bluuebunny/crossref_metadata_2025_split huggingface dataset:
Photocatalytic-Assisted Electrochemical Machining of SiCp/Al: An Exploration of Mechanisms and Effects
Authors: Zhou Jing , Wang Feng , Wei Hongfei , Kang Xiaoming 
Published: March 2024

Abstract

Silicon carbide particle reinforced aluminum matrix composites (SiCp/Al), combine the properties of aluminum matrix materials and reinforced silicon carbide (SiC) particles, finding widespread application in aerospace, optics, and electronics. The increased hardness and electrochemical inertness of SiC particles present significant challenges in their processing. This research investigates the impact of photocatalytic processes on machining accuracy in electrochemical processing (ECM). The dissolution characteristics of SiCp/Al during ECM and photocatalytic-assisted electrochemical machining (PAECM) were analyzed with varying electrolyte compositions, using polarization curves and oxidation-reduction potential (ORP). Validation of improved machining accuracy in PAECM was achieved through no-feeding machining. PAECM, with its photocatalytic reactions, effectively blocked stray currents and stray corrosion, enhancing accuracy in profile creation. Comparative analysis revealed that PAECM exhibited smoother profiles and reduced Overcut Ratio (OCR) in hole and groove machining compared to ECM, particularly in groove machining. Adjusting machining gaps and reducing abrasive particle size in PAECM influenced groove depth and width, achieving optimal morphology with a 0.4 mm gap and a 2-3 nm abrasive particle size, resulting in a 370 μm depth.
Unveiling Piperazine-Quinoline Hybrids as Potential Multi-Target Directed Anti- Alzheimer’s Agents: Design, Synthesis and Biological Evaluation
Authors: Nagani Afzal , Shah Moksh , Patel Salman , Patel Harnisha , Parikh Vruti , Patel Ashish , Patel Sagar , Patel Kirti , Parmar Hardik , Bhimani Bhargav , Yadav M R 
Published: March 2024

Abstract
Multi-target directed ligands (MTDLs) have recently been popularized due to their outstanding efficacy in combating the complicated features of Alzheimer's disease. This study details the synthesis of piperazine-quinoline-based MTDLs through a multicomponent Petasis reaction, targeting multiple factors such as AChE, BuChE, metal chelation to restore metal dyshomeostasis, and antioxidant activity. Some of the synthesized compounds exhibited notable inhibitory activity against AChE and BuChE enzymes at specific concentrations. Among the synthesized compounds compound (95) containing a 4-chloroaniline moiety and a 4-methoxybenzyl group displayed the most promising inhibitory activities against AChE (IC50 3.013 µM) and BuChE (IC50 = 3.144 µM). Compound (83) featuring 2-methoxyaniline and 4-fluorobenzyl substituents, exhibited the highest BuChE inhibition (IC50 1.888 µM). Notably, compound (79) demonstrated 93-times higher selectivity for BuChE over AChE. Out of these compounds nine compounds were assessed for antioxidant activity, displaying significant potential at a concentration of 100 μM. Moreover, all the compounds demonstrated metal chelating activity with Cu+2, Zn+2, Fe+2, Fe+3 and Al+3. This study provides insights into the design of novel MTDLs, highlighting compound (95) as a potential candidate for Inhibiting Alzheimer's disease and emphasizing its role in the development of anti-AD medication.
Occupational radiation protection for the medical staff during the fluoroscopic spinal procedure: Are we protected adequately?; A cadaveric simulation study
Authors: Yamashita Kazuta , Nakajima Daiki , Tamaki Yasuaki , Omichi Yasuyuki , Hayashi Hiroaki , Tsuruo Yoshihiro , Sairyo Koichi 
Published: March 2024

Abstract
Background
Fluoroscopy is crucial in spine surgery, but exposes medical staff to scattered radiation, raising concerns about health risks. The effectiveness of using protective equipment to reduce occupational exposure remains uncertain.
Objective
The purpose of this study was to evaluate actual radiation exposure doses and shielding effectiveness during simulated spine surgery.
Methods
Using seven fresh cadavers and mannequins, radiation exposure was measured with and without protective equipment. The X-ray source was positioned under the table, over the table, and laterally. Radiation exposure doses were measured at different simulated areas such as the optic lens, thyroid gland, and hand of the medical staff.
Results
There were significant differences in the radiation exposure dose under different conditions and for different irradiated areas. The protective equipment significantly reduced radiation exposure. However, exposure remained relatively high, especially to the surgeon's lens and hand when the radiation source was positioned over and laterally. Exposure to scrub nurses and anesthesiologists was relatively low.
Conclusions
Shielding is critical, but the complete elimination of radiation exposure is challenging. Overall, protective equipment is effective in reducing occupational radiation exposure, but additional measures may be necessary, especially when the radiation source is positioned over the table and laterally. Our results provide a guide for medical staff to understand the risk of radiation exposure.
Toward Optimal Feature Selection: A Framework Harnessing Ensemble Metaheuristics
Authors: Zipp Logan R. , Mai Garrett A. , Osti Manish , Al-Fuqaha Ala , MULAHUWAISH AOS , Qolomany Basheer 
Published: March 2024

Abstract

The increasing complexity of datasets in various domains necessitates more effective and efficient feature selection techniques for predictive analytics. This study presents an ensemble approach that integrates multiple metaheuristic algorithms to optimize feature selection across different datasets. Utilizing five datasets—NSL KDD, DARWIN, Period Changer, Toxicity, and SMK CAN 187—our experimental evaluation compared the ensemble of metaheuristic algorithms against individual algorithms in terms of both feature selection and predictive accuracy. Our results revealed that the ensemble approach significantly reduced the dimensionality of the feature space without sacrificing much in terms of predictive performance. For instance, on the NSL KDD dataset, the ensemble method averaged just 4.76% selected features with a competitive accuracy rate of 93.9%. Conversely, on the Period Changer dataset, the ensemble not only reduced the feature set but also outperformed individual algorithms with a higher average accuracy rate of 56.7%. These findings affirm the potential of ensembles of metaheuristics to offer a trade-off between computational efficiency and prediction accuracy, making them highly applicable in resource-constrained and real-world scenarios.
Lost diagnoses? A multi-year trajectory of patients with childhood ADHD in the criminal justice system in Switzerland
Authors: Wyler Helen , Wijnkoop Moritz van , Smith Alexander , Retz Wolfgang , Liebrenz Michael , Buadze Ana 
Published: March 2024

Abstract

Background: Attention-deficit/hyperactivity disorder (ADHD) is prevalent amongst offenders, increasing risks for aggressive and delinquent behaviours. Since ADHD and its symptoms can persist into adulthood, accurately diagnosing and maintaining diagnoses in offenders is crucial to ensure appropriate treatment and reduce recidivism.
Methods: This study employed a retrospective longitudinal design to investigate ADHD amongst adult offenders at a Swiss forensic outpatient clinic between 2008 and 2021. N = 181 patient files were reviewed, including forensic expert witness assessments and treatment reports. We charted the adulthood trajectory of patients with a confirmed childhood/adolescence ADHD diagnosis, examining the course of their diagnoses. Results are reported using descriptive statistics and graphical representations.
Results: Of 181 patients, evidence indicated that 12 (7%) had an ADHD diagnosis in childhood/adolescence. In 1 (8%) of these 12 cases, the diagnosis was maintained throughout the observation period. For 4 patients (33%), a diagnosis was given in the first forensic psychiatric expert witness assessment in adulthood but subsequently dropped. In another 4 cases (33%), the diagnosis was dropped in adulthood but later re-assigned, whereas in 3 cases (25%), the diagnosis was discontinued throughout the observation period. In 50% of cases with a diagnostic change, the discontinuation of an adult ADHD diagnosis coincided with a newly diagnosed personality disorder (or vice versa).
Conclusions: Our findings highlighted considerable inconsistencies in the assignment of adult ADHD diagnoses amongst offenders. Whilst ADHD remission in adulthood occurs, the diagnostic variability in our results warrants detailed scrutiny. One possibility is that ADHD has similar fluctuations to conditions like depression, as argued elsewhere. Equally, diagnoses may become “lost”, meaning they are not given even when applicable and replaced by other diagnoses. Additionally, residual symptoms may remain but beyond the diagnostic threshold. This is significant because untreated ADHD can increase re-offending risks and adverse health outcomes.
Preoperative CT-Based Radiomic Prognostic Index to Predict the Benefit of Postoperative Radiotherapy in Patients with Non-Small Cell Lung Cancer: A Multicenter Study
Authors: Ma Zeliang , Men Yu , liu yunsong , Bao Yongxing , Liu Qian , Yang Xu , Wang Jianyang , Deng Lei , Zhai Yirui , Bi Nan , Wang Luhua , Hui Zhouguang 
Published: March 2024

Abstract
Background
The value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT.
Methods
Six cohorts were included. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI.
Results
In total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1–6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18–4.52), cohort 2 (HR = 1.64, 95% CI: 1.26–2.14), cohort 3 (HR = 2.53, 95% CI: 1.45–4.3), cohort 4 (HR = 1.24, 95% CI: 1.01–1.52), cohort 5 (HR = 2.56, 95% CI: 0.73–9.02), cohort 6 (HR = 2.30, 95% CI: 0.53–10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60–0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50–0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46–0.70), and RPI (C-index: 0.65, 95% CI: 0.54–0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40–0.91; p = 0.02), while patients in the low-risk and high-risk groups did not.
Conclusions
We developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.
Investigation of Structural, Optical and Mechanical Properties of Poly (methyl methacrylate) / Zirconium oxide (PMMA/ZrO2 ) Nanocomposite Films
Authors: Horti N. C. , Mathapati S. I. , Banapurmath N. R. , Pujari V. S. , Inamdar S. R. , Kamatagi M. D. 
Published: March 2024

Abstract
This research article explain the fabrication of poly (methyl methacrylate)/ zirconium oxide (PMMA/ZrO2) nanocomposite films via a solution casting technique. The fabricated nanocomposite films were examined for their structural, morphological and optical properties through X-ray diffraction, Atomic force microscopy, Fourier infrared transform, UV-Vis absorption and fluorescence emission spectroscopy techniques. Thermogravimetric test was performed to check the thermal stability of nanocomposite films and the mechanical properties was assessed using a universal testing machine. XRD patterns of samples showed the formation of pure PMMA films and the successful incorporation of ZrO2 nano-fillers into polymer matrix and the results are in good agreement with the FTIR results. The agglomeration of particles and change in surface roughness of films was noticed from AFM images. UV-Vis absorption analysis revealed that the absorption onset of PMMA films shifted towards a longer wavelength with an increasing content of ZrO2 nano-fillers. The photoluminescence spectra exhibited the significant enhancement of photoluminescence intensity and a red shift in the emission peak of PMMA films as the content of ZrO2 nanofillers increases. With an increase of ZrO2 nanofiller concentration, the mechanical properties of composite films change significantly. The sample with 3% nano-filler exhibited the good mechanical strength, including a break energy of 4665 MJ/m3 and a break stress of 3.390 MPa and superior photoluminescence intensity making it suitable composite material for denture-based applications.
IIFS2.0: An Improved Incremental Feature Selection Method for Protein Sequence Processing Based on a Caching Strategy
Authors: Meng Chaolu , Pei Yue , Bu Yongbo , Liu Qing , Li Qun , Zhang Ying , Zou Quan 
Published: March 2024

Abstract
The purpose of feature selection in protein sequence recognition problems is to select the optimal feature set and use it as training input for classifiers and discover key sequence features of specific proteins. In the feature selection process, relevant features associated with the target task will be retained, and irrelevant and redundant features will be removed. Therefore, in an ideal state, a feature combination with smaller feature dimensions and higher performance indicators is desired. This paper proposes an algorithm called IIFS2.0 based on the cache elimination strategy, which takes the local optimal combination of cached feature subsets as a breakthrough point. It searches for a new feature combination method through the cache elimination strategy to avoid the drawbacks of human factors and excessive reliance on feature sorting results. We validated and analyzed its effectiveness on the protein dataset, demonstrating that IIFS2.0 significantly reduces the dimensionality of feature combinations while also improving various evaluation indicators. In addition, we provide IIFS2.0 on http://112.124.26.17:8006/ for researchers to use.
Adversarial transfer learning based hybrid recurrent network for air quality prediction
Authors: Hao Yanqi , Luo Chuan , Li Tianrui , Zhang Junbo , Chen Hongmei 
Published: March 2024

Abstract

Air quality modeling and forecasting has become a key problem in environmental protection. The existing prediction models typically require large-scale and high-quality historical data to achieve better performance. However, insufficient data volume and significant differences between data distribution across different regions will definitely reduce the effectiveness of the model reuse. To address the above issues, we propose a novel hybrid recurrent network based on domain adversarial transfer to achieve a stronger generalization ability when training air quality data from multi-source domains. The proposed model mainly consists of three fundamental modules, i.e., feature extractor, regression predictor, and domain classifier. One-dimensional convolutional neural networks (1D-CNNs) are used to extract temporal feature of data from source and target stations. Bi-directional gated recurrent unit (bi-GRU) and bi-directional long short-term memory (bi-LSTM) are utilized to learn temporal dependencies pattern of multivariate time series data. Two adversarial transfer strategies are employed to ensure that our model is capable of finding domain invariant representations automatically. Experiments with different number of source domains are conducted to demonstrate the effectiveness of the proposed domain transfer strategies. The experimental results also show that our composite model has superior performance for forecasting air quality in various regions. As further evidence, the adversarial training method could promote the positive transfer and alleviate the negative effect of irrelevant source data. Besides, our model exhibits preferable generalization capability as more robust prediction results are achieved on both unseen target domains and original source domains.
An Inverse Distance Weighting and Radial BasisFunction Based Particle Swarm Optimizer forHigh-dimensional Expensive Optimization
Authors: Huang Xun , Li Fei , Liu Xiang , Zhou Baiyu 
Published: March 2024

Abstract
Surrogate-assisted evolutionary algorithms is one of the effective solutions tohigh-dimensional expensive optimization problems. It builds a surrogate modelto reduce the computational cost of expensive problems. However, the quality ofmodel management will directly affect the optimization performance of the algorithm. Inverse distance weighting and radial basis function (IDW-RBF) modelis constructed with low time cost and provides relatively estimated fitness valueand estimated uncertainty respectively. In order to reduce the calculation cost,this paper adopts a social learning particle swarm optimization algorithm namedIR-SLPSO which based on IDW-RBF model. In addition, IR-SLPSO proposes asimplified multiobjective infill criterion (SMIC), selects the last layer and the firstlayer of two prominent particles into the expensive real function operation in theprocess of nondominated sorting. We simulate IR-SLPSO with the existing fourSAEAs on the six commonly used benchmark functions from 50 to 100 dimension.Experimental results show that IR-SLPSO is competitive on high-dimensionalexpensive optimization problems.
Effect of Berberis vulgaris L. Extract on Beef Patties Quality Parameters
Authors: Topcu Kübra Cinar , UGUR Gamze 
Published: March 2024

Abstract
The effects of adding Berberis vulgaris L. fruit extract to beef patties (patties) on physicochemical, sensory, texture and safety features were investigated. The addition of the extract did not cause a significant change in the approximate composition (moisture and ash) of the patties. It had a significant effect on the pH value in all groups of raw and cooked patties (P < 0.01). Initially, it had a significant effect on the TBARS value in all groups (P < 0.05), while there was no significant difference in TBARS values after cooking (p > 0.05). While no significant difference was observed for the color values, cooking caused a fall in L*, a* and b* values. Cooking yield had a significant effect in all groups (P < 0.01). The use of Berberis extract in patties reduced the cooking yield. The addition of the extract did not change the sensory features of the patties by a significant degree. Additionally, texture analysis showed very significant effects of hardness (N), gumminess (N) and chewiness (mJ) values in all groups (P < 0.01). The findings indicate that the extract does not significantly alter the patties' properties but reduces lipid oxidation and enhances textural attributes. This study supports the potential use of Berberis vulgaris L. extract as a natural additive to improve the quality of beef patties.
Electrochemical Indicators of Sarcoplasmic Meat Discoloration: A Novel Food Technology
Authors: Krishnan Sadagopan , KD Sandun Bogahawaththa , Bhandari Silan , Devage Sachinthani , Avery Jacob , Kumar Rishav , Ramanathan Ranjith 
Published: March 2024

Abstract
Meat discoloration is a major issue in the meat industry that affects quality and shelf life. In this report, we present a new user-friendly electroanalytical tool for directly probing signature redox peaks associated with meat discoloration and its delay when controlled biochemically and validated by spectrophotometry. The designed approach is valuable in potentially reducing nutritious beef waste, environmental impacts from waste disposal, and CO2 emissions due to discoloration problems.
Techniques to Predict Employee Attrition Using Optimized Levy Fruit Fly Optimization Algorithm
Authors: Preena Romela 
Published: March 2024

Abstract
Competent people are a valuable asset for strong businesses. The issue of retaining competent staff with expertise poses a challenge to business owners. Companies may incur losses due to employee turnover if they are unable to replace lost expertise and productivity. Consequently, this research suggests a new model that uses machine learning to forecast staff turnover. The datasets are collected from Kaggle resource. The dataset has been pre-processed using standard scalar with Label Encoding method. The dataset has been trained with ML algorithm. The best features are selected by using modified genetic algorithm (MGA). The classification has been done with KNN, Gradient Boosting and Extra tree classifier. Finally, the attrition prediction using optimized levy fruit fly optimization (OLFFO). The experimental results are compared with ML algorithms with classification metrics (Accuracy, Precision, recall and f-measure).
Traffic modeling using Machine Learning methods for predicting vehicle numbers at junctions: A case study in Colombo, Sri Lanka
Authors: Hasan Farasath 
Published: March 2024

Abstract
Traffic modeling is a fundamental tool for comprehending the complex dynamics of urban mobility. It provides vital insights for transportation planning and the construction of infrastructure. This study aims to employ sophisticated machine-learning techniques to forecast the volume of vehicles at certain main intersections in Colombo, Sri Lanka. The study emphasizes the importance of traffic modeling in influencing policy development and optimizing transportation networks, based on a thorough evaluation of relevant literature. The research utilizes machine learning methods, namely random forest regression, to analyze temporal and spatial trends in traffic flows. This analysis yields valuable insights for urban planners and transportation authorities. An examination of the dataset, utilizing methodologies such as rolling statistics and time series analysis, reveals subtle variations in traffic levels over time, including the influence of external influences like as the COVID-19 pandemic. The study's results highlight the significant impact of incorporating machine learning techniques into traffic modeling, providing a solution to improve urban mobility, decrease congestion, and create more sustainable transportation systems. This research ultimately enhances the development of data-driven solutions to tackle the changing mobility requirements of Colombo and establishes the groundwork for future research in the field of urban transportation modeling.
Evolving Advances in the Applications of Carbon Nanotubes (CNTs) for Management of Rheumatoid Arthritis (RA)
Authors: Patil Sarika J. , Thorat Vandana M. , Koparde Akshada A. , Bhosale Rohit R. , Chavan Dhanashri D. , Tiwari Devkumar D. 
Published: March 2024

Abstract:
Rheumatoid arthritis (RA) is a chronic condition causing joint pain and inflammation that
has now spurred the interest in nanotechnology-based drug delivery for more effective treatment, and
in this regard, carbon nanotubes (CNTs) are being explored for their potential to deliver the drugs
steadily to manage the RA. Many investigators have been investigating both single-walled carbon
nanotubes (SWCNT) as well as multi-walled carbon nanotubes (MWCNT) for managing arthritis via
targeted drug delivery. Moreover, functionalized CNTs show promise in delivering the drugs precisely
and in a controlled manner, thereby minimizing toxicity. However, research on applications of
CNTs as drug carriers for RA remains limited, thus necessitating further exploration to address the
various challenges. In this present piece of writing, challenges in RA treatment and the advances in
applications of CNTs for RA management are reported, consequently reflecting the CNTs as advanced
drug delivery vehicles for arthritis treatment.