Miracle-dz/UniFAR-FTUs huggingface dataset:
Beryllium-boron relative sensitivity factors for melilitic glasses measured with a NanoSIMS ion microprobe

The short-lived radionuclide 10Be (t1/2 = 1.4 Myr) present in the Early Solar System (ESS) provides important information about the astrophysical environment in which the solar system formed. However, the origin of 10Be in the ESS remains controversial. To reveal its origin, it is important to determine precise and accurate 10Be abundances in meteoritic components. The initial 10Be/9Be ratio can be estimated using ion microprobe analysis and a 10B/11B-9Be/11B correlation. The relative sensitivities for Be and B must be determined to obtain 9Be/11B ratios of unknowns. Here, we report Be/B relative sensitivities for synthetic melilitic glasses and silica-rich NIST SRM 610 glass measured with a NanoSIMS ion microprobe. The Be/B relative sensitivities for melilitic glasses are identical to that of the NIST 610 glass within uncertainties, which suggests that the matrix effects, at least between the NIST 610 glass and melilite, are not significant with respect to Be-B measurements. The present results confirm that the observed variations in the 10Be/9Be ratios for CV calcium-aluminum-rich inclusions (CAIs) are real and the origin of 10Be must be attributed to irradiation by the active early sun. It is also inferred that the 10Be/9Be ratios of fractionation and unidentified nuclear effect (FUN) CV CAIs are not significantly lower than those for normal (non-FUN) CV CAIs, suggesting similar formation conditions.
PHYSICAL QUALITY OF AN ALFISOL AS INFLUENCED BY CULTIVATION OF JATROPHA CURCAS L. AT SAMARU, NORTHERN NIGERIA

A THESIS SUBMITTED TO THE SCHOOL OF POST GRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA, NIGERIA IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF MASTERS DEGREE IN SOIL SCIENCE DEPARTMENT OF SOIL SCIENCE, FACULTY OF AGRICULTURE, AHAMADU BELLO UNIVERSITY, ZARIA FEBRUARY, 2013
Morbidity Cost and Willingness to Pay for Healthcare Insurance among Wheat Farmers in Jigawa State of Nigeria

The study determined the costs of morbidity and farmers' willingness to pay for health insurance in the Jigawa State of Nigeria using a cross-sectional data collected from 284 farmers through a multi-stage sampling technique. Using an easy-route cost approach, a well-structured questionnaire coupled with interview schedule was used for data collection. Besides, the collected data was then analysed using both descriptive and inferential statistics. Empirically, the majority of farmers utilised an accumulation strategy for livelihood sustenance, alongside enterprise diversification – which acted as a catalyst in increasing their stocks and consumption outcomes, thus smoothing their income and consumption. However, financial and, at worst, physical livelihood capitals posed challenges that affected farmers' livelihood assets in the study area. Furthermore, malaria emerged as the major health issue affecting livelihoods; consequently, slightly over half of the sampled population agreed to the notion of a social health insurance scheme for a healthy livelihood. However, this inclination was largely influenced by the overlooked or nearly neglected social learning aspect of extension service delivery. Therefore, as a method to reduce public capital expenditure on healthcare for livelihoods in the study area, the study recommends that policymakers expand the healthcare scheme to include the farming community, going beyond formal organisations, thereby enhancing farm family livelihoods specifically and overall economic growth and development in general. Nonetheless, enhance institutional factors, alongside social extension, financial and infrastructural facilities are recommended.
Evaluating the Impact of the Inspiring School Program in High Schools

This research aims to evaluate the Inspirational School Program using the CIPP (Context, Input, Process, Product) model, focusing on providing insights and recommendations for enhancing its effectiveness and sustainability. Data collection methods in qualitative descriptive research include interviews, observation, and documentation. The study's sample population comprises individuals associated with SMA Negeri 5 in Banjarmasin, including educators, school leaders, and grade XI students. In-person interviews were conducted with the school principal, homeroom teacher, counselling teacher, and students. Data analysis techniques encompass data collection, reduction, presentation, and verification/conclusion. The results indicate that the school's needs align with the Ministry of Education and Culture guidelines. Despite limited local government support, the program planning and evaluation processes have been implemented effectively despite obstacles. The Product Evaluation Stage reflects positive outcomes, including literacy, numeracy, character development, and school safety climate improvements. Educator and staff competence have also seen enhancements. It is recommended to align implementation with the Independent Curriculum, bolster human resources through educator and staff training, enhance infrastructure support, and seek increased government assistance to sustain and improve the program. These measures aim to enhance productivity further and ensure the continued success of the Inspirational School Program.
Exploring perceived dignity among AIDS/HIV patients in behavioral disease counseling centers in Iran

Background and aims: People with HIV/AIDS may encounter several problems, such as discrimination and social challenges, which affect their perceived dignity. The purpose of this study was to evaluate the perception of dignity among HIV/AIDS patients in a healthcare setting in Iran. Methods: This cross-sectional study was done on HIV/AIDS patients referring to behavioral disease consulting centers affiliated with a mainstay of health and medical education, Iran, in 2023. The study sample consisted of 220 patients selected using the available sampling method. A patient dignity questionnaire was used to collect data. The validity and reliability of the questionnaire were confirmed. The collected data were analyzed by SPSS 25 software using parametric tests. Results: Findings showed that the mean age of patients was 37; 51.1% of participants were men, 46.1% were married, 4.2% had primary education, 55.9% were employed, and 66.7% were in the asymptomatic stage of the disease. The overall score for perceived dignity was 1/96 out of 5 and was evaluated well. The mean scores for mental abilities and perceptions, personal and social concerns were 1.37 (SD: 0.44), 2.85 (SD: 0.94), and 1.37 (SD: 0.48), respectively. There was also a statistically significant relationship between perceived dignity score and the levels of education and being under treatment. Conclusion: Although the studied patients had a well-perceived status of dignity and were satisfied with their abilities and social communication, their greatest concern was personal concerns. It is recommended that psychological support and therapies be provided to these patients.
Observed and Simulated Lake Effect Precipitation Over the Tibetan Plateau: An Initial Study at Nam Co Lake

Thousands of lakes are located on the Tibetan Plateau. However, few studies examine the impacts of these lakes on local climate. To investigate lake effect precipitation over the plateau environment, we take Nam Co Lake as a case study, due to its large size and the availability of in situ observations. Using the Weather Research and Forecasting (WRF) model, the impacts of Nam Co Lake on downwind precipitation events (i.e., the generation and causes) are studied quantitatively. First, we compare meteorological station observations downwind of the lake with observations from other nearby stations. In total, four stations are located around Nam Co Lake in different directions, and these stations accurately represent the spatial distribution of observed precipitation. The largest amount of precipitation is observed at Nam Co station (downwind) in October, and this precipitation amount is more than twice that observed at the other three stations. Second, simulations are carried out using the WRF model, and the results show that WRF model accurately captures the precipitation events that occurred at Nam Co station in October 2006. Finally, the WRF_lake and WRF_nolake simulations, which do and do not include the lake, respectively, are carried out using the WRF model. Nam Co Lake caused an increase in precipitation of up to 70% downwind of the lake on 24 October 2006. The simulation results also show that the lake caused an increase in precipitation of up to 60% over the lake's area and downwind of the lake in October of 2006.
Sustainability Reporting in IT Sector vs. Other Sectors: The Impacts on Multidimensional Performance

This study investigates the relationship between the level of sustainability reporting and information technology (IT) sectors' performance (operational, financial, and market) and compares the results with other sectors. The findings elicited from the empirical results demonstrate that there is a significant negative relationship between ESG and IT sector market performance (TQ). The study contributes to the literature of sustainability accounting by providing records of the cross-sector's experiences. It could serve as a guide to firms that wish to adopt sustainability reporting. Moreover, as the study adopted worldwide and used macroeconomic variables, it contributes to the literature of the economic implications of sustainability reporting. ‎
Impacts of Magnetosheath High‐Speed Jets on the Magnetosphere and Ionosphere Measured by Optical Imaging and Satellite Observations

Magnetosheath high‐speed jets (HSJs) are dayside upstream transient disturbances with enhanced flow velocity and dynamic pressure. They are associated with significant magnetopause perturbations, ultralow frequency waves in the dayside magnetosphere, and localized flow enhancements in the ionosphere. However, whether HSJs have corresponding dayside aurora signatures is still an open question. If auroral signatures are found, 2‐D structure and evolution of HSJ effects on the magnetosphere can be imaged in a much higher precision than possible by other means. In this study, eight HSJ events are identified by the THEMIS satellites located within ±1 MLT of the center of the field‐of‐view of the South Pole station all‐sky imager. In all of those cases, the HSJs are observed to have a nearly one‐to‐one relationship with individual localized discrete/diffuse auroral brightenings. The azimuthal size of HSJ‐related diffuse aurora signatures is ~800 km at 230‐km altitude in the ionosphere and ~3.7 Re in the magnetosphere, which is slightly larger but of the order of the cross‐sectional diameter of HSJs (~1 Re). Furthermore, most of those aurora signatures have azimuthal motion, whose magnitude and direction agree with magnetosheath background flows. This study for the first time shows high‐resolution, two‐dimensional observations of localized structure and fast propagation of precipitation due to magnetosheath HSJs. We conclude that magnetosheath HSJs can have substantial impacts on the coupled magnetosphere‐ionosphere system, causing localized magnetospheric compression and aurora brightening, in a similar manner to responses during interplanetary shocks except with a smaller scale size.
High-resolution analysis of the initiation of deep convection forced by boundary-layer processes

The initiation of atmospheric convection is investigated using the synergy of different instruments. The impact of increased spatial data resolution on the detection of the initiation of deep convection is analysed, and a methodology is developed to determine the likelihood of deep convection over flat and complex terrains. Intensive Observation Periods (IOPs) are used from the Convective Storm Initiation Project (CSIP), and the Convective and Orographically-Induced Precipitation Study (COPS).
Global Structure and Sodium Ion Dynamics in Mercury's Magnetosphere With the Offset Dipole

We conducted global magnetohydrodynamics (MHD) simulation of Mercury's magnetosphere with the dipole offset, which was revealed by MESSENGER (Mercury Surface, Space Environment, Geochemistry, and Ranging) observations, in order to investigate its global structure under northward interplanetary magnetic field conditions. Sodium ion dynamics originating from the Mercury's exosphere is also investigated based on statistical trajectory tracing in the electric and magnetic fields obtained from the MHD simulations. The results reveal a north‐south asymmetry characterized by open field lines around the southern polar region and northward deflection of the plasma sheet in the far tail. The asymmetry of magnetic field structure near the planet drastically affects trajectories of sodium ion and thus their pressure distributions and precipitation pattern onto the planet. Weaker magnetic field strength in the southern hemisphere than in the north increases ion loss by precipitation onto the planetary surface in the southern hemisphere. The “sodium ring,” which is formed by high‐energy sodium ions drifting around the planet, is also found in the vicinity of the planet. The sodium ring is almost circular under nominal solar wind conditions. The ring becomes partial under high solar wind density, because dayside magnetosphere is so compressed that there is no space for the sodium ions to drift around. In both cases, the sodium ring is formed by sodium ions that are picked up, accelerated in the magnetosheath just outside the magnetopause, and reentered into the magnetosphere due to combined effects of finite Larmor radius and convection electric field in the dawnside magnetosphere.
Enhancing Robot Task Planning and Execution through Multi-Layer Large Language Models

Large language models have found utility in the domain of robot task planning and task decomposition. Nevertheless, the direct application of these models for instructing robots in task execution is not without its challenges. Limitations arise in handling more intricate tasks, encountering difficulties in effective interaction with the environment, and facing constraints in the practical executability of machine control instructions directly generated by such models. In response to these challenges, this research advocates for the implementation of a multi-layer large language model to augment a robot’s proficiency in handling complex tasks. The proposed model facilitates a meticulous layer-by-layer decomposition of tasks through the integration of multiple large language models, with the overarching goal of enhancing the accuracy of task planning. Within the task decomposition process, a visual language model is introduced as a sensor for environment perception. The outcomes of this perception process are subsequently assimilated into the large language model, thereby amalgamating the task objectives with environmental information. This integration, in turn, results in the generation of robot motion planning tailored to the specific characteristics of the current environment. Furthermore, to enhance the executability of task planning outputs from the large language model, a semantic alignment method is introduced. This method aligns task planning descriptions with the functional requirements of robot motion, thereby refining the overall compatibility and coherence of the generated instructions. To validate the efficacy of the proposed approach, an experimental platform is established utilizing an intelligent unmanned vehicle. This platform serves as a means to empirically verify the proficiency of the multi-layer large language model in addressing the intricate challenges associated with both robot task planning and execution.
Machine Learning to Predict Enzyme–Substrate Interactions in Elucidation of Synthesis Pathways: A Review

Enzyme–substrate interactions play a fundamental role in elucidating synthesis pathways and synthetic biology, as they allow for the understanding of important aspects of a reaction. Establishing the interaction experimentally is a slow and costly process, which is why this problem has been addressed using computational methods such as molecular dynamics, molecular docking, and Monte Carlo simulations. Nevertheless, this type of method tends to be computationally slow when dealing with a large search space. Therefore, in recent years, methods based on artificial intelligence, such as support vector machines, neural networks, or decision trees, have been implemented, significantly reducing the computing time and covering vast search spaces. These methods significantly reduce the computation time and cover broad search spaces, rapidly reducing the number of interacting candidates, as they allow repetitive processes to be automated and patterns to be extracted, are adaptable, and have the capacity to handle large amounts of data. This article analyzes these artificial intelligence-based approaches, presenting their common structure, advantages, disadvantages, limitations, challenges, and future perspectives.
Time-series analysis of chlorophyll-a, sea surface temperature, and sea surface height anomalies during 2003–2014 with special reference to El Niño, La Niña, and Indian Ocean Dipole (IOD) Years

ABSTRACT The present study was carried out to find the variability of chlorophyll-a (chl-a) concentration, sea surface temperature (SST), and sea surface height anomalies (SSHa) during 2003–2014, covering the Bay of Bengal (BoB) and Arabian Sea (AS) waters. These parameters were linked with El Niño, La Niña, and Indian Ocean Dipole (IOD) years. The observed results during 2003–2014 were evaluated and it was found that the monthly mean value for 12-year data ranged as follows: chl-a (0.11–0.46 mg m−3), SST (27–31 °C), and SSHa (−0.2 to 20 cm). The annual mean range of chl-a for 12-year data was 0.1–0.23 mg m−3, the SST range was 27–28 °C, and the SSHa range was 2.14–13.91 cm. It has been observed that with the SST range of 27–28 °C and the SSHa range of 7–9 cm, the chl-a concentration enhanced to 0.20–0.23 mg m−3. With a higher SST range of 28–29 °C and with a positive SSHa range of 11–14 cm, the chl-a concentration appeared to be low (0.17–0.18 mgm−3). During normal years, SSHa was positive with the >5 to <10 cm range during the months of April–June, which coincided with an increase in SST, >2 to <4 °C. During the normal years, SSHa (>−0.2 to <−10 cm) was observed to be negative during October–December, with a decrease in SST (<3 °C) observed. The high monthly mean chl-a concentration (>0.3 to <0.5 mg m−3) was noticed during December–February in the BoB and AS. Compared to the BoB chl-a range (<0.4 mg m−3), a high chl-a concentration was observed in AS (>0.4 mg m−3). However, during the phenomenon years, the study area had experienced low chl-a (<0.2 mg m−3), high SST (>5 °C), and more positive SSHa (>10 to <20 cm) during January–March and October–December in AS and BoB. The present study infers that a positive IOD leads to low chl-a concentration (<2 mg m−3) and low primary productivity in AS. El Niño caused the down-welling process, it results in a low chl-a concentration (<1 mg m−3) in BoB and AS. La Niña caused the upwelling process, and it results in a high chl-a concentration (>2.0 mg m−3) in BoB and AS. In the recent past years (2003–2014), the intensity and frequency of El Niño, La Niña, and IOD have been increasing, evidenced with few studies, and have impacts on the Indian Ocean climate. Therefore, the influences of the relative changes of these phenomena on the BoB and AS need to be understood for productivity assessment and ocean state monitoring.
Fighting Against the Repetitive Training and Sample Dependency Problem in Few-Shot Named Entity Recognition

Few-shot named entity recognition (NER) systems recognize entities using a few labeled training examples. The general pipeline consists of a span detector to identify entity spans in text and an entity-type classifier to assign types to entities. Current span detectors rely on extensive manual labeling to guide training. Almost every span detector requires initial training on basic span features followed by adaptation to task-specific features. This process leads to repetitive training of the basic span features among span detectors. Additionally, metric-based entity-type classifiers, such as prototypical networks, typically employ a specific metric that gauges the distance between the query sample and entity-type referents, ultimately assigning the most probable entity type to the query sample. However, these classifiers encounter the sample dependency problem, primarily stemming from the limited samples available for each entity-type referent. To address these challenges, we proposed an improved few-shot NER pipeline. First, we introduce a steppingstone span detector that is pre-trained on open-domain Wikipedia data. It can be used to initialize the pipeline span detector to reduce the repetitive training of basic features. Second, we leverage a large language model (LLM) to set reliable entity-type referents, eliminating reliance on few-shot samples of each type. Our model exhibits superior performance with fewer training steps and human-labeled data compared with baselines, as demonstrated through extensive experiments on various datasets. Particularly in fine-grained few-shot NER settings, our model outperforms strong baselines, including ChatGPT. We will publicly release the code, datasets, LLM outputs, and model checkpoints.
Research Progress on the Depression Status and Nursing Intervention in Patients after Percutaneous Coronary Intervention

Percutaneous coronary intervention (PCI) is an effective treatment method for myocardial ischemic necrosis. Postoperative depression caused by PCI stress will adversely affect the prognosis of patients. This article reviews the current status and influencing factors of postoperative depression after PCI and summarizes the corresponding nursing interventions, to provide a literature reference to implement effective nursing interventions for depressed patients after clinical PCI.
A sporadic low‐velocity layer atop the 410 km discontinuity beneath the Pacific Ocean

Waveforms of SS precursors recorded by global stations are analyzed to investigate lateral heterogeneities of upper mantle discontinuities on a global scale. A sporadic low‐velocity layer immediately above the 410 km discontinuity (LVL‐410) is observed worldwide, including East Asia, western North America, eastern South America, the Pacific Ocean, and possibly the Indian Ocean. Our best data coverage is for the Pacific Ocean, where the LVL‐410 covers 33–50% of the resolved region. Lateral variations of our LVL‐410 observations show no geographical correlation with 410 km discontinuity topography or tomographic models of seismic velocity, suggesting that the LVL‐410 is not caused by regional thermal anomalies. We interpret the LVL‐410 as partial melting due to dehydration of ascending mantle across the 410 km discontinuity, which is predicted by the transition zone water filter hypothesis. Given the low vertical resolution of SS precursors, it is possible that the regions without a clear LVL‐410 detection also have a thin layer. Therefore, the strong lateral heterogeneity of the LVL‐410 in our observations suggests partial melting with varying intensities across the Pacific and further provides indirect evidence of a hydrous mantle transition zone with laterally varying water content.