High Fluctuations Research Articles

Effect of fear and non-linear predator harvesting on a predator–prey system in presence of environmental variability

In this paper, we have proposed and analyzed a predator–prey system introducing the cost of predation fear into the prey reproduction with Holling type-II functional response in the stochastic environment with the consideration of non-linear harvesting on predators. The system experiences Transcritical, Saddle–node, Hopf, and Bogdanov-Taken (BT) bifurcation with respect to the intrinsic growth rate and competition rate of the prey populations. We have discussed the existence and uniqueness of positive global solution of the stochastic model with the help of Ito’s integral formula and the long-term behavior of the solution is derived here. The existence of stationary distribution and explicit form of the density function is established here when only prey populations survive or both populations. We have shown that due to high fluctuation, the regime changes from one stable state to another state when bistability occurs in the system. The paper ends with some conclusions.

Constraints in Production and Marketing in Perception of Fish Farmers and Market Intermediaries of Inland Fisheries in Khammam District of Telangana State, India

The study focuses on Khammam district in Telangana, a significant region for fish farming. Using primary data collected through personal interviews with fish farmers (60) and market intermediaries. The present study aims to identify the constraints in fish production and status of marketing using Garrett’s ranking technique. The major constraints in fish production were identified include high feed costs (Garrett score- 71.41), lack of market facilities (68.55), and inadequate capital (68.15). For marketing, addressing the key issues include high price fluctuation (60.4) in the fishes, high transportation costs (56.13), and lack of market infrastructure (53.53).These results point to important issues that must be resolved in order to increase the productivity and sustainability of fish farming and marketing. The most significant difficulties are those concerning infrastructure, such as lack of market facilities and high transportation costs, along with financial limitations like high feed costs and insufficient money. Addressing these challenges is essential for enhancing the fisheries sector contribution to food security and economic development, particularly in the regions of Khammam District in Telangana

Lightweight single-phase Al-based complex concentrated alloy with high specific strength

Developing light yet strong aluminum (Al)-based alloys has been attracting unremitting efforts due to the soaring demand for energy-efficient structural materials. However, this endeavor is impeded by the limited solubility of other lighter components in Al. Here, we propose to surmount this challenge by converting multiple brittle phases into a ductile solid solution in Al-based complex concentrated alloys (CCA) by applying high pressure and temperature. We successfully develop a face-centered cubic single-phase Al-based CCA, Al55Mg35Li5Zn5, with a low density of 2.40 g/cm3 and a high specific yield strength of 344×103 N·m/kg (typically ~ 200×103 N·m/kg in conventional Al-based alloys). Our analysis reveals that formation of the single-phase CCA can be attributed to the decreased difference in atomic size and electronegativity between the solute elements and Al under high pressure, as well as the synergistic high entropy effect caused by high temperature and high pressure. The increase in strength originates mainly from high solid solution and nanoscale chemical fluctuations. Our findings could offer a viable route to explore lightweight single-phase CCAs in a vast composition-temperature-pressure space with enhanced mechanical properties.

An experimental study on improving the thermal environment of data centers by optimizing fan wall arrangement

For a fan-wall data center, a large number of fans can be installed on one side of the server room to facilitate the introduction of outdoor cold air into the server room for effective heat dissipation, thereby accomplishing the objective of energy conservation. However, fewer researches are currently available on optimizing the fan array layout of fan-wall data centers to improve the thermal environment. This study aims to explore the impact of fan array layout parameters, including the area, distance, and height of the fan array, on the cooling efficiency of server rooms through both single-factor experiments and orthogonal experiments. The results indicate that: (1) In a fan-wall cooling system, racks closest to the fan wall have high temperature fluctuations and less orderly airflow organization. (2) The optimization of the fan array layout in a data center is primarily influenced by the height, area and distance of the fan array. (3) For a DC, the fan array layout, with an area of 4320 cm2, a distance of 81 cm, and a height of 18 cm, can achieve optimal conditions and effectively enhances the overall thermal performance of the rack. By optimizing the layout of the fan array, a more efficient combination of parameters for air distribution can be achieved, providing a reference for the arrangement and layout of fan arrays in actual engineering applications.

Accurate neuron segmentation method for one-photon calcium imaging videos combining convolutional neural networks and clustering

One-photon fluorescent calcium imaging helps understand brain functions by recording large-scale neural activities in freely moving animals. Automatic, fast, and accurate active neuron segmentation algorithms are essential to extract and interpret information from these videos. One-photon imaging videos’ low resolution, high noise, and high background fluctuation pose significant challenges. Here, we develop a software pipeline to address the challenges of processing one-photon calcium imaging videos. We extend our previous two-photon active neuron segmentation algorithm, Shallow U-Net Neuron Segmentation (SUNS), to better suppress background fluctuations in one-photon videos. We also develop additional neuron extraction (ANE) to locate small or dim neurons missed by SUNS. To train our segmentation method, we create ground truth neurons by developing a manual labeling pipeline assisted with semi-automatic refinement. Our method is more accurate and faster than state-of-the-art techniques when processing simulated videos and multiple experimental datasets acquired over various brain regions with different imaging conditions.

Predicting Workers' Stress: Application of a High-Performance Algorithm Using Working-Style Characteristics.

Work characteristics, such as teleworking rate, have been studied in relation to stress. However, the use of work-related data to improve a high-performance stress prediction model that suits an individual's lifestyle has not been evaluated. This study aims to develop a novel, high-performance algorithm to predict an employee's stress among a group of employees with similar working characteristics. This prospective observational study evaluated participants' responses to web‑based questionnaires, including attendance records and data collected using a wearable device. Data spanning 12 weeks (between January 17, 2022, and April 10, 2022) were collected from 194 Shionogi Group employees. Participants wore the Fitbit Charge 4 wearable device, which collected data on daily sleep, activity, and heart rate. Daily work shift data included details of working hours. Weekly questionnaire responses included the K6 questionnaire for depression/anxiety, a behavioral questionnaire, and the number of days lunch was missed. The proposed prediction model used a neighborhood cluster (N=20) with working-style characteristics similar to those of the prediction target person. Data from the previous week predicted stress levels the following week. Three models were compared by selecting appropriate training data: (1) single model, (2) proposed method 1, and (3) proposed method 2. Shapley Additive Explanations (SHAP) were calculated for the top 10 extracted features from the Extreme Gradient Boosting (XGBoost) model to evaluate the amount and contribution direction categorized by teleworking rates (mean): low: <0.2 (more than 4 days/week in office), middle: 0.2 to <0.6 (2 to 4 days/week in office), and high: ≥0.6 (less than 2 days/week in office). Data from 190 participants were used, with a teleworking rate ranging from 0% to 79%. The area under the curve (AUC) of the proposed method 2 was 0.84 (true positive vs false positive: 0.77 vs 0.26). Among participants with low teleworking rates, most features extracted were related to sleep, followed by activity and work. Among participants with high teleworking rates, most features were related to activity, followed by sleep and work. SHAP analysis showed that for participants with high teleworking rates, skipping lunch, working more/less than scheduled, higher fluctuations in heart rate, and lower mean sleep duration contributed to stress. In participants with low teleworking rates, coming too early or late to work (before/after 9 AM), a higher/lower than mean heart rate, lower fluctuations in heart rate, and burning more/fewer calories than normal contributed to stress. Forming a neighborhood cluster with similar working styles based on teleworking rates and using it as training data improved the prediction performance. The validity of the neighborhood cluster approach is indicated by differences in the contributing features and their contribution directions among teleworking levels. UMIN UMIN000046394; https://www.umin.ac.jp/ctr/index.htm.

Effects of structure parameter and material property on thermal performance of on-board hydrogen storage tanks during fast refueling

As a power generation fuel, high-pressure hydrogen gas is widely used in transportation, and significantly contributed to development of hydrogen fuel cell electric vehicles. However, obvious temperature rise generated during fast refueling causes serious safety risk to reliable operation of fuel cell stacks. Therefore, it is necessary to conduct in-depth research on fast filling process. Based on safety reasons, the SAE J2601/ISO 15869 strictly regulates the maximum temperature limit of 85 °C in specifications for refueling hydrogen gas tanks. In this study, a 70 MPa, Type IV on-board hydrogen storage tank is adopted as the research object, and a 2-dimensional axisymmetric numerical model is established to study the temperature rise of the vapor within storage tanks during fast refueling. The Realizable k-ε turbulence model is employed to simulate high intensity turbulent fluctuation during fast filling. Considering the compressibility of vapor, the state of equation for real hydrogen gas is used by obtaining fluid thermodynamic properties from the National Institute of Standards and Technology database. Three groups of fast filling experiments are selected to validate the developed numerical model. The comparison result shows that the numerical model has good prediction ability on hydrogen fast refueling. The final mass average temperature differences between numerical results and experimental data are 2.6 K, 4.3 K, and 0.03 K, respectively. Based on the developed numerical model, the influences of structure parameters and material properties on the temperature rise of hydrogen storage tanks are investigated. The results indicate that the inlet diameter and wall thickness of the inner and outer layers of the storage tank have weaken impacts on the temperature rise during rapid charging, with the maximum temperature differences of 1.8 K, 0.74 K, and 0.54 K, respectively. Due to the influence of heat capacity, the material properties of different layers exhibit a similar variation trend. This work can deepen researchers' understanding of the temperature rise of on-board hydrogen storage tanks during fast filling, and provide technical references for the structural optimal design of tanks and rapid refueling at hydrogen stations.

Biogeography of oribatid mites (Acari) reflects their ancient origin and points to Southeast Asia as centre of radiation

AbstractAimUnderstanding the worldwide distribution of species has fascinated scientists at least since Alfred Russel Wallace. Global patterns of belowground biodiversity may fundamentally differ from those of aboveground organisms. Here, we examine the global pattern and potential mechanisms driving the endemism and overlap of a soil microarthropod taxon.LocationGlobal.TaxonOribatida, Acari.MethodsWe investigated the global distribution of oribatid mites in five biogeographic regions including North America, South America, Eurasia, Africa and Oceania using a world list on the distribution of ~11,400 described species (including subspecies) at species, genera and family levels.Results and Main ConclusionWe found that most “endemic” (i.e., only occurring in one of the regions) species (4297) and also genera (143) occurred in Eurasia indicating that this region was the centre of radiation of oribatid mites. Within Eurasia, oribatid mite diversity was highest in Southeast Asia (1975 species) likely reflecting the long tropical history of this region (at least 200 million years), and also high fluctuations in water levels and island formation on the Sunda Shield. On a family level, oribatid mite diversity did not differ between biogeographic regions indicating that oribatid mites are much older than the tropics and older than the existence of Pangaea supporting the view of their origin in the early Palaeozoic. Finally, among the 55 species that occurred in all biogeographic regions nearly 50% reproduce by thelytoky and colonize high latitude ecosystems, indicating that compared to sexual species a disproportionally high number of parthenogenetic species is distributed worldwide suggesting that they possess a general‐purpose genotype. The other ~50% sexuals predominantly occur in tropical/subtropical regions across biogeographic regions supporting the view that resource shortage in tropical regions favours sex. Our findings highlight the importance of biogeographical studies of soil animals to better understand their diversity, distribution and life history strategies.

Presenting a new deep learning-based method with the incorporation of error effects to predict certain cryptocurrencies

In recent years, with the emergence of blockchain technology, we have witnessed a remarkable increase in the use of digital currencies. However, investing in the digital currency market carries a high level of risk due to the market's erratic behavior and high price fluctuations. Consequently, the need for an appropriate model for intelligent prediction and risk management is perceived. Motivated by the above subject, we propose a novel approach based on a deep neural network with a focus on error patterns. The proposed approach is based on the theory of non-random walks and assumes that there are predictable components in the price movements of cryptocurrencies. This new approach attempts to improve prediction results by modeling residual values and incorporating their impact on the main predictions. The time scope of this research is from October 31, 2018, to December 30, 2023, on a daily basis, spanning Five years. In this study, we utilized Long Short-Term Memory (LSTM) as the main prediction model and Vector Autoregression (VAR) for forecasting noise in three well-known cryptocurrencies: Bitcoin, Ethereum, and Binance Coin (BNB). The results indicate that the proposed approach has been able to enhance the predictions.

Electricity Price Analysis for Renewable Energy Dominated Electricity Markets under Extreme Scenarios

The COVID-19 pandemic, the energy crisis, the outbreak of the Russia-Ukraine war, and the rapid development of renewable energy electricity generation in European countries brought risks to electricity market operations, which are shown as high fluctuations in electricity prices. In order to explore the price risks of electricity markets in European countries, this paper studies the annual average electricity spot price and their fluctuations in each country, and compares them based on the renewable energy percentage of the country and inter-country power exchanges. It was found that under periods with extreme energy supply/consumption situations, a high proportion of hydropower generation may aggravate the price fluctuation of the electricity market. Meanwhile, electricity systems with high wind and solar generations are highly rely on natural gas, and hence be affected by gas/oil markets. Finally, cross-border electricity transactions might trigger electricity price fluctuations under extreme circumstances.

STRATEGIC PLANNING USING THE TOTAL COST OF OWNERSHIP MODEL

In a highly competitive global scenario, companies seek new ways to optimize production and implement continuous improvement mechanisms to reduce costs. An essential tool in this context is the Total Cost of Ownership model, applied to the acquisition processes of inputs to determine the costs involved and compare the economic viability between domestic and international suppliers. Given the unstable economic scenario and high exchange rate fluctuations, the possibility of nationalizing production is evaluated. The case study method is adopted, characterized as applied and exploratory research, with the objective of generating knowledge for practical application and solving specific problems. This method combines a literature review with exploratory field analysis. The studied company faces a loss of profitability due to rising import costs, reduced demand, and the loss of purchasing power of the national currency against international suppliers. The increase in costs resulting from exchange rate fluctuations necessitates the search for new supply options, primarily in the domestic market, where exchange rate influence is lower. Thus, the nationalization of inputs becomes crucial in business decisions to minimize costs and maintain competitiveness. The results indicate that the nationalization of production using the Total Cost of Ownership model is viable. This model considers all costs involved in the acquisition of inputs, unlike the traditional acquisition model, which focuses exclusively on the unit price.

Supply organ development of young broilers in response to increased carbohydrates and amino acids in the starter period

The growth of broiler chickens is marked by high fluctuations, varying nutrient requirement, early growth is characterized by high allometric growth rates of supply organs, which if underdeveloped, can impede nutrient efficiency and growth of demand organs like muscle and skeleton. This study aimed to investigate the impact of carbohydrate- and amino-acid-rich diets on the development of supply organs in broiler chickens. Four dietary treatments were used in a 2 × 2 factorial arrangement of treatments with apparent metabolizable energy (AME) at 2 levels (low: 2,750 kcal/kg and high: 3,050 kcal/kg) and standardized ileal digestible (SID) lysine at 2 levels (low: 1.0% and high: 1.2%) in the starter diets. Feed intake (FI) and BW gain were measured weekly; dissections were conducted at d 4 and d 11 to determine supply organ weights. Allometric growth of the liver was higher (P < 0.001) in the high AME and low lysine group compared to the other groups. For the pancreas, the highest (P < 0.001) allometric growth rate was in the high lysine groups. The small intestines responded differently; the duodenum had the highest (P < 0.001) allometric growth rate in the high AME groups and the jejunum in the low lysine groups, whereas the ileum showed an effect of diet density. For performance, high AME from carbohydrates, via maize starch, had a negative effect (P < 0.001) on FI and BW gain. High lysine had a positive effect (P < 0.001) on BW gain and FI, and high lysine alleviated part of the detrimental effect of high AME from carbohydrates. This effect was visible from d 0 to d 11, and persisted till the end of the trial on d 35. In conclusion, feeding a diet with a high AME from carbohydrates has negative consequences for the development of the supply organs of broilers.

Increasing and fluctuating resource availability enhances invasional meltdown.

Exotic plant invaders can promote others via direct or indirect facilitation, known as "invasional meltdown." Increased soil nutrients can also promote invaders by increasing their competitive impacts, but how this might affect meltdown is unknown. In a mesocosm experiment, we evaluated how eight exotic plant species and eight Eurasian native species responded individually to increasing densities of the invasive plant Conyza canadensis, while varying the supply and fluctuations of nutrients. We found that increasing density of C. canadensis intensified competitive suppression of natives but intensified facilitation of other exotics. Higher and fluctuating nutrients exacerbated the competitive effects on natives and facilitative effects on exotics. Overall, these results show a pronounced advantage of exotics over native target species with increased relative density of C. canadensis under high nutrient availability and fluctuation. We integrate these results with the observation that exotic species commonly drive increases in soil resources to suggest the Resource-driven Invasional Meltdown and Inhibition of Natives hypothesis in which biotic acceleration of resource availability promotes other exotic species over native species, leading to invasional meltdown.

Taxonomic diversity and environmental tolerance of cultivable extremophilic bacteria from a high-altitude meltwater pond on Ojos del Salado (Chile).

Earth harbors unique environments where only microorganisms adapted to extreme conditions, known as extremophiles, can survive. This study focused on a high-altitude meltwater pond, located in the Puna de Atacama, Dry Andes. The extremophilic bacteria of this habitat must adapt to a range of extremities, including cold and dry climate, high UV radiation, high daily temperature fluctuations, low-nutrient availability, and negative water balance. This study aimed to explore the taxonomic diversity of cultivable extremophilic bacteria from sediment samples of a desiccated, high-altitude, meltwater pond using media with different organic matter contents and different incubation temperatures. Based on the 16S rRNA gene sequence analysis, the isolates were identified as members of the phyla Actinobacteria, Proteobacteria, and Firmicutes. The most abundant genera were Arthrobacter and Pseudoarthrobacter. The isolates had oligocarbophilic and psychrotrophic properties, suggesting that they have adapted to the extreme environmental parameters of their natural habitats. The results indicate a positive correlation between nutrient concentration and temperature tolerance.

Evaluating High-Frequency Fluctuations And Its Effect On Hemodynamics At Varying Heart Rates In Intracranial Aneurysms Via 4D PTV

Hemodynamics have been shown to affect the growth and rupture of intracranial aneurysms (IA). Studies have increasingly reported the presence of high frequency velocity fluctuations in aneurysms. However, it remains undetermined how transient physiological factors like heart rate and inflow blood waveform amplitude affect the hemodynamic parameters and high frequency fluctuations in and around an IA, as well as the risk of IA progression. In this work, we address this question using a volumetric particle tracking velocimetry (PTV) study where a patient-specific right middle cerebral artery (R MCA) aneurysm model was subjected to two heart rate conditions (60 bpm and 137 bpm). The input waveform amplitude for the 137 bpm case was increased by a factor of 1.6 to be physiologically consistent. Captured PTV images were processed using the Shake-the-Box (STB) workflow. Flow parameters including velocity, vorticity, and turbulent kinetic energy (TKE) were evaluated. To evaluate the presence of high frequency fluctuations, a direct numerical simulation (DNS) using the R MCA geometry and same two heart rate cases was conducted and a method for use with STB particle tracks was proposed. As compared to the 60 bpm case, the 137 bpm case had a more concentrated inflow jet that extended further into the aneurysmal sac and a stronger and larger vortical flow structure in the aneurysmal sac. The 60 bpm case maintained a region of slightly elevated TKE at the main junction point where the aneurysmal sac and all vessels merge. In the 137 bpm case, TKE in this region was dramatically higher. Using probe points, the DNS data revealed high frequency velocity fluctuations in an outflow vessel during the deceleration phase of the cycle. Notably, these fluctuations were only observed for the 137 bpm case. For both the DNS and STB data, high frequency components were more prevalent and stronger outside of the aneurysmal sac as opposed to inside the aneurysmal sac. Overall, these results suggest that changes in heart rate induce changes to the flow structure and can induce significant increases in TKE and high frequency fluctuations. Based on our findings, a higher heart rate would be expected to yield a flow field more akin to IA progression. The specific correlation between the high frequency components computed using our proposed STB-based method and the high frequency fluctuations in the flow field could not be well understood. Thus, additional analysis and development of this method is needed.

Exploring Long-Term Persistence in Sea Surface Temperature and Ocean Parameters via Detrended Cross-Correlation Approach

Long-term cross-correlational structures are examined for pairs of sea surface temperature anomalies (SSTAs) and advective forcing parameters and sea surface height anomalies (SSHAs) and current velocity anomalies (CVAs) in the East/Japan Sea (EJS); all these satellite datasets were collected between 1993 and 2023. By utilizing newly modified detrended cross-correlation analysis algorithms, incorporating local linear trend and local fluctuation level of an SSTA, the analyses were performed on timescales of 400–3000 days. Long-term cross-correlations between SSTAs and SSHAs are strongly persistent over nearly the entire EJS; the strength of persistence is stronger during rising trends and low fluctuations of SSTAs, while anti-persistent behavior appears during high fluctuations of SSTAs. SSTA-CVA pairs show high long-term persistence only along main current pathways: the zonal currents for the Subpolar Front and the meridional currents for the east coast of Korea. SSTA-CVA pairs also show negative long-term persistent behaviors in some spots located near the coasts of Korea and Japan: the zonal currents for the eastern coast of Korea and the meridional currents for the western coast of Japan; these behaviors seem to be related to the coastal upwelling phenomena. Further, these persistent characteristics are more conspicuous in the recent decades (2008~2023) rather than in the past (1993~2008).

Are we focusing on the right parameters? Insights from Global Sensitivity Analysis of a Functional-Structural Plant Model

Abstract Performing global sensitivity analysis on functional-structural plant models (FSP models) can greatly benefit both model development and analysis by identifying the relevance of parameters for specific model outputs. Setting unimportant parameters to a fixed value decreases dimensionality of the typically large model parameter space. Efforts can then be concentrated on accurately estimating the most important input parameters. In this work, we apply the Elementary Effects method for dimensional models with arbitrary input types, adapting the method to models with inherent randomness. Our FSP model simulated a maize stand for 160 days of growth, considering three outputs, namely yield, peak biomass and peak leaf area index (LAI). Of 52 input parameters, 12 were identified as important for yield and peak biomass and 14 for LAI. Over 70 % of parameters were deemed unimportant for the outputs under consideration, including most parameters relating to crop architecture. Parameters governing shade avoidance response and leaf appearance rate (phyllochron) were also unimportant; variations in these physiological and developmental parameters do lead to visible changes in plant architecture but not to significant changes in yield, biomass or LAI. Some inputs identified as unimportant due to their low sensitivity index have a relatively high standard deviation of effects, with high fluctuations around a low mean, which could indicate non-linearity or interaction effects. Consequently, parameters with low sensitivity index but high standard deviation should be investigated further. Our study demonstrates that global sensitivity analysis can reveal which parameter values have the most influence on key outputs, predicting specific parameter estimates that need to be carefully characterized.

Association between Systolic Blood Pressure Variability and Incident Aortic Stenosis.

Background: This study investigated the potential link between blood pressure variability (BPV) and the incidence of aortic stenosis (AS) using Korean National Health Insurance Service data from 2002 to 2019. Methods: We collected annual systolic blood pressure variability (SBPV) measurements consisting of three consecutive blood pressure readings each year over three years. The obtained SBPV data was divided into five quantiles, with the highest quintile representing a high fluctuation of blood pressure. Results: Analyzing 9,341,629 individuals with a mean age of 40.7 years, the study found 3981 new AS diagnoses during an average 8.66-year follow-up. Independent predictors for AS included higher blood pressure levels and elevated systolic blood pressure variability (SBPV). The hazard ratios (HR) for different SBPV quintiles compared to the reference (1st quintile) were as follows: 2nd quintile HR 1.09 (p = 0.18), 3rd quintile HR 1.13 (p = 0.04), 4th quintile HR 1.13 (p = 0.04), and 5th quintile HR 1.39 (p < 0.001). Conclusion: Our findings suggest that both hypertension and high fluctuations in SBP during consecutive visits are associated with an increased risk of incident AS. These results emphasize the importance of blood pressure management and stability in the prevention of AS.

The spiking output of the mouse olfactory bulb encodes large-scale temporal features of natural odor environments.

In natural odor environments, odor travels in plumes. Odor concentration dynamics change in characteristic ways across the width and length of a plume. Thus, spatiotemporal dynamics of plumes have informative features for animals navigating to an odor source. Population activity in the olfactory bulb (OB) has been shown to follow odor concentration across plumes to a moderate degree (Lewis et al., 2021). However, it is unknown whether the ability to follow plume dynamics is driven by individual cells or whether it emerges at the population level. Previous research has explored the responses of individual OB cells to isolated features of plumes, but it is difficult to adequately sample the full feature space of plumes as it is still undetermined which features navigating mice employ during olfactory guided search. Here we released odor from an upwind odor source and simultaneously recorded both odor concentration dynamics and cellular response dynamics in awake, head-fixed mice. We found that longer timescale features of odor concentration dynamics were encoded at both the cellular and population level. At the cellular level, responses were elicited at the beginning of the plume for each trial, signaling plume onset. Plumes with high odor concentration elicited responses at the end of the plume, signaling plume offset. Although cellular level tracking of plume dynamics was observed to be weak, we found that at the population level, OB activity distinguished whiffs and blanks (accurately detected odor presence versus absence) throughout the duration of a plume. Even ~20 OB cells were enough to accurately discern odor presence throughout a plume. Our findings indicate that the full range of odor concentration dynamics and high frequency fluctuations are not encoded by OB spiking activity. Instead, relatively lower-frequency temporal features of plumes, such as plume onset, plume offset, whiffs, and blanks, are represented in the OB.

Optimization of garnet-type solid-state lithium batteries via synergistic integration of an advanced composite interface for elevated performance

Solid-state batteries (SSBs) represent a pivotal avenue of development owing to their superior energy density and enhanced safety profile. However, the widespread utilization about SSBs confronts challenges such as inadequate interfacial connectivity resulting in high resistance, dendrite formation, and volumetric fluctuations in the lithium metal anode during plating and stripping. In this study, we introduce an innovative and remarkably efficient approach, leveraging the transformative potential of TiN-induced conversion. This method yields a lithium-ion-conductive TiN material concurrently addressing pre-existing porosity. The LiTiN| LLZTO| LiTiN symmetric cell is particularly noteworthy for its remarkable long-term cycle stability, which exceeds 1000 h at 0.2 mA cm−2, and its remarkable critical current density of 1.4 mA cm−2 at 25 °C. By subjecting TiN, the formation of the Li-Ti-N phase is induced, thereby establishing an additional Li3N-conductive layer that significantly enhances battery performance. Of paramount significance is the validation of the exceptional attributes of the composite through the deployment of LiFePO4 (LFP) full cells. In this configuration, the LFP coupled full cell manifests a remarkable discharge rate capacity of about 147 mAh g−1 at 1C, along with a noteworthy discharge capacity retention rate of 90 % even following 1000 charge and discharge cycles. These outcomes underscore the material’s robust lithium affinity and uniform lithium-ion distribution, which together mitigate dendrite growth and enhance the cycle stability of lithium-metal batteries.