Estimation of willingness to pay for improvement in dirnking water quality: a study of wasa, lahore

This study examines the existing water quality of Lahore and measures domestic household's willingness to pay for improvement in water quality services. To this end, a Tobit model is estimated by conducting a contingent valuation survey about household perceptions in six towns of Lahore. The results show that the factors affecting household's willingness to pay are coping costs that a household pay for ensuring quality of water.The study also finds the education level of head of family is an important factor in determining the willingness to pay for improved water services. It is recommended that by ensuring the supply and quality to the household additional revenue of 4.22 million rupees could be earned by the authority.Willingness to Pay, Tobit, Drinking Water Quality

How vital are the vital signs? a multi-center observational study from emergency departments of Pakistan.

BACKGROUND: Vital signs play a critical role in prioritizing patients in emergency departments (EDs), and are the foundation of most triage methods and disposition decisions. This study was conducted to determine the frequency of vital signs documentation anytime during emergency department treatment and to explore if abnormal vital signs were associated with the likelihood of admission for a set of common presenting complaints. METHODS: Data were collected over a four-month period from the EDs of seven urban tertiary care hospitals in Pakistan. The variables included age, sex, hospital type (government run vs. private), presenting complaint, ED vital signs, and final disposition. Patients who were \u3e12 years of age were included in the analysis. The data were analyzed to describe the proportion of patients with documented vitals signs, which was then crossed-tabulated with top the ten presenting complaints to identify high-acuity patients and correlation with their admission status. RESULTS: A total of 274,436 patients were captured in the Pakistan National Emergency Department Surveillance (Pak-NEDS), out of which 259,288 patients were included in our study. Vital signs information was available for 90,569 (34.9%) patients and the most commonly recorded vitals sign was pulse (25.7%). Important information such as level of consciousness was missing in the majority of patients with head injuries. Based on available information, only 13.3% with chest pain, 12.8% with fever and 12.8% patients with diarrhea could be classified as high-acuity. In addition, hospital admission rates were two- to four-times higher among patients with abnormal vital signs, compared with those with normal vital signs. CONCLUSION: Most patients seen in the EDs in Pakistan did not have any documented vital signs during their visit. Where available, the presence of abnormal vital signs were associated with higher chances of admission to the hospital for the most common presenting symptoms

Characterization of Fading Statistics of mmWave (28 GHz and 38 GHz) Outdoor and Indoor Radio Propagation Channels

Extension of usable frequency spectrum from microwave to millimeter-wave (mmWave) is one of the key research directions in addressing the capacity demands of emerging 5th-generation communication networks. This paper presents a thorough analysis on the azimuthal multipath shape factors and second-order fading statistics (SOFS) of outdoor and indoor mmWave radio propagation channels. The well-established analytical relationship of plain angular statistics of a radio propagation channel with the channel’s fading statistics is used to study the channel’s fading characteristics. The plain angle-of-arrival measurement results available in the open literature for four different outdoor radio propagation scenarios at 38 GHz, as well as nine different indoor radio propagation scenarios at 28 GHz and 38 GHz bands, are extracted by using different graphical data interpretation techniques. The considered quantifiers for energy dispersion in angular domain and SOFS are true standard-deviation, angular spread, angular constriction, and direction of maximum fading; and spatial coherence distance, spatial auto-covariance, average fade duration, and level-crossing-rate; respectively. This study focuses on the angular spread analysis only in the azimuth plane. The conducted analysis on angular spread and SOFS is of high significance in designing modulation schemes, equalization schemes, antenna-beams, channel estimation, error-correction techniques, and interleaving algorithms; for mmWave outdoor and indoor radio propagation environments

SELWAK: A Secure and Efficient Lightweight and Anonymous Authentication and Key Establishment Scheme for IoT Based Vehicular Ad hoc Networks

In recent decades, Vehicular Ad Hoc Networks (VANET) have emerged as a promising field that provides real-time communication between vehicles for comfortable driving and human safety. However, the Internet of Vehicles (IoV) platform faces some serious problems in the deployment of robust authentication mechanisms in resource-constrained environments and directly affects the efficiency of existing VANET schemes. Moreover, the security of the information becomes a critical issue over an open wireless access medium. In this paper, an efficient and secure lightweight anonymous mutual authentication and key establishment (SELWAK) for IoT-based VANETs is proposed. The proposed scheme requires two types of mutual authentication: V2V and V2R. In addition, SELWAK maintains secret keys for secure communication between Roadside Units (RSUs). The performance evaluation of SELWAK affirms that it is lightweight in terms of computational cost and communication overhead because SELWAK uses a bitwise Exclusive-OR operation and one-way hash functions. The formal and informal security analysis of SELWAK shows that it is robust against man-in-the-middle attacks, replay attacks, stolen verifier attacks, stolen OBU attacks, untraceability, impersonation attacks, and anonymity. Moreover, a formal security analysis is presented using the Real-or-Random (RoR) model

Semantic similarity based food entities recognition using WordNet

International audienceUnstructured text processing is the first step for several applications such as question answering systems, information retrieval, and recipe classification. In the field of recipe classification, number of frameworks have been proposed. However, it is still very tedious and time consuming to extract the food items from the unstructured text and then process for classification. In this research, an automatic food item detection from unstructured text is proposed based on semantic sense modeling. The candidate nouns are detected which can be food items and then the similarity of those nouns is computed with possible food categories. The candidate noun is treated as food item if the similarity is high. For similarity between possible food item and food category is computed by WordNet ontology. The proposed framework is evaluated on benchmark datasets and competitive performance have been achieved. The F-score on large dataset that contains around 20 K recipes is 0.89 which is improved from 0.56

Machine learning, Water Quality Index, and GIS-based analysis of groundwater quality

Water is essential for life, as it supports bodily functions, nourishes crops, and maintains ecosystems. Drinking water is crucial for maintaining good health and can also contribute to economic development by reducing healthcare costs and improving productivity. In this study, we employed five different machine learning algorithms – logistic regression (LR), decision tree classifier (DTC), extreme gradient boosting (XGB), random forest (RF), and K-nearest neighbors (KNN) – to analyze the dataset, and their prediction performance were evaluated using four metrics: accuracy, precision, recall, and F1 score. Physiochemical parameters of 30 groundwater samples were analyzed to determine the Water Quality Index (WQI) of Pano Aqil city, Pakistan. The samples were categorized into the following four classes based on their WQI values: excellent water, good water, poor water, and unfit for drinking. The WQI scores showed that only 43.33% of the samples were deemed acceptable for drinking, indicating that the majority (56.67%) were unsuitable. The findings suggest that the DTC and XGB algorithms outperform all other algorithms, achieving overall accuracies of 100% each. In contrast, RF, KNN, and LR exhibit overall accuracies of 88, 75, and 50%, respectively. Researchers seeking to enhance water quality using machine learning can benefit from the models described in this study for water quality prediction. HIGHLIGHTS Groundwater quality is evaluated using the Water Quality Index method.; Machine learning algorithms are used for forecasting groundwater quality.; The predictive capabilities of decision tree classifier, extreme gradient boosting, logistic regression, random forest, and K-nearest neighbors models have been evaluated and compared.

Epidemiology and outcomes of out of hospital cardiac arrest in Karachi, Pakistan - A longitudinal study

Background: Out-of-hospital cardiac arrest (OHCA) is a major cause of morbidity and mortality globally, with survival outcomes remaining poor particularly in many low- and middle-income countries. We aimed to establish a pilot OHCA registry in Karachi, Pakistan to provide insights into OHCA patient demographics, pre-hospital and in-hospital care, and outcomes.Methods: A multicenter longitudinal study was conducted from August 2015-October 2019 across 11 Karachi hospitals, using a standardized Utstein-based survey form. Data was retrospectively obtained from medical records, patients, and next-of-kin interviews at hospitals with accessible medical records, while hospitals without medical records system used on-site data collectors. Demographics, arrest characteristics, prehospital events, and survival outcomes were collected. Survivors underwent follow-up at 1 month, 6 months, 1 year, and 5 years.Results: In total, 1068 OHCA patients were included. Mean age was 55 years, 61.1 % (n = 653) male. Witnessed arrests accounted for 94.9 % of the cases (n = 1013), whereas 89.4 % of the cases (n = 955) were transported via non-EMS. Bystander CPR was performed in 10.3 % (n = 110) cases whereas pre-hospital defibrillation performed in 0.4 % (n = 4). In-hospital defibrillation was performed in 9.9 % (n = 106) cases despite \u3c 5 % shockable rhythms. Overall survival to discharge was 0.75 % (n = 8). Of these 8 patients, 7 patients survived to 1-year and 2 to 5-years. Neurological outcomes correlated with long-term survival.Conclusion: OHCA survival rates are extremely low, necessitating public awareness interventions like CPR training, developing robust pre-hospital systems, and improving in-hospital emergency care through standardized training programs. This pilot registry lays the foundation for implementing interventions to improve survival and emergency medical infrastructure

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population