bluuebunny/crossref_metadata_2025_split huggingface dataset:
An effective automatic processing engine for improving the multi-GNSS constellation precise orbit prediction
Authors: Chen Xinghan , Ge Maorong , Zuo Xiang , Schuh Harald 
Published: February 2024

AbstractOrbit prediction (OP) recently tends to be a very crucial step for supporting real-time GNSS orbit services due to the dynamic stability of navigation satellite orbits. The OP performance depends on the length of the predicted orbits and the accuracy of precise orbit determination (POD) as basis. Considering this, a new automatic processing engine is established for improving the multiple global navigation satellite systems (multi-GNSS) constellation OP performance. From the architecture-oriented high-performance parallel processing perspective, the multi-node and multi-core computer sources are fully exploited to implement the hourly update of the current multi-GNSS POD. For MEO-type satellites (e.g., Galileo satellites), the accuracy of predicted orbits is improved from 3.8 cm, 6.5 cm, and 12.3 cm to 3.5 cm, 4.3 cm, and 6.3 cm, in the radial, cross, and along directions, respectively, compared to the three-hour POD update. Despite the shortened OP length, the OP performance of regional navigation satellite system (RNSS) satellites is still limited due to their regional observability. The BDS-IGSO and QZSS-IGSO satellitesexhibit radial directional orbital errors of up to 36.9 cm and 28.9 cm, respectively. Therefore, an orbit fitting (OF) processing method with orbit reconstruction is implemented into the processing engine. By utilizing this method, the radial orbital errors for BDS-IGSO and QZSS-IGSO satellites can be reduced to 7.0 cm and 10.4 cm, respectively. The mean real-time positioning errors are thus reduced from 28.3 to 18.4 cm and from 24.4 to 18.2 cm in the horizontal and vertical components, respectively.
The application of AI techniques in requirements classification: a systematic mapping
Authors: Kaur Kamaljit , Kaur Parminder 
Published: February 2024

AbstractRequirement Analysis is the essential sub-field of requirements engineering (RE). From the last decade, numerous automatic techniques are widely exploited in requirements analysis. In this context, requirements identification and classification is challenging for RE community, especially in context of large corpus and app review. As a consequence, several Artificial Intelligence (AI) techniques such as Machine learning (ML), Deep learning (DL) and transfer learning (TL)) have been proposed to reduce the manual efforts of requirement engineer. Although, these approaches reported promising results than traditional automated techniques, but the knowledge of their applicability in real-life and actual use of these approaches is yet incomplete. The main objective of this paper is to systematically investigate and better understand the role of Artificial Intelligence (AI) techniques in identification and classification of software requirements. This study conducted a systematic literature review (SLR) and collect the primary studies on the use of AI techniques in requirements classification. (1) this study found that 60 studies are published that adopted automated techniques in requirements classification. The reported results indicate that transfer learning based approaches extensively used in classification and yielding most accurate results and outperforms the other ML and DL techniques. (2) The data extraction process of SLR indicates that Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are widely used in selected studies. (3) Precision and Recall are the commonly used metrics for evaluating the performance of automated techniques. This paper revealed that while these AI approaches reported promising results in classification. The applicability of these existing techniques in complex and real-world settings has not been reported yet. This SLR calls for the urge for the close alliance between RE and AI techniques to handle the open issues confronted in the development of some real-world automated system.
Milestones in speaker recognition
Authors: Sharma R. , Govind D. , Mishra J. , Dubey A. K. , Deepak K. T. , Prasanna S. R. M. 
Published: February 2024

AbstractThis article reviews significant research in the domain of speaker recognition, i.e., the task of determining the speaker’s identity from its speech. Unlike conventional review articles, this document strives to be concise and selective, provide a historical context, and reach a wider audience. In this endeavour, a summary of selected key works of every decade is provided which highlights the theme(s) of research of that period. At first, an overview of the humble beginnings of the 1960s and 70s is provided, followed by the key developments in the 80s and 90s. The prime focus of the research community in the 2000s is then discussed, leading to various non-conventional features, modelling techniques, and hybrid or fusion systems. The developments of the last decade (the 2010s), such as the i-vector-based systems, are then discussed. Modern speaker recognition based on Artificial Intelligence (AI), such as the x-vector system, and refinements of the i-vector-based systems using deep neural networks, are then discussed. The article concludes with a concise discussion of the evolving recent trends and allied research in speaker recognition.
Egerváry's theorems for harmonic trinomials
Authors: Barrera G. , Barrera W. , Navarrete J. P. 
Published: February 2024

AbstractWe study the arrangements of the roots in the complex plane
for the lacunary harmonic polynomials called harmonic trinomials. We provide
necessary and sufficient conditions so that two general harmonic trinomials have
the same set of roots up to a rotation around the origin in the complex plane, a
reflection over the real axis, or a composition of the previous both transformations.
This extends the results of Jenő Egerváry given in [19] for the setting of
trinomials to the setting of harmonic trinomials.
Efficiency of Persicaria salicifolia as a natural alternative antifungal against Aspergillus flavus infection in Oreochromis niloticus from Aswan Governorate, Egypt
Authors: El-Zayat Soad A. , Abdel-Motaal Fatma F. , Mohamed Sahar H. , Hamouda Awatef H. 
Published: February 2024

AbstractFungal diseases in fish cause economic losses all over the world, and knowledge about them is scarce and outdated in Aswan Governorate, Egypt, making interpretation, prevention, and treatment difficult. The necessity to find a fungicide that is natural, environmentally friendly, and does not emerge drug resistance is a must. Therefore, the current study aimed to isolate and diagnose fungal infection in farmed Oreochromis niloticus, causing mortalities, in Aswan Governorate. During 2021, 200 fresh O. niloticus samples were collected from the Sahary Fish Hatchery and Aswan General Authority for Fish Resources Development fish farm. Some fish showed hemorrhagic lesions all over the body, detachment of scales, and fin erosion. Collected tissue samples were cultured on potato dextrose agar and Sabouraud dextrose agar for phenotypic characterization. Macroscopic and microscopic analyses were used to identify the isolated fungi. A total of 18 fungal species and two varieties appertaining to ten fungal genera were recovered from 48 samples out of 200 examined O. niloticus (24%), with Aspergillus flavus being the most prevalent at a rate of 25.6%. The isolated A. flavus was proven to be pathogenic to farmed O. niloticus, as by experimental infection. The natural herb Persicaria salicifolia had an LC50 value of 41.68 mg/l in exposed O. niloticus and was used to treat A. flavus-infected O. niloticus. It can be concluded that A. flavus poses a major hazard to O. niloticus aquaculture and can be treated with 40 mg/kg in feed or 20 mg/l in water of P. salicifolia for 6 days.
CircRNA ITCH Inhibits Epithelial–Mesenchymal Transformation and Promotes Apoptosis in Papillary Thyroid Carcinoma via miR-106a-5p/JAZF1 Axis
Authors: Chen Yijun , Lian Zhiming , Zhang Guolie , Lin Yuanmei , Zhang Guoliang , Liu Wei , Gao Jian , Zheng Zifang 
Published: February 2024

AbstractCircular RNA ITCH (circ-ITCH) is implicated in papillary thyroid carcinoma (PTC) development. Nevertheless, the more detailed molecular mechanism remains uncovered. The transcriptional level of circ-ITCH was tested via quantitative real-time PCR. Transwell assay was introduced to assess the migrative and invasive abilities of cells. RNA interference technology was employed to reduce the level of circ-ITCH as well as JAZF1 in PTC cells. Western blot assay was utilized to reveal the content of JAZF1 and proteins related to epithelial–mesenchymal transformation (EMT) progression. Circ-ITCH was downregulated in PTC tissues as well as cells. Overexpression of circ-ITCH suppressed EMT, migration, invasion, facilitated apoptosis in PTC cells, while silencing circ-ITCH exhibited reversed effects. Additionally, miR-106a-5p was the target of circ-ITCH and negatively regulated through circ-ITCH. MiR-106a-5p mimic partly eliminated the influences of overexpressed circ-ITCH in PTC cells. Moreover, JAZF1 could interact with miR-106a-5p, then it was regulated via circ-ITCH. Silencing JAZF1 partially counteracted the role of circ-ITCH in PTC cells progress. This study uncovered that circ-ITCH suppressed the development of PTC cells at least partly by mediating miR-106a-5p/JAZF1 network.
Exploring the Intuitive Eating Scale-2 and Dyadic Associations Between Mental Health and Relational Dynamics in a Sample of Married Male Sexual Minority Couples
Authors: Parsons L. E. , Wei Menglin , Novak Josh R. , de Almeida Giovanna Veiga , Gast Julie , Peak Terry 
Published: February 2024

AbstractAcross studies, intuitive eating is associated with many health indicators. The Intuitive Eating Scale-2 (IES-2), the instrument for measuring intuitive eating, had not previously been explored with a sample of sexual minority [Sexual minority refers to individuals who experience consensual sexual identity, attraction, or behavior that is non normative (typically sexual minority individuals are lesbian, gay, bisexual, queer, or non-heterosexual)] individuals despite the disparities in eating behaviors and stress that persist among this population. Further, past work suggests the context of being partnered can be both protective and harmful for eating behaviors, highlighting an important avenue for investigating intuitive eating at the dyad level. This study sought to investigate the factor structure of the IES-2 and to explore the associations between both partner’s mental health, relational quality, and interpersonal eating interactions within a dyadic sample of 228 married male sexual minority couples (88% white, 8.8% Latino or Hispanic, 5.3% African American or Black, and 5.9% in other racial or ethnic groups). The original four-factor solution did not fit the current sample; instead, a new, fifth factor emerged, which was labeled as Eating Not as Coping. Intuitive eating behaviors were dyadically associated with mental and relational health and partners’ health-related interactions with one another. Future use of the IES-2 is informed by present results which may also inform recommendations for practice with and study of health and relationships.
Genetic diversity and population structure of wild and cultivated apricots collected from Kyrgyzstan
Authors: Uzun Aydın , Pinar Hasan , Gürcan Kahraman , Turgunbaev Kubanychbek , Yıldız Ercan , Ilgın Mürüvvet , Dolgikh Svetlana 
Published: February 2024

AbstractCentral Asia contains rich genetic resources for apricots. With its mountainous geography, Kyrgyzstan is the country where wild and cultivated apricot forms are preserved. The present study revealed genetic diversity and population structures in 91 apricot accessions from different regions of Kyrgyzstan. Fifteen simple sequence repeat (SSR) primers were used, capillary electrophoresis was performed, and the number of alleles per primer ranged from 4 to 11. A significant level of variation was determined among apricots originating from Kyrgyzstan. In the resulting dendrogram, all apricot accessions were divided into five groups, and the materials belonging to the northern and southern regions were grouped separately. At the same time, the population structure of apricots was analyzed. Accordingly, individuals have 0.80 or more membership coefficients; therefore, they are likely pure and non-admixed. There were 34 apricot accessions that have been determined as pure. The remaining 53 individuals had varying membership coefficients and were likely to be admixed by at least two subpopulations. This study is the most comprehensive research on Kyrgyzstan-origin apricots, providing vital information on their identification, preservation, and use in future research.
Interaction of Resting Heart Rate with Empathy in Predicting Externalizing Behavior
Authors: Oskarsson Sofi , Bertoldi Bridget M. , Andersson Anneli , Siponen Rebecca , Ling Shichun , Raine Adrian , Baker Laura , Evans Brittany , Tuvblad Catherine 
Published: February 2024

AbstractBiopsychosocial criminological theories suggest that it is important to consider interactions between risk factors from different domains in the prediction of externalizing behavior. Lower resting heart rate is considered the best replicated biological risk factor for externalizing behavior. The psychological construct of empathy has also shown to be predictive of such behavior, but little is known about the potential interaction between these two different risk factors in predicting externalizing behavior. We examined the moderating role of empathy on the association between resting heart rate in childhood and adolescence with externalizing behavior by young adulthood using two subsets of participants from the Longitudinal Risk Factors for Antisocial Behavior project: Subsample 1 (n = 697) at ages 9–10 and 19–20 years and Subsample 2 (n = 394) at ages 14–15 and 19–20 years. Linear and logistic regressions showed that empathy moderated the association between resting heart rate in adolescence and externalizing behavior by young adulthood. Among individuals with low but not high levels of empathy, increased resting heart rate predicted lower levels of externalizing behavior. Interventions enhancing empathic skills in individuals with psychophysiological risk profiles could be beneficial.
The > Uncommon < Factor in Psychotherapy and the Role of Negative Skills: Why and How Psychoanalysis Offers an Important Contribution for Mental Health Practice Today
Authors: Storck Timo 
Published: February 2024

AbstractPsychoanalysis brings some specific aspects of treatment technique to the field of mental health practice today, such as highlighting the dynamics within therapeutic relationship (especially regarding emotional and unconscious elements), the role of defense mechanisms etc. Moreover, by means of taking a particular therapeutic stance, psychoanalysis offers some shared mental space for patients. The present paper argues that this stance is characterized by the capacity to “not act”, that is: by passive receptivity. To view this as some specific “negative skill” in psychotherapy in general means to align common elements of effective psychotherapy with the capacity to explore the uncommon, unfamiliar or unforeseen in particular psychotherapeutic processes. The paper sketches how this can be employed in psychotherapy training as well as in psychotherapy integration.
A K-means Supported Reinforcement Learning Framework to Multi-dimensional Knapsack
Authors: Bushaj Sabah , Büyüktahtakın İ. Esra 
Published: February 2024

AbstractIn this paper, we address the difficulty of solving large-scale multi-dimensional knapsack instances (MKP), presenting a novel deep reinforcement learning (DRL) framework. In this DRL framework, we train different agents compatible with a discrete action space for sequential decision-making while still satisfying any resource constraint of the MKP. This novel framework incorporates the decision variable values in the 2D DRL where the agent is responsible for assigning a value of 1 or 0 to each of the variables. To the best of our knowledge, this is the first DRL model of its kind in which a 2D environment is formulated, and an element of the DRL solution matrix represents an item of the MKP. Our framework is configured to solve MKP instances of different dimensions and distributions. We propose a K-means approach to obtain an initial feasible solution that is used to train the DRL agent. We train four different agents in our framework and present the results comparing each of them with the CPLEX commercial solver. The results show that our agents can learn and generalize over instances with different sizes and distributions. Our DRL framework shows that it can solve medium-sized instances at least 45 times faster in CPU solution time and at least 10 times faster for large instances, with a maximum solution gap of 0.28% compared to the performance of CPLEX. Furthermore, at least 95% of the items are predicted in line with the CPLEX solution. Computations with DRL also provide a better optimality gap with respect to state-of-the-art approaches.
Consensus-based optimization for multi-objective problems: a multi-swarm approach
Authors: Klamroth Kathrin , Stiglmayr Michael , Totzeck Claudia 
Published: February 2024

AbstractWe propose a multi-swarm approach to approximate the Pareto front of general multi-objective optimization problems that is based on the consensus-based optimization method (CBO). The algorithm is motivated step by step beginning with a simple extension of CBO based on fixed scalarization weights. To overcome the issue of choosing the weights we propose an adaptive weight strategy in the second modeling step. The modeling process is concluded with the incorporation of a penalty strategy that avoids clusters along the Pareto front and a diffusion term that prevents collapsing swarms. Altogether the proposed K-swarm CBO algorithm is tailored for a diverse approximation of the Pareto front and, simultaneously, the efficient set of general non-convex multi-objective problems. The feasibility of the approach is justified by analytic results, including convergence proofs, and a performance comparison to the well-known non-dominated sorting genetic algorithms NSGA2 and NSGA3 as well as the recently proposed one-swarm approach for multi-objective problems involving consensus-based optimization.
A new framework for assessing ecosystem health with consideration of the sustainable supply of ecosystem services
Authors: Huang Ying , Gan Xiaoyu , Feng Yaofa , Li Jin , Niu Shaofei , Zhou Bo 
Published: February 2024

Abstract
Context
The establishment of an ecosystem health assessment framework from a human–environment view is vital to landscape sustainability. Although several studies have improved the assessment framework by integrating ecosystem services (ESs) supply or demand, consideration of the sustainable supply of ESs is lacking.

Objectives
The objective of this paper is to improve the current methodological framework by integrating ecological integrity and the sustainable supply of ESs to establish an ecosystem health assessment framework.

Methods
An improved assessment framework, including four indicators, vigor, organization, resilience, and ecosystem services supply rate, was established from the perspective of human–environment systems. Then, the performance of the improved assessment framework was demonstrated in a case study in China from 2000 to 2020.

Results
From 2000 to 2020, the overall spatial pattern of ecosystem health values in China was high in the southern and southeastern coastal regions, and low health values were mostly located in the western region, parts of Inner Mongolia, and metropolitan areas, with a descending trend from southeast to northwest. The imbalance between the potential and actual supply of ESs greatly contributed to the deterioration of regional ecosystem health. During the study period, the regional ecosystem in China was found to be in a more unhealthy state than in traditional Vigor-Organization-Resilience-Ecosystem services (VORES) evaluation.

Conclusions
The improved assessment framework that incorporates the ecological integrity and sustainable supply of ESs provides a new perspective for understanding the complex inherent characteristics of ecosystems and the regional human-nature connectedness in coupled human–environment systems. Our results could serve as a scientific reference for practical landscape governance in a changing world to achieve landscape sustainability.
MNPDenseNet: Automated Monkeypox Detection Using Multiple Nested Patch Division and Pretrained DenseNet201
Authors: Demir Fahrettin Burak , Baygin Mehmet , Tuncer Ilknur , Barua Prabal Datta , Dogan Sengul , Tuncer Turker , Ooi Chui Ping , Ciaccio Edward J. , Acharya U. Rajendra 
Published: February 2024

Abstract
Background
Monkeypox is a viral disease caused by the monkeypox virus (MPV). A surge in monkeypox infection has been reported since early May 2022, and the outbreak has been classified as a global health emergency as the situation continues to worsen. Early and accurate detection of the disease is required to control its spread. Machine learning methods offer fast and accurate detection of COVID-19 from chest X-rays, and chest computed tomography (CT) images. Likewise, computer vision techniques can automatically detect monkeypoxes from digital images, videos, and other inputs.

Objectives
In this paper, we propose an automated monkeypox detection model as the first step toward controlling its global spread.

Materials and method
A new dataset comprising 910 open-source images classified into five categories (healthy, monkeypox, chickenpox, smallpox, and zoster zona) was created. A new deep feature engineering architecture was proposed, which contained the following components: (i) multiple nested patch division, (ii) deep feature extraction, (iii) multiple feature selection by deploying neighborhood component analysis (NCA), Chi2, and ReliefF selectors, (iv) classification using SVM with 10-fold cross-validation, (v) voted results generation by deploying iterative hard majority voting (IHMV) and (vi) selection of the best vector by a greedy algorithm.

Results
Our proposal attained a 91.87% classification accuracy on the collected dataset. This is the best result of our presented framework, which was automatically selected from 70 generated results.

Conclusions
The computed classification results and findings demonstrated that monkeypox could be successfully detected using our proposed automated model.