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Due to increasing urbanization and population growth, municipal solid waste management (MSWM) is a significant environmental concern in developing countries. Inadequate waste management systems lead to environmental pollution, health hazards, and economic losses. While considering the challenges and limitations, policymakers and authorities need to opt for such waste management scenarios that are environmentally friendly and resolve energy issues. Ten MSWM scenarios were developed and evaluated using seven different criteria. Four multi-criteria decision-making (MCDM) techniques, namely fuzzy logic, AHP, TOPSIS, and PROMETHEE II, were employed to rank the scenarios and identify the most appropriate option for solid waste management in Lahore. This study highlights that the optimal waste management approach comprises a composition of 54% anaerobic digestion, 37% gasification, and 9% landfill technologies. These percentages collectively represent the most suitable and effective strategies for the city's waste management needs. All the MCDM techniques consistently produce similar results. These scenarios have broader applicability across cities in Central Asia and beyond. The study's findings are aligned to promote sustainable and environmentally friendly MSWM practices. These findings endorse implementing strategies and measures aimed at fostering environmental sustainability and the responsible handling of waste, serving as a valuable reference for various regions.

Objective: To determine post procedural immediate and short-term outcomes of transcatheter Ventricular Septal Defect (VSD) device closure. Study Design: Longitudinal Cross-sectional study. Place and Duration of Study: Pediatric Cardiology Unit at Tertiary Cardiac Care Centre, Rawalpindi, Pakistan, from Feb to Oct 2022. Methodology: Total of 62 patients fulfilling inclusion and exclusion criteria who underwent transcatheter VSD device closure were retrospectively identified from our institutional database by non-probability consecutive sampling technique. Preprocedural evaluation [Transthoracic Echocardiography (TTE) & Electrocardiography(ECG)], procedural details and immediate post procedure outcomes were documented. Follow-up evaluations were done at 6 and 12 months. It included clinical examination, TTE and 12 lead ECG. New onset and status of old complications were documented in each visit. SPSS version 24.00 was used to analyze data. Chi-square test was used to find association between study variables. p-

The carbon footprint (CFP) is a measure of greenhouse gases (GHGs) emitted throughout the lifecycle of a product or activity, while the energy footprint (EFP) and water footprint (WFP) measure energy and water consumption, respectively. These footprints are essential for managing emissions and consumption and promoting low-carbon consumption. A carbon labeling scheme could help consumers make informed choices. Asia is a major textile producer and consumer, so studying textiles' carbon, energy, and water footprints is essential for managing domestic emissions, energy and water consumption, and international trade negotiations. This paper presents a method and framework for assessing CFP, EFP, and WFP at the product level and calculates the footprints for textile products. The results show that the total CFP of all textile products produced is 42,624.12 MT CO2e, with indirect emissions contributing significantly more than direct emissions. The total EFP is 248.38 PJ, with electricity consumption being the main contributor, while the total WFP is 80.71 billion liters. The spinning stage of production has the highest CFP and EFP, and energy consumption is the main contributor to all footprints. These results can help compare different products and reduce the footprints of the textile sector.

We report a successful closure of large patent ductus arteriosus (PDA) in a low-birth weight preterm neonate using an ADO IIdevice for the first time in AFIC/NIHD Rawalpindi. A hemodynamically significant PDA in a preterm neonate carries anincreased risk of morbidity, including chronic lung disease, necrotizing enterocolitis and renal impairment. After the failure ofmedical therapy, successful, timely and safe transcatheter closure of PDA using appropriate equipment is a milestone inmanaging PDA in preterm neonates.

Cyber threats and vulnerabilities present an increasing risk to the safe and frictionless execution of business operations. Bad actors (“hackers”), including state actors, are increasingly targeting the operational technologies (OTs) and industrial control systems (ICSs) used to protect critical national infrastructure (CNI). Minimisations of cyber risk, attack surfaces, data immutability, and interoperability of IoT are some of the main challenges of today’s CNI. Cyber security risk assessment is one of the basic and most important activities to identify and quantify cyber security threats and vulnerabilities. This research presents a novel i-TRACE security-by-design CNI methodology that encompasses CNI key performance indicators (KPIs) and metrics to combat the growing vicarious nature of remote, well-planned, and well-executed cyber-attacks against CNI, as recently exemplified in the current Ukraine conflict (2014–present) on both sides. The proposed methodology offers a hybrid method that specifically identifies the steps required (typically undertaken by those responsible for detecting, deterring, and disrupting cyber attacks on CNI). Furthermore, we present a novel, advanced, and resilient approach that leverages digital twins and distributed ledger technologies for our chosen i-TRACE use cases of energy management and connected sites. The key steps required to achieve the desired level of interoperability and immutability of data are identified, thereby reducing the risk of CNI-specific cyber attacks and minimising the attack vectors and surfaces. Hence, this research aims to provide an extra level of safety for CNI and OT human operatives, i.e., those tasked with and responsible for detecting, deterring, disrupting, and mitigating these cyber-attacks. Our evaluations and comparisons clearly demonstrate that i-TRACE has significant intrinsic advantages compared to existing “state-of-the-art” mechanisms.

We report a technically difficult retrieval of embolized Atrial Septal Defect device from left atrium. Device was captured after multiple attempts using different types of snares and was brought to inferior vena cava without delivery sheath. As we were unable to slenderize the device into short sheath, it was finally retrieved using an unconventional and innovative technique.We emphasize the importance of safe improvised methods during challenging catheterization procedures resulting in successful outcomes.

In congenital cardiac surgery, intracardiac repair procedures like ventricular septal defect (VSD), atrioventricular septal defect and tetralogy of fallot (TOF) are associated with conduction system injury. Fewer cases are reported describing life threatening complication of late post-operative complete heart block. We are reporting 2 patients who presented with late complete heart block after ventricular septal defect repair and managed by permanent pacemaker placement in AFIC Cath lab.

Traffic accidents have become a major issue for researchers, academia, government and vehicle manufacturers over the last few years. Many accidents and emergency situations frequently occur on the road. Unfortunately, accidents lead to health injuries, destruction of some infrastructure, bad traffic flow, and more importantly these events cause deaths of hundreds of thousands of people due to not getting treatment in time. Thus, we need to develop an efficient and smart emergency system to ensure the timely arrival of an ambulance service to the place of the accident in order to provide timely medical help to those injured. In addition, we also need to communicate promptly with other entities such as hospitals so that they can make appropriate arrangements and provide timely medical information to emergency personnel on the scene including alerting those related to the injured person(s). In this paper, we have developed an intelligent protocol that uses connected and autonomous vehicles' scenarios in Intelligent Transportation System (ITS) so that prompt emergency services can be provided to reduce the death rate caused. The proposed protocol smartly connects with all the relevant entitles during the emergency while maintaining a smooth traffic flow for the arrival of the ambulance service. Moreover, our protocol also mitigates the broadcasting of messages circulating over the network for delay sensitive tasks. The evaluation results, based on the performance metrics such as channel collision, average packet delay, packet loss, and routing-overhead demonstrate that our proposed protocol outperforms previously proposed protocols such as Emergency Message Dissemination for Vehicular (EMDV), Contention Based Broadcasting (CBB), and Particle Swarm Optimization Contention-based Broadcast (PCBB) protocols. Finally, we discuss several issues and challenges that need to be addressed in the network in order to achieve more a reliable, efficient, connected, and autonomous vehicular network.

Security has been the most widely researched topic, particularly within IoT, and has been considered as the major hurdle in the adoption of different applications of IoT. When it comes to IoV, security is considered as the most inevitable component to ensure a safe and smooth driving experience. CAV is the new era of transportation, integrating intelligence and self-driving capabilities within vehicles and that requires strong security measures to ensure safety. Security alone is not enough. Instead, a complete package including privacy of the vehicles and passengers needs to be added in addition to secure communication. This is because CAVs are under continuous cyber threats and attacks and the most important among them is the DDoS, where a remote attacker can hijack/launch attacks on vehicles remotely. Single point of failure attacks target the centralized trusted body in order to mislead the connected vehicles for personal gains. In this paper, the authors have proposed a secure communication system for CAVs using blockchain, which also ensures the privacy of the vehicle/people. The paper highlights the major components of the proposed system, and its performance is evaluated to check its efficiency against DDoS and Eclipse attacks. The unlinkability and anonymity of the vehicles have been ensured using the zk-SNAKR protocol over Blockchain.

Objective: To determine the frequency of congenital cardiac lesions in pregnant women reporting for fetal echocardiogram and correlate with obstetrical risk factors. Study Design: Descriptive cross-sectional study. Place and Duration of Study: To be conducted at pediatric cardiology and obstetrical unit of AFIC/NIHD and CMH Rawalpindi, from Jan 2009 to Jan 2020. Methodology: Pregnant women referred for fetal echocardiogram from 19-49 years of age were enrolled. Echo was done between 20 to 36 weeks gestation. Primary outcome was the nature of cardiac lesion and gestation at diagnosis. Neonatal scan was done at follow up to confirm cardiac diagnosis. Secondary outcome was obstetrical risk factors which were the reason for referral. This included age consanguinity, family history, previous history of intrauterine death or early neonatal death, maternal and paternal diseases. Data was collected and analyzed n frequencies and percentage. Chi-square was applied for association between variable and p-valve 0.05 was considered significant. Results: A total of n=967 pregnant women referred to outpatient department for fetal echocardiogram. Congenital cardiac lesion were detected in n=83 patients. Majority of patients had CAVSD (1.2%) CCAVB (1.2%) followed by ASD (1.1%) VSD (0.9%) Abnormal heart rate (0.8%) Single ventricle (0.3%). Most significant maternal age group was between 21 to 30 and 30-40 years (43.7 and 46.2%). Only 3% below 20 years and 6.7% above 40 years of age. Most important maternal risk factor was previous off spring 0.5% past family history 0.3% and abnormal obstetrical ultrasound 0.3% Followed by maternal diabetes 0.1% and maternal hypertension 0.1%. The yield of detection of congenital cardiac lesions was 8.3%. Conclusions: Congenital cardiac diseases are the second most common congenital anomaly and huge burden n pediatric population. Timely diagnosis and referral can be lifesaving.

Computer networks are subject to an unprecedented number and variety of attack, the majority of which are distributed denial of service (DDoS). The nature and mechanisms employed in these DDoS attacks continually change, creating a significant challenge for detection and management. To address this evolving nature of attacks, approaches are required that can effectively detect and mitigate emerging attacks. In this paper, we provide a mechanism that not only detects the presence of a DDoS attacks but also identifies the route of attack and commences a process of mitigation at the initial stage of identification. The proposed research involves an optimized SVM classification algorithm integrated with SNORT IPS to provide prevention mechanisms for the entire network when subject to DDoS attack. The proposed IPS method allows traffic identified as legitimate to pass through the network, whereas suspect traffic is flagged and has to go through an identification system. We present the algorithm with experimental results that show better performance than simple Snort IPS, Probabilistic Neural Network (PNN), Back Propagation (BP), Chi-square, and PSO-SVM in terms of accuracy, exposure and specificity. These results show that the average accuracy rate of our method is 97 percent.

Detection of Black Hole attacks is one of the most challenging and critical routing security issues in vehicular ad hoc networks (VANETs) and autonomous and connected vehicles (ACVs). Malicious vehicles or nodes may exist in the cyber-physical path on which the data and control packets have to be routed converting a secure and reliable route into a compromised one. However, instead of passing packets to a neighbouring node, malicious nodes bypass them and drop any data packets that could contain emergency alarms. We introduce an intelligent black hole attack detection scheme (IDBA) tailored to ACV. We consider four key parameters in the design of the scheme, namely, Hop Count, Destination Sequence Number, Packet Delivery Ratio (PDR), and End-to-End delay (E2E). We tested the performance of our IDBA against AODV with Black Hole (BAODV), Intrusion Detection System (IdsAODV), and EAODV algorithms. Extensive simulation results show that our IDBA outperforms existing approaches in terms of PDR, E2E, Routing Overhead, Packet Loss Rate, and Throughput.

Grey wolf optimization (GWO) algorithm is a relatively recent and novel optimization approach. GWO showed performance improvement over all competing algorithms. However, the relevant literature identified that the primary GWO due to its position update equation shows superiority in exploitation, but is inefficient in exploration. It shows slow convergence and low precision, too. Motivated by the outlined issues in the primary GWO, this work presents two new and improved GWO algorithms. The first proposed variant modifies all the three models, encircling model of prey, position update equation and the hunting equation of canonical GWO. Further, a new parameter is introduced to scale the encircling and position update equations. As a result, the exploration issue of the algorithm is tackled. Unlike the first variant, the second proposed variant does not modify the position update models, but it incorporates Minkowski’s information into GWO. To the best of our knowledge, no such modifications to GWO have been done before. The proposed modified versions of GWO are tested on a well-known test functions suit and then compared with different population-based algorithms, including fast evolutionary programming and particle swarm optimization. It was identified from the simulation results that proposed algorithms outperform different algorithms in comparison on majority of problems. The sensitivity study of the proposed algorithms to their various parameters is also provided.

BackgroundOn October 23, 2016, 79 dengue fever cases were reported from the Union Council Tarlai to Federal Disease Surveillance and Response Unit Islamabad. A team was established to investigate the suspected dengue outbreak. ObjectiveThe aim of this study was to determine the extent of the outbreak and identify the possible risk factors. MethodsActive case finding was performed through a house-to-house survey. A case was defined as an acute onset of fever ≥38℃ in a resident of Tarlai from October 2 to November 11, 2016, with a positive dengue virus (nonstructural protein, NS-1) test and any of the two of following signs and symptoms: retroorbital/ocular pain, headache, rash, myalgia, arthralgia, and hemorrhagic manifestations. A structured questionnaire was used to collect data. Age- and sex-matched controls (1:1) were identified from residents in the same area as cases. Blood samples were taken and sent to the National Institute of Health for genotype identification. ResultsDuring the active case search, 145 cases of dengue fever were identified by surveying 928 houses from October 23 to November 11, 2016. The attack rate (AR) was 17.0/10,000. The mean age was 34.4 (SD 14.4) years. More than half of the cases were male (80/145, 55.2%). Among all cases, 29% belonged to the 25-34 years age group and the highest AR was found in the 35-44 years age group (35.6/10,000), followed by the 55-64 years age group (35.5/10,000). All five blood samples tested positive for NS-1 (genotype DENV-2). The most frequent presenting signs/symptoms were fever and headache (both 100%). Stagnant water around houses (odds ratio [OR] 4.86, 95% CI 2.94-8.01; P

Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems.

In today's world, social media provides a valuable platform for conveying expressions, thoughts, point-of-views, and communication between people, from diverse walks of life. There are currently approximately 2.62 billion active users' social networks, and this is expected to exceed 3 billion users by 2021. Social networks used to share ideas and information, allowing interaction across communities, organizations, and so forth. Recent studies have found that the typical individual uses these platforms between 2 and 3 h a day. This creates a vast and rich source of data that can play a critical role in decision-making for companies, political campaigns, and administrative management and welfare. Twitter is one of the important players in the social network arena. Every scale of companies, celebrities, different types of organizations, and leaders use Twitter as an instrument for communicating and engaging with their followers. In this paper, we build upon the idea that Twitter data can be analyzed for crowd source sensing and decision-making. In this paper, a new framework is presented that uses Twitter data and performs crowd source sensing. For the proposed framework, real-time data are obtained and then analyzed for emotion classification using a lexicon-based approach. Previous work has found that weather, understandably, has an impact on mood, and we consider these effects on crowd mood. For the experiments, weather data are collected through an application-programming-interface in R and the impact of weather on human sentiments is analyzed. Visualizations of the data are presented and their usefulness for policy/decision makers in different applications is discussed.

Smart offices are places that aim to provide a favorable, interactive, and healthy environment for the employees. As a result, their working efficiency is enhanced. In order to achieve these numerous systems such as lie detector, emotion detection, and recognition, automation of office equipment has been proposed and implemented. To contribute toward one of the goals of smart offices, a novel ”multi-modal aptitude detection system” is being proposed. The proposed system helps in analyzing the working aptitude of employees in order to help them in providing the more conducive and healthy working environment. In this paper, a detailed layered framework, implementation details, and achieved outcomes related to the proposed novel system are presented. The results achieved show that this novel system after successfully becoming a part of smart office can bring significant enhancements in the efficiency of employees. The proposed system helps in learning and identifying job preferences of employees based on their working aptitude. The system is validated and showed an accuracy of 96% in determining an employee's aptitude. The results are further verified by computing the F1 scores, and more details regarding testing and validation are provided.

The vehicular ad hoc network (VANET) is one of the promising and encouraging technologies, and it is going to attract great attention in the near future. VANET has turned into a main module of the intelligent transport system. It is a self-controlled, wheeled network (also called network on wheels), and a wider and stimulating class of mobile ad hoc network (MANET). VANETs raise many innovative challenges because of their high-class and unique features, such as high-node mobility, dynamic topology changes, wireless links breakage, network constancy, and network scalability. A well-organized routing protocol is one of the most challenging matters of such networks. In this paper, we propose an intelligent naïve Bayesian probabilistic estimation practice for traffic flow to form a stable clustering in VANET, briefly named ANTSC. The proposed scheme aims to improve routing by employing awareness of the current traffic flow as well as considering the blend of several factors, such as speed difference, direction, connectivity level, and node distance from its neighbors by using the intelligent technique. The proposed technique has proven to be more strong, stable, robust, and scalable than existing ones.

For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer's attention. Android allows downloading and installation of apps from other unofficial market places. This gives malware developers an opportunity to put repackaged malicious applications in third-party app-stores and attack the Android devices. A large number of malware analysis and detection systems have been developed which uses static analysis, dynamic analysis, or hybrid analysis to keep Android devices secure from malware. However, the existing research clearly lags in detecting malware efficiently and accurately. For accurate malware detection, multilayer analysis is required which consumes large amount of hardware resources of resource constrained mobile devices. This research proposes an efficient and accurate solution to this problem, named SAMADroid, which is a novel 3-level hybrid malware detection model for Android operating systems. The research contribution includes multiple folds. First, many of the existing Android malware detection techniques are thoroughly investigated and categorized on the basis of their detection methods. Also, their benefits along with limitations are deduced. A novel 3-level hybrid malware detection model for Android operating systems is developed, that can provide high detection accuracy by combining the benefits of the three different levels: 1) Static and Dynamic Analysis; 2) Local and Remote Host; and 3) Machine Learning Intelligence. Experimental results show that SAMADroid achieves high malware detection accuracy by ensuring the efficiency in terms of power and storage consumption.

Wireless sensor networks, due to their nature, are more prone to security threats than other networks. Developments in WSNs have led to the introduction of many protocols specially developed for security purposes. Most of these protocols are not efficient in terms of putting an excessive computational and energy consumption burden on small nodes in WSNs. This paper proposes a knowledge-based context-aware approach for handling the intrusions generated by malicious nodes. The system operates on a knowledge base, located at the base station, which is used to store the events generated by the nodes inside the network. The events are categorized and the cluster heads (CHs) are acknowledged to block maliciously repeated activities generated. The CHs can also get informational records about the maliciousness of intruder nodes by using their inference engines. The mechanism of events logging and analysis by the base station greatly affects the performance of nodes in the network by reducing the extra security-related load on them.

Wireless sensor networks (WSNs) often consist of hundreds of sensor nodes that may be deployed in relatively harsh and complex environments. In views of hardware cost, sensor nodes always adopt relatively cheap chips, which make these nodes become error-prone or faulty in the course of their operation. Natural factors and electromagnetic interference could also influence the performance of the WSNs. When sensor nodes become faulty, they may have died which means they cannot communicate with other members in the wireless network, they may be still alive but produce incorrect data, they may be unstable jumping between normal state and faulty state. To improve data quality, shorten response time, strengthen network security, and prolong network lifespan, many studies have focused on fault diagnosis. This survey paper classifies fault diagnosis methods in recent five years into three categories based on decision centers and key attributes of employed algorithms: centralized approaches, distributed approaches, and hybrid approaches. As all these studies have specific goals and limitations, this paper tries to compare them, lists their merits and limits, and propose potential research directions based on established methods and theories.

Go to: We report a case of an atrial septal defect where a Figulla Occlutech device embolized into the right ventricle. As this device has no left atrial hub, we brought the bare device into the inferior vena cava and used a novel two-snare technique to slenderize the device into the sheath. This report highlights additional tips in transcatheter device retrieval.

With the rapid development in mobile Wireless Sensor Networks (WSNs), it has become very essential to focus on the efficiency in performance of small sensing nodes operating in WSNs. While designing a routing protocol for mobile sensor nodes, the quality parameters like end-to-end delays and routing overhead are always considered. Moreover, the nodes in wirelessly connected mobile networks consume considerable power on routing more than other functionalities. Any modification in a standard routing protocol can also affect routing overhead, end-to-end delays, and energy consumption of nodes. In this paper a new hybrid routing protocol, named as State-Aware Link Maintenance Approach (SALMA), is introduced which is based on Dynamic Source Routing (DSR) and Optimized Link State Routing (OLSR) protocols. The work also focuses on the activeness of nodes in the network operations and defines three states of nodes, that is, white, gray, and black. The work concludes that the proposed protocol gives improvements in some quality of service metrics like lower delay than DSR, lower routing overhead than OLSR, and lesser energy consumption by the network nodes.

Although wireless sensor networks (WSNs) have been the object of research focus for the past two decades, fault diagnosis in these networks has received little attention. This is an essential requirement for wireless networks, especially in WSNs, because of their ad-hoc nature, deployment requirements and resource limitations. Therefore, in this paper we survey fault diagnosis from the perspective of network operations. To the best of our knowledge, this is the first survey from such a perspective. We survey the proactive, active and passive fault diagnosis schemes that have appeared in the literature to date, accenting their advantages and limitations of each scheme. In addition to illuminating the details of past efforts, this survey also reveals new research challenges and strengthens our understanding of the field of fault diagnosis.

Traffic accidents have become an important problem for governments, researchers and vehicle manufacturers over the last few decades. However, accidents are unfortunate and frequently occur on the road and cause death, damage to infrastructure, and health injuries. Therefore, there is a need to develop a protocol to avoid or prevent traffic accidents at the extreme level in order to reduce human loss. The aim of this research is to develop a new protocol, named as the Traffic Accidents Reduction Strategy (TARS), for Vehicular Ad-hoc NETworks (VANETs) to minimize the number of road accidents, decrease the death rate caused by road accidents, and for the successful deployment of the Intelligent Transportation System (ITS). We have run multiple simulations and the results showed that our proposed scheme has outperformed DBSR and POVRP routing protocols in terms of the Message Delivery Ratio (MDR), Message Loss Ratio (MLR), Average Delay, and Basic Safety Message.

In Pakistan, residential energy consumption is predominantly devoted to ensuring thermal comfort, making energy reduction a significant task in building load management. Windows, which are notorious for having poor thermal barriers, contribute considerably to energy losses under harsh weather conditions. Incorporating high thermal inertia materials in windows, such as transparent insulation materials (TIM) and phase change materials (PCM), offers the potential for energy reduction. Using numerical simulations in ANSYS Fluent, this study compares three window types and explores their influence on interior temperature. The findings show that PCM-based windows have a low temperature increase during the melting phase, indicating their great energy-saving potential. Furthermore, PCM absorbs almost 90% of exposed heat, emphasizing its usefulness for energy saving in the Pakistani building industry. [ABSTRACT FROM AUTHOR]