Depth Search Research Articles

Performance shortfalls and innovative search: Evidence from the biopharmaceutical firms in China

ABSTRACT Performance feedback literature acknowledges that firms’ innovative behaviour in response to performance shortfalls. However, it overlooks the impact of multidimensional heterogeneity in innovative activities on the direction of search following performance shortfalls. Based on the framework of gain and loss, we propose that underperforming firms are inclined to minimise breadth search and are more likely to engage in depth search, influenced by varying perceptions of risk associated with different innovative search strategies. This hypothesis is tested using a panel dataset of 234-listed biopharmaceutical firms in China from 2008 to 2022. Our findings indicate a negative relationship between performance shortfalls and innovative search breadth, and a positive relationship between performance shortfalls and innovative search depth. Furthermore, we found that both knowledge diversity and knowledge distance negatively moderate the relationship between performance shortfalls and innovative search breadth. Conversely, knowledge distance positively moderates the relationship between performance shortfalls and innovative search depth. This study enriches the understanding of performance feedback mechanisms and their influence on innovative search strategies.

Event-based depth estimation with dense occlusion.

Occlusions pose a significant challenge to depth estimation in various fields, including automatic driving, remote sensing observation, and video surveillance. In this Letter, we propose a novel, to the best of our knowledge, depth estimation method for dense occlusion to estimate the depth behind occlusions. We design a comprehensive procedure using an event camera that consists of two steps: rough estimation and precise estimation. In the rough estimation, we reconstruct two segments of the event stream to remove occlusions and subsequently employ a binocular intersection measurement to estimate the rough depth. In the precise estimation, we propose a criterion that the maximum total length of edges of reconstructed images corresponds to the actual depth and search for the precise depth around the rough depth. The experimental results demonstrate that our method is implemented with relative errors of depth estimation below 1.05%.

Optimizing Enhanced Extended Topological Active Nets Model Using Parallel Processing

In numerous clinical applications that support the diagnosis and treatment planning of a broad variety of disorders, medical image segmentation is essential. Medical picture segmentation using the Enhanced Extended Topological Active Net (EETAN) model has proven to be successful in correctly identifying structures. This study suggests a novel way to combine the best clustering techniques and parallel processing approaches to maximize the segmentation performance of the EETAN model. The Probabilistic Depth Search Optimization (PDSO) Algorithm, which makes the parallel searching technique to find the ideal contour set, is responsible for this. This work implements parallel processing and ideal clustering to improve the EETAN model's performance in medical image segmentation. Performance metrics like accuracy, precision, recall, dice similarity, and computational time are used for a comparison study. The results demonstrate the notable enhancements attained by employing parallel processing and effective clustering.

Sensor placement algorithm for faults detection in electrical secondary distribution network using dynamic programming method: focusing on dynamic change and expansion of the network configurations

Modern power grids are developing toward smartness through the use of sensors in gathering data for situation awareness, visibility, and fault detection. In most developing countries, fault detection in the electrical secondary distribution network (SDN) is very challenging due to the lack of automated systems for network monitoring. Systems for monitoring faults require sensor placement on each node, which is not economically feasible. Hence, optimal placement algorithms are required to ensure that the network is observable with few sensors possible. The existing sensor placement methods based on mathematical and heuristic approaches are efficient for transmission and primary distribution networks which are mostly static in size and layout. Such methods may not be efficient in SDN which is dynamic in size and have a relatively large number of nodes. This study proposes an enhanced dynamic programming method for sensor placement to enhance fault detection in SDN. The proposed algorithm employs the depth search concepts and the parent–children relationship between nodes to determine sensor types and locations considering the optimal cost. The proposed algorithm was compared with other methods including particle swarm optimization, genetic algorithm, and chaotic crow search algorithm using different network configurations. The results revealed that the proposed algorithm suggested the minimum number of sensors and shortest convergence time of 1.27 min. The results also revealed that, on network expansion, maintaining the location of the existing sensors is more cost-effective by 20% than reallocating the existing sensors. Furthermore, the results revealed that an average of 30% of nodes, need sensors to observe the entire network, hence cost optimization.

Prospects for studying the paleo archive of lake sediment from the Sarskoe Bog

In the center of the East European Plain, lake sediments provide the most detailed and high-resolution paleoarchives of the Late Pleistocene and Holocene periods. However, not all lake sediments can be used to reliably reconstruct regional paleoclimate and landscape changes. Factors such as the shape of the lake basin, the type of rocks beneath it, the number and flow of tributaries, and the vegetation present all play a role in determining the rate and composition of sedimentation in lake deposits, creating a unique record of the local environmental conditions. The Sarskoe Bog Basin has promising potential for providing a high-resolution record of the Late Pleistocene period. The core sample collected from the central part of the swamp met all expectations, but during the geophysical study of the geological structure and search for the maximum depth of lake sediments, it was found that the reference well was not in the most appropriate location. The first geophysical surveys conducted in the bog have improved our understanding of the paleorelief of the basin and will allow us to choose a location for the main well that is suitable for our research objectives.

Adaptive educational game design incorporating the psychology of early childhood development: a study of enhancing learning experiences and promoting cognitive development

Abstract Grounded in the principles of developmental psychology, this study develops an innovative online educational game for young children titled “Fill in the Picture.” Utilizing the AppCan platform in conjunction with Photoshop technology, the game involves the automatic generation of randomly sampled, unordered point sets using network depth search algorithms. It captures the sequential order in which children connect these points onscreen to form coherent graphical representations, subsequently assessing the correctness of these connections against computer-judged standards. The effectiveness of “Fill in the Picture” was evaluated through a questionnaire survey, which revealed a weighting coefficient of 0.248 for the game’s educational quality. Additionally, it scored above 4 on various dimensions, including educational value, scientific basis, playfulness, artistic integration, immersion, and technological engagement. Importantly, in terms of pedagogical design satisfaction, 81.3% of respondents recognized that the game significantly enhances understanding and mastery of scientific concepts, while 81% appreciated the logical organization of its content. Thus, the “Fill in the Picture” game, designed and developed by integrating a tailored educational game design model based on developmental psychology, demonstrates substantial educational efficacy and user satisfaction.

A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly

ABSTRACT Most existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment.

Guided Direct Time-of-Flight Lidar Using Stereo Cameras for Enhanced Laser Power Efficiency

Self-driving vehicles demand efficient and reliable depth-sensing technologies. Lidar, with its capability for long-distance, high-precision measurement, is a crucial component in this pursuit. However, conventional mechanical scanning implementations suffer from reliability, cost, and frame rate limitations. Solid-state lidar solutions have emerged as a promising alternative, but the vast amount of photon data processed and stored using conventional direct time-of-flight (dToF) prevents long-distance sensing unless power-intensive partial histogram approaches are used. In this paper, we introduce a groundbreaking ‘guided’ dToF approach, harnessing external guidance from other onboard sensors to narrow down the depth search space for a power and data-efficient solution. This approach centers around a dToF sensor in which the exposed time window of independent pixels can be dynamically adjusted. We utilize a 64-by-32 macropixel dToF sensor and a pair of vision cameras to provide the guiding depth estimates. Our demonstrator captures a dynamic outdoor scene at 3 fps with distances up to 75 m. Compared to a conventional full histogram approach, on-chip data is reduced by over twenty times, while the total laser cycles in each frame are reduced by at least six times compared to any partial histogram approach. The capability of guided dToF to mitigate multipath reflections is also demonstrated. For self-driving vehicles where a wealth of sensor data is already available, guided dToF opens new possibilities for efficient solid-state lidar.

Analysis of the integration of traditional culture and Civic Education based on the full convolutional net model

Abstract A traditional cultural text detection model is constructed using a full convolutional net model and a feature fusion method in this paper. Bilinear interpolation calculates the aspect ratio between the original and target features maps. Combined with transpose convolution, the feature map is reduced to pixel space to extract traditional cultural features. A residual network is used to achieve constant mapping, which improves the network’s overall performance and increases its depth. The mapping coordinates of the input image are obtained by calculating anchor box positive samples using text box truth values. Combined with the depth search algorithm, all segments of the same text are detected, articulated, and combined. The results show that combining traditional culture and Civic Education is beneficial to improve students’ overall ability, with 65% improvement in grades and 55% improvement in ideology.

Cross-entropy boosted CRO-SL for optimal power flow in smart grids

Optimal power flow (OPF) is a complex, highly nonlinear, NP-hard optimization problem, in which the goal is to determine the optimal operational parameters of a power-related system (in many cases a type of smart or micro grid) which guarantee an economic and effective power dispatch. In recent years, a number of approaches based on metaheuristics algorithms have been proposed to solve OPF problems. In this paper, we propose the use of the Cross-Entropy (CE) method as a first step depth search operator to assist population-based evolutionary methods in the framework of an OPF problem. Specifically, a new variant of the Coral Reefs Optimization with Substrate Layers algorithm boosted with CE method (CE+CRO-SL) is presented in this work. We have adopted the IEEE 57-Bus System as a test scenario which, by default, has seven thermal generators for power production for the grid. We have modified this system by replacing three thermal generators with renewable source generators, in order to consider a smart grid approach with renewable energy production. The performance of CE+CRO-SL in this particular case study scenario has been compared with that of well-known techniques such as population’s methods CMA-ES and EPSO (both boosted with CE). The results obtained indicate that CE+CRO-SL showed a superior performance than the alternative techniques in terms of efficiency and accuracy. This is justified by its greater exploration capacity, since it has internally operations coming from different heuristics, thus surpassing the performance of classic methods. Moreover, in a projection analysis, the CE+CRO-SL provides a profit of millions of dollars per month in all cases tested considering the modified version of the IEEE 57-Bus smart grid system.

Modified Jaya algorithm with depth search and perturbation

Modified Jaya algorithm with depth search and perturbation

Modified Jaya algorithm with depth search and perturbation

Modified Jaya algorithm with depth search and perturbation

An Improved Chicken Swarm Optimization Algorithm for Solving Multimodal Optimization Problems.

To solve the premature convergence problem of the standard chicken swarm optimization (CSO) algorithm in dealing with multimodal optimization problems, an improved chicken swarm optimization (ICSO) algorithm is proposed by referring to the ideas of bacterial foraging algorithm (BFA) and particle swarm optimization (PSO) algorithm. First, in order to improve the depth search ability of the algorithm, considering that the chicks have the weakest optimization ability in the whole chicken swarm, the replication operation of BFA is introduced. In the role update process of the chicken swarm, the chicks are replaced by the same number of chickens with the strongest optimization ability. Then, to maintain the population diversity, the elimination-dispersal operation is introduced to disperse the chicks that have performed the replication operation to any position in the search space according to a certain probability. Finally, the PSO algorithm is integrated to improve the global optimization ability of the algorithm. The experimental results on the CEC2014 benchmark function test suite show that the proposed algorithm has good performance in most test functions, and its optimization accuracy and convergence performance are also better than BFA, artificial fish swarm algorithm (AFSA), genetic algorithm (GA), and PSO algorithm, etc. In addition, the ICSO is also utilized to solve the welded beam design problem, and the experimental results indicate that the proposed algorithm has obvious advantages over other comparison algorithms. Its disadvantage is that it is not suitable for dealing with large-scale optimization problems.

Uncertainty Guided Multi-View Stereo Network for Depth Estimation

Deep learning has greatly promoted the development of multi-view stereo in recent years. However, how to measure the reliability of the estimated depth map for practical applications and make reasonable depth hypothesis sampling for the cost volume building in the coarse-to-fine architecture are still unresolved crucial problems. To this end, an Uncertainty Guided multi-view Network (UGNet) is proposed in this paper. In order to enable the network to perceive the uncertainty, an uncertainty-aware loss function is introduced, which not only can infer uncertainty implicitly in an unsupervised manner but also can reduce the bad impact of high uncertainty regions and the erroneous labels in the training set during training. Moreover, an uncertainty-based depth hypothesis sampling strategy is further proposed to adaptively determine the depth search range of each pixel for finer stages, which helps to generate more rational depth intervals compared with other methods and build more compact cost volumes without redundancy. Experimental results on DTU dataset, BlendedMVS dataset, Tanks and Temples dataset and ETH3D high-res benchmark show that our method achieves promising reconstruction results compared with other state-of-the-art methods.

Optimization of a Rural Portfolio Credit Granting System Using Improved Two-Dimensional Strip Packing Grouping Delay Problem

Rural preferential loans usually take the form of portfolio credits. From the perspective of public interest, the total delay time for obtaining loans is expected to be minimized. To use rural portfolio credits effectively, the two-dimensional strip packing grouping delay problem (2SPGDP) is improved to optimize the rural portfolio credit granting system. First, 2SPGDP is established by adding grouping constraints and the latest start time constraints to the two-dimensional strip packing problem, and the total delay is taken as the optimization objective. Second, based on the depth search reverse spanning tree (DSRST) and the insert spare space (ISS) method, the branch-and-bound reverse order insert algorithm (BB-RIA) is designed. Finally, the lag pruning operator (LPO) is designed to reduce lag. The improved model (2SPGDP) and BB-RIA-LPO algorithm are used to solve several classical two-dimensional strip packing problems and a specific rural portfolio credit case. Compared with the Bottom-Left and Branch and Bound Algorithm, our model and algorithm improve the success rate by 25% and reduce the total delay by 6%. The case of rural portfolio credit illustrates the operability and effectiveness of this method.

A novel depth search based light weight CAR network for the segmentation of brain tumour from MR images

In this modern era, brain tumour is one of the dreadful diseases that occur due to the growth of abnormal cells or by the accumulation of dead cells in the brain. If these abnormalities are not detected in the early stages, they lead to severe conditions and may cause death to the patients. With the advancement of medical imaging, Magnetic Resonance Images (MRI) are developed to analyze the patients manually. However, this manual screening is prone to errors. To overcome this, a novel depth search-based network termed light weight channel attention and residual network (LWCAR-Net) is proposed by integrating with a novel depth search block (DSB) and a CAR module. The depth search block extracts the pertinent features by performing a series of convolution operations enabling the network to restore low-level information at every stage. On other hand, CAR module in decoding path refines the feature maps to increase the representation and generalization abilities of the network. This allows the network to locate the brain tumor pixels from MRI images more precisely. The performance of the depth search based LWCAR-Net is estimated by testing on different globally available datasets like BraTs 2020 and Kaggle LGG dataset. This method achieved a sensitivity of 95%, specificity of 99%, the accuracy of 99.97%, and dice coefficient of 95% respectively. Furthermore, the proposed model outperformed the existing state-of-the-art models like U-Net++, SegNet, etc by achieving an AUC of 98% in segmenting the brain tumour cells.

The Influence of External Knowledge Searches on Enterprises’ Innovation Performance: A Meta-Analysis

Innovation is one aspect of the pursuit of the harmonious development of the environment, economy, and society. Although prior research has examined the factors that influence corporate innovation, how external knowledge search affects it remains unclear. We contribute to this literature by examining how knowledge searches influence firms’ innovation performance from a knowledge-based point of view and organizational search theory. We utilized meta-analysis to conduct empirical testing on 15,059 samples from 58 independent studies. The conclusions are as follows: first, the overall test showed a significant positive correlation between external knowledge search and firm innovation performance; second, the group test shows that the positive impact of external knowledge search on the innovation performance of mature enterprises is more obvious; scaled measurement and collectivism have more significant influences on the relationship between external knowledge search and firm innovation performance. Further research concluded that a breadth search could provide higher innovation performance than a depth search. Meanwhile, the same results were obtained in the subgroup tests of start-ups, non-high-tech enterprises, high-tech enterprises, scale measurement, and collectivism. Finally, both balanced knowledge search and joint knowledge searches significantly improved the innovation performance of enterprises, and the innovation effect brought by balanced knowledge search is more prominent than the latter.

Bisphenol A (BPA) Leading to Obesity and Cardiovascular Complications: A Compilation of Current In Vivo Study.

BPA is one of the most common endocrine disruptors that is widely being manufactured daily nationwide. Although scientific evidence supports claims of negative effects of BPA on humans, there is also evidence suggesting that a low level of BPA is safe. However, numerous in vivo trials contraindicate with this claim and there is a high possibility of BPA exposure could lead to obesity. It has been speculated that this does not stop with the exposed subjects only, but may also cause transgenerational effects. Direct disruption of endocrine regulation, neuroimmune and signaling pathways, as well as gut microbiata, has been identified to be interrupted by BPA exposure, leading to overweight or obesity. In these instances, cardiovascular complications are one of the primary notable clinical signs. In regard to this claim, this review paper discusses the role of BPA on obesity in the perspective of endocrine disruptions and possible cardiovascular complications that may arise due to BPA. Thus, the aim of this review is to outline the changes in gut microbiota and neuroimmune or signaling mechanisms involved in obesity in relation to BPA. To identify potentially relevant articles, a depth search was done on the databases Nature, PubMed, Wiley Online Library, and Medline & Ovid from the past 5 years. According to Boolean operator guideline, selected keywords such as (1) BPA OR environmental chemical AND fat OR LDL OR obese AND transgenerational effects or phenocopy (2) Endocrine disruptors OR chemical AND lipodystrophy AND phenocopy (3) Lipid profile OR weight changes AND cardiovascular effect (4) BPA AND neuroimmune OR gene signaling, were used as search terms. Upon screening, 11 articles were finalized to be further reviewed and data extraction tables containing information on (1) the type of animal model (2) duration and dosage of BPA exposure (3) changes in the lipid profile or weight (4) genes, signaling mechanism, or any neuroimmune signal involved, and (5) transgenerational effects were created. In toto, the study indicates there are high chances of BPA exposure affecting lipid profile and gene associated with lipolysis, leading to obesity. Therefore, this scoping review recapitulates the possible effects of BPA that may lead to obesity with the evidence of current in vivo trials. The biomarkers, safety concerns, recommended dosage, and the impact of COVID-19 on BPA are also briefly described.

In depth search of the Sequence Read Archive database reveals global distribution of the emerging pathogenic fungus Scedosporium aurantiacum.

Scedosporium species are emerging opportunistic fungal pathogens causing various infections mainly in immunocompromised patients, but also in immunocompetent individuals, following traumatic injuries. Clinical manifestations range from local infections, such as subcutaneous mycetoma or bone and joint infections, to pulmonary colonization and severe disseminated diseases. They are commonly found in soil and other environmental sources. To date S. aurantiacum has been reported only from a handful of countries. To identify the worldwide distribution of this species we screened publicly available sequencing data from fungal metabarcoding studies in the Sequence Read Archive (SRA) of The National Centre for Biotechnology Information (NCBI) by multiple BLAST searches. S. aurantiacum was found in 26 countries and two islands, throughout every climatic region. This distribution is like that of other Scedosporium species. Several new environmental sources of S. aurantiacum including human and bovine milk, chicken and canine gut, freshwater, and feces of the giant white-tailed rat (Uromys caudimaculatus) were identified. This study demonstrated that raw sequence data stored in the SRA database can be repurposed using a big data analysis approach to answer biological questions of interest.Lay summaryTo understand the distribution and natural habitat of S. aurantiacum, species-specific DNA sequences were searched in the SRA database. Our large-scale data analysis illustrates that S. aurantiacum is more widely distributed than previously thought and new environmental sources were identified.

Positive Performance Feedback and Innovation Search: New Ideas for Sustainable Business Development

Although the literature suggests that firms tend to adopt “conservative” behavior in the face of positive performance feedback, there are also studies that take the opposite view on the impact of positive performance feedback. Based on the behavior theory of the firm and regulatory focus theory, this study explored the impact of positive performance feedback on innovation search behavior and the boundary effect of CEO regulatory focus to gain insight into the mechanisms of innovation search behavior and to promote innovation for sustainable development. Based on data from 230 biopharmaceutical companies in China, the analysis found that: (1) positive performance feedback had a significant positive effect on depth search behavior and a significant negative effect on breadth search behavior; (2) CEOs’ promotion focus had a negative moderating effect on the relationship between positive performance feedback and depth search behavior and a positive moderating effect on the relationship between positive performance feedback and breadth search behavior; and (3) CEOs’ prevention focus positively moderated the relationship between positive performance feedback in relation to depth search behavior. This study extends the behavior theory of the firm and reveals the mechanism of the differential impact of positive performance feedback on innovation search behavior, which has implications for the study of which innovation search practices should be conducted by high-performing firms to promote sustainable development.