Diverse Combinations Research Articles

Ecosystem style of thinking in economic analysis: principle of unity of the continuous and the discrete

Objective: to substantiate the unity of the discrete and the continuous in the socio-economic ecosystems theory development. Methods: system economic theory; the theory of the system structure influence on the changing homogeneity of the intraecosystem space-time; the systemic-integrational theory of an enterprise; the principle of complementarity. Results: the article substantiates the expediency of introducing the “ecosystem style of thinking” concept into scientific circulation. It is shown that the basic principle of the ecosystem style of thinking is the principle of complementarity, expressed as the unity of the continuous and the discrete. This principle is due to the inherent characteristic of the ecosystem – a combination of continuity and discreteness of its intra-ecosystem space-time (in particular, a combination of homogeneity and diversity of relationships between ecosystem participants). In order to form methods for determining the qualitative relationship between the ecosystem homogeneity and heterogeneity, the author proposes a typology of relationships between ecosystem participants, formed on the basis of the systemic-integrational theory of an enterprise. The paper substantiates the importance of a rational correlation of the homogeneity and diversity of the ecosystem space-time for the ecosystem functioning and for identifying the imbalances in the ecosystem spatiotemporal development. Scientific novelty: It was found that the main characteristic of the ecosystem style of thinking is the principle of unity of the continuous and the discrete in economic analysis. The author concludes that the ecosystem style of thinking is based on the post-non-classical style of thinking as interpreted by V. S. Stepin. The article proposes a typology of the economic analysis principles and substantiates the typology of relationships in the ecosystem based on the system integration theory of an enterprise. The author draws attention to the need for a rational balance between homogeneity and diversity in the ecosystem relationships for its functioning. Practical significance: the research expands the arsenal of objective methods for studying economic phenomena and processes by applying the ecosystem approach principles.

How Good Is GPT’s “Emojinal Intelligence”? Investigating Emoji Patterns in LLM-Generated Social Media Text

Recent advancement in Large Language Models (LLMs) has opened the prospect of generating text for social media content that mimics human writing. The misuse of these tools presents urgent dilemmas, motivating the need to better understand the structure and patterns of LLM-generated content. Human communication on the Internet has developed relevant linguistic adaptations, including the use of emoji to augment traditional text. This study investigates the ability of one LLM, OpenAI’s GPT-3.5, to replicate human emoji usage in social media contexts. Drawing upon a dataset of nearly three thousand US English human-written tweets, we employed GPT-3.5-Turbo to generate social-media-style content and analyzed the use of emoji in the resulting text. We compared the patterns of emoji usage between the LLM-generated and the human-written datasets, particularly frequency, types of emoji commonly used, and emoji sequences (n-grams). Our results revealed notable differences in all categories. While human-written tweets were more likely to use faces, hearts, and repetitive sequences of emoji, LLM-created content had a broader variety of emoji, with a preference for literal representations of the text’s subject matter, producing diverse and unique emoji combinations.

Plant litter crust enhances nitrogen accumulation by regulating microbial diversity and urease activity in semi-arid sandy soils

Plant litter plays a crucial role in regulating soil nitrogen (N) cycling in dryland ecosystems. However, the mechanisms by which plant litter crust drives microbial community composition to influence N levels in sandy soils remain unclear. In this study, we examined the effects of litter crust on sandy soil microhabitat characteristics (temperature, moisture, and porosity), microbial diversity and composition, urease activity, and soil N variables (total-N, nitrate-N, and ammonium-N) across different stages of litter crust development (including early-, mid-, and post-term) in a field setting. We found that litter crust promotes bacterial and fungal alpha diversity in sandy soils, leading to a shift in bacterial community composition from oligotrophs (i.e., Actinobacteria) to copiotrophs (i.e., Proteobacteria and Bacteroidetes), and a shift in fungal community composition dominance from Ascomycota to Basidiomycota. Litter crust enhances the complexity and stability of bacterial and fungal co-occurrence networks in sandy soils, especially in early- and mid-term stages of crust development. Additionally, litter crust increases nitrification (aerobic ammonia oxidation) and decreases denitrification (nitrate reduction) in these soils. Notably, the increase in soil moisture and urease activity, along with the decrease in soil temperature due to litter crust, effectively promotes N accumulation in sandy soils. Our results demonstrate that N levels during the litter crust period are mainly influenced by a combination of bacterial beta diversity and fungal alpha diversity at the 0–5 cm depth, and bacterial alpha diversity along with soil properties (i.e., soil variables and urease activity) at the 5–10 cm depth. Overall, our results reveal that litter crust contributes to N accumulation in sandy soils by regulating bacterial and fungal community composition and diversity, as well as by buffering soil temperature and soil moisture, and enhancing urease activity. These findings provide new insights into the critical role of soil microbes in maintaining N functions during litter crust development in semi-arid sandy ecosystems.

Fabrication of Engineered Drug-Polymer Composite Particles via Piezoelectric Inkjet Technique for Floating Drug Delivery Systems.

This study presents a simple approach for fabricating low-density drug-polymer amorphous solid dispersions (ASDs) using a piezoelectric inkjet method, demonstrating potential applications for floating drug delivery systems (FDDS). By adjusting the ratio of two polymers, polylactic acid, and Eudragit RLPO, the floatability and drug release rate of the drug-polymer ASD particles can be easily manipulated. Kinetic model analyses have been conducted to interpret the drug release mechanism. This work offers a robust platform for exploring diverse polymer-drug combinations that are applicable to FDDS.

The abscopal effects of sonodynamic therapy in cancer.

The abscopal effect is a phenomenon wherein localised therapy on the primary tumour leads to regression of distal metastatic growths. Interestingly, various pre-clinical studies utilising sonodynamic therapy (SDT) have reported significant abscopal effects, however, the mechanism remains largely enigmatic. SDT is an emerging non-invasive cancer treatment that uses focussed ultrasound (FUS) and a sonosensitiser to induce tumour cell death. To expand our understanding of abscopal effects of SDT, we have summarised the preclinical studies that have found SDT-induced abscopal responses across various cancer models, using diverse combination strategies with nanomaterials, microbubbles, chemotherapy, and immune checkpoint inhibitors. Additionally, we shed light on the molecular and immunological mechanisms underpinning SDT-induced primary and metastatic tumour cell death, as well as the role and efficacy of different sonosensitisers. Notably, the observed abscopal effects underscore the need for continued investigation into the SDT-induced 'vaccine-effect' as a potential strategy for enhancing systemic anti-tumour immunity and combating metastatic disease. The results of the first SDT human clinical trials are much awaited and are hoped to enable the further evaluation of the safety and efficacy of SDT, paving the way for future studies specifically designed to explore the potential of translating SDT-induced abscopal effects into clinical reality.

Phosphorylation of disordered proteins tunes local and global intramolecular interactions

Protein post-translational modifications, such as phosphorylation, are important regulatory signals for diverse cellular functions. In particular, intrinsically disordered protein regions (IDRs) are subject to phosphorylation as a means to modulate their interactions and functions. Toward understanding the relationship between phosphorylation in IDRs and specific functional outcomes, we must consider how phosphorylation affects the IDR conformational ensemble. Various experimental techniques are suited to interrogate the features of IDR ensembles; molecular simulations can provide complementary insights and even illuminate ensemble features that may be experimentally inaccessible. Therefore, we sought to expand the tools available to study phosphorylated IDRs by all-atom Monte Carlo simulations. To this end, we implemented parameters for phosphoserine (pSer) and phosphothreonine (pThr) into the OPLS version of the continuum solvent model, ABSINTH, and assessed their performance in all-atom simulations compared with published findings. We simulated short (<20 residues) and long (>80 residues) phospho-IDRs that, collectively, survey both local and global phosphorylation-induced changes to the ensemble. Our simulations of four well-studied phospho-IDRs show near-quantitative agreement with published findings for these systems via metrics including changes to radius of gyration, transient helicity, and persistence length. We also leveraged the inherent advantage of sequence control in molecular simulations to explore the conformational effects of diverse combinations of phospho-sites in two multiphosphorylated IDRs. Our results support and expand on previous observations that connect phosphorylation to changes in the IDR conformational ensemble. Herein, we describe phosphorylation as a means to alter sequence chemistry, net charge and charge patterning, and intramolecular interactions, which can collectively modulate the local and global IDR ensemble features.

From words to wealth: Decoding the language markers and compositions of shop names in Banda Aceh

This qualitative study examines the names of 100 local and independent businesses to study the linguistic diversity and features of the linguistic landscape in Banda Aceh, Indonesia. The names here refer to those written in specific languages used in Aceh (Indonesian, Acehnese, English, Arabic, and other indigenous languages spoken in the province). The data was collected from five main roads, capturing the shop names for analysis. The results show that English is the dominant language choice, composing a majority of 45% of signage, showing the influence of globalization and the desire for international appeal. While English appears to convey prestige, some businesses blend it with Indonesian or Acehnese, connecting with local culture and engaging diverse audiences. This deliberate language choice reflects a commitment to cultural preservation and authentic customer experiences. Meanwhile, diverse linguistic combinations, emphasizing global influences, cultural preservation, and strategic branding, were also revealed. The most prevalent linguistic structure of signs is product+X (57%), which highlights product types, followed by place+X (14%) which emphasizes regional or cultural identity. In addition, a name/person’s name+X establishes a personalized connection (6%). Finally, in language markers, shop names reveal globalization’s prevalence and demonstrate efforts to convey a global image. The revitalization marker reflects the commitment to the Acehnese language and cultural pride. Identity markers, which include place, name, abbreviation, product, plant name, and brand, establish trust, familiarity, and resonance, creating a dynamic linguistic and cultural landscape where businesses steer global influences and local heritage to connect authentically with customers. To conclude, shop names in Banda Aceh illustrate a vibrant linguistic and cultural environment where businesses use global influences and local heritage to create meaningful bonds with customers.

Unravelling the knowledge matrix: exploring knowledge-sharing behaviours on market-based platforms using regression tree analysis

PurposeThis study aims to enhance the effectiveness of knowledge markets and overall knowledge management (KM) practices within organisations. By addressing the challenge of internal knowledge stickiness, it seeks to demonstrate how machine learning and AI approaches, specifically a text-based AI method for personality assessment and regression trees for behavioural analysis, can automate and personalise knowledge market incentivisation mechanisms.Design/methodology/approachThe research employs a novel approach by integrating machine learning methodologies to overcome the limitations of traditional statistical methods. A natural language processing (NLP)-based AI tool is used to assess employees’ personalities, and regression tree analysis is applied to predict and categorise behavioural patterns in knowledge-sharing contexts. This approach is designed to capture the complex interplay between individual personality traits and environmental factors, which traditional methods often fail to adequately address.FindingsCognitive style was confirmed as a key predictor of knowledge-sharing, with extrinsic motivators outweighing intrinsic ones in market-based platforms. These findings underscore the significance of diverse combinations of environmental and individual factors in promoting knowledge sharing, offering key insights that can inform the automatic design of personalised interventions for community managers of such platforms.Originality/valueThis research stands out as it is the first to empirically explore the interaction between the individual and the environment in shaping actual knowledge-sharing behaviours, using advanced methodologies. The increased automation in the process extends the practical contribution of this study, enabling a more efficient, automated assessment process, and thus making critical theoretical and practical advancements in understanding and enhancing knowledge-sharing behaviours.

Optimizing CAR-T cell therapy for solid tumors: current challenges and potential strategies.

Chimeric antigen receptor (CAR)-T cell therapy demonstrates substantial efficacy in various hematological malignancies. However, its application in solid tumors is still limited. Clinical studies report suboptimal outcomes such as reduced cytotoxicity of CAR-T cells and tumor evasion, underscoring the need to address the challenges of sliding cytotoxicity in CAR-T cells. Despite improvements from fourth and next-generation CAR-T cells, new challenges include systemic toxicity from continuously secreted proteins, low productivity, and elevated costs. Recent research targets genetic modifications to boost killing potential, metabolic interventions to hinder tumor progression, and diverse combination strategies to enhance CAR-T cell therapy. Efforts to reduce the duration and cost of CAR-T cell therapy include developing allogenic and in-vivo approaches, promising significant future advancements. Concurrently, innovative technologies and platforms enhance the potential of CAR-T cell therapy to overcome limitations in treating solid tumors. This review explores strategies to optimize CAR-T cell therapies for solid tumors, focusing on enhancing cytotoxicity and overcoming application restrictions. We summarize recent advances in T cell subset selection, CAR-T structural modifications, infiltration enhancement, genetic and metabolic interventions, production optimization, and the integration of novel technologies, presenting therapeutic approaches that could improve CAR-T cell therapy's efficacy and applicability in solid tumors.

Sentiments Unleashed : Pioneering the Frontier of Sentiment Analysis through Cutting-Edge Applications and Methodologies

Sentiment analysis is a powerful tool within Natural Language Processing (NLP), assists businesses and researchers in discerning the emotions, attitudes, and beliefs conveyed in written language. The increasing availability of textual data, including social media posts, product reviews, and customer feedback, has driven a significant increase in the demand for sentiment analysis. The primary goal is to uncover underlying emotions expressed through diverse word combinations, sentences, or paragraphs. By scrutinizing language nuances, it identifies a wide range of emotions, from joy and sadness to criticism and admiration. This profound understanding, facilitated by sentiment analysis, provides valuable insights into public perception, market trends, and consumer behaviours, empowering businesses to refine strategies, improve products, and enhance customer experiences. As sentiment analysis tools and techniques continue to advance, they present opportunities for progress, assisting businesses in navigating shifts in public opinion and enabling researchers to delve into the intricacies of human expression through text. With the continuous surge in textual data, sentiment analysis retains its significance in guiding stakeholders from diverse industries toward informed decisions and a comprehensive understanding of human sentiment. This research explores advanced approaches in sentiment analysis, including machine learning, lexicon-based methods, hybrid and ontology-based models, and multi-modal techniques, examining various levels, classifications, and methodologies.

An experimental study on self-propulsion of Leidenfrost drops levitating on heated ratchet plates with various orientations and built-in line angles

The self-propelled motion of Leidenfrost drops has become a significant area of interest in recent years. This study introduces diverse combinations of ratchet surfaces in horizontal, angled, and vertical alignments with the aim of augmenting drop speed and manipulating its path in a chosen direction. Four pairs of symmetric ratchet surfaces were machined with different built-in line angles (θ). Each pair of ratchet surfaces was positioned at different plate inclination angles from the ground (α), and the drop motion through these binary surfaces was investigated. A series of experiments were conducted to examine the velocity of Leidenfrost drops levitating on the surface of ratchets with built-in line angles that were positioned at horizontal, inclined, and vertically channeled configurations. It was observed that employing horizontal V-shaped ratchets with built-in line angles resulted in an increased drop velocity when compared to the conventional horizontal ratchets. The effect of three different fluids, including water, ethanol, and methanol on the drop velocity was also studied with water drops demonstrating a superior velocity compared to the others. The results showed that the inclined and vertically channeled configurations can respectively lead to 24.2 % and 33 % higher drop velocities compared to conventional ratchets.

What drives the development of digital rural life in China?

Background and objectiveEmpowering rural life through digital technology reflects the collective aspirations of millions of farmers striving for a better quality of life. Ensuring that the benefits of digital advancements reach every corner of the population is a crucial and inevitable choice. To expedite the establishment of an inclusive digital life for all citizens, the Chinese government has exerted substantial efforts by positioning the development of digital villages as a national strategy. Through comprehensive initiatives in digital village construction, facilitating the extension of "Internet+" services such as education, healthcare, transportation, and entertainment to rural areas. 24-hour digital village libraries, smart health stations, intelligent homes, facial recognition payments, unmanned supermarkets, and a variety of digitized, networked, and intelligent lifestyle applications are increasingly prevalent in Chinese rural regions. The digital gap between urban and rural areas in China is gradually diminishing, and the ongoing evolution of rural digital lifestyles paints a picturesque picture of an enhanced rural life. This study endeavors to provide a meticulous analysis of the digital landscape in Chinese villages, seeking to unravel the intricacies behind the swift development of rural digital life. MethodsUsing the top 100 counties in China's 2020 Rural Life Digitization Index ranking as case studies, this research collected the necessary data through industry research reports and official statistical sources. To begin with, based on the Technology-Organization-Environment (TOE) analysis framework, variables such as digital infrastructure, government policy support, funds are put into utilization, digital economy level, and farmer digital literacy were identified. The fuzzy set qualitative comparative analysis (fsQCA) method was then employed to recognize the influencing factors of rural digital life and combinations of condition variables. Subsequently, utilizing the system dynamics methodology, the relationships between factors were analyzed, simulating changes in rural digital life development to gain a deeper understanding of the key elements affecting rural digital life. Sensitivity analysis was conducted to further reveal the extent of influence of these key elements on rural digital life. ResultsThe fuzzy set qualitative comparative analysis reveals that the evolution of rural digital life in China is a complex outcome influenced by the simultaneous interplay of multiple factors. It demonstrates a pattern of diverse combinations and parallel pathways encompassing various variables. Among these configurations, six paths stand out with the highest coverage. These six paths can be further classified into three modes of rural digital life development in China: environment-empowered type, organization-pushed type, and compound-driven type. Further dissection of causal combinations through a system dynamics model unveils that the progression of rural digital life is intricately linked to key factors such as information infrastructure, policy intensity, and digital learning and development. Over time, there is a discernible upward trajectory in the level of rural digital life, characterized by an accelerating pace of improvement. The influence of policy intensity on rural digital life surpasses that of information infrastructure and digital learning and development, indicating that policy intensity exerts the most significant impact overall. ConclusionThe evolution of rural digital life in China is a complex, long-term process propelled by a combination of multiple factors. China's experience demonstrates that achieving high-level development in rural digital life demands attention not only to the synergistic interplay of various factors but also a strategic focus on leveraging specific key elements. This entails skillfully integrating diverse factors and identifying pivotal areas to maximize the impact of relevant conditions to the greatest extent possible.

Influence of highly dispersed phase of titanium carbide on physical and mechanical properties of alloys AM4.5Kd and AK10M2N

Dispersion-strengthened composite materials belong to the group of promising structural materials characterized by a diverse combination of properties. The work presents examples of the creation and heat treatment of composite materials based on aluminum alloys, strengthened by a dispersed phase of titanium carbide, and characterized by high hardness, elastic modulus and good wettability by the melt. The most accessible, inexpensive and effective way to obtain them is self-propagating high-temperature synthesis (SHS).The work shows the possibility of obtaining new aluminum matrix composite materials based on industrial aluminum alloys AM4.5Kd and AK10M2N by reinforcing them with 10 wt.% highly dispersed titanium carbide or AM4.5Kd–5.95 vol.% TiC and AK10M2N–5.78 vol.% TiC. The reinforcing phase is formed in alloy melts using the technology of SHS from the initial elemental components—titanium powder and carbon black. Using the obtained samples, an assessment was made of the uniformity of the ceramic phase distribution over the volume of the matrix alloys, which amounted to 0.15 and 0.12 for the samples AM4.5Kd–10%TiC and AK10M2N–10%TiC, respectively, which constitutes a high degree of uniformity.An assessment was made of physical properties such as porosity, density, electrical conductivity, as well as the coefficient of thermal linear expansion. Analysis of the data allows us to say that the final composite materials AM4.5Kd–10%TiC and AK10M2N–10%TiC have a slightly higher density (↑~4%) than the matrix alloys, due to the presence of a ceramic phase, low porosity values (~1%), lower TCLE (↓~6%) than matrix alloys and low electrical conductivity (~25% IACS). This article also presents data on the values of the mechanical properties of composite materials AM4.5Kd–10%TiC and AK10M2N–10%TiC. It has been shown that reinforcement with a ceramic phase contributes to a significant increase in hardness by 15 and 42 HB, as well as higher values of the compressive yield strength by 31 and 17 MPa, respectively, while maintaining a high level of relative deformation. The results obtained allow us to conclude that the developed composite materials can be recommended for products used under conditions of elevated temperatures and significant wear.

DeLoSo: Detecting Logic Synthesis Optimization Faults Based on Configuration Diversity

Logic synthesis tools are the core components of digital circuit design, which convert programs written in hardware description languages into gate-level netlists, and optimize the netlists. However, the netlist optimization is complex, with numerous optimization parameters to be configured. Any minor optimization faults in logic synthesis tools may cause circuit diagrams to significantly deviate from the original design, posing risks in target systems. We propose DeLoSo, the De tector of Lo gic S ynthesis o ptimization faults, the first method specifically designed to identify potential faults in the optimization processes of logic synthesis tools. DeLoSo relies on netlist differences and parameter variations to guide the generation of diverse Logic Synthesis Optimization Configuration (LSOC) combinations to thoroughly test the optimization process. DeLoSo consists of three components: LSOC generator, which generates diverse LSOC combinations through configuration recombination and mutation; LSOC diversity evaluator, which assesses the diversity of optimization configurations; and LSOC validator, which validates the generated LSOC combinations to discover optimization faults. DeLoSo identified 19 faults in two established logic synthesis tools (i.e., Vivado and Yosys); 15 of them have been fixed by vendors. Particularly, the community maintainers of Yosys have considered incorporating DeLoSo into Yosys’ existing test suite.

Multi-attribute balanced dataset generation framework AutoSyn and KinFace Channel-Spatial Feature Extractor for kinship recognition

In the field of kinship verification, facial recognition technology is becoming increasingly vital due to privacy concerns, ethical disputes, and the high costs associated with DNA testing. We have developed a novel method, the AutoSyn framework, to synthesize facial images and enhance kinship image datasets, effectively addressing the challenges of scale and quality in existing datasets. By employing a strategy that mixes ages and genders in the synthesized images, we minimize the impact of these factors on kinship recognition tasks. We have enhanced the original KinFaceW-I dataset by integrating ten distinct styles, including diverse combinations of gender, ethnicity, and age. This enrichment significantly improves both the quality and quantity of the images. Furthermore, this paper introduces an efficient feature extractor for kinship tasks, KinFace-CSFE, within a siamese neural network framework. This model not only utilizes meticulously designed channel feature extraction but also incorporates mixed kernel size spatial attention mechanisms to better focus on local features. We have also integrated YOCO data augmentation techniques to simulate complex imaging conditions, enhancing the model’s robustness and accuracy. The effectiveness of these innovations has been validated through experiments on the KinFaceW-I, KinFaceW-II, and synthesized Syn-KinFaceW-I datasets, achieving accuracy rates of 82.7%, 94.1%, and 83.2% respectively. These results significantly surpass both traditional models and current advanced models.

Mass Transportation Facilitated Porous Fe/Co Dual‐Site Catalytic Cathodes for Ultrahigh‐Power‐Density Al–Air Fuel Cells

AbstractThe development of oxygen‐dependent fuel cells is frequently hindered by the high cost of cathode materials and the sluggish kinetics of the oxygen reduction reaction (ORR). It is thus crucial to explore low‐cost and high‐activity catalysts with accelerated mass transport. Here, a high‐throughput screening method is developed by integrating an explicit solvation model with machine learning potential‐based molecular dynamics simulations, which enables the efficient evaluation of a vast array of diverse diatomic combinations and ultimately identifying Fe–Co dual‐atomic sites as the optimal ORR electrocatalysts. The superior electrocatalytic performance of the diatomic Fe/Co sites loaded on 3D‐interconnected ordered macroporous carbon (FeCo‐3DMNC) is experimentally verified, achieving high ORR activity with half‐wave potentials of 0.806 V in acidic and 0.905 V in alkaline environments. Additionally, innovative hybrid acid/alkali aluminum–air fuel cells (hA/A‐AAFCs) incorporating FeCo‐3DMNC as the cathode catalyst demonstrate a notably high open‐circuit voltage of 2.72 V and a record‐breaking power density of 827 mW cm−2, significantly outperforming conventional alkaline aluminum–air fuel cells. This work marks a significant advancement by combining cutting‐edge computational screening with rigorous experimental validation to develop promising electrocatalysts, potentially paving the way for the advanced energy storage and conversion technologies.

Patterns of early childhood adversity and neighborhood deprivation predict unique challenges in adolescence: A UK birth cohort study.

This study applies a comprehensive bioecological perspective to address a significant gap in the childhood adversity literature by employing latent profile analysis to examine the impact of diverse combinations of early childhood adversities and protective factors on adolescent psychosocial and behavioral outcomes. Drawing from the United Kingdom's Millennium Cohort Study (N = 19,444), we identified eight unique profiles of early childhood adversity and protective factors. These profiles provide a nuanced understanding of adversity combinations and allow for differentiation between groups with similar profiles. Latent profile membership was a significant predictor of all adolescent outcome variables, indicating that profiles differed significantly from one another on psychosocial and behavioral outcomes (Wald values ranged from 10.10-623.22; p < .001). Some findings support the cumulative risk model, indicating that exposure to multiple early adversities increases the likelihood of adverse outcomes. However, we also found that specific adversities, such as parental psychopathology, parental alcohol use, and neighborhood deprivation, uniquely impact adolescent outcomes. This study highlights the necessity for tailored interventions and policies to support children with distinct early life experiences, emphasizing the importance of addressing both cumulative and specific adversities at multiple levels to prevent psychosocial and behavioral problems in adolescence.

Detecting m6A RNA modification from nanopore sequencing using a semisupervised learning framework.

Direct nanopore-based RNA sequencing can be used to detect posttranscriptional base modifications, such as N6-methyladenosine (m6A) methylation, based on the electric current signals produced by the distinct chemical structures of modified bases. A key challenge is the scarcity of adequate training data with known methylation modifications. We present Xron, a hybrid encoder-decoder framework that delivers a direct methylation-distinguishing basecaller by training on synthetic RNA data and immunoprecipitation (IP)-based experimental data in two steps. First, we generate data with more diverse modification combinations through in silico cross-linking. Second, we use this data set to train an end-to-end neural network basecaller followed by fine-tuning on IP-based experimental data with label smoothing. The trained neural network basecaller outperforms existing methylation detection methods on both read-level and site-level prediction scores. Xron is a standalone, end-to-end m6A-distinguishing basecaller capable of detecting methylated bases directly from raw sequencing signals, enabling de novo methylome assembly.

Working hours influence preferences of residents for micro features of small urban green spaces

This study investigates whether duration of working hours affect residents’ preferences for the micro-features of Small Urban Green Spaces (SUGS), and explores differences across genders and visitation purposes. Little is known about this subject, yet it is crucial for creating more equitable green spaces. In phase 1, participants (n = 209; 30.9 ± 6.73 years, 59.3%male) were categorized by average daily working hours (0–8 h, 8–10 h, 10 h+), and rated 41 micro-feature images on their importance for visiting SUGS. Phase 2 included the top ten features ranked from phase 1, with participants (n = 256; 30.8 ± 5.12 years, 35.2% female) completing the Kano model survey to identify and explain the importance of features and different types of needs. Findings reveal a clear trend: extended work hours pivot resident preferences towards green space attributes promoting physical activity. For residents of 0–8 h valued aesthetic elements like sculptures and vegetation, while lighting became paramount for those with 8–10 h, and for those working over 10 h prioritized facilities for active engagement, such as playground equipment and slow runway. These distinctions provide valuable guidance for designing SUGS with diverse feature combinations that cater to the needs of residential areas with varying socio-economic backgrounds and occupational lifestyles, thereby enhancing urban livability.

A review of the literature on spirituality and religion in information research – 1990 to 2022

PurposeMore than eight in ten people worldwide identify with a religious group. In addition, people often engage with spiritual and religious content despite having no formal beliefs or affiliations. Spirituality remains a prominent feature of Western and Westernised information-based societies and cultures; however, people’s everyday interactions with spiritual and religious information have received disproportionate attention in information and library science research. Accordingly, this paper aims to understand how scholars have explored religion and spirituality in information research and identify current and emerging trends in the literature.Design/methodology/approachThis paper analyses 115 peer-reviewed articles, 44 book chapters, 24 theses and 17 unrefereed papers published between 1990 and 2022 to present a narrative review of how scholars have explored spirituality and religion in information research. The reviewed literature is first organised into spirituality-related and religion-related articles and thereafter analysed in Internet studies, information behaviour studies and galleries, libraries, archives and museums-related research groups.FindingsOur analysis indicates scholars in Internet studies have researched both established and alternative religious interactions, and emerging research agendas seek to explore intersections between traditional religious authority and modern Internet-facilitated engagements. Information behaviour scholars have examined interactions in Christianity and Islam, focused primarily on Western contexts and conventional interactions, with emerging research aiming to explore diverse contextual and methodological combinations. Finally, GLAM researchers have investigated the practicality, suitability, and appropriateness of spirituality and religion-related service provisions; however, a clear research agenda is currently lacking in spirituality and religion information research more broadly.Originality/valueThis paper is the first review of the spirituality and religion-related information research spanning Internet studies, information behaviour studies and galleries, libraries, archives and museums research domains.