Sebastian Frey, Edoardo Beccari, Maximilian Kranz
et al.
Cost-per-click (CPC) in paid search is a volatile auction outcome generated by a competitive landscape that is only partially observable from any single advertiser's history. Using Google Ads auction logs from a concentrated car-rental market (2021--2023), we forecast weekly CPC for 1,811 keyword series and approximate latent competition through complementary signals derived from keyword text, CPC trajectories, and geographic market structure. We construct (i) semantic neighborhoods and a semantic keyword graph from pretrained transformer-based representations of keyword text, (ii) behavioral neighborhoods via Dynamic Time Warping (DTW) alignment of CPC trajectories, and (iii) geographic-intent covariates capturing localized demand and marketplace heterogeneity. We extensively evaluate these signals both as stand-alone covariates and as relational priors in spatiotemporal graph forecasters, benchmarking them against strong statistical, neural, and time-series foundation-model baselines. Across methods, competition-aware augmentation improves stability and error profiles at business-relevant medium and longer horizons, where competitive regimes shift and volatility is most consequential. The results show that broad market-outcome coverage, combined with keyword-derived semantic and geographic priors, provides a scalable way to approximate latent competition and improve CPC forecasting in auction-driven markets.
Giorgio Castellaro Galdames, Carla Orellana Mardones, Juan Pablo Escanilla Cruzat
et al.
With the purpose of understanding the trophic relationships between three herbivores that use humid high-mountain grassland and evaluating a possible interspecific competition between them and depending on the importance of the hydromorphic vegetation formations of high-mountain areas, relations were established between the attributes of these grasslands and the botanical composition of the diet of grey-breasted seedsnipe (<i>Thinocorus orbignyianus</i>), brown hares (<i>Lepus europaeus</i>), and horses (<i>Equus ferus caballus</i>). For two summer seasons, the botanical composition of the grassland and dry matter availability were assessed. In parallel, the botanical composition of the diets of the three herbivores was estimated through fecal microhistology. Based on the botanical composition data for both the grasslands and herbivores’ diets, their relative diversity was estimated. The Pianka index was established among the three herbivores. Hares showed greater dietary diversity (J) than horses and grey-breasted seedsnipes, factors that were negatively correlated in all three cases with the vegetation diversity patch. The same response amplitude was found when analyzing the food web. The dietary diversity for all species showed no relation to the dry matter productivity of the vegetable patches. Through analyzing the correlation of the abundance of two species of Cyperaceae in the grassland with the presence of the same in the diet of herbivores, we found a negative relationship between the abundance of <i>Carex</i> sp. and grey-breasted seedsnipe diet, and a positive relationship between the <i>Eleocharis pseudoalbibracteata</i> species abundance and frequency in the diet of hares and horses. About the group of species content of graminoids in the diet, a dietary overlap of 30% was determined in the animal species assessed; depending on that, it could identify the existence of interspecific competition between herbivores, which would be conditioned by the response of individuals to the environment. However, and according to the magnitude of the dietary overlap, a low probability of interspecific trophic competition among the studied herbivore species can be expected, which enables the use of the highland wet grassland habitat in sympatry.
Badminton players normally prioritize technical and tactical training over physical conditioning before competition, presenting a challenge in enhancing physical fitness within a constrained timeframe. While evidence have indicated complex contrast training can enhance strength and power, it is still unclear whether the French Contrast Method Training (FCMT) can bring greater explosive power gains to young badminton players. This study evaluated the effects of French Contrast Method Training versus Complex Training on lower limb explosive strength and its endurance in elite adolescent male badminton players. In a single-blind randomized controlled trial, 20 athletes were allocated to either an FCMT group (n = 10) or a CT group (n = 10) and completed an 8-week intervention. Performance was assessed pre- and post-intervention using standardized tests, including countermovement jump (CMJ), squat jump (SJ), drop jump (DJ), 10-m sprint, 5-0-5 agility test, eccentric utilization ratio (EUR), one-repetition maximum (1RM) squat, and lower extremity explosive endurance (LEEE). Subjective fatigue was monitored using the rating of perceived exertion (RPE) scale. Results demonstrated that while both training modalities improved explosive strength, the French Contrast Method Training led to significantly greater improvements in CMJ, DJ, Reaction Strength Index (RSI), EUR, agility, and LEEE (p < 0.05). The French Contrast Method Training also induced a larger effect size across most performance indicators. In contrast, the Complex Training improved strength and speed-related measures but showed limited effects on stretch-contraction cycle (SSC) utilization and endurance. Perceived fatigue was consistently higher in the CT group than in the FCMT group. These findings suggest that adaptation occurred more rapidly in the CT group (after roughly 3 weeks), whereas the FCMT group showed a more prolonged adaptation period (~1 month). The findings suggest that the French Contrast Method Training is more effective than the Complex Training in developing reactive strength, agility, SSC efficiency, and explosive endurance in youth badminton athletes within a short-term training period. Given its neuromechanical advantages across the force-velocity spectrum, the French Contrast Method Training is recommended for use in the pre-competition phase of training, while Complex Training may be more suitable for foundational strength development. These results provide practical insights for coaches and support the strategic integration of French Contrast Method Training into performance enhancement programs for adolescent athletes.
Molecular communication (MC), a biologically inspired technology, enables applications in nanonetworks and the Internet of Everything (IoE), with great potential for intra-body systems such as drug delivery, health monitoring, and disease detection. This paper extends our prior work on the Flexure-FET MC receiver by integrating a competitive binding model to enhance performance in high-interference environments, where multiple molecular species coexist in the reception space. Previous studies have largely focused on ligand concentration estimation and detection, without fully addressing the effects of inter-species competition for receptor binding. Our proposed framework captures this competition, offering a more biologically accurate model for multitarget environments. By incorporating competition dynamics, the model improves understanding of MC behavior under interference. This approach enables fine-tuning of receptor responses by adjusting ligand concentrations and receptor affinities, thereby optimizing the performance of the Flexure-FET MC receiver. Comprehensive analysis shows that accounting for competitive binding is crucial for improving reliability and accuracy in complex MC systems. Factors such as signal-to-noise ratio (SNR), symbol error probability (SEP), interferer concentration, and receptor dynamics are shown to significantly affect performance. The proposed framework highlights the need to manage these factors effectively. Results demonstrate that modeling interference through competitive binding offers a realistic system perspective and allows tuning of receiver response, enabling robust detection in environments with multiple coexisting species.
In recent years, image semantic segmentation technology has developed rapidly, but image annotation usually requires a significant amount of human and financial resources, especially for remote sensing image annotation, which can be expensive and sometimes even unaffordable. To address this issue, this paper integrates the idea of curriculum learning into the self-training method and screens reliable pseudo-labels through computing image-level confidence, significantly reducing the confirmation error problem. Furthermore, the semi-supervised model in this paper combines implicit semantic enhancement with strong data augmentation, which can reduce the coupling between the teacher model and the student model’s prediction distribution and enhance the model’s robustness. Finally, the proposed semi-supervised method is experimentally verified using the ISPRS competition dataset and compared with existing state-of-the-art (SOTA) methods. Experimental results show that the proposed semi-supervised segmentation method achieves higher segmentation accuracy compared to self-training methods. Moreover, despite not using iterative training to simplify the training process, the proposed method still yields satisfactory segmentation results.
The coastal wetlands in the Yellow River Delta play a vital role in the ecological function of the area. However, the impact of primary restoration on microbial communities is not yet fully understood. Hence, this study aimed to analyze the bacterial and archaeal communities in the soil. The results indicated that <i>Marinobacter</i> and <i>Halomonas</i> were predominant in the bacterial community during spring and winter. On the other hand, <i>Muribaculaceae</i> and <i>Helicobacter</i> were prevalent during the core remediation of soil, while <i>Inhella</i> and <i>Halanaerobium</i> were predominant in non-vegetation-covered high-salinity soil. The bacterial Shannon index showed significant differences in vegetation-covered areas. For archaea, <i>Salinigranum</i>, <i>Halorubrum</i>, and <i>Halogranum</i> were dominant in vegetation areas, while <i>Halolamina</i>, <i>Halogranum</i>, and <i>Halorubrum</i> were prevalent in non-vegetation areas. The colonization of <i>Suaeda salsa</i> led to differences in the composition of bacteria (22.6%) and archaea (29.5%), and salt was one of the significant reasons for this difference. The microflora was more diverse, and the elements circulated after vegetation grounding, while the microbial composition in non-vegetation areas was similar, but there was potential competition. Therefore, vegetation restoration can effectively restore soil ecological function, while the microorganisms in the soil before restoration provide germplasm resources for pollutant degradation and antimicrobial development.
Mubarik Salifu, James Atta Peprah, Joshua Sebu
et al.
Purpose: This study investigated the relationship between openness policies (trade and finance) and the degree of financial development in Ghana.Design/methodology/approach: The data for this study were extracted from the World Bank's World Development Indicators from 1960 to 2020. The data consists of annual time series data for Ghana. This study employs the ARDL bounds testing approach for cointegration to estimate the short- and long-run effects of openness policies on financial development in Ghana. In addition, a Markov Switching Model is used as a robustness check to estimate the effect of openness policies and financial development and to handle issues of structural breaks in the dataset.Findings: The study finds that openness policies have a substantial impact on financial development in Ghana and supports the McKinnon–Saw hypothesis. Specifically, financial and trade openness have positive effects on financial development in the long run. However, in the short run, trade openness is the only significant determinant of financial development. The results from the Markov model also indicate that the effect of openness policies on financial development is positive in high and low regimes of financial development, even though foreign investment does not affect financial development in less volatile environments.Originality/value: This study examines the relationship between openness policies (trade and finance) and the degree of financial development in Ghana.Research implications: This study suggests that government policies should focus on enhancing trade agreements to increase trade volumes and promote financial development in the long run. Additionally, reducing restrictions on capital movement across borders could encourage capital inflows and competition among financial institutions, leading to greater efficiency and development of the financial sector. Moreover, policies that promote financial openness can promote long-term financial development in the long run. Therefore, policymakers should work towards liberalizing capital accounts and promoting foreign investment. However, policymakers must be cautious about the potential short-term effects of such policies and work towards mitigating any negative impacts.
As industrial informatization progresses, virtual simulation technologies are increasingly demonstrating their potential in industrial applications. These systems utilize various sensors to capture real-time factory data, which are then transmitted to servers via communication interfaces to construct corresponding digital models. This integration facilitates tasks such as monitoring and prediction, enabling more accurate and convenient production scheduling and forecasting. This is particularly significant for flexible or mixed-flow production modes. Bionic optimization algorithms have demonstrated strong performance in factory scheduling and operations. Centered around these algorithms, researchers have explored various strategies to enhance efficiency and optimize processes within manufacturing environments.This study introduces an efficient migratory bird optimization algorithm designed to address production scheduling challenges in an assembly shop with mold quantity constraints. The research aims to minimize the maximum completion time in a batch flow mixed assembly flow shop scheduling problem, incorporating variable batch partitioning strategies. A tailored virtual simulation framework supports this objective. The algorithm employs a two-stage encoding mechanism for batch partitioning and sequencing, adapted to the unique constraints of each production stage. To enhance the search performance of the neighborhood structure, the study identifies and analyzes optimization strategies for batch partitioning and sequencing, and incorporates an adaptive neighborhood structure adjustment strategy. A competition mechanism is also designed to enhance the algorithm’s optimization efficiency. Simulation experiments of varying scales demonstrate the effectiveness of the variable batch partitioning strategy, showing a 5–6% improvement over equal batch strategies. Results across different scales and parameters confirm the robustness of the algorithm.
The competition-common enemy graph (CCE graph) of a digraph $D$ is the graph with the vertex set $V(D)$ and an edge $uv$ if and only if $u$ and $v$ have a common predator and a common prey in $D$. If each vertex of a digraph $D$ has indegree at most $i$ and outdegree at most $j$, then $D$ is called an $\langle i,j \rangle$ digraph. In this paper, we fully characterize the CCE graphs of $\langle 2,2\rangle$ digraphs. Then we investigate the CCE graphs of acyclic $\langle 2,2 \rangle$ digraphs, and prove that any CCE graph of an acyclic $\langle 2,2 \rangle$ digraph with at most seven components is interval, and the bound is sharp. While characterizing acyclic $\langle 2,2 \rangle$ digraphs that have interval graphs as their competition graphs, Hefner~{\it et al}. (1991) initiated the study of competition graphs of degree-bounded digraphs. Recently, Lee~{\em et al}. (2017) and Eoh and Kim (2021) studied phylogeny graphs of degree-bounded digraphs to extend their work.
Petar Veličković, Alex Vitvitskyi, Larisa Markeeva
et al.
Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady progress has been made, the highest percentiles still remain out of reach for these methods on standard competition platforms such as Codeforces. Here we instead focus on combinatorial competitive programming, where the target is to find as-good-as-possible solutions to otherwise computationally intractable problems, over specific given inputs. We hypothesise that this scenario offers a unique testbed for human-AI synergy, as human programmers can write a backbone of a heuristic solution, after which AI can be used to optimise the scoring function used by the heuristic. We deploy our approach on previous iterations of Hash Code, a global team programming competition inspired by NP-hard software engineering problems at Google, and we leverage FunSearch to evolve our scoring functions. Our evolved solutions significantly improve the attained scores from their baseline, successfully breaking into the top percentile on all previous Hash Code online qualification rounds, and outperforming the top human teams on several. Our method is also performant on an optimisation problem that featured in a recent held-out AtCoder contest.
The competition focuses on Multiparty Multiobjective Optimization Problems (MPMOPs), where multiple decision makers have conflicting objectives, as seen in applications like UAV path planning. Despite their importance, MPMOPs remain understudied in comparison to conventional multiobjective optimization. The competition aims to address this gap by encouraging researchers to explore tailored modeling approaches. The test suite comprises two parts: problems with common Pareto optimal solutions and Biparty Multiobjective UAV Path Planning (BPMO-UAVPP) problems with unknown solutions. Optimization algorithms for the first part are evaluated using Multiparty Inverted Generational Distance (MPIGD), and the second part is evaluated using Multiparty Hypervolume (MPHV) metrics. The average algorithm ranking across all problems serves as a performance benchmark.
The article aims to highlight the particularities of photomontage as a representation of the architectural project, using photography as a supporting image. It develops an image analysis of a photomontage produced by the architects Lacaton & Vassal in the context of the competition for the construction of the FRAC Grand-Large, in Dunkirk, France, 2009. Through this examination, the following issues are addressed: the indiciality of the photographs used as a support for the photomontages, the way in which the image registers deployed by a photomontage can be presented as embodiments of an architectural project. Thus, the images become both representations and metaphors of the project, capable of serving the architects’ purpose.