Heap fermentation as a case-based ecological context for interpreting variability in solid-state fermented foods.

H-FEX: A symbolic learning method for Hamiltonian systems.

Supramolecular self-assembly, driven by noncovalent interactions, serves as a fundamental principle for constructing complex functional systems (biological membranes, proteins, and DNA). Inspired by these natural paradigms, biomimetic self-assembly strategies have promoted the development of drug delivery systems (DDSs), including liposomes, lipid nanoparticles (LNPs), and virus-like particles. While these nanomedicines are clinically established, they continue to face persistent challenges, including maintaining drug stability and activity, achieving targeted delivery, overcoming biological barriers, and ensuring efficient intracellular release. Overcoming these obstacles requires the rational design of next-generation drug delivery materials. In this review, a comprehensive analysis of supramolecular self-assembled materials (SAMs) is provided. It traces the development and self-assembly mechanisms of SAMs. It defines the characteristics of next-generation SAMs, centered on their programmability, and focuses on introducing the innovative design strategy guidelines for next-generation SAMs. These guidelines fully embody the high programmability, precise size/morphology control capabilities, and multifunctional properties of SAMs. Furthermore, it discusses the latest application progress of SAMs in treating various diseases, and emphasizes the future strategies and challenges of SAM-based DDSs, aiming to facilitate broader clinical applications and benefit human health.

The functioning of complex systems depends on the coordination of diverse components, often supported by regulatory structures that incur costs. In human organizations, such costs manifest as administrative burden, which has been rising despite often reducing efficiency. Classic explanations point to bureaucrat self-interest or regulation, yet they do not explain variation across organizations or clarify how this burden can be reduced. Here, we develop a dynamical model of administrative growth that integrates known behavioral mechanisms of process creation, obsolescence, and removal. The model conceptualizes processes as developed for problem solving, but becoming obsolete as conditions change, while continuing to consume resources until actively pruned. This interplay generates two long-term outcomes: stable equilibrium or run-away growth. The threshold separating these outcomes is shaped by organizations' propensity to create new processes when faced with problems, and their propensity to prune obsolete ones in response to administrative burden. Importantly, their effects are asymmetric: Sufficiently high creation propensity leads to bloat regardless of pruning propensity. Faster environmental change shifts this threshold, making bloat more likely. Simulations of interventions show that lasting reductions in administrative costs and waste require permanent shifts in priorities and investments in distinguishing obsolete from useful processes. Temporary efforts or indiscriminate cuts provide only short-lived relief, and counterintuitively, prioritizing direct production can increase waste. Our work highlights a general mechanism by which well-intentioned problem-solving can create self-reinforcing inefficiencies in complex systems, offering insights possibly generalizable to broader applications, such as legal, policy, and software systems where obsolete elements accumulate.

2,2’-Dihalo-1,1’-binaphthyl compounds can introduce coordinating groups and other complex functional systems into the binaphthyl skeleton; however, their structural diversity renders preparation challenging. In this study, a streamlined approach is developed to prepare dihalobinaphthyl compounds that uses designed, easily obtained α-hydroxyl haloalkynes as the starting materials, which combine activity of haloalkynes and traceless directional hydroxyl groups. This process involves a palladium-catalysed 1,2-halo shift, electrophilic carbocyclisation, dehalogenative coupling with another α-hydroxyl haloalkyne, and halogenated electrophilic cyclisation. Density functional theory calculations shows the occurrence of 1,2-halo shift is primarily governed by the coordination of the aromatic ring in the palladium catalyst. The target 2,2’-dihalo-1,1’-binaphthyl compounds can be prepared on the gram scale, and afford a series of ligands, catalysts, and high-value binaphthyl-based materials. This method will significantly expand the synthetic toolbox for dihalobinaphthyl compounds and create possible opportunities for preparing functionally diverse binaphthalene-based architectures with tailored properties.

Purpose The objective of this study is to develop a comprehensive framework that integrate probabilistic failure modeling techniques such as Bayesian network and fault tree to evaluate failure dependency, linguistic fuzzy assessment to incorporate the likelihood and impact of human errors on system failure. The proposed framework also seeks to facilitate better decision-making in maintenance strategies by offering insights into both technical and human-related failure mechanisms. This holistic approach will ultimately aim to improve system performance and resilience in the face of potential failures. Design/methodology/approach Maintenance is crucial for the efficient functioning of complex systems, as it addresses faults, failures, system degradation, and their consequences, including costs, reliability, availability, and performance. The study began by identifying critical components, followed by modeling the failure and repair rate distribution graphs. It utilized fault tree analysis for failure modeling, incorporating real-time data and the application of fuzzy set theory. The Fussel–Vesely measure was employed to identify critical elements from a reliability standpoint. Furthermore, a Bayesian network was used to analyze the probabilistic dependencies among failure causes. Findings The result obtained from fault tree failure modeling analysis revealed that the machine currently exhibits a failure rate of 0.64 per day, reliability of 0.53, and availability of 0.40. The result of the Bayesian network shows the failure probability of the machine is 0.44 per day and the comparison reveals that failure analysis for the system using the Bayesian network is more precise than using the fault tree, which decreases the error of failure probability from 49% to 44% and have 10.2% of reduction. The ability to more accurately represent the likelihood of failure not only aids in operational efficiency but also supports better decision-making processes in managing system reliability and availability. Originality/value This study makes a distinct contribution by filling a significant gap in the literature using a Bayesian network (BN) to model the failure behavior of air jet looms machine using integrated quantitative failure/repair data and qualitative expert insights on human factors. Current textile maintenance studies often rely on conventional techniques that overlook this crucial integration. This study provides the first evidence-based framework for BN resilience in capturing the intricate, human-influenced failure mechanisms inherent in the textile industry. This fills a gap in the literature and establishes a new standard for reliability analysis in the industry.

An important part of ensuring the continuity of operation of complex systems is information security monitoring which is a continuous process inseparable from the context of the functioning of the protected object. The operational use of monitoring results requires the interpretability of the obtained data and the presentation of key cause-andeffect relationships in a formal and provable form. If the protected object exhibits statistical, behavioral, and process regularities, it becomes possible to form an informative space for identifying information security events. This paper formulates and validates hypotheses regarding the possibility of identifying information security events when the abovementioned types of regularity are violated as well as the search for a rational interval for the formation of a state. The scientific novelty of the results is determined by the adaptation of formal methods for constructing an informative space for identifying information security events, the introduction and experimental confirmation of hypotheses regarding the impact of an information security event on statistical, behavioral, and process regularities, and the search for a rational analysis interval. The goal of this paper is to provide a qualitatively new method for constructing an informative space for the automatic detection of information security events. The object of the study is the process of monitoring the information security status of a corporate computer network. The subject of this study is heuristic methods for forming an informative space for identifying information security events based on the statistical analysis of retrospective data in real time. This paper proposes a method for automatically forming an informative space for identifying information security events in corporate computer networks. This method is based on the dynamics of two adjacent states of end devices determined over discrete time intervals. The set of such state transitions across all devices forms the state matrix of the computer network under study. This study defined an informative space for calculating the dynamics of the obtained state vectors and found a rational interval for forming the device state when studying the dependence of the difference in the vectors of two adjacent states on the analysis interval in various informative spaces. To experimentally confirm the operability of the proposed solution, a set of network data in the PCAP (Packet CAPture) format was analyzed, including legitimate and botnet activity of Internet of Things devices. Graphical interpretation of the obtained result allows one to determine the attack preparation and attack start times, which significantly simplifies the task of information security monitoring at the input data analysis stage and reduces the amount of data analyzed by the information security analyst. Distinguishing features of the proposed method include real-time operation, the absence of a preprocessing stage for input data, and the interpretability of detected information security events. Clearly discernible trends in device status dynamics allow for a reduction in the volume of analyzed information and the focus on irregularities that characterize potential information security events. The scope of application of the proposed method includes monitoring information security events, identifying information security incidents, and detecting intrusions in corporate computer networks.

The Markovian dynamics of open quantum systems is typically described through Lindblad equations, which are derived from the Redfield equation via the full secular approximation. The latter neglects the rotating terms in the master equation corresponding to pairs of jump operators with different Bohr frequencies. However, for many physical systems this approximation breaks down, and thus a more accurate treatment of the slowly rotating terms is required. Indeed, more precise physical results can be obtained by performing the partial secular approximation, which takes into account the relevant time scale associated with each pair of jump operators and compares it with the time scale arising from the system-environment coupling. In this work, we introduce a general code for performing the partial secular approximation in the Redfield equation for structured open quantum systems. The code can be applied to a generic Hamiltonian of any multipartite system coupled to bosonic baths. Moreover, it can also reproduce the unified master equation , which captures the same physical behavior as the Redfield equation under the partial secular approximation, but is mathematically guaranteed to generate a completely positive dynamical map. Finally, the code can compute both the local and global version of the master equation for the same physical problem. We illustrate the code by studying the steady-state heat flow in a structured open quantum system composed of two superconducting qubits, each coupled to a bosonic mode, which in turn interacts with a thermal bath. The results in this work can be employed for the numerical study of a wide range of complex open quantum systems.

Physics-informed neural networks (PINNs) represent a methodology that integrates physical equations into neural networks. By incorporating the residuals of physical equations as part of the loss function, PINNs enable the network to learn data features while simultaneously satisfying the constraints imposed by physical laws. The underlying principle of PINNs involves using optimization algorithms to iteratively update the network parameters until the value of a specified physics-informed loss function decreases to an acceptable level, thereby driving the network towards the solution of the differential equations. This approach is not merely a tool but serves as a bridge integrating scientific computing with artificial intelligence, offering novel paradigms for modeling and predicting complex systems. In this paper, we propose a non-Hermitian shortcut to adiabaticity (STA) scheme based on physics-informed neural networks. We utilize the physics-informed neural networks to solve parameterized differential equations, employing the neural networks as an approximating function for the quantum adiabatic evolution process. This provides an interpretable and end-to-end framework (termed STAPINNs) for control-field optimization, distinct from traditional gradient-based approaches like gradient ascent pulse engineering (GRAPE), which require pre-defined pulse parameterization. The parameterized differential equations, along with various physical constraints, are incorporated into the network's loss function. Training the networks allows them to fit the quantum system's evolution process and obtain the driving control function for population inversion. Compared to conventional STA techniques, our approach introduces machine learning into non-Hermitian quantum systems, enabling highly robust and high-fidelity population transfer. Numerical simulations demonstrate that the proposed method significantly outperforms traditional STA protocols under dissipative conditions, achieving fidelities above 0.99 even in the presence of substantial decoherence. Moreover, the framework demonstrates strong generalization across wide parameter ranges and is inherently scalable to multi-level and many-body systems due to the mesh-free, high-dimensional handling capability of PINNs. Neural networks possess strong computational capabilities suitable for generating driving control functions in complex systems, and are equally applicable to non-Hermitian STA techniques. The STAPINNs framework not only enhances control flexibility but also provides a powerful tool for optimizing quantum operations in open and noisy systems.

In the operation of large complex systems, a substantial amount of process data is collected and stored. The extraction, utilization, and identification of features from complex data are crucial for system modeling, simulation system construction, and the design of control algorithm. The experimental data of the flight environment simulation system (FESS) was investigated. Firstly, an information entropy calculation method based on the probability cloud space was proposed, which could effectively identify the dynamic characteristics of the rate of change in the signal amplitude of the experimental data. Secondly, the information entropy was used to supportthe research on the adaptive tracking differentiator algorithm of integral step parameter and the signal correlation mining algorithm combined with the Apriori algorithm. The results show that the developed adaptive tracking differentiator algorithm can achieve superior signal extraction quality for both steady and dynamic states within the constraints of computational cycles. The proposed signal correlation mining algorithm can effectively reflect the internal patterns among different experimental parameters.

Multimodal MRI data fusion reveals distinct structural, functional and neurochemical correlates of depression in patients with Parkinson's disease.

ABSTRACT This study investigates the soliton solutions, stability, and chaotic characteristics of the M fractional (3+1)‐dimensional generalized B‐type Kadomtsev–Petviashvili (gBKP) equation, where a Galilean transformation is performed to get the related system of equations. Advanced mathematical and analytical techniques are utilized to explore the soliton solutions and bifurcation analysis in a fractional‐order nonlinear system, which helps to understand the functioning of complex systems. Perturbations are introduced to those systems to enable the observation of bifurcation analysis, including phase portraits. We also apply analytical technique the unified method to derive soliton solutions for the M‐fractional gBKP model. This work reveals various soliton solutions, including kink, anti‐kink, periodic waves, kinky periodic waves, and periodic lump waves. The solutions are graphically analyzed to explore their dynamic properties for fractional parameters. Moreover, we examine the suggested model's stability. We visualize the diverse range of soliton‐like solutions to demonstrate the significance of the findings and the effectiveness of the proposed methodology. These results enhance the understanding of soliton behaviors in M fractional gBKP equation and portray how effective the medium approach is for solving complicated nonlinear systems.

On the basis of modern mathematical models, methods and information and technologies, for example, an integrated stochastic nonlinear model of man-made processes and objects, suitable for the conditions of systemic crises, were investigated and developed. Considered in this article are some aspects of integration of a lot of domains and sectors of operation of modern complex systems which are functioning and developing in the present-day conditions of instability, crises and nonlinearity. In order to predict the development of the state of an innovation economy, the nonlinear integrated stochastic model of growthing dynamic in the phase space has been investigated and developed. In the article, problem of the optimization of the management of the activity of modern complex systems that develop and function regarding current conditions of the instability are considered. The prospect of further research based on the developed models is to conduct computer experiments and their practical usisng. The development and study of criterias, methods and models for optimal management of man-made objects and the creation of making decision systems based on the proposed integral models in the state space are also promising.

The subject of the article is the methodological basis for ensuring the adaptability of training systems (TS), utilizing real-time biometric feedback to the user, which provides personalization of the training process for vehicle drivers, aircraft pilots, operators of complex technical systems, etc. The goal is to increase the efficiency of computer-based training tools by developing a method for online dispatching of thematic training modules (TTM), which is based on the use of a user profile vector and a user state vector. These vectors are formed by integrating previous psychometric assessments of the level of training, analyzing learning results, and subsequently converting information about the user's state into discrete levels of complexity for dispatching TTM as part of the TS. The tasks addressed include: conducting a critical analysis of approaches to developing software for adaptive TS with biometric feedback and means of personalizing the training process; and developing a method for the online dispatching of training modules based on the integration of user profile assessments and biometric state, which aggregates previous task performance results and takes into account the variability of the user's heart rate. The following results were obtained. The practical advantage of the developed method lies in ensuring the personalization of training in adaptive TS software architectures across various subject areas, based on effective online dispatching of ТTM, using vectors of the individual profile and the current state of the learners. Conclusions. As a result of the research, a method was developed to increase the level of ТS adaptability by the real-time dispatching of software modules in the system, considering data on the individual characteristics of a particular user and his current psycho-physiological state. The scientific novelty of the results obtained is as follows. For the first time, a method for the dynamic dispatching of ТTM in adaptive e-learning systems was proposed. The method, unlike existing ones, considers the level of training, the current psychophysical state of the user, as well as the degree of novelty of the tasks in the ТTM. The method uses preliminary filtering of modules based on the degree of alignment with the level of user training, which makes it possible to significantly reduce the computational costs for dispatching and ensures the operation in the module in online mode. The results of experimental testing have shown the effectiveness of the developed methodological tools in solving problems related to increasing the efficiency of ТS operation, in terms of their adaptability to the individual characteristics of the user.

Quantitative imaging of endogenous proteins in biological tissues is essential for understanding their roles in cellular signaling and metabolism. Mass spectrometry imaging (MSI) using nanospray desorption electrospray ionization (nano-DESI) is a label-free approach for mapping intact proteins in biological tissues with minimal sample preparation. However, signal suppression during ionization presents a challenge for quantification. In this study, we introduce a quantitative nano-DESI MSI approach by incorporating protein internal standards (IS) into the extraction solvent. Ion images were generated using iFAMS deconvolution software, which efficiently isolates protein-specific signals from a complex background. A systematic evaluation of normalization strategies indicates that accurate quantification in each pixel is achieved using charge-weighted signal normalization to the IS signal. We demonstrate quantitative imaging of endogenous proteins using only one IS and validate the approach using immunofluorescence imaging and bulk analysis. This robust approach enables accurate protein mapping and quantification in MSI experiments, providing deeper insights into protein function in complex biological systems.

The quantum control of spin-correlated radical pairs may enable the targeted manipulation of magnetic field effects, with potential long-term applications across molecular quantum technologies, from prospective noise-resilient quantum information processors to genetically encodable quantum sensors. However, achieving precise handles over the intricate interplay between coherent electron spin dynamics and incoherent relaxation processes in photoexcited radical-pair reactions requires tractable approaches for numerically obtaining controls for large, complex open quantum systems. Employing techniques relying on full Liouville-space propagators to that end becomes computationally infeasible for large spin systems of realistic complexity. Here, we demonstrate how a control engineering approach based on the Pontryagin Maximum Principle (PMP) can offer a viable alternative by reporting on the successful application of PMP-optimal control to steer the coherent and incoherent spin dynamics of noisy radical pairs. This enables controls for prototypical radical-pair models that exhibit robustness in the face of relevant noise sources and paves the way to incoherent control of radical-pair spin dynamics.

This study investigates the use of ozonized water as a novel reaction medium for generating Maillard reaction products (MRPs) from fructose and glycine, comparing their physicochemical properties and antioxidant performance with those produced in phosphate buffer. Heating in ozonized water delayed early Maillard stages, as indicated by slower browning, lower A294 and A420 absorbance, and higher L* values. However, prolonged heating led to intensified reddish-brown coloration and elevated intermediate formation, suggesting ozone-modified reaction pathways. pH declined more sharply in the ozone system, while conductivity increased significantly after 60 min, reflecting accelerated late-stage reactions. Antioxidant activity, assessed via DPPH and ABTS assays, developed more slowly in the ozone system but reached comparable levels to the buffer after 120 min. In emulsion models, MRPs from either system alone exhibited pro-oxidant effects, while blends, especially those produced using ozonized water and buffer at ratios of 75:25 and 50:50, significantly enhanced oxidative stability. Zeta-potential analysis showed that emulsions containing MRP blends had less negative initial charges but exhibited greater stability over 3 days compared to those with individual treatments. These findings highlight the potential of ozonized water to modulate Maillard reaction kinetics and suggest that blending MRPs from different reaction media can enhance antioxidant functionality and emulsion stability in complex food systems.

We propose that current Neuroscience approaches can benefit from further integrating morphodynamics across different scales of brain organization and neural network emergent functions in complex systems. While emergence in neuroscience is commonly addressed at higher organizational levels, here we consider neuronal morphology itself as an emergent level of organization. Progressing from form-based complexity views, early models of neuronal morphogenesis, and functional approaches, we integrate cell morphology to behavior with particular relevance to the following issues: (1) Neuronal Morphological Diversity and Circuitry Function, (2) Mother-Infant Relationships, and (3) Epilepsy and Neuropsychiatric Comorbidities. The structure of neurons and their connectivity within the brain volume are morphodynamic features that emerge from dynamic interactions among morphogenetic elements, the local cell neighborhood, and synaptic connections. In turn, the emergent functions of networks are organized around a series of conceptual, experimental, and computational foundations. Complex systems neuroscience combines such data with additional high- and multiscale information to develop models organized around structure, function, and behavioral displays in both normal and pathological conditions. Here, we present and discuss examples that approximate this framework, drawing on animal models and human data. Such an integrated approach aligns with the ongoing efforts promoted by UNESCO's "UniTwin Complex Systems Digital Campus" (CS-DC) to collaboratively address open, multiscale problems in neuroscience and complex systems.

Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as autonomous vehicles, healthcare diagnostics, and financial fraud detection. Despite its benefits, the deployment of ML models brings significant security challenges, such as adversarial attacks, which can compromise the integrity and reliability of these systems. To address these challenges, this paper builds upon the concept of Secure Machine Learning Operations (SecMLOps), providing a comprehensive framework designed to integrate robust security measures throughout the entire ML operations (MLOps) lifecycle. SecMLOps builds on the principles of MLOps by embedding security considerations from the initial design phase through to deployment and continuous monitoring. This framework is particularly focused on safeguarding against sophisticated attacks that target various stages of the MLOps lifecycle, thereby enhancing the resilience and trustworthiness of ML applications. A detailed advanced pedestrian detection system (PDS) use case demonstrates the practical application of SecMLOps in securing critical MLOps. Through extensive empirical evaluations, we highlight the trade-offs between security measures and system performance, providing critical insights into optimizing security without unduly impacting operational efficiency. Our findings underscore the importance of a balanced approach, offering valuable guidance for practitioners on how to achieve an optimal balance between security and performance in ML deployments across various domains.

Functional validation of candidate genes in congenital anomalies of the kidneys and urinary tract (CAKUT) and other disorders is essential for translating genetic discoveries into clinical applications. Conditional knockout mouse models are indispensable for studying gene function in complex organ systems. The Short Conditional intrON (SCON) system accelerates the generation of such models by inserting the artificial SCON into a coding exon. SCON is designed to be spliced out after transcription, without affecting gene expression. Upon Cre activity, SCON is converted into the ΔSCON allele which cannot be spliced out, introducing premature termination codons (PTCs) to inactivate the gene. Previous validation of the SCON system in mice has focused primarily on phenotypic outcomes. Here, we provide a molecular characterization of the SCON system in Cdh12—a candidate gene implicated in kidney damage in CAKUT. We found that both Cdh12SCON and Cdh12ΔSCON alleles caused unintended skipping of the exon downstream of the insertion site, culminating in a frameshift and PTC. Consequently, the Cdh12SCON allele led to a ~ 25% reduction in mRNA expression, indicating that it was not transcriptionally inert as designed. Despite unintended exon skipping, the Cdh12ΔSCON allele still effectively suppressed mRNA expression. These findings highlight the importance of transcript-level characterization of engineered alleles prior to functional studies, as artefactual splicing events may occur across multiple gene-targeting strategies, including artificial intron-based conditional alleles as shown here.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11248-026-00496-7.