DECODING PEDIATRIC MENINGITIS UNRAVELING THE INTRICACIES OF ANTIMICROBIAL RESISTANCE IN IRAQI PEDIATRIC PATIENTS.

BACKGROUND AND OBJECTIVES: Bacterial meningitis is a common serious infectious diseases in children with tough therapy due to resistance to commonly indicated antibacterial therapy. We sought to characterize the bacterial resistance spectrum of Bacterial meningitis in Iraqi pediatric patients. METHODS: Fifty-seven patients' CSF (cerebrospinal fluid) microbiological data were reviewed before enrollment of children (less than 4 years old), Gram-stain identification, white blood cells, protein, and glucose assays, and growing the bacterial pathogen from CSF or blood samples. A probable case of bacterial meningitis was determined by leukocytosis (>100 cells/mm 3), turbidity, decreased glucose (<40 mg/dl), and raised protein levels (>100 mg/dl), which provided enough evidence to start antibiotic regimen. RESULTS: Three quarters of the patients diagnosed with meningitis 71.9 % were males, although there was no significant association neither between gender nor age group and drug resistance. There was a significant association between the level of hemoglobin (low level of Hb) and drug resistance (single 68.3%, multiple 29.3%, P value 0.004). Also, there was a significant association between vaccination (unvaccinated patient) and drug resistance (multiple drug resistance) (100%, P value 0.001). CONCLUSION: This study offers valuable new insights into the characteristics and prognoses of individuals with varying degrees of therapy resistance.

Effects of Doula Care on Mother and Infant Health Outcomes in Al-Ahsa Region, Saudi Arabia: A Retrospective Cohort Study.

Introduction Continued supportive care during childbirth may be the key to preventing unfavorable outcomes for both mother and child. It is important to assess and comprehend the sources of assistance available during pregnancy in order to enhance the birthing process and promote favorable outcomes. Objectives The current study aimed to assess the impact of ongoing doula support on mother and infant health outcomes compared to standard care. Methods A retrospective cohort study using both medical records and direct interviews based on information in the data collection form was carried out in the Eastern Province of Saudi Arabia, Al-Ahsa Region. The data that were collected included demographic information, obstetric history, delivery data, and postpartum outcome. Results A total of 50 pregnant women receiving doula care and 100 pregnant women receiving standard care were included. Regarding the mode of delivery, 43 (86%) of the doula group had vaginal deliveries compared to 73 (73%) of the standard care group, while CSs were needed for seven (14%) and 27 (27%), respectively (P = 0.78). Only seven (14%) neonates in the doula group required neonatal intensive care unit admission, versus 22 (22%) in the standard care group (P = 0.246). Additionally, initial breastfeeding within the golden hour occurred in 27 (54%) of the doula group compared to 16 (16%) of the standard care group, while exclusive breastfeeding was reported in 32 (64%) of the doula group compared to 26 (26%) of the standard care group (P < 0.001). Conclusion The current study showed more advantageous delivery and postpartum outcomes among doula care women and their infants compared to standard care, mainly for increasing the rate of initiated breastfeeding within the golden hour, exclusive breastfeeding, and reducing postpartum depression.

PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features.

Post-translational modifications (PTMs) are fundamental to essential biological processes, exerting significant influence over gene expression, protein localization, stability, and genome replication. Sumoylation, a PTM involving the covalent addition of a chemical group to a specific protein sequence, profoundly impacts the functional diversity of proteins. Notably, identifying sumoylation sites has garnered significant attention due to their crucial roles in proteomic functions and their implications in various diseases, including Parkinson's and Alzheimer's. Despite the proposal of several computational models for identifying sumoylation sites, their effectiveness could be improved by the limitations associated with conventional learning methodologies. In this study, we introduce pseudo-position-specific scoring matrix (PsePSSM), a robust computational model designed for accurately predicting sumoylation sites using an optimized deep learning algorithm and efficient feature extraction techniques. Moreover, to streamline computational processes and eliminate irrelevant and noisy features, sequential forward selection using a support vector machine (SFS-SVM) is implemented to identify optimal features. The multi-layer Deep Neural Network (DNN) is a robust classifier, facilitating precise sumoylation site prediction. We meticulously assess the performance of PSSM-Sumo through a tenfold cross-validation approach, employing various statistical metrics such as the Matthews Correlation Coefficient (MCC), accuracy, sensitivity, specificity, and the Area under the ROC Curve (AUC). Comparative analyses reveal that PSSM-Sumo achieves an exceptional average prediction accuracy of 98.71%, surpassing existing models. The robustness and accuracy of the proposed model position it as a promising tool for advancing drug discovery and the diagnosis of diverse diseases linked to sumoylation sites.

Computational investigation of stochastic Zika virus optimal control model using Legendre spectral method.

This study presents a computational investigation of a stochastic Zika virus along with optimal control model using the Legendre spectral collocation method (LSCM). By accumulation of stochasticity into the model through the proposed stochastic differential equations, we appropriating the random fluctuations essential in the progression and disease transmission. The stability, convergence and accuracy properties of the LSCM are conscientiously analyzed and also demonstrating its strength for solving the complex epidemiological models. Moreover, the study evaluates the various control strategies, such as treatment, prevention and treatment pesticide control, and identifies optimal combinations that the intervention costs and also minimize the proposed infection rates. The basic properties of the given model, such as the reproduction number, were determined with and without the presence of the control strategies. For R 0 < 0 , the model satisfies the disease-free equilibrium, in this case the disease die out after some time, while for R 0 > 1 , then endemic equilibrium is satisfied, in this case the disease spread in the population at higher scale. The fundamental findings acknowledge the significant impact of stochastic phonemes on the robustness and effectiveness of control strategies that accelerating the need for cost-effective and multi-faceted approaches. In last the results provide the valuable insights for public health department to enabling more impressive mitigation of Zika virus outbreaks and management in real-world scenarios.

Correction: Enantioselective total synthesis of atisane diterpenoids: (+)-sapinsigin H, (+)-agallochaol C, and (+)-16α, 17-dihydroxy-atisan-3-one.

Correction for 'Enantioselective total synthesis of atisane diterpenoids: (+)-sapinsigin H, (+)-agallochaol C, and (+)-16α, 17-dihydroxy-atisan-3-one' by Dattatraya H. Dethe et al., Chem. Commun., 2024, 60, 7866-7869, https://doi.org/10.1039/D4CC01982B.

Impact of Sociodemographic Characteristics on the Quality of Life of Frontline Nursing Staff During COVID-19 in Saudi Arabia.

Background The COVID-19 pandemic imposed unprecedented challenges on healthcare systems worldwide. The pandemic placed frontline nursing staff working in the ICU and ER at the epicenter of this global crisis. This study aimed to assess the multifaceted impact of sociodemographic characteristics on the quality of life (QOL) of nursing staff during the pandemic. Method A cross-sectional survey was conducted to evaluate the impact of sociodemographic characteristics on the QOL of 322 frontline nurses working in the ICU and ER of five Saudi hospitals from May to July 2022. Participants completed the electronic survey questionnaire including demographic characteristics and four domains of QOL from the World Health Organization Quality of Life Questionnaire (WHOQOL-BREFF). The data was evaluated using descriptive and inferential statistics. Results Among 322 nurse participants, the majority were female (84.8%), married (64.4%), and held a bachelor's degree (92.4%). Age (above 40 years), gender (male), and marital status (married) reported a higher individual domain and overall QOL scores which shows that these characteristics have a direct influence on QOL. Years of work experience, extra working hours, and direct contact with COVID-19 patients were additional significant factors. Pearson correlation coefficients among QOL domains ranged from 0.54 to 0.91, indicating a strong interrelation among these domains. The highest transformed score was in the social domain (70.10) while the lowest score was in the psychological domain (59.20). The overall QOL mean score (SD) was 3.49(0.14) and the mean score (SD) of general health was 3.46(0.15). Conclusion The findings of this study suggest that sociodemographic and work-related factors have a complex and multifaceted impact on the QOL of nurses during the COVID-19 pandemic in Saudi Arabia. It also presents an insight into developing specific interventions to enhance nurses' resilience and well-being amidst pandemic challenges and to improve their QOL.

Value of the Brain and Spinal Injury Center Score in Assessment and Prognosis of Acute Traumatic Spinal Cord Injury.

The objective was to assess the severity of neurological injury in acute traumatic spinal cord injury (ATSCI) using the BASIC (Brain and Spinal Injury Center) score, to correlate with the American Spinal Injury Association (ASIA) Impairment Scale (AIS) grade at admission and at 3 months postinjury in patients managed for ATSCI at National Hospital, Abuja, and thereby validate the novel BASIC score. This was a prospective longitudinal hospital-based study involving consecutive patients diagnosed with ATSCI and managed at the National Hospital, Abuja. Sixty-five participants met the inclusion criteria. Each patient was resuscitated along the Advanced Trauma Life Support protocol, followed by history, neurological examination according to the International Standards for the Neurological Classification of Spinal Cord Injury (ISNCSCI), and AIS grades that were recorded. Magnetic resonance imaging scan of the injured spinal cord was done, and BASIC scores were assigned. Further management was as per the standard. Three months after injury, neurological examination was again carried out based on ISNCSCI and AIS grades assigned. Data were collected, analyzed, and correlated using Excel and SPSS version 23. Means, medians, correlation coefficients, and Fisher's exact t-tests were determined. p-Value <0.05 was considered statistically significant. Results show mean age was 39.1 ± 12.3 years. The majority (81.5%) were males, whereas 18.5% were females. The majority (67.7%) were skilled professionals, 13.8% were unskilled, and 18.5% were students. Most injuries (90.8%) were due to road traffic accidents, whereas 9.2% were due to falls. Majority (72.3%) of the patients had complete SCI (AIS grade A), whereas AIS grade E accounted for the least number (3.1%). Cervical spine injury affected 92.3% of patients, whereas 7.7% had thoracic spine injury. Most patients had BASIC 4 pattern on MRI (44.6%), whereas BASIC 1 pattern was the fewest (3.1%). Surgery was not done for 58.5% of patients, whereas 41.5% had surgical decompression and spine fusion. At 3 months postinjury, 15.4% of patients had AIS grade improvement, whereas 84.6% maintained their AIS grade. The largest AIS grade improvement was from grade B to C (6.2%), which was statistically significant (p = 0.04). BASIC score correlated moderately with admission AIS grade (p = 0.532). BASIC score also correlated moderately with AIS grade at 3 months postinjury (p = 0.546). BASIC score 4 was best at predicting poor outcome in ATSCI. In conclusion, BASIC score has a moderate correlation with AIS grade in ATSCI and can predict poor outcomes in ATSCI. BASIC score of 4 has the best discriminant value in prognosticating and represents severe SCI.

Photophysical Investigation of One Pot Synthesized Novel Indenofluorene Derivative (BDP) as a Fluorescent Chemosensor for the Detection of Fe3+ Ion.

An Indane-1-one derivative 11-(1-benzyl-1H-indol-3-yl)-10,12-dihydrodiindeno[1,2-b:2',1'-e]-pyridine (BDP) has been synthesized by the reaction of Indan-1-one with 1-benzyl-1H-indole-3-carbaldehyde. FT-IR, 1H-NMR, 13N-NMR and Mass spectroscopic techniques has been used to confirmed the structure of BDP. The observed photophysical changes in BDP across various solvents were associated. The impact of various interactions on photophysical parameters, including Stokes shift, dipole moment, oscillator strength, and fluorescence quantum yields, has been assessed in relation to solvent polarity. Moreover, BDP demonstrates potential as a selective fluorescent chemosensor for detecting Fe3+ ion within a range of cations in an aqueous DMSO environment. A thorough investigation into the recognition mechanism of BDP towards Fe3+ ion has been conducted using Benesi-Hildebrand and Stern-Volmer, measurements. BDP forms a 2:1 complex with the Fe3+ ion, exhibiting fluorescent quenching behaviour.

Enantioselective total synthesis of atisane diterpenoids: (+)-sapinsigin H, (+)-agallochaol C, and (+)-16α, 17-dihydroxy-atisan-3-one.

Enantioselective total synthesis of (+)-sapinsigin H, (+)-agallochaol C, and (+)-16α, 17-dihydroxy-atisan-3-one has been accomplished starting from enantiopure Wieland-Miescher ketone. Key features of the syntheses include a benzannulation step to construct the tricyclic core, an oxidative dearomatization step to generate the diene, and a Diels-Alder reaction with ethylene gas to establish the bicyclo[2.2.2]octane framework. Efficient late-stage functionalisation of the A-ring by aerobic oxidation and Baeyer-Villiger oxidation completed the atisane target molecules.

Human and ecological risk assessments of potentially toxic elements in sediments around a pharmaceutical industry.

Potentially toxic elements (PTEs) in sediment can be highly hazardous to the environment and public health. This study aimed to assess the human and ecological risks of PTEs in sediments around a pharmaceutical industry in Ilorin, Nigeria. Physicochemical parameters and the concentrations of lead (Pb), chromium (Cr), cadmium (Cd), cobalt (Co), arsenic (As), and nickel (Ni) were analyzed in sediment samples collected from seven locations in the wet and dry seasons. Standard two-dimensional principal component analysis (PCA) and risk assessments were also conducted. The concentrations of Pb, Co, Ni, Cr, Cd, and As in the sediments ranged from 0.001 to 0.031 mg/kg, 0-0.005 mg/kg, 0.005-0.012 mg/kg, 0.001-0.014 mg/kg, 0.005-0.024 mg/kg, and 0.001-0.012 mg/kg, respectively. The mean concentrations of the total PTEs content were found in decreasing order of concentration: Pb > Cd > Ni > Cr > As > Co. PCA showed that some of the PTEs were highly concentrated in samples obtained at other locations as well as at the discharge point. The Hazard Index was mostly <1 across locations, indicating little to no probable non-cancerous effect. However, the incremental lifetime cancer risk for arsenic and nickel was high and required attention. The ecological risk assessment showed that lead and arsenic were the major PTEs pollutants in all locations. The study identifies PTEs profiles in sediments and emphasises the necessity of continual monitoring and action to stop long-term negative impacts on the local environment and public health.

Modeling different infectious phases of hepatitis B with generalized saturated incidence: An analysis and control.

Hepatitis B is one of the global health issues caused by the hepatitis B virus (HBV), producing 1.1 million deaths yearly. The acute and chronic phases of HBV are significant because worldwide, approximately 250 million people are infected by chronic hepatitis B. The chronic stage is a long-term, persistent infection that can cause liver damage and increase the risk of liver cancer. In the case of multiple phases of infection, a generalized saturated incidence rate model is more reasonable than a simply saturated incidence because it captures the complex dynamics of the different infection phases. In contrast, a simple saturated incidence rate model assumes a fixed shape for the incidence rate curve, which may not accurately reflect the dynamics of multiple infection phases. Considering HBV and its various phases, we constructed a model to present the dynamics and control strategies using the generalized saturated incidence. First, we proved that the model is well-posed. We then found the reproduction quantity and model equilibria to discuss the time dynamics of the model and investigate the conditions for stabilities. We also examined a control mechanism by introducing various controls to the model with the aim to increase the population of those recovered and minimize the infected people. We performed numerical experiments to check the biological significance and control implementation.

Detection of Alteration in Carotid Artery Volumetry Using Standard-of-care Computed Tomography Surveillance Scans Following Unilateral Radiation Therapy for Early-stage Tonsillar Squamous Cell Carcinoma Survivors: A Cross-Sectional Internally-Matched Carotid Isodose Analysis.

Purpose: Radiation induced carotid artery disease (RICAD) is a major cause of morbidity and mortality among survivors of oropharyngeal cancer. This study leveraged standard-of-care CT scans to detect volumetric changes in the carotid arteries of patients receiving unilateral radiotherapy (RT) for early tonsillar cancer, and to determine dose-response relationship between RT and carotid volume changes, which could serve as an early imaging marker of RICAD. Methods and Materials: Disease-free cancer survivors (>3 months since therapy and age >18 years) treated with intensity modulated RT for early (T1-2, N0-2b) tonsillar cancer with pre- and post-therapy contrast-enhanced CT scans available were included. Patients treated with definitive surgery, bilateral RT, or additional RT before the post-RT CT scan were excluded. Pre- and post-treatment CTs were registered to the planning CT and dose grid. Isodose lines from treatment plans were projected onto both scans, facilitating the delineation of carotid artery subvolumes in 5 Gy increments (i.e. received 50-55 Gy, 55-60 Gy, etc.). The percent-change in sub-volumes across each dose range was statistically examined using the Wilcoxon rank-sum test. Results: Among 46 patients analyzed, 72% received RT alone, 24% induction chemotherapy followed by RT, and 4% concurrent chemoradiation. The median interval from RT completion to the latest, post-RT CT scan was 43 months (IQR 32-57). A decrease in the volume of the irradiated carotid artery was observed in 78% of patients, while there was a statistically significant difference in mean %-change (±SD) between the total irradiated and spared carotid volumes (7.0±9.0 vs. +3.5±7.2, respectively, p<.0001). However, no significant dose-response trend was observed in the carotid artery volume change withing 5 Gy ranges (mean %-changes (±SD) for the 50-55, 55-60, 60-65, and 65-70+ Gy ranges [irradiated minus spared]: -13.1±14.7, -9.8±14.9, -6.9±16.2, -11.7±11.1, respectively). Notably, two patients (4%) had a cerebrovascular accident (CVA), both occurring in patients with a greater decrease in carotid artery volume in the irradiated vs the spared side. Conclusions: Our data show that standard-of-care oncologic surveillance CT scans can effectively detect reductions in carotid volume following RT for oropharyngeal cancer. Changes were equivalent between studied dose ranges, denoting no further dose-response effect beyond 50 Gy. The clinical utility of carotid volume changes for risk stratification and CVA prediction warrants further evaluation.

A Review on Pyrazolines as Colorimetric Fluorescent Chemosensors for Cu2.

The Pyrazoline derivatives display promising potential as sensitive and selective chemosensors for detecting Cu2+ ions. It has undergone screening for its sensing behavior with various metals using absorption, emission spectroscopic techniques. Their unique structure incorporates both donating and accepting sites, characterized by delocalized orbitals. These derivatives exhibit notable chromogenic and fluorogenic capabilities facilitated by intramolecular charge transfer. The sensors based on pyrazoline demonstrate exceptional selectivity, low detection limits, and precise detection of metal ions, particularly Cu2+. This review offers a comprehensive summary of recent discoveries concerning as pyrazoline-based "On-Off" chemosensors. The discussion places emphasis on exploring the design and photophysical properties of these chemosensors, with the primary objective of detecting Cu2+ metal ions. The unique features of pyrazoline derivatives make them promising candidates for practical applications in environmental and biological monitoring, showcasing their potential significance in advancing sensing technologies.

Advanced stability analysis of a fractional delay differential system with stochastic phenomena using spectral collocation method.

In recent years, there has been a growing interest in incorporating fractional calculus into stochastic delay systems due to its ability to model complex phenomena with uncertainties and memory effects. The fractional stochastic delay differential equations are conventional in modeling such complex dynamical systems around various applied fields. The present study addresses a novel spectral approach to demonstrate the stability behavior and numerical solution of the systems characterized by stochasticity along with fractional derivatives and time delay. By bridging the gap between fractional calculus, stochastic processes, and spectral analysis, this work contributes to the field of fractional dynamics and enriches the toolbox of analytical tools available for investigating the stability of systems with delays and uncertainties. To illustrate the practical implications and validate the theoretical findings of our approach, some numerical simulations are presented.

Up-gradation of the dielectric, physical & chemical properties of cottonseed-based, non-edible green nanofluids as sustainable alternatives for high-voltage equipment's insulation fluids.

The use of natural ester oils as electrically insulating fluids has gained significant attention from industries and electrical utilities as they aim to replace traditional mineral oils. However, most natural ester oils are derived from edible products, which has the potential to contribute to the food crisis. Therefore, nonedible green nanofluids made from cottonseed oil (CSO) have been targeted as a keen solution to this issue. However, Al2O3, TiO2, Fe2O3, SiO2, and graphene nanoparticles at (0.025, 0.05, and 0.075 wt/vol%) were used as additives, along with surfactant Olic Ac-id and Ethanol (1:5) due to their promising impact on the dielectric and thermal properties of the nanofluid. The nanofluid synthesis process was practically conducted in HV & Chemical Laboratories using one-step and two-step methods, and their breakdown voltage results and chemical properties (e.g., fire point, flash point, cloud point, pour point, viscosity, acidity, moisture content, resistivity, and dissipation factor) were compared. The physical mechanisms underlying these properties were also analyzed and tested. For the validation of the proposed vegetable oil the results have been compared with traditional mineral oil for high-voltage equipment's. The findings suggest that the proposed nonedible green nanofluids-based cottonseed oil (CSO) has a high potential to be used as electrically insulating fluids, providing a sustainable alternative to conventional mineral oils. Overall, this study provides insights into the use of non-edible green nanofluids as a solution to the potential contribution of natural ester oils to the food crisis. The findings highlight the importance of sustainable solutions in the energy industry and the need for further research in this area.

A rigorous theoretical and numerical analysis of a nonlinear reaction-diffusion epidemic model pertaining dynamics of COVID-19.

The spatial movement of the human population from one region to another and the existence of super-spreaders are the main factors that enhanced the disease incidence. Super-spreaders refer to the individuals having transmitting ability to multiple pathogens. In this article, an epidemic model with spatial and temporal effects is formulated to analyze the impact of some preventing measures of COVID-19. The model is developed using six nonlinear partial differential equations. The infectious individuals are sub-divided into symptomatic, asymptomatic and super-spreader classes. In this study, we focused on the rigorous qualitative analysis of the reaction-diffusion model. The fundamental mathematical properties of the proposed COVID-19 epidemic model such as boundedness, positivity, and invariant region of the problem solution are derived, which ensure the validity of the proposed model. The model equilibria and its stability analysis for both local and global cases have been presented. The normalized sensitivity analysis of the model is carried out in order to observe the crucial factors in the transmission of infection. Furthermore, an efficient numerical scheme is applied to solve the proposed model and detailed simulation are performed. Based on the graphical observation, diffusion in the context of confined public gatherings is observed to significantly inhibit the spread of infection when compared to the absence of diffusion. This is especially important in scenarios where super-spreaders may play a major role in transmission. The impact of some non-pharmaceutical interventions are illustrated graphically with and without diffusion. We believe that the present investigation will be beneficial in understanding the complex dynamics and control of COVID-19 under various non-pharmaceutical interventions.

The epidemiological and clinicopathological features of breast cancer in Riyadh, Saudi Arabia.

OBJECTIVES: To investigate the epidemiological and clinicopathological features of breast cancer (BC) in Saudi Arabia to improve decisions regarding resource allocation, disease control, and management. METHODS: We retrieved the records of all patients who presented with histologically proven BC at King Fahad Medical City between 2019 and 2020. The data were analyzed quantitatively, and the results were expressed as percentages and frequencies. RESULTS: This study comprised 419 patients. The mean age was 50.13 (± 10.96) years. The majority of the patients were obese (56.6%), and approximately a quarter had a history of oral contraceptive pill use, breast biopsy, or an affected family member. Most cases were from the central region (80.1%), followed by the southern provinces (12.7%). Breast lumps were the most common complaint (89%), whereas hypertension and diabetes mellitus were the most common comorbidities. Invasive ductal carcinoma was the most common pathologic type (89.7%). Most patients presented with TNM stages II and III (55.2%), and 27.7% had metastasis. The main therapeutic modalities included radical mastectomy (63.8%), neoadjuvant chemotherapy (60.4%), and adjuvant radiotherapy (82.9%). CONCLUSION: In Saudi Arabia, a trend of BC incidence migration towards older patients may be ensuing. However, prediction of an advanced and aggressive presentation requires the enhancement of screening programs and standardized protocols for disease management.

Numerical simulation of a fractional stochastic delay differential equations using spectral scheme: a comprehensive stability analysis.

The fractional stochastic delay differential equation (FSDDE) is a powerful mathematical tool for modeling complex systems that exhibit both fractional order dynamics and stochasticity with time delays. The purpose of this study is to explore the stability analysis of a system of FSDDEs. Our study emphasizes the interaction between fractional calculus, stochasticity, and time delays in understanding the stability of such systems. Analyzing the moments of the system's solutions, we investigate stochasticity's influence on FSDDS. The article provides practical insight into solving FSDDS efficiently using various numerical techniques. Additionally, this research focuses both on asymptotic as well as Lyapunov stability of FSDDS. The local stability conditions are clearly presented and also the effects of a fractional orders with delay on the stability properties are examine. Through a comprehensive test of a stability criteria, practical examples and numerical simulations we demonstrate the complexity and challenges concern with the analyzing FSDDEs.

Frequency regulation of interconnected hybrid power system with Assimilation of electrical vehicles.

Recent widespread connections of renewable energy resource (RESs) in place of fossil fuel supplies and the adoption of electrical vehicles in place of gasoline-powered vehicles have given birth to a number of new concerns. The control architecture of linked power networks now faces an increasingly pressing challenge: tie-line power fluctuations and reducing frequency deviations. Because of their nature and dependence on external circumstances, RESs are analogous to continually fluctuating power generators. Using a fractional order-based frequency regulator, this work presents a new method for improving the frequency regulation in a two-area interconnected power system. In order to deal with the frequency regulation difficulties of the hybrid system integrated with RES, the suggested controller utilizes the modified form of fractional order proportional integral derivative (FOPID) controller known as FOI-PDN controller. The new proposed controllers are designed using the white shark optimizer (WSO), a current powerful bioinspired meta heuristic algorithm which has been motivated by the learning abilities of white sharks when actively hunting in the environment. The suggested FOI-PDN controller's performance was compared to that of various control methodologies such as FOPID, and PID. Furthermore, the WSO findings are compared to those of other techniques such as the salp swarm algorithm, sine cosine algorithm and fitness dependent optimizer. The recommended controller and design approach have been tested and validated at different loading conditions and different circumstances, as well as their robustness against system parameter suspicions. The simulation outcomes demonstrate that the WSO-based tuned FOI-PDN controller successfully reduces peak overshoot by 73.33%, 91.03%, and 77.21% for region-2, region-1, and link power variation respectively, and delivers minimum undershoot of 89.12%, 83.11%, and 78.10% for both regions and tie-line. The obtained findings demonstrate the new proposed controller's stable function and frequency controlling performance with optimal controller parameters and without the requirement for a sophisticated design process.

Aiding the well celebrated Kuk's randomized response technique through auxiliary and prior information.

Asking direct questions in face to face surveys about sensitive traits is an intricate issue. One of the solutions to this issue is the randomized response technique (RRT). Being the most widely used indirect questioning technique to obtain truthful data on sensitive traits in survey sampling RRT has been applied in a variety of fields including behavioral science, socio-economic, psychological, epidemiology, biomedical, criminology, data masking, public health engineering, conservation studies, ecological studies and many others. This paper aims at exploring the methods to subsidize the randomized response technique through additional information relevant to the parameter of interest. Specifically, we plan to contribute by proposing more efficient hybrid estimators compared to existing estimator based on (Kuk, 1990) [31] family of randomized response models. The proposed estimators are based on the methodology of incorporating the pertinent information, available on the basis of either historical records or expert opinion. Specifically, in case of availability of auxiliary information, the regression-cum-ratio estimator is found to be the best to further enhance the estimation through (Kuk, 1990) [31] model while the (Thompson, 1968) [49] shrinkage estimation is observed to be yielding more precise and accurate estimator of sensitive proportion. The findings in this study signify the importance of the proposed methodology. Additionally, to support the mathematical findings, a detailed numerical investigation to evaluate the comparative performances is also conducted. Based on performance analysis, overwhelming evidences are witnessed in the favor of proposed strategies.