bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effects of quantum hardware properties on the performances of variational quantum learning algorithms
Authors: Buonaiuto Giuseppe , Gargiulo Francesco , De Pietro Giuseppe , Esposito Massimo , Pota Marco 
Published: February 2024

AbstractIn-depth theoretical and practical research is nowadays being performed on variational quantum algorithms (VQAs), which have the potential to surpass traditional, classical, algorithms on a variety of problems, in physics, chemistry, biology, and optimization. Because they are hybrid quantum-classical algorithms, it takes a certain set of optimal conditions for their full potential to be exploited. For VQAs, the construction of an appropriate ansatz in particular is crucial, since it lays the ground for efficiently solving the particular problem being addressed. To prevent severe negative effects that hamper quantum computation, the substantial noise, together with the structural limitations, characteristic of currently available devices must be also taken into consideration while building the ansatz. In this work the effect of the quantum hardware structure, namely the topological properties emerging from the couplings between the physical qubits and the basis gates of the device itself, on the performances of VQAs is addressed. Specifically, it is here experimentally shown that a complex connectivity in the ansatz, albeit being beneficial for exploring wider sets of solutions, introduces an overhead of gates during the transpilation on a quantum computer that increases the overall error rate, thus undermining the quality of the training. It is hence necessary, when implementing a variation quantum learning algorithm, to find the right balance between a sufficiently parametrized ansatz and a minimal cost in terms of resources during transpilation. Moreover, the experimental finding allows to construct a heuristic metric function, which aids the decision-making process on the best possible ansatz structure to be deployed on a given quantum hardware, thus fostering a more efficient application of VQAs in realistic situations. The experiments are performed on two widely used variational algorithms, the VQE (variational quantum eigensolver) and the VQC (variational quantum classifier), both tested on two different problems, the first on the Markowitz portfolio optimization using real-world financial data, and the latter on a classification task performed on the Iris dataset.
Novel Curtailment Control Strategy for Wind Power Plants Based on the SQP Optimization
Authors: Seo KangWon , Choi DongHee , Lim SungHoon 
Published: February 2024

AbstractAs the penetration level of renewable energy sources (RESs) increases, the output power of RESs needs to be curtailed to balance the power supply and load demand. Nevertheless, depending on the curtailment control strategy for wind power plants (WPPs), while the total amount of output power curtailment remains the same, the overall stored inertial energy within WPPs may vary. Furthermore, this stored inertial energy within WPPs can be used during disturbances to enhance frequency stability. This paper proposes a novel curtailment control strategy based on the sequential quadratic programming (SQP) optimization algorithm to effectively curtail WPPs and increase the overall stored inertial energy within WPPs. Then, the proposed solution can enhance frequency stability by providing a more inertial response from WPPs during disturbances. To verify the effectiveness of this novel curtailment control strategy, several case studies are conducted using the IEEE 39-bus system. The results show that the proposed curtailment control strategy effectively increases the stored inertial energy within WPPs while satisfying the required output power curtailment.
Evil in Turkish Muslim horror film: the demonic in “Semum”
Authors: Erdağı Deniz Özkan 
Published: February 2024

AbstractThe aim of the following study is to offer a better understanding of the Turkish religious consciousness. Turkey is a secular country with a Muslim majority. Movie characters with different attitudes towards religion and their relationship to supernatural evil within Islamic cosmology will be examined. This will provide better insights into the religiosity among Turkish Muslims and their relationship with religious metaphysics in a secular world through the popular horror movie “Semum” (2008). “Semum” is unique by not utilizing jinn, but a devil as the chosen monster. By that, “Semum” conceptualizes an entity of what is perceived as evil in contrast to the moral greyness presented in jinn-horror movies. It reflects a supernatural world of good and evil to its audience, in which metaphysical questions are addressed. Turkey, as a Muslim-majority society, is less often examined when it comes to theological/metaphysical matters regarding the Islamic faith, than when it comes to social issues. For an accurate understanding, this paper first investigates the director’s own statements about his monster. Afterwards, Islamic sources will be compared to the movie’s depiction of said monsters. Next, the roles of the main characters and their relationships towards the demonic will be explained. By that, it will be seen how the roles of the characters allegedly deal with evil. It will be shown that secularism is considered to be insufficient to deal with possible threats to humanity. Yet, the director asserts that science and religion should co-exist to deal with mankind’s fears.
Teaching and learning science as inquiry: an outlook of teachers in science education
Authors: Twizeyimana Etienne , Shyiramunda Theophile , Dufitumukiza Balthazard , Niyitegeka Gratien 
Published: February 2024

AbstractFor years, educators have recognized inquiry-based learning as a cutting-edge and promising approach in science education. However, the connection between teachers’ beliefs and practices and students’ attitudes within inquiry-based classrooms has not been fully explored. This study employs a mixed-methods approach, combining quasiexperimental and descriptive research designs with quantitative data analysis. The results indicate a statistically significant difference in the performance of students who participated in a Chemistry Achievement Test between the control and experimental groups (t = 5.66, p < 0.05). Furthermore, our research highlights teachers’ optimistic views and eagerness to embrace inquiry-based learning. They perceive it as a means to ignite students’ enthusiasm for science subjects and foster the acquisition of essential science process skills through practical activities. This study contributes to a deeper understanding of the interplay between teacher perspectives, instructional methods, and students’ grasp of the scientific process in science education, emphasizing the potential benefits of inquiry-based learning for enhancing science education.
Unveiling the dynamic relationship of viruses and/or symbiotic bacteria with plant resilience in abiotic stress
Authors: Sharma Vasudha , Mohammed Shakeel A. , Devi Nisha , Vats Gourav , Tuli Hardeep S. , Saini Adesh K. , Dhir Yashika W. , Dhir Sunny , Singh Bharat 
Published: February 2024

AbstractIn the ecosphere, plants interact with environmental biotic and abiotic partners, where unbalanced interactions can induce unfavourable stress conditions. Abiotic factors (temperature, water, and salt) are primarily required for plants healthy survival, and any change in their availability is reflected as a stress signal. In certain cases, the presence of infectious pathogens such as viruses, bacteria, fungi, protozoa, nematodes, and insects can also create stress conditions in plants, leading to the emergence of disease or deficiency symptoms. While these symptoms are often typical of abiotic or biotic stress, however, there are instances where they can intensify under specific conditions. Here, we primarily summarize the viral interactions with plants during abiotic stress to understand how these associations are linked together during viral pathogenesis. Secondly, focus is given to the beneficial effects of root-associated symbiotic bacteria in fulfilling the basic needs of plants during normal as well as abiotic stress conditions. The modulations of plant functional proteins, and their occurrence/cross-talk, with pathogen (virus) and symbiont (bacteria) molecules are also discussed. Furthermore, we have highlighted the biochemical and systematic adaptations that develop in plants due to bacterial symbiosis to encounter stress hallmarks. Lastly, directions are provided towards exploring potential rhizospheric bacteria to maintain plant-microbes ecosystem and manage abiotic stress in plants to achieve better trait health in the horticulture crops.
Investigating the structure under the Pingting Terrace from the co-seismic surface rupture of the 2022 Guanshan earthquake
Authors: Hsu Yi-Chun , Chang Chung-Pai , Yen I-Chin , Huang Shao-Yi , Yen Jiun-Yee 
Published: February 2024

AbstractOn September 17th and 18th, 2022, shallow earthquakes with magnitudes of 6.6 (ML) and 6.8 (ML) occurred in the eastern Taiwan Longitudinal Valley, which marks the collision zone between the Philippine Sea plate and the Eurasian plate, and led to noticeable surface deformation and ruptures within 70 km. This study primarily focuses on the southernmost section of the rupture zone—the Pingting Terrace. Surface rupture locations and behaviors correspond to changes in topography, providing mutual confirmation that the deformation behavior of Pingting Terrace is complex. Based on the distribution of surface ruptures and topography changes, this study roughly divides the Pingting Terrace into northern and southern segments, using the central concave feature as a boundary. The Riedel shear model analysis results show that the principal shear directions in the northern and southern segments are N–S trending and azimuth 20°, respectively. The maximum principal stress orientations are around 135° for the northern and 155° for the southern segments. These findings align with the fault mechanical investigation of the Lichi Mélange in the northern Muken River area of the Pingting Terrace. This suggests spatial changes in shear zone orientations within the Lichi Mélange, which contribute to developing pressure ridges due to transpressional forces. As a result, the Pingting Terrace experiences rapid uplift, causing the Luliao River to migrate southward into the Beinan River, while the eastern Beinan River turns to the eastward edge of the Pingting Terrace.
Transient Data Caching Based on Maximum Entropy Actor–Critic in Internet-of-Things Networks
Authors: Zhang Yu , Chen Ningjiang , Yu Siyu , Hu Liangqing 
Published: February 2024

AbstractWith the rapid development of the Internet-of-Things (IoT), a massive amount of transient data is transmitted in edge networks. Transient data are highly time-sensitive, such as monitoring data generated by industrial devices. Due to their inefficiency, traditional caching strategies in edge networks are inadequate for handling transient data. Thus, to improve the efficiency of transient data caching, we construct a freshness model of transient data and propose a maximum entropy Actor–Critic-based caching strategy, TD-MEAC-which can improve the freshness of cached data and reduce the long-term caching cost. Simulation results show that the proposed TD-MEAC achieves a higher cache hit rate and maintains a higher average freshness of cached transient data compared with the existing DRL and baseline caching strategies.
Segmentation of Lung Lesions through Bilateral Learning Branches to Aggregating Contextual and Local Characteristics
Authors: Niu Hao , Li Linjing , Yuan Bo , Zhu Min , Xu Xiuyuan , Lu Xi , Luo Fengming , Yi Zhang 
Published: February 2024

AbstractDetecting and analyzing lung lesion regions using artificial intelligence is of great significance in the medical diagnosis of lung CT images, which can substantially improve the efficiency of doctors. However, segmentation of the inflammatory region in the CT image of the lung remains challenging due to the varied sizes, blurry local details, irregular shapes, and limited sizes of datasets. Faced with these challenges, this paper proposes a novel lung lesion segmentation network that incorporates two feature extraction branches to achieve a balance of speed and accuracy. We first design a context branch (CB) to preserve the scale-invariant global context information by the transformer-like module. Besides, a shallow detail branch (DB) based on a deep aggregation pyramid (DAP) module is designed to provide detailed information. Extensive experiments are conducted on two datasets, including the public COVID-19 dataset and a private dataset. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods. Moreover, the trade-off between accuracy and inference speed is achieved.
Breast Tumor Classification with Enhanced Transfer Learning Features and Selection Using Chaotic Map-Based Optimization
Authors: Chakravarthy Sannasi , Nagarajan Bharanidharan , Kumar V. Vinoth , Mahesh T. R. , Sivakami R. , Annand Jonnakuti Rajkumar 
Published: February 2024

AbstractAmong women, breast cancer remains one of the most dominant cancer types. In the year 2022, around 2,87,800 new cases were diagnosed, and 43,200 women faced mortality due to this disease. Analysis and processing of mammogram images is vital for its earlier identification and thus helps in reducing mortality rates and facilitating effective treatment for women. Accordingly, several deep-learning techniques have emerged for mammogram classification. However, it is still challenging and requires promising solutions. This study proposed a newer automated computer-aided implementation for breast cancer classification. The work starts with enhancing the mammogram contrast using a haze-reduced adaptive technique followed by augmentation. Afterward, EfficientNet-B4 pre-trained architecture is trained for both original and enhanced sets of mammograms individually using static hyperparameters’ initialization. This provides an output of 1792 feature vectors for each set and then fused using a serial mid-value-based approach. The final feature vectors are then optimized using a chaotic-crow-search optimization algorithm. Finally, the obtained significant feature vectors were classified with the aid of machine learning algorithms. The evaluation is made using INbreast and CBIS-DDSM databases. The proposed framework attained a balanced computation time with a maximum classification performance of 98.459 and 96.175% accuracies on INbreast and CBIS-DDSM databases, respectively.
Genetic Clustering Algorithm-Based Feature Selection and Divergent Random Forest for Multiclass Cancer Classification Using Gene Expression Data
Authors: Senbagamalar L. , Logeswari S. 
Published: February 2024

AbstractComputational identification and classification of clinical disorders gather major importance due to the effective improvement of machine learning methodologies. Cancer identification and classification are essential clinical areas to address, where accurate classification for multiple types of cancer is still in a progressive stage. In this article, we propose a multiclass cancer classification model that categorizes the five different types of cancers using gene expression data. To perform efficient analysis of the available clinical data, we propose feature selection and classification methods. We propose a genetic clustering algorithm (GCA) for optimal feature selection from the RNA-gene expression data, consisting of 801 samples belonging to the five major classes of cancer. The proposed feature selection method reduces the 1621 gene expressions into a cluster of 21 features. The optimum feature set acts as input data to the proposed divergent random forest. Based on the features computed, the proposed classifier categorizes the data samples into 5 different classes of cancers, including breast cancer, colon cancer, kidney cancer, lung cancer, and prostate cancer. The proposed divergent random forest provided performance improvisation in terms of accuracy with 95.21%, specificity with 93%, and sensitivity with 94.29% which outperformed all the other existing multiclass classification algorithms.
Understanding Researcher Risk and Safety in Qualitative Research Online
Authors: Yadlin Aya , Tsuria Ruth , Nissenbaum Asaf 
Published: February 2024

AbstractIn this reflective essay, we seek to advance our understanding about the risk and safety of the researcher within the changing landscape of academia and media as one. We maintain that over the years, risk and safety protocols have prioritized discussions about the participant, the subject in the study, or the studied community, overlooking some important aspects related to the risk and safety of the researcher. Thus, this reflection directly corresponds with the journal’s main aim: advancing knowledge about the impact of digital technologies on society, and specifically in researching society.
Removal of diesel oil from water using biochar derived from waste banana peels as adsorbent
Authors: Urgel Jefrick Jun Daniel T. , Briones Justin Marwin A. , Diaz Emmanuel B. , Dimaculangan Kirstin Margaux N. , Rangel Kayla L. , Lopez Edgar Clyde R. 
Published: February 2024

AbstractHere, banana peel biochar (BPBC) generated from discarded saba banana peels powder (SBPP) was utilized as an adsorbent in this study to remove diesel oil from water. The BPBC was synthesized using a slow pyrolysis method and characterized using SEM, EDX, FTIR, DSC, TGA, BET, contact angle analyzer, and XRD. The results showed that BPBC exhibited high porosity, thermal stability, and hydrophobic character, making it a promising adsorbent for oil-water separation and environmental remediation. The adsorption capacity of BPBC for diesel oil removal was examined in terms of adsorbent dose, pH level, salinity, and contact time. Increasing the BPBC dosage, contact time, and salinity significantly enhanced the sorption capacity, however, pH variations had no significant effect on adsorption. Adsorption parameters were correlated using a reduced cubic model, and an adsorbent dose of 2.50 g, pH of 7.00, salinity of 44,999.95 mg/L, and contact duration of 240 minutes were found to be optimal, producing a sorption capacity of 5.3352 g diesel oil/g adsorbent. The adsorption process was characterized by the first-order kinetic model. The creation of multilayer adsorption on the BPBC surface was confirmed by the BET isotherm. Adsorption characterization revealed changes in the surface morphology, elemental analysis, and functional groups of BPBC after adsorption. SEM revealed occupied surface pores, and EDX analysis verified an increase in carbon content. The presence of adsorbed diesel oil molecules on the BPBC surface was detected by FTIR analysis, which exhibited changes in peak appearance and functional group shifts. Overall, this study presents an adsorbent derived from waste material for diesel oil adsorption, which is useful for remediating oil spills and for wastewater treatment.
Graphical Abstract
‘Of’ the community but not ‘of’ the health system: Translating community health workers’ knowledge into credible advice in Aceh, Indonesia
Authors: Randell Madeleine , Li Mu , Rachmi Cut Novianti , Jusril Hafizah , Abimbola Seye , Tama Andi Yoga , Aswitama Tira , Phebe Natassya , Sulasmi  , Alam Neeloy Ashraful , Negin Joel , Bernays Sarah 
Published: February 2024

AbstractCommunity health workers (CHWs)—or ‘cadres’ as they are known in Indonesia—are intermediaries between the health system and the community, providing maternal and child health services at a village level. However, systemic and contextual factors inhibit CHWs from realising their potential impact. Training is essential for improving CHW performance; however, there is a need to understand whether and how this affects the care communities receive. This paper explores how communities in Aceh Province, Indonesia, receive care from cadres and the extent to which training interventions of CHWs influence this care. Semi-structured interviews were conducted remotely with 10 cadres in two districts in Aceh between June and July 2021, and five focus group discussions were conducted face-to-face with 21 caregivers of young children in one district in September 2021. Data were iteratively and thematically analysed throughout data collection. Cadres were seen as ‘of’ the community but not ‘of’ the health system. Cadres found training valuable. However, training did not adequately address the contextual challenges cadres face. From the caregivers’ perspective, cadres’ enhanced knowledge did not translate into credible guidance. Their proximity to the community and perceived distance from the health system undermined caregiver perceptions of cadres’ credibility. Infrastructural limitations of Posyandu inhibited CHW’s perceived ability to perform their roles effectively. To maximise the potential of their role, there must be more investment in enhancing the credibility of cadres within their communities—and programmatic factors such as training must better account for and adapt to the local context.
Deep learning enabled seismic fragility evaluation of structures subjected to mainshock-aftershock earthquakes
Authors: He Shan , Liao Yuchen , Sun Peng Patrick , Zhang Ruiyang 
Published: February 2024

AbstractMainshock-aftershock earthquakes have gained significant attention since accumulated damages induced by multiple shocks are likely to cause failure of structures. This paper presents a deep learning approach based on a Gated Recurrent Unit (GRU) network for assessing the seismic fragility of structures under mainshock-aftershock scenarios. The GRU network is utilized to create a surrogate model that captures the nonlinear relationship between seismic responses and mainshock-aftershock earthquakes. Subsequently, seismic fragility analysis is conducted based on double incremental dynamic analysis, employing the trained GRU network. A single-degree-of-freedom system with Bouc-Wen hysteretic behavior was investigated to demonstrate the proposed approach. The results indicate that the approach shows a substantial reduction in computational costs and holds promising potential for evaluating the seismic fragility of structures exposed to mainshock-aftershock earthquakes.
Investigating the Reliability and Validity of the Portable Osteometric Device
Authors: Anderson Eric , Malis Sierra W. , Osterholtz Anna J. , Zuckerman Molly K. 
Published: February 2024

ABSTRACTMetric analysis of skeletal material is integral to the analysis and identification of human remains, though one commonly used measuring device, the osteometric board, has lagged in recent advancement. Traditional boards are bulky and require manual measurement recording, potentially generating intra- and interobserver error. To address these limitations, we tested the reliability, validity, and error rates of a novel device, the Portable Osteometric Device Version 1 (PODv1), which measures distance using laser sensors with time-of-flight technology. Forty-five volunteers measured four skeletal elements with the PODv1 and a PaleoTech osteometric board in three rounds. Comparison of tibia, humerus, and femur measurements with both devices showed no significant differences, although the maximum length of the ulna did differ, potentially because of observer confusion regarding the PODv1's user instructions for this element. Our results suggest that the PODv1 is a reliable, valid measurement device compared to traditional osteometric boards. Although both device types can produce calibration, transcription, and observer errors, the time-of-flight technology and the absence of manual recording built into the PODv1 may limit those errors. These advancements and their potential positive impacts on the accuracy of osteometric data collection may have far-reaching benefits for osteological, bioarchaeological, paleopathological, and forensic anthropological data collection.