Evaluation and improvement of energy flexibility and performance of building heating, ventilation, and air-conditioning systems

The foreseen reduction of available fossil fuels, the continued increase in global energy demand, and the irrefutable evidence of climate change, along with the implementation of a global commitment to achieve a net-zero emissions target, have greatly sharpened commercial interest in using renewable energy resources (RER). However, the high penetration of RER-based stochastic power generation systems has resulted in a significant requirement for increased flexibility on the demand side that can allow buildings to adapt to increasingly dynamic energy supply conditions to support power grid operation and optimization. Failure to adapt may carry serious electrical blackouts and can compromise the safety of the supply side. The building sector accounts for a substantial amount of global energy usage and offers great opportunities for energy flexibility. Building energy flexibility is an important and emerging concept in the modern energy landscape, which can support the sustainable transition of the power sector. Building heating, ventilation, and air-conditioning (HVAC) systems are one of the leading energy consumers in buildings, which can be used as a key flexible source. The HVAC systems with integrated thermal energy storage (TES) can further enhance building energy flexibility. This thesis contributes to the evolving field of demand flexibility and introduces methodologies to evaluate and improve energy flexibility and performance of building HVAC systems

Macroeconomic Covariates of Default Risk: Case of Pakistani Non-Financial Firms

Empirical estimation of default probability through structural approach in the context of macroeconomic dynamics turn out be an emerging idea. However, various aspects of these studies are still needs to be explored to make these models more reliable. This study explored the structural model of default risk (Moody’s KMV) application with macroeconomic dynamics in Pakistani non-financial firm’s context and confirm whether Moody’s KMV model of default prediction could be applicable in Pakistan where the markets are highly speculative and stock markets are highly volatile. The study approximate about the expected default frequency (hereafter EDF) of 307 Pakistani non-financial firms, categorized in 12 industries for a span of 8 years from 2004 to 2011. It further check the macroeconomic variables effects on EDF with the use of generalized method of moments (hereafter GMM). Empirical results compared with the real life scenarios over the said years and on the basis of results we infer that Moody’s KMV model can predict default probability in a much better way than traditional ratio based approach

Electron emission characterization of laser-induced gaseous plasma

Laser induced hydrogen plasma has been investigated as a source of electrons. The energy, temperature and flux of the electrons have been determined by using two different temporal detectors; Faraday cup and Rogowski coil. The energy, temperature and flux were measured from the electrons extracted from laser induced hydrogen plasma. The Rogowski coil has been used for measurement of flux and current whereas Faraday cup has been used to determine the current density in laser induced plasma. The energy and temperature of the electrons are measured by the time of flight method in the range of 1.23 - 5.04 keV and 2516.26 eV, respectively. The flux of the electrons has been measured ≈ 1015 by Rogowski coil and the maximum current has been measured to be 39.6 kA. The current density has been measured by the Faraday cup ranges from 20 Am-2 to 23 Am-2

Examining the contribution of fiscal policy on economic growth: Analytical insights from Pakistan

To better understand the influence of fiscal policy (FP) on economic growth (EG) in Pakistan, this study investigates the importance of the amount of output produced by different factors of production in Pakistan's economy. The annual time series data has been collected from the State Bank of Pakistan and World Bank Data-Base from the years (2001-2020). The Autoregressive Distributed Lag (ARDL) model is used for empirical research to assess the significant factors of EG, and the Augmented Dickey-Fuller (ADF) unit root test is used to ensure that all of the variables are stationary. Using annual time series data from 2001 to 2020. Based on these findings, this study recommends adopting a proactive fiscal policy framework that incorporates expansionary measures. We argue that this strategy has the capacity to stimulate and maintain Pakistan's economic growth path, thereby fostering a more promising and prosperous future. Moreover, the study found the impact of government expenditures (GE), gross fixed capital creation (GFCC), and direct and indirect taxes on Gross domestic Product (GDP). Additionally, findings showed that government expenditures, gross fixed capital creation, indirect, and direct taxes have a strong effect on economic growth. It is argued that an expansionary fiscal policy in the future could greatly benefit Pakistan's economic growth

Effect of Blood Pressure Lowering Therapy in Stroke Patients

Objective: The objective of the study is to assess the effect of blood pressure lowering with Candesartan in patients with stroke and elevated blood pressure admitted in this hospital.Study Design: Prospective descriptive observational study.Setting: Neurosurgery, Medical Emergency / OPD, Lady Reading Hospital, Peshawar.Materials and Methods: This descriptive study was done at the Department of Medicine and Neurosurgery, Postgraduate Medical Institute, Lady Reading Hospital Peshawar from January 2013 to May 2014 (for One year and 5 months period) in a total of 357 patients. In this descriptive study, patients presenting to Emergency department or OPD with stroke and elevated blood pressure, presenting within 30 hours of symptom onset and with SBP ≥ 140 mmHg, diastolic > 90 mmHg, were eligible for inclusion. Exclusion criteria were contraindicat-ions to or ongoing treatment with an angiotensin receptor blocker, markedly reduced consciousness, patients with chronic heart failure and intolerance to ACE inhibitors, patient unavailability for follow-up and pregnancy or breast – feeding. The acute phase treatment was a fixed dose of 4 mg on day 1, 8 mg on day 2 and 16 mg on days 3 to 7. Blood pressure was measured daily with the patient in the supine position using a blood pressure monitor. All patients were follow-up on day 7 and at 1 and 6 months after discharged from hospital.Results: Among 357 cases, 68.06% were males and 31.93% females. Majority (37.25%) belongs to age group of 61 – 70 years. Out of these, 66.10% patients were found to have ischemic and 33.89% patients had hemorrhagic stroke. Highest (40.05%) patients belonged to severe hypertensive group i.e. ≥ 180/110 mmHg. Target was achieved in 75.91% patients.Conclusions: Our data suggests that lowering BP in acute ICH is probably safe; however, it remains to be seen if this decreases hematoma expansion or improves outcome

Intercloud Message Exchange Middleware

ABSTRACT Cloud Interoperability has been a core issue pertaining Intercloud and Cloud Federation. Several vendor-based proprietary solutions and open-source middleware are present for the resolution; however, these solutions are highly coupled to particular cloud environments. For heterogeneous clouds to exist in an interoperable environment, the need of a vendor-independent, secure and reliable message exchange middleware is critical. In this paper, considering general cloud architecture, we are presenting a Publish-Subscribe based middleware for Intercloud Message Exchange. Intercloud Message Exchange is an implementation of Data Distribution Service (DDS). DDS's reliable pub-sub messaging in conjunction with our devised Information Model can be a novel candidate for messaging domain of Intercloud Interoperability Standards. This Information Model also hosts an OWL based Cloud Resource Description Ontology, utilized by cloud environments for resource cataloguing and possible matchmaking prior to workload migration between heterogeneous clouds

A Hybrid Duo-Deep Learning and Best Features Based Framework for Action Recognition

Human Action Recognition (HAR) is a current research topic in the field of computer vision that is based on an important application known as video surveillance. Researchers in computer vision have introduced various intelligent methods based on deep learning and machine learning, but they still face many challenges such as similarity in various actions and redundant features. We proposed a framework for accurate human action recognition (HAR) based on deep learning and an improved features optimization algorithm in this paper. From deep learning feature extraction to feature classification, the proposed framework includes several critical steps. Before training fine-tuned deep learning models – MobileNet-V2 and Darknet53 – the original video frames are normalized. For feature extraction, pre-trained deep models are used, which are fused using the canonical correlation approach. Following that, an improved particle swarm optimization (IPSO)-based algorithm is used to select the best features. Following that, the selected features were used to classify actions using various classifiers. The experimental process was performed on six publicly available datasets such as KTH, UT-Interaction, UCF Sports, Hollywood, IXMAS, and UCF YouTube, which attained an accuracy of 98.3%, 98.9%, 99.8%, 99.6%, 98.6%, and 100%, respectively. In comparison with existing techniques, it is observed that the proposed framework achieved improved accuracy

Current state of knowledge of basic life support in health professionals of the largest city in Pakistan: A cross-sectional study

Background: Basic Life Support (BLS) is the recognition of sudden cardiac arrest and activation of the emergency response system, followed by resuscitation, and rapid defibrillation. According to WHO, Pakistan has one of the highest mortality rates from accidental deaths therefore assessment and comparison of BLS knowledge in health professionals is crucial. We thereby aim to assess and compare the knowledge of BLS in doctors, dentists and nurses. Methods: A multi-centric cross-sectional survey was conducted in Karachi at different institutions belonging to the private as well as government sector from January to March 2018. We used a structured questionnaire which was adapted from pretested questionnaires that have been used previously in similar studies. Descriptive statistics were analyzed using SPSS v22.0, where adequate knowledge was taken as a score of at least 50%. P \u3c 0.05 was considered as significant. Logistic regression was used to identify the factors affecting the knowledge regarding BLS in health care professionals. Results: The responders consisted of 140 doctors, nurses and dentists each. Only one individual (dentist) received a full score of 100%. In total, 58.3% of the population had inadequate knowledge. Average scores of doctors, dentists and nurses were 53.5, 43.3 and 38.4% respectively. Doctors, participants with prior training in BLS and those with 6 to 10 years after graduation were found to be a significant predictor of adequate knowledge, on multivariate analysis. Conclusion: Even though knowledge of BLS in doctors is better than that of dentists and nurses, overall knowledge of health care professionals is extremely poor. Present study highlights the need for a structured training of BLS for health care workers

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach

Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning’s contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures —InceptionV3, ResNet50, and VGG19—on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively

Trackable CEMB-Klean Cotton Transgenic Technology: Affordable Climate Neutral Agri-biotech Industrialization for Developing Countries

Background: Transgenic technology reflects the incorporation of novel useful traits in crop plants like cotton for economic benefits by overcoming the problems including insects’ pests and weeds in special. The present study is the success story of the continuous effort of CEMB team started back in the 1990s.Methods: This study includes characterization of a large number of Bacillus thuringiensis (Bt) strains taken from local soil and subjected to direct transformation of isolated BT genes into local cotton cultivars. Protocols for transformation into cotton plants were optimized and validated by the development of double gene codon optimized (Cry1Ac and Cry2A) transgenic cotton varieties.Results: The resulting GMOs in the form of CEMB-33, CA-12, CEMB-66 have been approved by Punjab Seed Council in 2013 and 2016 respectively. Double Bt and weedicide resistant cotton harboring CEMB-Modified and codon optimized cp4EPSPS (GTGene). These varieties can tolerate glyphosate spray @ 1900ml per acre without the appearance of necrotic spots/shedding and complete removal of all surrounding weeds in the cotton field is a significant advance to boost cotton production without spending much on insecticides and herbicides.Conclusion: In the current report, two unique sets of primers which amplify 1.1 Kb for CEMB-double Bt genes and 660 bp product for CEMB-Modified cp4EPSPS (GTGene) were tested. CEMB cotton variety CKC-01 is specially designed as low cost and easy to use by local farmer’s technology has the potential to revolutionize the cotton growing culture of the country.Keywords: Event detection; Bt Cotton; CEMB transgenic technology; GTGen