Burak Dayı, Hüseyin Üzen, İpek Balıkçı Çiçek et al.

Diagnostics • 2023

The study aims to evaluate the diagnostic performance of an artificial intelligence system based on deep learning for the segmentation of occlusal, proximal and cervical caries lesions on panoramic radiographs. The study included 504 anonymous panoramic radiographs obtained from the radiology archive of Inonu University Faculty of Dentistry's Department of Oral and Maxillofacial Radiology from January 2018 to January 2020. This study proposes Dental Caries Detection Network (DCDNet) architecture for dental caries segmentation. The main difference between DCDNet and other segmentation architecture is that the last part of DCDNet contains a Multi-Predicted Output (MPO) structure. In MPO, the final feature map split into three different paths for detecting occlusal, proximal and cervical caries. Extensive experimental analyses were executed to analyze the DCDNet network architecture performance. In these comparison results, while the proposed model achieved an average F1-score of 62.79%, the highest average F1-score of 15.69% was achieved with the state-of-the-art segmentation models. These results show that the proposed artificial intelligence-based model can be one of the indispensable auxiliary tools of dentists in the diagnosis and treatment planning of carious lesions by enabling their detection in different locations with high success.

Hyunduk Jang, Sang Eun Lee, Jimin Yang et al.

European Heart Journal • 2019

We identified CAP1 as a new binding partner of PCSK9 and a key mediator of caveolae-dependent endocytosis and lysosomal degradation of LDLR.

Pieter Verbeke, Tom Verguts

PLoS Computational Biology • 2019

We provide a novel computational framework on how biological and artificial agents can learn to flexibly couple and decouple neural task modules for cognitive processing. In this way, they can address the stability-plasticity dilemma. For this purpose, we combine two prominent computational neuroscience principles, namely Binding by Synchrony and Reinforcement Learning. The model learns to synchronize task-relevant modules, while also learning to desynchronize currently task-irrelevant modules. As a result, old (but currently task-irrelevant) information is protected from overwriting (stability) while new information can be learned quickly in currently task-relevant modules (plasticity). We combine learning to synchronize with task modules that learn via one of several classical learning algorithms (Rescorla-Wagner, backpropagation, Boltzmann machines). The resulting combined model is tested on a reversal learning paradigm where it must learn to switch between three different task rules. We demonstrate that our combined model has significant computational advantages over the original network without synchrony, in terms of both stability and plasticity. Importantly, the resulting models' processing dynamics are also consistent with empirical data and provide empirically testable hypotheses for future MEG/EEG studies.

Laifang Li, Raymond W. Schmitt, Caroline C. Ummenhofer et al.

Journal of Climate • 2016

Abstract Moisture originating from the subtropical North Atlantic feeds precipitation throughout the Western Hemisphere. This ocean-to-land moisture transport leaves its imprint on sea surface salinity (SSS), enabling SSS over the subtropical oceans to be used as an indicator of terrestrial precipitation. This study demonstrates that springtime SSS over the northwestern portion of the subtropical North Atlantic significantly correlates with summertime precipitation over the U.S. Midwest. The linkage between springtime SSS and the Midwest summer precipitation is established through ocean-to-land moisture transport followed by a soil moisture feedback over the southern United States. In the spring, high SSS over the northwestern subtropical Atlantic coincides with a local increase in moisture flux divergence. The moisture flux is then directed toward and converges over the southern United States, which experiences increased precipitation and soil moisture. The increased soil moisture influences the regional water cycle both thermodynamically and dynamically, leading to excessive summer precipitation in the Midwest. Thermodynamically, the increased soil moisture tends to moisten the lower troposphere and enhances the meridional humidity gradient north of 36°N. Thus, more moisture will be transported and converged into the Midwest by the climatological low-level wind. Dynamically, the increases in soil moisture over the southern United States enhance the west–east soil moisture gradient eastward of the Rocky Mountains, which can help to intensify the Great Plains low-level jet in the summer, converging more moisture into the Midwest. Owing to these robust physical linkages, the springtime SSS outweighs the leading SST modes in predicting the Midwest summer precipitation and significantly improves rainfall prediction in this region.

Areej Shahid, Jong-Hyeok Choi, Abu ul Hassan S. Rana et al.

Sensors • 2018

Over the last few decades, the development of the electronic nose (E-nose) for detection and quantification of dangerous and odorless gases, such as methane (CH₄) and carbon monoxide (CO), using an array of SnO₂ gas sensors has attracted considerable attention. This paper addresses sensor cross sensitivity by developing a classifier and estimator using an artificial neural network (ANN) and least squares regression (LSR), respectively. Initially, the ANN was implemented using a feedforward pattern recognition algorithm to learn the collective behavior of an array as the signature of a particular gas. In the second phase, the classified gas was quantified by minimizing the mean square error using LSR. The combined approach produced 98.7% recognition probability, with 95.5 and 94.4% estimated gas concentration accuracies for CH₄ and CO, respectively. The classifier and estimator parameters were deployed in a remote microcontroller for the actualization of a wireless E-nose system.

Romain Escudier, Emanuela Clementi, Andrea Cipollone et al.

Frontiers in Earth Science • 2021

In order to be able to forecast the weather and estimate future climate changes in the ocean, it is crucial to understand the past and the mechanisms responsible for the ocean variability. This is particularly true in a complex area such as the Mediterranean Sea with diverse dynamics like deep convection and overturning circulation. To this end, effective tools are ocean reanalyses or reconstructions of the past ocean state. Here we present a new physical reanalysis of the Mediterranean Sea at high resolution, developed in the Copernicus Marine Environment Monitoring Service (CMEMS) framework. The hydrodynamic model is based on the Nucleus for European Modelling of the Ocean (NEMO) combined with a variational data assimilation scheme (OceanVar). The model has a horizontal resolution of 1/24° and 141 unevenly distributed vertical z* levels. It provides daily and monthly temperature, salinity, current, sea level and mixed layer depth as well as hourly fields for surface velocities and sea level. ECMWF ERA-5 atmospheric fields force the model and daily boundary conditions in the Atlantic are taken from a global reanalysis. The reanalysis covers the 33 years from 1987 to 2019. Initialized from SeaDataNet climatology in January 1985, it reaches a nominal state after a 2-years spin-up. In-situ data from CTD, ARGO floats and XBT are assimilated into the model in combination with satellite altimetry observations. This reanalysis has been validated and assessed through comparison to in-situ and satellite observations as well as literature climatologies. The results show an overall improvement of the comparison with observations and a better representation of the main dynamics of the region compared to a previous, lower resolution (1/16°), reanalysis. Temperature and salinity RMSD are decreased by respectively 14 and 18%. The salinity biases at depth of the previous version are corrected. Climate signals show continuous increase of the temperature and salinity, confirming estimates from observations and other reanalysis. The new reanalysis will allow the study of physical processes at multi-scales, from the large scale to the transient small mesoscale structures and the selection of climate indicators for the basin.

Bing Zhu, Xin Pan, Seppe vanden Broucke et al.

Information Sciences • 2022

Monica F. Contrino, Maribell Reyes-Millán, Patricia Vázquez‐Villegas et al.

Smart Learning Environments • 2024

Abstract It is becoming increasingly clear that not all students require the same education, and the requirement of personalized education is increasingly in demand. The incorporation of adaptive learning (AL) has increased in recent years. However, research on this subject is still evolving at the university level. In this study, we investigated the impact of integrating an AL tool (CogBooks®) in a university course (statistics for decision making) taught in an innovative online modality called FIT (flexible, interactive, and with technology), in which the course is designed in the CANVAS® platform and uses Zoom® as a means of communication with students. Learning outcomes were compared between the FIT courses with or without AL and between AL strategies in online and face-to-face courses. It was clear that AL improved the students’ achievement regardless of the modality. In addition, we conclude that students achieve better in AL courses in the classroom than in distance courses. Satisfaction surveys favor a preference for FIT courses with AL over classroom classes with AL. Our results suggest that AL is a solid strategy for teaching undergraduate courses. Graphical abstract

Zhenyu Zhang, Simona Liguori, Thomas F. Fuerst et al.

ACS Sustainable Chemistry & Engineering • 2019

Liquid ammonia is a high density (17.7 wt. %) hydrogen carrier with a well-established production and distribution infrastructure. Efficient decomposition and purification are essential for its use as a hydrogen storage material. Here we demonstrate the production of high purity (> 99.7%) H 2 from NH 3 using a catalytic membrane reactor (CMR) in which a Ru catalyst is impregnated within a porous yttria-stabilized zirconia (YSZ) tube coated with a thin, 6 µm Pd film by electroless deposition. The intimate proximity of catalyst and membrane eliminates transport resistances that limit performance in the conventional packed bed membrane reactor (PBMR) configuration. The addition of a Cs promoter enabled complete NH 3 conversion at temperatures as low as 400°C, exceeding equilibrium constraints without the need for a sweep gas. A reactor model was developed that captured CMR performance with high fidelity. NH 3 decomposition was observed to follow first order kinetics due to efficient H 2 removal. Relative to a comparable PBMR, the Ru loading in the CMR was reduced an order of magnitude and the H 2 recovery increased 35%, enabling record volumetric productivity rates (> 30 mol m-3 s-1) that validate its promise for efficient, compact H 2 delivery from ammonia.

Abeer Ali Alnuaim, Mohammed Zakariah, Aseel Alhadlaq et al.

Computational Intelligence and Neuroscience • 2022

Emotions play an essential role in human relationships, and many real-time applications rely on interpreting the speaker's emotion from their words. Speech emotion recognition (SER) modules aid human-computer interface (HCI) applications, but they are challenging to implement because of the lack of balanced data for training and clarity about which features are sufficient for categorization. This research discusses the impact of the classification approach, identifying the most appropriate combination of features and data augmentation on speech emotion detection accuracy. Selection of the correct combination of handcrafted features with the classifier plays an integral part in reducing computation complexity. The suggested classification model, a 1D convolutional neural network (1D CNN), outperforms traditional machine learning approaches in classification. Unlike most earlier studies, which examined emotions primarily through a single language lens, our analysis looks at numerous language data sets. With the most discriminating features and data augmentation, our technique achieves 97.09%, 96.44%, and 83.33% accuracy for the BAVED, ANAD, and SAVEE data sets, respectively.

Josh V. Vermaas, Richard A. Dixon, Fang Chen et al.

Proceedings of the National Academy of Sciences • 2019

Lignin is an abundant aromatic polymer found in plant secondary cell walls. In recent years, lignin has attracted renewed interest as a feedstock for bio-based chemicals via catalytic and biological approaches and has emerged as a target for genetic engineering to improve lignocellulose digestibility by altering its composition. In lignin biosynthesis and microbial conversion, small phenolic lignin precursors or degradation products cross membrane bilayers through an unidentified translocation mechanism prior to incorporation into lignin polymers (synthesis) or catabolism (bioconversion), with both passive and transporter-assisted mechanisms postulated. To test the passive permeation potential of these phenolics, we performed molecular dynamics simulations for 69 monomeric and dimeric lignin-related phenolics with 3 model membranes to determine the membrane partitioning and permeability coefficients for each compound. The results support an accessible passive permeation mechanism for most compounds, including monolignols, dimeric phenolics, and the flavonoid, tricin. Computed lignin partition coefficients are consistent with concentration enrichment near lipid carbonyl groups, and permeability coefficients are sufficient to keep pace with cellular metabolism. Interactions between methoxy and hydroxy groups are found to reduce membrane partitioning and improve permeability. Only carboxylate-modified or glycosylated lignin phenolics are predicted to require transporters for membrane translocation. Overall, the results suggest that most lignin-related compounds can passively traverse plant and microbial membranes on timescales commensurate with required biological activities, with any potential transport regulation mechanism in lignin synthesis, catabolism, or bioconversion requiring compound functionalization.

Rei Akaishi, Nils Kolling, Joshua W. Brown et al.

Journal of Neuroscience • 2016

Learners should infer which features of environments are predictive of significant events, such as rewards. This "credit assignment" problem is particularly challenging when any of several cues might be predictive. We show that human subjects solve the credit assignment problem by implicitly "hypothesizing" which cue is relevant for predicting subsequent outcomes, and then credit is assigned according to this hypothesis. This process is associated with a distinctive pattern of activity in a part of medial frontal cortex. By contrast, when unexpected outcomes occur, hypotheses are redirected toward alternative cues, and this process is associated with activity in lateral orbitofrontal cortex.

Yang Zhao, Deliang Chen, Jiao Li et al.

Climate Dynamics • 2020

Abstract This study investigates the roles of atmospheric moisture transport under the influence of topography for summer extreme precipitation over North China (NC) during 1979–2016. Based on rain gauge precipitation data and a reanalysis, 38 extreme precipitation days in NC during the 38 years were selected and associated moisture fluxes estimated. The results show that there is a dominant moisture influx of 311.8 kg m −1 s −1 into NC along its southern boundary from tropical oceans, and a secondary influx of 107.9 kg m −1 s −1 across its western boundary carried by mid-latitude westerlies. The outflux across the eastern boundary is 206.9 kg m −1 s −1 and across the northern boundary is 76.0 kg m −1 s −1 , giving a net moisture gain over NC of 136.8 kg m −1 s −1 . During extreme precipitation days, the moisture flux convergence (MFC) was much larger, exceeding 4 × 10 −5 kg m −1 s −1 . The MFC maximum core, the pronounced moisture transport, and the striking extreme precipitation zone over NC are all anchored to the east of the steep slopes of the surrounding topography. Moreover, a remarkably high humidity and strong upward motion also occur near steep slopes, indicating the critical role of the adjacent topography on the extreme precipitations. Simulations with and without the topography in NC using the Weather and Research Forecasting model for six selected out of the 38 extreme precipitation days demonstrate that the surrounding topography reinforces the MFC over NC by 16% relative to the case without terrain, primarily through enhanced wind convergence and higher moisture content, as well as stronger vertical motion induced by diabatic heating. The interactions between moisture convergence and topographic settings strengthen the extreme precipitation over NC.

Mohsen Ashrafi, Mansour Bayat, Pejman Mortazavi et al.

Journal of nanostructure in chemistry • 2020

Abstract Candida is a common yeast in opportunistic fungal diseases around the world and is usually colonized on the skin and mucosal membranes. The purpose of this study was to synthesize chitosan–silver–copper nanocomposite and to investigate its antifungal effects on Candida albicans . Silver, copper and chitosan nanoparticles were synthesized individually. Then, copper–silver–chitosan nanocomposite was synthesized. These nanoparticles are approved by transmission electron microscope, and nanocomposite structure was also confirmed by scanning electron microscope. Then, the minimum inhibitory concentrations and minimum fungicidal of these nanostructures were examined on C. albicans . The results of this study indicate that the properties and effects of the investigated nanocomposite are comparable to amphotericin B as standard material. The results show that this effect was higher for copper–silver–chitosan nanocomposite than for other nanoparticles studied. Antifungal effect of copper nanoparticles and chitosan nanoparticles was not established separately, but it was found that their composition had antifungal effects that were effective. The combination of nanoparticles of chitosan with silver has been shown to have some antifungal effects. The most antifungal effect for the nanoparticles studied is related to copper–silver–chitosan nanocomposite and, which has had a significant effect on the growth of C. albicans in the laboratory environment compared to other nanoparticles. Graphic abstract

Alberto Pliego Marugán, Fausto Pedro Garcı́a Márquez, Jesús María Pinar-Pérez

Energies • 2016

Nowadays offshore wind energy is the renewable energy source with the highest growth. Offshore wind farms are composed of large and complex wind turbines, requiring a high level of reliability, availability, maintainability and safety (RAMS). Firms are employing robust remote condition monitoring systems in order to improve RAMS, considering the difficulty to access the wind farm. The main objective of this research work is to optimise the maintenance management of wind farms through the fault probability of each wind turbine. The probability has been calculated by Fault Tree Analysis (FTA) employing the Binary Decision Diagram (BDD) in order to reduce the computational cost. The fault tree presented in this paper has been designed and validated based on qualitative data from the literature and expert from important European collaborative research projects. The basic events of the fault tree have been prioritized employing the criticality method in order to use resources efficiently. Exogenous variables, e.g., weather conditions, have been also considered in this research work. The results provided by the dynamic probability of failure and the importance measures have been employed to develop a scheduled maintenance that contributes to improve the decision making and, consequently, to reduce the maintenance costs.

Abha Singh, Gayatri Vaidya, Vishal Jagota et al.

Journal of Food Quality • 2022

Agriculture is an important component of the concept of sustainable development. Given the projected population growth, sustainable agriculture must accomplish food security while also being economically viable, socially responsible, and having the least possible impact on biodiversity and natural ecosystems. Deep learning has shown to be a sophisticated approach for big data analysis, with several successful cases in image processing, object identification, and other domains. It has lately been applied in food science and engineering. Among the issues and concerns addressed by these systems were food recognition; quality detection of fruits, vegetables, meat, and aquatic items; food supply chain; and food contamination. In precision agriculture, Artificial Intelligence (AI) is a commonly used technology for estimating food quality. It is especially important when evaluating crops at different phases of harvest and postharvest. Crop disease and damage detection is a high-priority activity because some postharvest diseases or damages, such as decay, can destroy crops and produce poisons that are toxic to humans. In this paper, we use Convolutional Neural Networks (CNNs)-based U-Net, DeepLab, and Mask R-CNN models to detect and predict postharvest deterioration zones in stored apple fruits. Our approach is unique in that it segmented and predicted postharvest decay and nondecay zones in fruits separately. This review will focus on postharvest physiology and management of fruits and vegetables, including harvesting, handling, packing, storage, and hygiene, to reduce postharvest loss (PHL) and improve crop quality. It will also cover postharvest handling under extreme weather conditions and potential impacts of climate change on vegetable postharvest and postharvest biotechnology on PHL.

Luna Rizik, Loai Danial, Mouna Habib et al.

Nature Communications • 2022

Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their revolutionary impact on computing, neuro-inspired models can transform synthetic gene circuit design in a manner that is reliable, efficient in resource utilization, and readily reconfigurable for different tasks. To this end, we introduce the perceptgene, a perceptron that computes in the logarithmic domain, which enables efficient implementation of artificial neural networks in Escherichia coli cells. We successfully modify perceptgene parameters to create devices that encode a minimum, maximum, and average of analog inputs. With these devices, we create multi-layer perceptgene circuits that compute a soft majority function, perform an analog-to-digital conversion, and implement a ternary switch. We also create a programmable perceptgene circuit whose computation can be modified from OR to AND logic using small molecule induction. Finally, we show that our approach enables circuit optimization via artificial intelligence algorithms.

Shichao Jin, Yanjun Su, Xiaoqian Zhao et al.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing • 2020

Airborne laser scanning (ALS) data is one of the most commonly used data for terrain products generation. Filtering ground points is a prerequisite step for ALS data processing. Traditional filtering methods mainly use handcrafted features or predefined classification rules with preprocessing/post-processing operations to filter ground points iteratively, which is empirical and cumbersome. Deep learning provides a new approach to solve classification and segmentation problems because of its ability to self-learn features, which has been favored in many fields, particularly remote sensing. In this article, we proposed a point-based fully convolutional neural network (PFCN) which directly consumed points with only geometric information and extracted both point-wise and tile-wise features to classify each point. The network was trained with 37449157 points from 14 sites and evaluated on 6 sites in various forested environments. Additionally, the method was compared with five widely used filtering methods and one of the best point-based deep learning methods (PointNet++). Results showed that the PFCN achieved the best results in terms of mean omission error (T1 = 1.10%), total error (Te = 1.73%), and Kappa coefficient (93.88%), but ranked second for the root mean square error of the digital Terrain model caused by the worst commission error. Additionally, our method was on par with or even better than PointNet++ in accuracy. Moreover, the method consumes one-third of the computational resource and one-seventh of the training time. We believe that PFCN is a simple and flexible method that can be widely applied for ground point filtering.

Antonio Artuñedo, Marcos Moreno-Gonzalez, Jorge Villagrá

Annual Reviews in Control • 2023

The selection of an appropriate control strategy is essential for ensuring safe operation in autonomous driving. While numerous control strategies have been developed for specific driving scenarios, a comprehensive comparative assessment of their performance using the same tuning methodology is lacking in the literature. This paper addresses this gap by presenting a systematic evaluation of state-of-the-art model-free and model-based control strategies. The objective is to evaluate and contrast the performance of these controllers across a wide range of driving scenarios, reflecting the diverse needs of autonomous vehicles. To facilitate the comparative analysis, a comprehensive set of performance metrics is selected, encompassing accuracy, robustness, and comfort. The contributions of this research include the design of a systematic tuning methodology, the use of two novel metrics for stability and comfort comparisons and the evaluation through extensive simulations and real tests in an experimental instrumented vehicle over a wide range of trajectories.

Raquel Busqué, Ricardo Torres, Joan Grau et al.

International Journal of Hydrogen Energy • 2017

Dallas Card, Justin H. Gross, Amber E. Boydstun et al.

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing • 2016

We present an unsupervised model for the discovery and clustering of latent "personas" (characterizations of entities). Our model simultaneously clusters documents featuring similar collections of personas. We evaluate this model on a collection of news articles about immigration, showing that personas help predict the coarse-grained framing annotations in the Media Frames Corpus. We also introduce automated model selection as a fair and robust form of feature evaluation.

Onel L. Alcaraz López, Osmel Martínez Rosabal, David E. Ruíz‐Guirola et al.

IEEE Open Journal of the Communications Society • 2023

Technology solutions must effectively balance economic growth, social equity, and environmental integrity to achieve a sustainable society. Notably, although the Internet of Things (IoT) paradigm constitutes a key sustainability enabler, critical issues such as the increasing maintenance operations, energy consumption, and manufacturing/disposal of IoT devices have long-term negative economic, societal, and environmental impacts and must be efficiently addressed. This calls for self-sustainable IoT ecosystems requiring minimal external resources and intervention, effectively utilizing renewable energy sources, and recycling materials whenever possible, thus encompassing energy sustainability. In this work, we focus on energy-sustainable IoT during the operation phase, although our discussions sometimes extend to other sustainability aspects and IoT lifecycle phases. Specifically, we provide a fresh look at energy-sustainable IoT and identify energy provision, transfer, and energy efficiency as the three main energy-related processes whose harmonious coexistence pushes toward realizing self-sustainable IoT systems. Their main related technologies, recent advances, challenges, and research directions are also discussed. Moreover, we overview relevant performance metrics to assess the energy-sustainability potential of a certain technique, technology, device, or network, together with target values for the next generation of wireless systems, and discuss protocol, integration, and implementation issues. Overall, this paper offers insights that are valuable for advancing sustainability goals for present and future generations.

Takaya Ogawa, Mizutomo Takeuchi, Yuya Kajikawa

Sustainability • 2018

Water electrolysis for hydrogen production has received increasing attention, especially for accumulating renewable energy. Here, we comprehensively reviewed all water electrolysis research areas through computational analysis, using a citation network to objectively detect emerging technologies and provide interdisciplinary data for forecasting trends. The results show that all research areas increase their publication counts per year, and the following two areas are particularly increasing in terms of number of publications: “microbial electrolysis” and “catalysts in an alkaline water electrolyzer (AWE) and in a polymer electrolyte membrane water electrolyzer (PEME).”. Other research areas, such as AWE and PEME systems, solid oxide electrolysis, and the whole renewable energy system, have recently received several review papers, although papers that focus on specific technologies and are cited frequently have not been published within the citation network. This indicates that these areas receive attention, but there are no novel technologies that are the center of the citation network. Emerging technologies detected within these research areas are presented in this review. Furthermore, a comparison with fuel cell research is conducted because water electrolysis is the reverse reaction to fuel cells, and similar technologies are employed in both areas. Technologies that are not transferred between fuel cells and water electrolysis are introduced, and future water electrolysis trends are discussed.

Ishtiaq Ahmed, Muhammad Anjum Zia, Huma Afzal et al.

Sustainability • 2021

In the late twentieth century, the only cost-effective opportunity for waste removal cost at least several thousand dollars, but nowadays, a lot of improvement has occurred. The biomass and waste generation problems attracted concerned authorities to identify and provide environmentally friendly sustainable solutions that possess environmental and economic benefits. The present study emphasises the valorisation of biomass and waste produced by domestic and industrial sectors. Therefore, substantial research is ongoing to replace the traditional treatment methods that potentially acquire less detrimental effects. Synthetic biology can be a unique platform that invites all the relevant characters for designing and assembling an efficient program that could be useful to handle the increasing threat for human beings. In the future, these engineered methods will not only revolutionise our lives but practically lead us to get cheaper biofuels, producing bioenergy, pharmaceutics, and various biochemicals. The bioaugmentation approach concomitant with microbial fuel cells (MFC) is an example that is used to produce electricity from municipal waste, which is directly associated with the loading of waste. Beyond the traditional opportunities, herein, we have spotlighted the new advances in pertinent technology closely related to production and reduction approaches. Various integrated modern techniques and aspects related to the industrial sector are also discussed with suitable examples, including green energy and other industrially relevant products. However, many problems persist in present-day technology that requires essential efforts to handle thoroughly because significant valorisation of biomass and waste involves integrated methods for timely detection, classification, and separation. We reviewed and proposed the anticipated dispensation methods to overcome the growing stream of biomass and waste at a distinct and organisational scale.

Michail Makridis, Konstantinos Mattas, Biagio Ciuffo

IEEE Transactions on Intelligent Transportation Systems • 2019

Road vehicles are characterized by increasing levels of automation and it is vital to understand the future impact on transport efficiency. Adaptive Cruise Control (ACC) is one of the first and most common automated functionalities available in privately owned vehicles. The effect of ACC on traffic flow has been widely studied by making assumptions on its operating strategy and on some of its important parameters such as the response time and the desired time headway. In the literature, these parameters are usually set to low values, based on the vehicle controller's theoretical ability to respond within a very short time frame. Response time is known to be an important parameter in defining the capacity of the road and therefore, assuming a very short response time, studies usually conclude that systems like the ACC will contribute increasing the road capacity significantly. The present study aims at measuring the actual response time of an ACC-enabled vehicle in car-following conditions. A new methodology for the estimation of the controller's response time and the desired time-gap was developed to this objective. Results show that the response time of the particular ACC controller was in the range 0.8s-1.2s, which is similar to what is commonly assumed for human drivers. In this light, the results of the present study question the common assumption that ACC or other automation technologies necessarily improve traffic flow and increase road capacity.

Ali Wagdy Mohamed, Abdulaziz S. Almazyad

Applied Computational Intelligence and Soft Computing • 2017

This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better, and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems.

Rafic M. Ajaj, Muhammed S. Parancheerivilakkathil, Mohammadreza Amoozgar et al.

Progress in Aerospace Sciences • 2021

P. A. G. S. Giachini, Sachin Sean Gupta, Wendong Wang et al.

Science Advances • 2020

Functionally graded materials (FGMs) enable applications in fields such as biomedicine and architecture, but their fabrication suffers from shortcomings in gradient continuity, interfacial bonding, and directional freedom. In addition, most commercial design software fail to incorporate property gradient data, hindering explorations of the design space of FGMs. Here, we leveraged a combined approach of materials engineering and digital processing to enable extrusion-based multimaterial additive manufacturing of cellulose-based tunable viscoelastic materials with continuous, high-contrast, and multidirectional stiffness gradients. A method to engineer sets of cellulose-based materials with similar compositions, yet distinct mechanical and rheological properties, was established. In parallel, a digital workflow was developed to embed gradient information into design models with integrated fabrication path planning. The payoff of integrating these physical and digital tools is the ability to achieve the same stiffness gradient in multiple ways, opening design possibilities previously limited by the rigid coupling of material and geometry.

Hossein Shayeghi, Elnaz Shahryari, Mohammad Moradzadeh et al.

Energies • 2019

Aggregation of distributed generations (DGs) along with energy storage systems (ESSs) and controllable loads near power consumers has led to the concept of microgrids. However, the uncertain nature of renewable energy sources such as wind and photovoltaic generations, market prices and loads has led to difficulties in ensuring power quality and in balancing generation and consumption. To tackle these problems, microgrids should be managed by an energy management system (EMS) that facilitates the minimization of operational costs, emissions and peak loads while satisfying the microgrid technical constraints. Over the past years, microgrids’ EMS have been studied from different perspectives and have recently attracted considerable attention of researchers. To this end, in this paper a classification and a survey of EMSs has been carried out from a new point of view. EMSs have been classified into four categories based on the kind of the reserve system being used, including non-renewable, ESS, demand-side management (DSM) and hybrid systems. Moreover, using recent literature, EMSs have been reviewed in terms of uncertainty modeling techniques, objective functions (OFs) and constraints, optimization techniques, and simulation and experimental results presented in the literature.

Clay B. Holroyd, José J. F. Ribas-Fernandes, Danesh Shahnazian et al.

Proceedings of the National Academy of Sciences • 2018

The function of midcingulate cortex (MCC) remains elusive despite decades of investigation and debate. Complicating matters, individual MCC neurons respond to highly diverse task-related events, and MCC activation is reported in most human neuroimaging studies employing a wide variety of task manipulations. Here we investigate this issue by applying a model-based cognitive neuroscience approach involving neural network simulations, functional magnetic resonance imaging, and representational similarity analysis. We demonstrate that human MCC encodes distributed, dynamically evolving representations of extended, goal-directed action sequences. These representations are uniquely sensitive to the stage and identity of each sequence, indicating that MCC sustains contextual information necessary for discriminating between task states. These results suggest that standard univariate approaches for analyzing MCC function overlook the major portion of task-related information encoded by this brain area and point to promising new avenues for investigation.

Aneta Brzezicka, Jan Kamiński, Chrystal M. Reed et al.

Journal of Cognitive Neuroscience • 2019

Holding information in working memory (WM) is an active and effortful process that is accompanied by sustained load-dependent changes in oscillatory brain activity. These proportional power increases are often reported in EEG studies recording theta over frontal midline sites. Intracranial recordings, however, yield mixed results, depending on the brain area being recorded from. We recorded intracranial EEG with depth electrodes in 13 patients with epilepsy who were performing a Sternberg WM task. Here, we investigated patterns of theta power changes as a function of memory load during maintenance in three areas critical for WM: dorsolateral prefrontal cortex (DLPFC), dorsal ACC (dACC), and hippocampus. Theta frequency power in both hippocampus and dACC increased during maintenance. In contrast, theta frequency power in the DLPFC decreased during maintenance, and this decrease was proportional to memory load. Only the power decreases in DLPFC, but not the power increases in hippocampus and dACC, were predictive of behavior in a given trial. The extent of the load-related theta power decreases in the DLPFC in a given participant predicted a participant's RTs, revealing that DLPFC theta explains individual differences in WM ability between participants. Together, these data reveal a pattern of theta power decreases in the DLPFC that is predictive of behavior and that is opposite of that in other brain areas. This result suggests that theta band power changes serve different cognitive functions in different brain areas and specifically that theta power decreases in DLPFC have an important role in maintenance of information.

Alberto de Ramón-Fernández, M.J. Salar-García, Daniel Ruíz Fernández et al.

Energy • 2020

Microbial fuel cell (MFC) power performance strongly depends on the biofilm growth, which in turn is affected by the feed flow rate. In this work, an artificial neural network (ANN) approach has been used to simulate the effect of the flow rate on the power output by ceramic MFCs fed with neat human urine. To this aim, three different second-order algorithms were used to train our network and then compared in terms of prediction accuracy and convergence time: Quasi-Newton, Levenberg-Marquardt, and Conjugate Gradient. The results showed that the three training algorithms were able to accurately simulate power production. Amongst all of them, the Levenberg-Marquardt was the one that presented the highest accuracy (R = 95%) and the fastest convergence (7.8 s). These results show that ANNs are useful and reliable tools for predicting energy harvesting from ceramic-MFCs under changeable flow rate conditions, which will facilitate the practical deployment of this technology.

min liu, Hengbo Hu, Jian Wu et al.

Engineering Research Express • 2026

Abstract Transmission lines play a crucial role in power transmission, where external mechanical damage constitutes a major hidden risk for line faults. The detection of external damage on power transmission lines faces several challenges due to the complex and variable nature of their surroundings, as well as the significant size variation of targets caused by changing distances. These challenges primarily include interference from complex environmental backgrounds and insufficient accuracy in detecting small targets. This paper aims to address the issue of degraded detection accuracy resulting from background noise in complex scenarios and the loss of small-target information during the feature fusion process. Furthermore, it seeks to improve detection accuracy without increasing the model’s computational cost. This paper proposes EMF-YOLO: an improved YOLOv8 detection model with enhanced multi-scale feature extraction and fusion for detecting external damage risks in transmission line scenarios. Firstly, we propose a Diverse Branch Downsampling (DBS) module to preserve richer semantic information during feature extraction, which enhances representation capability without introducing extra computational cost. Secondly, we integrate the SENetV2 (Aggregated dense layer for channelwise and global representations) attention mechanism into the last layer of the bottleneck in the C2f module. By incorporating multi-branch fully connected layers and channel attention, this mechanism optimizes the global feature representation, thereby boosting the model’s robustness to noise and its ability to capture discriminative features. These improvements to the backbone network effectively mitigate the accuracy degradation caused by complex environments. For the neck structure, we propose a Converge-Diffuse Feature Pyramid Network (CDFPN), which optimizes cross-scale connections and incorporates an innovative diffusion mechanism. It includes a Multi-scale Feature Convergence (MFC) module that effectively integrates multi-layer features while minimizing the loss of fine-grained details from small objects during fusion. This design improves the detection accuracy for small objects and enables more effective fusion of features across different scales. Experimental results on the transmission line external damage dataset demonstrate that the improved model achieves 92.1% mAP50 and 73.3% mAP50-95, representing 3.5% and 6.5% improvements over YOLOv8m, respectively, with negligible increases in computational complexity and parameters. On the PASCAL VOC2012 dataset, EMF-YOLO achieved a 73.4% mAP50 and a 53.8% mAP50-95. Compared to YOLOv8m, there are improvements of 1.2% and 1.6% respectively. The model’s detection ability is enhanced. And through comparative experiments with mainstream existing models. The proposed EMF-YOLO exhibits superior performance compared to other existing models.

Chunyu Chen, Mingjian Cui, Xin Fang et al.

Applied Energy • 2020

Peilin Xie, Hossein Asgharian, Josep M. Guerrero et al.

International Journal of Hydrogen Energy • 2023

The hybrid combination of hydrogen fuel cells (FCs) and batteries has emerged as a promising solution for efficient and eco-friendly power supply in maritime applications. Yet, ensuring high-quality and cost-effective energy supply presents challenges. Addressing these goals requires effective coordination among multiple FC stacks, batteries, and cold-ironing. Although there has been previous work focusing it, the unique maritime load characteristics, variable cruise plans, and diverse fuel cell system architectures introduce additional complexities and therefore worth to be further studied. Motivated by it, a two-layer energy management system (EMS) is presented in this paper to enhance shipping fuel efficiency. The first layer of the EMS, executed offline, optimizes day-ahead power generation plans based on the vessel's next-day cruises. To further enhance the EMS's effectiveness in dynamic real-time situations, the second layer, conducted online, dynamically adjusts power splitting decisions based on the output from the first layer and instantaneous load information. This dual-layer approach optimally exploits the maritime environment and the fuel cell features. The presented method provides valuable utility in the development of control strategies for hybrid powertrains, thereby enabling the optimization of power generation plans and dynamic adjustment of power splitting decisions in response to load variations. Through comprehensive case studies, the effectiveness of the proposed EMS is evaluated, thereby showcasing its ability to improve system performance, enhance fuel efficiency (potential fuel savings of up to 28%), and support sustainable maritime operations.

Joost-Pieter Katoen

Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science • 2016

Randomization is a key element in sequential and distributed computing. Reasoning about randomized algorithms is highly non-trivial. In the 1980s, this initiated first proof methods, logics, and model-checking algorithms. The field of probabilistic verification has developed considerably since then. This paper surveys the algorithmic verification of probabilistic models, in particular probabilistic model checking. We provide an informal account of the main models, the underlying algorithms, applications from reliability and dependability analysis---and beyond---and describe recent developments towards automated parameter synthesis.

Thomas‐Martin Dutschmann, Lennart Kinzel, Antonius ter Laak et al.

Journal of Cheminformatics • 2023

It is insightful to report an estimator that describes how certain a model is in a prediction, additionally to the prediction alone. For regression tasks, most approaches implement a variation of the ensemble method, apart from few exceptions. Instead of a single estimator, a group of estimators yields several predictions for an input. The uncertainty can then be quantified by measuring the disagreement between the predictions, for example by the standard deviation. In theory, ensembles should not only provide uncertainties, they also boost the predictive performance by reducing errors arising from variance. Despite the development of novel methods, they are still considered the "golden-standard" to quantify the uncertainty of regression models. Subsampling-based methods to obtain ensembles can be applied to all models, regardless whether they are related to deep learning or traditional machine learning. However, little attention has been given to the question whether the ensemble method is applicable to virtually all scenarios occurring in the field of cheminformatics. In a widespread and diversified attempt, ensembles are evaluated for 32 datasets of different sizes and modeling difficulty, ranging from physicochemical properties to biological activities. For increasing ensemble sizes with up to 200 members, the predictive performance as well as the applicability as uncertainty estimator are shown for all combinations of five modeling techniques and four molecular featurizations. Useful recommendations were derived for practitioners regarding the success and minimum size of ensembles, depending on whether predictive performance or uncertainty quantification is of more importance for the task at hand.

B. Anil Kumar, Neeraj Kumar Misra

Soft Computing • 2025

Xushen Xiong, Benjamin T. James, Carles A. Boix et al.

Cell • 2023

Recent work has identified dozens of non-coding loci for Alzheimer's disease (AD) risk, but their mechanisms and AD transcriptional regulatory circuitry are poorly understood. Here, we profile epigenomic and transcriptomic landscapes of 850,000 nuclei from prefrontal cortexes of 92 individuals with and without AD to build a map of the brain regulome, including epigenomic profiles, transcriptional regulators, co-accessibility modules, and peak-to-gene links in a cell-type-specific manner. We develop methods for multimodal integration and detecting regulatory modules using peak-to-gene linking. We show AD risk loci are enriched in microglial enhancers and for specific TFs including SPI1, ELF2, and RUNX1. We detect 9,628 cell-type-specific ATAC-QTL loci, which we integrate alongside peak-to-gene links to prioritize AD variant regulatory circuits. We report differential accessibility of regulatory modules in late AD in glia and in early AD in neurons. Strikingly, late-stage AD brains show global epigenome dysregulation indicative of epigenome erosion and cell identity loss.

Xu Zhang, Vesselina Pearsall, Chase M. Carver et al.

Nature Communications • 2022

Cellular senescence is a plausible mediator of inflammation-related tissue dysfunction. In the aged brain, senescent cell identities and the mechanisms by which they exert adverse influence are unclear. Here we used high-dimensional molecular profiling, coupled with mechanistic experiments, to study the properties of senescent cells in the aged mouse brain. We show that senescence and inflammatory expression profiles increase with age and are brain region- and sex-specific. p16-positive myeloid cells exhibiting senescent and disease-associated activation signatures, including upregulation of chemoattractant factors, accumulate in the aged mouse brain. Senescent brain myeloid cells promote peripheral immune cell chemotaxis in vitro. Activated resident and infiltrating immune cells increase in the aged brain and are partially restored to youthful levels through p16-positive senescent cell clearance in female p16-InkAttac mice, which is associated with preservation of cognitive function. Our study reveals dynamic remodeling of the brain immune cell landscape in aging and suggests senescent cell targeting as a strategy to counter inflammatory changes and cognitive decline.