bluuebunny/crossref_metadata_2025_split huggingface dataset:
Instagram for recruiting: a content analysis and effects of information and interactions in hiring-related Instagram posts
Authors: Cho Inchul , Joa Claire Youngnyo 
Published: October 2024

PurposeThis study aims to understand how organizations use social media for recruitment by identifying (1) what type of information is promoted and (2) what type of engagement and interaction occurs in hiring-related Instagram posts. Moreover, drawing on the social media affordance approach, this study further examines whether certain information and interactions in Instagram posts lead to better outcomes.Design/methodology/approachUsing a hashtag-based sampling strategy, we conducted a content analysis of 274 hiring-related Instagram posts by organizations to identify different types of information and interaction and then conducted a series of t-tests to examine the impact of the observed information and interactions on outcomes.FindingsWe found four types of hiring-related information and identified ten types of interactions driven by both organizations and audiences. Additionally, we found that hiring-related Instagram posts with a high degree of visibility and association led to more likes and comments.Practical implicationsThis study’s results can benefit organizations that use Instagram to attract jobseekers and potential applicants, as different types of information and interaction lead to different outcomes.Originality/valueSince this is the first study to content analyze Instagram posts associated with hiring, it will provide important implications for both practice and research.
Non-isothermal crystallization kinetics of organic montmorillonite/SiO2 aerogel/PET composites
Authors: Gao Hongyu , Xu Shi , Yang Fukang , Zhang Ziyue , Wang Shuolei , Jiang Xiaojian , Dong Yubing 
Published: October 2024

Purpose
Crystallization kinetics is a key factor that controls the crystallization process of polymers and influences the crystallinity and morphology of polymers. This study aims to explore the effects of functional filler SiO2 aerogel and co-modified filler organic montmorillonite (OMMT) on the crystallization process of polyester polyethylene terephthalate (PET). In this study, the nonisothermal crystallization kinetics of OMMT/SiO2 aerogel/PET composites were studied by Jeziorny method.

Design/methodology/approach
The catalyst (Sb2O3), OMMT and SiO2 aerogel were uniformly dispersed in ethylene glycol (EG). Then, the mixture and terephthalic acid (PTA) were put into a semicontinuous polyester synthesis reactor, and the SiO2 aerogel/PET composites were prepared by esterification and polycondensation.

Findings
Non-isothermal kinetic results showed that the high cooling rate hindered the movement of the molecular chain of the composites and made the crystallization peak move toward the low-temperature direction. With the increase of crystallization temperature (Tc), the melt crystallization rate decreases, but the cold crystallization rate increases. The introduction of OMMT and SiO2 aerogel condensation affected the nucleation and growth mode of crystals, lengthened the time with a relative crystallinity of 50% (t1/2) and decreased the crystallization rate. OMMT improved the crystallinity and Avrami index of the composites.

Research limitations/implications
Effects of thermal insulation functional filler SiO2 aerogel and co-modified filler OMMT on the crystallization process of PET were studied by non-isothermal crystallization kinetics, and the effects of SiO2 aerogel and OMMT on the nucleation mechanism of PET were clarified, which provided a theoretical reference for the preparation and performance optimization of PET matrix composites.

Originality/value
In this study, the OMMT/SiO2 aerogel/PET composites were prepared by in-situ polymerization, the crystallinity of PET matrix composites was improved, and the effects of OMMT and SiO2 aerogel on the crystallization process of PET were clarified.
Not all maps are equal: Evaluating approaches for mapping vessel collision risk to large baleen whales
Authors: Hague E. L. , Halliday W. D. , Dawson J. , Ferguson S. H. , Heide‐Jørgensen M. P. , Serra Sogas N. , Gormley K. , Young B. G. , McWhinnie L. H. 
Published: October 2024

Abstract

A growing and increasingly globalised human population, requiring the movement of goods and commodities, is placing increasing demands on the maritime industry, resulting in a concurrent increase in global shipping activities. This has consequences for the marine environment, particularly for species vulnerable to the impacts of vessel traffic. For example, vessel collisions can result in sub‐lethal or fatal injuries for marine mammals, whilst vessel noise can cause acoustic masking that effectively reduces an animal's listening space, potentially impacting their communication, navigation and foraging capacity.

While a number of parallel approaches to mapping collision risk to large whales have arisen, these methods vary in their focus, usually on either co‐occurrence, collision probability, or probability of mortality. However, little attention has been given to the implications of methodological choice and data selection on subsequent risk predictions.

To assess differences between these approaches, we used a standardised input dataset comprised of telemetry‐point data from tagged bowhead whales, and satellite‐based Automated Identification System (AIS) data of spatial vessel movements covering the Davis‐Baffin Arctic Marine Area. We applied this data to eight different, previously published analyses for deriving areas of vessel risk.

We found that the choice of risk mapping approach affected the location, and total area, identified as ‘high risk’, and that more computationally complex approaches did not necessarily equate to different predictions. There was considerable variation in the total area of ‘high risk’ predicted within each map (range = 20–42,246 km2).

Synthesis and Applications. The results underscore the importance of methodological transparency, informed data selection and careful interpretation when predicting collision risk. We provide practical recommendations for enhancing transparency when predicting risk, and discuss choice of approach suitable for different situations or management applications. It is critical that managers and policy makers are aware of the implications of applying different approaches when interpreting risk evaluation outputs.
An adjunctive multi‐family intervention based on non‐verbal techniques in a day hospital for parents and toddlers with feeding problems
Authors: Jespers Ine , De Groote Isabel 
Published: October 2024

AbstractFeeding problems in early childhood are common and affect parental behaviour with more use of maladaptive feeding strategies. Although multi‐family therapy is well known as an effective intervention for adolescents and adults with eating disorders/problems, the use of multi‐family therapy for toddlers with feeding problems is rather rare. In this paper we describe how we adapted the evidence‐based goals of multi‐family therapy to a specific format for parents and toddlers and how we used it in our clinical work. We explain the importance of creating a supportive culture in which parents may develop effective feeding strategies. We describe how we used non‐verbal techniques in multi‐family therapy as a tool for growth for both toddlers and parents. We end with some retrospective reflections and directions for future therapy and research.
Should I stay or should I go again: Multiple switching between fee‐for‐service Medicare and Medicare advantage among older beneficiaries
Authors: Hoffman Geoffrey J. , Jiao Yang Amy , Fan Zhaohui , Kim H. Myra , Min Lillian , Maust Donovan 
Published: October 2024

AbstractObjectiveTo evaluate whether having previously disenrolled from Medicare Advantage (MA) is associated with lower hazards of future MA enrollment.Data Sources and Study SettingSecondary data from Medicare.Study DesignWe examined beneficiaries with baseline FFS enrollment from 2017–2019 using a 20% sample of Medicare claims. Cox proportional hazard models were used to examine the association of prior MA enrollment (in the three years prior to baseline FFS enrollment) with MA re‐enrollment, and whether this association is modified by Alzheimer's Disease and Related Dementias (ADRD), prior nursing home use, chronic illness, dual eligible status, and availability of MA plans and quality.Data CollectionNot applicable.Principal FindingsOverall, 3.3% of beneficiaries switched to MA annually. Of those with prior MA enrollment, MA switching percentages were 9.0%, 4.6%, and 6.8% for those whose most recent MA enrollments were 1, 2, and 3 years prior to their baseline FFS year. Comparatively, the switching percentages was 3.2% for those with no prior MA enrollment. The hazards of switching to MA were 2.73 (p < 0.001), 1.29 (p < 0.001), and 1.97 (p < 0.001) times greater than remaining in FFS for beneficiaries whose most recent MA enrollments were one, two, and three years prior to their baseline FFS year. Hazards of switching were generally similar between those with and without ADRD, stratified by recency in prior MA experience, except those with dual eligibility. Among those with ADRD, switching hazards were greatest for 3 years prior MA enrollees in counties with the fewest available (HR: 3.84, p < 0.001) and lowest‐rated plans (HR: 4.02, p < 0.001).ConclusionsRecency of switching from MA to FFS was the strongest predictor of a FFS‐to‐MA switch, identifying a population of beneficiaries who multiply switch regardless of health status or MA access. Future health policy considerations should more closely examine the vulnerabilities and long‐term outcomes of this population.
Development of a forensic DNA research grade test material
Authors: Romsos Erica L. , Borsuk Lisa A. , Steffen Carolyn R. , Riman Sarah , Kiesler Kevin M. , Vallone Peter M. 
Published: October 2024

AbstractAdvancements in forensic DNA typing technology and methods have increased sensitivity and, while beneficial, carry the weight of more challenging profile interpretation. In response, the forensic DNA community has often requested more complex reference materials to address commonly encountered measurement and interpretation issues such as complex DNA mixtures, DNA degradation, and PCR inhibition. The National Institute of Standards and Technology (NIST) released Research Grade Test Material 10235: Forensic DNA Typing Resource Samples to support the forensic DNA community. Components include three single source samples, two degraded samples, and three mixture samples. As part of the Research Grade Test Material (RGTM) process, automated methods for bottling, alternative sample tube types, and the addition of carrier RNA for stabilizing low‐quantity samples were investigated. Both internal and external testing demonstrate the stability of the material over time at 4°C through qPCR testing. In the development of a data portal, users have been allowed to anonymously upload results and compare their data with NIST and others. This report describes the preparation and stability of this material.
Results of a large‐scale humeri pair matching study
Authors: LeGarde Carrie B. 
Published: October 2024

AbstractDuring the analysis of a commingled human remains assemblage, pair‐matching is often conducted as part of the inventory to inform the DNA sampling strategy and to calculate the minimum or most likely number of individuals. As commingled assemblages become larger, pair‐matching becomes more difficult, and it is unknown whether accuracy declines. Therefore, a study to determine the accuracy rates of visual pair‐matching for multiple observers was conducted on a large, commingled human assemblage. The sample consisted of 580 left and right humeri (n = 287 and n = 293, respectively) from the commingled remains of the USS Oklahoma, which were undergoing analysis at the Defense POW/MIA Accounting Agency. Five anthropologists completed this study, determining humeri pairs with varying degrees of confidence and non‐pairs. The overall precision was 81.0% and ranged from 74.1% to 95.9% for participants, including all confidence levels. When considering only confident matches, the overall precision for all participants increased to 91.4%.
Sugar inclusion influences the expansion characteristics of corn starch extrudates
Authors: Dey Debomitra , Gu Bon‐Jae , Ek Pichmony , Ross Carolyn F , Saunders Steven R , Ganjyal Girish M 
Published: October 2024

AbstractCorn starch‐based direct expanded products incorporated with 2% and 10% (w/w) sugar (fructose, glucose, sucrose, and xylose) were produced using a 20 mm co‐rotating twin‐screw extruder. The pasting and thermal properties of raw corn starch‐sugar mixes were analyzed before extrusion processing. The independent variables for extrusion processing included two sugar inclusion levels (2% and 10% w/w) and two screw speeds (150 and 250 rpm). The extrudates were characterized by their initial expansion ratio (IER), expansion ratio (ER), and shrinkage. ER values were high for fructose at 2% and 150 rpm and 10% glucose and sucrose extrudates at 250 rpm. The extrudates with 2% sucrose inclusion shrunk significantly higher than the control. Fourier transform infrared (FTIR) spectroscopy of the extruded blends did not indicate the presence of any new covalent bond formed between starch and sugar post‐extrusion. The interactions between sugar concentration and screw speed significantly influenced extrudate expansion characteristics. Due to their thermal and plasticizing properties, sugar inclusion (glucose, fructose, sucrose, and xylose) enhanced the extrudate expansion by altering their melt viscosity.Practical ApplicationThe findings of this study can improve the expansion characteristics of high‐fiber‐based extruded snacks. Ingredients high in fiber generally hinder the starch transformation during extrusion and negatively impact the expansion properties. The presence of sugar at low concentrations can improve melt properties during extrusion processing and, in turn, significantly improve the textural properties of snacks.
Improving storage duration of tomatoes (Solanum lycopersicum) through electron beam technology
Authors: Yoon Ki‐Nam , Yoon Yeong‐Seok , Hong Hae‐Jung , Yeom Seo‐Joon , Park Jong‐Heum , Song Beom‐Seok , Eun Jong‐Bang , Kim Jae‐Kyung 
Published: October 2024

AbstractElectron beam (EB) technology typically consists of high‐energy electron streams produced by a linear accelerator. Although promising, the use of EB irradiation as a technique to delay ripening and prevent spoilage in tomatoes has not been extensively investigated. In this study, the effectiveness of EB irradiation in prolonging the shelf life of tomatoes postharvest was investigated. The results indicated that EB irradiation successfully reduced microbial contamination and decay, preserved key quality attributes (such as total soluble solids, titratable acidity, pH, and firmness), and significantly minimized weight loss. Notably, the treatment delayed the biosynthesis of lycopene, a key indicator of ripening, without adversely affecting phenolic content and antioxidant activity, which remained consistent regardless of irradiation. Additionally, different methods for detecting irradiation were evaluated. Thermoluminescence analysis proved to be the most dependable technique, especially for doses exceeding 600 Gy, due to its high sensitivity and specificity. In contrast, photostimulated luminescence and electron spin resonance analyses showed limitations in accurately identifying the irradiation status of foods with high moisture content, such as tomatoes. This study confirms that EB irradiation, while maintaining postharvest quality, extends the shelf life of tomatoes by 5–10 days, suggesting its potential for commercial application in food preservation.
Predicting physicochemical properties of papayas (Carica papaya L.) using a convolutional neural networks model approach
Authors: An Sujin , Oh Geuncheol , Lee Dongyoung , Baek Kyungim , Jun Soojin 
Published: October 2024

AbstractThe current state of quality assessment methods for agricultural produce, particularly fruits, heavily relies on manual inspection techniques, which could be subjective, time‐consuming, and prone to human errors. Consequently, there have been emerging trends and needs for non‐destructive methods to evaluate fruit quality accurately and practically. This research aimed to develop a novel approach for predicting the physicochemical properties of papayas using a convolutional neural network (CNN) model that combines image analysis and weight assessment. This study involved capturing images of papayas at different ripening stages, measuring papaya weights, and determining various physicochemical properties such as texture, pH, total soluble solids, and seed weight. A total of 532 images were obtained from 132 papayas, and an additional 1064 images were generated through image augmentation. The dataset was divided into three sets with an 8:1:1 ratio for training, validation, and testing. The CNN model was trained using papaya images and weights as input values to predict and estimate the physicochemical property values. Model performance was evaluated using mean squared error (MSE) and the coefficient of determination (R2) as metrics. The CNN model, integrated with image processing, could predict the diverse physicochemical properties of papayas with high accuracy. The MSE values estimated for the training and validation sets were 0.0284 and 0.1729, respectively. The R2 values for the test dataset ranged from 0.71 to 0.94. These findings demonstrate that CNN‐based models could provide detailed and quantitative insights, facilitating improved understanding and management of papaya quality while enhancing predictive modeling accuracy in agriculture.Practical ApplicationThis research introduces a new method for accurately predicting the quality of papayas using a computer model. Instead of relying on manual inspection, which can be slow and prone to errors, this model uses images of papayas and their weights to predict properties, including texture, pH, total soluble solids, and seed weight. This can help manage papaya quality better while also improving agricultural production and transportation processes.
What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA
Authors: Daniel Alexandrine , Savary Paul , Foltête Jean‐Christophe , Vuidel Gilles , Faivre Bruno , Garnier Stéphane , Khimoun Aurélie 
Published: October 2024

AbstractModelling population connectivity is central to biodiversity conservation and often relies on resistance surfaces reflecting multi‐generational gene flow. ResistanceGA (RGA) is a common optimization framework for parameterizing these surfaces by maximizing the fit between genetic distances and cost distances using maximum likelihood population effect models. As the reliability of this framework has rarely been studied, we investigated the conditions maximizing its accuracy for both prediction and interpretation of landscape features' permeability. We ran demo‐genetic simulations in contrasted landscapes for species with distinct dispersal capacities and specialization levels, using corresponding reference cost scenarios. We then optimized resistance surfaces from the simulated genetic distances using RGA. First, we evaluated whether RGA identified the drivers of the genetic patterns, that is, distinguished Isolation‐by‐Resistance (IBR) patterns from either Isolation‐by‐Distance or patterns unrelated to ecological distances. We then assessed RGA predictive performance using a cross‐validation method, and its ability to recover the reference cost scenarios shaping genetic structure in simulations. IBR patterns were well detected and genetic distances were predicted with great accuracy. This performance depended on the strength of the genetic structuring, sampling design and landscape structure. Matching the scale of the genetic pattern by focusing on population pairs connected through gene flow and limiting overfitting through cross‐validation further enhanced inference reliability. Yet, the optimized cost values often departed from the reference values, making their interpretation and extrapolation potentially dubious. While demonstrating the value of RGA for predictive modelling, we call for caution and provide additional guidance for its optimal use.
Lipid droplet‐associated proteins in alcohol‐associated fatty liver disease: A proteomic approach
Authors: Perumal Sathish Kumar , Day Le Z. , Arumugam Madan Kumar , Chava Srinivas , Kumar Vikas , Osna Natalia A. , Jacobs Jon , Rasineni Karuna , Kharbanda Kusum K. 
Published: October 2024

AbstractBackgroundThe earliest manifestation of alcohol‐associated liver disease (ALD) is steatosis characterized by deposition of fat in specialized organelles called lipid droplets (LDs). While alcohol administration causes a rise in LD numbers in the hepatocytes, little is known regarding their characteristics that allow their accumulation and size to increase. The aim of the present study is to gain insights into underlying pathophysiological mechanisms by investigating the ethanol‐induced changes in hepatic LD proteome as a function of LD size.MethodsAdult male Wistar rats (180–200 g BW) were fed with ethanol liquid diet for 6 weeks. At sacrifice, large‐, medium‐, and small‐sized hepatic LD subpopulations (LD1, LD2, and LD3, respectively) were isolated and subjected to morphological and proteomic analyses.ResultsMorphological analysis of LD1‐LD3 fractions of ethanol‐fed rats clearly demonstrated that LD1 contained larger LDs compared with LD2 and LD3 fractions. Our preliminary results from principal component analysis showed that the proteome of different‐sized hepatic LD fractions was distinctly different. Proteomic data analysis identified over 2000 proteins in each LD fraction with significant alterations in protein abundance among the three LD fractions. Among the altered proteins, several were related to fat metabolism, including synthesis, incorporation of fatty acid, and lipolysis. Ingenuity pathway analysis revealed increased fatty acid synthesis, fatty acid incorporation, LD fusion, and reduced lipolysis in LD1 compared to LD3. Overall, the proteomic findings indicate that the increased level of protein that facilitates fusion of LDs combined with an increased association of negative regulators of lipolysis dictates the generation of large‐sized LDs during the development of alcohol‐associated hepatic steatosis.ConclusionSeveral significantly altered proteins were identified in different‐sized LDs isolated from livers of ethanol‐fed rats. Ethanol‐induced increases in specific proteins that hinder LD lipid metabolism led to the accumulation and persistence of large‐sized LDs in the liver.