bluuebunny/crossref_metadata_2025 huggingface dataset:
Influenza Virus Genomic Surveillance, Arizona, USA, 2023-2024
Authors: Maqsood Rabia , Smith Matthew F. , Holland LaRinda A. , Sullins Regan A. , Holland Steven. C. , Tan Michelle , Hernandez Barrera Gabrielle M. , Thomas Alexis W. , Islas Mario , Kramer Joanna L. , Nordstrom Lora , Mulrow Mary , White Michael , Murugan Vel , Lim Efrem S.
Published: February 2024

We conducted genomic sequencing of 110 influenza A and 30 influenza B viruses from specimens collected between October 2023 and February 2024 in Arizona, USA. We identified mutations in the hemagglutinin (HA) antigenic sites. Real-time genomic surveillance is important to ensure influenza vaccine effectiveness.
Reconsider photoplethysmogram signal quality assessment in the free living environment
Authors: Su Yan-Wei , Hao Chia-Cheng , Liu Gi-Ren , Sheu Yuan-Chung , Wu Hau-Tieng
Published: February 2024

Background

Assessing signal quality is crucial for biomedical signal processing, yet a precise mathematical model for defining signal quality is often lacking, posing challenges for experts in labeling signal qualities. The situation is even worse in the free living environment.

Method

We propose to model a PPG signal by the adaptive non-harmonic model (ANHM) and apply a decomposition algorithm to explore its structure, based on which we advocate a reconsideration of the concept of signal quality.

Result

We demonstrate the necessity of this reconsideration and highlight the relationship between signal quality and signal decomposition with examples recorded from the free living environment. We also demonstrate that relying on mean and instantaneous heart rates derived from PPG signals labeled as high quality by experts without proper reconsideration might be problematic.

Conclusion

A new method, distinct from visually inspecting the raw PPG signal to assess its quality, is needed. Our proposed ANHM model, combined with advanced signal processing tools, shows potential for establishing a systematic signal decomposition based signal quality assessment model.
A targetable PREX2/RAC1/PI3Kβ signalling axis confers resistance to clinically relevant therapeutic approaches in melanoma
Authors: Ford Catriona A. , Koludrovic Dana , Centeno Patricia P. , Foth Mona , Tsonou Elpida , Vlahov Nikola , Sphyris Nathalie , Gilroy Kathryn , Bull Courtney , Nixon Colin , Serrels Bryan , Munro Alison F. , Dawson John C. , Carragher Neil O. , Pavet Valeria , Hornigold David C. , Dunne Philip D. , Downward Julian , Welch Heidi C. E. , Barry Simon T. , Sansom Owen J. , Campbell Andrew D.
Published: February 2024

Metastatic melanoma remains a major clinical challenge. Large-scale genomic sequencing of melanoma has identifiedbona fideactivating mutations inRAC1, with mutations of its upstream regulator, the RAC-GEFPREX2, also commonly detected. Crucially,RAC1mutations are associated with resistance to BRAF-targeting therapies. Despite the role of its homologue PREX1 in melanomagenesis, and evidence that some truncatingPREX2mutations drive increased RAC1 activity, no hotspot mutations have been identified, and the impact ofPREX2mutation remains contentious. Here, we use genetically engineered mouse models and patient-derived BRAFV600E-driven melanoma cell lines to dissect the role of PREX2 in melanomagenesis and response to therapy. We show that while PREX2 is dispensable for the initiation and progression of melanoma, its loss confers sensitivity to clinically relevant therapeutics. Importantly, genetic and pharmacological targeting of the RAC1 effector kinase PI3Kβ phenocopiesPREX2loss, sensitizing our model systems to therapy. Our data reveal a druggable PREX2/RAC1/PI3Kβ signalling axis inBRAF-mutant melanoma that could be exploited clinically.

Statement of Significance

Metastatic melanoma remains both a clinical problem, and an opportunity for therapeutic benefit. Co-targeting of the MAPK pathway and the PREX2/RAC1/PI3Kβ has remarkable efficacy and outperforms monotherapy MAPK targetingin vivo.
Proteomic Analysis of Urine from Youths Indulging in Gaming
Authors: Yang Minhui , Liu Yuqing , Shen Ziyun , Wang Haitong , Gao Youhe
Published: February 2024

Video game addiction manifests as an escalating enthusiasm and uncontrolled use of digital games, yet there are no objective indicators for gaming addiction. This study employed mass spectrometry proteomics to analyze the proteomic differences in the urine of adolescents addicted to gaming compared to those who do not play video games. The study included 10 adolescents addicted to gaming and 9 non-gaming adolescents as a control group. The results showed that there were 125 significantly different proteins between the two groups. Among these, 11 proteins have been reported to change in the body after the intake of psychotropic drugs and are associated with addiction: Calmodulin, ATP synthase subunit alpha, ATP synthase subunit beta, Acid ceramidase, Tomoregulin-2, Calcitonin, Apolipoprotein E, Glyceraldehyde-3-phosphate dehydrogenase, Heat shock protein beta-1, CD63 antigen, Ephrin type-B receptor 4, Tomoregulin-2. Additionally, several proteins were found to interact with pathways related to addiction: Dickkopf-related protein 3, Nicastrin, Leucine-rich repeat neuronal protein 4, Cerebellin-4. Enriched biological pathways discovered include those related to nitric oxide synthase, amphetamine addiction, and numerous calcium ion pathways, all of which are associated with addiction. Moreover, through the analysis of differentially expressed proteins, we speculated about some proteins not yet fully studied, which might play a significant role in the mechanisms of addiction: Protein kinase C and casein kinase substrate in neurons protein, Cysteine-rich motor neuron 1 protein, Bone morphogenetic protein receptor type-2, Immunoglobulin superfamily member 8. In the analysis of urinary proteins in adolescents addicted to online gaming, we identified several proteins that have previously been reported in studies of drug addiction.
Decoding the spatiotemporal dynamic neural representation of repetitive facial expression imitation
Authors: Liu Qi , Zhou Xinqi , Zhu Siyu , Liu Can , Yang Yanmiao , Lan Chunmei , Song Xinwei , Becker Benjamin , Kendrick Keith M. , Zhao Weihua
Published: February 2024

Imitating facial emotion expressions can facilitate social interactions, although the behavioral and neural spatiotemporal dynamics is unclear. Here participants (N=100) imitated facial emotions repeatedly over one month (16 times in total) with neural activity measured on three occasions using functional near-infrared spectroscopy. Additionally, the transfer effect of repeated imitation on emotional face and scene perception was assessed by fMRI with multivariate pattern analysis. Valence specific imitation performance was facilitated by the alterations in the similarity of spatiotemporal patterns evoked in the mirror neuron system (MNS) with information flow moving progressively towards the inferior frontal gyrus as the as the number of times of imitation increase. Furthermore, MNS representation predictive patterns of processing emotional faces, but not scenes, were enhanced. Overall, these findings provide a neural changes of information flow within MNS and advance our understanding of the spatiotemporal dynamics from novice to proficient of facial emotion imitation.
Selection of movement rules to simulate species dispersal in a mosaic landscape model
Authors: Gold Susannah , Croft Simon , Budgey Richard , Aegerter James
Published: February 2024

Dispersal is an ecological process central to population dynamics, representing an important driver of movement between populations and across landscapes. In spatial population models for terrestrial vertebrates, capturing plausible dispersal behaviour is of particular importance when considering the spread of disease or invasive species. The distribution of distances travelled by dispersers, or the dispersal kernel, is typically highly skewed, with most individuals remaining close to their origin but some travelling substantially further. Using mechanistic models to simulate individual dispersal behaviour, the dispersal kernel can be generated as an emergent property. Through stepwise simulation of the entire movement path, models can also account for the influence of the local environment, and contacts during the dispersal event which may spread disease. In this study, we explore a range of simple rules to emulate individual dispersal behaviour within a mosaic model generated using irregular geometry. Movement rules illustrate a limited range of behavioural assumptions and when applied across these simple synthetic landscapes generated a wide range of emergent kernels. Given the variability in dispersal distances observed within species, our results highlight the importance of considering landscape heterogeneity and individual-level variation in movement, with simpler rules approximating random walks providing less plausible emergent kernels. As a case study, we demonstrate how rule sets can be selected by comparison to an empirical kernel for a study species (red fox;Vulpes vulpes). These results provide a foundation for the selection of movement rules to represent dispersal in spatial agent-based models, however, we also emphasise the need to corroborate rules against the behaviour of specific species and within chosen landscapes to avoid the potential for these rules to bias predictions.
The recovery of parabolic avalanches in spatially subsampled neuronal networks at criticality
Authors: Srinivasan Keshav , Ribeiro Tiago L. , Kells Patrick , Plenz Dietmar
Published: February 2024

Scaling relationships are key in characterizing complex systems at criticality. In the brain, they are evident in neuronal avalanches—scale-invariant cascades of neuronal activity quantified by power laws. Avalanches manifest at the cellular level as cascades of neuronal groups that fire action potentials simultaneously. Such spatiotemporal synchronization is vital to theories on brain function yet avalanche synchronization is often underestimated when only a fraction of neurons is observed. Here, we investigate biases from fractional sampling within a balanced network of excitatory and inhibitory neurons with all-to-all connectivity and critical branching process dynamics. We focus on how mean avalanche size scales with avalanche duration. For parabolic avalanches, this scaling is quadratic, quantified by the scaling exponent,χ= 2, reflecting rapid spatial expansion of simultaneous neuronal firing over short durations. However, in networks sampled fractionally,χis significantly lower. We demonstrate that applying temporal coarse-graining and increasing a minimum threshold for coincident firing restoresχ= 2, even when as few as 0.1% of neurons are sampled. This correction crucially depends on the network being critical and fails for near sub- and supercritical branching dynamics. Using cellular 2-photon imaging, our approach robustly identifiesχ= 2 over a wide parameter regime in ongoing neuronal activity from frontal cortex of awake mice. In contrast, the common ’crackling noise’ approach fails to determineχunder similar sampling conditions at criticality. Our findings overcome scaling bias from fractional sampling and demonstrate rapid, spatiotemporal synchronization of neuronal assemblies consistent with scale-invariant, parabolic avalanches at criticality.
A high throughput CRISPR perturbation screen identifies epigenetic regulators impacting primordial germ cell development
Authors: Li Liangdao , Ding Xinbao , Sheft Alex P. , Schimenti John C.
Published: February 2024

Certain environmental factors can impact fertility and reproductive parameters such as the number and quality of sperm and eggs. One possible mechanism is the perturbation of epigenetic landscapes in the germline. To explore this possibility, we conducted a CRISPRi screen of epigenetic-related genes to identify those that specifically perturb the differentiation of embryonic stem cells (ESCs) into primordial germ cell-like cells (PGCLCs), exploiting a highly scalable cytokine-free platform. Of the 701 genes screened, inhibition of 53 decreased the efficiency of PGCLC formation. NCOR2, a transcriptional repressor that acts via recruitment of Class I and Class IIa histone deacetylases (HDACs) to gene targets, was particularly potent in suppressing PGCLC differentiation. Consistent with evidence that histone deacetylation is crucial for germline differentiation, we found that the HDAC inhibitors (HDACi) valproic acid (VPA; an anti-convulsant) and sodium butyrate (SB; a widely-used dietary supplement) also suppressed ESC>PGCLC differentiation. Furthermore, exposure of developing mouse embryos to SB or VPA caused hypospermatogenesis. Transcriptome analyses of HDACi-treated, differentiating ESC>PGCLC cultures revealed suppression of germline-associated pathways and enhancement of somatic pathways. This work demonstrates the feasibility of conducting large-scale functional screens of genes, chemicals, or other agents that may impact germline development.
A 2D Gabor-wavelet baseline model out-performs a 3D surface model in scene-responsive cortex
Authors: Shafer-Skelton Anna , Brady Timothy F. , Serences John T.
Published: February 2024

Understanding 3D representations of spatial information, particularly in naturalistic scenes, remains a significant challenge in vision science. This is largely because of conceptual difficulties in disentangling higher-level 3D information from co-occurring features and cues (e.g., the 3D shape of a scene image is necessarily defined by spatial frequency and orientation information). Recent work has employed newer models and analysis techniques that attempt to mitigate these in-principle difficulties. For example, one such study reported 3D-surface features were uniquely present in areas OPA, PPA, and MPA/RSC (areas typically referred to as ‘scene- selective’), above and beyond a Gabor-wavelet baseline (“2D”) model. Here, we tested whether these findings generalized to a new stimulus set that, on average, dissociated static Gabor- wavelet baseline (“2D”) features from 3D scene-surface features. Surprisingly, we found evidence that a Gabor-wavelet baseline model better fit voxel responses in areas OPA, PPA and MPA/RSC compared to a model with 3D-surface information. This raises the question of whether previous findings of greater 3D information could have been due to a baseline condition that didn’t model some potentially critical low-level features (e.g., motion). Our findings also emphasize that much of the information in “scene-selective” regions—potentially even information about 3D surfaces—may be in the form of spatial frequency and orientation information often considered 2D or low-level, and they highlight continued fundamental conceptual challenges in disentangling the contributions of low-level vs. high-level features in visual cortex.
Yemeni university students public perceptions toward the use of artificial intelligence in healthcare: A cross-sectional study
Authors: Hatem Najmaddin A. H. , Ibrahim Mohamed Izham Mohamed , Yousuf Seena A.
Published: February 2024

The integration of artificial intelligence (AI) in healthcare has emerged as a transformative force, promising to enhance medical diagnosis, treatment, and overall healthcare delivery. Hence, this study investigates the university students perceptions toward using AI in healthcare. A cross-sectional survey was conducted at two major universities using a paper-based questionnaire from September 2023 to November 2023. Participants’ views regarding using artificial intelligence in healthcare were investigated using 25 items distributed across five domains. The Mann-Whitney U test was applied for the comparison of variables. The response rate for the survey was 75%, with a sample size of 279. More than half of the participants (52%, n = 145) expressed their belief in AI’s potential to reduce treatment errors in the future. However, about (61.6%, n = 172) of participants fear the influence of AI that could prevent doctors from learning to make correct patient care judgments, and it was widely agreed (69%) that doctors should ultimately maintain final control over patient care. Participants with experience with AI, such as engaging with AI chatbots, significantly reported higher scores in both the “Benefits and Positivity Toward AI in Healthcare” and “Concerns and Fears” domains (p= 0.024) and (p= 0.026), respectively. The identified cautious optimism, concerns, and fears highlight the delicate balance required for successful AI integration. The findings emphasize the importance of addressing specific concerns, promoting positive experiences with AI, and establishing transparent communication channels. Insights from such research can guide the development of ethical frameworks, policies, and targeted interventions, fostering a harmonious integration of AI into the healthcare landscape in developing countries.
Attitudes and Perceptions of Medical Researchers Towards the Use of Artificial Intelligence Chatbots in the Scientific Process: A Large-Scale, International Cross-Sectional Survey
Authors: Ng Jeremy Y. , Maduranayagam Sharleen G. , Suthakar Nirekah , Li Amy , Lokker Cynthia , Iorio Alfonso , Haynes R. Brian , Moher David
Published: February 2024

Background

Chatbots are artificial intelligence (AI) programs designed to simulate conversations with human users through text or speech. The use of artificial intelligence chatbots (AICs) in scientific research presents benefits and challenges. Although the stances of journals and publishing organizations on AIC use is increasingly clear, little is known about researchers’ perceptions of AICs in research. This survey study explores attitudes, familiarity, perceived benefits, limitations, and factors influencing adoption of AIC by researchers.

Methods

A cross-sectional online survey of published researchers was conducted. Corresponding authors and their e-mail addresses were identified by querying PubMed for articles (any type) published in a MEDLINE indexed journal in the most recent two months and using R script on PubMed metadata. e-Mail invitations were sent to 61560 study authors. The survey, administered on SurveyMonkey, opened on July 9, 2023, and closed on August 11, 2023. Respondents had 3 weeks to complete the survey and were sent 2 reminder e-mails during the weeks of July 17, 2023, and July 24, 2023.

Results

2165 respondents completed the survey (4.0% response rate; 94% completion rate of those who responded). Most were familiar with the concept of AICs (n=1294/2138, 60.5%). About half had used an AIC previously for purposes relating to the scientific process (n=1107/2125, 52.1%). Only 244/2137 (11.4%) respondents reported that their institution offered training on using AI tools of whom 64/244 (26.2%) completed the training. 211/2131 (9.9%) reported that their institution implemented policies regarding AIC use in the scientific process. Most respondents expressed interest in learning more and receiving training on AIC use in the scientific process (n=1428/2048, 69.7%). Respondents had mixed opinions about the potential benefits of using AICs, whereas most agreed on their cons/challenges. Respondents agreed AICs were most beneficial in reducing the workload and administrative burden on researchers (n=1299/1941, 66.9%) and they were most concerned about the lack of understanding behind how AICs make decisions and generate responses (n=1484/1923, 77.2%).

Conclusions

Most respondents are familiar with AICs and half used AICs in their own research. Although there is clear interest in understanding how AICs can be used, many hesitate due to existing limitations. Little formal instruction on using AICs is available across academic institutions.
Evaluating treatments for the protection of grapevine pruning wounds from natural infection by trunk disease fungi
Authors: Leal C. , Bujanda R. , López-Manzanares B. , Ojeda S. , Berbegal M. , Villa-Llop A. , Santesteban L. G. , Palacios J. , Gramaje D.
Published: February 2024

Infection of grapevines by fungal pathogens causing grapevine trunk diseases (GTDs) primarily arises from annual pruning wounds made during the dormant season. While various studies have showcased the efficacy of products in shielding pruning wounds against GTDs infections, most of these investigations hinge on artificial pathogen inoculations, which may not faithfully mirror real field conditions. This study aimed to evaluate and compare the efficacy of various liquid formulation fungicides (pyraclostrobin + boscalid) and paste treatments, as well as biological control agents (BCA:Trichoderma atrovirideSC1,T. atrovirideI-1237, andT. asperellumICC012 +T. gamsiiICC080), for their potential to prevent natural infection of grapevine pruning wounds by trunk disease fungi in two field trials located in Samaniego (Northern Spain) and Madiran (Southern France) over three growing seasons. Wound treatments were applied immediately after pruning in February. One year after pruning, canes were harvested from vines and brought to the laboratory for assessment ofTrichodermaspp. and fungal trunk pathogens. More than 1,200 fungal isolates associated with five GTDs (esca, Botryophaeria, Diaporthe and Eutypa diebacks, and Cytospora canker) were collected from the two vineyards each growing season. Our findings reveal that none of the products under investigation exhibited complete effectiveness against all the GTDs. The efficacy of these products was particularly influenced by the specific year of study. A notable exception was observed with the biocontrol agentT. atrovirideI-1237, which consistently demonstrated effectiveness against Botryosphaeria dieback infections throughout each year of the study, irrespective of the location. The remaining products exhibited efficacy in specific years or locations against particular diseases, with the physical barrier (paste) showing the least overall effectiveness. The recovery rates ofTrichodermaspp. in treated plants were highly variable, ranging from 17% to 100%, with both strains ofT. atrovirideyielding the highest isolation rates. This study underscores the importance of customizing treatments for specific diseases, taking into account the influence of environmental factors for BCA applications.
Spin polarization and spin alignment from quantum kinetic theory with self-energy corrections
Authors: Fang Shuo , Pu Shi , Yang Di-Lun
Published: February 2024

We derive the quantum kinetic theory for massive fermions with collision terms and self-energy corrections based on quantum field theory. We adopt an effective power-counting scheme with ℏ expansion to obtain the leading-order perturbative solutions of the vector and axial Wigner functions and the corresponding kinetic equations. We observe that both the on-shell relation and the structure of Wigner functions, along with the kinetic equations, are modified due to the presence of self-energies and their spacetime gradients. We further apply our formalism to investigate the spin polarization phenomena in relativistic heavy ion collisions and derive the modification to the spin polarization spectrum of massive quarks. We find that the gradient of vector self-energy plays a similar role to the background electromagnetic fields, which induces a more dominant contribution than the collisional effects by a naive power counting in the gradient expansion and weak coupling. Our findings could further modify the spin polarization of strange quarks and the spin alignment of ϕ mesons beyond local thermal equilibrium.

Published by the American Physical Society
2024
Symmetry analysis involving meson mixing for charmonium decay
Authors: Mo X. H.
Published: February 2024

For charmonium’s decaying to the final states involving merely light quarks, in light of SU(3) flavor symmetry, the effective interaction Hamiltonian in tensor form is obtained by virtue of group representation theory. The strong and electromagnetic breaking effects are treated as a spurion octet so that the flavor singlet principle can be utilized as the criterion to determine the form of effective Hamiltonian for all charmonium two body decays. Moreover, a synthetic nonet is introduced to include both octet and singlet representations for meson description, and resorting to the mixing angle, the pure octet and singlet states are combined into the observable pseudoscalar and vector particles, so that the empirically effective Hamiltonian can be obtained in a concise way. As an application, by virtue of this scenario the relative phase between the strong and electromagnetic amplitudes is studied for vector-pseudoscalar meson final state. In data analysis of samples taken in an e+e− collider, the details of experimental effects, such as energy spread and initial state radiative correction, are taken into consideration in order to make full use of experimental information and acquire accurate and delicate results.

Published by the American Physical Society
2024
Cosmological collider signal from non-Bunch-Davies initial states
Authors: Yin Yuan
Published: February 2024

We investigate the cosmological collider (CC) signal arising from the tree-level exchange of a scalar spectator particle with a non-Bunch-Davies (BD) initial state. We decompose the inflaton correlators into seed integrals, which we compute analytically by solving the bootstrap equations. We show that the non-BD initial state eliminates the Hubble-scale Boltzmann suppression e−πm/H that usually affects the CC signal. Consequently, in this scenario, the CC can probe an energy scale much higher than the inflationary Hubble scale H.

Published by the American Physical Society
2024
Causality and classical dispersion relations
Authors: Hoult Raphael E. , Kovtun Pavel
Published: February 2024

We explore the consequences of relativistic causality and covariant stability for short-wavelength dispersion relations in classical systems. For excitations described by a finite number of partial differential equations, as is the case in relativistic hydrodynamics, we give causality and covariant stability constraints on the excitation’s frequency at large momenta.

Published by the American Physical Society
2024
Vortex-antivortex lattices in a holographic superconductor
Authors: Su Jia-Hao , Xia Chuan-Yin , Yang Wei-Can , Zeng Hua-Bi
Published: February 2024

We employ the Einstein-Abelian-Higgs theory to investigate the structure of vortex-antivortex lattices within a superconductor driven by spatial periodic magnetic fields. By adjusting the parameters of the external magnetic field, including the period (T) and the amplitude (B0), various distinct vortex states emerge. These states encompass the Wigner crystallization state, the vortex cluster state, and the suppressed state. Additionally, we present a comprehensive phase diagram to demarcate the specific regions where these structures emerge, contributing to our understanding of superconductivity in complex magnetic environments.

Published by the American Physical Society
2024
Robust gates with spin-locked superconducting qubits
Authors: Zuk Ido , Cohen Daniel , Gorshkov Alexey V. , Retzker Alex
Published: February 2024

Dynamical decoupling is effective in reducing gate errors in most quantum computation platforms and is therefore projected to play an essential role in future fault-tolerant constructions. In superconducting circuits, however, it has proven difficult to utilize the benefits of dynamical decoupling. In this work, we present a theoretical proposal that incorporates a continuous version of dynamical decoupling, namely, spin locking, with a coupler-based CZ gate for transmons and provide analytical and numerical results that demonstrate its effectiveness.

Published by the American Physical Society
2024
Predicting and analysing initiator crime environments based on machine learning for improving urban safety
Authors: Hwang Yoonjae , Jung Sungwon , Park Eun Joo
Published: February 2024

Purpose

Initiator crimes, also known as near-repeat crimes, occur in places with known risk factors and vulnerabilities based on prior crime-related experiences or information. Consequently, the environment in which initiator crimes occur might be different from more general crime environments. This study aimed to analyse the differences between the environments of initiator crimes and general crimes, confirming the need for predicting initiator crimes.

Design/methodology/approach

We compared predictive models using data corresponding to initiator crimes and all residential burglaries without considering repetitive crime patterns as dependent variables. Using random forest and gradient boosting, representative ensemble models and predictive models were compared utilising various environmental factor data. Subsequently, we evaluated the performance of each predictive model to derive feature importance and partial dependence based on a highly predictive model.

Findings

By analysing environmental factors affecting overall residential burglary and initiator crimes, we observed notable differences in high-importance variables. Further analysis of the partial dependence of total residential burglary and initiator crimes based on these variables revealed distinct impacts on each crime. Moreover, initiator crimes took place in environments consistent with well-known theories in the field of environmental criminology.

Originality/value

Our findings indicate the possibility that results that do not appear through the existing theft crime prediction method will be identified in the initiator crime prediction model. Emphasising the importance of investigating the environments in which initiator crimes occur, this study underscores the potential of artificial intelligence (AI)-based approaches in creating a safe urban environment. By effectively preventing potential crimes, AI-driven prediction of initiator crimes can significantly contribute to enhancing urban safety.
Benchmarking design-thinking as a tool for education: a systematic review and future research agenda
Authors: Bathla Arushi , Chawla Ginni , Gupta Ashish
Published: February 2024

Purpose

Design-thinking (DT) in education has attracted significant interest from practitioners and academics, as it proffers new-age thinking to transform learning processes. This paper synthesises extant literature and identifies the current intellectual frontiers.

Design/methodology/approach

First, a systematic-literature-review was undertaken employing a robust process of selecting papers (from 1986 to 2022) by reading titles, abstracts and keywords based on a required criterion, backward–forward chaining and strict quality evaluations. Next, a bibliometric analysis was undertaken using VOSviewer. Finally, text analysis using RStudio was done to trace the implications of past work and future directions.

Findings

At first, we identify and explain 12 clusters through bibliometric coupling that include “interdisciplinary-area”, “futuristic-learning”, “design-process” and “design-education”, amongst others. We explain each of these clusters later in the text. Science, Technology, Engineering, Arts and Mathematics (STEAM), management education, design and change, teacher training, entrepreneurship education and technology, digital learning, gifted education and course development) Secondly, through co-word-analysis, we identify and explain four additional clusters that include “business education and pedagogy”, “content and learning environment”, “participants and outcome” and finally, “thinking-processes”. Based on this finding, we believe that the future holds a very positive presence sentiment for design thinking and education (DT&E) in changing the 21st century learning.

Research limitations/implications

For investigating many contemporary challenges related to DT&E, like virtual reality experiential learning, sustainability education, organisational learning and management training, etc. have been outlined.

Practical implications

Academics may come up with new or improved courses for the implementation of DT in educational settings and policymakers may inculcate design labs in the curricula to fortify academic excellence. Managers who would employ DT in their training, development and policy design, amongst others, could end up gaining a competitive advantage in the marketplace.

Originality/value

This study conducted a comprehensive review of the field, which to our limited knowledge, no prior studies have been done so far. Besides, the study also outlines interesting research questions for future research.
Key performance indicators for hospital planning and construction: a systematic review and meta-analysis
Authors: Liu Wenque , Chan Albert P.C. , Chan Man Wai , Darko Amos , Oppong Goodenough D.
Published: February 2024

Purpose

The successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).

Design/methodology/approach

The KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.

Findings

Results indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.

Originality/value

There is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.
Digital writing with AI platforms: the role of fun with/in generative AI
Authors: Stornaiuolo Amy , Higgs Jennifer , Jawale Opal , Martin Rhianne Mae
Published: February 2024

Purpose

With the rapid advancement of generative artificial intelligence (AI), it is important to consider how young people are making sense of these tools in their everyday lives. Drawing on critical postdigital approaches to learning and literacy, this study aims to center the experiences and perspectives of young people who encounter and experiment with generative AI in their daily writing practices.

Design/methodology/approach

This critical case study of one digital platform – Character.ai – brings together an adolescent and adult authorship team to inquire about the intertwining of young people’s playful and critical perspectives when writing on/with digital platforms. Drawing on critical walkthrough methodology (Light et al., 2018), the authors engage digital methods to study how the creative and “fun” uses of AI in youths’ writing lives are situated in broader platform ecologies.

Findings

The findings suggest experimentation and pleasure are key aspects of young people’s engagement with generative AI. The authors demonstrate how one platform works to capitalize on these dimensions, even as youth users engage critically and artfully with the platform and develop their digital writing practices.

Practical implications

This study highlights how playful experimentation with generative AI can engage young people both in pleasurable digital writing and in exploration and contemplation of platforms dynamics and structures that shape their and others’ literate activities. Educators can consider young people’s creative uses of these evolving technologies as potential opportunities to develop a critical awareness of how commercial platforms seek to benefit from their users.

Originality/value

This study contributes to the development of a critical and humanist research agenda around generative AI by centering the experiences, perspectives and practices of young people who are underrepresented in the burgeoning research devoted to AI and literacies.
Unpacking the effects of performance feedback on strategic change: the moderating role of industry conditions
Authors: Alnahedh Mishari , Alrashdan Abdullatif
Published: February 2024

Purpose

This paper aims to integrate insights from the behavioral theory of the firm and the dynamic capabilities perspective to explain how the historical and social attainment discrepancies motivate firms to change. Specifically, this paper proposes that a negative historical attainment discrepancy encourages the firm to engage in strategic change to solve its performance problems. In contrast, this paper advanced that a positive social attainment discrepancy motivates strategic change as a mechanism to bolster the firm’s position within the industry. Further, this paper integrated the moderating effects of industry dynamism and industry munificence.

Design/methodology/approach

This paper tests hypotheses using panel data on 2,435 US public firms over the years from 1996 to 2018. This paper uses a fixed-effects regression model to empirically test these hypotheses.

Findings

This paper finds empirical support for the effects of both the negative historical attainment discrepancy and the positive social attainment discrepancy on the firm’s tendency to engage in strategic change. As for the hypothesized moderating effects, this paper finds that industry munificence accentuated the effects of both attainment discrepancies on the firm’s tendency to engage in strategic change. However, the results do not support the hypothesized moderating effect of industry dynamism on either of these attainment discrepancies.

Originality/value

This paper contributes to the research on the separate effects of historical and social comparisons within the context of strategic change. Further, the paper bolsters our understanding of how performance feedback increases the firm’s tendency to change. Finally, the paper integrates theoretical views from the behavioral theory of the firm and the dynamic capabilities perspective on how socially high-performing firms may build and sustain their competitive advantage through organizational change.
Harnessing of carboxy containing starch nanoparticles as an adsorbent for Cd (II) ions and methylene blue from waste water effluent: synthesis, metrological characterization and properties
Authors: Mostafa Khaled , El-Sanabary Azza
Published: February 2024

Purpose

The novelty addressed here is undertaken by using tailor-made and fully characterized starch nanoparticles (SNPs) having a particle size ranging from 80 to 100 nm with a larger surface area, biodegradability and high reactivity as a starting substrate for cadmium ions and basic dye removal from wastewater effluent. This was done via carboxylation of SNPs with citric acid via esterification reaction using the dry preparation technique, in which a simple, energy-safe and sustainable process concerning a small amount of water, energy and toxic chemicals was used. The obtained adsorbent is designated as cross-linked esterified starch nanoparticles (CESNPs).

Design/methodology/approach

The batch technique was used to determine the CESNPs adsorption capacity, whereas atomic adsorption spectrometry was used to determine the residual cadmium ions concentration in the filtrate before and after adsorption. Different factors affecting adsorption were examined concerning pH, contact time, adsorbent dose and degree of carboxylation. Besides, to validate the esterification reaction and existence of carboxylic groups in the adsorbent, CESNPs were characterized metrologically via analytical tools for carboxyl content estimation and instrumental tools using Fourier-transform infrared spectroscopy (FTIR) spectra and scanning electron microscopy (SEM) morphological analysis.

Findings

The overall adsorption potential of CESNPs was found to be 136 mg/g when a 0.1 g adsorbent dose having 190.8 meq/100 g sample carboxyl content at pH 5 for 60 min contact time was used. Besides, increasing the degree of carboxylation of the CESNPs expressed as carboxyl content would lead to the higher adsorption capacity of cadmium ions. FTIR spectroscopy analysis elucidates the esterification reaction with the appearance of a new intense peak C=O ester at 1,700 cm−1, whereas SEM observations reveal some atomic/molecules disorder after esterification.

Originality/value

The innovation addressed here is undertaken by studying the consequence of altering the extent of carboxylation reaction expressed as carboxyl contents on the prepared CESNPs via a simple dry technique with a small amount of water, energy and toxic chemicals that were used as a sustainable bio nano polymer for cadmium ions and basic dye removal from wastewater effluent in comparison with other counterparts published in the literature.
Hidden truncation model with heteroskedasticity: S&P 500 index returns reexamined
Authors: Belhachemi Rachid
Published: February 2024

Purpose

This paper aims to introduce a heteroskedastic hidden truncation normal (HTN) model that allows for conditional volatilities, skewness and kurtosis, which evolve over time and are linked to economic dynamics and have economic interpretations.

Design/methodology/approach

The model consists of the HTN distribution introduced by Arnold et al. (1993) coupled with the NGARCH type (Engle and Ng, 1993). The HTN distribution nests two well-known distributions: the skew-normal family (Azzalini, 1985) and the normal distributions. The HTN family of distributions depends on a hidden truncation and has four parameters having economic interpretations in terms of conditional volatilities, kurtosis and correlations between the observed variable and the hidden truncated variable.

Findings

The model parameters are estimated using the maximum likelihood estimator. An empirical application to market data indicates the HTN-NGARCH model captures stylized facts manifested in financial market data, specifically volatility clustering, leverage effect, conditional skewness and kurtosis. The authors also compare the performance of the HTN-NGARCH model to the mixed normal (MN) heteroskedastic MN-NGARCH model.

Originality/value

The paper presents a structure dynamic, allowing us to explore the volatility spillover between the observed and the hidden truncated variable. The conditional volatilities and skewness have the ability at modeling persistence in volatilities and the leverage effects as well as conditional kurtosis of the S&P 500 index.
The effects of celebrity endorser on consumer advocacy behavior through the customization and entertainment intention – A multivariate analysis
Authors: Kalam Abul , Goi Chai Lee , Tiong Ying Ying
Published: February 2024

Purpose

The purpose of this study is to investigate the effects of celebrity endorsers on consumer advocacy, customization and entertainment intentions based on the notion of the stimulus–organism–response (SOR) framework. In addition, this study aims to examine the mediating and moderating role of customization and entertainment intentions on the proposed relationships. The authors also intend to highlight the comparative effects between male and female young social media consumers on those proposed associations.

Design/methodology/approach

In the pursuit of comprehensive and rigorous data collection, this study adopted a quantitative methodology using a meticulously crafted questionnaire. The questionnaire survey was conducted in major cities of Malaysia using the convenience and snowball sampling techniques. A total of 576 responses were collected, even though 549 retorts were used for data analysis. In this investigation, the authors strategically used covariance-based structural equation modeling through the use of AMOS v. 24 as the primary data analysis tool. Augmenting the analytical depth, the authors also conducted a supplementary bootstrap analysis. The additional layers of examination were crucial for appraising the mediating and moderating effects inherent within the model, in which the PROCESS MACRO v.4.20 was used.

Findings

The results of this study revealed the significant direct positive effects of celebrity endorsers on consumer customization, entertainment and advocacy intentions. Consumer customization and entertainment intentions also found significant direct affirmative effects on consumer advocacy intention, along with the significant direct positive effects of consumer entertainment intention on consumer customization intention. The results further revealed that consumer customization and entertainment intentions cannot mediate the relationship between celebrity endorsers and advocacy intention. The entertainment intention also declined the mediating effects between celebrity endorsers and consumer customization intention. On the contrary, consumer customization intention significantly and positively, and entertainment intention also significantly but negatively, moderate the association between celebrity endorsers and consumer advocacy intention. This study also illustrates that the effects of those examined relationships differ between male and female young social media consumers.

Originality/value

This study investigates the impact of celebrity endorsers on consumer behavior, focusing on their customization, entertainment and advocacy intentions. It extends current SOR framework, enhances source credibility theory, fills gaps in the literature on social media brand engagement and underscores the significance of customization and entertainment intentions. The findings provide insights for managers aiming to harness consumer brand advocacy through celebrity endorsers effectively.
Rapid quantitative detection system for measles virus‐neutralizing antibodies using HiBiT‐tagged virus‐like particles
Authors: Okura Takashi , Miyakawa Kei , Tahara Maino , Someya Kenji , Seki Fumio , Nishi Mayuko , Otsuki Noriyuki , Ryo Akihide
Published: February 2024

Immunological testing to detect neutralizing antibodies (NAbs) is important in measles (MV) infection control. Currently, the plaque reduction neutralization test is the only credible method for measuring actual virus NAbs; however, its feasibility is hampered by drawbacks, such as long turnaround times, low throughput, and the need for laboratory biosafety equipment. To solve these problems, we developed a simple and rapid MV‐NAb detection system using lentivirus‐based virus‐like particles incorporated with the NanoLuc fragment peptide HiBiT comprising the MV fusion protein and hemagglutinin on their exterior surface. Overall, this simple, safe, and rapid method could be used to detect MV NAbs.