Adaptive Capabilities Research Articles

Characteristics of Microbial Diversity and Metabolic Versatility in Dynamic Mid-Okinawa Trough Subsurface Sediments

Large-scale and multi-sample datasets have revealed that microbial diversity and geographic distribution patterns are distinct across various habitats, particularly between hydrothermal vent and cold seep ecosystems. To date, our understanding of the effects of spatial and geochemical gradients on marine microbial communities remains limited. Here, we report the microbial diversity and metabolic versatility of a remote seafloor sediment ecosystem at different sites (GC-2, -4, -5, -6, -8) in the Mid-Okinawa Trough (Mid-OT) using high-throughput metagenomic sequencing technology. Desulfobacteraceae (3.1%) were detected in a high abundance at GC-2 with intense methane concentrations (353 μL/L), which showed a clear correlation with cold seeping. Whereas Candidatus Brocadiaceae (1.7%), Rhodobacteraceae (0.9%), and Rhodospirillaceae (0.7%), which are commonly involved in denitrification and sulfur oxidation, were enriched at GC-8. Concurrently investigating the potential of deep-sea microbial metabolism, we gained insights into the adaptive capabilities and metabolic mechanisms of microorganisms within seafloor environments. Utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, the analysis of functional modules revealed a significant enrichment (71–74%) of genes associated with metabolic pathways. These results expand our knowledge of the relationship between microbial biodiversity and metabolic versatility in deep-sea extreme environments.

Cross-Cultural Communication Strategies of the Thai Advertising Industry and Their Implications for the International Communication of Chinese Advertising

Based on the theory of cultural adaptation, this paper explores the cross-cultural communication strategies and successes of Thailand’s advertising industry, analyzing their implications for China’s international advertising efforts. Through case studies and empirical analysis, the paper identifies key cross-cultural strategies in Thailand’s advertising industry, such as language adaptation, symbol interpretation, and cultural element integration, which effectively enhance advertising impact and audience acceptance. The study suggests that China’s advertising industry should draw from Thailand’s successes by aligning with target market cultural characteristics while maintaining Chinese cultural identity, achieving a balance of localization and internationalization. The paper also provides specific strategies for China’s advertising industry to enhance its cultural adaptation capabilities, addressing the challenges posed by globalization. The research aims to offer theoretical support and practical guidance for cultural adaptation strategies in China’s international advertising.

Informing infodemic management capacity building by piloting an adaptable capability tool

Abstract Background Infodemic management competencies and indicators have been included in key WHO pandemic preparedness documents and guidance such as the Preparedness and Resilience for Emerging Threats (PRET) initiative, the Health Emergency preparedness and response (HEPR) framework and in the WHO benchmarks for strengthening health emergency capacities. Training and capacity building is needed to strengthen infodemic management skills and capacity, and a tool to define capabilities and inform training needs was needed. Objectives An adaptable tool was developed and piloted to measure infodemic management capacity to inform training and capacity building needs. The tool was informed by previous work and adapted to cover a wide set of capabilities. It was piloted in March - May 2024. Results The tool was developed to cover the breadth of infodemic management work practice and structural support and included: who is conducting infodemic management work; structures in place to support; main activities; health topics focus areas; partnerships with other organisations; social listening and infodemic insights generation; as well as challenges, opportunities and training needs. The tool can be adapted to the specific situation, with a full survey able to be disseminated via an online survey platform as well as a modified interview guide for key informants or settings where that is more appropriate. Conclusions Preliminary results show that this tool can be a useful way to measure infodemic management competencies and to inform training and capacity building needs. The ability to adapt to specific settings is important to ensure the results are as useful and tailored as possible. As further testing in different setting continues the tool will continue to be iterated upon. Key messages • Infodemic management is an important component of pandemic preparedness and resilience and across the whole emergency response cycle. • A new adaptable tool to measure capabilities can help inform infodemic management trainings and capacity building.

Strategic Convergence: How Knowledge Sharing, Cross-Functional Collaboration, and Adaptive Leadership Drive Innovation Success

This study investigates the relationships among knowledge sharing, cross-functional collaboration, adaptive leadership, and innovation success in organizational settings. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the research analyzes data collected from employees across various industries. The findings reveal that knowledge sharing significantly influences innovation success both directly and indirectly through cross-functional collaboration, which also serves as a critical mediator between adaptive leadership and innovation outcomes. Furthermore, adaptive leadership is shown to enhance cross-functional collaboration, emphasizing the importance of leadership style in fostering a collaborative culture conducive to innovation. These results underscore the necessity for organizations to cultivate knowledge-sharing practices, promote cross-functional teamwork, and develop adaptive leadership capabilities to drive innovation success. The study contributes to the literature by elucidating the dynamic interplay among these constructs and providing practical implications for managers seeking to enhance their organization's innovative capacities.

INFLUENCE OF STRATEGIC DIRECTION ON THE PERFORMANCE OF FINTECH FIRMS IN NAIROBI CITY COUNTY, KENYA

This study investigated the influence of strategic direction on the performance of financial technology (FINTECH) firms in Nairobi City County, Kenya. The research was grounded in strategic leadership theory and employed a predictive research design. The target population consisted of 154 senior and middle-level managers from 22 FINTECH firms, with a sample size of 111 respondents determined using the Taro Yamane formula. Data was collected using structured questionnaires, achieving a response rate of 82.88%. Descriptive statistics revealed that firms excel in adapting strategic direction to changing circumstances (M = 4.80, SD = 0.399) and establishing clear strategic direction (M = 4.43, SD = 0.731), but face challenges in setting clear and measurable goals (M = 1.18, SD = 0.390). In the inferential analysis, multiple linear regression revealed that strategic direction has a statistically significant positive influence on FINTECH firm performance (β = 0.890, t = 11.797, p < .05). The coefficient of determination (R² = 0.685) indicated that 68.5% of the variance in organizational performance could be explained by the strategic factors studied. The study concludes that effective strategic direction is fundamental to FINTECH firm success in Kenya's dynamic financial technology sector, validating the theoretical frameworks that emphasize strategic leadership's role in organizational performance. The research recommends that FINTECH firms implement comprehensive strategic management frameworks that balance structured goal-setting processes with adaptive capabilities, while regulatory bodies develop supportive policies that encourage strategic innovation while maintaining industry stability. This research contributes to both the theoretical understanding of strategic management in the FINTECH sector and provides practical insights for industry leaders navigating complex market conditions.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0735/a.php" alt="Hit counter" /></p>

Thriving minds, thriving workplaces: unleashing creativity through psychological wellbeing and psychological capital

PurposeThis study examines the complicated relationship between psychological wellbeing, joy at work, creative ability and the mediating influence of psychological capital, drawing insights from self-determination theory within the context of the banking sector.Design/methodology/approachThe study utilized random sampling to gather comprehensive data from 465 banking employees. Following data collection, structural equation modeling was employed to test the hypotheses formulated based on the collected data.FindingsFindings underscore a significant association between psychological wellbeing and both joy at work and creative ability within the banking sector. Moreover, the study elucidates that psychological capital acts as a crucial mediator, illuminating the pathway through which psychological wellbeing influences joy at work and subsequently enhances creative ability.Practical implicationsThis research offers valuable insights for organizational leaders and policymakers, emphasizing the imperative of prioritizing psychological wellbeing initiatives and nurturing positive work environments to enhance employee satisfaction, productivity and innovative contributions within the banking sector.Originality/valueThe application of self-determination theory as a theoretical framework provides a robust foundation for understanding the dynamics between psychological factors and workplace outcomes. The banking industry, often characterized by high stress levels and demanding work environments, stands to benefit substantially from interventions aimed at fostering psychological wellbeing. By cultivating positive mental health and capitalizing on joy at work, organizations can stimulate employee creativity ability, thereby fostering innovation and adaptive problem-solving capabilities crucial in the contemporary banking landscape.

Leveraging Data-Driven Insights to Enhance Supplier Performance and Supply Chain Resilience

The study examines how artificial intelligence (AI) enhances supply chain performance (SCP) and resilience (SCRes), with adaptive capabilities (AC) and supply chain collaboration (SCC) as mediators. Data from X firms were analyzed using structural equation modeling (SEM) to explore the relationships between AI, AC, SCC, supply chain dynamism (SCD), and supply chain outcomes. The results show that AI significantly improves SCP (β = 0.49, p < 0.01), leading to a 49% boost in performance, particularly in metrics like SLAs and on-time delivery. Moreover, AI’s effect on SCRes is amplified through AC (β = 0.71, p < 0.01), resulting in a 66% increase in resilience, while SCC strengthens resilience further (β = 0.68, p < 0.01) by 71%. AI also helps firms manage dynamic environments more effectively (β = 0.38, p < 0.01). These findings underscore AI's role in improving operational efficiency and building resilient supply chains. The study offers insights for firms to leverage AI, develop adaptive capabilities, and enhance collaboration for better performance and resilience.

The effect of the composition of hepatoprotective action on biochemical and morphostructural changes in the body of laying hens under thermal stress

To reduce the negative effects of hyperthermia on the body of farm animals and poultry, various drugs and feed additives are currently used. Which do not have sufficiently adaptogenic and antitoxic properties. We studied the effect of a hepatoprotective composition consisting of dried live yeast, amorphous silicon dioxide, propylene glycol, calcium propionate, ascorbic acid, manganese, copper and zinc chelates, methionine and choline chloride on the variability of biochemical and morphological parameters of the body of laying hens under temperature stress. It was simulated by increasing the air temperature in a building where laying hens were kept from 18.0 ± 1.0 °C to 28.0 ± 1.0 °C for 48 hours. Due to hyperthermia, changes in biochemical and morphofunctional parameters were observed in the tissues and organs of birds. The obtained values of the biochemical parameters of blood serum in the birds of the control group indicated the intensity of the adaptive capabilities of their body. A complex of morphological changes confirmed a violation of protein metabolism and the regenerative-compensatory process. Pathological changes in the structure of the duodenum, characteristic of catarrhal-necrotic duodenitis, were identified. The stress reaction was also reflected in the condition of the heart muscle, in which an inflammatory process developed against the background of granular dystrophy of cardiomyocytes. The results of biochemical studies of blood serum in birds of the experimental group indicated an increase in the anti-stress response to a temperature stimulus under the influence of the studied composition (tendency to increase glucose and calcium, increase alkaline phosphatase activity by 47.4 %). The introduction of a hepatoprotective composition into the diet of laying hens during periods of temperature stress did not lead to disruption of the structure of tissues and organs, preserving cellular metabolic mechanisms. The

Determination of adaptive capabilities of various types, forms and varieties of apple trees based on endophytic and epiphytic microbiota indicators

The aim of the research was to identify the most resistant species, forms and varieties of apple trees based on the developmental specifics of the various types of microbiota. The research was conducted in 2014–2023 at the I. V. Michurin Federal Scientific Center, Michurinsk, Tambov region. The year of planting was 2009 with the planning scheme 6×3 m., the rootstock 54–118. The objects of the research are: Malus robusta species; forms 25–8, 64–143, and 54–118; varieties Korichnoe polosatoe, Orlovim, Iyulskoe Chernenko, and Slava Michurinska. The soil type was leached medium-power black soil with a loamy mechanical composition. Pseudomonas syringae bacteria prevailed in resistant plants: 96.6 % in Malus robusta hybrid apple, 89.3 % in Korichnoe polosatoe variety, and 88.7 % in 25–8 form, which is 9.7 %, 2.4 % and 1.8 % respectively, higher than the average value among the test subjects. In 64–143 form and Slava Michurinska variety, the indicators are lower than the average by 5.5 % and 4.2 % respectively. The yield of mixed microbiota in Malus robusta was not observed (0.0 %). Korichnoe polosatoe variety had an indicator below the average value by 0.7 %, in the form 25–8 by 1.3 %, and in the form 64–143 by 11.4 %, which is 4.5 % higher than the average. The magnitude of negative tests ranged from 3.4 % (Malus robusta) to 6.2 % (Iyulskoe Chernenko), which did not exceed the average value. Samples 64–143, 54–118 and Slava Michurinska had above-average values of 1.0 %, 2.0 % and 4.0 %, respectively. According to the complex of indicators of microbiota yield and the percentage of negative tests, Malus robusta, Korichnoe polosatoe variety and 25–8 form had a high potential for environmental resistance. They were recommended for further breeding for resistance to adverse environmental factors.

Artificial Neural Network-based Control of a Switched Reluctance Motor for a High-precision Positioning System

This paper presents a approach to control a switched reluctance motor (SRM) in the context of a high-precision positioning system using artificial neural networks (ANNs). The SRM is known for its robustness and simplicity, making it suitable for various applications, including positioning systems where precision is paramount. Traditional control methods often struggle to achieve the desired level of accuracy due to the non-linear and dynamic nature of the SRM. In this study, we propose an advanced control strategy leveraging the adaptive learning capabilities of ANNs. The neural network is trained to capture the intricate relationships between the motor's inputs and outputs, allowing for precise control in real-time. By measuring the electromagnetic torque and phase currents, the neural network is able to estimate the rotor position, facilitating the elimination of the rotor position sensor. The training data set of the neural network consists of magnetization data for the SRM with the electromagnetic torque and current as inputs and the corresponding position as outputs in this set. With a sufficiently large training data set, the artificial neural networks (ANN) can be correlated for appropriate network architecture.

Влияние насаждений Quercus robur L. и Juglans rupestris Engelm. на свойства степных почв

The aim of the study has been to determine the condition of Quercus robur L. and Juglans rupestris Engelm. and their influence on the properties of segregated chernozems. The study was conducted in 2020–2021 in the steppe Crimea. The control has been a perennial grass fallow. To characterize the general condition of the trees, a 4-point scale for assessing the condition of the introduced species has been used. Standard methods have been used for soil analysis. A better preservation and a greater number of 50-year old plants in excellent and good condition have been established for Q. robur (67 и 56 %, respectively) compared to J. rupestris (48 и 42 %, respectively) in these environmental conditions, which is explained by the greater adaptive capabilities of oak as an indegenous species. Both tree crops have influenced soil properties. In their phytocenoses, a litter has been formed, more significant under J. rupestris. The dark humus horizon has also turned out to be thicker under the J. rupestris plantation (80 cm) than under the Q. robur plantation and the fallow (72 and 70 cm, respectively). Under Q. robur the soil density has been lower than under J. rupestris by 0.02–0.18 g/cm3. Under the woody plants, the soil structure has improved: the number of lumpy aggregates has decreased, and the number of agronomically valuable and granular particles has increased. The maximum coefficient of structure has been in the soil under the oaks (4.4–6.6), which exceeded the control by 2.5–4.9 times. Under the woody plants, carbonates have been significantly washed out of the soil profile, especially under J. rupestris. The pHH2O value of the alkaline soil under the woody plants has decreased by 0.2–0.4 and has been closely related to carbonate content (r = 0.83–0.91). The humus content under both species has been significantly higher than under the fallow – by 0.30–0.86 %. Afforestation has resulted in the accumulation of total forms of nutrients, to a greater extent under J. rupestris, and the C:N ratio has decreased. Tree species have stored more labile forms of nutrients in the soil. Soil desalination has been noted in the tree cenoses. Thus, J. rupestris and Q. robur are ecologically stable in the conditions of the central steppe of Crimea and can be used to increase forest cover in steppe areas. This will help improve soil fertility.

Foreign migrants in the Moscow agglomeration: spatial and temporal analysis based on data from mobile operators

The article analyzes the modern ethnic landscape of the Moscow agglomeration on the basis of data from mobile operators. The estimation of the number of foreign migrants and its monthly dynamics from October 2021 to October 2022 is made. The main areas of residence of foreigners have been identified, the ethnic diversity of municipalities has been assessed, and the main types of settlement pattern of national-ethnic communities have been identified. The study showed that the total number of foreign migrants in the agglomeration reaches 1.8 million people or 9% of the total population, remaining almost unchanged in spite of events of 2022, including the special military operation. The share of foreigners is minimal in ZATO (below 3%), it is also insignificant in the far suburbs of Moscow and in expensive districts of the capital city. At the same time, the threshold of 17% (the so-called “boiling point”, reflecting a sharp increase in the risks of interethnic conflicts and ghettoization of urban space) was overcome by 8 municipalities in the agglomeration. The two most noticeable areas of increased concentration of foreign migrants are identified in the south-east at the junction of Moscow and Moscow region (Lublino – Kotelniki) and in New Moscow (Mosrentgen – Sosenskoye). The calculation of the Ekkel ethnic mosaic index confirmed the presence of pronounced interethnic contact zones here. Increased index values were also noted in most central and southwest districts of Moscow, which, with a smaller proportion of foreigners living, are associated with a high density of offices, diplomatic institutions and universities. Three types of settlement patterns of national-ethnic groups of migrants, determined by the adaptive capabilities of ethnic communities, were revealed. Diffuse and relatively uniform settlement pattern is characteristic for both the most massive ethnic groups (citizens of Tajikistan, Uzbekistan and Kyrgyzstan) and the most culturally close to the local population (citizens of Ukraine and Belarus). Concentric settlement pattern (in residential areas of Moscow and satellite cities) is typical for relatively large ethnic groups coming from post-Soviet countries (citizens of Azerbaijan, Armenia, Georgia). The local settlement pattern is typical for small ethnic communities that gravitate to specific districts of the capital city.

Efficient Ship Detection System for Maritime Surveillance using Deep Learning Approach

The usage of drones in maritime surveillance is a very effective means to observe ships. As the ship detection is vital for applications such as port monitoring, cross-border surveillance, Moreover, it is not only vital for maritime surveillance but also environmental conservation. Despite its effectiveness, the drone-captured images present itself with its own challenges, specifically when leveraging on a custom dataset which is tailored for specific application. To address the issues, we propose a novel ship detection approach called YOLOv9 with Adan optimizer (YOLOv9-Adan) which is more accurate to increase the efficiency of maritime surveillance. The YOLOv9-Adan model integrates the robust object detection capabilities of the adaptive learning capabilities of the Adan (ADAptive Nesterov momentum algorithm) optimiser, trained on the drone image dataset comprising 3200 images of maritime scenes and ship types in drone view. Our model is trained on a drone-image dataset comprising 3200 images of maritime scenes and ship types in drone views collected from various sources. The experimental results show that our approach using the YOLOv9-Adan model achieves 65.5% mAP, which exceeds the mAP of YOLOv9 by 4.3%. Additionally, This article also provides a comparative analysis of our model YOLOv9-Adan with other existing models in literature with consistently surpassing existing approaches

The contribution of seasonal variations and Zostera marina presence to the bacterial community assembly of seagrass bed sediments

BackgroundMicroorganisms play pivotal roles in seagrass ecosystems by facilitating material and elemental cycling as well as energy flux. However, our understanding of how seasonal factors and seagrass presence influence the assembly of bacterial communities in seagrass bed sediments is limited. Employing high-throughput sequencing techniques, this study investigates and characterizes bacterial communities in the rhizosphere of eelgrass (Zostera marina) and the bulk sediments across different seasons. The research elucidates information on the significance of seasonal variations and seagrass presence in impacting the microbial communities associated with Zostera marina.ResultsThe results indicate that seasonal variations have a more significant impact on the bacterial community in seagrass bed sediments than the presence of seagrass. We observed that the assembly of bacterial communities in bulk sediments primarily occurs through stochastic processes. However, the presence of seagrass leading to a transition from stochastic to deterministic processes in bacterial community assembly. This shift further impacts the complexity and stability of the bacterial co-occurrence network. Through LEfSe analysis, different candidate biomarkers were identified in the bacterial communities of rhizosphere sediments in different seasons, indicating that seagrass may possess adaptive capabilities to the environment during different stages of growth and development.ConclusionsSeasonal variations play a significant role in shaping these communities, while seagrass presence influences the assembly processes and stability of the bacterial community. These insights will provide valuable information for the ecological conservation of seagrass beds.

Double Enzyme Active Hydrogel Program Regulates the Microenvironment of Staphylococcus aureus-Infected Pressure Ulcers.

The treatment of infected pressure ulcers (IUPs) requires addressing diverse microenvironments. A pressing challenge is to effectively enhance the regenerative microenvironment at different stages of the healing process, tailoring interventions as needed. Here, a dual enzyme mimetic and bacterial responsive self-activating antimicrobial hydrogel designed to enhance IPUs healing is introduced. This hydrogel incorporates pH-responsive dual enzyme-active nanoplatforms (HNTs-Fe-Ag) encapsulated within a methacrylate-modified silk fibroin (SFMA) and dopamine methacrylamide (DMA) matrix. This composite hydrogel exhibits adaptive microenvironment regulation capabilities. Under the low pH microenvironment of bacterial infection, it has excellent antimicrobial activity by self-activating the •OH generation in conjunction with photothermal effects. Under the neutral and alkaline microenvironment of chronic inflammation, it catalyzes the decomposition of hydrogen peroxide (H2O2) to produce oxygen (O2), thereby alleviating hypoxia and scavenging reactive oxygen species (ROS), which in turn remodulates the phenotype of macrophages. The composite hydrogel demonstrates on-demand therapeutic effects in the microenvironment of infected wounds, significantly enhancing the regenerative microenvironment of IUPs by promoting wound closure, inflammation regulation, and collagen deposition through self-activated antimicrobial action during infection and adaptive hypoxia relief during recovery. This approach offers a novel strategy for developing smart wound dressings.

First use of Chironomid Pupal Exuvial Technique in freshwater biomonitoring in Cameroon: Ecological aspect and morphological description of Chironomidae (Insecta: Diptera)

The biodiversity of urban streams is gradually degrading as a result of anthropogenic pollution. In this study, chironomid pupal exuviae were used as surrogates to assess urban streams and a lake in Douala Cameroon. We compared these assessments with samples collected from a suburban forest stream. Based on their highly adaptive capability, we predicted that chironomid communities would present high diversity and abundance in urban habitats. Chironomid pupal exuviae were collected using the chironomid pupal exuvial technique in 14 urban and 2 forest reference stations during dry and rainy seasons. Measurements of the environmental variables were done simultaneously. Physicochemical analysis revealed that all urban sites have had very poor ecological health status whereas forest site had good water quality as shown by the hierarchical cluster analysis. This ecological survey identified 8 species/morphotypes of chironomids all belonging to the subfamily Chironominae, tribe Chironomini. These are Chironomus holomelas(?), Chironomus sp.1, Chironomus sp.2, Chironomus sp.3, Chironomus (Lobochironomus) sp., Stenochironomus sp., Dicrotendipes sp. and Dicrotendipes pulsus. No midge pupal exuviae were recorded at the forest stations (S1 & S2) and the stations S6 and S7 located downstream industrial mill effluents. The species richness of the study sites was significantly very low as compare to other Afrotropical studies. Canonical redundancy analysis revealed that high values of water temperature, ammonium, nitrates and phosphates, and low oxygen concentration were significant predictors of the distribution and high abundance of Chironomidae. This survey highlighted the impacts of anthropogenic activities on chironomid diversity and distribution, with great decrease of their diversity due to industrial pollution.

Pengaruh Kemampuan Adaptasi dan Dukungan Pemerintah terhadap Ketahanan Bisnis yang Dimediasi Kemampuan Teknologi Informasi pada UMKM di Koperasi Konsumen Wanita Pengusaha Indonesia (KOWAPI) Srikandi Daerah Istimewa Yogyakarta

This research aims to examine and analyze the influence of adaptability on business resilience, the influence of government support on business resilience, the influence of adaptability on information technology capabilities, the influence of government support on information technology capabilities, the influence of information technology capabilities on business resilience, the influence of adaptability on business resilience mediated by information technology capabilities and the influence of government support on business resilience mediated by information technology capabilities in MSMEs that are members of the Indonesian Women Entrepreneurs Cooperative (Kowapi) Srikandi DIY. The research method used was quantitative with a sample size of 80 respondents. The data collection technique was carried out using a questionnaire survey via Google Form and analyzed using SEM-PLS modeling analysis. The test results show that adaptability, government support and information technology capabilities have a positive effect on the resilience of MSME businesses in DIY. Information technology capability is a variable that explains the relationship between adaptability and government support on business resilience. This research focuses on confirming previous research regarding factors that influence MSME business resilience. It is hoped that future research will have broader and deeper objects, not only on MSME actors in the Kowapi "Srikandi" Cooperative.

Time-optimal ergodic search: Multiscale coverage in minimum time

Search and exploration capabilities are essential for robots to inspect hazardous areas, support scientific expeditions in extreme environments, and potentially save human lives in natural disasters. The variability of scale in these problems requires robots to reason about time alongside their dynamics and sensor capabilities to effectively assess and explore for information. Recent advances in ergodic search methods have shown promise in supporting trajectory planning for exploration in continuous, multiscale environments with dynamics consideration. However, these methods are still limited by their inability to effectively reason about and adapt the time to explore in response to their environment. This ability is crucial for adapting exploration to variable-resolution information-gathering tasks. To address this limitation, this paper poses the time-optimal ergodic search problem and investigates solutions for fast, multiscale, and adaptive robotic exploration trajectories. The problem is formulated as a minimum-time problem with an ergodic inequality constraint whose upper bound specifies the amount of coverage needed. We show the existence of optimal solutions using Pontryagin’s conditions of optimality, and we demonstrate effective, minimum-time coverage numerically through a direct transcription optimization approach. The efficacy of the approach in generating time-optimal search trajectories is demonstrated in simulation under several nonlinear dynamic constraints, and in a physical experiment using a drone in a cluttered environment. We find that constraints such as obstacle avoidance are readily integrated into our formulation, and we show through an ablation study the flexibility of search capabilities at various scales. Last, we contribute a receding-horizon formulation of time-optimal ergodic search for sensor-driven information-gathering and demonstrate improved adaptive sampling capabilities in localization tasks.

Meta Learning to Rank for Sparsely Supervised Queries

Supervisory signals are a critical resource for training learning to rank models. In many real-world search and retrieval scenarios, these signals may not be readily available or could be costly to obtain for some queries. The examples include domains where labeling requires professional expertise, applications with strong privacy constraints, and user engagement information that are too scarce. We refer to these scenarios as sparsely supervised queries which pose significant challenges to traditional learning to rank models. In this work, we address sparsely supervised queries by proposing a novel meta learning to rank framework which leverages fast learning and adaption capability of meta-learning. The proposed approach accounts for the fact that different queries have different optimal parameters for their rankers, in contrast to traditional learning to rank models which only learn a global ranking model applied to all the queries. In consequence, the proposed method would yield significant advantages especially when new queries are of different characteristics with the training queries. Moreover, the proposed meta learning to rank framework is generic and flexible. We conduct a set of comprehensive experiments on both public datasets and a real-world e-commerce dataset. The results demonstrate that the proposed meta-learning approach can significantly enhance the performance of learning to rank models with sparsely labeled queries.

HEART RATE VARIABILITY IN FOOTBALL PLAYERS AND TRACK AND FIELD ATHLETES

Objective of the study: To investigate the state of the autonomic nervous system in athletes based on heart rate variability indicators. Materials and methods: The study was conducted on 142 male athletes (67 football players and 75 track and field athletes) aged 17-20 years, with an average age of 18.5±1.5 years. Depending on the type of higher nervous activity, athletes were divided into 4 groups: sanguine (41.55%), choleric (28.87%), phlegmatic (19.01%), and melancholic (10.56%). Heart rate variability (HRV) was studied using the "BioMouse" system. A t-test for independent samples (Welch's t-test) was used to analyze the significance of differences between the groups. Results of the study: It was found that the highest adaptive capabilities were observed in phlegmatic athletes, who demonstrated high SDNN and HF values, as well as a low tension index (IN). Their calm temperament allows for better adaptation to physical exertion and maintaining a balance between sympathetic and parasympathetic activity. The lowest adaptive capabilities were observed in melancholics, who showed high IN and LF values, as well as low SDNN and HF, indicating an imbalance in the autonomic nervous system and lower resilience to physical stress. Overall, beginner athletes tended to have slightly higher heart rates and lower HRV indicators (SDNN), LF, and HF compared to more experienced athletes of the 2nd-3rd ranks. This is due to the fact that first-year athletes have not yet fully adapted to physical loads, leading to more pronounced dominance of the sympathetic nervous system and less parasympathetic activity. KEYWORDS: variability, heart rate, athletes, football players, track and field athletes, autonomic nervous system, physical exertion