Framework for optimizing chlorine dose in small- to medium-sized water distribution systems: A case of a residential neighbourhood in Lahore, Pakistan

To maintain desirable residual chlorine for a groundwater source, optimizing the chlorine dose in small- to medium-sized water distribution systems (SM-WDS) is a daunting task in developing countries. Mostly,  operators add a random chlorine dose without recognizing the smaller size of their distribution network.  In this research, a modelling framework for optimizing chlorine dose in SM-WDS is developed. In order to  evaluate its practicality, the proposed framework has been applied in a case study of a residential  neighbourhood in Lahore (Pakistan) with a small network spanning over 0.35 km2. Three datasets for residual chlorine were monitored at 6 locations spread over the study area. EPANET 2.0 software was  used for hydraulic and residual chorine modelling. The bulk decay coefficient (Kb) was determined in the  laboratory, whereas the wall decay coefficient (Kw) was estimated by calibrating the simulation results  with the residual chlorine determined in the field. Based on the calibrated EPANET simulations, a fuzzy  rule-based model was developed for pragmatic application of the proposed framework. Scenario analyses for different situations have also been carried out for achieving residual chlorine required at the consumer end. This exercise revealed that much lower chlorine doses than the existing practice can generate detectable chlorine residuals. Moreover, the model can be used to deal with emergency situations, which may arise in developing countries due to viral outbreaks and cross-contamination events in SM-WDS.Keywords: small- to- medium-sized water distribution systems, residual chlorine modelling, water  quality, chlorine decay coefficients, fuzzy rule-based modelling, EPANE

Rehabilitation of Acute and Chronic Ankle Sprain for Male Cricketers through Mixedbag (Hydrotherapy and Land-Based) Exercises

Ninety-five amateur cricketers of age, 15-35 years with confirmed acute or chronic ankle sprain, selected from four different cities of the Punjab, Pakistan were evaluated through Star Excursion Balance Test (SEBT), Single Leg Balance test (SLB) and subject to a set of progressively increasing exercises on ground and in water as well known as MixedBag rehab exercises which consisted of Hydro, Isometric, Isotonic and proprioception protocols. Another group of same level cricketer (n = 40) served as control. Comparisons of pre and post-exercise values showed significant increase (p \u3c 0.001) in Lateral direction reach and Posteromedial direction while the rest of the six directions showed non-significant results. MixedBag Rehab Group showed an overall improvement of 14.3 % and 9.2 % with an increase (cm) of 9.6 and 6.3 for the non-injured and injured leg respectively. However, the range of improvement in percentage for all eight directions lies between 5.5-6.3 and 7.7-11.8 for the non-injured and injured leg, respectively. Similarly, the difference between pre and post-exercise difference of two positions of non-injured leg and injured leg in seconds were 2.9, 12.1 and 4.1, 27.7. The results indicated that MixedBag exercises improved isometric and isotonic muscular strength, proprioception and stability that ultimately helped to recover, regaining strength and reinstall proprioception. After completing the MixedBag Rehab plan, the subjects were followed for four months to check the recurrence and it was found that the recurrence of Control Injured Group and MixedBag Rehab Group was 17.5 % and 7.5 %, respectively

Developing a Performance Evaluation Framework for Public Private Partnership Projects

The public–private partnership (PPP) is a potential procurement strategy for delivering complex construction projects. However, implementing PPPs has not been explored extensively in developing countries like Pakistan. A performance framework is developed in this study to evaluate the application of PPP projects based on 10 key performance indicators (KPIs) and 41 performance measures (PMs). This framework was reviewed by experts for coverage and relevance, then validated through two case studies involving road construction. A triangulation approach was adopted to collect the relevant data through multiparty focus group sessions, archives, and site observations, which enhances the reliability of the data. Results showed there is a difference in performance for six KPIs, but similar practices were reported for four KPIs. The developed performance evaluation framework (PEF) for PPP projects is suitable for developing countries transitioning toward adopting this procurement strategy

Fothergill Disease Wincing Facial Pain or Toothache: Incidence of Tooth extraction in Fothergill Disease

Background: Fothergill Disease commonly cause pain in jaws. Here, incidence of unnecessary tooth extraction and simple dental treatment was noted while managing this disease, is presented.Methods: A prospective review of 30 consecutive patients was done, who were treated for Fothergill disease at Department of Neurosurgery, PGMI / Lahore General Hospital Lahore from 2012 to 2013. The clinical presentation, dental surgeon evaluation, simple dental procedures and tooth extractions, were noted and analyzed.Results: In this study, thirty Fothergill disease patients were identified in 18 females (60%) and 12 males (40%) patients, with a mean age at diagnosis of 56.0 years. Right facial pain was relatively more common in 17 (56.66%) patients. Lower jaw was involved more alone in (n = 6) with upper jaw in (n = 12). Despite the typical nature of facial pain a large no of patients (n = 12) 40%, visited dental surgeons. Simple dental procedures in the form of local injections or root canal was done in (n = 5), while tooth extracted in (n = 7)Conclusions: All peridental area pain are not due to dental disease, Exact diagnosis and treatment of jaw pain is mandatory to avoid unnecessary dental treatment

An Event Matching Energy Disaggregation Algorithm Using Smart Meter Data

Energy disaggregation algorithms disintegrate aggregate demand into appliance-level demands. Among various energy disaggregation approaches, non-intrusive load monitoring (NILM) algorithms requiring a single sensor have gained much attention in recent years. Various machine learning and optimization-based NILM approaches are available in the literature, but bulk training data and high computational time are their respective drawbacks. Considering these drawbacks, we devised an event matching energy disaggregation algorithm (EMEDA) for NILM of multistate household appliances using smart meter data. Having limited training data, K-means clustering was employed to estimate appliance power states. These power states were accumulated to generate an event database (EVD) containing all combinations of appliance operations in their various states. Prior to matching, the test samples of aggregate demand events were decreased by event-driven data compression for computational effectiveness. The compressed test events were matched in the sorted EVD to assess the contribution of each appliance in the aggregate demand. To counter the effects of transient spikes and/or dips that occurred during the state transition of appliances, a post-processing algorithm was also developed. The proposed approach was validated using the low-rate data of the Reference Energy Disaggregation Dataset (REDD). With better energy disaggregation performance, the proposed EMEDA exhibited reductions of 97.5 and 61.7% in computational time compared with the recent smart event-based optimization and optimization-based load disaggregation approaches, respectively

Two dimensional holey graphyne: An excellent anode and anchoring material for metal–ion and metal–sulfur batteries

Based on first-principles calculations, the potential of holey graphyne is investigated for battery applications in terms of the storage capacity, volume expansion, diffusion barrier, and metal polysulfides binding. We found substantially higher storage capacities of Li (873 mAh/g) and Na (558 mAh/g) than typical graphite anodes (372 mAh/g for Li and &lt;35 mAh/g for Na) and other carbonaceous materials (450–750 mAh/g for Li and 200–500 mAh/g for Na). The migration barriers of Li and Na turn out to be 0.28 eV and 0.32 eV, respectively, lower than those theoretically reported for commercial anodes TiO2 (0.4–1.0 eV) and silicon (0.6–0.8 eV). Holey graphyne with maximum Li adsorption expands only 0.5%, in contrast to the 10% volume growth in graphite. The lithium and sodium polysulfides and S8 cluster adsorb with moderate binding energies ranging from −0.73 eV to −2.08 eV, which is sufficient to prevent the unintended decomposition of polysulfides. Our findings demonstrate that holey graphyne is a promising anode material for metal-ion batteries and an anchoring material for metal-sulfur batteries to mitigate the shuttle effect.Validerad;2023;Nivå 2;2023-08-15 (joosat);Licens fulltext: CC BY-NC-ND LicenseFunder: Khalifa University of Science and Technology (CIRA-2020-007), (ESIG-2023-004);</p

Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems

Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99%, 98.53% and 99.9% overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications

A Comparative Performance Analysis of Different Insulation Materials Installed in a Residential Building of a Cold Region in Pakistan

Globally, the building sector consumes approximately 60% of the total energy usage, while the energy consumption of residential buildings lies between 20% to 40%. The majority of this energy is operational energy, which comes mainly from the heating and cooling of houses. Innovative and cost-effective insulation materials have the potential to reduce the operational energy requirements and can therefore make the buildings more energy efficient. In this study, three commonly available insulation materials were experimentally evaluated for a case study of residential buildings, located in a cold region of Pakistan. Glass wool, extruded polystyrene, and polyethylene were used, as insulation materials, for monitoring the case study building performance. Thermal data were collected for 21 days in the year 2019 using a Testo Saveries System and were then used for analyzing the thermal performance of each of the three types of insulation materials. Other relevant data including the cost of insulation materials, thickness, ease of application, design life, and fire resistance of the selected insulation materials were obtained for broader (based on the scorecard) analysis based on a multi-weighted decision model. It was concluded that Polyethylene was the most economical insulation material amongst the others, which also showed the best thermal performance. Polyethylene was also found to be the best insulation material for the case study building based on a multi-weighted decision model and, hence, is recommended for application in buildings around cold regions of Pakistan