bluuebunny/crossref_metadata_2025 huggingface dataset:
Features of Vascular Endothelial Growth Factor А (Vegf-А) Expression in Atypical Teratoid-Rhabdoid Tumor of the Central Nervous System in Children
Authors: Sitovskaya Darya
Published: September 2024

Atypical teratoid rhabdoid tumors (AT/RT) are highly malignant neoplasms composed of poorly differentiated germline cells and a variable number of rhabdoid cells that can differentiate into different cell types. A key diagnostic factor for AT/RT is the absence of nuclear expression of the INI1 protein (SMARCB1 gene product) when tested with an anti-INI1 antibody in an immunohistochemical study. Currently, there is no standard treatment for AT/RT, but treatment approaches have become more aggressive and multimodal. In one study, AT/RT was found to be positive for VEGF. The purpose of our study was to examine the expression of VEGF-A in AT/RT in the central nervous system of children. We analyzed biopsy samples from 6 male patients aged 3 months to 8 years (average age 4.5 years). The level of VEGF-A expression was measured by counting the number of brown pixels in the cytoplasm of tumor cells using the PhotoM program in 1 mm2 of tumor tissue. Immunohistochemistry (IHC) revealed varying levels of cytoplasmic staining with an antibody to VEGF-A in 5 out of 6 patients, with the youngest patient (No. 6) showing staining only in endothelial cells. In conclusion, children with AT/RT exhibit different patterns of VEGF-A expression, potentially dependent on the molecular subgroup. Identifying this marker in these highly aggressive tumors could aid in developing new treatment protocols and determining which patients would benefit most from treatment.
Temptations, Stances, and Masses: Appraisal of a Likely Discrepancy
Authors: Shoja Shafti Saeed
Published: September 2024

The modern era is full of ideologies and doctrines that generally have roots in academic disputes, theoretical thoughts, or ethical concerns. Nevertheless, formulations, which may be full of recommendations and calculations, or doctrines, which may be packed with justifications or condemnations, or prophecies, which may be filled with wishes or fears, may not be calibrated without pragmatic measures, experiences, or outlooks.
The Effect of Α-Bisabolol on Alzheimer Disease
Authors: Doğukan Akarsu Gökhan
Published: September 2024

Alzheimer's disease is a condition that causes permanent effects due to damage in memory cells, leading to the inability to meet basic life needs and eventually resulting in loss of life. The increasing lifespan of individuals, particularly in advanced and developing cells, has raised the risk of developing this disease among people aged 65 and older. α-Bisabolol is a molecule derived from the essential oil of hemp. It is found in various plants, such as chamomile, similar to its presence in hemp. Due to its molecular structure, α-Bisabolol is considered to have strong effects against Alzheimer's disease. In this systematic review, clinical animal and cell line studies investigating α-Bisabolol for Alzheimer's disease have been examined. Given that α-Bisabolol is a newly discovered compound and there is a lack of sufficient clinical and experimental studies, there are still uncertainties. With further research in the future, the effects of α-Bisabolol on Alzheimer's disease can be determined conclusively.
Service quality and customer delight in the hospitality sector of Pakistan
Authors: Yasir Muhammad , Kanwal Noreen , Asad Khan Muhammad
Published: September 2024

The current study investigates the relationship between service quality and its dimensions with customers’ delight in the hospitality sector of Pakistan. Using a convenient sampling technique, data was collected through a survey questionnaire in order to examine customers’ hotel experiences. Returned and usable questionnaires were 157. The data was analyzed using descriptive statistics, common method bias, reliability and correlation analysis. Results of this study showed a positive link between service quality and customer delight in the context of Pakistani hotels. Finally, implications, limitations and future research suggestions are provided.
Clinical characteristics and therapeutic outcomes after endoscopic endonasal surgery for craniopharyngioma in the elderly
Authors: Bao You-Yuan , Pan Lai-Sheng , Cao Yong , Wu Jie , Xie Shen-Hao , Ouyang Feng , Tan Jian , Wang Qi-Fa , Zhou Dong-Wei , Tang Bin , Hong Tao
Published: September 2024

OBJECTIVE

Craniopharyngiomas (CPs) in adults are rare benign epithelial tumors, and few contemporary studies have explored outcomes after surgical treatment in elderly patients, especially with regard to endoscopic endonasal surgery (EES).

METHODS

A retrospective cohort study was conducted on patients aged ≥ 18 years with CP who were treated with EES from 2013 to 2022. The cohort was divided into nonelderly (18–64 years) and elderly (≥ 65 years) groups based on age. Various parameters, including patient and tumor characteristics, surgical outcomes, complications, and follow-up, were compared between the two age groups.

RESULTS

A total of 193 patients met the inclusion criteria, with 161 (83.4%) patients in the nonelderly group and 32 (16.6%) patients in the elderly group. Preoperatively, older patients were more likely to have memory impairment (4.3% vs 18.8%, p = 0.010), fatigue or decreased energy (9.3% vs 34.4%, p = 0.001), hypopituitarism (68.7% vs 90.6%, p = 0.012), or hydrocephalus (18% vs 40.6%, p = 0.005), and they were more likely asymptomatic (1.2% vs 9.4%, p = 0.033) and less likely to experience headache (57.8% vs 31.3%, p = 0.006). Patients in the elderly group had a longer symptom duration (median [IQR] 5 [10] months vs 9.5 [13] months, p = 0.001) and higher comorbidity scores (p < 0.001). Postoperatively, gross-total resection was achieved in 145 (90.1%) and 28 (87.5%) patients in the nonelderly and elderly groups, respectively. Older patients were more likely to develop pneumonia (5% vs 21.9%, p = 0.004). There were no significant differences in the extent of resection (p = 0.541), pathological subtypes (88.2% vs 75.0% adamantinomatous, p = 0.089), operation time (mean ± SD 307.8 ± 68.3 minutes vs 323.5 ± 86.0 minutes, p = 0.257), estimated blood loss (median [IQR] 300 [200] ml vs 300 [238] ml, p = 0.594), length of stay (median [IQR] 15 [8] days vs 15 [22] days, p = 0.964), perioperative mortality (2.5% vs 3.1%, p > 0.99), or postoperative severe hypothalamic dysfunction (37.9% vs 50.0%, p = 0.237) between the groups. Multivariate Cox regression analysis demonstrated that tumor calcification (HR 3.406, 95% CI 1.859–27.233, p = 0.038) and preoperative hydrocephalus (HR 3.688, 95% CI 1.310–10.386, p = 0.013) were independently associated with decreased survival. The median follow-up period in the elderly group was shorter (71 months vs 44 months, p = 0.001), and no recurrence was observed (7.1% vs 0%, p = 0.132).

CONCLUSIONS

This study demonstrates that EES is a viable treatment option for older CP patients. With appropriate perioperative management, EES does not significantly increase mortality and, in selected populations, is well tolerated by patients.
Value of difference in diameters between Pipeline embolization device and parent artery in assessing aneurysm outcome
Authors: Ma Yajing , Zhang Hang , Liu Chao , Deng Xin , Yuan Yongjie , Guan Sheng , Guo Xinbin
Published: September 2024

OBJECTIVE

In vitro trials have demonstrated that oversized stents are associated with reduced metal coverage and increased porosity. However, the relationship between stent selection and aneurysm outcome is inconclusive, and determination of this was the authors’ purpose in conducting this study.

METHODS

This was a single-center retrospective study. Patients who underwent Pipeline embolization device treatment at the authors’ center between January 1, 2018, and June 15, 2023, were enrolled. The authors constructed multiple logistic regression models and restricted cubic spline plots to examine the effect of the difference in diameters between the stent and parent artery (Dd) on aneurysm outcome. The authors also performed stratified analyses. Then, Dd was included in the logistic regression analysis as a categorical variable. The cutoff value for Dd was determined according to the principle of the maximum Youden’s index.

RESULTS

In total, 302 patients were included in this study. The median Dd was 0.52 mm. With a median follow-up time of 7 months, the aneurysm occlusion rate was 80.1%. The restricted cubic spline plots showed a decreasing aneurysm occlusion rate as Dd increased. After stratification by age and adjunctive embolization, the restricted cubic splines aligned with the results of the main analysis. Compared with the group with a smaller Dd (Dd < 0.3625 mm), the group with a larger Dd showed an OR of 0.439 (p = 0.026). Additionally, patients with diabetes mellitus (OR 0.306, p = 0.018), age ≥ 65 years (OR 0.968, p = 0.03), aneurysm incorporation with a branch (OR 0.253, p < 0.001), and aneurysm neck ≥ 4 mm (OR 0.872, p = 0.003) were independent predictors of aneurysm persistence, whereas Pipeline embolization device plus coiling (OR 4.949, p < 0.001) and smoking history (OR 5.86, p = 0.025) were predictors of aneurysm occlusion.

CONCLUSIONS

The authors’ retrospective analysis demonstrated that the aneurysm occlusion rate declined when Dd increased within a certain range. The authors suggested that Dd with an interval of −0.25 to 0.5 mm may be proper in clinical practice.
Complications of intrathecal baclofen therapy in children and young adults
Authors: Ryan Megan V. , Freeman Lindsey M. , Blasco Sophia , Sawyer Kim , Graber Sarah , Tong Suhong , Oleszek Joyce , Wilkinson Corbett
Published: September 2024

OBJECTIVE

The primary objective of this study was to determine the frequency and nature of complications that occur during intrathecal baclofen (ITB) therapy, including nonsurgical complications as well as complications associated with both implantation surgeries and subsequent revisions.

METHODS

The authors retrospectively reviewed the medical records of all patients who had baclofen pumps implanted at a tertiary children’s hospital from 2006 through June 2019. The study employed appropriate descriptive statistics and statistical models to analyze patient demographics, improvements in tone, and clinical complications. The authors evaluated the associations of complications as well as changes in modified Ashworth Scale (MAS) scores with various preexisting conditions (e.g., presence of gastrostomy tubes [G-tubes] and ventriculoperitoneal [VP] shunts) and surgical variations (e.g., use of newer [Ascenda] catheters and subfascial versus subcutaneous catheter tunneling).

RESULTS

One hundred forty-two pumps were implanted. Complications occurred in 111 patients (78.2%). The most frequent complications were catheter complications and pseudomeningoceles, each of which occurred in 63 (44%) patients. On multivariable analysis, pseudomeningoceles and percutaneous CSF leaks were significantly less likely when intrathecal catheters were placed via laminotomy versus spinal needle (OR 4.6, p = 0.044), and when catheters were passed from the posterior incision to the abdominal pump pocket deep to the thoracolumbar fascia rather than superficial to it (OR 2.7, p = 0.008). Preexisting G-tubes and VP shunts at the time of pump implantation were not associated with a significantly increased likelihood of pump malfunction or infection. Ascenda catheters were significantly less likely to have disconnections (p < 0.001) and obstructions (p = 0.016), and overall were less likely to have any catheter-related complications (p = 0.01). Patients with preexisting VP shunts at the time of implantation had a significantly greater mean decrease in MAS scores in both their lower extremities (1.8, p = 0.003) and upper extremities (1.3, p = 0.002) than did patients without shunts.

CONCLUSIONS

Various complications are associated with ITB therapy, most commonly catheter complications and pseudomeningoceles. There have been fewer catheter complications associated with the newest catheter model, while pseudomeningoceles have become less frequent since the surgical technique was modified to prevent them. VP shunts and G-tubes are not associated with significantly higher complication rates and shunts seem to be associated with greater efficacy of ITB therapy, as represented by a greater mean improvement in MAS scores.
Central nervous system dissemination in spinal cord astrocytomas: association with H3 K27M mutation
Authors: An Songyuan , Lin Han , Zhang Yaowu , Pang Bo , Yan Hao , Liu Yun , Wang Long , Wu Yilin , Chai Ruichao , Jia Wenqing , Wang Yongzhi
Published: September 2024

OBJECTIVE

Patients with spinal cord astrocytomas (SCAs) are at high risk for CNS dissemination, yet comprehensive data on characteristics of dissemination are lacking. This study depicts the exact incidence and patterns of dissemination by analyzing data from a large-scale dataset of SCA.

METHODS

The authors included 94 patients with SCA based on the 2021 WHO classification from 2011 to 2022, retrospectively collected their clinical and pathological characteristics, and analyzed factors influencing SCA dissemination.

RESULTS

CNS dissemination, encompassing leptomeningeal spreading and/or subarachnoid seeding, was evaluated in 94 patients with and without H3 K27 alterations, with an overall dissemination rate reaching 85.0% at 5-year follow-up. Patients with altered H3 K27 had a significantly higher 5-year CNS dissemination rate than patients with H3 K27 wildtype status (95.2% vs 68.0%, p = 0.002). The median dissemination-free survival in H3 K27–altered patients was 14.37 (95% CI 2.84–25.89) months, significantly shorter than those with H3 K27 wildtype (statistics not calculated; p < 0.001). Based on univariate Cox regression analysis, H3 K27M alteration, higher histopathological grade, Ki-67 index (≥ 10%), and tumor length (≥ 4 segments) were identified as potential factors associated with CNS dissemination in SCAs. Multivariate Cox regression analysis revealed that H3 K27M alteration appeared to be a risk factor for this phenomenon (HR 2.089, 95% CI 0.940–4.642, p = 0.070). Following dissemination, H3 K27–altered patients had a median postdissemination survival of 8.83 (95% CI 7.13–10.54) months, which was significantly shorter than the 13.40 (95% CI 3.98–34.26) months in those with H3 K27 wildtype (p = 0.008).

CONCLUSIONS

Factors indicative of higher SCA malignancy, such as H3 K27M alteration, higher histopathological grade, Ki-67 index (≥ 10%), and tumor length (≥ 4 segments), were similarly suggestive of higher rates of dissemination. The occurrence of dissemination is closely associated with the outcome events in patients with SCA.
Induction Motor Performance Improvement using Super Twisting SMC and Twelve Sector DTC
Authors: Zahraoui Yassine , Moutchou Mohamed , Tayane Souad , Fahassa Chaymae , Elbadaoui Sara
Published: September 2024

Induction motor (IM) direct torque control (DTC) is prone to a number of weaknesses, including uncertainty, external disturbances, and non-linear dynamics. Hysteresis controllers are used in the inner loops of this control method, whereas traditional proportional-integral (PI) controllers are used in the outer loop. A high-performance torque and speed system is consequently needed to assure a stable and reliable command that can tolerate such unsettled effects. This paper treats the design of a robust sensorless twelve-sector DTC of a three-phase IM. The speed controller is conceived based on high-order super-twisting sliding mode control with integral action (iSTSMC). The goal is to decrease the flux, torque, the current ripples that constitute the major conventional DTC drawbacks. The phase current ripples have been effectively reduced from 76.92% to 45.30% with a difference of 31.62%. A robust adaptive flux and speed observer-based fuzzy logic mechanism are inserted to get rid of the mechanical sensor. Satisfactory results have been got through simulations in MATLAB/Simulink under load disturbance. In comparison to a conventional six-sector DTC, the suggested technique has a higher performance and lower distortion rate.
Enhancing the Performance of Power System under Abnormal Conditions Using Three Different FACTS Devices
Authors: Fawzy Ibram Y. , Mossa Mahmoud A. , Elsawy Ahmed M. , Diab Ahmed A. Zaki
Published: September 2024

In this paper, a comparison between Flexible Alternating Current Transmission System (FACTS) devices including Static Synchronous Compensator (STATCOM), Static Synchronous Series Compensator (SSSC) and Unified Power Flow Controller (UPFC) for providing a better adaptation to changing operating conditions and improving the usage of current systems. The power system using FACTS devices is presented under different conditions such as single phase fault and three phase fault. A digital simulation using Matlab/Simulink software package is carried out to demonstrate the better performance including the voltage and the current of the presented system using FACTS that located between buses B1 and B2 under different faults types. The results obtained investigate that the presented system gives better response with FACTS as compared to not using them under abnormal conditions besides, the UPFC gives better performance of power system under several faults as compared to STATCOM or SSSC as It can absorb reactive power in a manner which significantly reduced the fault current. It is demonstrated that UPFC can reduce the peak fault current at bus B1 ‎to 63.85% of its value without ‎using FACTS devices under line to ground fault and 79.18% under three line to ‎ground fault whereas STATCOM and SSSC reduce it ‎to (75.21, 94.35%) and (75.40, 94.68%), respectively.
Industry 4.0 Readiness Trends: A Bibliometric and Visualization Analysis
Authors: Solikhah Efa Wakhidatus , Asih Hayati Mukti , Astuti Fatma Hermining , Ghazali Ihwan , Mohammad Effendi Bin
Published: September 2024

The Industrial Revolution 4.0 signifies a pivotal change in industrial paradigms, integrating advanced technologies like the Internet of Things (IoT), artificial intelligence (AI), robotics, and big data into production processes. This research aims to analyze the growth and readiness in industry for these changes through a detailed bibliometric analysis. It quantitatively tracks the expansion of Industry 4.0 readiness research, including publication counts, citation trends, and thematic shifts, reflecting heightened academic and industrial interests. A clear definition of Industry 4.0 readiness is provided, focusing on metrics and criteria used for assessment. The paper identifies key contributions and novel insights of the research, emphasizing its practical implications for industry and academia. It examines elements influencing Industry 4.0 readiness, such as infrastructure, policy, and workforce preparedness, offering a comprehensive overview of challenges. The practical implications of our findings are presented, suggesting actionable strategies for stakeholders. This research also highlights the gaps in the current literature, which offers a thorough and multidimensional understanding of Industry 4.0 readiness, its influences, and its impact on the global industrial system.
A Technological Review on Fast Chargers for Electric Vehicles: Standards, Architectures, Power Converter Topologies, Fast Charging Techniques, Impacts and Future Research Directions
Authors: Seleem Mohamed , Atia Yousry , Abou-Zalam Belal , Abd-Elhaleem Sameh
Published: September 2024

Within a universe in which concerns about petroleum resources depletion and ecological issues are increasing, the technological evolution of electric mobility has quickened to overcome these concerns. Vehicle electrification technology is seen as a promising and viable substitute for prospective transport systems. Electric vehicles (EVs) offer a solution for reducing the reliance on fossil fuels, improving air quality, integrating easily with renewable energies, and enhancing energy efficiency, especially when smart grids have become omnipresent. However, range anxiety and long charging times remain substantial barriers to the extensive embrace of these vehicles, impeding a seamless shift from conventional vehicles to EVs. Reducing EV recharging time is considered a pivotal element in promoting consumer interest and increasing their market appeal. Thus, EV commercial deployment relies heavily on the presence of an adequate fast-charging infrastructure. Fast-charging infrastructure will decrease drivers' wait times for vehicle charging, providing a refueling experience like that of gasoline vehicles. Hence, a significant portion of research efforts have been dedicated to the advancement of fast chargers (FCs) designed to rapidly recharge EV batteries. It is crucial to opt for the appropriate power electronic interfaces for these chargers to avoid possible grid-related issues and improve overall system efficiency. This review is concentrated on presenting pertinent information regarding EV FCs, including various standards, architectures, power converter topologies, compatible battery chemistries, and fast-charging techniques. Finally, the significant impacts of the integration of fast-charging with both the AC grid and traction batteries are presented in detail.
Intelligent Temperature-Controlled Poultry Feed Dispensing System with Fuzzy Logic Algorithm
Authors: Ramizares Ulysis V. , Teves Winbert James A. , Arboleda Edwin R. , Bangeles Julliana Marie C.
Published: September 2024

This study introduces a novel fuzzy logic algorithm tailored to the thermoneutral zone of poultry, offering a precise and adaptive approach to feed dispensation. This involved the utilization of an LCD module to present essential information such as the selected age, real-time ambient temperature, current time, and the dispensed feed quantity. Data gathered during the process were stored in a memory device. The design of the fuzzy logic algorithm centered on the thermoneutral zone of the chicken serves as the determinant for feed dispensed by the system. It's crucial to note that while the system lacked artificial intelligence (AI), its logical analysis operated based on the fuzzy logic algorithm. Rigorous testing ensued, encompassing the comparison of feed dispensation between automated and manual systems and the assessment of feed waste and broiler weight.  Significant feed waste reduction in the first week demonstrated the efficacy of the fuzzy-based method, with consistently low p-values of 0.00069, 0.015195, and 0.034 across subsequent weeks confirming the consistent outperformance in broiler weight compared to the traditional feeding technique. The findings contribute to the advancement of temperature-based poultry feed systems, addressing key challenges in optimizing feed quantity. The study successfully met its objectives, demonstrating the system's capability to dispense feeds effectively across varying ambient temperatures.  Notably, the study revealed a consistent alignment of system outputs with those obtained from a digital thermometer and digital weighing scale, confirming the accuracy and reliability of the temperature-based feed dispensing system.
Radial Basis Function Network Based Self-Adaptive PID Controller for Quadcopter: Through Diverse Conditions
Authors: Sahrir Nur Hayati , Basri Mohd Ariffanan Mohd
Published: September 2024

A quadcopter is an underactuated and nonlinear system which requires a robust controller to aid in maneuvering the quadcopter during flight. A Proportional-Integral-Derivative (PID) controller is easy and suitable to implement, and its efficiency is proved in quadcopter control. However, a PID controller with fixed parameters is inadequate enough to control a quadcopter system with different inputs or perturbations. This paper proposes the development of a self-adaptive PID controller assisted by Radial Basis Function (RBF) Network, to improve the function of the PID controller and help a quadcopter to better adapt towards different inputs and situations, independently.  This work contributes to introducing RBF-PID controller to adaptively fly the underactuated quadcopter through different trajectory and perturbations using simulation. By using the hidden Gaussian function to train the current input, estimate the suitable output and update the Jacobian Information during system control, the PID gains can change adaptively during flight, additionally with the help of Gradient Descent Method (GDM). The proposed method is compared to the traditional PID controller tuned using the PID Tuner App in Simulink. Different inputs are given to test the altitude, attitudes, and position tracking such as step, multistep, sine wave, circular and lemniscate trajectory. The simulated results proved the robustness of RBF-PID in enhancing the disturbance rejection capacity by 13% to 25% in the presence of perturbations (sine wave and wind gust) compared to PID controller. The proposed controller can ensure quadcopter’s flight stability through perturbations that is within the quadcopter’s limitations.
Experimental Validation of the Generation of Direct and Quadratic Reference Currents by Combining the Ant Colony Optimization Algorithm and Sliding Mode Control in PMSM using the Process PIL
Authors: Najem Adil , Moutabir Ahmed , Ouchatti Abderrahmane , Haissouf Mohammed El
Published: September 2024

This article aims to enhance the control efficiency of the Permanent Magnet Synchronous Motor (PMSM) by generating optimal reference currents  and using Ant Colony Optimization (ACO), while ensuring a minimal absorbed current condition to reduce energy consumption and optimize PMSM performance. The ACO algorithm is chosen for its ability to find global solutions and robustness in complex environments, while Sliding Mode Control (SMC) provides advantages in terms of robustness against disturbances and the ability to maintain the system in a desired state. The implementation of the processor-in-the-loop (PIL) technique using MATLAB software with code composer and the LAUNCHXL- F28069M board enables the controller to be implemented in real hardware (LAUNCHXL-F28069M) to test the simulation environment (inverter and PMSM). Our results demonstrate the efficiency of ACO compared to the analytical method (AM) in terms of response time and minimizing absorbed current for different load values. Artificial intelligence (AI) has successfully and efficiently addressed the non-linearity between torque and reference currents, thus reducing energy consumption. This has allowed for the optimization of PMSM performance in a straightforward and efficient manner.
Synthesis of Adaptive Sliding Mode Control for Twin Rotor MIMO System with Mass Uncertainty based on Synergetic Control Theory
Authors: Chiem Nguyen Xuan , Hai Bui Xuan , Phan T. C.
Published: September 2024

In this paper, the authors present a new method to synthesize an adaptive sliding controller for Twin Rotor MIMO System (TRMS) based on Synergetic Control Theory (SCT). This system represents a prototype of a helicopter with two degrees of freedom and is widely used in automatic control laboratories. The complexity of the control problem is due to the nonlinear cross-coupling between the main and tail rotors. Uncertainty in system parameters further increases the complexity of the control problem. In Synergetic Control Theory, manifolds are designed for each channel. The control law is found based on sequential manifolds and the Analytical Design of Aggregated Regulators (ADAR) method. The adaptive law when the parameters are uncertain is given based on the analysis of system stability thanks to the Lyapunov function of the first manifold. Finally, the effectiveness of the proposed controller is demonstrated by numerical simulation results and comparison with conventional Sliding Mode Control (SMC).
Analysis of Drone Wireless Communication System Performance Affected by Vibration based on 1DCNN
Authors: Abbas Ahmed Hussein , Hameed Hassanain Ghani , Abdulsadda Ahmad Taha
Published: September 2024

Developments in drone technology have made them crucial in various fields. Vibrations caused by external conditions or mechanical failures in a drone's design can significantly affect the efficiency of the drone's communication systems. The drone's antenna generates phase noise, which can degrade the performance of drone communications systems. This work presents an analysis and computational model of how drone vibration affects system performance. by using two steps. The first one uses the simulation Monte-Carlo in MATLAB when the iteration algorithm processes with various variable values as the frequency carriers and the order of the quadrature-amplitude-modulation (M-QAM) system and evaluates the performance of the communication system by measuring the symbol error rate. The second step uses the one-dimensional convolutional neural network to predict the symbol error rate. After creating the dataset in the first stage, reprocess it and split it into 70% training and 30% testing. Then, by MATLAB App Designer created a graphical user interface (GUI) for friendly use. The result appears to be that the performance of the drone communication system decreased when frequency carriers and modulation order for M-QAM increased due to the impact of a vibrating antenna. Our contribution to this work is using 1DCNN, unlike other works that only use simulation to evaluate the performance, because 1DCNN can automatically extract useful features from the input dataset to evaluate the effect. This study provides a valuable method to evaluate the efficiency of a communication system on the UAV, which is particularly important for drone wireless system planning. In our next work, we propose investigating other factors affecting UAV communication systems, including humidity and temperature.
Quadrotor Modeling Approaches and Trajectory Tracking Control Algorithms: A Review
Authors: M.A. Abitha , Saleem Abdul
Published: September 2024

Quadrotor unmanned aerial vehicles are utilized in basically every sector of society, including the business, civil, and military industries. Popular applications include delivery, agriculture, target-acquisition, surveying, surveillance, and rescue. They are widely used due to their exceptional features such as accuracy, capability to perform swift inspections, simplicity in deploying perilous and uncertain missions, and additional praiseworthy attributes. This article presents a comprehensive analysis of the theoretical frameworks that have been proposed for the purpose of quadrotor modelling and control. Detailed examinations are conducted on every methodology that underpins the control algorithms, spanning from traditional linear to modern. The analysis looks at hybrid control technique models, which incorporate adaptive components across multiple controllers to improve overall performance and resilience by addressing individual algorithm shortcomings. This analysis also delves deeper into potential future research avenues. These include the development of learning-based or hybrid methodologies that employ machine learning and artificial intelligence to optimize performance and adaptability. For instance, model reference adaptive control systems can learn adaptation laws through machine learning techniques, as opposed to depending on predefined adaptation laws. By training neural networks or fuzzy logic controllers to forecast optimal adaptation parameters based on sensor data, the quadrotor can adjust to fluctuating conditions more effectively. A comparison table is provided to elaborate on the advantages, disadvantages, and hybrid versions of each control algorithm. This will serve as a concise guide that will promote innovation, facilitate the selection and integration of appropriate control algorithms, and enhance the functionality of quadrotor control systems.
Optimizing Aircraft Pitch Control Systems: A Novel Approach Integrating Artificial Rabbits Optimizer with PID-F Controller
Authors: Abualigah Laith , Izci Davut , Ekinci Serdar , Zitar Raed Abu
Published: September 2024

The precise control of aircraft pitch angles is critical in aviation for maintaining specific attitudes during flight, including straight and level flight, ascents, and descents. Traditional control strategies face challenges due to the non-linear and uncertain dynamics of flight. To address these issues, this study introduces a novel approach employing the artificial rabbits optimizer (ARO) for tuning a PID controller with a filtering mechanism (PID-F) in aircraft pitch control systems. This combination aims to enhance the stability and performance of the aircraft pitch control system by effectively mitigating the kick effect through the incorporation of a filter coefficient in the derivative gain. The study employs a time-domain-based objective function to guide the optimization process. Simulation results validate the stability and consistency of the proposed ARO/PID-F approach. Comparative analysis with various optimization algorithm-based controllers from the literature demonstrates the effectiveness of the proposed technique. Specifically, the ARO/PID-F controller exhibits a rapid response, zero overshoot, minimal settling time, and precise control during critical phases. The obtained results position the proposed methodology as a promising and innovative solution for optimizing aircraft pitch control systems, offering improved performance and reliability.
A New Hybrid Intelligent Fractional Order Proportional Double Derivative + Integral (FOPDD+I) Controller with ANFIS Simulated on Automatic Voltage Regulator System
Authors: Mohammed Abdullah Fadhil , Marhoon Hamzah M. , Basil Noorulden , Ma'arif Alfian
Published: September 2024

In the dynamic realm of Automatic Voltage Regulation (AVR), the pursuit of robust transient response, adaptability, and stability drives researchers to explore novel avenues. This study introduces a groundbreaking approach—the Hybrid Intelligent Fractional Order Proportional Derivative2+Integral (FOPDD+I) controller—leveraging the power of the Adaptive Neuro-Fuzzy Inference System (ANFIS). The novelty lies in the comparative analysis of three scenarios: the AVR system without a controller, with a traditional PID controller, and with the proposed FOPDD+I-based ANFIS. By fusing ANFIS with a hybrid controller, we forge a unique path toward optimized AVR performance. The hybrid controller, based on FOPID (Fractional Order Proportional Integral Derivative) principles, synergizes individual integral factors with ANFIS, augmenting them with a doubled derivative factor. The ANFIS design employs a hybrid optimization learning scheme to fine-tune the Fuzzy Inference System (FIS) parameters governing the AVR system. To train the fuzzy inference system, we utilize a Proportional-Integral-Derivative (PID) simulation of the entire AVR system, capturing essential data over approximately seven seconds. Our simulations, conducted in MATLAB/Simulink, reveal impressive performance metrics for the FOPDD+I-ANFIS approach: Rise time: 1.1162 seconds, settling time: 0.5531 seconds, Overshoot: 0%, Steady-state error: 0.00272, These results position our novel approach favorably against existing works, underscoring the transformative potential of intelligent creation in AVR control.
A Novel Sea Horse Optimizer Based Load Frequency Controller for Two-Area Power System with PV and Thermal Units
Authors: Andic Cenk , Ozumcan Sercan , Varan Metin , Ozturk Ali
Published: September 2024

This study introduces the Sea Horse Optimizer (SHO), a novel optimization algorithm designed for Load Frequency Control (LFC) in two-area power systems including photovoltaic and thermal units. Inspired by the interactive behaviors of seahorses, this population-based metaheuristic algorithm leverages strategies like Brownian motion and Levy flights to efficiently search for optimal solutions, demonstrating quicker and more stable identification of global and local optima than traditional algorithms. The proposed SHO algorithm was tested in a two-region power system containing a photovoltaic system and a reheat thermal unit under three different scenarios. In the first scenario, the frequency response of the algorithm to a 0.1 p.u. load change in both regions was examined. In the second scenario, the algorithm's frequency response to sudden load changes from 0.1 p.u. to 0.4 p.u. was tested. Finally, the algorithm's frequency response was examined against different levels of solar irradiance for sensitivity analysis. This study compared the performance of the SHO-optimized controller with the optimization algorithms reported in the literature, including the Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA).  In this context, the optimization of PI controller gain parameters based on the ITAE metric resulted in SHO algorithm achieving the best performance with values of 2.5308, followed by WOA at 4.1211, FA at 7.4259, and GA at 12.1244. In tests, SHO significantly outperformed these algorithms in key performance metrics, such as Settling Time, Overshoot (M+), and Undershoot (M-). Specifically, SHO achieved 98.94% better overshoot and 85.25% reduced undershoot than GA, and concluded settling times 52.79% faster than GA in the first scenario. Similar superior outcomes were noted in subsequent tests. These results underline SHO's efficacy in enhancing system stability and control performance, marking it as a significant advancement over conventional LFC methods.
Analysis and Performance Validation of CRONE Controllers for Speed Control of a DC Motor
Authors: Velmurugan V. , Venkatesan M. , Praboo N. N
Published: September 2024

In recent decades controllers play a major role for an efficient control and reliable operation of an industrial process. Hence in this paper, a special kind of Commande Robuste d’Ordre Non Entier (CRONE) controller is designed for controlling the speed of DC Motor (DCM). The proper design procedure of two generations CRONE control strategies named as First-Generation CRONE (FGC) and Second-Generation CRONE (SGC) controllers are implemented through the transfer function of armature-controlled DCM. Simulations are performed on MATLAB software in order to investigate the servo responses of two designed CRONE controllers, besides the results are presented in terms of settling time(ts), rise time(tr), Integral Square Error (ISE) and Integral Absolute Error (IAE). In addition, the relay PI controller is designed and the simulation results are presented for comparison purpose. It is evident from the comparing results that the SGC controller is superior for effective process control.
Utilize the Prediction Results from the Neural Network Gate Recurrent Unit (GRU) Model to Optimize Reactive Power Usage in High-Rise Buildings
Authors: Rofii Ahmad , Soerowirdjo Busono , Irawan Rudi , Caesarendra Wahyu
Published: September 2024

The growing urbanization and the construction sector, efficient use of electric energy becomes important, especially the use of reactive power. If excessive use causes decreased efficiency and increased operational costs. Decreased efficiency contributes to increasing exhaust gas volumes and greenhouse emissions. Efficient energy can achieved if planning and predictions are correct. This research applies the GRU neural network method with grid search initialization as a novelty predictive model for energy-use high-rise buildings in form fast training without multiple iterations because optimal hyperparameters are obtained. Experimental show the MAE and RMSE performance metrics of the GRU better than LSTM in predicting energy consumption data peak loads, off-peak loads and reactive power. The accuracy of GRU predictions can optimize the use of energy to contribute to saving the environment from exhaust emissions and the greenhouse effect in urban systems. Experimental results demonstrate the superiority of GRU over LSTM, proof of the much lower MAE and RMSE values. This metric shows the accuracy of GRU in generalizing data both during peak and off-peak hours, as well as in reactive power usage. By Utilizing GRU's capabilities, building management can manage reactive power usage effectively, allocate reactive power resources appropriately, and mitigate peak load times and the power factor within the threshold, thus avoiding additional costs and electrical system efficiency and contributing to reducing the carbon footprint and gas emissions greenhouse. Research on GRU is widely open in the high-rise building sector, including its integration with sensors to automatically control energy use.
Using Active Filter Controlled by Imperialist Competitive Algorithm ICA for Harmonic Mitigation in Grid-Connected PV Systems
Authors: Hadi Husam Ali , Kassem Abdallah , Amoud Hassan , Nadweh Safwan , Ghazaly Nouby M. , Moubayed Nazih
Published: September 2024

Solar energy has been gaining momentum recently, with a focus on maximizing its investment potential due to its reputation as the most sustainable and efficient energy source. This shift towards solar power could potentially reduce the reliance on oil-based fuels in the future. As a result of the integration of photovoltaic (PV) energy sources into the grid, the reliability of power distribution and maintaining its quality in these systems has become increasingly important. The presence of non-linear loads in these grids causes distortion of both voltage and current waves on the grid side, so it is necessary to implement effective reduction techniques to reduce the distortions in these waves. The research contribution is TO introduce the integration of an active filter on the dc side of grid-connected PV systems, along with a control circuit for the filter switches. The control switches were operated using a Sinusoidal Pulse Width Modulation (SPWM) control scheme, while the controller parameters were tuned using the Imperialist Competitive Algorithm (ICA). The proposed system was simulated in the MATLAB/Simulink environment with variations in solar radiation and temperature. The simulation results demonstrated a reduction in the total harmonic distortion factor (THD) for voltage and current waveforms on the grid side, which are within the permissible limits. This confirms the effectiveness of the proposed filter and the efficiency of the control strategy and algorithm for parameter adjustment.
Intelligent PID Controller Based on Neural Network for AI-Driven Control Quadcopter UAV
Authors: Sahrir Nur Hayati , Basri Mohd Ariffanan Mohd
Published: September 2024

Unmanned Aerial Vehicle (UAV), specifically a quadcopter is publicly popular which it provides services in different applications such as aerial delivery, aerial photography, military, weather forecasting and more examples to date. A Proportional-Integral-Derivative (PID) controller is one of the control techniques that can provide stabilization and reliable trajectory tracking. However, proper PID gains are needed to ensure a stable flight and it should be hybridized or improved to increase the robustness, reliability, and stabilization during flight. In this paper, an intelligent PID controller using neural network is proposed based on Levenberg-Marquardt feedforward neural network training method. The PID gains are initialized using different ranges according to the optimal gains generated by Particle Swarm Optimization, and this contributes towards a good training performance using Mean Square Error (MSE) evaluation. The trained network takes desired output and references as input data to calculate the required combination of PID gains as the output. The including of the response characteristics as the input data for the network, together with reference, error, and control input is the significance of the work. The performance of this work is presented using MSE performances, attitudes and altitude stabilization, and trajectory tracking reliability through error index performances. The simulation results graphically prove that the proposed controller provides better stability with reduced overshoot and settling times. Disturbance rejection is also enhanced by 1.7% compared to manual tuned PID controller. The reliability of the proposed controller highlights avenues for further exploration in AI-driven control strategies for quadcopter systems.
Revolutionizing Anemia Classification with Multilayer Extremely Randomized Tree Learning Machine for Unprecedented Accuracy
Authors: Saputra Dimas Chaerul Ekty , Muryadi Elvaro Islami , Futri Irianna , Win Thinzar Aung , Sunat Khamron , Ratnaningsih Tri
Published: September 2024

Anemia is a prevalent global health issue that is characterized by a deficit in red blood cells or low levels of hemoglobin. This condition is influenced by various causes, including nutritional inadequacies, chronic diseases, and genetic predisposition. The incidence of the phenomenon exhibits variation across different geographical regions and demographic groups. This pioneering research investigates the identification and classification of anemia, potentially leading to transformative advancements in the discipline. The classification of anemia encompasses four distinct groups, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination. This comprehensive categorization offers clinicians a more refined and detailed comprehension of the condition. The integration of deep learning and machine learning in the Multilayer Extremely Randomized Tree Learning Machine (MERTLM) model represents a departure from traditional approaches and a significant advancement in the field of medical categorization accuracy. The MERTLM approach integrates randomized tree with multilayer extreme learning machine (M-ELM) representation learning, hence emphasizing the possibility of interdisciplinary collaboration in the field of diagnostics. In addition to its impact on anemia, artificial intelligence (AI) is playing a significant role in revolutionizing medical diagnosis by emphasizing the integration of innovative methods. This study utilizes the combined capabilities of machine learning and deep learning to improve accuracy. Notably, recent developments have resulted in an exceptional accuracy rate of 99.67%, precision of 99.60%, sensitivity of 99.47%, and an amazing F1-Score of 99.53%. This study represents a significant advancement in the field of anemia research, providing valuable insights that may be applied to intricate medical issues and enhancing the quality of patient care.
Photovoltaic Model Parameters Estimation Via the Fully Informed Search Algorithm
Authors: Bensidhoum Tarek , Lekouaghet Badis , Touil Sid-Ahmed
Published: September 2024

Effective parameter estimation for photovoltaic (PV) systems holds significant importance for both researchers and industry professionals. An accurate understanding of PV models, achieved through modeling and simulation, plays a pivotal role in optimizing the design, control, testing, and forecasting of PV system performance. Developing a precise and robust parameter identification method significantly contributes to enhancing the modeling, control, and optimization of photovoltaic systems. In this context, our research contribution introduces a novel version of Rao metaheuristic algorithm named the Fully Informed Search Algorithm (FISA). Which demonstrate acceptable performance to solving optimization problems in several applied fields. While, maintaining the simplicity and non-parametric nature of the original algorithm. The proposed algorithm holds promise for various industrial applications, particularly in optimizing complex systems such as photovoltaic (PV) systems. For which, we used it to efficiently identifying the parameters of the single-diode model (SDM). Thus, we demonstrate its effectiveness through the application in two distinct case studies within our simulation research. in the end, we compared the results of FISA algorithm to seven other well-known algorithms, the obtained results indicate the superiority of the proposed algorithm in term of the stability of the system, a faster convergence and higher accuracy.