Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand

This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1~6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73~2.98%; Summer: 8.41~14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts

Synthesis of Silver Nanoparticles from Extracts of Wild Ginger (Zingiber zerumbet) with Antibacterial Activity against Selective Multidrug Resistant Oral Bacteria

Antibiotic resistance rate is rising worldwide. Silver nanoparticles (AgNPs) are potent for fighting antimicrobial resistance (AMR), independently or synergistically. The purpose of this study was to prepare AgNPs using wild ginger extracts and to evaluate the antibacterial efficacy of these AgNPs against multidrug-resistant (MDR) Staphylococcus aureus, Streptococcus mutans, and Enterococcus faecalis. AgNPs were synthesized using wild ginger extracts at room temperature through different parameters for optimization, i.e., pH and variable molar concentration. Synthesis of AgNPs was confirmed by UV/visible spectroscopy and further characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy analysis (EDXA), and Fourier-transform infrared spectroscopy (FTIR). Disc and agar well diffusion techniques were utilized to determine the in vitro antibacterial activity of plant extracts and AgNPs. The surface plasmon resonance peaks in absorption spectra for silver suspension showed the absorption maxima in the range of 400–420 nm. Functional biomolecules such as N–H, C–H, O–H, C–O, and C–O–C were present in Zingiber zerumbet (Z. zerumbet) (aqueous and organic extracts) responsible for the AgNP formation characterized by FTIR. The crystalline structure of ZZAE-AgCl-NPs and ZZEE-AgCl-NPs was displayed in the XRD analysis. SEM analysis revealed the surface morphology. The EDXA analysis also confirmed the element of silver. It was revealed that AgNPs were seemingly spherical in morphology. The biosynthesized AgNPs exhibited complete antibacterial activity against the tested MDR bacterial strains. This study indicates that AgNPs of wild ginger extracts exhibit potent antibacterial activity against MDR bacterial strains

Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption

This study utilized different methods, namely classical multiple linear regression (MLR), statistical approach exponential smoothing (EXPS), and deep learning algorithm long short-term memory (LSTM) to forecast long-term electricity consumption in the Kingdom of Saudi Arabia. The originality of this research lies in (1) specifying exogenous variables that significantly affect electrical consumption; (2) utilizing the Bayesian optimization algorithm (BOA) to develop individual super learner BOA-LSTM models for forecasting the residential and total long-term electric energy consumption; (3) measuring forecasting performances of the proposed super learner models with classical and statistical models, viz. MLR and EXPS, by employing the broadly used evaluation measures regarding the computational efficiency, model accuracy, and generalizability; and finally (4) estimating forthcoming yearly electric energy consumption and validation. Population, gross domestic products, imports, and refined oil products significantly impact residential and total annual electricity consumption. The coefficient of determination (R2) for all the proposed models is greater than 0.93, representing an outstanding fitting of the models with historical data. Moreover, the developed BOA-LSTM models have the best performance with R2>0.99, enhancing the predicting accuracy (Mean Absolute Percentage Error (MAPE)) by 59.6% and 54.8% compared to the MLR and EXPS models, respectively, of total annual electricity consumption. This forecasting accuracy in residential electricity consumption for the BOA-LSTM model is improved by 62.7% and 68.9% compared to the MLR and EXPS models. This study achieved a higher accuracy and consistency of the proposed super learner model in long-term electricity forecasting, which can be utilized in energy strategy management to secure the sustainability of electric energy

Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management

This study aims to develop statistical and machine learning methodologies for forecasting yearly electricity consumption in Saudi Arabia. The novelty of this study include (i) determining significant features that have a considerable influence on electricity consumption, (ii) utilizing a Bayesian optimization algorithm (BOA) to enhance the model’s hyperparameters, (iii) hybridizing the BOA with the machine learning algorithms, viz., support vector regression (SVR) and nonlinear autoregressive networks with exogenous inputs (NARX), for modeling individually the long-term electricity consumption, (iv) comparing their performances with the widely used classical time-series algorithm autoregressive integrated moving average with exogenous inputs (ARIMAX) with regard to the accuracy, computational efficiency, and generalizability, and (v) forecasting future yearly electricity consumption and validation. The population, gross domestic product (GDP), imports, and refined oil products were observed to be significant with the total yearly electricity consumption in Saudi Arabia. The coefficient of determination R2 values for all the developed models are >0.98, indicating an excellent fit of the models with historical data. However, among all three proposed models, the BOA–NARX has the best performance, improving the forecasting accuracy (root mean square error (RMSE)) by 71% and 80% compared to the ARIMAX and BOA–SVR models, respectively. The overall results of this study confirm the higher accuracy and reliability of the proposed methods in total electricity consumption forecasting that can be used by power system operators to more accurately forecast electricity consumption to ensure the sustainability of electric energy. This study can also provide significant guidance and helpful insights for researchers to enhance their understanding of crucial research, emerging trends, and new developments in future energy studies

The use of artificial neural networks to control the concentration of a model drug released acoustically

Liposomes are designed to encapsulate chemotherapy drugs used in cancer treatment. Their small size (nano-scale) allows them to extravagate through the leaky vascular surroundings of the tumor. Ultrasound waves can be used as an external trigger to control drug release from these liposomes. It is essential that the therapeutic dose is released as cancer cells can develop drug resistance, in part due to the concentration levels of the chemotherapeutic agent dipping below therapeutic levels during the treatment. To address this issue, this study proposes a feedback drug release controller based on model predictive control theory (MPC) and neural networks (NN). Our preliminary simulation results suggest that using a feedback controller is capable of keeping drug concentration levels, in the tumor site, at or above therapeutic levels. This is achieved by controlling the amount of acoustic drug release from these lipid-based nanocarriers, thus ensuring a controlled, safe, and effective therapeutic dose

Improving the efficacy of anticancer drugs via encapsulation and acoustic release

Conventional chemotherapeutics lack the specificity and controllability, thus may poison healthy cells while attempting to kill cancerous ones. Newly developed nano-drug delivery systems have shown promise in delivering anti-tumor agents with enhanced stability, durability and overall performance; especially when used along with targeting and triggering techniques. This work traces back the history of chemotherapy, addressing the main challenges that have encouraged the medical researchers to seek a sanctuary in nanotechnological-based drug delivery systems that are grafted with appropriate targeting techniques and drug release mechanisms. A special focus will be directed to acoustically triggered liposomes encapsulating doxorubicin

Validation of the Bengali version of the Caregiver Collusion Questionnaire: A tool for measuring collusion among caregivers of terminally ill patients

Background: In palliative care settings, collusion or “conspiracy of silence” frequently interferes with communication and interpersonal relationships among patients, caregivers, and healthcare professionals. The “Caregiver Collusion Questionnaire” is the only tool available for assessing caregiver collusion. The purpose of the study is to translate and adapt the English version of this instrument into Bengali, followed by standard validation. Methods: The study was carried out in two stages. Four independent translators conducted forward and backward translations of the English version of the “Caregiver Collusion Questionnaire” into Bengali. The Bengali version of the instrument was finalized following expert committee reviews, pre-testing, and cognitive debriefing. The final validation was carried out among 71 caregivers of patients with advanced incurable illnesses admitted to the palliative medicine and internal medicine departments of two Bangladeshi hospitals. In the final phase, the validity (content, face, and construct validity) and reliability (interclass item-wise correlation coefficient) of the translated tool were assessed. Result: 60% of the participants fully understood 19 items, whereas 40% struggled with one or more items. The expert committee expressed their satisfaction with the face and content validity of the translated version. The Bengali version also had quite good reliability (α = 0.62). Seven components were identified using principal component analysis with the distribution of Varimax Rotation distribution. Items under each factor had adequate factor loading, ranging from 0.4 to 0.8. Conclusion: The Bengali version of the “Caregiver Collusion Questionnaire” was found valid reliable and culturally acceptable for measuring caregiver collusion among the Bengali-speaking people. Based on the scale, the reasons for collusion can be identified and measures can be taken for breaking the collusion

Factors influencing plasma donation behavior of COVID‐19 recovered patients in Bangladesh: A pilot study

Abstract Background and Aim The COVID‐19 pandemic has plagued our lives for more than 2 years, and the preference for convalescent plasma (CP) as a life‐saving treatment since CP has proven as a potential therapeutic option for acute COVID‐19 patients who were suffering from severe disease. It is important to identify which factors are associated with plasma donation. Therefore, this study aimed to assess the associated factors for CP donation to COVID‐19 patients. Methods A cross‐sectional study was conducted online from December 21, 2021 to February 15, 2022 to identify different socio‐demographic factors and knowledge related to CP donation. People who recovered from the COVID‐19 infections and those who are willing to participate were included in the study. A total of 60 participants were included in the study. The data were analyzed using descriptive statistics, correlation matrix, and factor analysis. Results The analysis results confirm that 41.67% (n = 25) of the participants aged 26–30 years; among the recovered patients, only about 23% (n = 14) of the participants donated plasma. Though 97% (n = 58) of the participants agreed to donate plasma when it will be needed, however, when someone asked to donate plasma then 76.67% (n = 46) of the patients declined it. Findings depict that gender had a weak positive relationship with ever decline in plasma donation at 5% level of significance and the age of the participants inversely related to plasma donation. Conclusion Almost all the recovered participants were willing to donate plasma, however, due to a lack of knowledge and misconception, relatively few people actually did. This study reemphasizes the importance of health education to overcome the misconception about plasma donation, which is crucial for the treatment of COVID‐19 infection

Attitudes of Patients Attending Omdurman Teaching Hospital VCT Center, Sudan toward HIV/AIDS Voluntary Counseling and Testing Services

Abstract Background: Despite the availability of HIV/AIDS voluntary counselling and testing services in Omdurman Teaching Hospital, the level of uptake remains low, and the prevalence of HIV/AIDS in Sudan is still high. This situation suggests that there may be some underlying factors, such as patients' attitudes toward the services provided, that are affecting their willingness to access them. Therefore, this study aimed to assess the attitude of patients attending HIV/AIDS voluntary counselling and testing services in Omdurman Teaching Hospital, Sudan. Methods: A descriptive hospital-based study was conducted at Omdurman Teaching Hospital, Sudan. All patients attending HIV/AIDs counseling and voluntary services center were invited to participate in this study, and of the 200 invited, 150 patients participated with a response rate of 75%. Data were collected using a structured interview questionnaire and then analyzed by SPSS (version 23). Results: The findings revealed that many patients (92%) have a positive attitude toward HIV voluntary counselling and testing and believed that the shared information is informative and influential. It was also observed that 80% of the patients who received counselling had lower levels of social and psychological stress and stigma. Conclusion: The study highlighted the positive attitude of patients to utilize HIV/AIDS voluntary counselling and testing services which reduces the social and psychological stresses and stigma among HIV patients. Females and Muslim patients had a positive attitude