Large Pool Research Articles

Diversity of bartonellae in mites (Acari: Mesostigmata: Macronyssidae and Spinturnicidae) of boreal forest bats: Association of host specificity of mites and habitat selection of hosts with vector potential.

Research into various bacterial pathogens that can be transmitted between different animals and may have zoonotic potential has led to the discovery of different strains of Bartonella sp. in bats and their associated ectoparasites. Despite their enormous species diversity, only a few studies have focussed on the detection of bacterial pathogens in insectivorous bats of boreal forests and their associated Macronyssidae and Spinturnicidae mites. We collected and molecularly analysed mite samples from forest-dwelling bat species distributed all along the boreal belt of the Palearctic, from Central Europe to Far East. Ectoparasitic mites were pooled for DNA extraction and DNA amplification polymerase chain reaction (PCRs) were conducted to detect the presence of various bacterial (Anaplasmataceae, Bartonella sp., Rickettsia sp., Mycoplasma sp.) and protozoal (Hepatozoon sp.) pathogens. Bartonella sp. DNA was detected in four different mite species (Macronyssidae: Steatonyssus periblepharus and Spinturnicidae: Spinturnix acuminata, Sp. myoti and Sp. mystacinus), with different prevalences of the targeted gene (gltA, 16-23S ribosomal RNA intergenic spacer and ftsZ). Larger pools (>5 samples pooled) were more likely to harbour Bartonella sp. DNA, than smaller ones. In addition, cave-dwelling bat hosts and host generalist mite species are more associated with Bartonella spp. presence. Spinturnicidae mites may transmit several distinct Bartonella strains, which cluster phylogenetically close to Bartonella species known to cause diseases in humans and livestock. Mites with ubiquitous presence may facilitate the long-term maintenance (and even local recurrence) of Bartonella-infestations inside local bat populations, thus acting as continuous reservoirs for Bartonella spp in bats.

Optimal consumption and investment in pooled annuity funds with and without fund managers

In this paper, we study two schemes of pooled annuity funds: pools with and without fund managers. In the pool without managers, there are no artificial rules or management cost and we introduce Nash equilibrium to depict the simultaneous non-cooperative game among participants. Given others' strategies, participants choose optimal consumption and investment strategies to maximize their own utility. In the pool with managers, the fund manager makes artificial rules for consumption and investment, ensuring cooperation among participants. The manager then determines optimal consumption withdrawal and investment strategies to maximize the total utility of all participants. By using variational methods and recursive algorithms, semi-analytical solutions in both pools are obtained. Due to prisoner's dilemma, larger pools without managers induce more intense competition in consumption and reduce utility. Thus, there exists an optimal pool size and only homogeneous participants form the pool. Meanwhile, larger pools with managers diversify investment and longevity risks more sufficiently and the subsidy effect among participants is mitigated. Therefore, the perfect pool with infinite participants is recommended as the optimal pool. Moreover, the preference between the two optimal pools depends on risk aversion degree, management cost and flexibility in selecting the strategies.

A Comparison of Functional Outcomes in Rotator Cuff Repairs Using Adjunctive Bone Marrow Aspirate Concentrate vs. Bone Marrow Aspirate Concentrate With Platelet-Rich Plasma: A Systematic Review and Meta-Analysis.

Regenerative medicine, specifically bone marrow aspirate concentrate (BMAC) and platelet-rich plasma (PRP), has become a novel adjunct that orthopedic surgeons have started to use with surgical rotator cuff repairs (RCR). Thus, we are conducting this systematic review to determine if either RCRs with BMAC alone or with BMAC and PRP result in superior functional outcomes. We conducted a comprehensive search using five databases including PubMed, Web of Science, Embase, Scopus, and Cochrane. After duplicates were removed, 1205 studies were screened by title and abstract using Rayyan, resulting in three included studies (one BMAC with PRP and two BMAC only). Only studies that reported functional outcomes using the American Shoulder and Elbow Surgeons Shoulder Score and the University of California Los Angeles Shoulder Score were included. Changes in assessment scores from baseline to follow-up evaluation were quantified using the effect size and used in the meta-analysis for each group. Interpretation of treatment efficacy was represented using Cohen's d. The effect size of BMAC with PRP (Cohen's d = 2.19) was not significantly different (p = 0.76) from that of BMAC alone (Cohen's d = 2.35). Between-group differences in functional outcomes were Cohen's d = 0.16, which was not significant. Given the lack of superiority and the small sample size, more research is required before a conclusion can be drawn as to the benefits of combining PRP with BMAC for RCR. If functional outcomes are the same, using BMAC alone as an adjunct may be optimal to reduce resources used and cost. Future studies should be conducted with a larger pool as our primary limitation is that only three studies were included.

PathNet: Path-Selective Point Cloud Denoising.

Current point cloud denoising (PCD) models optimize single networks, trying to make their parameters adaptive to each point in a large pool of point clouds. Such a denoising network paradigm neglects that different points are often corrupted by different levels of noise and they may convey different geometric structures. Thus, the intricacy of both noise and geometry poses side effects including remnant noise, wrongly-smoothed edges, and distorted shape after denoising. We propose PathNet, a path-selective PCD paradigm based on reinforcement learning (RL). Unlike existing efforts, PathNet enables dynamic selection of the most appropriate denoising path for each point, best moving it onto its underlying surface. We have two more contributions besides the proposed framework of path-selective PCD for the first time. First, to leverage geometry expertise and benefit from training data, we propose a noise- and geometry-aware reward function to train the routing agent in RL. Second, the routing agent and the denoising network are trained jointly to avoid under- and over-smoothing. Extensive experiments show promising improvements of PathNet over its competitors, in terms of the effectiveness for removing different levels of noise and preserving multi-scale surface geometries. Furthermore, PathNet generalizes itself more smoothly to real scans than cutting-edge models.

Let the algorithm speak: How to use neural networks for automatic item generation in psychological scale development.

Measurement is at the heart of scientific research. As many-perhaps most-psychological constructs cannot be directly observed, there is a steady demand for reliable self-report scales to assess latent constructs. However, scale development is a tedious process that requires researchers to produce good items in large quantities. In this tutorial, we introduce, explain, and apply the Psychometric Item Generator (PIG), an open-source, free-to-use, self-sufficient natural language processing algorithm that produces large-scale, human-like, customized text output within a few mouse clicks. The PIG is based on the GPT-2, a powerful generative language model, and runs on Google Colaboratory-an interactive virtual notebook environment that executes code on state-of-the-art virtual machines at no cost. Across two demonstrations and a preregistered five-pronged empirical validation with two Canadian samples (NSample 1 = 501, NSample 2 = 773), we show that the PIG is equally well-suited to generate large pools of face-valid items for novel constructs (i.e., wanderlust) and create parsimonious short scales of existing constructs (i.e., Big Five personality traits) that yield strong performances when tested in the wild and benchmarked against current gold standards for assessment. The PIG does not require any prior coding skills or access to computational resources and can easily be tailored to any desired context by simply switching out short linguistic prompts in a single line of code. In short, we present an effective, novel machine learning solution to an old psychological challenge. As such, the PIG will not require you to learn a new language-but instead, speak yours. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

MRFS: Mining Rating Fraud Subgraph in Bipartite Graph for Users and Products

Fraud in e-commerce fields (e.g., Amazon, Taobao, and so on) and social networks (e.g., Twitter and Weibo) has recently brought a very bad user experience. Rating fraud detection is an urgent issue for improving user experiences. However, existing methods have lots of limitations in some respects, because it is always very hard to acquire sufficient labeled data for fraud detection and detect new fraud patterns. Fortunately, the relationship for users rating (e.g., purchasing and following) products can be represented as a bipartite graph. So the problem of rating fraud detection can be transformed into the problem of abnormal subgraph detection in the bipartite graph. The major challenge of fraud detection is to distinguish fake rates from real user rates. In this article, we focus on mining rating fraud-connected subgraphs in a bipartite graph. The motivation for this work is fraud detection tasks, which can usually be formulated as mining a bipartite graph formed by source nodes (followers and users) and target nodes (followees and products) for malicious patterns. Now, smart fraudsters evade existing detection methods by buying a large pool of users and hijacking honest users, making them look “normal”-this behavior is called “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">camouflage</i> .” Accordingly, we propose a fraud detection approach for mining rating fraud subgraph (MRFS), which addresses the problem from the intrinsic metric (e.g., fraudulence, badness and unreliability). The proposed MRFS mines the intrinsic characteristics of nodes and edges from node behavior information, which is an effective and scalable (linear on the input size) algorithm. A large number of comparative experimental results on real-world rating networks show that our proposed MRFS is efficient and universal.

EPLERENONE VS IRBESARTAN AS FIRST LINE THERAPY IN OBESE HYPERTENSIVE PATIENTS: PRELIMINARY RESULTS OF HEBRO TRIAL IN ECHOCARDIOGRAPHIC DIASTOLIC FUNCTION INDEXES

Objective: Obesity is significantly associated with hypertension and cardiovascular outcomes. However, little is known about the impact of aldosterone blockade using eplerenone on hypertensive obese patients as first line therapy. The aim of this study is to compare the efficacy between eplerenone-based strategy and irbesartan-based strategy in obese hypertensive individuals, regarding blood pressure reduction and change of echocardiographic diastolic function parameters. Design and method: HEBRO (antiHypertensive treatment with Eplerenone vs. irBesartan in hypeRtensive Obese patients) is a prospective, randomized, open-labeled, multicenter trial enrolled 112 obese hypertensive patients aged 30-75 years old, evaluated by office and out-of-office BP measurements. Patients were randomized to either 25 mg eplerenone twice (59) or 150 mg irbesartan (53) once daily. The primary endpoint is the difference in ambulatory BP measurements after 6 months follow-up period and secondary endpoint is difference in diastolic function parameters. Results: We enrolled 112 patients, 54.6 years old, 53% female, mean BMI 34.4 kg/m2 and mean eGFR 82.5 ml/min/1.73m2. Diastolic function was assessed at baseline and 6 months, by calculating left ventricular mass index (LVMI) and left atrial volume index (LAVI). At baseline, mean LVMI was 102.9 g/m2 ± 18.1 in the irbesartan group vs. 110.1 g/m2 ± 30.5 in the eplerenone group, p=0.237. The change of LVMI was – 5.6 ± 17.8 vs. -4.5 ± 22.6, p=0.843. At baseline, mean LAVI was 30.6 ml/m2 ± 16.9 in the irbesartan group vs. 30.7 ml/m2 ± 11.1 in the eplerenone group, p=0.979. The change of LAVI in the two study arms was +0.9 ± 6.7 vs. -3.6 ± 11.5, p=0.063, respectively. Although, the result was not statistically significant, there was a numerical trend towards the eplerenone arm. Conclusions: There were no statistically significant differences at baseline and follow-up evaluation of changes in left ventricular and left atrial geometry, though there is a numerical trend towards eplerenone arm, regarding reduction of LAVI after 6 months follow-up period. Patients are currently still enrolled and data on larger pool are anticipated.

Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem.

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Size-dependent dynamics of the internal carbon pool drive isotopic vital effects in calcifying phytoplankton

Isotopic offsets in biogenic calcite from equilibrium values can provide unique insights into the physiology and mechanisms of carbon regulation in calcifying phytoplankton. This study examines the impact of varying CO2 (controlled via pH) on five coccolithophore species chosen for varied cell sizes, physiology, and calcification. The study investigates isotopic offsets in coccolith calcite and organic matter, in relation to carbon demand and supply (µ/CO2). Species-specific CO2 and/or pH optima for growth (µopt) were derived from variations in growth rates with varying CO2 concentrations. Growth rates for all species declined with rising CO2 (decreasing pH) due to H+-driven inhibition. C. braarudii and C. leptoporus exhibited µopt at high CO2 concentrations (suggesting high carbon-demand) and limited growth under low CO2 (high pH) suggesting carbon limitation. Under low CO2 supply, when growth rates were CO2-limited, both species exhibited coincident isotopic depletion in calcite and organic matter as a consequence of CO2 diffusion into the cell that experienced no equilibration as a result of a highly depleted internal carbon pool. In these two species, isotopic values in calcite remained unaffected by growth rates and CO2 concentration (µ/CO2) when CO2 was sufficient for optimal growth. G. huxleyi and G. oceanica displayed optima for growth (µopt) at low CO2 concentrations and showed no growth limitation under low CO2 (indicating low carbon-demand). Both species experienced depleted (negative) vital effects caused by an excess of CO2 diffusioninto the small internal carbon pool of the cell when diffusive carbon supply outpaced low demand (low µ/CO2). Enriched (positive) vital effects were observed under low carbon supply and high demand, likely due to increased HCO3– uptake and diffusive CO2 loss from the intracellular carbon pooldue to a lower intracellular pH than the seawater pH. C. carterae exhibited a µopt at intermediate CO2 concentrations and isotopically equilibrated intracellular carbon pool such that δ13C values in calcite and organic matter suggested a shared carbon pool. This study illustrates that pH and CO2 driven vital effects and fractionation into organic matter indicate the residence time for carbon in the intracellular carbon pool, where the size of the pool is proportional to cell size. Due to the increased buffering afforded by a larger pool, C. leptoporus and C. carterae may have elevated intracellular pH which minimises CO2 leakage, whereas vital effects in G. huxleyi and G. oceanica are caused by CO2 diffusion in or out of their small internal carbon pool with limited buffering capacity owing to its small size.

Distribution and bulk flow analyses of the intraflagellar transport (IFT) motor kinesin-2 support an "on-demand" model for Chlamydomonas ciliary length control.

Most cells tightly control the length of their cilia. The regulation likely involves intraflagellar transport (IFT), a bidirectional motility of multi-subunit particles organized into trains that deliver building blocks into the organelle. In Chlamydomonas, the anterograde IFT motor kinesin-2 consists of the motor subunits FLA8 and FLA10 and the nonmotor subunit KAP. KAP dissociates from IFT at the ciliary tip and diffuses back to the cell body. This observation led to the diffusion-as-a-ruler model of ciliary length control, which postulates that KAP is progressively sequestered into elongating cilia because its return to the cell body will require increasingly more time, limiting motor availability at the ciliary base, train assembly, building block supply, and ciliary growth. Here, we show that Chlamydomonas FLA8 also returns to the cell body by diffusion. However, more than 95% of KAP and FLA8 are present in the cell body and, at a given time, just ~1% of the motor participates in IFT. After repeated photobleaching of both cilia, IFT of fluorescent kinesin subunits continued indicating that kinesin-2 cycles from the large cell-body pool through the cilia and back. Furthermore, growing and full-length cilia contained similar amounts of kinesin-2 subunits and the size of the motor pool at the base changed only slightly with ciliary length. These observations are incompatible with the diffusion-as-a-ruler model, but rather support an "on-demand model," in which the cargo load of the trains is regulated to assemble cilia of the desired length.

Interactions Between Molten Sodium and Standard Pipe Insulation

Abstract Safety system design and implementation are critical to the operation of any nuclear plant. For sodium-cooled nuclear reactors, hazards external to the reactor core are present in the form of molten sodium that leaks through degraded piping structures. These structures are often clad in high-temperature insulation to preserve the heat needed to keep the sodium molten in the piping. While large sodium leaks are quite noticeable and often result in hazardous fire situations, small leaks of molten sodium are often masked by the shroud of insulation until a large pool of material has collected outside of the failed pipe. This study concentrated on the physical and chemical interactions between molten sodium and standard fiberglass insulation in temperatures ranging from 100 °C to 500 °C. The degradation of the insulation material begins with the volatilization of the organic binder around 250 °C, thereafter the insulation deteriorates at an advanced rate in areas that are in direct contact with the sodium. Chemical profile data were collected for a variety of sample locations that were in contact with the molten sodium, with only a slight increase in the amount of sodium present that can be attributed to the external sodium source. In this way, the molten sodium chemically reacts with the insulation, which accelerates the degradation of the insulation in locations where the sodium is in direct contact with the insulation. These reactions are enhanced by the temperature of the sodium.

Revolutionizing language learning: Unleashing the power of the engage model to supercharge writing skill in cognitively more and less active EFL learners

The ever-growing need for fluency in written English around the world, because of the role of English as the world’s international language, has given priority to finding more effective ways to its teaching. The present study aimed to investigate the effect of using the ENGAGE Model on writing performance of EFL learners through a mixed-method study. The participants of the study were 60 advanced level female EFL learners with the age range of 20 to 30 in one of the private language institutes in Isfahan, Iran. The participants were non-randomly selected from a large pool of advanced female students. The selected participants were assigned to the two groups of the ENGAGE Model (the experimental group) and TBLT (the control group), with 30 students in each. The participants were also specified in terms of their cognitive ability as cognitively more or less active based on their answers to a validated cognitive profile questionnaire. In the quantitative phase of the study, the participants went through the processes of pre-testing, intervention, and post-testing and the data collected were fed into the SPSS software version 26. The results revealed that the ENGAGE Model had a statistically significant effect on the writing development of cognitively more and less active EFL learners. The qualitative findings of the study proved that the cognitively more active learners enjoyed the ENGAGE Model class more than the cognitively less active ones. Likewise, the cognitively more active learners could benefit from the class more than their counterparts in the cognitively less active camp and assessed themselves more positively in terms of L2 writing. The finding of the study suggested that EFL teachers and stakeholders should increase interaction and higher-order thinking, and make connections to learners’ previous learning.

Set size and the orthographic/phonological neighbourhood size effect in serial recognition: The importance of randomization.

The neighbourhood size effect refers to the finding of better memory for words with more orthographic/phonological neighbours than otherwise comparable words with fewer neighbours. Although many studies have replicated this result with serial recall, only one has used serial recognition. Greeno et al. (2022) found no neighbourhood size effect when a large stimulus pool was used and a reverse effect-better performance for small neighbourhood words-when a small stimulus pool was used. We reexamined these results but made two methodological changes. First, for the large pool, we randomly generated lists for each subject rather than creating one set of lists that all subjects experienced. Second, for the small pool, we randomly generated a small pool for each subject rather than using one small pool for all subjects. In both cases, we observed a neighbourhood size effect consistent with results from the serial recall literature. Implications for methodology and theoretical accounts of both the neighbourhood size effect and serial recognition are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Reliable design rules for cold-formed ferritic stainless steel closed built-up beams

In the companion paper, test results were presented to explore the flexural performance of cold-formed ferritic stainless steel (FSS) closed built-up beams. Each specimen was fabricated by joining two lipped channels in a face-to-face configuration and securing them with spot welds at intermediate points along the beam span. Sectional aspect ratio, compression flange’s sectional slenderness and moment gradient were the key parameters varied to explore their influence on the flexural response of such members through tests. The current paper establishes a simplified finite element (FE) model of cold-formed FSS closed built-up beams and verifies it against the test results. Further, an extensive parametric study is carried out using the validated FE model to generate a large pool of data points. A wider database comprising of test results, numerical results obtained from this study and numerical results available in the companion paper were used to critically examine the adequacy of the current Australian Specifications (AS/NZ), American Standard (SEI/ASCE-8), European Code (EN 1993-1-4), Modified Eurocode approach suggested by Gardner and Theofanous, and Direct Strength Method (DSM) in North American Specification (AISI-S100: 2016), Modified DSM approach proposed by the authors in the literature and Continuous Strength Method (CSM) for cold-formed steel (CFS) flexural members. This makes the current study the first to extensively assess the accuracy of the various design guidelines available in the codes and other relevant literature, which explicitly highlighted their grave limitations. Accordingly, through suitable modifications and recommendations, new design rules are proposed to reliably and safely predict the flexural capacity of cold-formed FSS closed built-up beams.

Raising pre-service English language teachers’ awareness of sustainable development goals through literary texts

Purpose This study aims to examine the outcome of an online project to inform preservice English language teachers regarding the United Nations’ Sustainable Development Goals (SDGs). Design/methodology/approach A qualitative research method was used to understand participants’ overall knowledge. In total, 30 preservice teachers were selected from a larger pool after considering their grade point averages, sex, university affiliations and geographical backgrounds to ensure a balanced distribution and diversity. Qualitative data were collected through written productions, video recordings and a postproject survey. Findings Findings revealed that preservice teachers deepened their understanding of SDGs and adopted a positive attitude toward SDGs while increasing their pedagogical awareness and knowledge of teaching English in response to SDGs. They also regarded literary texts as a valuable resource for teaching SDGs. Originality/value This study contributes to the literature by demonstrating that literary works are valuable for educating preservice teachers on integrating SDGs. A detailed overview of the project and its outcomes can guide practitioners and teacher educators in integrating SDGs into their education programs and English language teaching.

Climate, fire, and anthropogenic disturbance determine the current global distribution of tropical forest and savanna

Tropical forest and savanna biomes are pivotal in the functioning of the Earth system. Both are biodiverse and under increasing threat due to land clearing and anthropogenic climate change, and play important roles in the global carbon cycle, through maintenance of a large carbon pool in tropical forests, and exchange in savannas through extensive landscape fires. Reliable mapping of tropical forest and savanna is essential to understand how the current distribution of these vegetation types is controlled by climate land clearing and fire. Using Google Maps satellite imagery, we manually classified 24 239 random points as forest, savanna, or anthropogenic landscapes within the tropics and applied this novel dataset to defining the climatic zone where forest and savanna exist as alternative states. Because fire and climate are correlated, we developed separate geospatial models to rank the importance of climate, topography, and human influence on vegetation present. This modeling confirmed that those areas with more fires had lower probabilities of tropical forest, that forest was most likely in areas with high mean annual rainfall with little seasonal variation in precipitation, and that anthropogenic factors disrupt this environmental predictability. We also identified areas where tropical forest and savanna both co-occur, but these were relatively uncommon. These relationships suggest that future drier climates projected under anthropogenic climate change, combined with clearing and burning that have reduced tropical forest extent to a subset of its theoretical distribution, will lead to irreversible loss of tropical forests. Our modeling provides global mapping that can be used track further changes to distribution of tropical forests.

A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data

The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the aid of such large data pools, researchers are able to develop clinical prediction models for improved disease classification, risk assessment, and beyond. To fully utilize this potential, Machine Learning (ML) methods are commonly required to process these large amounts of data on disease-specific patient cohorts. As a consequence, the Observational Health Data Sciences and Informatics (OHDSI) collaborative develops a framework to facilitate the application of ML models for these standardized patient datasets by using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). In this study, we compare the feasibility of current web-based OHDSI approaches, namely ATLAS and “Patient-level Prediction” (PLP), against a native solution (R based) to conduct such ML-based patient-level prediction analyses in OMOP. This will enable potential users to select the most suitable approach for their investigation. Each of the applied ML solutions was individually utilized to solve the same patient-level prediction task. Both approaches went through an exemplary benchmarking analysis to assess the weaknesses and strengths of the PLP R-Package. In this work, the performance of this package was subsequently compared versus the commonly used native R-package called Machine Learning in R 3 (mlr3), and its sub-packages. The approaches were evaluated on performance, execution time, and ease of model implementation. The results show that the PLP package has shorter execution times, which indicates great scalability, as well as intuitive code implementation, and numerous possibilities for visualization. However, limitations in comparison to native packages were depicted in the implementation of specific ML classifiers (e.g., Lasso), which may result in a decreased performance for real-world prediction problems. The findings here contribute to the overall effort of developing ML-based prediction models on a clinical scale and provide a snapshot for future studies that explicitly aim to develop patient-level prediction models in OMOP CDM.

Countering online marketing and user endorsements with enhanced cannabis warning labels: An online experiment among at-risk youth and young adults

As cannabis legalization expands and online marketing intensifies, this study examines whether online social cues can amplify youth-targeted cannabis advertising and whether cannabis warning labels (CWLs) can counteract these influences. A U.S. online sample of 970 adolescents and 1776 young adults susceptible to cannabis use were recruited from Qualtrics in summer 2022. Each participant was randomly assigned to one of the 3 (CWLs: none vs. textual vs. pictorial) by 3 (comments: none vs. anti-cannabis vs. pro-cannabis) conditions in an online experiment. Participants were exposed to three online marketing posts promoting marijuana edibles (randomly selected from a large pool, N = 1260), each with either no warning label, a textual warning, or a pictorial warning (text and picture), and with either five comments (pro- or anti-cannabis in valence) or none. Results showed that among adolescents, pro-cannabis comments increased product appeal (vs. anti-cannabis comments: b = 0.18, p = .025; vs. no comments: b = 0.21, p = .021), and did so more than young adults. For adolescents, only pictorial warnings reduced product appeal (b = −0.20, p = .028). For young adults, both pictorial (b = −0.18, p = .002) and textual warnings (b = −0.12, p = .029) reduced product appeal. Furthermore, both textual (adolescents: b = −0.20, p = .004; young adults: b = −0.15, p = .005) and pictorial (adolescents: b = −0.30, p < .001; young adults: b = −0.18, p = .001) warnings reduced cannabis use intentions. Findings support requiring enhanced CWLs accompany online marketing ads.

Exploring the Impact of E-Recruitment on the Performance of Human Resource Departments in Multinational Organizations

The human resource department is among the crucial units in organizations. Through the department, companies conduct recruitment and selection processes to ensure that they hire people with appropriate skills and other attributes to fit in workplace cultures. The study reviews the effects of e-recruitment on the performance of human resource units in multinational companies. Due to the rising utilization of modern technologies in organizational processes, e-recruitment has emerged as a way of using the Internet to identify suitable individuals to fill vacancies in companies. The benefits associated with e-recruitment include access to many candidates from different parts of the world, convenient communication between the company and job applicants, and lower advertising and information-sharing costs. The literature review in the report also indicates that recruitment processes in multinational companies transformed in recent years. The firms mostly use e-recruitment approaches to reach a large pool of candidates since attracting talent and persuading individuals to work in foreign countries can be challenging. The research will be based on surveys and experimental methods using questionnaires as data collection tools. The findings will illustrate the importance of e-recruitment in improving human resource departments’ capacity to select suitable candidates and reduce labour costs.

Fostering Competency Development Through Knowledge Sharing Capabilities in Onboarding

The study proposes and empirically validates a theoretical model suggesting that employee competency development during the onboarding period largely depends on the availability of knowledge sharing (KS) channels (both digital and traditional), on the KS culture experienced, and on employee satisfaction with the existing KS practices. The data for the study was collected using an online survey of a large pool of young public service employees working at various government agencies (n= 3,652 respondents). The results of a structural equation modeling analysis of the data provide support for the study's main premise. The implications of these results on the design of effective onboarding processes are discussed.