Search Results (13)

Traditional regression estimators like Ordinary Least Squares (OLS) and classical ridge regression often fail under multicollinearity and outlier contamination respectively. Although recently developed two-parameter ridge regression (TPRR) estimators improve efficiency by introducing dual shrinkage parameters, they remain sensitive to extreme observations. This study develops a new class of Two-Parameter Robust Ridge M-Estimators (TPRRM) that integrate dual shrinkage with robust M-estimation to simultaneously address multicollinearity and outliers. A Monte Carlo simulation study, conducted under varying sample sizes, predictor dimensions, correlation levels, and contamination structures, compares the proposed estimators with OLS, ridge, and the most recent TPRR estimators. The results demonstrate that TPRRM consistently achieves the lowest Mean Squared Error (MSE), particularly in heavy-tailed and outlier-prone scenarios. Application to the Tobacco and Gasoline Consumption datasets further validates the superiority of the proposed methods in real-world conditions. The findings confirm that the proposed TPRRM fills a critical methodological gap by offering estimators that are not only efficient under multicollinearity, but also robust against departures from normality. Full article

Groundwater is an important source of freshwater. At the same time, anthropogenic activities, in particular, industrialization, urbanization, population growth, and excessive application of fertilizers, are some of the major reasons for groundwater quality deterioration. Therefore, the present study is conducted to evaluate groundwater quality by using integrated water quality indices and a geospatial approach to identify the different water quality zones and propose management strategies for the improvement of groundwater quality. Groundwater quality was evaluated through the physicochemical parameters (pH, chloride (Cl⁻), fluoride(F⁻), iron (Fe⁻²), nitrate (NO₃⁻¹), nitrite (NO₂), arsenic (As), total hardness, bicarbonate (HCO₃⁻), calcium (Ca⁺²), magnesium (Mg⁺²), color, taste, turbidity, total dissolved solids (TDS)) and microbiological parameters including total coliforms, fecal coliforms, and Escherichia coli of samples collected from the water and sanitation agency (WASA) and urban units. Irrigation parameters crucial to the assessment, including (electrical conductivity (EC), residual sodium carbonates (RSC), and sodium adsorption ratio (SAR)), were also collected at more than 1100 sites within the study area of upper and central Punjab. After collecting the data of physicochemical parameters, the analysis of data was initiated to compute the water quality index for groundwater quality, a four-step protocol in which the Analytical Hierarchy Process (AHP) was used to determine the weights of selected parameters by generating a pairwise matrix, on the relative importance of parameters using the Satty scale. The index was then classified into five classes for quality assessment of drinking water (excellent, good, medium, bad, and very bad) and four classes for irrigation water quality assessment (excellent, good, permissible, and unsuitable). After computing the index values for drinking as well as irrigation purposes, the values were interpolated, and various maps were developed to identify the status of groundwater quality in different zones of the study area. Mitigation strategies for water pollution involve source control, such as monitoring industrial discharge points and managing waste properly. Additionally, treating wastewater through primary, secondary, or tertiary stages significantly improves water quality, reducing contaminants like heavy metals, microbiological agents, and chemical ions, safeguarding water resources. The findings highlight significant regional variations in water quality issues, with heavy metal concerns concentrated notably in Lahore and widespread emerging microbiological contamination across all studied divisions. This suggests a systemic problem linked to untreated industrial effluents and poorly managed sewerage systems. The computed indices for the Lahore, Sargodha, and Rawalpindi divisions indicate water quality ranging from marginal to unfit, underscoring the urgency for remediation. Conversely, other divisions fall within a medium class, potentially suitable for drinking purposes. Notably, microbiological contamination at 27% poses a major challenge for water supply agencies, emphasizing the critical need for pre-disposal primary, secondary, and tertiary treatments. These treatments could potentially rehabilitate 9%, 35%, and 41% of the study area, respectively, pointing toward tangible, scalable solutions critical for safeguarding broader water resources and public health. With the current pace of water quality deterioration, access to drinking water is a major problem for the public. The government should prioritize implementing strict monitoring mechanisms for industrial effluent discharge, emphasizing proper waste management to curb groundwater contamination. Establishing comprehensive pre-disposal treatments, especially primary, secondary, and tertiary stages, is imperative to address the prevalent heavy metal and microbiological issues, potentially rehabilitating up to 41% of affected areas. Additionally, creating proactive policies and allocating resources for sustainable groundwater management are crucial steps for ensuring broader water resource security and public health in the face of deteriorating water quality. Therefore, urgent regional action is needed to address escalating anthropogenic threats to groundwater, emphasizing the crucial need for proactive measures to safeguard public health and ensure sustainable water resources. Full article

The El Nino Southern Oscillation (ENSO) phenomenon is devastating as it negatively impacts global climatic conditions, which can cause extreme events, including floods and droughts, which are harmful to the region’s economy. Pakistan is also considered one of the climate change hotspot regions in the world. Therefore, the present study investigates the effect of the ENSO on extreme precipitation events across the Upper Indus Basin. We examined the connections between 11 extreme precipitation indices (EPIs) and two ENSO indicators, the Southern Oscillation Index (SOI) and the Oceanic Niño Index (ONI). This analysis covers both annual and seasonal scales and spans the period from 1971 to 2019. Statistical tests (i.e., Mann–Kendall (MK) and Innovative Trend Analysis (ITA)) were used to observe the variations in the EPIs. The results revealed that the number of Consecutive Dry Days (CDDs) is increasing more than Consecutive Wet Days (CWDs); overall, the EPIs exhibited increasing trends, except for the Rx1 (max. 1-day precipitation) and Rx5 (max. 5-day precipitation) indices. The ENSO indicator ONI is a temperature-related ENSO index. The results further showed that the CDD value has a significant positive correlation with the SOI for most of the UIB (Upper Indus Basin) region, whereas for the CWD value, high elevated stations gave a positive relationship. A significant negative relationship was observed for the lower portion of the UIB. The Rx1 and Rx5 indices were observed to have a negative relationship with the SOI, indicating that El Nino causes heavy rainfall. The R95p (very wet days) and R99p (extreme wet days) indices were observed to have significant negative trends in most of the UIB. In contrast, high elevated stations depicted a significant positive relationship that indicates they are affected by La Nina conditions. The PRCPTOT index exhibited a negative relationship with the SOI, revealing that the El Nino phase causes wet conditions in the UIB. The ONI gave a significant positive relationship for the UIB region, reinforcing the idea that both indices exhibit more precipitation during El Nino. The above observations imply that while policies are being developed to cope with climate change impacts, the effects of the ENSO should also be considered. Full article

In Pakistan, surface water supply for irrigation is decreasing, while water demand is increasing for agriculture production. Also, due to the fast rate of population growth, land holding capacity is decreasing. So, there is a need to develop appropriate technologies and design approaches for small-scale farmers to improve modern irrigation practices. In this study, a hydraulic and structural layout of CPIS was designed for small-scale farmers with some modifications. The hydraulic parameters and structural design of the CPIS were designed using IrriExpress and SAP2000 software, respectively. An economic analysis of the modified CPIS was carried out. The results revealed that in one complete revolution of the whole system, its span slope varied from 2.98 to 0.1%, and the wheel slope varied from 2.35 to −2.4%. The timing setting was 60% for one revolution, and the irrigation depth was 10 mm. When the time setting was reduced from 100% to 10%, the irrigation hours per cycle and irrigation depth both increased. Variendeel type-II trusses were designed for structural purposes using SAP2000 software. This design led to a 17% reduction in weight by lowering it from 1.916 to 1.5905 tons and a 44% reduction in joint count, decreasing it from 32 to 18. Our economic analysis revealed that the structural part of the system is more expensive than the hydraulic, electric and power parts for small-scale design. So, it was suggested that CPIS is suitable for land holdings from 100 to 250 acres, because when the area increases to more than 250 acres, there is no significant change in the cost. A towable system is more economical for small-scale farmers due to its lower cost per acre. This study will be helpful for the optimization of CPISs to improve water use efficiency and crop yield. Full article

In this study, hydrological modeling at the watershed level is used to assess the impacts of climate and land use changes on the catchment area of the Khanpur Dam, which is an important water source for Rawalpindi and Islamabad. The hydrological impact of past and anticipated precipitation in the Khanpur Dam watershed was forecast by using a HEC-HMS model. After calibration, the framework was employed to analyze the effects of changes in land cover and climate on the hydrological regime. The model used information from three climatic gauge stations (Murree, Islamabad Zero Point, and Khanpur Dam) to split the Khanpur Dam catchment area into five sub-basins that encompass the entire watershed region, each with distinctive characteristics. The model was evaluated and checked for 2016–2018 and 2019–2020, and it produced an excellent match with the actual and anticipated flows. After statistical downscaling with the CMhyd model, the most effective performing GCM (MPI-ESM1-2-HR) among the four GCMs was chosen and used to forecast projections of temperature and precipitation within two shared socioeconomic pathways (SSP2 and SSP5). The predictions and anticipated changes in land cover were incorporated into the calibrated HEC-HMS model to evaluate the potential impact of climate change and land cover change at the Khanpur Dam. The starting point era (1990–2015) and the projected period (2016–2100), which encompassed the basis in the present century, were analyzed annually. The results indicated a spike in precipitation for the two SSPs, which was predicted to boost inflows all year. Until the end of the twenty-first century, SSP2 predicted a 21 percent rise in precipitation in the Khanpur Dam catchment area, while SSP5 predicted a 28% rise in precipitation. Increased flows were found to be projected in the future. It was found that the calibrated model could also be used effectively for upcoming studies on hydrological effects on inflows of the Khanpur Dam basin. Full article

The impact of climate extremes, such as heat waves and extreme rainfall, can cause harvest failures, flooding, and droughts that ultimately threaten global food security, harming the region’s economy. Fluctuations in streamflow indicate the sensitivity of streamflow responding to extreme precipitation events and other climatic variables (temperature extremes) that play a significant role in its generation. Pakistan is also considered one of the climate change hotspot regions in the world. The devastating impacts have often occurred in recent decades due to an excess or shortage of streamflow, majorly generated from the Upper Indus Basin (UIB). To better understand climate extremes’ impact on streamflow, this study examined climate extremes and streamflow (Q) changes for three decades: 1990–1999, 2000–2009, and 2010–2019. Observed streamflow and meteorological data from nine sub-catchments across all climatic zones of the UIB were analyzed using RGui (R language coding program) and partial least squares regression (PLSR). Climatic variables were estimated, including precipitation extremes, temperature extremes, and potential evapotranspiration. The Mann–Kendal test was applied to the climatic indices, revealing that precipitation increased during the last 30 years, while maximum and minimum temperatures during the summer months decreased in the Karakoram region from 1990 to 2019. The spatiotemporal trend of consecutive dry days (CDD) indicated a more increasing tendency from 1990 to 2019, compared to the consecutive wet days (CWD), which showed a decreasing trend. PLSR was applied to assess the relation between climatic variables (extreme P, T indices, and evapotranspiration). It was found that the dominant climatic variables controlling annual streamflow include the r95p (very wet days) and R25mm (heavy precipitation days), maximum precipitation event amount, CWD, PRCPTOT (annual total precipitation), and RX5 (maximum five-day precipitation). The TXn (Min Tmax) and Tmax mean (average maximum temperature) dominate streamflow variables. Moreover, the impact of evapotranspiration (ET) on variations in streamflow is more pronounced in arid catchments. Precipitation is the predominant factor influencing streamflow generation in the UIB, followed by temperature. From streamflow quantification, it was found that climate-driven annual streamflow decreased during 1999–2019 in comparison to 1990–1999, with an increase in a few catchments like Kalam, which increased by about 3.94% from 2000 to 2010 and 10.30% from 2010 to 2019, and Shigar, which increased by 0.48% from 2000 to 2009 and 37.37% from 2010 to 2019 concerning 1990–1999. These variations were due to changes in these climatic parameters. The PLSR approach enables the identification of linkages between climatic variables and streamflow variability and the prediction of climate-driven floods. This study contributes to an enhanced identification and hydroclimatological trends and projections. Full article

Assessing the impacts of climate change and land use/land cover changes on water resources within a catchment is essential because it helps us understand how these dynamic factors affect the quantity, quality, and availability of freshwater. This knowledge is crucial for making informed decisions about water management, conservation, and adaptation strategies, especially in regions facing increasing environmental uncertainties and challenges to water resource sustainability. In Pakistan’s Kunhar River Basin (KRB), this investigation explores the potential effects of shifting land use/land cover (LULC), and climate on stream flows. The SWAT (Soil and Water Assessment Tool), a semi-distributed hydrological model, and the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) dataset from multiple global climate models (GCMs) were used to evaluate these effects. The temperature and precipitation data were downscaled using the CMhyd software; for both shared socioeconomic pathways (SSP2 and SSP5), the top-performing GCM out of four was required to produce downscaled precipitation and temperature predictions while taking future land use characteristics into account. The output from the chosen GCM indicated that by the conclusion of the 21st century, relative to the reference period (1985–2014), the study area’s average monthly precipitation, highest temperature, and lowest temperature will be increasing. Precipitation is anticipated to increase between 2015 and 2100 by 20.5% and 29.1% according to the SSP2 and SSP5 scenarios, respectively. This study’s findings, which emphasize the need for project planners and managers taking into account the effects of climate and land cover changes in their management techniques, show that climate change can have a significant impact on the changing seasons of flows in the Kunhar River basin. Full article

Background: Breast cancer is rare in men. This population-based study aimed to determine outcomes of male breast cancer in relation to residence and other variables. Methods: In this retrospective cohort study, men diagnosed with breast cancer in Saskatchewan during 2000–2019 were evaluated. Cox proportional multivariable regression analyses were performed to determine the correlation between survival and clinicopathological and contextual factors. Results: One hundred-eight eligible patients with a median age of 69 years were identified. Of them, 16% had WHO performance status ≥ 2 and 61% were rural residents. The stage at diagnosis was as follows: stage 0, 7%; I, 31%; II, 42%; III, 11%; IV, 8%. Ninety-eight percent had hormone receptor-positive breast cancer. The median disease-free survival of urban patients was 97 (95% CI: 50–143) vs. 64 (46–82) months of rural patients (p = 0.29). The median OS of urban patients was 127 (94–159) vs. 93 (32–153) months for rural patients (p = 0.27). On multivariable analysis, performance status ≥ 2, hazard ratio (HR) 2.82 (1.14–6.94), lack of adjuvant systemic therapy, HR 2.47 (1.03–5.92), and node-positive disease, HR 2.32 (1.22–4.40) were significantly correlated with inferior disease-free survival in early-stage invasive breast cancer. Whereas stage IV disease, HR 7.8 (3.1–19.5), performance status ≥ 2, HR 3.25 (1.57–6.71), and age ≥ 65 years, HR 2.37 (1.13–5.0) were correlated with inferior overall survival in all stages. Conclusions: Although residence was not significantly correlated with outcomes, rural men had numerically inferior survival. Poor performance status, node-positive disease, and lack of adjuvant systemic therapy were correlated with inferior disease-free survival. Full article

Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool with which to find underlying patterns in data, e.g., for cancer specific biomarkers. In the past, analyses were performed on RNA-Seq data pertaining to the same cancer class as positive and negative samples, i.e., without samples of other cancer types. To perform multiple cancer type classification and to find differentially expressed genes, data for multiple cancer types need to be analyzed. Several repositories offer RNA-Seq data for various cancer types. In this paper, data from the Mendeley data repository for five cancer types are analyzed. As a first step, RNA-Seq values are converted to 2D images using normalization and zero padding. In the next step, relevant features are extracted and selected using Deep Learning (DL). In the last phase, classification is performed, and eight DL algorithms are used. Results and discussion are based on four different splitting strategies and k-fold cross validation for each DL classifier. Furthermore, a comparative analysis is performed with state of the art techniques discussed in literature. The results demonstrated that classifiers performed best at 70–30 split, and that Convolutional Neural Network (CNN) achieved the best overall results. Hence, CNN is the best DL model for classification among the eight studied DL models, and is easy to implement and simple to understand. Full article

Metaheuristic algorithms have been widely used to solve diverse kinds of optimization problems. For an optimization problem, population initialization plays a significant role in metaheuristic algorithms. These algorithms can influence the convergence to find an efficient optimal solution. Mainly, for recognizing the importance of diversity, several researchers have worked on the performance for the improvement of metaheuristic algorithms. Population initialization is a vital factor in metaheuristic algorithms such as PSO and DE. Instead of applying the random distribution for the initialization of the population, quasirandom sequences are more useful for the improvement the diversity and convergence factors. This study presents three new low-discrepancy sequences named WELL sequence, Knuth sequence, and Torus sequence to initialize the population in the search space. This paper also gives a comprehensive survey of the various PSO and DE initialization approaches based on the family of quasirandom sequences such as Sobol sequence, Halton sequence, and uniform random distribution. The proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO) and DE (DE-TO, DE-WE, and DE-KN) have been examined for well-known benchmark test problems and training of the artificial neural network. The finding of our techniques shows promising performance using the family of low-discrepancy sequences over uniform random numbers. For a fair comparison, the approaches using low-discrepancy sequences for PSO and DE are compared with the other family of low-discrepancy sequences and uniform random number and depict the superior results. The experimental results show that the low-discrepancy sequences-based initialization performed exceptionally better than a uniform random number. Moreover, the outcome of our work presents a foresight on how the proposed technique profoundly impacts convergence and diversity. It is anticipated that this low-discrepancy sequence comparative simulation survey would be helpful for studying the metaheuristic algorithm in detail for the researcher. Full article

Titanium nitride thin films were grown on Si(001) and fused silica substrates by radio frequency reactive magnetron sputtering. Post-growth annealing of the films was performed at different temperatures from 300 °C to 700 °C in nitrogen ambient. Films annealed at temperatures above 300 °C exhibit higher surface roughness, smaller grain size and better crystallinity compared to the as-grown film. Bandgap of the films decreased with the increase in the annealing temperature. Hall effect measurements revealed that all the films exhibit n-type conductivity and had high carrier concentration, which also increased slightly with the increase in the annealing temperature. A detailed depth profile study of the chemical composition of the film was performed by x-ray photoelectron spectroscopy confirming the formation of Ti-N bond and revealing the presence of chemisorbed oxygen in the films. Annealing in nitrogen ambient results in increased nitrogen vacancies and non-stoichiometric TiN films. Full article

Thin films of tin oxide mixed cerium oxide were grown on unheated substrates by physical vapor deposition. The films were annealed in air at 500 °C for two hours, and were characterized using X-ray photoelectron spectroscopy, atomic force microscopy and optical spectrophotometry. X-ray photoelectron spectroscopy and atomic force microscopy results reveal that the films were highly porous and porosity of our films was found to be in the range of 11.6–21.7%. The films were investigated for the detection of carbon monoxide, and were found to be highly sensitive. We found that 430 °C was the optimum operating temperature for sensing CO gas at concentrations as low as 5 ppm. Our sensors exhibited fast response and recovery times of 26 s and 30 s, respectively. Full article