bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal treatment strategy for low-grade spinal cord astrocytoma: a retrospective, multicenter analysis by the Neurospinal Society of Japan
Authors: Shigekawa Seiji , Inoue Akihiro , Endo Toshiki , Muto Jun , Inoue Tomoo , Kanematsu Ryo , Mitsuhara Takafumi , Umebayashi Daisuke , Mizuno Masaki , Hida Kazutoshi , Kunieda Takeharu , _ _ 
Published: September 2024

OBJECTIVE
Primary spinal cord gliomas are rare, and among these astrocytomas (WHO grade II) are much rarer. The optimal treatment strategy thus remains unclear. The authors conducted a multicenter study led by the Neurospinal Society of Japan (NSJ) to analyze treatment policies and outcomes. The aim was to present optimal treatment methods for spinal cord astrocytoma and to identify predictors of better outcomes.

METHODS
Among 1033 consecutive cases of spinal cord intramedullary tumors treated surgically at 58 centers affiliated with the NSJ, 57 patients were diagnosed with diffuse astrocytoma (WHO grade II) and were enrolled in the present study. Among these 57 patients, treatment methods, outcomes, and tumor proliferation rate as evaluated by the MIB-1 staining index (SI) were analyzed, and the optimal treatment method for spinal cord astrocytomas (grade II) was determined. In addition, the authors searched for factors predicting better treatment outcomes.

RESULTS
Treatment for spinal cord astrocytoma comprised three methods: surgery alone in 30 patients, adjuvant radiation therapy in 13 patients, and adjuvant chemoradiotherapy in 13 patients. One patient who did not undergo surgery was excluded from survival analysis. Treatment with surgery alone or surgery with radiotherapy was associated with significantly longer overall and progression-free survivals than that with adjuvant chemoradiotherapy. Patients treated with radiation therapy had a higher MIB-1 SI than those treated with surgery alone. The extent of tumor resection tended to correlate with longer survival. In contrast, postoperative neurological worsening showed the inverse order. Adjuvant chemoradiotherapy was associated with the shortest survival in both total cases and recurrent cases. The optimal cutoff value of MIB-1 SI for predicting longer survival by surgery alone was less than 4.0%.

CONCLUSIONS
The optimal treatment for spinal cord astrocytoma is maximal tumor resection without neurological impairment. When some tumor remains in patients with an MIB-1 SI less than 4.0%, a wait-and-see approach is optimal. If the MIB-1 SI is higher than 4.0%, local radiation therapy is recommended. Adjuvant chemotherapy is not recommended for the treatment of grade II spinal cord astrocytoma.
Does disease etiology matter in long-term patency in extracranial-intracranial bypass?
Authors: McGuire Laura Stone , Abou-Mrad Tatiana , Atwal Gursant , Amin-Hanjani Sepideh , Charbel Fady T. 
Published: September 2024

OBJECTIVE
Extracranial-intracranial (EC-IC) bypass has been well described in chronic vaso-occlusive cerebrovascular diseases, including both moyamoya disease (MMD) and atherosclerotic disease (AD). This study aimed to compare factors associated with bypass occlusion between these two diseases.

METHODS
An institutional database of 357 patients with intracranial bypass procedures performed between August 2001 and May 2022 was retrospectively reviewed. Patients with MMD and AD were selected for study. Baseline characteristics, surgical technique, and flow-related measurements were compared in relation to the outcome of bypass occlusion.

RESULTS
A total of 232 patients met inclusion criteria (AD, n = 108; MMD, n = 124). The average age and sex differed significantly between groups (AD 57.2 years, 56.5% male; MMD 36.6 years, 31.5% male; p < 0.001). The modified Rankin Scale scores at surgery and at follow-up were higher in the AD group (p = 0.004 and p < 0.001, respectively), showing a slightly worse baseline functional status, and higher rates of stroke were observed in the AD group by last follow-up (p = 0.005). Patients with AD also were more likely to require an interpositional graft (p < 0.001). At last follow-up, rates of occlusion did not differ between AD and MMD groups (25.2% vs 25.4%, respectively). Of occluded bypasses, the AD group had more occlusions within 1 week compared to MMD (51.9% vs 35.5%, p = 0.176), although the difference was not significant. In patients with more than 1 year of follow-up and in those with more than 2 years of follow-up, MMD tended to have higher rates of occlusion (31.2% vs 26.1% [p = 0.558], and 26.4% vs 20.7% [p = 0.564]). Flow measurements did not differ between AD and MMD groups, but in subgroup analyses of patients with AD and those with MMD, both bypass flow and cut flow index predicted occlusion in both groups.

CONCLUSIONS
Despite different disease etiologies treated with bypass, rates of occlusion at last follow-up did not vary between groups, although short-term follow-up would suggest earlier bypass failure in AD, and extended follow-up trended toward higher occlusion rates in MMD. Additionally, patients with AD were more likely to have further occurrences of stroke by last follow-up. Importantly, the bypass flow and cut flow index at the time of surgery predicted occlusion in both AD and MMD.
Intraoperative MRI in pediatric epilepsy and neuro-oncology: a systematic review and meta-analysis
Authors: Adegboyega Gideon , Momin Sheikh , Gillespie Conor S. , Jeelani Noor ul Owase , Sudhakar Sniya , Mankad Kshitij , Tisdall Martin M. , Aquilina Kristian , Toescu Sebastian M. 
Published: September 2024

OBJECTIVE
Intraoperative magnetic resonance imaging (iMRI) use is becoming increasingly widespread in neurosurgical practice, and most of the data reporting its use are in adult populations. There is less evidence on the use of iMRI in pediatric neurosurgery. The aim of this paper was to synthesize the available literature into a systematic review and meta-analysis to evaluate the evidence for iMRI in pediatric neurosurgery, with a particular focus on neuro-oncology and epilepsy surgery.

METHODS
This review was registered on PROSPERO and conducted according to PRISMA guidelines. A comprehensive search strategy of Medline via Ovid and Embase was conducted with predetermined key terms. Studies in English reporting the outcomes of patients < 21 years of age who underwent neuro-oncological or epilepsy surgery with the use of iMRI were included in the study. The types of studies eligible for inclusion were observational case-control and cohort studies, randomized clinical trials, cross-sectional studies, editorials, case series, and commentaries. Articles were de-duplicated and abstracts independently screened for inclusion by two reviewers. Full texts were screened, and data on demographic characteristics, etiology, outcome (extent of resection for neuro-oncology and Engel class for epilepsy), and technical iMRI data were extracted.

RESULTS
Thirty-five articles were included in the review, 25 of which were observational cohort studies. Four articles were suitable for meta-analysis. In total, 1217 patients underwent iMRI-guided neuro-oncology surgery in 26 studies, most commonly for gliomas (n = 443). A total of 148 patients underwent iMRI-guided epilepsy surgery in 9 studies, with focal cortical dysplasia being the most common diagnosis (n = 95). The mean ± SD operating time was 357 ± 94 minutes (12 studies), with a mean of 1.32 scans per patient. There was a mean re-entry rate into the operative field of 42% (across 20 studies). Complications were noted in 21% of epilepsy surgery patients and 11% of neuro-oncology surgery patients. Meta-analysis of 4 eligible studies revealed that iMRI was more likely to lead to better Engel outcomes in terms of seizure freedom (OR 3.84, 95% CI 1.38–10.68, p = 0.69) and complete tumor resection (OR 3.19, 95% CI 0.28–36.92, p = 0.06).

CONCLUSIONS
iMRI appears to be a useful adjunct in optimizing resective pediatric epilepsy and neuro-oncology surgery, with a low complication rate.
Canopy garden model for synergy of land and sea area on Papan Island in Tojo Una Una Regency, Central Sulawesi
Authors: Bakri Muhammad Bakri , Kasim Anita Ahmad , Timbang Gator 
Published: September 2024

Pulau Papan is one part of the Togean archipelago, Kabupaten Tojo Una Una. Initially, the Pulau Papan area was part of Tiga Pulau Village, Walea Islands District and Malenge Village, Talatako District, because the people who lived in this area came from these two villages. However, in 2011 Pulau Papan was expanded along with the increase in the number of family heads by 638 people and became part of the Kadoda Village area, Talatako District. The aim of this research is to build synergy in destination development between sea space and land/island space by creating regional nodes that are beautiful and function for the public. The public function that is formed can be utilized by the community both as a public space and a functional area that is beneficial for the residents of Pulau Papan, for example a green area containing vegetables for the residents. This vegetable green area is needed because of the limited vegetables on Pulau Papan. The solution proposed in this research is to design a canopy garden model that can become a green structure in the Pulau Papan area. The canopy garden will function to synergize the arrangement of land and sea areas on Pulau Papan. This canopy garden modeling was obtained through a survey process of the Pulau Papan area, then determining the meeting points for the mobility of residents on Pulau Pulau Papan. The public function that is formed can be utilized by the community both as a public space and a functional area that is beneficial for the residents of Pulau Papan, for example a green area containing vegetables for the residents.
Optimization Combining with Digital Transformation of the Men's Shirts Processing at Small and Medium-Sized Garment Enterprises in Vietnam
Authors: Le Tieu-Thanh , Bui Phuong-Thao Thi , La Ngoc-Anh Thi , Dang Thai-Viet 
Published: September 2024

Industry 4.0 has become a hype among the manufacturing industries across the globe. Recent developments require significant capital investments, but these technologies are yet to be established in developing countries such as Vietnam, especially the apparel industry. Based on a survey of the current situation at small and medium-sized enterprises in Vietnam's textile industry, the paper proposes to apply technology, test and evaluate the effectiveness of applying and coordinating digital systems in management and chain supply. Multifaceted applications have been specifically explored including automatic equipment and digital systems, spanning the domains of automation, robotics, artificial intelligence, data analytics, and the Internet of Things (IoT). These technologies are posited as catalysts for transformative improvements in production efficiency and resource utilization. Furthermore, experimental results point out the symbiotic relationship between technology adoption and effective management strategies to achieve holistic operational enhancements. As the Vietnamese textile industry strives for competitive excellence in the global arena, this research offers actionable insights for industry practitioners, policymakers, and researchers.
Power Regulation of a Three-Phase L-Filtered Grid-Connected Inverter Considering Uncertain Grid Impedance Using Robust Control
Authors: Yay Socheat , Soth Panha , Tang Heng , Cheng Horchhong , Ang Sovann , Choeung Chivon 
Published: September 2024

Uncertain grid impedance is often common in power distribution networks; therefore, it is crucial to design an efficient controller in this situation. An issue that frequently occurs is the problem of unpredictable grid impedance, which can cause voltage fluctuations, power quality problems, and potential damage to equipment. This work provides a systematic control strategy to tackle these issues by supplying well-regulated power from a DC source to an AC power grid. A linear matrix inequality (LMI)-based robust optimal control is proposed in this paper to provide stability to the inverter system without offset error at the output side. The convergence time to steady state is minimized by solving the LMI problem to maximize the eigen value of the closed-loop system with the inclusion of the uncertainty of the filter parameter and grid impedance. Furthermore, the uncertainties in this study include the potential variation of values for the filters and the grid's impedance. These uncertainties occur because the grid impedance can fluctuate fast in the event of a fault or termination of a transmission line, while the filter's impedance can also be affected by changes in operating temperature. The simulation study of this proposed control includes a comparison between wide and narrow uncertainty ranges, as well as a performance comparison under uncertain parameters. Furthermore, this approach exhibits a lower power ripple in comparison to existing PI control method.
Enhanced Hybrid Robust Fuzzy-PID Controller for Precise Trajectory Tracking Electro-Hydraulic Actuator System
Authors: Ali Nur Husnina Mohamad , Ghazali Rozaimi , Tahir Abdul Wafi , Jaafar Hazriq Izzuan , Ghani Muhammad Fadli , Soon Chong Chee , Has Zulfatman 
Published: September 2024

The Electro-Hydraulic Actuator (EHA) system integrates electrical and hydraulic elements, enabling it to generate a rapid reaction, a high power-to-weight ratio, and significant stiffness. Nevertheless, EHA systems demonstrate non-linear characteristics and modeling uncertainties, such as friction and parametric uncertainty. Designing a controller for accurate trajectory tracking is greatly challenging due to these limitations. This paper introduces a hybrid robust fuzzy proportional-integral-derivative (HFPID) and (HF+PID) controller. The controller is designed to effectively control a third-order model of an EHA system for trajectory tracking. It is a significant contribution to the development of an intelligent robust controller that can perform well in different environments. Initially, a mathematical model for the EHA system was created using a first-principle approach. Subsequently, the Ziegler-Nichols method was employed to fine-tune the PID controller, while a conventional Fuzzy Logic Controller (FLC) was constructed in MATLAB Simulink utilizing linguistic variables and rule-based control. Without further tuning, the FL and PID controller are combined as a hybrid controller with different structures: Hybrid Fuzzy-PID (HFPID) and Hybrid Fuzzy+PID (HF+PID) controller. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) are utilized as indices to assess the tracking accuracy and robustness of the four controllers. A greater value of MSE and RMSE indicates poorer performance of the controller. The results demonstrate that the HF+PID controller surpasses the other controllers by reaching the lowest MSE and RMSE values. It showcases the efficacy and accuracy in monitoring sinusoidal, multi-sinusoidal, and point-to-point trajectory tracking. Future work should focus on implementing the designed controller on hardware for real-time performance and experimenting with various types of FLC or Hybrid controllers, such as self-tuning fuzzy-PID, to further explore their potential.
Adaptive Load Frequency Control in Microgrids Considering PV Sources and EVs Impacts: Applications of Hybrid Sine Cosine Optimizer and Balloon Effect Identifier Algorithms
Authors: Hassan Ahmed Tawfik , Banakhr Fahd A. , Mahmoud Mohamed Metwally , Mosaad Mohamed I. , Rashwan Asmaa Fawzy , Mosa Mohamed Roshdi , Hussein Mahmoud M. , Mohamed Tarek Hassan 
Published: September 2024

The negative impacts of microgrids (µGs) on the load frequency highlight the importance of implementing a robust, efficient, and adaptable controller to ensure stability. This work introduces an adaptive load frequency control (LFC) for an isolated µG that includes a PV system and electric vehicles (EVs), which have a significant impact on frequency. This control utilizes a combination of sine cosine optimization (SCO) and balloon effect identifier (BEI) algorithms. The controller presented in this work transforms the LFC process into an optimization problem that is highly compatible with various random situations encountered in the control process. The suggested control method is a novel approach by utilizing SCO+BEI for adaptive LFC application, resulting in a highly efficient response. The effectiveness of the proposed adaptive controller is assessed under the conditions of 17 MW variable load, system parameters uncertainties, and installed PV systems of 6 MW. MATLAB / Simulink package is rummage-sale as a digital test environment. According to simulation results, the proposed adaptive controller succeeds in regulating the frequency and power of an islanded µG. To measure the efficiency of the proposed control scheme, a comparison between other control techniques (such as adaptive controller using Jaya+BEI and classical integral controller) is done. The findings of the studied scenarios assured that the not compulsory control method using (SCO+BEI) has an obvious superiority over other control methods in terms of frequency solidity in case of random load instabilities and parameter uncertainties. Finally, it can be said that the proposed controller can better ensure the safe operation of the µGs.
Investigating the Influence of Temperature on UAV Signal Quality
Authors: Abbas Ahmed Hussein , Abdulsadda Ahmad Taha , Hameed Hassanain Ghani 
Published: September 2024

Advancements in drone technology make them important in many areas. military, industry, and disaster The efficacy of a drone's communication systems can be greatly impacted by temperature fluctuations, either from environmental conditions or mechanical problems in the drone's construction. This study gives an analysis and computational model of the impact of temperature on the performance of drone communication. Utilizing a one-dimensional convolutional neural network, we aim to forecast the signal-to-noise ratio (SNR) and received signal strength indicator (RSSI). Following the initial stage of dataset creation in the drone laboratory, proceed to reprocess the dataset and divide it into a 70% training set and a 30% testing set. Subsequently, a graphical user interface (GUI) was developed using MATLAB App Designer to enhance user friendliness. The outcome suggests that the efficiency of the drone communication system declines with rising temperatures. Using 1DCNN is our contribution to this work; other studies depend only on simulation to assess performance. One benefit of 1DCNN is that the impact may be evaluated by automatically extracting important features from the input dataset. Using 1DCNN is our special addition to this project; other research evaluate the UAV communication system's effectiveness only through simulation. We propose in this work to optimize system characteristics for improved performance, including power transfer, by adding a feedback loop between the CNN result and the communication system. Furthermore, we investigate how different weather conditions, such wind and rain, affect UAV communication systems.
Comparative Study of ANN and SVM Model Network Performance for Predicting Brake Power in SI Engines Using E15 Fuel
Authors: Hofny Mohamed S. , Ghazaly Nouby M. , Shmroukh Ahmed N. , Abouelsoud Mostafa 
Published: September 2024

Currently, artificial neural networks (ANNs) and support vector machines (SVMs) are the most common applications of machine learning approaches. In this study, a comparative study of ANN and SVM is presented to evaluate the performance of each model in predicting the brake power (BP) of GX35-OHC 4-stroke, air-cooled, single cylinder gasoline engine with E15 (15% ethanol + 85% gasoline) fuel. Two models are compared based on experimental dataset that has single output (BP) and five inputs, engine speed (S), engine torque (T), intake air temperature (Tair), intake air flow (Qair), and fuel consumption (ṁ). The samples were split into three sets: Training set (70%), Validation set (15%), and the Test set (15%) based on 60 samples. The results are compared through different graphs such as target vs actual values, regression plots, histograms of prediction errors, residual plots, learning curves, and error distributions. The results showed that SVM model is indicated to have lower RMSE (0.0044) and higher EVS (0.9953), while ANN is shown to have lower value of MAPE (1.51%). These results have significant implications for the use of ANN and SVM models in real-world applications that need gradual comprehensibility and model generalization. In addition, work done with the models outlined above should try and test them in other engines and operating conditions to demonstrate the model’s and performance.
Design and Implementation of Smell Agent Optimizer for Parameters Estimation of Single and Double Diode in PV System: A Comparative Analysis
Authors: Elnaggar Mohamed F. 
Published: September 2024

One of the most important and desirable options for moving toward clean electric energy sources is solar energy. Therefore, a PV system's characteristics play a significant role in determining how effective it is across a range of temperature and radiation scenarios. One can consider the PV model's parameter estimation to be a nonlinear optimization situation. This work makes use of a novel application of the smell agent optimizer (SAO) created to forecast the undefined parameters of the PV model's single- and two-diode equivalent circuits. The goal of this effort is to create an accurate photovoltaic model that can accurately represent its performance under variable operating conditions. The square of the mean squared error between the actual measured curve and the current-voltage curve derived from the model defines the intended objective function. The suggested system is constructed and tested experimentally in a range of temperature and light conditions. Next, the MATLAB software is used to create the simulated PV model integrated with the SAO. The PV parameters are then predicted by comparing the experimental data with the convergence of the SAO based on the PV model. Based on the observed properties, the suggested approach for determining the parameters of an actual solar cell has been put into practice and contrasted with eight other optimization techniques. The outstanding efficacy of the method utilized compared with alternate methods is demonstrated by the statistical comparison of the ideal objective function resulting from the difference in the current-voltage curve produced from the optimized circuit model and the measurement.