A Review on MPC Based Self Recovering Intelligent Advance Meter for Smart Grid: Scheme and Challenges

The Model Predict Control (MPC) based Intelligent Advance Metering (IAM) is a core maneuver of future smart grids (SG). SG is the advanced generation of electric power and utility system that improve operation technology (OT) and information technology (IT) to provide nonstop, self-recovery, self-configuration, low-cost, and security-based electricity to the consumer in real-time. Smart metering (SM) allows SG to connect the electric, gas, and oil utilities through sensors. Power plants, consumers, and utility companies will be received real-time wireless control IAM with fifth generation (5G) network technology. The aim of 5G network technology is to enable power grid digitalization (PGD) and facilitate the (IOT) Internet of Things for the future advance SG with benefits such as high-rate public safety, low latency, ultra-high speed, large number of connectivity, and reliability. In this paper, we analyze future predictions about energy needs by using MPC, fast self-recovery system, self-configuration, and upgradation, better performance of service provider, faster power connecting after an outage, control electric theft, minimize electric leakage, a large number of wireless connecting of IAM home-based, and real-time monitoring via human machine interface (HMI) and for customer end IAM operation over 5G networks to reduce billing price, reduce meter cost, lower outage cost, and as well as personalized control over electricity consumption and future challenge in this area

Deliberate self-harm and attachment: mediating and moderating roles of depression, anxiety, social support and interpersonal problems among Pakistani school going adolescents

Introduction: In Pakistan there is dearth of research on deliberate self-harm (DSH) and its predictors among adolescents. While the lack of research in Pakistan can be partly attributed to the sacrilegious status, criminalization and stigmatization attached to DSH, it is also an attribute of paucity of Urdu versions of the standardized psychological instruments. Previous research in developed countries has indicated that attachment theory can be used as a useful framework to understand the development of austere psychopathologies like DSH, as well as for studying pathways of interaction of interpersonal and intrapersonal factors of psychopathologies. In this study, standardized psychological instruments are translated into Urdu language as a first step. These instruments are then used to study pathways of interaction of interpersonal and intrapersonal factors of DSH, conceptualized within attachment framework. Method: The study was conducted in two steps. In step 1, Youth Health Risk Behavior Survey (YHRB), Inventory of Interpersonal Problems-32 (IIP-32) and Significant Others Scale (SOS), were translated into Urdu language. Along with these scales, Urdu translated versions of Hospital Anxiety and Depression Scale (HADS), Adolescent Relationship Scales Questionnaire (ARSQ), Life Events scale (LES) from CASE questionnaire and Family Affluence Scale-II (FAS-II) were reviewed for accuracy of translation through expert judgement and psychometric evaluation. Secondly, a cross sectional survey was conducted with 1290 adolescents (10 - 19 years age) using the translated Urdu versions of the instruments and demographic pro forma. Structural equation modelling was used to study the pathways of associations between predictors of DSH. Results: The extensive process of translation resulted in establishment of semantic, content, technical and construct equivalence of the translated instruments with the original English versions. Multiple imputation was performed to account for missing values in SPSS 20. Important structural adaptations were made in the scales based on factor analyses conducted in M plus. After modifications, all scales showed satisfactory CFI (≥ 0.90) and RMSEA (≤ 0.06). Results of the survey indicated that the prevalence of DSH (with, without and ambivalent suicidal intentions) was 7%. Two SEM models were constructed involving both mediation and moderation pathways. Results of Model 1 showed association of attachment with DSH was double mediated by social support, depression and anxiety. Model 2 also confirmed association of attachment with DSH with double mediation through relationship style problems, depression and anxiety. In order to understand the contextual picture of the concepts studied in this research both SEM models were also constructed by controlling for demographic factors. This resulted in confirming age, gender and family affluence as significant contributors but with very small effects. Discussion and conclusion: In the present study translation of the instruments helped in building a reservoir for future research. The results of translation and validation of instruments indicated that cultural differences, language needs and age must be accounted for while using standardized psychological instruments. Taking into consideration specific cultural and demographic background of Pakistan, this study also confirms the key role of attachment in influencing interaction of predictors of DSH. It is suggested that intrapersonal and interpersonal factors are influential points of intervention for designing clinical, school and community based awareness and prevention programs for DSH. The thesis also discusses the implications for policy guidelines along with recommendations for future research and other applications of the study

A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm

Spatial distribution of meteorological stations has a significant role in hydrological research. The meteorological data play a significant role in drought monitoring; in this regard, accurate and suitable provision of meteorological stations is becoming crucial to improve and strengthen the skill of drought prediction. In this perspective, the choice of meteorological stations in a specific region has substantial importance for accurate estimation and continuous monitoring of drought hazards at the regional level. However, installation and data mining on a large number of meteorological stations require high cost and resources. Therefore, it is necessary to rank and find dependencies among existing meteorological stations in a particular region for further climatological analysis and reanalysis of databases. In this paper, the Monte Carlo feature selection and interdependency discovery (MCFS-ID) algorithm-based framework is proposed to identify the important meteorological station in a particular region. We applied the proposed framework on 12 meteorological stations situated in varying climatological regions of Punjab (Pakistan). We employed the drought index SPTI on 1-, 3-, 6-, 9-, 12-, 24-, and 48-month time-scale data to find the interdependencies among meteorological stations at various locations. We found that Sialkot has significance regional importance for studying SPTI-3, SPTI-6, and SPTI-48 indices. This regional importance is based on scores of relative importance (RI); for example, the RI values for SPTI-3, SPTI-6, and SPTI-48 indices are 0.1570, 0.1080, and 0.0270, respectively. Furthermore, the Jhelum station has more relative importance (RI = 0.1410 and 0.1030) for SPTI-1 and SPTI-9 indices, while varying concentration behaviour is observed in the remaining time scales

A Novel Appraisal Protocol for Spatiotemporal Patterns of Rainfall by Reconnaissance the Precipitation Concentration Index (PCI) with Global Warming Context

In global warming contexts, continuous increment in temperature triggers several environmental, economic, and ecological challenges. Its impacts have severe effects on energy, agriculture, and socioeconomic structure. Moreover, the strong correlation between temperature and dynamic changing of rainfall patterns greatly influences the natural cycles of water resources. Therefore, it is necessary to examine the spatiotemporal variation of precipitation to improve precipitation monitoring systems. Thereby, it helps to make future planning for flood control and water resource management. Considering the importance of the spatiotemporal assessment of precipitation, the current study provides a new method: regional contextual precipitation concentration index (RCPCI) to analyze spatial-temporal patterns of annual rainfall intensities by reconnaissance the precipitation concentration index (PCI) in the global warming context. The current study modifies the existing version of PCI by propagating the role of temperature as auxiliary information. Further, based on spatial and nonspatial correlation analysis, the current study compares the performance of RCPCI and PCI for 45 meteorological stations of Pakistan. Tjøstheim’s coefficient and the modified t-test are used for testing and estimating the spatial correlation between both indices. In addition, the Poisson log-normal spatial model is used to assess the spatial distribution of each rainfall pattern. Outcomes associated with the current analysis show that the proposed method is a good and efficient substitute for PCI in the global warming scenario in the presence of temperature data. Therefore, to make accurate and precise climate and precipitation mitigation policies, the proposed method may incorporate uncovering the yearly pattern of rainfall.Validerad;2022;Nivå 2;2022-08-05 (hanlid);Part of special issue: Multivariate and Big Data Modeling and Related Issues</p

Monitoring and assessment of heavy metal contamination in surface water of selected rivers

The current research aimed to monitor and assess the heavy metal contamination in the surface water of 53 sampling sites along the selected rivers using principal component analysis and cluster analysis. For this purpose, both physiochemical parameters such as the temperature (T), the potential of hydrogen (pH), total dissolved solids (TDS) and electroconductivity (EC), and heavy metals such as iron (Fe), chromium (Cr), nickel (Ni), cadmium (Cd), lead (Pb) and arsenic (As) are analyzed as potential water contaminants. The average values of pH, TDS, EC and T are found at 7.75, 70.89 mg/L, 139.11 µs/cm and 20.29 °C, respectively, and heavy metals including Cr, Ni, Cd, Pb, As and Fe are observed at 0.04, 0.04, 0.04, 0.03, 0.001 and 0.04 mg/L, respectively. Moreover, it is found that in both rivers hazardous metals, including Cr (100%), Cd (92.30%), Pb (100%), Ni (100%) and Fe (91%), exceed the permissible limits of the WHO

Identifying inter-seasonal drought characteristics using binary outcome panel data models

This study mainly focuses on spatiotemporal and inter-seasonal meteorological drought characteristics. Random Effect Logistic Regression Model (RELRM) and Conditional Fixed Effect Logistic Regression Model (CFELRM) are used to identify the spatiotemporal and inter-seasonal characteristics of meteorological drought in selected stations. The log-likelihood Ratio Chi-Square (LRCST) and Wald chi-square tests (WCTs) are used to assess the significance of RELRM and CFELRM. The Hausman test (HT) is applied to select the appropriate model between RELRM and CFELRM. For instance, HT suggests the CFELRM as an appropriate model in spring-to-summer spatiotemporal drought modelling. The significant coefficient from CFELRM indicates that an increment in moisture conditions of the spring season will decrease the probability of drought in the summer. The odds ratio of 0.1942 means that 19.42% chance of being in a higher category. Similarly, in summer-to-autumn using RELRM the computed odds ratio of 0.0673 shows that 6.73% chance of being in a higher category

Proposing a new framework for analyzing the severity of meteorological drought

The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan

A new comprehensive approach for regional drought monitoring

The Standardized Precipitation Index (SPI) is a vital component of meteorological drought. Several researchers have been using SPI in their studies to develop new methodologies for drought assessment, monitoring, and forecasting. However, it is challenging for SPI to provide quick and comprehensive information about precipitation deficits and drought probability in a homogenous environment. This study proposes a Regional Intensive Continuous Drought Probability Monitoring System (RICDPMS) for obtaining quick and comprehensive information regarding the drought probability and the temporal evolution of the droughts at the regional level. The RICDPMS is based on Monte Carlo Feature Selection (MCFS), steady-state probabilities, and copulas functions. The MCFS is used for selecting more important stations for the analysis. The main purpose of employing MCFS in certain stations is to minimize the time and resources. The use of MCSF makes RICDPMS efficient for drought monitoring in the selected region. Further, the steady-state probabilities are used to calculate regional precipitation thresholds for selected drought intensities, and bivariate copulas are used for modeling complicated dependence structures as persisting between precipitation at varying time intervals. The RICDPMS is validated on the data collected from six meteorological locations (stations) of the northern area of Pakistan. It is observed that the RICDPMS can monitor the regional drought and provide a better quantitative way to analyze deficits with varying drought intensities in the region. Further, the RICDPMS may be used for drought monitoring and mitigation policies

A generalized framework for quantifying and monitoring the severity of meteorological drought

The current study proposes a new framework for quantifying and monitoring the severity of meteorological drought. The proposed framework consists of three phases. The first phase of the framework uses K-component Gaussian Mixture Distribution (GMD) in the computation. The second phase is mainly based on the dissimilarity matrix-based clustering using C-index and Monte Carlo Feature-based Selection (MCFS) method. The third phase uses the Markov chain, transition probabilities and a non-homogeneous Poisson process under the Bayesian estimation. The Relative Importance (RI) values are used to choose appropriate stations. The Deviance Information Criteria (DIC) is used to check model suitability, and Root Mean Square Error (RMSE) is utilized for determining model performance. The proposed framework is validated to the 52 meteorological stations in Pakistan for 49 years from 1968 to 2016. Moreover, the outcomes of the current analysis provide insight to quantify and monitor meteorological drought comprehensively and accurately

Assessing the Probability of Drought Severity in a Homogeneous Region

The standardized precipitation index (SPI) is one of the most widely used indices for characterizing and monitoring drought in various regions. SPI's applicability has regional and time-scale constraints when it observes in several homogeneous climatic regions with similar characteristics. It also does not provide sufficient knowledge about precipitation deficits and the spatiotemporal evolution of drought. Therefore, a new method, the regional spatially agglomerative continuous drought probability monitoring system (RSACDPMS), is proposed to obtain spatiotemporal information and monitor drought characteristics more expeditiously. The proposed framework uses spatially agglomerative precipitation (SAP) and copulas’ functions to continuously monitor the drought probability in the homogenous region. The RSACDPMS is validated in the region of the Northern area of Pakistan. The outcomes of the current study provide a better quantitative way to obtain appropriate information about precipitation deficits and the spatiotemporal evolution of drought.Validerad;2022;Nivå 2;2022-02-03 (johcin);Funder: King Khalid University (RGP.2/4/43)</p