High Complexity Research Articles

A reservoir computing approach in active noise control for vehicle applications

Reservoir Computing (RC) is an alternative approach to Recurrent Neural Networks (RNN) that can avoid some shortcomings of conventional gradient decent-training that are mainly related to high computational complexity and the reduced ability to "learn" long-range dependencies. In RC the input data are transformed into patterns in a high-dimensional space by an RNN called the "reservoir", which remains unchanged during training. The desired output signal is generated as a linear combination of the neuron's signals from the input-excited reservoir. The linear part of the RC architecture, which is called the "readout" can be trained with a simple learning algorithm such as linear regression. In this work an RC architecture is proposed for the attenuation of acoustic disturbances encountered in vehicle cabins such as aircrafts and yachts. At first, a dataset of acoustic pressure measurements from real-world cabins was used to generate the readout and reservoir layers. Subsequent computer simulations demonstrated that the proposed architecture can successfully attenuate such acoustic disturbances. Finally, the performance of RC in Active Noise Cancellation task was compared with both the linear FxLMS algorithm and a conventional RNN, showing that it has several advantages regarding acoustic pressure reduction combined with relatively low computational complexity.

Photocatalytic furan-to-pyrrole conversion.

The identity of a heteroatom within an aromatic ring influences the chemical properties of that heterocyclic compound. Systematically evaluating the effect of a single atom, however, poses synthetic challenges, primarily as a result of thermodynamic mismatches in atomic exchange processes. We present a photocatalytic strategy that swaps an oxygen atom of furan with a nitrogen group, directly converting the furan into a pyrrole analog in a single intermolecular reaction. High compatibility was observed with various furan derivatives and nitrogen nucleophiles commonly used in drug discovery, and the late-stage functionalization furnished otherwise difficult-to-access pyrroles from naturally occurring furans of high molecular complexity. Mechanistic analysis suggested that polarity inversion through single electron transfer initiates the redox-neutral atom exchange processes at room temperature.

Spatio-temporal distribution and international context of bovine viral diarrhoea virus genetic diversity in France

Bovine viral diarrhoea (BVD) is one of the most economically damaging livestock enzootic diseases in the world. BVD aetiological agents are three pestiviruses (BVDV-1, -2 and HoBi-like pestivirus), which exhibit high genetic diversity and complex transmission cycles. This considerably hampers the management of the disease, which is why eradication plans have been implemented in several countries. In France, a national plan has been in place since 2019. Our understanding of its impact on the distribution of BVDV genotypes is limited by the availability of French genetic data. Here, we conducted a molecular epidemiology study to refine our knowledge of BVDV genetic diversity in France, characterise its international relationships, and analyse national spatio-temporal genotypic distribution. We collated 1037 BVDV-positive samples throughout France between 2011 and 2023, with a greater sampling effort in two major cattle production areas. We developed a high-throughput sequencing protocol which we used to complete the 5’UTR genotyping of this collection. We show that two main BVDV-1 genotypes, 1e and 1b, account for 88% of genotyped sequences. We also identified seven other BVDV-1 genotypes occurring at low frequencies and three BVDV-2 samples (genotype 2c). Phylogenetic analyses indicate different worldwide distribution patterns between the two main BVDV-1 genotypes. Their relative frequencies present no major changes in France since the 1990s and few variations at the national scale. We also found some degree of local spatial structuring in western France. Overall, our results demonstrate the potential of large-scale sequence-based surveillance to monitor changes in the epidemiological situation of enzootic diseases.

Spectral expansion methods for prediction uncertainty quantification in systems biology

Uncertainty is ubiquitous in biological systems. For example, since gene expression is intrinsically governed by noise, nature shows a fascinating degree of variability. If we want to use a model to predict the behaviour of such an intrinsically stochastic system, we have to cope with the fact that the model parameters are never exactly known, but vary according to some distribution. A key question is then to determine how the uncertainties in the parameters affect the model outcome. Knowing the latter uncertainties is crucial when a model is used for, e.g., experimental design, optimisation, or decision-making. To establish how parameter and model prediction uncertainties are related, Monte Carlo approaches could be used. Then, the model is evaluated for a huge number of parameters sets, drawn from the multivariate parameter distribution. However, when model solutions are computationally expensive this approach is intractable. To overcome this problem, so-called spectral expansion (SE) methods have been developed to quantify prediction uncertainty within a probabilistic framework. Such SE methods have a basis in, e.g., computational mathematics, engineering, physics, and fluid dynamics, and, to a lesser extent, systems biology. The computational costs of SE schemes mainly stem from the calculation of the expansion coefficients. Furthermore, SE effectively leads to a surrogate model which captures the dependence of the model on the uncertainty parameters, but is much simpler to execute compared to the original model. In this paper, we present an innovative scheme for the calculation of the expansion coefficients. It guarantees that the model has to be evaluated only a restricted number of times. Especially for models of high complexity this may be a huge computational advantage. By applying the scheme to a variety of examples we show its power, especially in challenging situations where solutions slowly converge due to high computational costs, bifurcations, and discontinuities.

THE STRUGGLE TO ANTICIPATE THE FUTURE Political uses, technological approaches and moral debates on climate change

This paper explores major academic debates that address why, as our scientific understanding of climate change increases, controversies about its causes and responses multiply. To this end, this research presents a discussion of the literature on the political, technological, and social conditions that generate divergent ideas on climate change. This article is divided into three sections. First, it is argued that climate knowledge tends to be contextualized to local concerns. Second, inherent technical limitations of climate models limit a direct translation between global climate science and local policies. Third, greater knowledge could not resolve controversies on climate change since values are inevitably part of understanding and elaborating solutions. Due to the high complexity of climate change, the intellectual fragmentation of its study, and the multiplication of contradictory information, it is crucial to present a broad overview of current academic contributions to strengthen public debate and informed decision-making.

Vessel Trajectory Prediction at Inner Harbor Based on Deep Learning Using AIS Data

This study aims to improve vessel trajectory prediction in the inner harbor of Busan Port using Automatic Identification System (AIS) data and deep-learning techniques. The research addresses the challenge of irregular AIS data intervals through linear interpolation and focuses on enhancing the accuracy of predictions in complex port environments. Recurrent neural network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU models were developed, with LSTM delivering the highest performance. The primary scientific question of this study is how to reliably predict vessel trajectories under varying conditions in inner harbors. The results demonstrate that the proposed method not only improves the precision of predictions but also identifies critical areas where Vessel Traffic Service Operators (VTSOs) can better manage vessel movements. These findings contribute to safer and more efficient vessel traffic management in ports with high traffic density and complex navigational challenges.

Inertial microfluidic mixer for biological CubeSat missions

Nanosatellites of CubeSat type due to, i.a., minimized costs of space missions, as well as the potential large application area, have become a significant part of the space economy sector recently. The opportunity to apply miniaturized microsystem (MEMS) tools in satellite space missions further accelerates both the space and the MEMS markets, which in the coming years are considered to become inseparable. As a response to the aforementioned perspectives, this paper presents a microfluidic mixer system for biological research to be conducted onboard CubeSat nanosatellites. As a high complexity of the space systems is not desired due to the need for failure-free and remotely controlled operation, the principal concept of the work was to design an entirely passive micromixer, based on lab-on-chip technologies. For the first time, the microfluidic mixer that uses inertial force generated by rocket engines during launch to the orbit is proposed to provide an appropriate mixing of liquid samples. Such a solution not only saves the space occupied by standard pumping systems, but also reduces the energy requirements, ultimately minimizing the number of battery modules and the whole CubeSat size. The structures of the microfluidic mixers were fabricated entirely out of biocompatible resins using MultiJet 3D printing technology. To verify the functionality of the passive mixing system, optical detection consisting of the array of blue LEDs and phototransistors was applied successfully. The performance of the device was tested utilizing an experimental rocket, as a part of the Spaceport America Cup 2023 competition.Graphical abstract

B-133 Uric acid: A description of the values found in adult patients treated during 2022 in a High Complexity Hospital

Abstract Background An association between uric acid and kidney disease, glucose intolerance, high blood pressure, metabolic syndrome and cardiovascular disease has been recognized. It is the obligation of the Clinical Laboratory to have valid reference intervals (RI) for this marker. Each particular population has genetics and a diet that represents it, which cannot be extrapolated in many cases. The difficulty and high cost of using direct methods for its establishment drive us to use the existing data in our electronic archive retrospectively. Objective: to describe the range of uric acid values in adult outpatients treated at the Posadas Hospital during the year 2022. Evaluate the influence of gender and age range on the observed values. Methods A retrospective observational approach was carried out from the System database Laboratory Informatics (SIL), Roche Infinity, from the year 2022. The data corresponding to all adult outpatient patients treated in the service were processed. Analytical System: Roche Cobas 6000, Uricase-Oxidase method. Exclusion criteria: oncology patients, kidney patients, diabetics, hyperglycemic patients, obstetric patients, hormonal patients, treated in wards and referred patients. Outliers excluded by Tukey method. The statistical treatment was carried out with the SPSS 17.0 program. Results We worked with 91922 data groups. Hence 12331 of uric acid. After meeting the exclusion criteria: 3899, of which 2289 (3.96 ± 1.05 mg/dl) correspond to women < 50 years , 747 (4.36 ± 1.15 mg/dl at ≥ 50 years and 863 (5.59 ± 1.33 mg/dl) men, all with normal distributions. Applying ANOVA and Student's test it was possible to verify in addition to the described significant difference between sexes, a difference between the 2 age groups of women (p<0.01) which is not equal to that of men in women ≥ 50 years of age. Conclusions A more extensive study is necessary to define own reference ranges that enable the early alarm guidelines, especially in the group of women < 50 years of age. This will allow an adequate evaluation of risk and evolution of patients. The application of Indirect Methods will make it possible to exclude values corresponding to underlying pathologies not registered in the SIL.

Transient voltage stability assessment and margin calculation based on disturbance signal energy feature learning

With the increasing variation of the network topology and the high complexity of the processing measurement data, the transient voltage stability assessment of the new power system is facing significant challenges in low accuracy and high time costs. To address the shortcomings of the existing method and apply it to online assessment, this paper proposes an assessment method based on feature learning for disturbance signal energy (DSE) from bus voltages. Firstly, the relationship between DSE and system transient voltage stability is established, and the calculation of DSE from bus voltage time series is detailed. Subsequently, a transient voltage stability assessment method based on the ID3 Decision Tree algorithm and DSE is proposed. Finally, by employing the Support Vector Machine (SVM) to construct the optimal boundary in the feature space formed by the key buses, the transient voltage stability margin (TVSM) for specific scenarios is proposed. Simulation results on the IEEE 39-bus system demonstrate that the proposed method can rapidly and accurately assess the transient voltage stability of the system and calculate the stability margin, providing intuitive and interpretable results with high engineering application value.

Multi-objective two-stage robust optimization of wind/PV/thermal power system based on meta multi-agent reinforcement learning

The integration of renewable energy into the power grid poses significant challenges for optimization and scheduling of the power system. In recent years, methods based on deep reinforcement learning have surpassed traditional methods on the high complexity and long-term decision-making of power system optimization and scheduling. However, faced with the inherent uncertainty of renewable energy generation and the different optimization objectives in power system, the deep reinforcement learning methods are unable to effectively address them. This paper proposes a method that combines meta reinforcement learning with multi-agent reinforcement learning to solve the multi-objective two-stage robust optimization of wind/PV/thermal power system. We conducts optimization and scheduling experiments on the IEEE39 bus system. The results indicate that our method not only enhances the robustness of the scheduling strategy, but also outperforms baseline methods in terms of convergence, diversity, and uniformity of the Pareto frontier.

Carbon Price Point–Interval Forecasting Based on Two-Layer Decomposition and Deep Learning Combined Model Using Weight Assignment

With the intensification of global warming, the demand for carbon emissions reduction has gradually increased in various countries. Carbon price is crucial for promoting the activation of the carbon trading market and facilitating emissions reduction. However, the current carbon price has non-linear characteristics, large fluctuations, and high complexity, making accurate predictions challenging. To effectively predict the trends and change of carbon price, this study proposed a hybrid deep learning point–interval prediction model. First, an improved variational mode decomposition–symplectic geometry mode decomposition (IVMD–SGMD) two-layer decomposition model was constructed to decompose the carbon prices into regular subsequences. Then, attention–temporal convolutional network–bidirectional gated recursive unit (Attention-TCN-BiGRU) and Encoder–Decoder long short-term memory (LSTM) combined prediction models were constructed for the prediction of subsequences. The entropy method (EM) was used to assign weights to the predictions of two models to achieve model complementarity and a linear reconstruction of the models’ results. Then the error correction was performed to obtain the final prediction results. This study conducted an experiment on carbon prices in the Guangdong and Shenzhen markets. The mean absolute error (MAE) of the two datasets were reduced by 89.69% and 87.43% lower than that for LSTM. To demonstrate the model's adaptability, prediction experiments conducted on natural gas and crude oil prices were employed, confirming its strong predictive accuracy in energy price forecasting. Based on the point prediction error, the interval prediction using the improved kernel density estimation (IKDE) provides more carbon market information for decision makers. The proposed model aids government energy policy formulation and fosters ongoing efforts to reduce carbon emissions.

A low-complexity error-feedback lattice-equalizer with phase tracking for underwater acoustic communications.

Recursive least squares (RLS)-based equalizers are hindered by their high complexity in underwater acoustic (UWA) communications. This article proposes an adaptive equalizer with a phase tracking method for the UWA communication, named the error-feedback lattice-equalizer (EFLE). First, we derive the algorithm for recursively solving the least squares problem from EFLE, introducing a lattice structure using time and order updates, thereby reducing the complexity to be linearly related to its length. The error-feedback mechanism used in computing reflection coefficients ensures the numerical stability of the algorithm. By focusing on the rapid tap rotation in time-varying channels, we design phase tracking in EFLE to further improve equalization performance. To verify the bit error rate (BER) performance of the proposed EFLE, we study the UWA communication system and conduct UWA simulations and at-sea experiments. Comparisons include linear complexity equalizers such as least mean square (LMS), leaky LMS, least mean mixed-norm, and ϵ-normalized LMS equalizers, and quadratic complexity RLS equalizers. At-sea experiment results show that the BER performance of EFLE significantly outperforms its counterparts.

Large coercivity and enhanced magnetization in La-substituted Sm6Mn23 alloys

The magnetic properties of Sm6Mn23-based alloys are still not fully understood for their high complexity in magnetic and crystallographic structures. This work presents studies of the structure and magnetic properties of Sm6Mn23 and Sm5LaMn23 alloys. The results show that both the binary and La-substituted ternary alloys crystallize in a cubic Th6Mn23 type structure (Fm-3m) with trace amount of Mn impurities. The lattice parameter of Sm5LaMn23 is slightly larger than that of Sm6Mn23. The Curie temperature of Sm5LaMn23 is ∼403 K, which is lower than that of Sm6Mn23. A field-dependent magnetic transition was observed in Sm5LaMn23, which shows a decreasing temperature at which Sm5LaMn23 exhibiting maximal magnetization with increasing field, indicating a suppression of the antiferromagnetic coupling of the rare earth and Mn moments under applied field. Large coercivities up to 0.74 T and 1.2 T are observed in Sm5LaMn23 bulk alloys and powders, respectively. The saturate magnetization, the remanent magnetization, and the coercivity of Sm5LaMn23 at 5 K are much higher than that of Sm6Mn23. The ball-milled Sm5LaMn23 powders exhibit significantly enhanced coercivity and magnetization. The refinement of the grain size during ball-milling results in the enhanced coercivity. The magnetic purification of the powders and possible local ferromagnetic configuration of the surface Mn atoms may result in the enhanced magnetization.

DB-FL: DAG blockchain-enabled generalized federated dropout learning

To protect user privacy and data security, the integration of Federated Learning (FL) and blockchain has become an emerging research hotspot. However, the limited throughput and high communication complexity of traditional blockchains limit their application in large-scale FL tasks, and the synchronous traditional FL will also reduce the training efficiency. To address these issues, in this paper, we propose a Directed Acyclic Graph (DAG) blockchain-enabled generalized Federated Dropout (FD) learning strategy, which could improve the efficiency of FL while ensuring the model generalization. Specifically, the DAG maintained by multiple edge servers will guarantee the security and traceability of the data, and the Reputation-based Tips Selection Algorithm (RTSA) is proposed to reduce the blockchain consensus delay. Second, the semi-asynchronous training among Intelligent Devices (IDs) is adopted to improve the training efficiency, and a reputation-based FD technology is proposed to prevent overfitting of the model. In addition, a Hybrid Optimal Resource Allocation (HORA) algorithm is introduced to minimize the network delay. Finally, simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor

Colorectal neuroendocrine tumors (NETs) differ significantly from colorectal carcinoma (CRC) in terms of treatment strategy and prognosis, necessitating a cost-effective approach for accurate discrimination. Here, we propose an approach for distinguishing between colorectal NET and CRC based on pathological images by utilizing pathological prior information to facilitate the generation of robust slide-level features. By calculating the similarity between morphological descriptions and patches, our approach selects only 2% of the diagnostically relevant patches for both training and inference, achieving an area under the receiver operating characteristic curve (AUROC) of 0.9974 on the internal dataset, and AUROCs of 0.9724 and 0.9513 on two external datasets. Our model effectively identifies NETs from CRCs, reducing unnecessary immunohistochemical tests and enhancing the precise treatment for patients with colorectal tumors. Our approach also enables researchers to investigate methods with high accuracy and low computational complexity, thereby advancing the application of artificial intelligence in clinical settings.

Tantalum in hydrothermal fluids

Understanding the behaviour of tantalum (Ta) in hydrothermal systems is pivotal for understanding its geochemical enrichment processes and economic extraction via hydrometallurgy. Yet, its behaviour in hydrothermal systems remains poorly characterised. This study investigates the coordination chemistry, speciation, and solubility of pentavalent Ta(V) in fluoride (F) − and chloride (Cl) −rich hydrothermal solutions up to 413 °C and 800 bar, utilising in-situ High Energy Resolution Fluorescence Detected X-ray Absorption Spectroscopy (HERFD-XAS). The results reveal the stability of high order fluoridotantalate complexes in fluoride-rich fluids solutions up to the highest investigated temperature, highlighting fluoride’s paramount role in enhancing Ta solubility through the formation of stable complexes in aqueous solutions. A transition from nonafluoridotantalate tetraanion (TaF94−) to heptafluoridotantalate dianion (TaF72−) complexes was observed as a function of temperature in solutions containing ≥1 m fluoride. Conversely, our findings indicate a negligible role for chloride in Ta complexation even in high Cl (∼6 m) aqueous solutions, suggesting that Ta chloride complexes do not contribute significantly to Ta transport in hydrothermal systems. Existing solubility data were reinterpreted based on an updated speciation model that integrates the in-situ XAS results. This confirms that Ta(OH)5(aq) predominates in solutions containing <0.02 m fluoride; oxyfluoridotantalate anions such as [TaF3(OH)3−] dominate in solutions containing intermediate fluoride concentrations (0.02–1 m), and the fluoridotantalate anions [TaF94− to TaF72−] occur in more concentrated fluoride solutions (>1 m) at hydrothermal conditions (∼100–400 °C). Derived thermodynamic data for these species enable better understanding and geochemical modelling of Ta transport in hydrothermal fluids, highlighting the potential of F-rich fluids to transport significant amounts of Ta.

Complex Systems and Sustainable Leadership: Enhancing Resilience and Sustainability of Community-Based Social Enterprises in Soweto, South Africa

In urban South Africa, community-based social enterprises (CBSEs) face challenges at the intersection of financial and environmental sustainability and social purpose. These enterprises address social exclusion and community development within complex institutional environments. Despite increasing governmental support, CBSEs struggle with historical inequalities, resource limitations, high poverty levels, inadequate infrastructure, and complex social dynamics. These challenges contribute to a high failure rate among SMMEs, including CBSEs, which represent approximately 71% of community-level businesses. This study examines how complex system dynamics in marginalized South African suburbs impact the sustainable leadership and resilience of CBSEs. Utilizing a qualitative approach, the study conducted 15 in-depth semi-structured interviews with CBSE leaders. The findings revealed that effective leadership in CBSEs requires extraordinary abilities to traverse complexity, adapt to changes, and engage in dynamic organizational change. The key themes identified included adaptive and flexible leadership, interdependencies and network collaboration, community engagement and co-creation, continuous learning and feedback loops, policy influence and response, and resource utilization and constraints. By applying complex systems leadership theory (CSLT) and sustainable leadership principles, the research offers insights into the mechanisms enabling CBSEs to thrive in challenging environments. These findings contribute to Social Enterprise Management and Leadership Studies, providing practical implications for policymakers and practitioners to support CBSE development and sustainability in marginalized urban communities.

Hybrid high and low molecular weight chains of hyaluronan for clitoral injection is an effective modality treatment for increasing female sexual satisfaction: an interventional, randomized-controlled parallel study.

Hormonal, behavioral, psychological, surgical, and pharmacopsychological treatment approaches contribute to female sexual dysfunction (FSD). Aim: The study is conducted to assess the effectiveness of hyaluronan high and low molecular weight hybrid cooperative complexes (hybrid H-HA/L-HA) in treating females with SD and to compare the female sexual function index (FSFI), dermatological life quality index (DLQI), and female genital self-image scale (FGSIS) before and after therapy. We divided the 60 female participants into two groups. Hybrid H-HA/L-HA was administered to form pili of 0.25cc around the clitoris in the direction of clock positions of 12, 3, 6, and 9. In Skene's gland; 0.25cc for each and 0.5cc into the corpus/body of the clitoris. Two treatments were held 30days apart.The same procedure was repeated on the control group, but with saline as a placebo. Women completed self-report questionnaires assessing sexual functioning using the FSFI, DLQI, and FGSIS before and after therapy. There was a significant (P= 634.152; P< .05) increase in the study group's weekly sexual interactions compared with the controls. The study group showed statistically significant amelioration in desire, arousal, lubrication, orgasm, satisfaction domains, overall score, and a decrease in pain following the first and second injection sessions (P= .014, .031, .003, .001, .011, .004, and .011, respectively). A comparison of the results between the two groups revealed significant improvement were found (P= .025).There were significant improvements in the domains of the FGSIS compared with the controls (P= .026). The study group showed a substantial improvement in satisfaction with the way their genital area looked, comfort level when allowing a sexual partner to view their genital area, belief that their genitals smell perfectly fine without being self-conscious about them, and overall score (P= .022, .031, .003, .001, and .004, respectively) (P< .05).The hybrid H-HA/L-HA sessions resulted in significantly greater positive perceptions and feelings, leisure activities, interpersonal interactions, and general assessments (P= .021, .021, and .020, respectively) (P< .05). Female individuals with SD experience sexual improvements after hybrid H-HA/L-HA injection. This is the first study focusing on female individuals with SD. We recommend conducting the study on a larger population and including their partners. Hybrid H-HA/L-HA injection for rejuvenating the clitoral injection appears to be a reliable and safe method for enhancing female genital self-image, sexuality, and quality of life.

Sustaining superior visibility within digital platforms through inside and outside competitive action repertoires

Although often credited with leveling the competitive playing field, platforms pose novel challenges for millions of complementors within their ecosystems. This study explores the tactics complementors use to maintain superior visibility on these platforms. Building on competitive repertoire theory, we conceptualize two categories of competitive actions that capture the dual environmental complexity faced by complementors: inside and outside competitive moves. We assemble a unique dataset from a leading food delivery platform in Europe, providing a comprehensive view of complementors’ competitive repertoires and visibility over ten months. We find that complementors’ inside competitive repertoires with high volume and complexity are associated with sustained superior visibility. However, we also find that complementors whose competitive repertoires diverge from those of their competitors are more likely to exit the superior visibility strata. Additionally, we identify outside action repertoires as a second pathway to differentiation, built on complementors’ idiosyncratic resources and less dependent on platform architecture and rules.

Alternative and simple method to determine the refractive index of human dentin.

Determining a substance's refractive index (RI) is of great importance in a wide variety of disciplines, such as chemistry, biomedicine, engineering, and optics, to name just a few. This work utilizes critical-angle reflectometry to measure the refractive index of dentin, a biological tissue characterized by its high RI and complex structure. We fabricated a hollow semicircular prism and filled it with bromonaphthalene (1-B), allowing us to measure RIs up to 1.65. We used 5 tooth samples with exposed dentin and a laser diode beam (λ = 650nm) incident on interface 1-B/Dentine; the reflectance curve was measured from 60° to 75°. The critical angle is determined, and dentin RI is calculated. The real part of the calculated dentine's RI coincided well with those determined by other more expensive and specialized techniques with high technical support.