Fostering Joint Innovation: A Global Online Platform for Ideas Sharing and Collaboration

In today's world, where moving forward hinges on innovation and working together, this article introduces a new global online platform that is all about sparking teamwork to come up with new ideas. This platform goes beyond borders and barriers between different fields, creating an exciting space where people from all over the world can swap ideas, get helpful feedback, and team up on exciting projects. What sets our platform apart is its ability to tap into the combined brainpower of a diverse bunch of users, giving people the power to come up with game-changing ideas that tackle big global problems. By making it easy for people to share ideas and promoting a culture of working together, our platform is like a buddy for innovation, boosting creativity and problem-solving on a global level. This article spills the details on what the platform aims to do, how it works, and what makes it special, emphasizing how it can kickstart creativity, ramp up problem-solving skills, and get different fields collaborating. It is not just a tool it is a whole new way of teaming up to make daily life better and build a global community of problem-solving pals.Comment: 5 pages, 5 figures, ITNG 2024 21st International Conference on Information Technology. Las Vegas, Nevada, US

Enhancing Talent Development Using AI-Driven Curriculum-Industry Integration

The specific hiring needs render low-skill-based job-seeking invalid in coping with the nation's economic development. There needs to be more graduate readiness for the industry's needs. This paper explores the transformative potential of Artificial Intelligence (AI) in fostering a symbiotic relationship between academic curricula and industry demands, aimed at building a robust talent pool for the future. A new hiring selection model that matches industry-identified hiring parameters with the knowledge and skills obtained from the university. By aligning educational programs with real-world challenges and market needs, this novel approach seeks to propel the growth of talents

Early detection of dysphoria using electroencephalogram affective modelling

Dysphoria is a trigger point for maladjusted individuals who cannot cope with disappointments and crushed expectations, resulting in negative emotions if it is not detected early. Individuals who suffer from dysphoria tend to deny their mental state. They try to hide, suppress, or ignore the symptoms, making one feel worse, unwanted, and unloved. Psychologists and psychiatrists identify dysphoria using standardized instruments like questionnaires and interviews. These methods can boast a high success rate. However, the limited number of trained psychologists and psychiatrists and the small number of health institutions focused on mental health limit access to early detection. In addition, the negative connotation and taboo about dysphoria discourage the public from openly seeking help. An alternative approach to collecting ‘pure’ data is proposed in this paper. The brain signals are captured using the electroencephalogram as the input to the machine learning approach to detect negative emotions. It was observed from the experimental results that participants who scored severe dysphoria recorded ‘fear’ emotion even before stimuli were presented during the eyes-close phase. This finding is crucial to further understanding the effect of dysphoria and can be used to study the correlation between dysphoria and negative emotions

MELPF version 1: Modeling Error Learning based Post-Processor Framework for Hydrologic Models Accuracy Improvement

This paper studies how to improve the accuracy of hydrologic models using machine-learning models as postprocessors and presents possibilities to reduce the workload to create an accurate hydrologic model by removing the calibration step. It is often challenging to develop an accurate hydrologic model due to the time-consuming model calibration procedure and the nonstationarity of hydrologic data. Our findings show that the errors of hydrologic models are correlated with model inputs. Thus motivated, we propose a modeling-error-learning-based post-processor framework by leveraging this correlation to improve the accuracy of a hydrologic model. The key idea is to predict the differences (errors) between the observed values and the hydrologic model predictions by using machine-learning techniques. To tackle the nonstationarity issue of hydrologic data, a moving window-based machine-learning approach is proposed to enhance the machine-learning error predictions by identifying the local stationarity of the data using a stationarity measure developed based on the Hilbert–Huang transform. Two hydrologic models, the Precipitation–Runoff Modeling System (PRMS) and the Hydrologic Modeling System (HEC-HMS), are used to evaluate the proposed framework. Two case studies are provided to exhibit the improved performance over the original model using multiple statistical metrics

A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm

Cloud computing is a concept introduced in the information technology era, with the main components being the grid, distributed, and valuable computing. The cloud is being developed continuously and, naturally, comes up with many challenges, one of which is scheduling. A schedule or timeline is a mechanism used to optimize the time for performing a duty or set of duties. A scheduling process is accountable for choosing the best resources for performing a duty. The main goal of a scheduling algorithm is to improve the efficiency and quality of the service while at the same time ensuring the acceptability and effectiveness of the targets. The task scheduling problem is one of the most important NP-hard issues in the cloud domain and, so far, many techniques have been proposed as solutions, including using genetic algorithms (GAs), particle swarm optimization, (PSO), and ant colony optimization (ACO). To address this problem, in this paper, one of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. The performance of the proposed algorithm has been compared with that of GAs, PSO, continuous ACO, and the basic SSA. The results show that our algorithm has generally higher performance than the other algorithms. For example, compared to the basic SSA, the proposed method has an average reduction of approximately 21% in makespan.Comment: 15 pages, 6 figures, 2023 IFIP International Internet of Things Conference. Dallas-Fort Worth Metroplex, Texas, US

Sloan Digital Sky Survey Imaging of Low Galactic Latitude Fields: Technical Summary and Data Release

The Sloan Digital Sky Survey (SDSS) mosaic camera and telescope have obtained five-band optical-wavelength imaging near the Galactic plane outside of the nominal survey boundaries. These additional data were obtained during commissioning and subsequent testing of the SDSS observing system, and they provide unique wide-area imaging data in regions of high obscuration and star formation, including numerous young stellar objects, Herbig-Haro objects and young star clusters. Because these data are outside the Survey regions in the Galactic caps, they are not part of the standard SDSS data releases. This paper presents imaging data for 832 square degrees of sky (including repeats), in the star-forming regions of Orion, Taurus, and Cygnus. About 470 square degrees are now released to the public, with the remainder to follow at the time of SDSS Data Release 4. The public data in Orion include the star-forming region NGC 2068/NGC 2071/HH24 and a large part of Barnard's loop.Comment: 31 pages, 9 figures (3 missing to save space), accepted by AJ, in press, see http://photo.astro.princeton.edu/oriondatarelease for data and paper with all figure

Led into Temptation? Rewarding Brand Logos Bias the Neural Encoding of Incidental Economic Decisions

Human decision-making is driven by subjective values assigned to alternative choice options. These valuations are based on reward cues. It is unknown, however, whether complex reward cues, such as brand logos, may bias the neural encoding of subjective value in unrelated decisions. In this functional magnetic resonance imaging (fMRI) study, we subliminally presented brand logos preceding intertemporal choices. We demonstrated that priming biased participants' preferences towards more immediate rewards in the subsequent temporal discounting task. This was associated with modulations of the neural encoding of subjective values of choice options in a network of brain regions, including but not restricted to medial prefrontal cortex. Our findings demonstrate the general susceptibility of the human decision making system to apparently incidental contextual information. We conclude that the brain incorporates seemingly unrelated value information that modifies decision making outside the decision-maker's awareness

Malaria vector research and control in Haiti: a systematic review

BACKGROUND: Haiti has a set a target of eliminating malaria by 2020. However, information on malaria vector research in Haiti is not well known. This paper presents results from a systematic review of the literature on malaria vector research, bionomics and control in Haiti. METHODS: A systematic search of literature published in French, Spanish and English languages was conducted in 2015 using Pubmed (MEDLINE), Google Scholar, EMBASE, JSTOR WHOLIS and Web of Science databases as well other grey literature sources such as USAID, and PAHO. The following search terms were used: malaria, Haiti, Anopheles, and vector control. RESULTS: A total of 132 references were identified with 40 high quality references deemed relevant and included in this review. Six references dealt with mosquito distribution, seven with larval mosquito ecology, 16 with adult mosquito ecology, three with entomological indicators of malaria transmission, eight with insecticide resistance, one with sero-epidemiology and 16 with vector control. In the last 15 years (2000–2015), there have only been four published papers and three-scientific meeting abstracts on entomology for malaria in Haiti. Overall, the general literature on malaria vector research in Haiti is limited and dated. DISCUSSION: Entomological information generated from past studies in Haiti will contribute to the development of strategies to achieve malaria elimination on Hispaniola. However it is of paramount importance that malaria vector research in Haiti is updated to inform decision-making for vector control strategies in support of malaria elimination

Transmission of Vibrio cholerae Is Antagonized by Lytic Phage and Entry into the Aquatic Environment

Cholera outbreaks are proposed to propagate in explosive cycles powered by hyperinfectious Vibrio cholerae and quenched by lytic vibriophage. However, studies to elucidate how these factors affect transmission are lacking because the field experiments are almost intractable. One reason for this is that V. cholerae loses the ability to culture upon transfer to pond water. This phenotype is called the active but non-culturable state (ABNC; an alternative term is viable but non-culturable) because these cells maintain the capacity for metabolic activity. ABNC bacteria may serve as the environmental reservoir for outbreaks but rigorous animal studies to test this hypothesis have not been conducted. In this project, we wanted to determine the relevance of ABNC cells to transmission as well as the impact lytic phage have on V. cholerae as the bacteria enter the ABNC state. Rice-water stool that naturally harbored lytic phage or in vitro derived V. cholerae were incubated in a pond microcosm, and the culturability, infectious dose, and transcriptome were assayed over 24 h. The data show that the major contributors to infection are culturable V. cholerae and not ABNC cells. Phage did not affect colonization immediately after shedding from the patients because the phage titer was too low. However, V. cholerae failed to colonize the small intestine after 24 h of incubation in pond water—the point when the phage and ABNC cell titers were highest. The transcriptional analysis traced the transformation into the non-infectious ABNC state and supports models for the adaptation to nutrient poor aquatic environments. Phage had an undetectable impact on this adaptation. Taken together, the rise of ABNC cells and lytic phage blocked transmission. Thus, there is a fitness advantage if V. cholerae can make a rapid transfer to the next host before these negative selective pressures compound in the aquatic environment

The Second Data Release of the Sloan Digital Sky Survey

The Sloan Digital Sky Survey (SDSS) has validated and made publicly available its Second Data Release. This data release consists of 3324 deg2 of five-band (ugriz) imaging data with photometry for over 88 million unique objects, 367,360 spectra of galaxies, quasars, stars, and calibrating blank sky patches selected over 2627 deg2 of this area, and tables of measured parameters from these data. The imaging data reach a depth of r ≈ 22.2 (95% completeness limit for point sources) and are photometrically and astrometrically calibrated to 2% rms and 100 mas rms per coordinate, respectively. The imaging data have all been processed through a new version of the SDSS imaging pipeline, in which the most important improvement since the last data release is fixing an error in the model fits to each object. The result is that model magnitudes are now a good proxy for point-spread function magnitudes for point sources, and Petrosian magnitudes for extended sources. The spectroscopy extends from 3800 to 9200 Å at a resolution of 2000. The spectroscopic software now repairs a systematic error in the radial velocities of certain types of stars and has substantially improved spectrophotometry. All data included in the SDSS Early Data Release and First Data Release are reprocessed with the improved pipelines and included in the Second Data Release. Further characteristics of the data are described, as are the data products themselves and the tools for accessing them