bluuebunny/crossref_metadata_2025 huggingface dataset:
AlloPool: An Adaptive Graph Neural Network for Dynamic Allosteric Network Prediction in Protein Systems
Authors: Marfoglia M. , Guirardel L. , Barth P.
Published: November 2024

Allosteric communication is essential to protein function, facilitating the dynamic regulation of biological responses through the propagation of structural and dynamic changes between regulatory and effector sites in response to stimuli. Traditional approaches to studying protein allostery often rely on static protein structures or abstract representations involving fully connected interaction graphs, which do not capture the temporal and state-dependent nature of these dynamic systems. Here, we introduce AlloPool, a graph neural network (GNN)-based model that iteratively prunes residue interactions to identify minimal, time-dependent interaction networks that govern long-range structural and dynamic responses to chemical or mechanical stimuli. Using temporal attention and graph aggregation, AlloPool accounts for evolving protein conformations in both molecular dynamics (MD) and steered MD (SMD) simulations to predict MD and SMD trajectories. We validate AlloPool on the Pin-1 protein and the ADGRG1 and B1AR receptors, showcasing its ability to accurately recapitulate protein motions, infer allosteric communication pathways, and identify critical allosteric sites. Additionally, AlloPool identifies force-dependent changes in GAIN domain structure and reconstructs directed information flow under mechanical load. Comparative analyses indicate that AlloPool outperforms existing models in MD and SMD trajectory reconstruction, presenting a new framework for analyzing force- and ligand-induced allosteric motions. This work advances the modeling of allosteric systems and offers broad potential for applications in drug discovery, synthetic biology, and protein engineering.
Blended Length Genome Sequencing (blend-seq): Combining Short Reads with Low-Coverage Long Reads to Maximize Variant Discovery
Authors: Magner Ricky , Cunial Fabio , Basu Sumit , Paulsen Ron , Saponas Scott , Shand Megan , Lennon Niall , Banks Eric
Published: November 2024

We introduce blend-seq, a method for combining data from traditional short-read sequencing pipelines with low-coverage long reads, with the goal of substantially improving variant discovery for single samples without the full cost of high-coverage long reads. We demonstrate that with only 4x long read coverage augmenting 30x short reads, we can improve SNP discovery across the genome and achieve precision and recall beyond what is possible with short reads, even at very high coverage (60x). For genotype-agnostic discovery of structural variants, we see a threefold improvement in recall while maintaining precision by using the low-coverage long reads on their own. For the more specialized scenario of genotype-aware structural variant calling, we show how combining the long and short reads in a graph-based approach results in greater performance than either technology on its own. The observed gains highlight the complementary nature of short and long read technologies: long reads help with SNP discovery by better mapping to difficult regions, and they provide better performance with long insertions and deletions (structural variants) by virtue of their length, while the larger number of short-read layers help with genotyping structural variants discovered by long reads. In this way, blend-seq offers many of the benefits of long-read pipelines without incurring the cost of high-coverage long reads.
Squeakuences: a portable tool for formatting ‘squeaky-clean’ sequences to eliminate bioinformatic software incompatibilities
Authors: Lane Linnea E , Doerfler Ashley N , L’Argent Luna A , Touchette Emily , Mills Bronson , Bryant Jessika , Roller Savannah N , Forsythe Evan S
Published: November 2024

Computational analysis of biological sequences is the cornerstone of modern bioinformatics research. Complex processing and interpretation of data often entails multi-step workflows. The specific requirements and limitations of individual applications can require laborious reformatting and piecemeal ‘data-wrangling’ to produce a satisfactory input for each step in a pipeline. We presentSqueakuences, a command line tool developed to simplify and automate FASTA file preparation for applications such as phylogenetics, gene annotation, and genome analysis. Implemented in a lightweight Python script,Squeakuencesidentifies and removes potentially problematic elements in sequence identifiers, such as non-alphanumeric characters, white space, and excessive character count.Squeakuencesoutputs a new clean version of the sequence file for analysis alongside metadata files to track changes. The user can customizeSqueakuences’ behavior using optional arguments to meet individual processing and formatting requirements. We tested the performance ofSqueakuenceson molecular data from the human reference genome and found that runtime correlates with the number of sequences processed but not with file size. We expectSqueakuencesto save time and manual effort when analyzing sequence data.Squeakuencescode is freely available athttps://github.com/EvanForsythe/Squeakuences.
Dampened α7 nAChR activity contributes to audiogenic seizures and hyperactivity in a mouse model of Fragile X Syndrome
Authors: Goebel Sarah , Cordova-Martinez Dylann , Verselis Vytas K. , Francesconi Anna
Published: November 2024

Fragile X Syndrome (FXS) is the most common form of inherited intellectual disability and often accompanied with debilitating pathologies including seizures and hyperactivity. FXS arises from a trinucleotide repeat expansion in the 5’ UTR of theFMR1gene that silences expression of the RNA-binding protein FMRP. Despite progress in understanding FMRP functions, the identification of effective therapeutic targets has lagged and at present there are no viable treatment options. Here we identify the α7 nicotinic acetylcholine receptor (nAChR) as candidate target for intervention in FXS. In the early postnatal hippocampus ofFmr1knockout (KO) mice, an established pre-clinical model of FXS, the α7 nAChR accessory protein Ly6H is abnormally enriched at the neuronal surface and mislocalized in dendrites. Ly6H, a GPI-anchored protein, binds α7 nAChRs with high affinity and can limit α7 nAChR surface expression and signaling. We find that α7 nAChR-evoked Ca2+responses are dampened in immature glutamatergic and GABAergicFmr1KOneurons compared to wild type. Knockdown of endogenous Ly6H inFmr1KOneurons is sufficient to rescue dampened α7 nAChR Ca2+responses in vitro, providing evidence of a cell-autonomous role for Ly6H aberrant expression in α7 nAChR hypofunction. In line with intrinsic deficits in α7 nAChR activity inFmr1KOneurons, in vivo administration of the α7 nAChR-selective positive allosteric modulator PNU-120596 reduced hyperactivity and seizure severity in adolescentFmr1KOmice. Our mechanistic studies together with evidence of the in vivo efficacy of α7 nAChR augmentation implicate α7 nAChR hypofunction in FXS pathology.

Correction of α7 nAChR hypofunction in a preclinical murine model of Fragile X syndrome ameliorates seizure severity and hyperactivity.
Generalized cell phenotyping for spatial proteomics with language-informed vision models
Authors: Wang Xuefei (Julie) , Dilip Rohit , Bussi Yuval , Brown Caitlin , Pradhan Elora , Jain Yashvardhan , Yu Kevin , Li Shenyi , Abt Martin , Börner Katy , Keren Leeat , Yue Yisong , Barnowski Ross , Van Valen David
Published: November 2024

We present a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels. Our approach utilizes a transformer with channel-wise attention to create a language-informed vision model; this model’s semantic understanding of the underlying marker panel enables it to learn from and adapt to heterogeneous datasets. Leveraging a curated, diverse dataset with cell type labels spanning the literature and the NIH Human BioMolecular Atlas Program (HuBMAP) consortium, our model demonstrates robust performance across various cell types, tissues, and imaging modalities. Comprehensive benchmarking shows superior accuracy and generalizability of our method compared to existing methods. This work significantly advances automated spatial proteomics analysis, offering a generalizable and scalable solution for cell phenotyping that meets the demands of multiplexed imaging data.
Neuron-astrocyte Coupling in Lateral Habenula Mediates Depressive-like Behaviors
Authors: Xin Qianqian , Wang Junying , Zheng Jinkun , Tan Yi , Jia Xiaoning , Ni Zheyi , Feng Jiesi , Wu Zhaofa , Li Yulong , Li Xiaoming , Ma Huan , Hu Hailan
Published: November 2024

The lateral habenular (LHb) neurons and astrocytes have been strongly implicated in depression etiology but it was not clear how the two dynamically interact during depression onset. Here, using multi-brain-region calcium photometry recording in freely-moving mice, we discover that stress induces a unique, bimodal neuronal response and a most rapid astrocytic response in the LHb. LHb astrocytic calcium requires the α1A-adrenergic receptor, and depends on a recurrent neural network between the LHb and locus coeruleus (LC). Through the gliotransmitter glutamate and ATP/Adenosine, LHb astrocytes mediate the second-wave activation of local LHb neurons as well as release of norepinephrine (NE). Activation or inhibition LHb astrocytic calcium signaling facilitates or prevents stress-induced depressive-like behaviors respectively. These results identify a stress-induced positive feedback loop in the LHb-LC axis, with astrocytes being a critical signaling relay. The identification of this prominent neuron-glia interaction may shed light on stress management and depression prevention.
A Perturbation Cell Atlas of Human Induced Pluripotent Stem Cells
Authors: Nourreddine Sami , Doctor Yesh , Dailamy Amir , Forget Antoine , Lee Yi-Hung , Chinn Becky , Khaliq Hammza , Polacco Benjamin , Muralidharan Monita , Pan Emily , Zhang Yifan , Sigaeva Alina , Hansen Jan Niklas , Gao Jiahao , Parker Jillian A. , Obernier Kirsten , Clark Timothy , Chen Jake Y. , Metallo Christian , Lundberg Emma , Ideker Trey , Krogan Nevan , Mali Prashant
Published: November 2024

Towards comprehensively investigating the genotype-phenotype relationships governing the human pluripotent stem cell state, we generated an expressed genome-scale CRISPRi Perturbation Cell Atlas in KOLF2.1J human induced pluripotent stem cells (hiPSCs) mapping transcriptional and fitness phenotypes associated with 11,739 targeted genes. Using the transcriptional phenotypes, we created a minimum distortion embedding map of the pluripotent state, demonstrating rich recapitulation of protein complexes, such as strong co-clustering of MRPL, BAF, SAGA, and Ragulator family members. Additionally, we uncovered transcriptional regulators that are uncoupled from cell fitness, discovering potential novel pluripotency (JOSD1, RNF7) and metabolic factors (ZBTB41). We validated these findings via phenotypic, protein-interaction, and metabolic tracing assays. Finally, we propose a contrastive human-cell engineering framework (CHEF), a machine learning architecture that learns from perturbation cell atlases to predict perturbation recipes that achieve desired transcriptional states. Taken together, our study presents a comprehensive resource for interrogating the regulatory networks governing pluripotency.
Molecular Signatures of Resilience to Alzheimer’s Disease in Neocortical Layer 4 Neurons
Authors: Dharshini S Akila Parvathy , Sanz-Ros Jorge , Pan Jie , Tang Weijing , Vallejo Kristen , Otero-Garcia Marcos , Cobos Inma
Published: November 2024

Single-cell omics is advancing our understanding of selective neuronal vulnerability in Alzheimer’s disease (AD), revealing specific subtypes that are either susceptible or resilient to neurodegeneration. Using single-nucleus and spatial transcriptomics to compare neocortical regions affected early (prefrontal cortex and precuneus) or late (primary visual cortex) in AD, we identified a resilient excitatory population in layer 4 of the primary visual cortex expressingRORB,CUX2, andEYA4. Layer 4 neurons in association neocortex also remained relatively preserved as AD progressed and shared overlapping molecular signatures of resilience. Early in the disease, resilient neurons upregulated genes associated with synapse maintenance, synaptic plasticity, calcium homeostasis, and neuroprotective factors, includingGRIN2A, RORA, NRXN1, NLGN1, NCAM2, FGF14, NRG3, NEGR1, andCSMD1. We also identifiedKCNIP4, which encodes a voltage-gated potassium (Kv) channel-interacting protein that interacts with Kv4.2 channels and presenilins, as a key factor linked to resilience.KCNIP4was consistently upregulated in the early stages of pathology. Furthermore, AAV-mediated overexpression ofKcnip4in a humanized AD mouse model reduced the expression of the activity-dependent genesArcandc-Fos, suggesting compensatory mechanisms against neuronal hyperexcitability. Our dataset provides a valuable resource for investigating mechanisms underlying resilience to neurodegeneration.
Boosted imaginary time evolution of matrix product states
Authors: Symons Benjamin C. B. , Manawadu Dilhan , Galvin David , Mensa Stefano
Published: November 2024

In this work, we consider the imaginary time evolution of matrix product states. We present a quantum-inspired classical method that, when combined with time evolving block decimation (TEBD), is able to potentially speed up the convergence to a ground state compared to TEBD alone. Our method, referred to as boosted imaginary time evolution, relies on the use of reflections to boost to lower energy states. Interleaving TEBD steps with boosts reduces the total number of TEBD steps and potentially the computational cost required to imaginary time evolve a matrix product state to a ground state. We give the mathematical details of the method followed by an algorithmic implementation and finally some results for a simple test case.

Published by the American Physical Society
2024
Efficient quantum circuits based on the quantum natural gradient
Authors: Roy Ananda , Erramilli Sameer , Konik Robert M.
Published: November 2024

Efficient preparation of arbitrary entangled quantum states is crucial for quantum computation. This is particularly important for noisy intermediate-scale quantum simulators relying on variational hybrid quantum-classical algorithms. To that end, we propose symmetry-conserving modified quantum approximate optimization algorithm (SCom-QAOA) circuits. The depths of these circuits depend not only on the desired fidelity to the target state but also on the amount of entanglement the state contains. The parameters of the SCom-QAOA circuits are optimized using the quantum natural gradient method based on the Fubini-Study metric. The SCom-QAOA circuit transforms an unentangled state into a ground state of a gapped one-dimensional Hamiltonian with a circuit depth that depends not on the system size but rather on the finite correlation length. In contrast, the circuit depth grows proportionally to the system size for preparing low-lying states of critical one-dimensional systems. Even in the latter case, SCom-QAOA circuits with depth less than the system size were sufficient to generate states with fidelity in excess of 99%, which is relevant for near-term applications. The proposed scheme enlarges the set of the initial states accessible for variational quantum algorithms and widens the scope of investigation of nonequilibrium phenomena in quantum simulators.

Published by the American Physical Society
2024
Phase transitions of ice VIII-VII-X: A potential energy landscape perspective
Authors: Li Lu , Gao Pengyue , Zhang Wei , Liu Guoji , Lv Jian , Wang Yanchao
Published: November 2024

Water ice, an archetypal molecular system, exhibits a complex phase diagram characterized by numerous phase transitions under varying pressure-temperature conditions. However, it remains a significant challenge to theoretical modeling of these transitions owing to the high dimensionality in representing the potential energy landscapes (PEL) of ice crystalline phases. In an attempt to deeply understand the mechanisms of phase transitions between ice VII, VIII, and X, a graph-based stationary-point network was employed to simplify the representation of the PEL and locally constructed the PEL of ice at different pressures by our developed high-index saddle dynamic scheme. The stationary-point networks at 40 and 80 GPa indicate a pronounced flattened PEL of ice over a wide range of pressure (up to at least 80 GPa), where multiple structures (i.e., local minima) are energetically degenerate with phase VIII and separated by low-energy barriers (∼10 meV/atom at 40 GPa). These features indicate the disordered VII phase is more favorable at finite temperatures, in agreement with previous experimental observations. Additionally, we demonstrated that the PEL topology undergoes a fundamental alteration at higher pressures, with a single funnel emerging at 120 GPa, facilitating the crystallization of phase X. This work serves as a proof of concept for using the stationary-point network to explicitly represent the PEL, highlighting their potential in elucidating complex phase transitions.

Published by the American Physical Society
2024
Chemical reactions regulated by phase-separated condensates
Authors: Laha Sudarshana , Bauermann Jonathan , Jülicher Frank , Michaels Thomas C. T. , Weber Christoph A.
Published: November 2024

Phase-separated liquid condensates can spatially organize and thereby regulate chemical processes. However, the physicochemical mechanisms underlying such regulation remain elusive as the intramolecular interactions responsible for phase separation give rise to a coupling between diffusion and chemical reactions at nondilute conditions. Here, we derive a theoretical framework that decouples the phase separation of scaffold molecules from the reaction kinetics of diluted clients. As a result, phase volume and client partitioning coefficients become control parameters, which enables us to dissect the impact of phase-separated condensates on chemical reactions. We apply this framework to two chemical processes and show how condensates affect the yield of reversible chemical reactions and the initial rate of a simple assembly process. In both cases, we find an optimal condensate volume at which the respective chemical reaction property is maximal. Our work can be applied to experimentally quantify how condensed phases alter chemical processes in systems biology and unravel the mechanisms of how biomolecular condensates regulate biochemistry in living cells.

Published by the American Physical Society
2024
Criticality-enhanced precision in phase thermometry
Authors: Yu Mei , Nguyen H. Chau , Nimmrichter Stefan
Published: November 2024

Temperature estimation of interacting quantum many-body systems is both a challenging task and topic of interest in quantum metrology, given that critical behavior at phase transitions can boost the metrological sensitivity. Here we study noninvasive quantum thermometry of a finite, two-dimensional Ising spin lattice based on measuring the dephasing dynamics of a spin probe coupled to the lattice. We demonstrate a strong critical enhancement of the achievable precision in terms of the quantum Fisher information, which depends on the coupling range and the interrogation time. Our numerical simulations are compared to instructive analytic results for the critical scaling of the sensitivity in the Curie-Weiss model of a fully connected lattice and to the mean-field description in the thermodynamic limit, both of which fail to describe the critical spin fluctuations on the lattice the spin probe is sensitive to. Phase metrology could thus help to investigate the critical behavior of finite many-body systems beyond the validity of mean-field models.

Published by the American Physical Society
2024
Relaxed hydrodynamic theory of electrically driven nonequilibrium steady states
Authors: Brattan Daniel K. , Matsumoto Masataka , Baggioli Matteo , Amoretti Andrea
Published: November 2024

The capability of hydrodynamics to accurately describe slow and long-wavelength fluctuations around nonequilibrium steady states (NESS), characterized by a stationary flow of energy or matter in the presence of a driving force, remains an open question. In this study, we explicitly construct a hydrodynamic description of electrically driven nonequilibrium charged steady states in the limit in which the relaxation of the first nonhydrodynamic excitation is parametrically slow. Our approach involves introducing gapped modes and extending the effective description into a relaxed hydrodynamic theory (RHT). Leveraging the gauge-gravity duality as a tool for controlled computations within nonequilibrium systems, we establish an ultraviolet complete model for these NESS that confirms the validity of our RHT. In summary, our findings provide a concrete realization of the validity of hydrodynamics beyond thermal equilibrium, offering valuable insights into the dynamics of nonequilibrium systems.

Published by the American Physical Society
2024
Effect of Microstructure on Chemo-Mechanical Damage Evolution in Aluminum Foil Anodes for Lithium-Ion Batteries
Authors: Chen Timothy S. , Wang Congcheng , Wright Salem C. , Cavallaro Kelsey Anne , Jeong Won Joon , Das Sazol , Majumdar Diptarka , Gopalaswamy Rajesh , McDowell Matthew T.
Published: November 2024

Aluminum-based foil anodes could enable lithium-ion batteries with high energy density comparable to silicon and lithium metal. However, mechanical pulverization and lithium trapping within aluminum tend to cause capacity fading. The complex interplay between these damage modes is not well understood, as well as the role of microstructure on reaction front evolution. Here, we investigate aluminum foils with different compositions and processing conditions to understand how microstructure influences chemo-mechanical degradation. Backscattered electron imaging of ion-milled foil cross sections is used to visualize reaction front evolution and chemo-mechanical degradation. We find that the shape of the reaction front during lithiation is strongly affected by foil composition, defect distribution, and grain size, which, in turn, affects the evolution of chemo-mechanical damage within the foils. Furthermore, a key finding is that the extent of fracture upon delithiation is inversely related to lithium trapping, with foil compositions that exhibit uniform lithiation, and therefore, less fracturing showing greater lithium trapping. Nonreactive precipitate inclusions within alloy foils are also found to induce void formation. This work shows that material design strategies designed to overcome the inverse fracture-lithium trapping relationship could be useful for improving performance.

Published by the American Physical Society
2024
AnACor2.0: a GPU-accelerated open-source software package for analytical absorption corrections in X-ray crystallography
Authors: Lu Yishun , Adámek Karel , Stefanic Tihana , Duman Ramona , Wagner Armin , Armour Wesley
Published: November 2024

Analytical absorption corrections are employed in scaling diffraction data for highly absorbing samples, such as those used in long-wavelength crystallography, where empirical corrections pose a challenge. AnACor2.0 is an accelerated software package developed to calculate analytical absorption corrections. It accomplishes this by ray-tracing the paths of diffracted X-rays through a voxelized 3D model of the sample. Due to the computationally intensive nature of ray-tracing, the calculation of analytical absorption corrections for a given sample can be time consuming. Three experimental datasets (insulin at λ = 3.10 Å, thermolysin at λ = 3.53 Å and thaumatin at λ = 4.13 Å) were processed to investigate the effectiveness of the accelerated methods in AnACor2.0. These methods demonstrated a maximum reduction in execution time of up to 175× compared with previous methods. As a result, the absorption factor calculation for the insulin dataset can now be completed in less than 10 s. These acceleration methods combine sampling, which evaluates subsets of crystal voxels, with modifications to standard ray-tracing. The bisection method is used to find path lengths, reducing the complexity from O(n) to O(log2 n). The gridding method involves calculating a regular grid of diffraction paths and using interpolation to find an absorption correction for a specific reflection. Additionally, optimized and specifically designed CUDA implementations for NVIDIA GPUs are utilized to enhance performance. Evaluation of these methods using simulated and real datasets demonstrates that systematic sampling of the 3D model provides consistently accurate results with minimal variance across different sampling ratios. The mean difference of absorption factors from the full calculation (without sampling) is at most 2%. Additionally, the anomalous peak heights of sulfur atoms in the Fourier map show a mean difference of only 1% compared with the full calculation. This research refines and accelerates the process of analytical absorption corrections, introducing innovative sampling and computational techniques that significantly enhance efficiency while maintaining accurate results.
Towards a framework for implementing blockchain technology in the construction industry of Sri Lanka
Authors: Gamage Namal , Ambagala A.P. Krishni Kavindya , Nanayakkara Samudaya , Perera Srinath
Published: November 2024

Purpose

The construction industry is often criticised due to inherited challenges: poor payment practices, inadequate collaboration, etc. Blockchain has the potential to address these issues with its salient features. Nonetheless, adopting blockchain and smart contracts (B&SC) within the construction industry is a comprehensive endeavour due to its intricate nature. The situation is bleak in Sri Lanka due to numerous barriers such as limited technical expertise, cost implications and many others. Hence, this paper aims to examine these barriers, appropriate strategies to overcome them, explore potential blockchain-applicable areas and formulate a framework to adopt B&SC in the construction industry of Sri Lanka (CISL).

Design/methodology/approach

This study employed a mixed research approach. The barriers for the adoption of B&SC to the CISL and their applicable areas were distinguished through a questionnaire survey and analysed using the Relative Importance Index. Strategies to overcome them were identified through expert interviews and analysed utilising manual-content analysis.

Findings

The study identified 15 barriers, 12 strategies and 9 areas for integrating B&SC for the CISL. The analysis indicated that having fewer blockchain-powered applications, the reluctance of the enterprises to bear costs to integrate blockchain and sluggish adaptation to new digital technologies are significant barriers. Further, conducting an industry-wide digitalisation analysis, developing an industry-wide digitalisation strategy and recruiting skilful IT staff were pointed out as the pivotal strategies. Moreover, payment and supply chain management were identified as areas with high potential.

Originality/value

This study unveils an analysis of barriers, strategies and areas in adopting B&SC for CISL and formulates a framework. It can be deployed as a guideline for implementing B&SC in the CISL.
Understanding enablers and inhibitors of digital rupee: a dual-factor theory perspective
Authors: Balasubramanian Senthil Arasu , Thirumaran Pirasad
Published: November 2024

Purpose

Central banks globally are actively investigating the issuance of central bank digital currencies, a digital form of fiat money. In this light, this study aims to explore and empirically validate the factors that enable or inhibit user behavioral intentions to adopt the digital rupee in India.

Design/methodology/approach

The study employed dual-factor theory (DFT) to capture the users’ perceptions of both enablers and inhibitors of the digital rupee. The authors gathered survey data from 351 individuals in India through online questionnaires. The authors used partial least squares structural equation modeling and multigroup analysis (MGA) to evaluate the proposed conceptual model.

Findings

The findings reveal that enablers such as perceived government support, trialability and similarity positively influence users’ attitudes toward the digital rupee. In contrast, inhibitors such as usage, value and risk barriers increase users’ resistance. Attitude has a significant positive impact on the intention to use the digital rupee, while resistance significantly reduces the intention to adopt it. MGA results highlight the importance of gender and income status in understanding intention to use the digital rupee.

Originality/value

By applying DFT, the study identifies a set of enablers and inhibitors that influence the behavioral intention to use the digital rupee in India. It provides actionable insights for governments and central bankers to devise effective policies, design considerations and targeted interventions, ensuring a sustainable environment for the successful implementation of the digital rupee.
Constrained solutions of generalized coupled discrete-time periodic matrix equations with application in state observer design for linear periodic systems
Authors: Wang Wenli , Song Caiqin
Published: November 2024

Purpose

It is desired to provide a diversified iterative scheme for solving the constrained solutions of the generalized coupled discrete-time periodic (GCDTP) matrix equations from the perspective of optimization.

Design/methodology/approach

The paper considers generalized reflexive solutions of the GCDTP matrix equations by applying the Jacobi gradient-based iterative (JGI) algorithm, which is an extended variant of the gradient-based iterative (GI) algorithm.

Findings

Through numerical simulation, it is verified that the efficiency and accuracy of the JGI algorithm are better than some existing algorithms, such as the GI algorithm in Hajarian, the RGI algorithm in Sheng and the AGI algorithm in Xie and Ma.

Originality/value

It is the first instance in which the GCDTP matrix equations are solved applying the JGI algorithm.
Survival of the greenest: how evolutionary motives affect sustainability campaign message persuasiveness
Authors: Wilkie Dean Charles Hugh , Rao Hill Sally , Silva Ruhani Angana , Mirzaei Abas
Published: November 2024

Purpose

The growing concern around sustainability highlights the need to change individual consumption behaviour. Using evolutionary theory, the purpose of this paper is to present a framework that demonstrates the process by which a consumer’s ultimate motives (thoughts, emotions, and perceptions that come from the evolutionary motives) impact sustainability campaign message persuasiveness (SCMP).

Design/methodology/approach

The framework was tested using partial least squares analysis with data from an online survey of 237 consumers.

Findings

Ultimate motives influence the SCMP through a mediating relationship, via a consumer’s sustainable consumption attitude and behaviour. The perceived sense of urgency around sustainability issues and the consumer’s connection to nature were also identified as key moderators that impact relationships between ultimate motives and consumers’ sustainability attitudes and behaviours.

Research limitations/implications

Future research could explore the impact of other factors, such as values, social norms, culture and governmental regulations, on individuals’ reactions to sustainability marketing.

Practical implications

The findings indicate the importance for managers to grasp the multifaceted nature of ultimate motives. By heightening particular ultimate motives, marketers can steer individuals towards sustainable attitudes and behaviours, consequently increasing the probability of favourable reactions to sustainable consumption messages.

Originality/value

This study addressed the two main limitations of research into SCMP by providing a theoretical grounding and focusing on the drivers of consumer behaviour, instead of campaign messaging aspects (e.g. framing). The empirical analysis, including identifying interactions between ultimate motives, supports the use of evolutionary theory to explain SCMP.
The impact of corporate digital transformation on stock market activity: evidence from the Chinese listed manufacturing companies
Authors: Elafify Mohamed Gamal , Wang Qinggang
Published: November 2024

Purpose

This research aims to investigate the impact of corporate digital transformation (CDT) on stock market activity.

Design/methodology/approach

A data set of Chinese listed manufacturing enterprises from 2012–2021 is used as a research sample.

Findings

This research finds that CDT can promote stock market activity. This study validates two potential mechanisms: increasing financial performance and mitigating information asymmetry. This study further suggests that internal control and analyst coverage can strengthen the impact of CDT on stock market activity.

Research limitations/implications

The research exhibits certain limitations that should be considered in future research. Because the findings are based on the Chinese context, the applicability and generalizability of the findings to other environments may be limited. This research enriches the literature on the determinants of stock market activity from a technological perspective and incrementally contributes to understanding the impact of CDT on stock markets. After analyzing two opposing perspectives on the economic consequences of CDT, the favorable effect of CDT on stock market activity is proven based on the resource-based view and agency theory. This research extends the literature on the relationship between CDT and investor behavior, demonstrating that investors perceive CDT as beneficial. The results provide evidence that CDT can increase financial performance and improve the information environment, leading to increased investor attention and enhanced trading activity.

Practical implications

This research has incremental practical implications for enterprises and regulatory authorities to comprehend the economic consequences of CDT in developing countries. First, enterprises should increase their digital investments to improve their performance and decrease information asymmetry. Furthermore, enterprise managers should strengthen information systems to adapt to the process of CDT and train employees on digital skills. Second, regulatory authorities should provide comprehensive digital policies and programs supported by tax incentives, subsidies and digital infrastructure projects (Wang et al., 2023).

Originality/value

This research strengthens the debate on the market impact of CDT. Unlike prior literature, this study explores the influence of CDT on stock market activity for the first time, enriching the literature on CDT and stock market activity. Furthermore, the outcomes guide regulatory authorities to actively support CDT and expedite the digital upgrading of manufacturing industries to promote stock market activity.
Integrating real-world entrepreneurship with international learning: insights from a blended intensive programme
Authors: Laine Igor , Pirrone Giuseppe , Phan Khanh Hoang Quoc , Milotta Margherita , Väätänen Juha , Hagen Birgit
Published: November 2024

Purpose

This study aims to illustrate how a university can leverage a Blended Intensive Programme (BIP) to act as a boundary spanner in international collaborations and multi-stakeholder value co-creation. This research explores the potential of a reimagined study abroad program to connect disparate entrepreneurial ecosystems and enhance the university’s role in fostering international collaborative projects.

Design/methodology/approach

This study uses a case study methodology to investigate an Erasmus+ BIP aimed at integrating real-world entrepreneurship with international learning. Data were collected through surveys, interviews and participant observation, providing a robust analysis of how such programs can bridge entrepreneurial ecosystems internationally.

Findings

The study shows how a blended study-abroad program not only enhances students’ learning outcomes but also can bridge local and international entrepreneurial ecosystems. By facilitating rich exchanges and value co-creation among students, faculty, industry and government stakeholders, the blended format of the program—integrating virtual and in-person elements—proved crucial in maintaining continuity and engagement amid global disruptions. The study highlights the university’s pivotal role both as a facilitator of global business education and international collaboration.

Research limitations/implications

The study’s findings are based on a single BIP, so they may not apply to all similar programs worldwide. To understand the broader applicability and impact across different contexts, future research should include diverse BIPs from various regions and sectors.

Practical implications

This research highlights the multifaceted benefits of blended intensive study-abroad programs. The collaborative model serves as a template for enhancing the practical value of higher education globally.

Originality/value

The study provides insights into the potential of blended intensive programs for universities to extend their role as boundary-spanners through a unique model for international collaboration and multi-stakeholder engagement. This approach addresses the challenges of global disruptions and sets a precedent for future educational practices in international business.
Service experience lies in the eye of the beholder: contextualizing extant literature toward integrating eye-tracking in service research
Authors: Wetzels Ruud , Wetzels Martin , Lemmink Jos G.A.M. , Grewal Dhruv , Kuppelwieser Volker
Published: November 2024

Purpose

In spite of offering clear benefits and increased availability, relatively few service research studies rely on eye-tracking. Therefore, this paper aims to assist service researchers in harnessing the vast capabilities of eye-tracking technologies and methods for their own inquiries.

Design/methodology/approach

In addition to systematically reviewing extant research in the service domain and the wider marketing field with respect to the fundamentals and practices of eye-tracking, this paper presents a concise, empirical eye-tracking demonstration. These contributions suggest future outlooks for how to incorporate eye-tracking more effectively in service research.

Findings

The systematic literature review informs a comprehensive framework for integrating eye-tracking in service research that comprises research question focus regarding higher-order psychological constructs of interest, appropriate study settings, sample composition, optimal eye-tracking equipment and operationalization considerations.

Research limitations/implications

By establishing a common ground and recommended uses of eye-tracking for service research, this study equips service scholars with the knowledge they need regarding eye-tracking fundamentals, common practices and future outlooks. A simple, empirical example further demonstrates some options for unlocking the unique capabilities of eye-tracking in service research and uncovering the complexities inherent to service experiences and other core service concepts.

Originality/value

This paper compiles and contextualizes insights from existing eye-tracking research, which it uses to draw important lessons for deploying eye-tracking in service domains.
Storm resilience of subtidal soft‐bottom mussel beds: Mechanistic insights, threshold quantification and management implications
Authors: de Smit Jaco C. , Zhao Zhiyuan , Capelle Jacob J. , Gerkema Theo , van de Koppel Johan , Bouma Tjeerd J.
Published: November 2024

With the projected escalation of extreme storm events, coastal ecosystems risk undergoing catastrophic shifts and losing essential ecosystem services. Subtidal soft‐bottom mussel beds, vital components of these ecosystems, are particularly vulnerable to hydrodynamically‐induced dislodgement (i.e., detachment of mussel clumps from the bed), especially during storms. However, the mechanisms underlying the resilience—comprising both resistance and recovery—of these beds to storms remain unclear, despite being essential for informed management.

This study addresses this knowledge gap regarding subtidal soft‐bottom mussel beds by: (i) quantifying their dislodgement threshold (i.e., the hydrodynamics causing widespread dislodgement of mussel clumps) using novel in situ monitoring methodologies in a representative region, namely the Dutch Wadden Sea; (ii) unveiling the influence of prior life history (here, wave exposure extent) and storm durations on their dislodgement thresholds through a flume study; and (iii) assessing the impacts of repeated storms and prior life histories (here, wave exposure extent and substrate types) on their recovery (i.e. mussel re‐aggregation) through mesocosm experiments.

Integrated experimental evidence indicates that: (i) hydrodynamic‐induced dislodgement is a sudden process characterized by distinct near‐bed orbital velocity thresholds, which were identified at our study site to be between 0.45 and 0.50 m s−1; (ii) peak storm intensity, rather than storm duration, primarily drives the dislodgement of subtidal soft‐bottom mussel beds, and prior wave exposure extent regulates the dislodgement threshold; (iii) repeated storms do not seem to affect the recovery of these beds following storm‐related disturbances when the conditions between storms are conducive to mussel re‐aggregation, whereas substrate type significantly impacts recovery.

Synthesis and applications. Overall, concerns regarding subtidal soft‐bottom mussel beds degradation primarily stem from increasing storm intensity and their limited resistance to such events. The methodology we developed enables low‐cost quantification of mussel resistance thresholds across broad spatiotemporal scales, facilitating the pinpointing of vulnerable areas. Our findings inform strategic management by highlighting the influential role of prior life histories in shaping mussel bed resistance and the potential to accelerate mussel bed recovery through substrate modification (e.g., shell additions). Both our methodology and findings hold promise for application in comparable ecosystems, such as oyster and coral reefs.
The Women of Corsican Nationalism: Between Tradition and Modernity (1975–98)
Authors: PACI DEBORAH
Published: November 2024

This article investigates late twentieth‐century Corsican nationalism through the lens of gender, drawing attention to how women have sought to play an active and creative role in the movement. Through a series of interviews with female nationalist militants, this article will focus on what belonging to the nationalist movement meant to Corsican women. Gender relations within the nationalist movement reflected the changes taking place in a society fluctuating between tradition and modernity.
Recent advances in fish cutting: From cutting schemes to automatic technologies and internet of things innovations
Authors: Li Qing , Ma Huawei , Min Weiqing , Wang Yang , Zhao Ran , Zhou Yongjie , Tan Yuqing , Luo Yongkang , Hong Hui
Published: November 2024

Fish‐cutting products are widely loved by consumers due to the unique nutrient composition and flavor in different cuts. However, fish‐cutting faces the issue of labor shortage due to the harsh working environment, huge workload, and seasonal work. Hence, some automatic, efficient, and large‐scale cutting technologies are needed to overcome these challenges. Accompanied by the development of Industry 4.0, the Internet of Things (IoT), artificial intelligence, big data, and blockchain technologies are progressively applied in the cutting process, which plays pivotal roles in digital production monitoring and product safety enhancement. This review focuses on the main fish‐cutting schemes and delves into advanced automatic cutting techniques, showing the latest technological advancements and how they are revolutionizing fish cutting. Additionally, the production monitoring architecture based on IoT in the fish‐cutting process is discussed. Fish cutting involves a variety of schemes tailored to the specific characteristics of each fish cut. The cutting process includes deheading and tail removal, filleting, boning, skinning, trimming, and bone inspection. By incorporating sensors, machine vision, deep learning, and advanced cutting tools, these technologies are transforming fish cutting from a manual to an automated process. This transformation has significant practical implications for the industry, offering improved efficiency, consistent product quality, and enhanced safety, ultimately providing a modernized manufacturing approach to fish‐cutting automation within the context of Industry 4.0.
Controlling strategies of methanol generation in fermented fruit wine: Pathways, advances, and applications
Authors: Shen Ju , Huang Weidong , You Yilin , Zhan Jicheng
Published: November 2024

Methanol is widely existed in fermented fruit wines (FFWs), and the concentration is excessive at times due to inappropriate fermentation conditions. Methanol is neurotoxic, and its metabolites of formaldehyde and formic acid can cause organic lesions and central respiratory system disorders. FFWs with unspecified methanol limits are often produced with reference to grape wine standards (250/400 mg/L). To clarify the causes of methanol production in FFWs and minimize the methanol content, this study summarizes the current process methods commonly applied for methanol reduction in FFWs and proposes novel potential controlling strategies from the perspective of raw materials (pectin, pectinase, and yeast), which are mainly the low esterification modification and removal of pectin, passivation of the pectinase activity, and the gene editing of yeast to target the secretion of pectinases and modulation of the glycine metabolic pathway. The modified raw materials combined with optimized fermentation processes will hopefully be able to improve the current situation of high methanol content in FFWs. Methanol detection technologies have been outlined and combined with machine learning that will potentially guide the production of low‐methanol FFWs and the setting of methanol limits for specific FFW.