used for above ground biomass (AGB) estimation of Shisham (Dalbergia sissoo) tree species in the managed forest of Chichawatni Irrigated Plantation, Sahiwal District, Punjab, Pakistan. A total of 15 plots were surveyed during the field campaign and locally developed tree species-specific allometric equation was used for AGB calculation. The SAR images were pre-processed and calibrated to sigma nought (dB) for statistical AGB modeling. Nonlinear regression models between field-based AGB (ton/ha) and SAR backscatter sigma nought (dB) values give R2 (0.47 and 0.55) and RMSE (1.18 and 1.87) values for HH and HV-pol data, respectively. AGB maps generated through these regression models from both HH and HV-pol data show similar AGB values and spatial patterns. Due to a small number of sample plots, the Leave-One-Out Cross-Validation (LOOCV) proxy validation method was used for model validation, between estimated and predicted backscatter sigma nought (dB) values. The validation R2 values are small; however, outliers are identifiable in the validation scatterplots. The AGB maps from this study are useful as a first estimate of AGB values from remote sensing in the study region, especially in the context of the REDD+ program.

Flash floods which occur due to heavy rainfall in hilly and semi-hilly areas may prove deleterious when they hit urban centers. The prediction of such localized and heterogeneous phenomena is a challenge due to a scarcity of in-situ rainfall. A possible solution is the utilization of satellite-based precipitation products. The current study evaluates the efficacy of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) three-hourly products, i.e., 3B42 near-real-time (3B42RT) and 3B42 research version (3B42V7) at a sub-daily time scale. Various categorical indices have been used to assess the capability of products in the detection of rain/no-rain. Hourly rain rates are assessed by employing the most commonly used statistical measures, such as correlation coefficients (CC), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). Further, a diurnal analysis is performed to authenticate TMPA’s performance in specific hours of the day. In general, the results show the good capability of both TMPA products in the detection of rain/no-rain events in all seasons except winter. Specifically, 3B42V7 performed better than 3B42RT. Moreover, both products detect a high number of rainy days falsely in light rain ranges. Regarding rainfall measurements, TMPA products exhibit an overall underestimation. Seasonally, 3B42V7 underestimates rainfall in monsoon and post-monsoon, and overestimates in winter and pre-monsoon. 3B42RT, on the other hand, underestimates rainfall in all seasons. A greater MBE and RMSE are found with both TMPA rain measurements in monsoon and post-monsoon seasons. Overall, a weak correlation and high MBE between the TMPA (3B42RT, 3B42V7) and reference gauge hourly rain rates are found at a three-hourly time scale (CC = 0.41, 0.38, MBE = −0.92, −0.70). The correlation is significant at decadal (CC = 0.79, 0.77) and monthly (CC = 0.91, 0.90) timescales. Furthermore, diurnal rainfall analysis indicates low credibility of 3B42RT to detect flash flooding. Within the parameters of this study, we conclude that the TMPA products are not the best choice at a three-hourly time scale in hilly/semi-hilly areas of Pakistan. However, both products can be used at daily, yet more reliably above daily, time scales, with 3B42V7 preferable due to its consistency.

Intrusion Detection Systems (IDSs) play a crucial role in safeguarding network infrastructures from cyber threats and ensuring the integrity of highly sensitive data. Conventional IDS technologies, although successful in achieving high levels of accuracy, frequently encounter substantial model bias. This bias is primarily caused by imbalances in the data and the lack of relevance of certain features. This study aims to tackle these challenges by proposing an advanced machine learning (ML) based IDS that minimizes misclassification errors and corrects model bias. As a result, the predictive accuracy and generalizability of the IDS are significantly improved. The proposed system employs advanced feature selection techniques, such as Recursive Feature Elimination (RFE), sequential feature selection (SFS), and statistical feature selection, to refine the input feature set and minimize the impact of non-predictive attributes. In addition, this work incorporates data resampling methods such as Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTE_ENN), Adaptive Synthetic Sampling (ADASYN), and Synthetic Minority Oversampling Technique–Tomek Links (SMOTE_Tomek) to address class imbalance and improve the accuracy of the model. The experimental results indicate that our proposed model, especially when utilizing the random forest (RF) algorithm, surpasses existing models regarding accuracy, precision, recall, and F Score across different data resampling methods. Using the ADASYN resampling method, the RF model achieves an accuracy of 99.9985% for botnet attacks and 99.9777% for Man-in-the-Middle (MITM) attacks, demonstrating the effectiveness of our approach in dealing with imbalanced data distributions. This research not only improves the abilities of IDS to identify botnet and MITM attacks but also provides a scalable and efficient solution that can be used in other areas where data imbalance is a recurring problem. This work has implications beyond IDS, offering valuable insights into using ML techniques in complex real-world scenarios. [ABSTRACT FROM AUTHOR]

Cell wall disrupted and dried Microchloropsis gaditana (Mg), Tetraselmis chui (Tc) and Chlorella vulgaris (Cv) microalgae biomasses, with or without ethanol pre-treatment, were added to wheat bread at a wheat flour substitution level of 12%, to enrich bread protein by 30%. Baking performance, protein quality and basic sensory properties were assessed. Compared to wheat, Mg, Tc and Cv contain higher amounts of essential amino acids and their incorporation markedly improved protein quality in the bread (DIAAS 57–66 vs. 46%). The incorporation of microalgae reduced dough strength and bread volume and increased crumb firmness. This was most pronounced for Cv and Tc but could be improved by ethanol treatment. Mg gave adequate dough strength, bread volume and crumb structure without ethanol treatment. To obtain bread of acceptable smell, appearance, and colour, ethanol treatment was necessary also for Mg as it markedly reduced the unpleasant smell and intense colour of all algae breads. Ethanol treatment reduced the relative content of lysine, but no other essential amino acids. However, it also had a negative impact on in vitro protein digestibility. Our results show that Mg had the largest potential for protein fortification of bread, but further work is needed to optimize pre-processing and assess consumer acceptance.

In the late 1960s, the Islamic Republic of Pakistan’s capital shifted from Karachi to Islamabad, officially named Islamabad Capital Territory (ICT). In this aspect, the ICT is a young city, but undergoing rapid expansion and urbanization, especially in the last two decades. This study reports the measurement and characterization of ICT land cover change dynamics using Landsat satellite imagery for the years 1976, 1990, 2000, 2010, and 2016. Annual rate of change, landscape metrics, and urban forest fragmentation spatiotemporal analyses have been carried out, along with the calculation of the United Nations Sustainable Development Goal (SDG) indicator 11.3.1 Land Consumption Rate to the Population Growth Rate (LCRPGR). The results show consistent increase in the settlement class, with highest annual rate of 8.79% during 2000–2010. Tree cover >40% and 500 acres’ class decreased from 392 km2 (65.41%) to 241 km2 (55%), and ‘patch forest’ class increased from 15 km2 (2.46%) to 20 km2 (4.54%), from 1976 to 2016. The LCRPGR ratio was 0.62 from 1976 to 2000, increasing to 1.36 from 2000 to 2016.

People who suffer from swallowing disorders, commonly referred to as dysphagia, are often restricted to a texture-modified diet. In such a diet, the texture of the fluid is modified mainly by the addition of gum or starch-based thickeners. For optimal modification of the texture, tunable rheological parameters are shear viscosity, yield stress, and elasticity. In this work, the flow properties of commercial thickeners obtained from major commercial suppliers were measured both in shear and extensional flow using a laboratory viscometer and a newly developed tube viscometry technique, termed Pulsed Ultrasound Velocimetry plus Pressure Drop (PUV + PD). The two methods gave similar results, demonstrating that the PUV + PD technique can be applied to study flow during the swallowing process in geometry similar to that of the swallowing tract. The thickeners were characterized in relation to extensional viscosity using the Hyperbolic Contraction Flow method, with microscopy used as a complementary method for visualization of the fluid structure. The gum-based thickeners had significantly higher extensional viscosities than the starch-based thickeners. The rheological behavior was manifested in the microstructure as a hydrocolloid network with dimensions in the nanometer range for the gum-based thickeners. The starch-based thickeners displayed a granular structure in the micrometer range. In addition, the commercial thickeners were compared to model fluids (Boger, Newtonian, and Shear-thinning) set to equal shear viscosity at 50/s and it was demonstrated that their rheological behavior could be tuned between highly elastic, extension-thickening to Newtonian. Practical applications Thickeners available for dysphagia management were characterized for extensional viscosity to improve the understanding of these thickeners in large scale deformation. Extensional deformation behavior was further explained by using microcopy as corresponding technique for better understanding of structure/rheology relationship. Moreover, the major challenge in capturing human swallowing process is the short transit times of the bolus flow (<1 s). Therefore, the ultrasound-based rheometry method; PUV+PD which measures the real-time flow curve in ∼50 ms was used in addition to classical shear rheometry. The two methods complimented each other indicating that the PUV+PD method can be applied to study the transient swallowing process which is part of our future research, where we are studying the flow properties of fluids in an in vitro swallowing tract. [ABSTRACT FROM AUTHOR]