Variety Of Environments Research Articles

Dynamic Active Sites in Electrocatalysis.

In-depth understanding of the real-time behaviors of active sites during electrocatalysis is essential for the advancement of sustainable energy conversion. Recently, the concept of dynamic active sites has been recognized as a potent approach for creating self-adaptive electrocatalysts that can address a variety of electrocatalytic reactions, outperforming traditional electrocatalysts with static active sites. Nonetheless, the comprehension of the underlying principles that guide the engineering of dynamic active sites is presently insufficient. In this review, we systematically analyze the fundamentals of dynamic active sites for electrocatalysis and consider important future directions for this emerging field. We reveal that dynamic behaviors and reversibility are two crucial factors that influence electrocatalytic performance. By reviewing recent advances in dynamic active sites, we conclude that implementing dynamic electrocatalysis through variable reaction environments, correlating the model of dynamic evolution with catalytic properties, and developing localized and ultrafast in-situ/operando techniques are keys to designing high-performance dynamic electrocatalysts. This review paves the way to the development of the next-generation electrocatalyst and the universal theory for both dynamic and static active sites.

Optimal load distribution control for airport terminal chiller units based on deep reinforcement learning

The building sector consumes substantial energy, with HVAC systems accounting for nearly half of the total energy use. Optimizing chiller unit operations is crucial for reducing energy consumption. This research introduces a novel model-free control method for optimizing central chiller load distribution using deep reinforcement learning (DRL). The aim is to address the challenges posed by the high-dimensional complexity and parameter tuning in traditional meta-heuristic algorithms. The proposed method leverages deep neural networks combined with reinforcement learning to optimize chiller control based on real-world data. Case studies conducted in a large airport terminal demonstrate that this approach achieves a 7.71 % energy savings, closely matching the performance of model-based control methods with only a 0.26 % difference. The results indicate that the DRL method not only outperforms traditional expert control systems but also offers a feasible alternative for optimal control in complex, variable environments with limited data. This study contributes to the field by filling a research gap in applying DRL for optimal chiller load (OCL) control and demonstrates its potential for large-scale public building applications. The novelty of this research lies in its model-free approach, which provides a robust solution for energy optimization in dynamic and uncertain environments.

Genetic requirements for uropathogenic E. coli proliferation in the bladder cell infection cycle.

Urinary tract infections (UTIs) are one of the most frequent infections worldwide. Uropathogenic Escherichia coli (UPEC), which accounts for ~80% of UTIs, must rapidly adapt to highly variable host environments, such as the gut, bladder sub-surface, and urine. In this study, we searched for UPEC genes required for bacterial growth and survival throughout the cellular infection cycle. Genes required for de novo synthesis of biomolecules and cell envelope integrity appeared to be important, and other genes were also implicated in bacterial dispersal and recovery from infection of cultured bladder cells. With further studies of individual gene function, their potential as therapeutic targets may be realized. This study expands knowledge of the UTI cycle and establishes an approach to genome-wide functional analyses of stage-resolved microbial infections.

Plasticity of life-history traits in the Draa barbel <i>Luciobarbus lepineyi</i> (Pellegrin, 1939) (Actinopterygii: Cyprinidae) in the sub-Saharan Draa basin, Morocco: effect of change in flow regime and salinity

The Draa barbel Luciobarbus lepineyi (Pellegrin, 1939), an endemic and unique cyprinid of the arid Draa river basin, southern Morocco, faces a range of multiple environmental stressors, especially in intermittent rivers and ephemeral streams (IRES). We assessed the effects of flow intermittence and salinisation on the somatic condition, and life-history traits in Draa barbels. We used the scaled-mass index (SMi) to evaluate the mean individual somatic body condition in nine populations. For three contrasted localities: Iriri (IR), Mghimima (MG), and Sid El Mokhtar (SM), we aged fish from scales and calculated von Bertalanffy growth parameters using the Beverton-Holt procedure. We used these parameters to estimate growth indices and performance, size and age at maturity, longevity, and mortality rates. Mean SMi decreased markedly with increasing salinity, revealing a normal condition at 0.4 PSU in IR, a poor condition in MG at 4 PSU, and a very poor condition at 10 PSU in SM. Growth rates (k) increased with salinity. Conversely, asymptotic lengths L∞ and growth performances (phi-prime, φ′) decreased with salinity and flow intermittency. The potential longevity tmax decreased with the increase in salinity. On the other hand, the mortality rate increased with flow intermittence and salinity. The Draa barbels in the saline IRES of the arid Draa basin compensate for the high mortality by growing faster but less efficiently. They mature at an earlier age and have shorter longevity. With this high life-history plasticity, the Draa barbel would constitute a good ecological indicator and a sentinel for salinisation in variable aquatic environments in the arid Draa river basin.

A Novel Method for Increasing Phase‐Change Microcapsules in Nanofiber Textile through Electrospinning

AbstractPhase‐change textiles can achieve temperature regulation in variable ambient environments, however, augmenting the amounts of phase‐change materials (PCMs) in textiles remains a significant challenge due to the occurrence of leakage with higher amounts. Herein, a novel approach is proposed for the fabrication of a hierarchical nanofiber textile embedded with a substantial quantity of phase‐change microcapsules (PCMC) using electrospinning. Such nanofiber textile is composed of a polyvinylidene fluoride‐hexafluoropropylene fibers (PVDF‐HFP) layer and a polyvinyl butyral fibers doped with 60 wt% of PCMC (PVB/PCMC‐60) layer. Gratifyingly, doped PCMC shows no signs of rupture and exhibits excellent cycling stability. Furthermore, the incorporation of the PCMC does not affect the spectral characteristics of the PVDF‐HFP layer while providing a substantial enthalpy of fusion (92.6 J g−1) to the PVB/PCMC‐60 layer. This serves to compensate for the deficiency in radiative cooling capacity and effectively mitigates temperature fluctuations and overheating of the textile. Outdoor test results indicate that the nanofiber textile can achieve temperature drops of 3.7 and 14.8 °C compared to textile without the PCMC (namely PVDF‐HFP/PVB) and cotton, and attains a subambient temperature drop of 6.5 °C. Additionally, the nanofiber textile exhibits desirable mechanical strength, flexibility, washability, breathability, moisture permeability, and sun protection.

Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach.

Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms.

PSII-7 Development and assessment of a mobility scoring system for beef cattle

Abstract Lameness is a disruption of normal gait due to infection, injury, or abnormal conformation, causing pain and discomfort. Several scoring systems have been developed to determine lameness severity; a key indicator in deciding if an animal is fit to withstand transport. However, most scoring systems were developed for dairy cattle and may not be suited for use in feedlot cattle due to the large genetic variation between beef breeds as well as the highly variable environment in which they are housed. Lack of consistency when scoring lameness and reduced ability to detect early lame cattle can result in poor animal welfare and increased costs. The objectives of this study were 1) to develop a mobility scoring system that aids in transport decisions for beef cattle, and 2) to determine observer repeatability using this scoring system. Over a 7-mo period, video cameras were placed in an alley to record the ambulation of individual cattle exiting the processing barn at 4 commercial feedlots in Southern Alberta. Cameras were set to record whole-body videos of cattle walking, running, or trotting. Five researchers trained to use this mobility scoring system, individually evaluated 174 cattle from the collected videos, for fitness for transport and the presence/absence of gait abnormalities: shortness of stride, stiffness (mild, moderate, or severe), limp (mild, moderate, or severe), hip hike, head dropped, head bob, arched back, non-weight bearing, or reluctance to move. The mobility scoring system used was a modified (NCBA and Zinpro) 4-point scale (0-3) for locomotion: 0) an animal with no gait abnormalities; 1) an animal with mild stiffness and shortness of stride; 2) an animal with difficulty taking some steps and one or more of the following: severe stiffness, limp favoring affected limb, limps with a head bob when walking but still bears weight; and 3) an animal reluctant or unable to move, bearing little or no weight on affected limb, the head of the animal is dropped and back arched with pronounced head bob and severe limp detected, or incoordination. Fitness for transport was scored as fit, compromised, or unfit according to the humane transportation guidelines of the Canadian Food Inspection Agency. A kappa statistic was calculated to assess inter-observer reliability for both mobility and transport fitness scoring systems as well as gait abnormalities. The combined mobility scores had substantial agreement between observers (Κ = 0.63; P < 0.001). Mobility score of 2 had the highest agreement among all variables (Κ = 0.88; P < 0.001), followed by head bob (Κ = 0.85; P < 0.001), and arched back (Κ = 0.76; P < 0.001). Results of this study will aid in the development of a mobility score that improves industry stakeholder agreement regarding lameness severity and transport loading decisions.

Ideotype map research based on a crop model in the context of a climatic gradient

Due to increasing climate uncertainties, optimizing plant traits is essential for sustainable agriculture. This article presents an approach that combines advanced modelling techniques to identify optimal plant traits under various agro-environmental conditions. By integrating a crop model, a climate generator, and our PEQI algorithm (Profile Expected Quantile Improvement), our method aims to create ideotype maps tailored to specific regions.We use the SAMARA model (Simulator of crop trait Assembly, MAnagement Response, and Adaptation), calibrated with trials carried in Sahel on a set of local varieties, to simulate crop growth in diverse environments. The PEQI algorithm adjusts varietal parameters to maximize expected yield, defining precise selection objectives known as ideotypes, which are particularly important in regions with irregular rainfall patterns like the Sahel.With the PEQI algorithm based on a kriging metamodel, we ensure effective adaptation to spatially variable environments. By leveraging a climate generator to simulate meteorological variability, our integrated approach optimizes crop yields in regions such as Senegal, southern Mali, Burkina Faso, and Guinea-Bissau. The outcome is an ideotype map for sorghum, providing breeders with a robust decision-support tool to enhance crop performance amidst climate uncertainty.

Magnetic Torque-Driven All-Terrain Microrobots.

All-terrain microrobots possess significant potential in modern medical applications due to their superior maneuverability in complex terrains and confined spaces. However, conventional microrobots often struggle with adaptability and operational difficulties in variable environments. This study introduces a magnetic torque-driven all-terrain multiped microrobot (MTMR) to address these challenges. By coupling the structure's multiple symmetries with different uniform magnetic fields, such as rotating and oscillating fields, the MTMR demonstrates various locomotion modes, including rolling, tumbling, walking, jumping, and their combinations. Experimental results indicate that the robot can navigate diverse terrains, including flat surfaces, steep slopes (up to 75°), and gaps over twice its body height. Additionally, the MTMR performs well in confined spaces, capable of passing through slits (0.1 body length) and low tunnels (0.25 body length). The robot shows potential for clinical applications like minimally invasive hemostasis in internal bleeding and thrombus removal from blood vessels through accurate cargo manipulation capability. Moreover, the MTMR can carry temperature sensors to monitor environmental temperature changes in real time while simultaneously manipulating objects, displaying its potential for in-situ sensing and parallel task implementation. This all-terrain microrobot technology demonstrates notable adaptability and versatility, providing a solid foundation for practical applications in interventional medicine.

Altruism and natural selection in a variable environment

Hamilton's rule provides the cornerstone for our understanding of the evolution of all forms of social behavior, from altruism to spite, across all organisms, from viruses to humans. In contrast to the standard prediction from Hamilton's rule, recent studies have suggested that altruistic helping can be favored even if it does not benefit relatives, as long as it decreases the environmentally induced variance of their reproductive success ("altruistic bet-hedging"). However, previous predictions both rely on an approximation and focus on variance-reducing helping behaviors. We derived a version of Hamilton's rule that fully captures environmental variability. This shows that decreasing (or increasing) the variance in the absolute reproductive success of relatives does not have a consistent effect-it can either favor or disfavor the evolution of helping. We then empirically quantified the effect of helping on the variance in reproductive success across 15 species of cooperatively breeding birds. We found that a) helping did not consistently decrease the variance of reproductive success and often increased it, and b) the mean benefits of helping across environments consistently outweighed other variability components of reproductive success. Altogether, our theoretical and empirical results suggest that the effects of helping on the variability components of reproductive success have not played a consistent or strong role in favoring helping.

The microbiome of bioreactors containing mass-cultivated marine diatoms for industrial carbon capture and utilization

Marine microalgae are a promising innovation platform for carbon capture and utilization (CCU) biotechnologies to mitigate industrial greenhouse gas emissions. However, industrial-scale cultivation of algal mono-cultures is challenging and often unscalable. Non-axenic microalgae in large semi-open photobioreactors lead to the co-cultivation of diverse microbial communities. There is limited knowledge about the “bioreactor ecology” involving microalgae interacting with the microbiome and its subsequent impact on process stability and productivity. In this study, we describe the semi-continuous industrial mass cultivation of the cold-adapted marine diatom, Porosira glacialis UiT201, by investigating the prokaryotic and microeukaryotic (phytoplankton and heterotrophic protist) communities. Data were collected in two consecutive time series experiments, representing the initiation and operation of an industrial-scale CCU photobioreactor (300,000 L). The first experiment experienced a culture “crash” of the focal strain after 39 days, while the second culture remained stable and “healthy” for 60 days. The results highlight that this mass cultivation system represents a unique industrial marine microbial ecosystem. The succession of the prokaryotic community was primarily driven by species replacement, indicating turnover due to selective bioreactor conditions and/or biological interactions. Nonetheless, the bioreactor consistently harbors a recurring and abundant core microbiome, suggesting that the closely associated bacterial community is influenced by microalgae-specific properties and can endure a dynamic and variable environment. The observed culture collapse of P. glacialis coincided with changes in the core microbiome structure and different environmental growth conditions compared to the stable and “healthy” experiment. These findings imply that cohabiting microbial taxa within industrial microalgae cultivation likely play a critical role in stabilizing the conversion of industrial CO2 into marine biomass, and changes in community structure serve as an indicator of process stability.

Dynamics of a SVEIR Epidemic Model with a Delay in Diagnosis in a Changing Environment

A SVEIR epidemic model with a delay in diagnosis is studied in a constant and variable environment. The mathematical analysis shows that the dynamics of the model in the constant environment are completely determined by the magnitude of the delay-induced reproduction number . We established that if < 1, the disease-free equilibrium is globally asymptotically stable, and when > 1 the endemic equilibrium is globally asymptotically stable. In the variable environment, the model undergoes a transcritical bifurcation for = 1 leading to changes in the stability of the equilibrium points. The analytical effect of the delays in epidemic diagnosis is investigated. A minimum diagnosis rate αmin has been determined to face or control the disease effectively. Finally, numerical illustrations were presented to support the theoretical results.

METHODS: Validating an immunoassay to measure fecal glucocorticoid metabolites in yellow-bellied marmots

Quantifying physiological stress in wild animals is essential for understanding their health, reproductive success, and survival in a variable environment. The yellow-bellied marmot (Marmota flaviventer) study at the Rocky Mountain Biological Laboratory near Crested Butte, Colorado, USA is the world's second longest study of free-living mammals. Historically, we used a validated corticosterone radioimmunoassay (RIA) to measure fecal glucocorticoid metabolites (FGMs) as a proxy for physiological stress. However, the costs and risks associated with working with radioisotopes drove us to consider a more sustainable method. Here we evaluate the suitability of two competitive corticosterone enzyme assays (EIA), one from Cayman Chemical Company (CCC) and one from Arbor Assays (AA), to measure marmot FGMs via their cross-reaction. The findings revealed that the AA EIA better matched the RIA in terms of accuracy across high and low FGM concentrations, had superior assay parameters, showed the highest correlations with RIA results and effectively captured the annual variations in FGM concentrations, thus demonstrating its reliability for use in longitudinal studies. We further analytically validated the AA EIA for FGMs and confirmed its efficacy and lack of matrix effects, thus establishing its suitability for ongoing and future studies of FGMs in marmots. The transition to the AA EIA from the RIA ensures continued data integrity while enhancing safety and environmental sustainability.

The Influence of the Work Environment and Work Discipline on Employee Performance in the Karawaci District, Tangerang City

This research aims to determine the influence of the work environment and work discipline on employee performance in the Karawaci District, Tangerang City. The research method used in this research is descriptive quantitative. The sample method used was the Simple Random Sampling technique, with a total of 87 employees. The work environment variable (X1) obtained a coefficient of determination value of 9.7%. Hypothesis testing obtained calculated t value > ttable or (3.025 > 1.988) p This strengthened by a significance value of 0.03 <0.05. then Ho1 is rejected and H1 is accepted, meaning that the work environment has a partial influence on employee performance. The results of the work discipline variable (X2) obtained a coefficient of determination value of 54.5%. Hypothesis testing obtained t value > t table or (10.0091 > 1.988) p This strengthened by a significance value of 0.00 <0.05. With Thus , H02 is rejected and Ha2 is accepted It means that discipline Work own influence in a way Partial to performance employee . As for from variable environment work and discipline Work in a way simultaneous obtained mark coefficient determination of 57.2%, the simultaneous F hypothesis test was obtained The value obtained is fcount>ftable or (56.141 > 3.11), this is also reinforced by a significance value <0.05 or (0.00 <0.05). Thus, H03 is rejected and H03 is accepted, this shows that there is a significant simultaneous influence between the work environment and work discipline on employee performance in the environment. Subdistrict Karawaci, Tangerang City.

Integrating self-hygroscopic hydrogel electrolyte and porous electrode for supercapacitor operating in variable humidity environments

The application of supercapacitors (SCs) based on the conventional polyvinyl alcohol‑potassium hydroxide (PVA-KOH) polymer electrolyte is challenged in variable humidity environments, especially in low relative humidity (RH) environments. Herein, taking advantage of the high ionic conductivity and strong hygroscopicity of highly concentrated KOH solutions, we have prepared a hygroscopic hydrogel polymer electrolyte composed of sodium alginate (SA)/PVA/poly(acrylic acid) (PAA)-KOH. The SA/PVA/PAA-KOH gel electrolyte exhibits high ionic conductivity, superior water retention and even water adsorption capability. A three-dimensional CoCu-layered double hydroxide/zeolitic imidazole framework-67 (CuCo-LDH/ZIF-67) porous electrode has also been prepared for the construction of the high-performance SC operating in variable RH environments. The CuCo-LDH/ZIF-67 porous electrode allows deep penetration of the self-hygroscopic electrolyte, facilitating electron/ion diffusion and providing fast water transport channels. As a result, our asymmetric SC show significantly improved electrochemical performance than that of SC based on conventional PVA-KOH electrolyte under different RH environments. This integration strategy can advance the practical application of SC in extreme environments.

Modeling and SOC estimation of on-board energy storage device for trains under emergency traction

The sudden interruption of train power supply in an extreme environment will seriously threaten the safety of passengers and affect the operational efficiency of the railway system. In this case, the focus of attention becomes a method of running the train to the nearest rescue point based on the limited capacity of the on-board emergency energy storage device. Therefore, this paper reports research on the state of charge (SOC) estimation of train energy storage equipment to optimize the emergency traction strategy and energy utilization rate of trains to the maximum extent. First, with the emergency power supply of on-board lithium titanate batteries, the energy flow model of train emergency power supply is constructed for the first time. Second, based on the second-order equivalent circuit model and considering the utilization of regenerative braking energy, the relationship between battery capacity and open circuit voltage (OCV), temperature, current and SOC are constructed through experimental data, which significantly improves the accuracy and robustness of SOC estimation in complex and variable environments. Then, to effectively eliminate the impact of environmental on the estimation especially in the case of extremely low temperature (−20 °C), online model parameter discrimination and SOC estimation of lithium batteries are realized by combining recursive least squares with an optimal forgetting factor (FRLS) algorithm and a new temperature forgetting factor based adaptive extended Kalman filter (TFFAEKF) algorithm. The experimental results show that the proposed method achieves accurate SOC estimation, and the maximum error is less than 1% at different temperatures.

Airborne observations of highly variable and complex freezing drizzle and mixed phase environments

Airborne observations of highly variable and complex freezing drizzle and mixed phase environments

Superior energy storage performance of Sr0.7Bi0.2TiO3-modified Na0.5Bi0.5TiO3-K0.7La0.1NbO3 lead-free ferroelectric ceramics

Na0.5Bi0.5TiO3 (NBT)-based ceramics exhibit significant potential as energy storage dielectric materials due to their high maximum polarization (Pmax). However, their limited energy storage density significantly restricts their practical applications. To address this, this study optimizes the dielectric energy storage characteristics of lead-free relaxor ferroelectric ceramics based on 0.91Na0.5Bi0.5TiO3-0.09 K0.7La0.1NbO3 (NBT-KLN) by incorporating Sr0.7Bi0.2TiO3 (SBT) relaxor additives. The introduction of SBT helps maintain large polarization and induces local disorderly fields, promoting the formation of polar nanoregions. Subsequently, a viscous polymer processing (VPP) technique was employed to reduce defects and enhance density, markedly improving the breakdown strength (BDS). The findings indicate that the BDS of the optimized 0.30SBT (VPP) ceramics increased to 440 kV/cm, while achieving a high energy storage efficiency (η) of 78 % and an elevated energy storage density (Wrec) of 6.29 J/cm3. Additionally, the 0.30SBT (VPP) ceramics demonstrate excellent temperature stability across a broad temperature range from 30 to 120 °C, making them ideal for long-term operation in variable environments. This study demonstrates superior results compared to previous research, thereby opening up new avenues for developing novel lead-free relaxor ferroelectric ceramics with superior energy storage characteristics.

Machine-Learning-Based Path Loss Prediction for Vehicle-to-Vehicle Communication in Highway Environments

Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gümüşhane provinces in Türkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R2 values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems.

Sensing volatiles throughout the body: geographic- and tissue-specific olfactory receptor expression in the fig wasp.

An essential adaptive strategy in insects is the evolution of olfactory receptors (ORs) to recognize important volatile environmental chemical cues. Our model species, Ceratosolen fusciceps, a specialist wasp pollinator of Ficus racemosa, likely possesses an OR repertoire that allows it to distinguish fig-specific volatiles in highly variable environments. Using a newly assembled genome-guided transcriptome, we annotated 63 ORs in the species and reconstructed the phylogeny of Ceratosolen ORs in conjunction with other hymenopteran species. Expression analysis showed that though ORs were mainly expressed in the female antennae, 20% were also expressed in nonantennal tissues such as the head, thorax, abdomen, legs, wings, and ovipositor. Specific upregulated expression was observed in OR30C in the head and OR60C in the wings. We identified OR expression from all major body parts of female C. fusciceps, suggesting novel roles of ORs throughout the body. Further examination of the OR expression of C. fusciceps in widely separated geographical locations, that is, South (urban) and Northeast (rural) India, revealed distinct OR expression levels in different locations. This discrepancy likely parallels the observed variation in fig volatiles between these regions and provides new insights into the evolution of insect ORs and their expression across geographical locations and tissues.