Multiple Networks Research Articles

The language network ages well: Preserved selectivity, lateralization, and within-network functional synchronization in older brains.

Healthy aging is associated with structural and functional brain changes. However, cognitive abilities differ from one another in how they change with age: whereas executive functions, like working memory, show age-related decline, aspects of linguistic processing remain relatively preserved (Hartshorne et al., 2015). This heterogeneity of the cognitive-behavioral landscape in aging predicts differences among brain networks in whether and how they should change with age. To evaluate this prediction, we used individual-subject fMRI analyses ('precision fMRI') to examine the language-selective network (Fedorenko et al., 2024) and the Multiple Demand (MD) network, which supports executive functions (Duncan et al., 2020), in older adults (n=77) relative to young controls (n=470). In line with past claims, relative to young adults, the MD network of older adults shows weaker and less spatially extensive activations during an executive function task and reduced within-network functional synchronization. However, in stark contrast to the MD network, we find remarkable preservation of the language network in older adults. Their language network responds to language as strongly and selectively as in younger adults, and is similarly lateralized and internally synchronized. In other words, the language network of older adults looks indistinguishable from that of younger adults. Our findings align with behavioral preservation of language skills in aging and suggest that some networks remain young-like, at least on standard measures of function and connectivity.

Highly sensitive and structure stable polyvinyl alcohol hydrogel sensor with tailored free water fraction and multiple networks by reinforcement of conductive nanocellulose

The wearable composite hydrogel sensors with high stretchability have attracted much attention in recent years, while the traditional hydrogels have weak molecular (chain) interaction and contain a lot of free water, leading to poor mechanical properties, unstable environmental tolerance and sensing ability. Herein, a novel ice crystal extrusion-crosslinking strategy is used to obtain polyvinyl alcohol (PVA) hydrogel with conductive nanocellulose-poly (3,4-ethylenedioxythiophene) (CNC-PEDOT) as skeleton network, sodium alginate (SA) and Ca2+ as tough segment of multi-bonding network. This strategy synergistically enhanced the interaction of hydrogen bonds and calcium (Ca2+) ion chelation within the hydrogel, building highly sensitive and stable multiple tough-elastic networks. Therefore, the optimal hydrogel sensor (PVA/SA-CP45) shows good structural stability, robust mechanical performance, excellent compress (Sensitivity = 68.7), stretching sensitivity (Gauge factor = 4.16), ultra-wide application range (−105–60 °C), fast response/relaxation time and outstanding dynamic durability with 6000 stretching-releasing cycles. Especially, it can give good sensing performance for omnidirectional monitoring of human motion and weak signals. Moreover, it was also designed into multifunctional sensing systems for gait guidance of model training and real-time monitoring ammonia gas for food preservation and public environmental safety, demonstrating great potential in flexible sensors devices for health monitoring.

Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

PPDistiller: Weakly-supervised 3D point cloud semantic segmentation framework via point-to-pixel distillation

Despite the significant growth in the availability of 3D light detection and ranging (LiDAR) point cloud data in recent years, annotation remains expensive and time-consuming. This has led to an increasing demand for weakly-supervised semantic segmentation (WSSS) methods in applications such as autonomous driving, mapping, and robotics. Existing approaches typically rely solely on LiDAR point cloud data for WSSS, which often results in lower segmentation accuracy due to the sparsity of point clouds. To address these challenges, we propose a novel architecture, PPDistiller, which employs multiple teacher networks from different modalities. Compared to other WSSS and multimodal approaches, PPDistiller achieves superior segmentation accuracy with fewer annotations. This is facilitated through the novel Mean Multi-Teacher Framework (MMT), which incorporates multiple modalities and teachers. To address the issue of lacking 2D labels, we propose the Distance-CAM Self-Training (DCAM-ST) module, which utilizes sparse 3D weak annotations to produce accurate 2D pixel-level annotations. To enable adaptive fusion of 2D and 3D data, we introduce the Attention Point to Pixel Fusion (APPF) module, facilitating bidirectional transfer of cross-modal knowledge. Additionally, to fully leverage the spatial semantic information in point cloud, we propose the Pyramid Semantic-context Neighbor Aggregation (PSNA) module, aiming to exploit spatial and semantic correlations to improve performance. Extensive experimentation on SemanticKITTI, ScribbleKITTI and nuScenes datasets demonstrates the superiority of our proposed method. Compared to state-of-the-art fusion and weakly-supervised methods, PPDistiller achieves the highest mean Intersection over Union (mIoU) scores under both fully-supervised and weakly-supervised settings.

Time-Efficient RSA over Large-Scale Multi-Domain EON.

The poor timeliness of routing has always been an urgent problem in practical operator networks, especially in situations with large-scale networks and multiple network domains. In this article, a pruning idea of routing integrated with Dijkstra's shortest path searching is utilized to accelerate the process of routing in large-scale multi-domain elastic optical networks (EONs). The layered-graph approach is adopted in the spectrum allocation stage. To this end, an efficient heuristic algorithm is proposed, called "Branch-and-Bound based Routing and Layered Graph based Spectrum Allocation algorithm (BBR-LGSA)", which is an integrated RSA algorithm. Notably, the significant reduction in algorithm time complexity is not only reflected in the pruning method used in the routing stage but also in the construction of auxiliary graphs during the spectrum allocation stage utilizing the Branch-and-Bound method. Simulation results show that the proposed BBR-LGSA significantly reduces the average running time by nearly 78% with higher spectrum utilization in large-scale multi-domain EONs, compared with benchmark algorithms. In addition, the impact of key parameters on performance comparisons of different algorithms is evaluated.

The Association of Prenatal Alcohol Exposure With Brain Development During the First 1000Days of Life: A Systematic Review.

Prenatal exposure to alcohol (PAE) can impact short- and long-term offspring health. However, knowledge on PAE and brain development in early life is limited. This systematic review investigated associations between PAE and brain development during the first 1000days of life, and was registered in PROSPERO at CRD42022355144. The literature search was performed from inception until February 2024 in EMBASE, MEDLINE, Web of Science, PsycINFO, and the Cochrane Library. Studies investigating PAE and brain development during the first 1000days using ultrasound, magnetic resonance imaging (MRI) or diffusion tensor imaging (DTI) were included. Our search initially identified 3618 articles, of which 16 were included. The findings suggest inconsistent associations between PAE and early brain development. Ultrasound studies on PAE and brain size report no clear relationship. Some postnatal MRI studies reported smaller thalami, amygdalae and hippocampi in alcohol exposed neonates. Postnatal DTI studies (n=5) examining network integrity and connectivity reported bidirectional results in multiple brain networks. Our results highlight the need for further research on first trimester brain development, timing and quantity of alcohol exposure using a core set of validated instruments. Longitudinal assessments and standardized procedures for neuroimaging are crucial to understand the impact of PAE on early brain development.

A Review on Secure Outsourcing and Privacy-Preserving Traffic Monitoring in Fog-Enabled VANETs

New possibilities for fog-based vehicle monitoring have emerged with the expansion of fog computing, but present privacy concerns provide a significant barrier that limits the extent to which vehicles can participate. Because of its potential to improve road network security and vehicle productivity, the field of Vehicle-based Ad hoc Networks (VANETs) is gaining prominence. The security problems with VANETs, such as data confidentiality and message access control are still need better solutions to provide better Quality of Service (QoS). The effectiveness of VANET networks is diminished due to their instability problem. Vehicles continually add requests to the Road Side Unit (RSU) queue whenever they want specific information. For ever-evolving networks like VANETs, better routing is a continual process. Fundamental problems arise in large-scale systems when centralized procedures are used to assign jobs to the nodes along a route. The present centralized system for computing and safety has many needs, including the protection of data storage, user authentication, access control, system availability across multiple network connections, and the provision of a real-time data flow overview. Distributed problem solving and work sharing between multiple agents can improve the system's scalability. This paper provides a brief survey on the secured outsourcing and privacy preservation-based traffic monitoring model with routing and task scheduling models in Fog-enabled VANETS. This survey presents the limitations of the traditional models that help the researchers design new solutions for secure outsourcing in VANETs.

Finding future associations in complex networks using multiple network features

Finding future associations in complex networks using multiple network features

Cardiorespiratory Fitness and Sleep, but not Physical Activity, are Associated with Functional Connectivity in Older Adults.

Aging results in changes in resting state functional connectivity within key networks associated with cognition. Cardiovascular function, physical activity, sleep, and body composition may influence these age-related changes in the brain. Better understanding these associations may help clarify mechanisms related to brain aging and guide interventional strategies to reduce these changes. In a large (n = 398) sample of healthy community dwelling older adults that were part of a larger interventional trial, we conducted cross sectional analyses of baseline data to examine the relationships between several modifiable behaviors and resting state functional connectivity within networks associated with cognition and emotional regulation. Additionally, maximal aerobic capacity, physical activity, quality of sleep, and body composition were assessed. Associations were explored both through correlation and best vs. worst group comparisons. Greater cardiovascular fitness, but not larger quantity of daily physical activity, was associated with greater functional connectivity within the Default Mode (p = 0.008 r = 0.142) and Salience Networks (p = 0.005, r = 0.152). Better sleep (greater efficiency and fewer nighttime awakenings) was also associated with greater functional connectivity within multiple networks including the Default Mode, Executive Control, and Salience Networks. When the population was split into quartiles, the highest body fat group displayed higher functional connectivity in the Dorsal Attentional Network compared to the lowest body fat percentage (p = 0.011; 95% CI - 0.0172 to - 0.0023). These findings confirm and expand on previous work indicating that, in older adults, higher levels of cardiovascular fitness and better sleep quality, but not greater quantity of physical activity, total sleep time, or lower body fat percentage are associated with increased functional connectivity within key resting state networks.

A compressor blade crack detection method based on the multilevel information fusion of acoustic and vibration signals

Single-signal crack detection methods perform unsatisfactorily in the presence of noise, prompting the need for alternative approaches. Leveraging multisource signals, which contain rich and complementary information, is a promising direction. In light of this, a compressor blade crack detection method based on the multilevel information fusion of acoustic and vibration signals is proposed. First, a multiscale data-level fusion convolutional neural network is designed, which fuses multisource homogeneous signals for crack detection. Second, multiple networks are trained with acoustic and vibration signals as inputs, respectively. Softmax layer of each network provides preliminary probabilities for each category, while the precision for each category in the validation set is calculated. Finally, an improved Dempster–Shafer theory approach is proposed to derive the final probability for each category according to the preliminary probabilities and precision, after which crack detection is realized. The proposed method is validated with experimental data from five categories of blades.

Energy-Saving Multi-Agent Deep Reinforcement Learning Algorithm for Drone Routing Problem.

With the rapid advancement of drone technology, the efficient distribution of drones has garnered significant attention. Central to this discourse is the energy consumption of drones, a critical metric for assessing energy-efficient distribution strategies. Accordingly, this study delves into the energy consumption factors affecting drone distribution. A primary challenge in drone distribution lies in devising optimal, energy-efficient routes for drones. However, traditional routing algorithms, predominantly heuristic-based, exhibit certain limitations. These algorithms often rely on heuristic rules and expert knowledge, which can constrain their ability to escape local optima. Motivated by these shortcomings, we propose a novel multi-agent deep reinforcement learning algorithm that integrates a drone energy consumption model, namely EMADRL. The EMADRL algorithm first formulates the drone routing problem within a multi-agent reinforcement learning framework. It subsequently designs a strategy network model comprising multiple agent networks, tailored to address the node adjacency and masking complexities typical of multi-depot vehicle routing problem. Training utilizes strategy gradient algorithms and attention mechanisms. Furthermore, local and sampling search strategies are introduced to enhance solution quality. Extensive experimentation demonstrates that EMADRL consistently achieves high-quality solutions swiftly. A comparative analysis against contemporary algorithms reveals EMADRL's superior energy efficiency, with average energy savings of 5.96% and maximum savings reaching 12.45%. Thus, this approach offers a promising new avenue for optimizing energy consumption in last-mile distribution scenarios.

Prediction of potential conflicts between active targets in airport flight zones based on LSTM

Aiming at the problem of constant conflicts in airport flight zones, a method based on LSTM to predict potential conflicts of active targets in airport flight zones is proposed. Firstly, based on the complex network theory, two types of active targets, aircraft and vehicles, are taken as the research objects, and the flight zone active target network is established. The network dynamic evolution model is set up, and the operation data is input to calculate multiple network characteristic indicators. The principal component analysis of the indicator time series is performed to fit the potential conflict index; then, the Keras framework is used to build a long short-term memory neural network (LSTM) model, and the indicator time series is input into LSTM for training and prediction, and compared with other prediction methods; finally, the actual operation data of Xi'an Xianyang Airport is used for experiments, and the predicted values are compared with the true values. The mean square errors of the prediction results of each indicator are respectively 1.61%、13.13%、0.72%、0.004%、0.014%; The research shows that by establishing the flight zone active target network model, the network characteristic indicators can be used to characterize potential conflicts from different angles; LSTM can effectively predict the potential conflicts of the flight zone active target network, remind relevant personnel to pay attention to preventing conflicts and reducing the probability of conflicts.

Multiple Neural Networks Originating from the Lateral Parabrachial Nucleus Modulate Cough-like Behavior and Coordinate Cough with Pain.

It has been reported that experimental pain could diminish cough sensitivity, the lateral parabrachial nucleus (LPBN) coordinated pain with breathing, whether LPBN regulates cough-like behaviors and pain-induced changes in cough sensitivity remains elusive. We investigated the roles of LPBN γ-aminobutyric acidergic (GABAergic) and glutamatergic neurons in the regulation of cough sensitivity and its relationship with pain in mice via chemogenetic approaches. Adeno-associated virus (AAV) tracing combined with chemogenetics was used to map the projections of LPBN GABAergic and glutamatergic neurons to the periaqueductal gray (PAG). LPBN neurons were activated by cough challenge, and nonspecific inhibition of LPBN neurons suppressed cough-like behavior. Chemogenetic suppression of LPBN GABAergic neurons reduced cough sensitivity in mice, whereas suppression of LPBN glutamatergic neurons counteracted the pain-driven decrease in cough sensitivity, so did silencing LPBN glutamatergic neurons projecting to the PAG. Our data suggest that GABAergic and glutamatergic neurons in the LPBN critically are involved in cough sensitivity and coordinate pain with cough through inhibitory or activating mechanisms at the midbrain level.

Structural resilience disparities in regional city networks under the influence of Typhoon Fitow and COVID-19

The integration of regional city networks plays a pivotal role in the regional development. Assessing and analyzing the structural resilience of these networks holds significance in enhancing regional comprehensive disaster prevention. This study focuses on the Yangtze River Delta, examining its regional city networks encompassing information, economic, transportation, and innovation domains. Utilizing the social network analysis, projection pursuit model based on the genetic algorithm, coupling coordination degree model, coefficient of variation and spatial Gini coefficient, we scrutinized the structural resilience and evolution difference of these networks under different disasters. We found that the structural resilience of economic and transportation networks that depend on infrastructure construction are more limited by geographical distance, and information and innovation networks with weaker physical dependence have stronger structural resilience. The impact of Typhoon Fitow on the structural resilience of individual networks was mainly concentrated in coastal affected cities, while the impact after COVID-19 was mainly in marginal cities. The spatial pattern of the structural resilience of multiple networks shifted from low level equilibrium to agglomeration development. Finally, nodes were identified into three types according to the resilience performance. This research offers a scientific foundation for quantifying regional resilience and formulating network optimization strategies.

Modeling CSF circulation and the glymphatic system during infusion using subject specific intracranial pressures and brain geometries

BackgroundInfusion testing is an established method for assessing CSF resistance in patients with idiopathic normal pressure hydrocephalus (iNPH). To what extent the increased resistance is related to the glymphatic system is an open question. Here we introduce a computational model that includes the glymphatic system and enables us to determine the importance of (1) brain geometry, (2) intracranial pressure, and (3) physiological parameters on the outcome of and response to an infusion test.MethodsWe implemented a seven-compartment multiple network porous medium model with subject specific geometries from MR images using the finite element library FEniCS. The model consists of the arterial, capillary and venous blood vessels, their corresponding perivascular spaces, and the extracellular space (ECS). Both subject specific brain geometries and subject specific infusion tests were used in the modeling of both healthy adults and iNPH patients. Furthermore, we performed a systematic study of the effect of variations in model parameters.ResultsBoth the iNPH group and the control group reached a similar steady state solution when subject specific geometries under identical boundary conditions was used in simulation. The difference in terms of average fluid pressure and velocity between the iNPH and control groups, was found to be less than 6% during all stages of infusion in all compartments. With subject specific boundary conditions, the largest computed difference was a 75% greater fluid speed in the arterial perivascular space (PVS) in the iNPH group compared to the control group. Changes to material parameters changed fluid speeds by several orders of magnitude in some scenarios. A considerable amount of the CSF pass through the glymphatic pathway in our models during infusion, i.e., 28% and 38% in the healthy and iNPH patients, respectively.ConclusionsUsing computational models, we have found the relative importance of subject specific geometries to be less important than individual differences in resistance as measured with infusion tests and model parameters such as permeability, in determining the computed pressure and flow during infusion. Model parameters are uncertain, but certain variations have large impact on the simulation results. The computations resulted in a considerable amount of the infused volume passing through the brain either through the perivascular spaces or the extracellular space.

Combining GLP-1 receptor agonists and SGLT-2 inhibitors for cardiovascular disease prevention in type 2 diabetes: A systematic review with multiple network meta-regressions.

Glucagon-like peptide-1 receptor agonists (GLP-1RA) and sodium-glucose co-transporter-2 inhibitors (SGLT-2I) are associated with significant cardiovascular benefit in type 2 diabetes (T2D). However, GLP-1RA or SGLT-2I alone may not improve some cardiovascular outcomes in patients with prior cardiovascular co-morbidities. To explore whether combining GLP-1RA and SGLT-2I can achieve additional benefit in preventing cardiovascular diseases in T2D. The systematic review was conducted according to PRISMA recommendations. The protocol was registered on PROSPERO (ID: 42022385007). A total of 107049 participants from eligible cardiovascular outcomes trials of GLP-1RA and SGLT-2I were included in network meta-regressions to estimate cardiovascular benefit of the combination treatment. Effect modification of prior myocardial infarction (MI) and heart failure (HF) was also explored to provide clinical insight as to when the combination treatment should be considered. The estimated hazard ratios (HR)GLP-1RA/SGLT-2I vs Placebo (0.75-0.98) and HRCombination vs GLP-1RA/SGLT-2I (0.26-0.86) for primary and secondary cardiovascular outcomes suggested that the combination treatment may achieve additional cardiovascular benefit compared with GLP-1RA or SGLT-2I alone. In patients with prior MI or HF, the mono-therapies may not improve the overall cardiovascular outcomes, as the estimated HRMI+/HF+ (0.57-1.52) suggested that GLP-1RA or SGLT-2I alone may be associated with lower risks of hospitalization for HF but not cardiovascular death. Considering its greater cardiovascular benefit in T2D, the combination treatment of GLP-1RA and SGLT-2I might be prioritized in patients with prior MI or HF, where the monotherapies may not provide sufficient cardiovascular protection.

Optimizing Virtual Network Embedding for Avionics with Time-Triggered Traffic Scheduling

Network virtualization technology abstracts the nodes and links resources in the physical network, and enables multiple virtual networks (VNs) to share the substrate network (SN) resources through the Virtual Netw ork Embedding (VNE) method. For time-triggered Ethernet (TTE) used in avionics, a time-triggered traffic scheduling oriented virtual network embedding (TT-VNE) method is proposed. While meeting the total resource constraints of traditional VNE problem, it ensures the strict periodicity of time-triggered (TT) traffic. During the solution process, the method sorts the virtual nodes according to the TT traffic bandwidth requirements and the total bandwidth requirements of the links connected to the virtual nodes, embeds the virtual nodes using the breadth first search algorithm, and plans the virtual links in candidate shortest path aggregation. If the TT traffic in the current virtual link is not schedulable, the iterative design of local virtual node re embedding and path planning is carried out. The simulation shows that the request acceptance rate of TT-VNE is not lower than that of existing methods, and when the number of virtual network requests in the star topology exceeds 30 , its request acceptance rate is about 14.3%higher than the VNE-NTANRC-D method which only considers the network topology and resource attributes.

BENN: Balanced Ensemble Neural Network for Handling Class Imbalance in Big Data

ABSTRACTClass imbalance is a critical challenge in big data analytics, often leading to biased predictive models. This imbalance can lead to biased models that perform well on the majority class but poorly on the minority class. Many machine learning models tend to be biased towards the majority class because they aim to minimise overall error, often leading to poor performance on the minority class. This paper presents the balanced ensemble neural network, a novel solution to effectively address class imbalance in big data. Balanced ensemble neural network combines the robust capabilities of neural networks with the power of ensemble learning, incorporating class balancing strategies to ensure fair representation of minority classes. The methodology involves integrating multiple neural networks, each trained on balanced subsets of data using techniques like Synthetic Minority Over‐sampling Technique and Random Undersampling. This integration aims to leverage the strengths of individual networks while reducing their inherent biases. Our extensive experiments across various datasets reveal that BENN achieves an AUC‐ROC score of 0.94, surpassing other models such as random forest (0.88), support vector (0.84) and single neural net (0.80). It was also observed that BENN's performance is better compared to traditional neural network models and standard ensemble methods in key metrics like accuracy, precision, recall, F1‐score and AUC‐ROC. The results specifically highlight BENN's effectiveness in accurately classifying instances of minority classes, a notable challenge in many existing models. These findings underscore BENN's potential as a substantial advancement in handling class imbalance within big data environments, offering a promising direction for future research and application in machine learning.

Citric acid/sucrose-modified pMDI for constructing high-performance straw particleboard based on multiple cross-linked networks

Polymeric diphenylmethane diisocyanate (pMDI) is an indispensable material used to produce straw particleboard. However, prolonged exposure to high concentrations of pMDI significantly increases the health risks to relevant professionals. Herein, we report a strategy for reducing safety hazards by partially replacing the pMDI with a bio-based adhesive. An adhesive with multiple cross-linked networks were synthesized using citric acid (CA), sucrose (SU), and pMDI first, and then applied to the preparation of straw particleboard. The amide and esterification reactions between the adhesives and rice straw particles form covalent bonds, enhancing boards adhesion and water resistance. Furthermore, acidic conditions provided by citric acid facilitate to convert SU into 5-hydroxymethyl furfural (5-HMF), which plays a positive role in straw adhesion. Adhesives made with 2.4 % pMDI, 8 %, and 4.8 % SU were used to bond particleboard, achieving internal bond strength, bending strength and elastic modulus of 0.57 MPa, 25.87 MPa, and 3.77 GPa, respectively, whose mechanical properties are comparable to the particleboard prepared with 4 % pMDI. The board showed superb mechanical performances, mould resistance, as well as water resistance. The CA/SU-modified pMDI adhesive system shows significant potential and promising application value in straw particleboard.

SnapE-ResNet: A novel electronic nose classification algorithm for gas data collected by open sampling systems

Increasing the depth of the neural network and implementing the ensemble of neural networks are two main methods to improve the accuracy of gas recognition algorithms. However, the issues of optimization difficulties or excessive resource consumption may occur when training deep neural networks or integrating multiple neural networks. In this study, an algorithm of snapshot ensemble is combined with Residual network (ResNet) to form a SnapE-ResNet which could simplify the training of the network due to the residual structure of ResNet, while realizes the ensemble of multiple ResNet models without additional time consumption and helps the network escape from local minimum using cyclic cosine annealing algorithm. The effectiveness of the proposed SnapE-ResNet is verified through classification experiments on the public dataset. Furthermore, the long-term drift and short-term drift are reduced by applying a zero-offset baseline compensation algorithm and a gaussian noise that added to the baseline signal. The experimental result indicating a high gas classification accuracy of 99.8 % from SnapE-ResNet is obtained, outperforming the comparative models. In addition, the necessity for snapshot ensemble is validated by network ablation experiments. This study could offer a reference for gas classification tasks in the gas detection fields.