bluuebunny/crossref_metadata_2025_split huggingface dataset:
Field programmable gate array‐based energy‐efficient and fast epileptic seizure detection using support vector machine and quadratic discriminant analysis classifier
Authors: Alam Md. Shamshad , Hasan Mohd , Farooq Omar , Hasan Mehedi 
Published: November 2023

AbstractEpilepsy is a serious neurological disorder that results in seizures. It can be diagnosed by analyzing the brain's electrical activity using an electroencephalogram (EEG). However, the detection of seizures from massive EEG datasets is a challenging task. To address this challenge, researchers have developed several machine‐learning classifiers and feature extraction techniques for detecting seizures. This paper proposes an energy‐efficient and fast field programmable gate array (FPGA) architecture for detecting epileptic seizures using minimal computational resources. The seizure detection system uses the one Hjorth parameter (mobility) and another statistical parameter (nonlinear energy) as features and employs two efficient classifiers, quadratic discriminant analysis and linear support vector machine (LSVM), for classifying signals into seizure and nonseizure categories. The feature extractor block is connected individually to each of these classifiers. Subsequently, the performance of these two proposed models is evaluated in terms of accuracy, sensitivity, power consumption, resource utilization, and other metrics. The results demonstrate that the SVM classifier‐based model achieved the highest accuracy (99.4%) and sensitivity (98.8%) while consuming minimal dynamic power (0.057 mW) and utilizing the minimum FPGA resources. Thus, the proposed hardware system offers a reliable and energy‐efficient solution for detecting seizures in clinical and real‐time applications.
A convolutional neural network deep learning method for model class selection
Authors: Impraimakis Marios 
Published: November 2023

AbstractThe response‐only model class selection capability of a novel deep convolutional neural network method is examined herein in a simple, yet effective, manner. Specifically, the responses from a unique degree of freedom along with their class information train and validate a one‐dimensional convolutional neural network. In doing so, the network selects the model class of new and unlabeled signals without the need of the system input information, or full system identification. An optional physics‐based algorithm enhancement is also examined using the Kalman filter to fuse the system response signals using the kinematics constraints of the acceleration and displacement data. Importantly, the method is shown to select the model class in slight signal variations attributed to the damping behavior or hysteresis behavior on both linear and nonlinear dynamic systems, as well as on a 3D building finite element model, providing a powerful tool for structural health monitoring applications.
Pore structure of tight sandstones with differing permeability: The He 8 Member of the Middle Permian Lower Shihezi Formation, Gaoqiao area, Ordos Basin
Authors: Chen Dayou , Zhu Yushuang , Wang Wei , Zhang Liling , Tang Jianyun , Ren Jiangli , Wang Yizhe 
Published: November 2023

AbstractTight sandstone has strong pore heterogeneity and complex pore structure, and the pore structure of tight sandstone varies with different permeability. To study the differences in the pore structure of tight sandstone with different permeability, this study investigated the tight sandstone of the He 8 Member in the Gaoqiao area of the Ordos Basin. Factors influencing pore formation are analyzed through experiments utilizing methods, such as distinguishing casting thin sections, scanning electron microscopy, high‐pressure mercury intrusion, and low‐temperature nitrogen adsorption. The results indicate that in this area, the pores of the tight sandstone are primarily dissolution and intercrystalline pores, with occasional intergranular pores and microcracks. Type Ⅱ samples (permeability > 0.2 × 10−3 μm2) are primarily composed of dissolution and intercrystalline pores, with a few visible intergranular and microcracks. In contrast, Type Ⅰ samples (permeability < 0.2 × 10−3 μm2) mainly consist of micropores and intercrystalline pores, where microcracks are observed locally. Pore size research demonstrates that Type Ⅰ samples have pore size under 1000 nm, with peaks primarily at 4–5 nm. There are also peaks between 10–1000 nm, but without a consistent pattern. For Type Ⅱ samples with sizes smaller than 1000 nm, pore distribution is evident. Peak values when the pore size exceeds 1000 nm. Type Ⅰ samples have fewer micron‐level pores, while Type Ⅱ samples are primarily dissolution pores. The nanopores of Type Ⅰ samples consist of flat pores with good connectivity, whereas those of Type Ⅱ samples are mainly blind pores with poor connectivity. Type Ⅰ samples have larger pore fractal dimensions, more intricate pore morphology, and rougher and irregular pore throat surfaces compared to Type II samples. Hence, the sedimentary environment, diagenesis, and mineral composition affect the pore distribution in tight sandstone. The research findings highlight the variations in the pore structure of tight sandstone with varying permeability, providing crucial guidance for classifying and assessing pore structure in tight sandstone reservoirs.
Anomalous vehicle recognition in smart cities using persistent bloom filter: Memory efficient and intelligent monitoring
Authors: Kumar Mandeep , Singh Amritpal 
Published: November 2023

AbstractGlobally, there is a rapid and significant change in the distribution of the urban population. Concerning which there is an increase in traffic rule violations in smart cities, resulting in traffic congestion and accidents. Anomalous vehicle recognition in smart cities is a critical issue that demands prompt and real‐time responsive solutions to address such situations. This article proposes an effective technique utilizing a persistent bloom filter to store and retrieve vehicle data, to improve road safety in urban locations. The technique employs distinct bloom filters to store vehicle data based on the hour, day, month, and year, which enables effective temporal membership queries on vast data. The proposed approach of anomalous vehicle recognition in smart cities using a persistent bloom filter is a memory‐efficient and intelligent monitoring technique. According to experimental analysis on large datasets such as “Montgomery County 911 Calls", “EMS and Fire Calls", “Seattle Fire Dept: 911", and “Traffic Violations in Montgomery County", the approach has the potential to boost query efficiency by up to 80% in comparison to conventional methods, in addition to decreasing storage space demands by up to 60%. The proposed approach has the potential to bridge the gap between research and practice in the field of urban safety and to enhance road safety while considering the dynamic changes in the road network. Employing traditional approaches to detect violations and monitor real‐time changes in a vast dataset of vehicles can be a laborious undertaking. The proposed technique provides a reliable and efficient approach to handling big data and improving traffic safety in smart cities.
Volatility forecasting for stock market index based on complex network and hybrid deep learning model
Authors: Song Yuping , Lei Bolin , Tang Xiaolong , Li Chen 
Published: November 2023

AbstractThe existing literature on the volatility forecasting less considered the co‐movement among stock markets from the spatial dimension. This paper builds the hybrid convolutional neural networks (CNNs)–gated recurrent unit (GRU) model for volatility forecasting under high frequency financial data based on transaction information and the topological characteristics constructed through the complex network of multi‐market symbol patterns. The hybrid neural network CNN‐GRU combines the advantages of CNN automatically extracting features for the input indicators and GRU processing long and short‐term serially dependent features, which can better improve the forecasting accuracy. The empirical results show that with the integration of topological characteristics as the indicators based on complex network, the deep learning model has a significant improvement of one‐step and multi‐step volatility forecasting accuracy for the China's and the US stock markets. The research in this paper provides a complete index system from the spatial dimension and a more accurate and robust volatility forecasting method under the high‐frequency financial data.
A Markov chain model of crop conditions and intrayear crop yield forecasting
Authors: Stokes J. R. 
Published: November 2023

AbstractCrop condition reports are an important source of information for producers, grain traders, businesses, and policymakers to assess and manage the price and yield risk inherent in a given crop. A Markov chain model is proposed for describing the weekly dynamic behavior of reported crop conditions. Empirical transition probabilities are estimated for corn grown in Nebraska, and forecasted crop conditions from the Markov chain are used as inputs to forecast final crop yields prior to harvest time. The results suggest that the modeling and forecasting approach has value for estimating crop yields as intrayear information about crop conditions materializes.
Synergistic antimicrobial interaction of plant essential oils and extracts against foodborne pathogens
Authors: Angane Manasweeta , Swift Simon , Huang Kang , Perera Janesha , Chen Xiao , Butts Christine A. , Quek Siew Young 
Published: November 2023

AbstractEssential oils (EOs) and plant extracts have demonstrated inhibitory activity against a wide range of pathogenic bacteria. In this study, the chemical composition of manuka, kanuka, peppermint, thyme, lavender, and feijoa leaf and peel EOs and feijoa peel and leaf extracts were analyzed, and their antimicrobial activity against Escherichia coli, Salmonella enterica Typhimurium, Staphylococcus aureus, Bacillus cereus, and Listeria monocytogenes were determined. The results showed that the major compounds varied among different EOs and extracts, with menthol in peppermint EO, thymol and carvacrol in thyme EO, linalool in lavender EO, β‐caryophyllene in feijoa EO, and flavones in feijoa extract being the most prevalent. The study found that while EOs/extracts had antimicrobial activity alone, no individual EO/extract was highly effective against all tested species. Therefore, their combinations were tested to identify those that could broaden the spectrum of activity and act synergistically. The checkerboard method was applied to assess the possible synergism between the paired combinations of EOs/extract. The peppermint/thyme, peppermint/lavender, and peppermint/feijoa peel extract combinations exhibited a synergistic effect against E. coli and L. monocytogenes, with the peppermint/thyme and peppermint/feijoa peel extract combinations being the most effective against all five pathogens. Time‐to‐kill kinetics assays demonstrated that peppermint/thyme and peppermint/feijoa peel extract combinations achieved complete eradication of E. coli within 10–30 min and L. monocytogenes within 4–6 h. This study provides a promising approach to developing a natural alternative for food preservation using synergistic combinations of EOs/extracts, which could potentially reduce the required dosage and broaden their application in food products as natural preservatives.
Comparative analysis of the physicochemical, proximate, and antioxidant characteristics of stingless bee (Meliponula beccarii) honey from modern and wild beehives in Ethiopia
Authors: Negera Taye , Degu Asfaw , Tigu Fitsum 
Published: November 2023

AbstractThere is a dearth of information on the comparative studies of the physicochemical, proximate, and antioxidant properties as well as quality standards of stingless bee honey (SBH) in Ethiopia. Hence, this study was designed to assess and compare the physicochemical, proximate, and antioxidant properties of SBH, specifically sourced from Meliponula beccarii, and produced under both wild and modern apiary conditions at two distinct geographical locations. A total of forty‐six honey samples were meticulously collected from domesticated stingless bee colonies and naturally occurring wild nests at Wolmera and Cheliya districts. Pollen analysis unveiled eleven distinct bee plant species distributed across six families, with Asteraceae being the most prevalent, primarily represented by Guizotia scabra and Vernonia amygdalina. Notably, the physicochemical, proximate, and antioxidant properties of SBH collected from modern pot hives exhibited significant variances (p < .05) when compared to SBH from wild nests. Principal component analysis (PCA) delineated the differentiation of SBH sources based on both geographical location and the type of beehive. One‐way ANOVA corroborated these distinctions, underscoring significantly higher levels (p < .05) of ash, electrical conductivity, free acidity, hydroxymethylfurfural, sucrose, total phenolic content, total flavonoid content, and radical scavenging activities of SBH from modern pot hives in Wolmera. Whereas, Cheliya modern pot hives recorded higher values in pH, hydroxymethylfurfural and maltose contents compared to the wild nest SBH. Further analysis through Pearson correlation highlighted a strong positive association between total phenolic content and total flavonoid content with the antioxidant capacity of SBH. These findings underscore the significance of integrating modern pot hives to enhance the quality of SBH within Ethiopia's beekeeping sector.
The information content of wheat derivatives regarding the Ukrainian war
Authors: Branger Nicole , Hanke Michael , Weissensteiner Alex 
Published: November 2023

AbstractWe extract implied price densities from wheat options and futures prices during the first 17 months of the Ukrainian war. Changing differences between short‐ and long‐term densities indicate that market expectations about the dynamics of the underlying changed over time. Before the signing of the Black Sea Grain Initiative, wheat derivatives prices showed predictive power for the further development of the conflict, and implied volatilities from wheat options were highly correlated with geopolitical risk (GPR). Afterwards, wheat prices lost their predictive power for the conflict, but instead reflected the market's opinion regarding the viability of the Black Sea Grain Initiative. By that time, correlations between wheat price risk and GPR dropped sharply.
Carboplatin and paclitaxel after anti‐PD‐1 or anti‐PD‐L1 antibody therapy in recurrent and/or metastatic squamous cell carcinoma of head and neck
Authors: Humphries Audrey , Zhou Connie J. , Welsh Madeleine , Lem Melinda , Kang Hyunseok , Algazi Alain P. 
Published: November 2023

AbstractBackgroundThe impact of concurrent chemotherapy and immunotherapy has been well characterized in patients with recurrent and metastatic head and neck squamous cell carcinoma (RM‐SCCHN). Here, we report outcomes in patients treated sequentially with immune checkpoint inhibition (ICI) followed by carboplatin and paclitaxel.MethodsPatients with RM‐SCCHN treated with ICI followed by carboplatin/paclitaxel at a single institution were identified retrospectively. ICI therapy history, p16, and PD‐L1 status were collected. The best overall response was assessed by RECIST v1.1.ResultsTwelve patients met inclusion criteria. Eight patients received pembrolizumab, three durvalumab, and one nivolumab. The median duration of ICI was 3.44 months, median PFS was 5.8 months, and median OS was 15.2 months. 66.7% of patients had an objective response on carboplatin/paclitaxel.ConclusionsCarboplatin/paclitaxel can induce objective responses in patients with prior treatment with ICI and clinical outcomes in this small series compare favorably to those seen in ICI naïve patients.
Probing the Nature of Surface Hydrides by Deuterium Quadrupolar Parameters: A Case Study on Silica‐Supported Zirconium Hydrides
Authors: Docherty Scott R. , Schärz Philipp , Gioffrè Domenico , Yakimov Alexander V. , Copéret Christophe 
Published: November 2023

AbstractSupported metal hydrides are key reactive intermediates in various catalytic processes, such as hydrogenation and dehydrogenation, but are often challenging to characterize spectroscopically. Here, deuterium solid state nuclear magnetic resonance spectroscopy is used to understand the structure of the corresponding silica‐supported zirconium hydrides after H/D exchange as an illustrative example of supported metal hydrides, which have been shown to display notable reactivity towards small molecules (e. g., CO2 and N2O) and to activate both C−H and C−C bonds, hence their use in to the conversion of hydrocarbons (alkanes, polyolefins etc.)
Psychosexual counseling intervention to improve women's genital self‐image: Study protocol for a randomized controlled trial based on a multistage mixed method design
Authors: Malary Mina , Hamzehgardeshi Zeinab , Keramat Afsaneh , Yunesian Masoud , Farjamfar Maryam 
Published: November 2023

AbstractBackgroundGenital self‐image (GSI) is a major barrier to reproductive and sexual health for women. This practical randomized trial aims to evaluate the effect of psychosexual counseling based on a cognitive‐behavioral approach to promoting GSI for reproductive‐age married Iranian women.MethodsMarried women aged 15–49 years under the coverage of health centers will be invited to join the study. The study will be conducted using a multistage mixed method design in three phases. In the first phase, semi‐structured interviews with women and key informants will be conducted to understand better the GSI concept, the factors influencing it, and the interventions promoting it. Simultaneously, a literature review will be conducted by searching electronic databases to find the factors influencing GSI and the interventions to enhance it.In the second phase, the GSI‐related factors, sexual and reproductive health outcomes correlated with GSI, and intervention programs for promoting GSI in women will be extracted from the integration of qualitative study and literature review. Then, based on the expert panel's priority, a suitable program will be prepared.In the third phase, at first, a cross‐sectional study will be conducted to identify women with poor GSI and the factors relating to GSI, and then psychosexual counseling intervention will be performed. For the randomized controlled trial study, participants will be randomly allocated into two groups: (1) the intervention group and (2) the control group. Data will be collected using the Female Genital Self‐Image Scale and sexual and reproductive health outcomes correlated with GSI at baseline, immediate, and 2‐month follow‐up assessments. The impact of the intervention on the promotion of GSI will be evaluated.DiscussionThis study provides a counseling program for promoting GSI. If this interventional program is successful, it has practical potential to be generalized for Iranian Women with poor GSI.
Partial identification and inference for conditional distributions of treatment effects
Authors: Lee Sungwon 
Published: November 2023

SummaryThis paper considers identification and inference for the distribution of treatment effects conditional on observable covariates. Since the conditional distribution of treatment effects is not point identified without strong assumptions, we obtain bounds on the conditional distribution of treatment effects by using the Makarov bounds. We also consider the case where the treatment is endogenous and propose two stochastic dominance assumptions to tighten the bounds. We develop a nonparametric framework to estimate the bounds and establish the asymptotic theory that is uniformly valid over the support of treatment effects. An empirical example illustrates the usefulness of the methods.
Epithelial‐like transport of mineral distinguishes bone formation from other connective tissues
Authors: Blair Harry C. , Larrouture Quitterie C. , Tourkova Irina L. , Nelson Deborah J. , Dobrowolski Steven F. , Schlesinger Paul H. 
Published: November 2023

AbstractWe review unique properties of bone formation including current understanding of mechanisms of bone mineral transport. We focus on formation only; mechanism of bone degradation is a separate topic not considered. Bone matrix is compared to other connective tissues composed mainly of the same proteins, but without the specialized mechanism for continuous transport and deposition of mineral. Indeed other connective tissues add mechanisms to prevent mineral formation. We start with the epithelial‐like surfaces that mediate transport of phosphate to be incorporated into hydroxyapatite in bone, or in its ancestral tissue, the tooth. These include several phosphate producing or phosphate transport‐related proteins with special expression in large quantities in bone, particularly in the bone‐surface osteoblasts. In all connective tissues including bone, the proteins that constitute the protein matrix are mainly type I collagen and γ−carboxylate‐containing small proteins in similar molar quantities to collagen. Specialized proteins that regulate connective tissue structure and formation are surprisingly similar in mineralized and non‐mineralized tissues. While serum calcium and phosphate are adequate to precipitate mineral, specialized mechanisms normally prevent mineral formation except in bone, where continuous transport and deposition of mineral occurs.
OGT‐1 regulates synaptic assembly through the insulin signaling pathway
Authors: Wu Mengting , Jiang Huihui , Li Qian , Liu Yunhe , Zhang Hongjun , Li Xuekun , Shao Zhiyong 
Published: November 2023

AbstractThe formation and maintenance of synapses are precisely regulated, and the misregulation often leads to neurodevelopmental or neurodegenerative disorders. Besides intrinsic genetically encoded signaling pathways, synaptic structure and function are also regulated by extrinsic factors, such as nutrients. O‐GlcNAc transferase (OGT), a nutrient sensor, is abundant in the nervous system and required for synaptic plasticity, learning, and memory. However, whether OGT is involved in synaptic development and the mechanism underlying the process are largely unknown. In this study, we found that OGT‐1, the OGT homolog in C. elegans, regulates the presynaptic assembly in AIY interneurons. The insulin receptor DAF‐2 acts upstream of OGT‐1 to promote the presynaptic assembly by positively regulating the expression of ogt‐1. This insulin‐OGT‐1 axis functions most likely by regulating neuronal activity. In this study, we elucidated a novel mechanism for synaptic development, and provided a potential link between synaptic development and insulin‐related neurological disorders.