Transportation-related emissions in Pakistan have been rapidly increasing in recent years. This study aims to determine how important it is to electrify road transportation in Pakistan to reduce greenhouse gas (GHG) emissions from the transportation sector. Motivated by the need to tackle the growing environmental issues related to conventional fuel-powered automobiles, this research explores the application of electrification techniques in the context of Pakistan’s transportation system. During the 2019 fiscal year, the transportation industry in Pakistan consumed 23 × 106 tonnes of energy from the burning of fossil fuels and produced 52.9 × 106 metric tons of CO2, which made up 31% of the country’s total carbon emissions. In this research, different scenarios, such as business as usual, low carbon, strengthen low carbon, and Pakistan National Electric Vehicle Policy 2040, are evaluated for the transportation sector of the country. Using the LEAP model, this study projects the effects of electrification on Pakistan road transportation over 30 years. When estimating how electrification will affect road transportation in Pakistan over the next 30 years, several factors were taken into account, including policy frameworks, changing consumer behavior, technology advancements, and infrastructure improvements. The analysis covered the emission levels, adoption hurdles, and possible advantages of transitioning to electric vehicles (EVs). The outcomes illustrate that adopting EVs can produce substantial drops in fuel consumption and environmental emissions, providing a sustainable solution to mitigate global warming. This work is directly associated with various Sustainable Development Goals, including SDG3 (good health and well-being), SDG7 (affordable and clean energy), and SDG13 (climate action). The results of this study highlight the considerable potential for GHG reduction associated with the widespread adoption of EVs, offering crucial insights to stakeholders and policymakers.

Despite efforts made over the past two decades, Pakistan continues to face electricity crises. The heavy reliance on fossil fuels, which make up 60% of the country’s energy mix, has raised concerns about energy security and environmental degradation due to greenhouse gas (GHG) emissions. Developing effective electricity generation scenarios has been challenging for policymakers and researchers, despite the steady increase in electricity demand. The LEAP software was used in this study to forecast the country’s power demand, and four supply-side scenarios were constructed and examined for the years 2018 through 2040. These scenarios include a baseline scenario, a renewable energy scenario, a more renewable energy scenario, and a near-zero emission scenario, focusing on electricity generation and carbon emissions. The study’s findings, projecting into 2040, indicate that the renewable energy scenarios are environmentally sustainable, with lower GHG emissions compared to the baseline scenario. According to the findings of this study, it is projected that around 615 TWh (terawatt-hours) of renewable energy and nuclear energy will be necessary by the year 2040. The anticipated contributions include 393 TWh from hydroelectric energy, 57 TWh from wind energy, 41 TWh from solar energy, and 62 TWh from other renewable sources. The surge in renewable energy is forecasted to bring near-zero CO2 emissions by 2040, a pivotal step toward a sustainable energy future. A projected energy generation of 615 TWh is expected, which adequately meets the country’s energy demand. Transition to renewable energy is critical for addressing Pakistan’s increasing electricity demands, emphasizing both energy security and environmental sustainability.

Shipping is the cornerstone of international trade and thus a critical economic sector. However, ships predominantly use fossil fuels for propulsion and electricity generation, which emit greenhouse gases such as carbon dioxide and methane, and air pollutants such as particulate matter, sulfur oxides, nitrogen oxides, and volatile organic compounds. The availability of Automatic Information System (AIS) data has helped to improve the emission inventories of air pollutants from ship stacks. Recent laboratory, shipborne, satellite and modeling studies provided convincing evidence that ship-emitted air pollutants have significant impacts on atmospheric chemistry, clouds, and ocean biogeochemistry. The need to improve air quality to protect human health and to mitigate climate change has driven a series of regulations at international, national, and local levels, leading to rapid energy and technology transitions. This resulted in major changes in air emissions from shipping with implications on their environmental impacts, but observational studies remain limited. Growth in shipping in polar areas is expected to have distinct impacts on these pristine and sensitive environments. The transition to more sustainable shipping is also expected to cause further changes in fuels and technologies, and thus in air emissions. However, major uncertainties remain on how future shipping emissions may affect atmospheric composition, clouds, climate, and ocean biogeochemistry, under the rapidly changing policy (e.g., targeting decarbonization), socioeconomic, and climate contexts.

This study explored cumulative 127.5MW waste to energy (WtE) potential in five populous cities of Pakistan based on local waste characterization profiles and global standards. The 50MW WtE plant in Lahore using National electricity regulator codes and practices resulted in an attractive Levelized cost of electricity (LCOE) of US¢ 7.86/kWh over 25 years with a $151.5 million investment cost. The net savings to Lahore Waste Management Company can be $103.4 and $137.7 million respectively with and without tipping fees on account of waste disposal cost, bricks revenue using bottom ash, and waste fee. The project developers can get net savings of $16.9 and $51.5 million respectively with and without tipping fees other than LCOE. Furthermore, the greenhouse gas emissions of 216.6 million tons of CO2eqcan be saved throughout plant life against 279 GWh/year energy generation, in terms of grid emission factor and current methane release into the atmosphere from the dumping site.

The present study was conducted to understand the relationship between Researcher-like disposition and teaching effectiveness of teacher educators in two phases. The first Phase was the quantitative Phase, in which Pearson product-moment correlation was carried out to understand the relationship between the variables. One hundred teacher educators are teaching B.Ed. A stratified sampling technique selected the course. The second Phase was the qualitative Phase, in which only 20 teacher educators were randomly selected and interviewed to understand the problems faced by teacher educators in teaching & conducting research. The present study's findings revealed a significant and positive relationship between researcher-like disposition and teaching effectiveness of teacher educators, and the researcher-like disposition has positively contributed to enhancing the teaching effectiveness of teacher educators (p<0.01 level). However, the findings also revealed that the teaching workload, inadequate research pre-service training, infrastructure constraints, Lack of training in ICT, and involvement in other administrative work were the various factors affecting teaching effectiveness and research performance. In a rapidly changing teaching and learning scenario, conducting research is one of the most potential approaches and thoughtful ways to inspire, motivate, and equip teacher educators with skills to teach evidence-based methods. The result of the present study provides a clear picture to the educationists and policymakers to improve the teaching effectiveness and research performance of teacher educators teaching in B.Ed. Institutions.

Protein–protein interactions be talking one of the fundamental utilitarian pertinence among qualities and integrative genomic information with available proteomic information that may oblige us with a thorough rough approximation of unimportant human issues. Amidst the strategies directed by numerous protein variations of human ailments, a large portion of them has unusual systems. Here, the profound neural system tends to utilize protein–protein interaction (DeepPPI) which selects the profound neural system to expert the portrayal of protein data refined from the protein–protein interaction (PPI) network. An out-of-the-container method of looking over the protein–protein interaction has been presented by the utilization of the PPI systems that will approve one to comprehend cell pathways and making practical medicines for the treatment of human ailments. In any case, our proposed dataset has likewise the amino acids constituent rate that permits one to anticipate the genuine site where the anticipated association can happen. The exploratory results exhibit that DeepPPI achieves pervasive execution in the test set with an exactness of 83.50%, an accuracy of 82.49%, and review of 83.89%. Along these lines, compared with the past profound neural system, our model achieved better or identically incredible execution. The PPI network is being broken down using Cytoscape.

Background The initial response to islet transplantation and the subsequent acute inflammation is responsible for significant attrition of islets following both autologous and allogenic procedures. This multicentre study compares this inflammatory response using cytokine profiles and complement activation. Methods Inflammatory cytokine and complement pathway activity were examined in two cohorts of patients undergoing total pancreatectomy followed either by autologous (n=11) or allogenic (n=6) islet transplantation. Two patients who underwent total pancreatectomy alone (n=2) served as controls. Results The peak of cytokine production occurred immediately following induction of anaesthesia and during surgery. There was found to be a greater elevation of the following cytokines: TNF-alpha (P<0.01), MCP-1 (P=0.0013), MIP-1α (P=0.001), MIP-1β (P=0.00020), IP-10 (P=0.001), IL-8 (P=0.004), IL-1α (P=0.001), IL-1ra (0.0018), IL-10 (P=0.001), GM-CSF (P=0.001), G-CSF (P=0.0198), and Eotaxin (P=0.01) in the allogenic group compared to autografts and controls. Complement activation and consumption was observed in all three pathways, and there were no significant differences in between the groups although following allogenic transplantation ∆IL-10 and ∆VEGF levels were significantly elevated those patients who became insulin-independent compared with those who were insulin-dependent. Conclusions The cytokine profiles following islet transplantation suggests a significantly greater acute inflammatory response following allogenic islet transplantation compared with auto-transplantation although a significant, non-specific inflammatory response occurs following both forms of islet transplantation.

Recent advances in networking technology and serverless architectures have enabled automated distribution of compute workloads at the function level. As heterogeneity and physical distribution of computing resources increase, so too does the need to effectively use those resources. This is especially true when leveraging multiple compute resources in the form of local, distributed, and cloud resources. Adding to the complexity of the problem is different notions of "cost" when it comes to using these resources. Tradeoffs exist due to the inherent difference between costs of computation for the end user. For example, deploying a workload on the cloud could be much faster than using local resources but using the cloud incurs a financial cost. Here, the end user is presented with the tradeoff between time and money. We describe preliminary work towards Delts+, a framework that integrates multidimensional cost objectives, cost tradeoffs, and optimization under constraints.

Meta-analysis aimed to quantify the relationship between intraductal papillary mucinous neoplasm (IPMN) and increased incidence of extra-pancreatic malignancy (EPM) previously reported in qualitative observational cohort studies. Study protocol was registered with PROSPERO (CRD42020169614) and conducted to the Meta-analysis Of Observational Studies in Epidemiology and systematic review reported with Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Assessing the Methodological Quality of Systematic Reviews guidelines. Sixteen studies (total of 8240 patients) were included in the pooled, and 7399 patients in the subgroup meta-analyses. The odds ratio (OR) for any EPM in the presence of IPMN was 57.9 (95% confidence interval 40.5–82.7), fixed effects, I2 = 59% (p < 0.0014). Subgroup analysis for any gastrointestinal EPM (i.e. oesophagus, stomach, colon and rectum) in the presence of an IPMN estimated an overall OR of 12.9 (95% confidence interval 8.8–19.0), fixed effects, I2 = 64% (p < 0.0004). Patients with an IPMN are categorically at increased risk for a higher incidence of EPM and particularly the odds of a gastrointestinal malignancy are also increased in comparison with the general population. We advocate that patients presenting with an IPMN should be considered for gastrointestinal screening including colonoscopy, upper gastrointestinal endoscopy or computed tomography.

Advances in network technologies have greatly decreased barriers to accessing physically distributed computers. This newfound accessibility coincides with increasing hardware specialization, creating exciting new opportunities to dispatch workloads to the best resource for a specific purpose, rather than those that are closest or most easily accessible. We present Delta, a service designed to intelligently schedule function-based workloads across a distributed set of heterogeneous computing resources. Delta implements an extensible architecture in which different predictors and scheduling algorithms can be integrated to provide dynamically evolving estimates of function execution times on different resources-estimates that can be used to determine the most appropriate location for execution. We describe predictors for function runtime, data transfer time, and cold-start resource provisioning and configuration delay; dynamic learning methods that update predictor models over time; and scheduling strategies that take into account both function and endpoint information. We show that these methods can halve workload makespan when compared with a strategy that selects the fastest resource, and decrease makespan by a factor of five when compared to a round robin strategy, when deployed on a heterogeneous testbed with resources ranging from a Raspberry Pi to a GPU node in an academic cloud.

In India, there are plans for 5 GW of installed capacity of wind power by 2022. This study establishes that the wind resource along the west coast of Gujarat is sufficient for offshore wind farms of between 500 MW to 2 GW rated power to be operated with capacity factors of 40% to 54%. For the majority of sites, a levelized cost of energy (LCOE) in the range 68 to 86 pound/MWh can be achieved with installation on floating support structures. The LCOE is up to 3.2 pound/MWh greater for installation on bed-fixed structures closer to shore. The wind resource and energy yield are predicted using Weather Research and Forecasting (WRF). Wind speed occurrence is predicted to within 1.3% for multiple sites simultaneously, and this provides wind turbine energy yield to within 3.4%. Wind speeds occurring due to sea breeze are accurately predicted and contribute 6.2% of annual energy generation at the nearshore locations and 3.8% at the deeper water offshore sites. While these events improve the viability of nearshore locations, floating installations offer lower LCOE for most locations along the Gujarat coast.

BACKGROUND:Numerous factors influence pancreatic islet survival following auto-transplantation. Of these, the host immune response in the early peri-operative period is one of the most important. In this study we investigated the role of the mannose-binding lectin (MBL)-dependent pathway in a group of total pancreatectomy (TP) islet auto-transplantation (TPIAT) patients and classified them as competent or deficient in MBL activity. Complement pathway activities, MBL protein and inflammatory cytokine concentrations were evaluated from eleven pancreatic islet auto-transplant patients from two institutions. METHODS:Eleven patients from two institutions were prospectively recruited. Serum was screened at different time points for 29 different cytokines and compared according to their MBL deficient or competent status. Twelve patients from previous TPIAT patients also underwent screening of MBL pathway activity. RESULTS:A total nine of twenty three patients (39%) were MBL pathway deficient. MCP-1, IL-7 and IL-1a concentrations were significantly lower in the MBL deficient cohort compared to the normal MBL group (P=0.0237, 0.0001 and 0.0051 respectively). IL-6 and IL-8 concentrations were significantly raised in the normal MBL group. MBL functional activity was lower in insulin-independent group compared to the insulin-dependent group. CONCLUSIONS:Complement activation is an important, possibly damaging response during intra-portal islet infusion. MBL pathway deficiency appears common in this population and the cytokine response was attenuated in MBL pathway deficient patients. Therapeutic MBL pathway blockade during and following islet auto-transplantation (IAT) may improve islet survival and function and thereby clinical outcome.

In this study, three different diagnostic tests for parvovirus were compared with vaccination status and parvovirus genotype in suspected canine parvovirus cases. Faecal samples from vaccinated (N17) and unvaccinated or unknown vaccination status (N41) dogs that had clinical signs of parvovirus infection were tested using three different assays of antigen tests, conventional and quantitative PCR tests. The genotype of each sample was determined by sequencing. In addition to the suspected parvovirus samples, 21 faecal samples from apparently healthy dogs were tested in three diagnostic tests to evaluate the sensitivity and specificity of the tests. The antigen test was positive in 41.2% of vaccinated dogs and 73.2% of unvaccinated diseased dogs. Conventional PCR and qPCR were positive for canine parvovirus (CPV) in 82.4% of vaccinated dogs and 92.7% of unvaccinated dogs. CPV type-2c (CPV-2c) was detected in 82.75% of dogs (12 vaccinated and 36 unvaccinated dogs), CPV-2b was detected in 5.17% dogs (one vaccinated and two unvaccinated) and CPV-2a in 1.72% vaccinated dog. Mean Ct values in qPCR for vaccinated dogs were higher than the unvaccinated dogs (p = 0.049), suggesting that vaccinated dogs shed less virus, even in clinical forms of CPV. CPV-2c was the dominant subtype infecting dogs in both vaccinated and unvaccinated cases. Faecal antigen testing failed to identify a substantial proportion of CPV-2c infected dogs, likely due to low sensitivity. The faecal samples from apparently healthy dogs (n = 21) showed negative results in all three tests. Negative CPV faecal antigen results should be viewed with caution until they are confirmed by molecular methods.

This study was aimed to utilize the decision-making capability of Analytical Hierarchy Process (AHP) and empirical rating capability of Bureau of Indian Standard code (BIS – 14496) methods to perform landslide susceptibility zonation (LSZ) mapping of slopes bounding Koteshwar reservoir. The key issue in selection of mapping unit in any LSZ mapping was simplified by adopting slope facet as the mapping unit. Slope facets were demarcated by using very high resolution ALOS PALSAR 12.5m digital elevation model. Total 54 facets were traced on the reservoir bounding slopes. For each slope facet, the influence of geo-environmental factors, namely, lithology, structural favourability, land use land cover, hydrogeological condition, slope angle and relative relief (as per BIS code) on the landslide propensity was evaluated using ratings proposed in the BIS code and the AHP method. LSZ map was created based on the procedure given in the BIS code. Landslide susceptibility index (LSI) values for each slope facet was calculated by simple arithmetic overlay operation which resembles total estimated hazard (TEHD) calculation given in BIS code. Further, LSI values were normalized to a range TEHD values. The LSI values were classified into five relative susceptible zones. Validation of both LSZ maps was carried out by establishing a relationship of observed landslides in the field with the facets identified under various susceptible zones.

High-level programming languages such as Python are increasingly used to provide intuitive interfaces to libraries written in lower-level languages and for assembling applications from various components. This migration towards orchestration rather than implementation, coupled with the growing need for parallel computing (e.g., due to big data and the end of Moore's law), necessitates rethinking how parallelism is expressed in programs. Here, we present Parsl, a parallel scripting library that augments Python with simple, scalable, and flexible constructs for encoding parallelism. These constructs allow Parsl to construct a dynamic dependency graph of components that it can then execute efficiently on one or many processors. Parsl is designed for scalability, with an extensible set of executors tailored to different use cases, such as low-latency, high-throughput, or extreme-scale execution. We show, via experiments on the Blue Waters supercomputer, that Parsl executors can allow Python scripts to execute components with as little as 5 ms of overhead, scale to more than 250 000 workers across more than 8000 nodes, and process upward of 1200 tasks per second. Other Parsl features simplify the construction and execution of composite programs by supporting elastic provisioning and scaling of infrastructure, fault-tolerant execution, and integrated wide-area data management. We show that these capabilities satisfy the needs of many-task, interactive, online, and machine learning applications in fields such as biology, cosmology, and materials science.

Since last four decades, remote sensing tools have become integral part of landslide studies. Remote sensing imageries captured from Arial, space borne and ground-based sensors in addition to digital elevation model (DEM) are frequently used by planners, researchers and mitigators. Active remote sensing tools such as LiDAR, RADAR, SAR etc., are being commonly used in landslide study. This work has been aimed to examine the capability of opensource remote sensing imageries in monitoring landslides in Part of Uttarakhand Himalaya. Open source remote sensing imageries available on google earth has been used in this study. From the Google Earth archives, imageries were captured and compared in GIS software as well as at the Google Earth platform itself. Using the archive images, historical landslide information, active landslide areas and potentially vulnerable area were identified. Visual image interpretation techniques such as color, contrast, shape, size pattern, texture etc., were used to identify present and past landslide scraps. Dimension of landslides were estimated using the vector tools available on the Google Earth platform. For few landslides, estimated dimensions were verified during the field observation. Landslide dimensions estimated using googles earth was found to be matching with the measurements carried out in the field.

This study was aimed to utilise important landslide causal factors for the delineation of the landslide susceptible area using the weights of evidence (WofE) method in the Tehri reservoir rim region on a macro scale. The Tehri reservoir extends up to 70km and bounded by moderate to steep slopes. Landslide susceptibility mapping (LSM) is an essential measure for identifying the potentially unstable slopes bounding the reservoir. With the help of ancillary data, remote sensing imagery and a digital elevation model, 10 causative factors along with landslide inventory were extracted. Initially, the WofE model was applied to obtain the association between landslides and causative factors. The process gave the numerical estimate of correlation between landslides and causative factors by means of positive and negative correlation. Important factor attributes, potentially causing landslides, were identified based on high positive correlation values. Later, the posterior probability of landslide occurrence for each mapping unit was also computed using the WofE model. Posterior probability was divided into five relative susceptibility classes. Validation of the posterior probability map was carried out by using the prediction rate curve technique and a reasonable accuracy of 83% was achieved. LSM of the Tehri reservoir rim area implicates unplanned road construction and settlements coupled with the reservoir slope settlement process for the present degradation of the geo-environmental system in that region.

INTRODUCTION:Cardiopulmonary exercise testing (CPET) is a reliable, reproducible and non-invasive measure of functional capacity. CPET has been increasingly used to assess pre-operative risk and stratify patients at risk of mortality and morbidity following surgery. CPET parameters that predict outcomes within liver and pancreas cancer surgery still remain to be defined. METHODS:A systematic review to assess CPET use in predicting post-operative outcomes in liver and pancreas cancer surgery was carried out using the following databases AMED, CINAHL, Cochrane Library, EMBASE, Google Scholar and PubMED. RESULTS:Data were extracted from four liver and four pancreas cancer studies. All were single institution, cohort series reporting outcomes with CPET used pre-operatively to assess patient morbidity, length of hospital stay and or mortality. In liver cancer surgery, all four papers reported outcome data on morbidity and patients who were more likely to suffer with complications tended to have an anaerobic threshold (AT) of less than 9.9-11.5 mL min-1.Kg-1. Whilst in pancreas cancer surgery, rates of pancreas fistulae tended to be higher in those patients who had an AT of less than 10 or 10.1 mL min-1.Kg-1. DISCUSSION:The CPET variable most reported and relevant to morbidity in both liver and pancreas cancer surgery appeared to be AT. A pre-operative AT of approximately 10.5 mL min-1.Kg-1 seems to be associated with a worse post-operative convalescence.