jimnoneill/paper-to-field-training huggingface dataset:
Feeding behavior of the kelp gull (larus dominicanus) during the non-breeding season at sandy beaches of península valdés, argentina.

Field: Agricultural and Biological Sciences (Life Sciences)

Las aves que se alimentan en la zona intermareal explotan recursos que frecuentemente estan distribuidos en parches disponibles durante intervalos regulares que dependen del ciclo de la marea. La calidad de los parches de alimento depende del tipo de presas que los componen y de la densidad, distribucion de tallas y disponibilidad de las mismas para los depredadores. En este trabajo analizamos el comportamiento de alimentacion de la Gaviota Cocinera ( Larus dominicanus ) en sectores del intermareal con diferentes densidades y tamanos de presas en una playa arenosa de Peninsula Valdes, Chubut, Argentina. Las gaviotas se alimentaron a lo largo de la linea de la marea desenterrando presas con movimientos acompasados de sus patas y su dieta estuvo principalmente compuesta por las almejas Tellina petitiana y Darina solenoides. Los tamanos promedio de las almejas de ambas especies identificadas en las egagropilas fueron mayores que aquellos encontrados en sedimentos de la playa. La actividad de alimentacion se concentro en areas con alta densidad de almejas y donde se encontraron las mayores tallas de ambas almejas. Aunque la Gaviota Cocinera es una especie generalista que usa un amplio espectro de alimentos, este trabajo muestra que tambien puede ser selectiva en su eleccion de presas.
Computational Study of Cannabinoid Receptors CB1 and CB2

Field: Biochemistry, Genetics and Molecular Biology (Life Sciences)

Marijuana has been used both recreationally and therapeutically, including to alleviate symptoms of cancer. The plant produces several interesting compounds, including the psychoactive D9-tetrahydrocannabinol (THC) and the structurally similar cannabidiol (CBD) which have also been chemically extracted from the plant and administered in pure form. THC and CBD interact with receptors within the cannabinoid system, the major ones being cannabinoid receptor 1 (CB1) and cannabinoid receptor 2 (CB2). Both are seven transmembrane G-protein coupled receptors (GPCRs). This report used the Catalogue of Somatic Mutations in Cancers (COSMIC) to map and analyze mutations arising in CB1 and CB2. Identified were several hotspot mutations in both CB1 and CB2. In silico 3-D structure protein analysis reveals that these hotspots cluster on the intracellular regions of the cannabinoid receptors and certain residues may be likely to destabilize the interaction with the G-alpha protein. Our analysis identifies two CB1 residues (R336 and M337) that are most likely to interact with G-alpha. Docking analysis reveals that hotspot substitution mutations at these two positions may affect G-alpha binding. Hotspot mutations in CB2 were not predicted to be as potentially destabilizing as those identified for CB1. Our data suggest that CB1 and CB2 mutations acquired during cancer development may impact the therapeutic effectiveness of cannabinoids. An additional study composed of molecular cross docking between CB1, CB2, and glucagon-like peptide type 1-receptor (GLP-1R) ligands was conducted. Like CB1 and CB2, GLP-1R is a GPCR. Due to the structural similarity and recent rise in cannabis use as well as GLP-1R targeted weight loss drugs, this study aimed to identify potential linkage between the systems. Results indicated that danuglipron, a small GLP-1R agonist, had a higher binding affinity for CB1 and CB2 when compared to cannabinoid ligands.
Advances in Computational Visual Information Processing for Neuroscience and Healthcare Applications

Field: Computer Science (Physical Sciences)

Advances in visual information processing have revolutionized computational neuroscience and healthcare by enhancing our understanding of neural mechanisms and developing new medical solutions. Techniques like deep learning algorithms, convolutional neural networks, and computer vision applications have provided unprecedented insights into brain activity and visual organ function. These technologies also enhance our ability to model and simulate cognitive processes, making them crucial in understanding and improving medical outcomes. Visual information processing enhances healthcare diagnostics, medical imaging, and treatment personalization, ranging from radiology to pathology. It integrates advanced technology with neural interfaces and AI, revolutionizing patient care and neurological research. Further, ethical considerations, data privacy, and the challenge of transforming complex visual data into actionable insights for healthcare professionals and neuroscientists are discussed in this chapter.
A Survey of Navy Cost Model Input Variables and their Data Sources,

Field: Decision Sciences (Social Sciences)

This survey of cost model input variables and their data sources is part of a series of tasks, sponsored by the U.S. Navy HARDMAN Development Office (OPNAV 112C), aimed toward the development of cost models which will be used by the Navy and industry to facilitate design/cost tradeoff analysis during the Weapon Systems Acquisition Process (WSAP). The survey was undertaken to determine whether the input data requirements of proposed HARDMAN cost models are consistent with and can be met by existing Navy data sources. It is concluded that the required source data are available for almost all input variables to the cost models. However, the data are widely dispersed. thus difficult and time consuming to obtain. Further, since most data bases are not originally intended to be used as source data for cost analysis, the information in the bases is often not in the form required for input to cost models. To convert the data into an appropriate form requires additional analysis.
Impact of Air Pollution on Climate Fluctuations in Tehran City

Field: Environmental Science (Physical Sciences)

The expansion of urbanisation and the development of cities along with increasing population and expansion of industrial activities and irregular consumption of fossil fuels have given rise to pollution. At the first stage, its consequences impact on citizens in terms of respiratory illness and increased intensity of heart and lung diseases and, at the second stage, it plays a role in increasing climate fluctuations and environmental impacts. In the current study, two types of data are used in investigating the relationship between parameters and elements. The first data under study are the climate parameters during a statistical period of 51 years from 1952 to 2003, divided into three 17- year periods - a clean period (1952-69), an intermediate period (1970-86) and a polluted period (1987-2003) - whose fluctuations and climate vicissitudes are compared. The second variable or data under study are the type and amount of pollutants in Tehran city during 1997 to 2003, taken as an annual average. Totally, after various calculations, the conclusion was that during the polluted period the climate of Tehran has more fluctuations than in the two previous periods and, during the polluted period, the fluctuations of the region were seen in temperature increases, increasing potential for flooding and change in climate towards a warmer and more humid state.
Acute searching and early diagnosis of HCV infected persons.

Field: Medicine (Health Sciences)

Hepatitis C virus (HCV) infection is still a major cause of chronic liver diseases, with approximately 71 million chronically infected persons worldwide. People who inject drugs currently or in the past (PWID), mostly intravenously, are the main risk group among HCV chronically infected persons. The efficacy of therapy with direct-acting antivirals (DAA) is almost 100 %. Currently, the main mission is to diagnose HCV infection in the most possible number of infected persons; it is in collision with poor adherence of PWID in particular. Changing the spectrum of chronic hepatitis C patients forces medical professionals to change their approach to diagnosis and treatment of HCV infection. Outreach testing and cooperation with support organizations showed to be an effective way to set a course to eliminate HCV in the PWID population.
Immobilizing Gold Nanoparticles in Mesoporous Silica Covered Reduced Graphene Oxide: A Hybrid Material for Cancer Cell Detection through Hydrogen Peroxide Sensing

Field: Chemistry (Physical Sciences)

A new kind of two-dimensional (2-D) hybrid material (RGO-PMS@AuNPs), fabricated by the immobilization of ultrasmall gold nanoparticles (AuNPs, ∼3 nm) onto sandwich-like periodic mesopourous silica (PMS) coated reduced graphene oxide (RGO), was employed for both electrocatalytic application and cancer cell detection. The hybrid-based electrode sensor showed attractive electrochemical performance for sensitive and selective nonenzymatic detection of hydrogen peroxide (H2O2) in 0.1 M phosphate buffered saline, with wide linear detection range (0.5 μM to 50 mM), low detection limit (60 nM), and good sensitivity (39.2 μA mM(-1) cm(-2)), and without any interference by common interfering agents. In addition, the sensor exhibited a high capability for glucose sensing and H2O2 detection in human urine. More interestingly, the hybrid was found to be nontoxic, and the electrode sensor could sensitively detect a trace amount of H2O2 in a nanomolar level released from living tumor cells (HeLa and HepG2). Because the hybrid presents significant properties for the detection of bioactive species and certain cancerous cells by the synergistic effect from RGO, PMS, and AuNPs, it could be able to serve as a versatile platform for biosensing, bioanalysis, and biomedical applications.
Publishing Efficient On-device Models Increases Adversarial Vulnerability

Field: Computer Science (Physical Sciences)

Recent increases in the computational demands of deep neural networks (DNNs) have sparked interest in efficient deep learning mechanisms, e.g., quantization or pruning. These mechanisms enable the construction of a small, efficient version of commercial-scale models with comparable accuracy, accelerating their deployment to resource-constrained devices. In this paper, we study the security considerations of publishing on-device variants of large-scale models. We first show that an adversary can exploit on-device models to make attacking the large models easier. In evaluations across 19 DNNs, by exploiting the published on-device models as a transfer prior, the adversarial vulnerability of the original commercial-scale models increases by up to 100x. We then show that the vulnerability increases as the similarity between a full-scale and its efficient model increase. Based on the insights, we propose a defense, $similarity$-$unpairing$, that fine-tunes on-device models with the objective of reducing the similarity. We evaluated our defense on all the 19 DNNs and found that it reduces the transferability up to 90% and the number of queries required by a factor of 10-100x. Our results suggest that further research is needed on the security (or even privacy) threats caused by publishing those efficient siblings.
The effect of managers’ ethical and behavioral characteristics on investors’sSentiment

Field: Economics, Econometrics and Finance (Social Sciences)

Behavioral and psychological factors, such as power, agency costs, profit management, and myopic behavior of managers can affect information transparency and cause significant changes. Evidence, on the other hand, suggests that investors determine the value of stocks under the influence of judgments based on mental imagery, unscientific information, and the psychological and emotional conditions of the stock exchange. However, investors can be present in this market relying on information transparency. Therefore, considering the importance of these relations for market, the present study is an attempt to investigate the effect of managers' ethical and behavioral characteristics on investors’ sentiments in companies listed on the Tehran Stock Exchange. For this purpose, hypotheses are developed and data of Companies listed on Tehran Stock Exchange during 2009 to 2018 are analyzed. The statistical population of the study includes 150 companies, selected by systematic elimination. The regression model of the research is tested using panel data method with integrated effects approach. The results show that with regard to the effect of managers’ ethical and behavioral characteristics, optimism, short-sightedness, agency behavior, and profit management intensify investors' sentiment and have a positive and significant effect on it by reducing transparency in firms’ decision-making environment. Moreover, the results confirm that the managerial power index, which reflects manager's authoritative behavior, has no significant effect on investors' sentiment.
Analog Self-Interference Cancellation in Dual-Polarization Full-Duplex MIMO Systems

Field: Engineering (Physical Sciences)

Full-duplex (FD) technology combined with dual-polarization (DP) multiple-input multiple-output (MIMO) systems is attractive to improve spectral efficiency and to enhance link capacity. Cancelling self-interference (SI) in such DPFD MIMO systems using beamforming techniques is very challenging due to a significant difference of the co-polarization and cross-polarization SI channels. In this letter, an analog adaptive filter structure is proposed to mitigate both co-polarization and cross-polarization SIs in DPFD MIMO systems. Stationary analysis is applied to evaluate the performance of the proposed structure. Simulation results show that about 45 dB to 55 dB of SI cancellation can be achieved regardless of the isolation differences between cross-polarization and co-polarization channels.
The Research on the Telecom Customer Churn Based on System Dynamics

Field: Decision Sciences (Social Sciences)

With the rapid development of telecommunication, the competition of telecom market becomes more and more drastic. It is very important for telecom operator to research how to keep from the telecom churn. In the paper, a model used to simulate the behavior of customers is constructed based on system dynamics. The causal loop diagram and flow diagram of the telecom churn is proposed and some simulation results are obtained. According to these simulation results, we give some suggestions to reduce the customer churn, which are valuable for telecom operators to maintain users, formulate marketing strategies and reduce operating costs.
Differences in Spiritual Coping Strategies of Mothers with Autism Children in Developed and Developing Countries: a Qualitative Systematic Review

Field: Social Sciences (Social Sciences)

Comprehensive community support is needed to help mothers cope with the psychological, physical, and social challenges of caring for a child with autism. This study aimed to identify differences in mothers' initial responses, spiritual coping strategies that influence psychological well-being, and factors supporting and inhibiting maternal coping in developed and developing countries. A literature search was conducted on EBSCO, PubMed, Wiley, Science Direct, ProQuest, Research Rabbit, and Google Scholar databases from 2014 to 2023, which was screened using Mendeley. Critical appraisal and data mapping were performed with the Joanna Briggs Institute (JBI) checklist for qualitative research and textual evidence. A total of 18 selected studies were analyzed using a narrative approach. The results identified five main themes: (1) acceptance and love in the face of adversity, (2) resilience through emotional turmoil to social and cultural complexities, (3) strength in community and shared experiences, (4) empowerment, and (5) advocacy. The spiritual coping strategies of mothers in developing countries differ from those in developed countries, influenced by social, cultural, and economic contexts. Social support proved crucial for maternal emotional resilience. Education of health workers on culture-based spiritual coping strategies is needed to support maternal well-being. An approach that considers the social, cultural, and economic context will facilitate access to resources, integration of relevant spirituality, and self-empowerment. This aims to improve maternal mental health and create an inclusive environment that supports families, especially mothers of children with autism.
Heart Disease Prediction Using Machine Learning Algorithms

Field: Computer Science (Physical Sciences)

Heart disease is a major issue that has become increasingly prevalent. According to current statistics, heart disease claims the life of one person every minute. In the last several years, one of the hardest problems facing the medical field is predicting heart disease. Reducing the death rate can be achieved with early detection of cardiac disease. Machine learning is the most effective approach to forecasting heart disease. This paper aims to create a lightweight, straightforward solution to detecting cardiac disease using machine learning. Machine learning can aid in heart disease prediction. This study analyzes several machine learning algorithms and performance indicators. This study compares cardiac disease detection methods using a publicly available dataset from the UCI machine learning repository. There are other datasets accessible, including the Switzerland and Cleveland databases. Here the dataset contains 303 patient records and 18 characteristics. The analysis shows that out of six machine learning algorithms, the Random Forest algorithm gives the best result with 94.50%. Keywords- cardiac disease detection, datasets, heart disease prediction, Machine Learning, Random Forest algorithm.
Progress on bound states in continuum emitters (Conference Presentation)

Field: Physics and Astronomy (Physical Sciences)

Orbital angular momentum is a fundamental degree of freedom of light that manifests itself even at the single photon level. The coherent generation and beaming of structured light usually requires bulky and slow components. Using wave singularities known as bound states in continuum, we report an integrated device that simultaneously generates and beams powerful coherent beams carrying orbital angular momentum. The device brings unprecedented opportunities in the manipulation of micro-particles and micro-organisms, and, will also find applications in areas such as biological sensing, microscopy, astronomy, and, high-capacity communications.
Influence Mechanism of Isolation Layer to Granular Media Flow

Field: Earth and Planetary Sciences (Physical Sciences)

By using particle flow code (PFC2D), influence mechanism of isolation layer to granular media flow was studied by simulating two ore-drawing modes of filling mining method (working condition Ⅰ) and synchronous filling shrinkage method(working condition Ⅱ). After analyzed simulation results, some laws were gotten: (1) by the fitting process conducted on released volume and height, it is found that the relationship of released volume and height was power equation, which is same as the stochastic medium theory for ore-drawing; (2) The released volume, the size in most width of loosing body and the location of working condition Ⅱ was both bigger than those of working condition Ⅰ; (3) the radius of curvature of drawn-funnel of working conditionⅡ was bigger than those of working conditionⅠ, but the volume of residual ore was opposite. Analysis shows that the isolation layer reduced the contact force in ore drawing regional, made the porosity bigger, so as to improve liquidity. The results provide a theoretical reference for the changes in underground mining techniques and ore-drawing control.
Optimizing the Flexibility of SDN Control Plane

Field: Computer Science (Physical Sciences)

Facing fast changing network traffic, Software Defined Networking (SDN) endows the capability of efficient traffic forwarding and control plane resource management which results in flexibility, in comparison to networking with legacy rigid hardware. Meanwhile, flexibility has become an implicit target of many novel network algorithms and designs. This paper answers an interesting research question: "Can we optimize the flexibility as an objectiveƒ" We study the impact of Data Centers (DCs) location on the flexibility of dynamic control plane. The flexibility is revealed when the control plane can adapt itself with controller migration and switch re-assignment in a timely manner for a new group of flows to satisfy the requirements of flow setup. We propose a model, named FLEXDC, for static DC placement and dynamic controller placement to optimize the control plane's flexibility. We also design heuristics to speed up the decision process. Our simulation over real network topology with synthetic flows shows the improved flexibility of the dynamic control plane. Furthermore, we can save up to 2 DCs while achieving the same flexibility, compared with a naive approach.
High Indexing Compression for Spatial Databases

Field: Computer Science (Physical Sciences)

The KDB-tree is a traditional point access method for retrieving multidimensional data. Many literatures frequently address the low storage utilization and insufficient retrieval performance as two bottlenecks for KDB-tree family of structures. A large amount of unnecessary splits caused by data insertion orders and data skewness is the fatal reason for these two bottlenecks. Compressing KDB-trees still has high appeal for practical applications. In this paper, dynamic-tuning partition (DT-partition) and leaf replication(l-replication) methods are proposed to mend the sufferings of data insertion orders and data skewness. Without loss the quantity of data selectivity, a better dynamic indexing scheme is presented for accommodating data to leaf nodes as many as possible. Moreover, the degradation of retrieval performance in heavily skewed spaces are carefully investigated and solved. Analytical and experimental results show our indexing method out performs the traditional methods.
Comparison of the Family Therapy Educational and Experience Requirements for Licensure or Certification in Six Mental Health Disciplines

Field: Health Professions (Health Sciences)

In a review of literature on clinical licensure programs and certified mental health disciplines, no studies specifically compared didactic methods and clinical training standards for family-based interventions. Consequently, consumers and insurers of these services have no basis from which to evaluate the relative competency of each of the mental health disciplines in providing family-based interventions. This study used content analysis to examine the amount of family based training that is required by six core mental health disciplines (Clinical Psychology, Psychiatry, Psychiatric Nursing, Professional Counseling, Marriage and Family Therapy, and Social Work) in each of the 50 United States. Results indicate that a marriage and family therapist is required to have three times more family therapy coursework than any other professional mental health discipline. Also, before becoming licensed a marriage and family therapist, must complete 16 times more face-to-face family therapy hours than a mental health professional from any other discipline. Implications for consumers and practitioners are discussed.
GAMIFICATION OF THE MATHEMATICS COURSE FOR PRE-SERVICE PRESCHOOL AND PRIMARY SCHOOL TEACHERS

Field: Arts and Humanities (Social Sciences)

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 6787-6794ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.1765Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference