Search Results (288)

Cyanobacteria are promising platforms for bioenergy, carbon sequestration, and bioproduct synthesis, but their photosynthetic efficiency is limited by suboptimal light utilization, electron transport constraints, and environmental stress. Titanium dioxide nanoparticles (n-TiO₂) have emerged as powerful photocatalytic materials that can enhance light absorption, modulate electron transport, and influence the redox balance in biological systems. This review advances the concept of photocatalytic-biological coupling, in which n-TiO₂ functions as artificial light amplifiers that augment cyanobacterial photosynthesis. Current evidence on the physicochemical properties of n-TiO₂, their interactions with cyanobacterial cells, and their effects on photosystems, electron transport chains, and downstream metabolic processes is examined. Particular emphasis is placed on the integration of photophysical and biological mechanisms, including reactive oxygen species (ROS)-mediated signaling, proton motive force (PMF) enhancement, and adenosine triphosphate (ATP) synthesis. Emerging approaches, including nano–bio interface engineering, environmental biotechnology applications, and artificial intelligence-guided optimization, are highlighted. By bridging photophysics, cellular bioenergetics, and computational design within a unified mechanistic framework, this review establishes the scientific foundation needed to translate photocatalytic–biological coupling into scalable and biotechnologically deployable nano-enabled photosynthetic systems. Full article

The semiconductor industry faces converging pressures from energy-constrained edge electronics and energy-bottlenecked high-performance computing, motivating heterogeneous monolithic three-dimensional (M3D) integration as a system-level response. M3D imposes a strict back-end-of-line (BEOL) thermal budget on upper-tier devices, restricting the channel materials and contact processes available and degrading conventional thin-film transistor performance. The source-gated transistor (SGT), in which drain saturation is set by gate-modulated injection across an engineered source barrier rather than by drain-side channel pinch-off, provides a device-level response: low saturation voltage, high output impedance, large intrinsic gain, and tolerance to channel-length variation, all achieved with moderate-mobility and nonideal-contact channel materials. This review organizes reported SGTs by source-barrier architecture and channel-material platform, develops a spatial characterization framework that complements electrical measurements for unambiguous identification of source-controlled operation, and surveys applications across standalone edge electronics and BEOL-compatible upper tiers in M3D stacks. Integrating non-volatile memory mechanisms into the source barrier further extends SGTs into a compute-in-memory and neuromorphic upper-tier role in which the voltage-invariant saturation current itself functions as a programmable, read-bias-robust state variable. Together, these considerations position SGTs as a flexible architectural primitive for heterogeneous M3D platforms that address the energy demands of both edge and high-performance computing. Full article

In the present study, we employed qualitative and quantitative content analytic methods to explore how fans use social chatbots to engage with the object of their fandom. Drawing on theories of self-disclosure, the Computers Are Social Actors (CASA) paradigm, and parasocial relationships, we investigated motivations underlying chatbot use among fandom communities. Data consisted of 813 Reddit comments discussing Character.ai use. Using qualitative thematic analysis, n-gram collocation analysis, and dictionary-based text analysis, the study identified five primary motivations: fan facilitation, safe harbor, virtual social compensation, real-life (IRL) social contact, and entertainment. Findings indicated that Character.ai was used by fans to extend fandom experiences, engage in role-play and creative storytelling, seek emotional support, compensate for unmet social needs, and explore interests in a judgment-free environment. Evidence from the thematic and n-gram analyses suggested that fandom engagement and social motivations were identified by users as important drivers of use, while utilitarian functions also played a role. The findings highlight how social chatbots may be reshaping fan practices, parasocial engagement, and digitally mediated companionship. Findings are discussed in terms of the dual character of fan C.ai use, namely as a tool to both meet social needs and avoid social contact, with its potential benefits and hazards for fans engaging with social chatbots. Full article

Objective: This study investigates the relationship between social media use and consumer purchase intentions or out-of-home food choices in the Polish gastronomic market, focusing specifically on variations across generations. Methodology: Data were collected between May and July 2024 through a diagnostic survey using the Computer-Assisted Web Interviewing (CAWI) technique (N = 1099). Respondents were recruited via a non-probability quota sampling approach based on strict demographic inclusion criteria. To test the research hypotheses regarding generational variations in market behaviour, a multivariate discriminant function analysis was performed using Statistica 13.1 PL. A preliminary pilot study (N = 30) confirmed the initial questionnaire readability and overall consistency (Cronbach’s alpha = 0.87), while subscales were treated independently during the main analysis. Results: Multivariate models were found to be highly significant, revealing clear differences between age groups. The youngest cohort (aged 18–35) relies heavily on Instagram and TikTok, showing distinct patterns regarding visual triggers such as “instagrammable” aesthetics, digital validation, and menu uniqueness alongside weight loss claims. Conversely, seniors (aged 61 and older) lean unexpectedly towards X. This older segment displays pragmatic, utility-driven motives, searching for detailed textual data about ingredients and the health-promoting properties of food. General product quality and calorie control were identified as universal factors that do not vary by generation. Conclusions and Managerial Implications: The digital transformation of the Polish restaurant industry does not follow a single path. Social media is closely linked to modern customer journeys, with physical dining spots frequently serving as spaces for socialisation. Consequently, restaurant operators should move away from mass communication and adopt a selective omnichannel strategy, where message formats shift from visual appeal to factual, nutrition-oriented text that is tailored to the digital literacy and dietary needs of each generation. Full article

Digital transformation in agricultural production requires automated approaches for monitoring, risk assessment, and decision support under uncertainty. This study proposes an automated risk assessment and control framework for feed production in digital agroengineering systems. The framework is designed for the quantitative evaluation of producer and consumer risks arising during the control of key feed-quality parameters, using the case of feed production for cattle in the OHMK agricultural holding. The proposed approach integrates probabilistic modeling, simulation-based risk estimation, fuzzy logic, expert evaluation, and a multi-agent representation of agroengineering processes. A three-dimensional risk model is developed to represent producer risk, consumer risk, and actuarial risk as interconnected components of a digital control environment. In addition, a fuzzy model is introduced to assess the robustness and digital maturity of management functions, including organization, planning, motivation, and control. Computer experiments based on statistical data for crude protein content in silage demonstrate that control risks depend nonlinearly on measurement uncertainty, parameter variability, and normative thresholds. In the analyzed single-indicator case study, the arithmetic mean of crude protein content in silage was 7.5% of dry matter, the standard deviation was 0.5, and the Weibull approximation parameters were α = 1.0, β = 2.5, and γ = 6.0. Under the most sensitive normative threshold scenario, producer risk increased to approximately 25%, while consumer risk showed a lower but nonlinear increase with measurement uncertainty. The results show that producer risk may reach significant levels when measurement uncertainty becomes comparable with the variability of the controlled parameter. The proposed framework can serve as a computational basis for future automated monitoring, risk-aware control, and decision-support systems in Industry 4.0-oriented agricultural production. Full article

This work constructs, analyzes and simulates a new general SIR epidemiological model for the spread of a generic long-time disease, in which the coefficients of infectivity and death rate are system variables. Diseases, such as COVID-19, have demonstrated clearly that infectivity and death rates can change over time, even for the same variant of the virus, due to vaccination, improved treatments, better analysis, better medications, etc. This motivates us to construct the SIR-ID model for a generic disease in which the rate coefficients are state variables as a part of the systems’s evolution in time. The model consists of a coupled system of five differential equations, where the equations for the infectivity and death rate have general source functions. The analysis shows the existence, positivity and boundedness of the solutions. A discussion of the Endemic (EE) and Disease-Free (DFE) equilibria and their stability is provided. A bifurcation analysis of the DFE and EE is conducted, as well as a sensitivity analysis. Then, computer simulations depict two typical cases of dynamic behavior, one when the DFE is stable and attracting, and one in which the EE is stable and attracting. These also show the way the system approaches the steady states. Full article

Classical Laplace-, Sumudu-, and Elzaki-type transforms are formulated within additive analytical frameworks and do not naturally accommodate multiplicative scaling structures arising in non-Newtonian calculus. Motivated by this limitation, this study introduces a non-Newtonian Laplace–Sumudu–Elzaki transform (NNLSET) based on logarithmic scaling mechanisms, multiplicative measures, and power-type kernels. The proposed framework is constructed by replacing the classical measure dt with the multiplicative measure $dt/t$ and the linear scaling structure $f\left(ut\right)$ with the nonlinear scaling structure $f\left(t^u\right)$. Using the logarithmic transformation $t=e^x$, a canonical kernel representation of the form $t^{-\alpha \left(u\right)}$ is derived, establishing a correspondence between multiplicative power-type kernels and weighted exponential structures in the logarithmic domain. Within an admissible weighted function framework, several analytical properties of the transform are established, including existence, boundedness, stability, uniqueness, restricted recoverability, and a logarithmic derivative representation associated with expressions of the form $t{f}^{\prime }\left(t\right)$. A comparative analysis with the classical Laplace–Sumudu–Elzaki framework, together with illustrative differential-equation examples, a representative nonlinear MEMS oscillator, and a numerical computation, is presented. The obtained results demonstrate that the NNLSET provides a mathematically consistent framework for the analysis of multiplicative structures, logarithmic scaling phenomena, and logarithmically structured differential equations. Its applicability is further illustrated through the analysis of a representative nonlinear MEMS oscillator. Full article

Hydrogen (H₂) is expected to play a critical role in modern industry, particularly in ammonia synthesis, petroleum refining, and low-carbon transportation. The safe storage of H₂ remains a major challenge due to its low volumetric density under ambient conditions. Metal–Organic Frameworks (MOFs), highly porous crystalline materials, have emerged as promising H₂ storage candidates owing to their high surface areas and tuneable pore structures. Molecular simulations such as grand canonical Monte Carlo or density functional theory are costly and limited in exploring large material spaces, motivating efficient predictive tools to accelerate discovery. Here, Machine Learning (ML) techniques are compared to an explainable artificial intelligence (XAI) approach using symbolic regression (SR), trained on 10,123 experimentally measured H₂ adsorption datapoints from real-world MOFs. The best performing model achieved a goodness of fit of 0.9986 with lower computational demand, but reduced interpretability, addressed using XAI analysis and clustering. SR achieves a lower goodness of fit of 0.914 but produces a physically meaningful equation highlighting structural features driving high gravimetric efficiencies. These results demonstrate strong ML capability for predicting how MOF properties and environmental conditions affect H₂ uptake. This offers engineers and researchers a practical means of screening potential MOFs for H₂ storage applications, with the XAI analyses providing additional confidence in the predictions. They allow researchers to understand the physical reasoning behind each output, assess the reliability of individual predictions, and make fully informed decisions, enabling predictive models to be acted upon with confidence in real-world contexts. Full article

Radial distribution networks are highly exposed to sustained service interruptions because faults occurring along upstream feeder sections can affect large groups of downstream customers. This condition motivates the development of cost-effective planning strategies capable of reducing expected energy not supplied (EENS) and improving reliability indices such as the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), and Customer Average Interruption Duration Index (CAIDI). This study formulates the optimal placement of sectionalizing devices as a binary combinatorial optimization problem in which the objective function minimizes the total expected cost, defined as the sum of customer interruption cost and the annualized investment and installation cost of the selected devices. The formulation considers candidate-branch eligibility, the maximum number of devices, the available investment budget, and the maximum allowable restoration time, while preserving the radial topology of the base feeder by construction. The Aquila Optimizer (AO) is implemented and compared with the Grey Wolf Optimizer (GWO) and a hybrid Genetic Algorithm–Particle Swarm Optimization (GA-PSO) algorithm using the IEEE 69-bus test system under identical population size, iteration budget, number of independent runs, and pseudo-random seed conditions. The results show that the best identified installation of eight sectionalizing devices reduces SAIDI by approximately 58% and the total expected cost by nearly 50% with respect to the base case. SAIFI remains unchanged because the analyzed radial configuration does not include load-transfer paths; therefore, sectionalizing primarily reduces interruption duration rather than interruption frequency. The three algorithms reached solutions of comparable quality around the best identified configuration. GWO exhibited the highest robustness, AO showed a slightly lower computational time than GWO under the adopted MATLAB R2025b-based evaluator, and GA-PSO converged to the same best identified configuration found by GWO. These findings indicate that AO is a competitive computational alternative for sectionalizing-device placement and that a moderate investment in sectionalizing infrastructure can support economically justified reliability improvements in radial distribution networks. Full article

Gamification increasingly influences learning experiences, cognitive engagement, and behavioral performance in digital learning, cognitive training, and health intervention contexts. However, the mechanisms underlying cognitive factors, along with related research hotspots and evolutionary trends, have not been adequately synthesized. Using the Web of Science Core Collection, this study analyzes 813 publications on gamification and cognitive factors published between 2012 and 2024. Using a bibliometric method, CiteSpace was utilized to analyze publication trends, collaborations, keyword co-occurrence, cluster structures, burst terms, cited references, and knowledge-map visualizations. The cluster analysis produced 10 interrelated themes: “flipped classroom,” “active learning,” “continuance intention,” “dementia,” “executive function,” “cognitive control training,” “computational thinking,” “cognitive training,” “cognitive load” and “user experience”. Potential future directions suggested by the bibliometric patterns include: (1) expanding gamification across educational contexts; (2) refining gamification theory models that focus on cognitive processes by examining user experience and cognitive load as potential mechanisms that link gamification design features to outcomes such as motivation, self-efficacy, task performance, and continuance intention; (3) promoting applications in cognitive training, cognitive impairment intervention, and digital health; (4) optimizing experimental design, data collection, interdisciplinary collaboration, and personalized design; and (5) clarifying how gamification shapes cognitive processes such as attention allocation, cognitive load regulation, problem solving, executive function, and computational thinking. This study does not aim to establish causal effects; rather, it uses bibliometric evidence to reveal the developmental trajectory, thematic structure, and emerging directions of research on gamification and cognitive factors. Full article

Background: Postoperative kidney dysfunction is a common and serious complication in surgical patients. Kidney function is typically assessed using the estimated glomerular filtration rate (eGFR), most often calculated with the CKD-EPI equation based on serum creatinine. While several machine learning models have been developed to predict acute kidney injury, few have focused on predicting postoperative eGFR category. This study aimed to develop a machine learning model capable of classifying surgical patients into eGFR categories G1, G2, or G3+ in the early postoperative period, based on preoperative and intraoperative data. Methods: We developed the two-layer “eGFR-AI” architecture. In the first layer, two XGBoost models compute the probability of eGFR being above 89 and 59 mL/min/1.73 m², respectively, and their outputs are passed to a second-layer logistic regression model that produces the final classification. The dataset included 200 patients admitted postoperatively to the intensive care unit of a tertiary academic hospital during the first half of 2024. Input features comprised age, sex, body mass index, type and duration of surgery, ASA status, presence of sepsis or shock at admission, and history of arterial hypertension, diabetes mellitus, or chronic kidney disease. Model performance was evaluated using accuracy, F1 score, and area under the ROC curve (ROC-AUC) on a held-out testing set. Feature importance analysis and statistical testing of associations with acute kidney injury were also performed. Results: On a held-out test set, the final model achieved an accuracy of 0.75, a weighted F1 score of 0.75, and a weighted ROC-AUC of 0.92 (balanced accuracy 0.76; Matthews correlation coefficient 0.65; Cohen’s κ 0.62). The first-layer models reached ROC-AUC values of 0.85 (eGFR > 89) and 0.96 (eGFR > 59). In a head-to-head comparison on the same partition, the stacked model performed comparably to standard baseline classifiers (multinomial logistic regression, random forest, support-vector machine, single multiclass XGBoost, CatBoost, LightGBM) without demonstrating superiority. Chronic kidney disease and presence of sepsis or shock at admission emerged as the strongest predictors. In an exploratory analysis (n = 8 events), all patients diagnosed with acute kidney injury fell into the G3+ category. Conclusions: In this single-center pilot study, “eGFR-AI” shows that early postoperative kidney function category can be predicted from a small set of routinely available preoperative and intraoperative variables, with performance comparable to standard classifiers and the added benefit of calibrated, interpretable category-level probabilities. Given the limited unicentric cohort and reduced category granularity, these findings should be regarded as preliminary and hypothesis-generating: they support the feasibility of the approach and motivate external, multicenter validation before any clinical application. Full article

Non-suicidal self-injury (NSSI) is a significant public health concern among university students. Accumulating evidence suggests that deficits in executive function (EF) are associated with NSSI; however, little is known about how specific EF components relate to the specific psychological functions that motivate self-injury. This study aimed to investigate the fine-grained associations between EF dimensions and NSSI functions in university students with past-year NSSI using network analysis. Altogether, 1078 Chinese university students (82.6% female; Mage = 19.07, SD = 1.03) who had engaged in at least one NSSI behavior in the past year were enrolled. Executive function was assessed using the Adolescent Executive Function Scale, which measures three dimensions: inhibitory control, cognitive flexibility, and working memory. NSSI functions were assessed using the 19-item function subscale of the Adolescent Non-Suicidal Self-Injury Assessment Questionnaire. A Gaussian graphical model was used to estimate the network. Expected influence (EI) and bridge expected influence (BEI) were computed to identify core nodes and bridge nodes, respectively. Of the 231 possible edges, 117 (50.65%) were non-zero, with the strongest within-community edge linking “coping with sadness and disappointment” (F3) and “expressing despair and hopelessness” (F4). Across communities, the most prominent edge was between “inhibitory control” and “having a desire to harm myself and cannot stop”. The three highest EI values were observed for “having a desire to harm myself and cannot stop,” “letting others make changes,” and “self-punishment”. “Inhibitory control” showed the highest BEI in the EF community, while “having a desire to harm myself and cannot stop” showed the highest BEI in the NSSI function community. Both EI and BEI demonstrated excellent stability (CS coefficients = 0.75). In university students with past-year NSSI, inhibitory control and the uncontrollable urge to self-injure function as critical bridge nodes linking executive dysfunction to NSSI functions, while self-punishment and interpersonal influence motives emerge as central drivers in the network. These findings highlight inhibitory control, the uncontrollable urge to self-injure, self-punishment, and interpersonal influence as promising targets for precision interventions aimed at disrupting the maladaptive cycle maintaining NSSI in this population. Full article

Infra-hepatic inferior vena cava (IVC) balloon occlusion is an effective strategy for reducing intraoperative bleeding during precision liver surgery, yet rapid balloon inflation can produce abrupt transient deviations in downstream venous pressure that are not yet quantitatively characterized. Current practice relies on operator experience, with no quantitative framework to balance occlusion efficacy against downstream pressure safety. A computational fluid dynamics (CFD) model of the balloon-occluded IVC was developed in ANSYS 2025 R2 with two-way fluid–structure interaction (FSI), Carreau–Yasuda blood rheology, and a balloon described by an Ogden hyperelastic model; the flow regime was laminar (Re ≈ 254). Reduced-order ARX models of four input–output subsystems were identified from CFD-generated data, and a model predictive control (MPC) strategy was formulated to penalize downstream pressure overshoot through a weighted cost function. The identified models achieved training normalized root-mean-square errors of 0.0363 to 0.1164 and out-of-sample validation errors of 0.1224 to 0.2381. Conventional sigmoid inflation induced a 45.82% overshoot in downstream pressure (P_aft); the optimal input signal (q = [0, 1, 0, 0], λ = 0.1) reduced this to 6.05%, a reduction of 39.77 percentage points, while preserving >90% flow occlusion at U_F = 3 × 10⁴ Pa. The proposed framework offers a quantitative basis for balloon-occlusion device design that limits downstream pressure overshoot, motivating subsequent benchtop, ex vivo, and in vivo validation. Full article

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph neural networks, and convolutional neural networks, have been introduced for intelligent decision-making tasks. From a biomimetic perspective, these models are inspired by biological information-processing mechanisms. Convolutional neural networks reflect hierarchical procedures similar to those in the visual cortex, graph neural networks mimic communication among biological neurons, and LSTM networks are motivated by short-term and long-term memory mechanisms in the brain. Inspired by these biomimetic computational principles, this study proposes a novel hybrid deep learning strategy composed of LSTM, convolutional layers and GraphSAGE geometric layers for smart supply chain logistics management. This strategy enables leveraging information pertaining to LSTM-based long-term dependencies, convolutional local patterns and graph-related hidden connections of the supply chain dataset for intelligent decision-making. The GraphSAGE framework helps with scalable graph learning, which enhances predictive accuracy in the case of unseen data. The optimizer in the proposed methodology performs sequential optimization using the biomimetic particle swarm optimizer and the Adam approach (PSO-Adam), considering the hybrid cost function. The prediction of logistics parameters is investigated using five datasets, including DataCo, Shipping, Smart Logistics, Hospital Supply Chain, and Pharmaceutical Supply Chain. The average accuracies of 97.8%, 100%, 96.6%, 98.7% and 99.4% are obtained for practical multi-category logistics parameter forecasts. The evaluation metrics for ten logistics predictions confirm the effectiveness of the proposed intelligent logistics model and highlight the potential of biomimetic geometric networks for complex supply chain decision-making. The model is a cost-efficient approach with consideration of the prediction capabilities, helping to reduce the occurrence of logistics risks, increase the productivity of the supply chain and affect the supply chain visibility, customer satisfaction, and industry reputation. Full article

Spiking neural networks (SNNs) are well suited for modeling temporally evolving information due to their event-driven and dynamic neuronal mechanisms. Nevertheless, the majority of existing SNN topologies are constructed through algorithmic procedures rather than guided by constraints from biological brain connectivity, which weakens their biological plausibility. In our earlier work, we developed a spiking neural network (SNN) by incorporating topological information from functional brain networks extracted from functional magnetic resonance imaging (fMRI) data of healthy individuals, and named the resulting model fMRISNN. Nevertheless, the fMRI data used in previous work were resting-state fMRI. Compared with resting-state fMRI, task-state fMRI can capture brain-region coordination patterns induced by specific task stimuli, and the resulting functional brain network is therefore more closely related to the corresponding task. Motivated by this advantage, this study replaces the resting-state topology used in previous fMRISNN studies with a task-state, number/digit-related fMRI topology and validates the resulting Task-fMRISNN on handwritten digit recognition. The experimental results demonstrate that the proposed Task-fMRISNN outperforms the Rest-fMRISNN in terms of recognition accuracy, lesion robustness, and noise robustness. In addition, the Task-fMRISNN achieves significantly better performance than several baseline models constructed using algorithmically generated topologies. While deep convolutional neural networks (CNNs) may deliver superior absolute recognition performance, the proposed fMRISNN provides a more compact model structure and shows potential resource-efficiency advantages due to its sparse and event-driven computational characteristics. Full article