Short-Term Load Forecasting Using AMI Data

Accurate short-term load forecasting is essential for efficient operation of the power sector. Predicting load at a fine granularity such as individual households or buildings is challenging due to higher volatility and uncertainty in the load. In aggregate loads such as at grids level, the inherent stochasticity and fluctuations are averaged-out, the problem becomes substantially easier. We propose an approach for short-term load forecasting at individual consumers (households) level, called Forecasting using Matrix Factorization (FMF). FMF does not use any consumers' demographic or activity patterns information. Therefore, it can be applied to any locality with the readily available smart meters and weather data. We perform extensive experiments on three benchmark datasets and demonstrate that FMF significantly outperforms the computationally expensive state-of-the-art methods for this problem. We achieve up to 26.5% and 24.4 % improvement in RMSE over Regression Tree and Support Vector Machine, respectively and up to 36% and 73.2% improvement in MAPE over Random Forest and Long Short-Term Memory neural network, respectively

Multi-Criteria Land Suitability Analysis for Agriculture Using AHP and Remote Sensing Data of Northern Region India

The purpose of this study was to identify adequate agricultural sites in Punjab’s Northern region India district (India). This research employed the “Analytic Hierarchy Process (AHP)” approach, which is extensively used in land use appropriateness studies. Great soil type, land use, land cover, soil moisture, slope, aspect, elevation, drainage, geology, and geomorphology were all incorporated into the application. The ranks of influencing criteria were calculated using expert judgments and correlation analysis, while the weights were determined using a pairwise comparison matrix. The scores for sub-parameters with internal variations in the criteria assigned based on field work and published norms. The study area is considered to be highly appropriate for agricultural production in 41.2% (39044.28 ha), moderately suitable in 14.3% (13498.76 ha), and marginally suitable in 4.2% (3993 ha). Furthermore, it was discovered that 1.9% of the land is now unfit for agricultural production (1766.6 ha), while 38.4% of the area is permanently unsuitable (36372.6 ha). The following facts were also discovered to be important in achieving these results: a large portion (approximately 45%) of the study area is covered with forests, built-up areas, and water bodies, the soil depth is insufficient for agricultural production, the slope in the study area is quite steep, and thus the erosion degree is high

Granulomatosis with polyangiitis: A 17 year experience from a tertiary care hospital in Pakistan

Objective: Granulomatosis with Polyangiitis (GPA) is an autoimmune, multi-system, small and medium vessel vasculitis with granulomatous inflammation. Aim of this study was to assess the clinical and radiological presentations of patients with GPA amongst the Pakistani population. It is a single centre retrospective single observation study. Results: Study was conducted at the Aga Khan University Hospital, Karachi with records were reviewed from January 2000 to December 2017. Definitive diagnosis was made using a combination of serological anti-neutrophil cytoplasmic antibody (ANCA) testing along with the clinical and radiological presentation. A total of 51 patients met the diagnostic criteria in the time frame of the study. There were 23 males and 28 females with mean age of 44.0 ± 17.8 years on presentation. Arthritis was the most common symptom present in 41.2% of the cases followed by cough in 32.0%. Sixteen patients showed pulmonary infiltrates on chest X-ray. C-ANCA was positive in all of the patients compared with 21.6% p-ANCA positivity. A total of 13 biopsies were done. The median Birmingham Vasculitis Activity Score was 12. We report a 17.6% mortality rate with 5 deaths occurring due to respiratory failure. GPA is a diagnostic challenge leading to late diagnosis which can contribute to significant morbidity and mortality specially in the Third World

Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers

Analysis of the fairness of machine learning (ML) algorithms recently attracted many researchers' interest. Most ML methods show bias toward protected groups, which limits the applicability of ML models in many applications like crime rate prediction etc. Since the data may have missing values which, if not appropriately handled, are known to further harmfully affect fairness. Many imputation methods are proposed to deal with missing data. However, the effect of missing data imputation on fairness is not studied well. In this paper, we analyze the effect on fairness in the context of graph data (node attributes) imputation using different embedding and neural network methods. Extensive experiments on six datasets demonstrate severe fairness issues in missing data imputation under graph node classification. We also find that the choice of the imputation method affects both fairness and accuracy. Our results provide valuable insights into graph data fairness and how to handle missingness in graphs efficiently. This work also provides directions regarding theoretical studies on fairness in graph data.Comment: Accepted at IEEE International Conference on Big Data (IEEE Big Data

DNA-based Eye Color Prediction of Pakhtun Population Living in District Swat KP Pakistan

Background: Forensic DNA Phenotyping (FDP) or the prediction of Externally Visible Characteristics (EVCs) from a DNA sample has gained importance in the last decade or so in the forensic community. If and when the traditional forensic DNA typing via Short Tandem Repeats (STR) fails due to the absence of a reference sample, an individual can be traced by a DNA sample using FDP. Amongst the many available EVCs, eye color is one such character that can be predicted by employing previously developed IrisPlex system using Single Nucleotide Polymorphism (SNP) assay. In this study, we applied the IrisPlex system to samples collected from population of District Swat for prediction of eye colours from DNA.Method: Eye colour digital photographs and buccal swab samples were collected from 267 Pakhtun individuals of District Swat. Any person with eye disease was excluded from the study. Genomic DNA was extracted through Phenol-Chloroform extraction method. The amplified SNPs were typed using Multiplexed Single Base Extension (SBE). The genotypes were checked for eye color phenotypes through IrisPlex online tool and correlation were checked between SNPs, Gender, pie score and eye color.Result: Brown eye color was found prevalent as compared to intermediate and blue. Females have highly brown eye color compared to males while males have intermediate and blue. Three SNPs rs12913832 (in the HERC2), rs1393350 (TYR gene), rs1800407 (OCA2 gene) were strongly significant to eye color. Pie score was also significant to eye color and rs12913832 SNP. IrisPlex analysis in 20 individuals of District Swat was performed. The prediction accuracy of IrisPlex for blue or brown was 100% in the studied individuals. However, the IrisPlex tool predicted the intermediate phenotype incorrectly as brown or blue.Conclusion: It is concluded from the data that intermediate eye colour was not predicted accurately, therefore, inclusion of more SNPs in the IrisPlex system is needed to predict intermediate eye colour accurately.Keywords: Eye colour, IrisPlex, SNPs, Multiplex genotyping, DNA, District Swa

Prevalence of Diabetic Retinopathy and Correlation with HbA1c in Patients Admitted in Khyber Teaching Hospital Peshawar

Objective: To determine the prevalence of diabetic retinopathy in patients admitted in Khyber Teaching Hospital Peshawar and to correlate different stages of diabetic retinopathy with HbA1C levels. Methodology: This cross sectional study was conducted at Department of Ophthalmology, Khyber Teaching Hospital, MTI, Peshawar from December 2019 to May 2020. All patients over the age of 15 years who were diagnosed with diabetes mellitus were included in the study while patients with cataract or retinopathy due to other pathologies were excluded. All diabetic patients were admitted through outpatient department. In the ward their blood pressures were recorded and HbA1c levels were also measured. Visual acuity (VA) was checked. Screening for diabetic retinopathy was done by a consultant ophthalmologist by Optos Ultrawide Field Imaging of retina and Optical Coherence Tomography (OCT) of macula to establish stages of diabetic retinopathy and presence of diabetic macular edema respectively. Results: A total of 103 diabetic patients were included. Their retina was photographed, viewed and analyzed. Diabetic retinopathy, irrespective of the type, was found in 69 patients with a prevalence of 66.9%. Patients with lower ranges of HbA1c (below 6%) showed no evidence of DR. The clustering of majority of patients with diabetic retinopathy with HbA1c levels of 8 to 12 %, showed a significant relationship between high blood sugar levels and severity. Conclusion: In our study the higher frequency of retinopathy is alarming by considering it one of the leading causes of blindness in working class. It is highly recommended that routine ophthalmologic examination may be carried out along with optimal diabetic control

Utilization of Integrative Technique for Partial Recovery of Proteases from Soil Microbes

Aqueous two-phase system (ATPS) is an efficient, cost effective, fast, simple and ecofriendly method for the recovery of biomolecules. In the present study, an ATPS composed of polyethylene glycol and ammonium sulphate (NH4)2SO4 was used for the partial purification of proteases from microbial source. The effects of different parameters such as molecular weight of PEG (4000, 6000 and 10000), concentration of PEG (15, 17.5 and 20 %) and concentration of (NH4)2SO4 (7.5, 8.3, 9.1 and 9.9 %) on the partitioning behavior of proteases at room temperature were investigated. Generally, increasing the concentration of PEG and (NH4)2SO4 moved the protease to the top i.e., polymer-rich phase. Increasing the molecular weight of PEG from 4000 to 10000 the partition coefficient decreased subsequently. The highest partition coefficient i.e., 3.32 and maximum activity i.e., 16.06 soxhlet unit was found in an optimum system composed of 20 % PEG 4000 and 9.9 % (NH4)2SO4

Impact of Length and Percent Dosage of Recycled Steel Fibers on the Mechanical Properties of Concrete

The global rapid increase in waste tyres accumulation, as well as the looming social and environmental concerns, have become major threats in recent times. The use of Recycled Steel Fiber (RSF) extracted from waste tyres in fiber reinforced concrete can be of great profitable engineering applications however the choice of suitable length and volume fractions of RSF is presently the key challenge that requires research exploration. The present experimental work aims at investigating the influence of varying lengths (7.62 and 10.16 cm) and dosages (1, 1.5, 2, 2.5, 3, 3.5, and 4%) of RSF on the various mechanical properties and durability of concrete. Test results revealed that the varying lengths and dosages of RSF significantly affect the mechanical properties of concrete. The improvements in the compressive strength, splitting tensile strength, and Modulus of Rupture (MOR) of RSF reinforced concrete observed were about 26, 70, and 63%, respectively. Moreover, the RSF reinforced concrete showed an increase of about 20 and 15% in the yield load and ultimate load-carrying capacity, respectively. The durability test results showed a greater loss in compressive strength and modulus of elasticity and a smaller loss in concrete mass of SFRC. Based on the experimental findings of this study, the optimum dosages of RSF as 2.5 and 2% for the lengths 7.62 and 10.16 cm lengths, respectively are recommended for production of structural concrete. Doi: 10.28991/cej-2021-03091750 Full Text: PD

GREEN SYNTHESIS OF SILVER NANOPARTICLES USING THE LEAF EXTRACT OF PUTRANJIVA ROXBURGHII WALL. AND THEIR ANTIMICROBIAL ACTIVITY.

 Objective: This study deals with the synthesis of silver nanoparticles (AgNP's) from the extract of the leaves of the plant Putranjiva roxburghii wall.Using biological method, i.e., green synthesis.Methods: The extract from the leaves acts as a reducing and stabilizing agent for the AgNP's. Further characterization was done using varioustechniques like ultraviolet (UV)-visible spectrophotometry, which shows surface plasmon resonance, Fourier transform infra-red spectroscopyanalysis shows formation of various bonds, scanning electron microscope (SEM) and transmission electron microscope (TEM) analysis depictsthe distribution and average size of nanoparticles. The antimicrobial activity was also checked against various bacteria and fungi using minimuminhibitory concentration and well diffusion assay.Result: UV analysis shows strong plasmon resonance between 420 and 480 nm SEM analysis shows the distribution of synthesized nanoparticles,whereas TEM analysis shows the average particle size to be near about 5 nm and well diffusion assay proved that these nanoparticles are effectiveagainst different microorganisms.Conclusion: P. roxburghii wall. shows strong potential for the reduction of silver from Ag+ to Ag0 and nanoparticles so formed are strongly activeagainst various microorganism.Keywords: Putranjiva roxburghii, Fourier transform infra-red, Scanning electron microscope, Transmission electron microscope