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The ever-increasing demand for electricity has presented a grave threat to traditional energy sources, which are finite, rapidly depleting, and have a detrimental environmental impact. These shortcomings of conventional energy resources have caused the globe to switch from traditional to renewable energy sources. Wind power significantly contributes to carbon-free energy because it is widely accessible, inexpensive, and produces no harmful emissions. Better and more efficient renewable wind power production relies on accurate wind speed predictions. Accurate short-term wind speed forecasting is essential for effectively handling unsteady wind power generation and ensuring that wind turbines operate safely. The significant stochastic nature of the wind speed and its dynamic unpredictability makes it difficult to forecast. This paper develops a hybrid model, L-LG-S, for precise short-term wind speed forecasting to address problems in wind speed forecasting. In this research, state-of-the-art machine learning and deep learning algorithms employed in wind speed forecasting are compared with the proposed approach. The effectiveness of the proposed hybrid model is tested using real-world wind speed data from a wind turbine located in the city of Karachi, Pakistan. Moreover, the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are used as accuracy evaluation indices. Experimental results show that the proposed model outperforms the state-of-the-art legacy models in terms of accuracy for short-term wind speed in training, validation and test predictions by 98% respectively. [ABSTRACT FROM AUTHOR]

Electric grid is vulnerable to power imbalance and inertia is the grid's response to overcome such disturbance. Augmentation of power electronic converter based renewable energy technologies like Photovoltaic Generators (PVG) and batteries in utility grid significantly reduces inertia. Inertia degradation is indicated by sharp Rate of Change of Frequency (ROCOF) events due to any grid component failure or imbalance. Fixed gain feedback Proportional Integral Derivative (PID) control is insufficient to deal with varying ROCOF events. This work proposes Sliding Mode (SM) robust droop control scheme assisted by Artificial Neural Network (ANN) algorithm for an islanded PVG integrated microgrid. Droop response is governed by swing equation that uses PVG Maximum Power Point (MPP) forecasted by ANN. ANN forecast is compared with optimized Gaussian process regression algorithm based on mean squared error and speed of training as key performance indicator. The algorithms are trained and validated based on climate dataset of Islamabad, Pakistan. SM control performance is compared with various PID gain settings and qualified as the most suitable against variable source, load and ROCOF scenarios. Finally, significance of accurate MPP forecast for droop control is established by comparing the ANN and deterministic forecaster assisted droop response in a microgrid case study. [ABSTRACT FROM AUTHOR]

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author's personal information, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic, semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, and marketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French, etc.However, the research on RomanUrdu is not up to the mark.Hence, this study focuses on detecting the author's age and gender based on Roman Urdu text messages. The dataset used in this study is Fire'18-MaponSMS. This study proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiple linguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model is contrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Naïve Bays (NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable, RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 with Random Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender prediction calculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and 60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguistic features for age and gender prediction. [ABSTRACT FROM AUTHOR]

Sugarcane (Saccharum officinarum) has gained more attention worldwide in recent decades because of its importance as a bioenergy resource and in producing table sugar. However, the production capabilities of conventional varieties are being challenged by the changing climates, which struggle to meet the escalating demands of the growing global population. Genome editing has emerged as a pivotal field that offers groundbreaking solutions in agriculture and beyond. It includes inserting, removing or replacing DNA in an organism's genome. Various approaches are employed to enhance crop yields and resilience in harsh climates. These techniques include zinc finger nuclease (ZFN), transcription activator-like effector nuclease (TALEN) and clustered regularly interspaced short palindromic repeats/associated protein (CRISPR/Cas). Among these, CRISPR/Cas is one of the most promising and rapidly advancing fields. With the help of these techniques, several crops like rice (Oryza sativa), tomato (Solanum lycopersicum), maize (Zea mays), barley (Hordeum vulgare) and sugarcane have been improved to be resistant to viral diseases. This review describes recent advances in genome editing with a particular focus on sugarcane and focuses on the advantages and limitations of these approaches while also considering the regulatory and ethical implications across different countries. It also offers insights into future prospects and the application of these approaches in agriculture. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the effectiveness of tissue plasminogen activator (r-tPA) in resistant toxic anterior segment syndrome (TASS) after Phacoemulsification. Study Design: Quasi experimental. Place and Duration of Study: Khyber Teaching Hospital, Peshawar from August 2021 to August 2023. Methods: Forty-six patients (46 eyes) with an anterior chamber fibrin reaction after cataract surgery were treated with intra-cameral injection of t-PA (30µg/0.1cc) following failure to respond to conventional treatment with intensive topical and subconjunctival steroids. Outcome measures were best corrected visual acuity (BCVA), clearance/recurrence of the fibrin reaction and complications. Results: Intra-cameral t-PA was injected2 to 6 weeks post Phacoemulsification in TASS cases. Mean post-operative injection time was 18.5 ± 10.2 days. After 1st day post injection, we observed total abolition of fibrinous exudate in 36(78.2%) cases and subtotal resolution in 10 (21.7%) cases. After 6 weeks the TASS was completely settled in 43(93.4%)cases. Mean BCVA improved from 0.59 ± 0.40 log MAR pre-injection to 0.38 ± 0.40 log MAR at 6th week post-injection (p=0.07). There was no statistically significant difference in BCVA and rate of fibrinolysis between the two groups after post r-tPA injections. Intra-cameral r-tPA was not associated with adverse effects like raised IOP or endophthalmitis. Conclusion: Intra-cameral t-PA injection (30µg/0.1cc)appears to be a safe and effective treatment option for resolving anterior chamber fibrin reaction refractory to conventional steroid therapy following cataract surgery. [ABSTRACT FROM AUTHOR]