Miracle-dz/UniFAR-FTUs huggingface dataset:
e-Publications@Marquette e-Publications@Marquette

To comply with the court’s ruling in U.S. v. Philip Morris USA Inc., tobacco companies must fund a large advertising campaign to “correct” smoking beliefs about which consumers may have been misled as a result of past deceptive practices of tobacco companies. The authors use an ad copy experiment to examine (1) the effects of different versions of corrective ad statements that plaintiff intervenors submitted to the court on multi-item belief measures and (2) the impact of the ad versions and beliefs on general attitudes toward smoking across current adult smokers and nonsmokers. The tested ad versions include a copy-only control condition, a copy-with-graphic-visual condition, and a version with a potentially distracting visual. The results indicate that the corrective statements in advertisements can have a positive effect on antismoking beliefs of focal interest in the case and that the test advertisements affect some beliefs more strongly than others. The authors discuss potential policy implications and limitations and provide suggestions for further research.
Exploring the Impact of Endophytic Fungus Aspergillus cejpii DMKU-R3G3 on Rice: Plant Growth Promotion and Molecular Insights through Proteomic Analysis

Rice is a crucial crop for many people worldwide, especially in regions like Asia, Latin America, and parts of Africa. Thailand is one of the largest exporters of rice. Nowadays, farmers use chemicals to control rice disease, which can have negative effects on humans and the environment. Therefore, the objective of this study was to examine the plant-promoting capabilities of the endophytic fungal strains DMKU-R3G3 in greenhouse settings. The endophytic fungi strain DMKU-R3G3, which was isolated from organic rice root, was identified as Aspergillus cejpii based on morphological characteristics and phylogenetic analysis. The production of IAA was detected using Salkowski’s reagent. After 7 days of incubation, the finding revealed that the strain cultivated in PDB supplemented with tryptophan yielded a greater concentration of IAA (25.45 μg/mL). The inoculation with A. cejpii DMKU-R3G3 significantly enhanced rice growth, as evidenced by notable increases in shoot height, root length, and fresh weight. Moreover, the chlorophyll content of the rice plants also increased by 1.78 times more than the control group. In addition, proteomic analysis revealed that rice responded toward the colonization of endophytic fungi by producing auxin-responsive proteins to regulate the IAA content in plant tissue and inducing total chlorophyll production due to the up-regulation of proteins in the chlorophyll biosynthesis pathway. The results obtained from this study lead to the conclusion that the A. cejpii strain DMKU-R3G3 possesses the capability to enhance plant growth through the production of phytohormones in greenhouse conditions. Therefore, endophytic A. cejpii DMKU-R3G3 has the potential to be a promising eco-friendly plant growth promoter for sustainable rice cultivation.
Change in precipitation over the Asian continent from 1901-2016 based on a new multi-source dataset

Asia experiences great regional differences in climatic variations, especially precipitation. Examining changes in precipitation will help us understand large-scale hydro-cycle processes against the background of global climate change. We used a new multi-source dataset of historical global land precipitation records developed by the China Meteorological Administration to analyze long-term change in annual and seasonal precipitation in Asia during 1901−2016. To solve the problem of inhomogeneity, the Russian Bias-corrected Monthly Precipitation dataset was also used for Russia. The normalized precipitation anomaly (NPA) was used to analyze spatial and temporal precipitation trend patterns. Regional average time series for the continent and its 6 subregions were obtained by an area-weighted averaging method. Results show that: (1) regional average annual precipitation increased significantly during this period, and the upward trend was mainly related to a shift in cold areas during the mid-20th century; (2) regional average spring and autumn precipitation amounts had similar characteristics to those of the long-term changes in annual precipitation, and winter precipitation increased slightly for the entire period, with a significant jump occurring from 1945−1955; (3) North and Central Asia experienced a significant upward trend in precipitation for every season except summer during 1901−2016, while South Asia witnessed a significant downward trend in winter during the same period; (4) during 1901− 2016, the proportion of summer precipitation declined uniformly in most parts of the continent, with a significant decline in cold areas. Although there are still uncertainties related to the observational data, the analysis results will help in understanding the general pattern of precipitation variation and the possible response of these regions to global climate change.
Machine learning's role in personalized medicine & treatment optimization

The advent of machine learning in personalized medicine has revolutionized the healthcare industry by providing an enhanced diagnosis and treatment regimen to patients based on their unique characteristics such as genetic predispositions, lifestyle variables, and medical history. Machine learning algorithms can analyze vast amounts of patient data to generate accurate diagnoses, establish tailored treatment plans, and improve patient outcomes. By combining multiple data sources, machine learning algorithms can identify patterns, predict the likelihood of specific illnesses, and recommend personalized treatment options. The technology has enabled healthcare professionals to access diverse datasets, including genetic information, medical history, and lifestyle variables, and derive insights from them that were previously inaccessible. However, the isolation of user data in silos across multiple hospitals and medical institutions presents challenges for researchers working in this sector. The article examines the possible obstacles and ethical implications associated with the widespread implementation of machine learning in personalized medicine and assesses the consequences of these breakthroughs for patient care, healthcare systems, and the future of medical research.
NATIONWIDE MULTICENTER DATABASE

A model that can immediately measure the severity of an epidemic would be of great help to the medical system. In this paper, we will use machine learning to create and analyze a model that can immediately measure the severity of SARS-CoV-2 patients. Since our model uses nationwide dataset that consists of basic personal information of patients, it is of great significance for the patient to be able to check their own severity. Moreover, based on the predicted severity score, we use our model to inform the patients to visit the appropriate clinic center.
State Speech Face / Expression MonitorNavigation Vision Communication Shared Memory

2000 American Association for Artificial Intelligence Mobile Robot Competition. The robots, developed by Swarthmore College, all used a modular hybrid architecture designed to enable reflexive responses to perceptual input. Within this architecture, the robots integrated visual sensing, speech synthesis and recognition, the display of an animated face, navigation, and interrobot communication. In the Hors d’Oeuvres, Anyone? event, a team of robots entertained the crowd while they interactively served cookies; and in the Urban– Search-and-Rescue event, a single robot autonomously explored a section of the test area, identified interesting features, built an annotated map, and exited the test area within the allotted time.
A survey of image-based rendering techniques

In this article, we survey the techniques for image-based rendering. In contrast with the conventional approach of using 3-D models for creating new virtual views, image-based rendering techniques require only the original images to produce new virtual views. We have identified four basic categories for image-based rendering techniques, namely non-physically based image mapping, mosaicking, interpolation from dense samples, and geometrically-valid pixel reprojection. This division is based primarily on the nature of the pixel indexing scheme. We discuss the characteristics of each of these categories and present representative techniques. From this survey, it is interesting to note that image-based rendering has evolved to be an area that has generated active interest from both computer vision and computer graphics communities.
Survey of image-based rendering techniques

In this article, we survey the techniques for image-based rendering. In contrast with the conventional approach of using 3-D models for creating new virtual views, image-based rendering techniques require only the original images to produce new virtual views. We have identified four basic categories for image-based rendering techniques, namely non- physically based image mapping, mosaicking, interpolation from dense samples, and geometrically-valid pixel reprojection. This division is based primarily on the nature of the pixel indexing scheme. We discuss the characteristics of each of these categories and present representative techniques. From this survey, it is interesting to note that image-based rendering has evolved to be an area that has generated active interest from both computer vision and computer graphics communities.
Aﬀective artiﬁcial agents as sui generis aﬀective artifacts

: AI-based technologies are increasingly pervasive in a number of contexts. Our aﬀective and emotional life makes no exception. In this article, we analyze one way in which AI-based technologies can aﬀect them. In particular, our investigation will focus on aﬀective artiﬁcial agents, namely AI-powered software or robotic agents designed to interact with us in aﬀectively salient ways. We build upon the existing literature on aﬀective artifacts with the aim of providing an original analysis of aﬀective artiﬁcial agents and their distinctive features. We argue that, unlike comparatively low-tech aﬀective artifacts, aﬀective artiﬁcial agents display a speciﬁc form of agency, which prevents them from being perceived by their users as extensions of their selves. In addition to this, we claim that their functioning crucially depends on the simulation of human-like emotion-driven behavior and requires a distinctive form of transparency – we call it emotional transparency – that might give rise to ethical and normative tensions.
on Semantic Web & Information Systems

Semantic Similarity relates to computing the similarity between conceptually similar but not necessarily lexically similar terms. Typically, semantic similarity is computed by mapping terms to an ontology and by examining their relationships in that ontology. We investigate approaches to computing the semantic similarity between natural language terms (using WordNet as the underlying reference ontology) and between medical terms (using the MeSH ontology of medical and biomedical terms). The most popular semantic similarity methods are implemented and evaluated using WordNet and MeSH. Building upon semantic similarity, we propose the Semantic Similarity based Retrieval Model (SSRM), a novel information retrieval method capable for discovering similarities between documents containing conceptually similar terms. The most effective semantic similarity method is implemented into SSRM. SSRM has been applied in retrieval on OHSUMED (a standard TREC collection available on the Web). The experimental results demonstrated promising performance improvements over classic information retrieval methods utilizing plain lexical matching (e.g., Vector Space Model) and also over state-of-the-art semantic similarity retrieval methods utilizing ontologies.
Coming Home: A Journey Back to the Authentic Self

The theory of coming home is a three-stage classic grounded theory that details an individual’s initial exploration of the world in childhood, followed by the subsequent abandoning of their authentic self and then the life-long journey back home to their most authentic being. In the first stage of the process, individuals act and express in authentic ways and receive feedback from the environment about which aspects of themselves are acceptable and which are not. In the second stage of the process, driven by feelings of shame and lowered self-worth or the awareness that certain aspects of themselves are unacceptable, individuals engage in abandoning behaviors to fit in and avoid rejection. After months, years, or even decades of increasing awareness about these behaviors, individuals may enter the third stage of the process, during which they come to re-explore, accept, and embrace their authentic selves
A Data-eﬃcient Neural ODE Framework for Optimal Control of Soft Manipulators

: This paper introduces a novel approach for modeling continuous forward kinematic models of soft continuum robots by employing Augmented Neural ODE (ANODE), a cutting-edge family of deep neural network models. To the best of our knowledge, this is the ﬁrst application of ANODE in modeling soft continuum robots. This formulation introduces auxiliary dimensions, allowing the system’s states to evolve in the augmented space which provides a richer set of dynamics that the model can learn, increasing the ﬂexibility and accuracy of the model. Our methodology achieves exceptional sample eﬃciency, training the continuous forward kinematic model using only 25 scattered data points. Additionally, we design and implement a fully parallel Model Predictive Path Integral (MPPI)-based controller running on a GPU, which eﬃciently manages a non-convex objective function. Through a set of experiments, we showed that the proposed framework (ANODE+MPPI) signiﬁcantly outperforms state-of-the-art learning based methods such as FNN and RNN in unseen-before scenarios and marginally outperforms them in seen-before scenarios.
The role of effective inventory management practices on organizational performance: a case of Tarkwa municipal hospital, western region, Ghana

The study aimed at investigating the role of effective inventory management practices on organisational performance; a case study of Municipal Hospital Tarkwa in the Western Region of Ghana. The specific objectives of the study were to establish the inventory management practices used by organisations, to find the effect of inventory management practices on organisational performance, and to determine the challenges of implementing inventory management practices in organizations. The study adopted descriptive and causal research designs and used questionnaires as the data collection instrument. Data were collected from 200 respondents and STATA 13 analytical tool was used to perform the analysis. The results were presented using frequency tables and descriptive statistics tables. The study also used Probit regression analysis to establish the relationship between the dependent and the independent variables. The findings show that the stakeholders of the hospital are generally happy with the service delivery of the hospital. Thus, the results show that the hospital adopts various inventory management practices including; activity-based costing, economic order quantity, material requirement planning, materials resource planning, enterprise resource planning, distribution requirement planning, and just in time. The study concludes that effective inventory management positively affects the performance of organizations, in this case, the quality of service delivery of organizations. The study recommends organizations successfully implement an effective inventory management system. This will help reduce the risks of shortages. In addition, the concept of inventory management could be simplified for organizations in Ghana. This can be done through education or training to all staff in organizations to enlighten them on the need for effective inventory management practices for their organization’s performance.
Automatic Verification Technology of Virtual Machine Software Patch on IaaS Cloud Yoji Yamato

Abstract—In this paper, we propose an automatic verification technology of software patches for user virtual environments on IaaS Cloud to decrease verification costs of patches. In these days, IaaS services have been spread and many users can customize virtual machines on IaaS Cloud like their own private servers. Regarding to software patches of OS or middleware installed on virtual machines, users need to adopt and verify these patches by themselves. This task increases operation costs of users. Our proposed method replicates user virtual environments, extracts verification test cases for user virtual environments from test case DB, distributes patches to virtual machines on replicated environments and conducts those test cases automatically on replicated environments. We have implemented the proposed method on OpenStack using Jenkins and confirmed the feasibility. Using the implementation, we confirmed the effectiveness of test case creation efforts by our proposed idea of 2-tier abstraction of software functions and test cases. We also evaluated the automatic verification performance of environment replications, test cases extractions and test cases conductions.
Genome variation and population structure among 1142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii

Citation for published version (APA): Clarkson, C. S. , Miles, A. , Harding, N. J., Lucas, E. R. , Battey, C. J., Amaya-Romero, J. E. , Cano, J., Diabaté, A. , Constant, E. , Nwakanma, D. C. , Jawara, M. , Essandoh, J., Dinis, J., Le Goff, G. , Robert, V. , Troco, A. D. , Costantini, C. , Rohatgi, K. R. , Elissa, N. , . Kwiatkowski, D. P. (2019). Genome variation and population structure among 1142 mosquitoes of the African malaria vector species Anopheles gambiae and Anopheles coluzzii. Manuscript submitted for publication. https://doi.org/10.1101/864314
The Promise and Potential of Continental‐Scale Limnology Using the U.S. Environmental Protection Agency's National Lakes Assessment

While there are many local and regional sampling efforts in lakes across the U.S., it is often difficult to compile this information into a cohesive framework to make national‐ or continental‐scale conclusions. To address this gap, the U.S. Environmental Protection Agency partners with States and Tribes to implement the National Lakes Assessment, which is a national‐scale, coordinated lake sampling program. The resulting data include colocated biological, chemical, habitat, and human use measures that are available to the scientific community. We provide a brief overview of the program, describe the data, and discuss some of the ways the results have been used. The availability of a consistent data set of this magnitude is an impressive and unique resource for the limnological community.
Computational Aspects of Distortion

The distortion framework in social choice theory allows quantifying the eﬃciency of (randomized) selection of an alternative based on the preferences of a set of agents. We make two fundamental contributions to this framework. First, we develop a linear-programming-based algorithm for computing the optimal randomized decision on a given instance, which is simpler and faster than the state-of-the-art solutions. For practitioners who may prefer to deploy a classical decision-making rule over the aforementioned optimal rule, we develop an algorithm based on non-convex quadratic programming for computing the exact distortion of any (and the best) randomized positional scoring rule. For a small number of alternatives, we ﬁnd that the exact distortion bounds are signiﬁcantly better than the asymptotic bounds established in prior literature and lead to diﬀerent recommendations on which rules to use. These results rely on a novel characterization of the instances yielding the worst distortion, which may be of independent interest.
Countermovement jump to monitor neuromuscular fatigue in endurance athletes A correlation study between training load and CMJ-variables

Training load monitoring is crucial to examine fatigue status. The currently available methods show diverse validity, and each come with their limitations. The advantages of CMJ are many and seem to be useful within team sports, although its use on endurance athletes needs more research. Using force plates is agreed to be the best method, but the uncertainty of which variables to use when monitoring fatigue creates difficulties, yet currently, no optimal method is identified. The purpose of this study was to investigate the use of CMJ and its variables on endurance athletes. Ten triathletes participated in the study, performing the CMJ two times a week for five weeks. The training load from each session was quantified using Lucia’s TRIMP. A correlation analysis between total TRIMP three days prior to testing and eight different CMJ-variables were made; jump height-flight time (JH-FT), jump height-impulse (JH-IM), Reactive Strength Index-flight time (RSI-FT), Reactive Strength Index-impulse (RSI-IM), braking duration (B-DUR), braking impulse (B-IM), propulsive duration (P-DUR), propulsive impulse (P-IM). A small positive correlation was found between TRIMP and JH-FT (r =0.23), JH-IM (r = 0.24), RSI-FT (r = 0.13), and RSI-IM (r = 0.13). B-DUR and B-IM had a small negative correlation (r = -0.10, r = -0.11) while P-DUR and P-IM showed no correlation (r = < 0.02). No significance was found in any correlations (p > 0.05). Results indicated that JH may provide better information about fatigue compared to other variables. Variables from the braking phase seem more sensitive to training load compared to the propulsive phase. Using flight time seems to provide similar results as impulse which could simplify fatigue monitoring. However, due to limitations of the study the results should be viewed with caution and more research is needed for further conclusions.