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Infertility is a growing concern in today’s technologically driven and mechanized world, with male related factors contributing to nearly half of all cases yet often remaining under diagnosed due to societal misconceptions and stigma. Prolonged sedentary behaviour, environmental exposures, and psychosocial stress further exacerbate reproductive health disorders. This study presents a hybrid diagnostic framework that combines a multilayer feedforward neural network with a nature-inspired ant colony optimization algorithm, integrating adaptive parameter tuning through ant foraging behaviour to enhance predictive accuracy and overcome the limitations of conventional gradient based methods. Unlike conventional fertility diagnostic approaches, this hybrid strategy demonstrates improved reliability, generalizability and efficiency. The model was evaluated on a publicly available dataset of 100 clinically profiled male fertility cases representing diverse lifestyle and environmental risk factors, with performance assessed on unseen samples. Remarkably, it achieved 99% classification accuracy, 100% sensitivity, and an ultra-low computational time of just 0.00006 seconds, highlighting its efficiency and real-time applicability. Clinical interpretability is achieved via feature-importance analysis, emphasizing key contributory factors such as sedentary habits and environmental exposures, thereby enabling healthcare professionals to readily understand and act upon the predictions. This cost effective, time efficient system has the potential to reduce diagnostic burden, enable early detection, and support personalized treatment planning, illustrating the effective synergy between machine learning and bio-inspired optimization in advancing male reproductive health diagnostics.

Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection. Without an index the total cost of NNG is quadratic. Using an index the cost would be sub-quadratic if the search for individual items is sublinear. Unfortunately, due to the so called curse of dimensionality the indexed and the brute force methods are almost equally inefficient. In this paper we present an efficient algorithm to build the Near Neighbor Graph (nNG), that is an approximation of NNG, using only the index construction, without actually searching for objects.

This paper presents an efficient online approach for complete coverage path planning of mobile robots in an unknown workspace based on online boustrophedon motion and an optimized backtracking mechanism. The presented approach first performs a single continuous boustrophedon motion until a critical point is reached. In order to completely cover the environment, next starting point is decided by using the accumulated knowledge of the environment map. An efficient backtracking technique based on proposed Two-way Proximity Search algorithm is used to plan a path from the critical point to the new starting point. Simulation results show the efficiency of proposed backtracking approach with improved total coverage time, coverage path length and memory requirements.

For a set of $n$ points in $\\mathbb{R} delta $, and parameters $k$ and $\\varepsilon$, we present a data structure that answers $(1+\\varepsilon,k)$ approximate nearest neighbor queries in logarithmic time. Surprisingly, the space used by the data structure is $\\widetilde{O}(n /k)$, where the $\\widetilde{O}(\\cdot)$ notation here hides terms that are exponential in $d$, roughly varying as $1/\\varepsilon delta $; as such, the space used is sublinear in the input size if $k$ is sufficiently large. Our approach provides a novel way to summarize geometric data, such that meaningful proximity queries on the data can be carried out using this sketch. Using this, we provide a sublinear space data structure that can estimate the density of a point set under various measures, including (i) sum of distances of $k$ closest points to the query point and (ii) sum of squared distances of $k$ closest points to the query point. Our approach generalizes to other distance-based estimations of densities of similar flavor. We also study the problem of approximating some of these quantities when using sampling. In particular, we show that a sample of size $\\widetilde{O} (n /k)$ is sufficient, in some restricted cases, to estimate the above quantities. Remarkably, the sample size has only linear dependency on the dimension.

Finding proximity information is crucial for massive database search. Locality Sensitive Hashing (LSH) is a method for finding nearest neighbors of a query point in a high-dimensional space. It classifies high-dimensional data according to data similarity. However, the “curse of dimensionality” makes LSH insufficiently effective in finding similar data and insufficiently efficient in terms of memory resources and search delays. The contribution of this work is threefold. First, we study a Token List based information Search scheme (TLS) as an alternative to LSH. TLS builds a token list table containing all the unique tokens from the database, and clusters data records having the same token together in one group. Querying is conducted in a small number of groups of relevant data records instead of searching the entire database. Second, in order to decrease the searching time of the token list, we further propose the Optimized Token list based Search schemes (OTS) based on index-tree and hash table structures. An index-tree structure orders the tokens in the token list and constructs an index table based on the tokens. Searching the token list starts from the entry of the token list supplied by the index table. A hash table structure assigns a hash ID to each token. A query token can be directly located in the token list according to its hash ID. Third, since a single-token based method leads to high overhead in the results refinement given a required similarity, we further investigate how a Multi-Token List Search scheme (MTLS) improves the performance of database proximity search. We conducted experiments on the LSH-based searching scheme, TLS, OTS, and MTLS using a massive customer data integration database. The comparison experimental results show that TLS is more efficient than an LSH-based searching scheme, and OTS improves the search efficiency of TLS. Further, MTLS per forms better than TLS when the number of tokens is appropriately chosen, and a two-token adjacent token list achieves the shortest query delay in our testing dataset.

When does a diffusing particle reach its target for the first time? This first-passage time (FPT) problem is central to the kinetics of molecular reactions in chemistry and molecular biology. Here, we explain the behavior of smooth FPT densities, for which all moments are finite, and demonstrate universal yet generally non-Poissonian long-time asymptotics for a broad variety of transport processes. While Poisson-like asymptotics arise generically in the presence of an effective repulsion in the immediate vicinity of the target, a time-scale separation between direct and reflected indirect trajectories gives rise to a universal proximity effect: Direct paths, heading more or less straight from the point of release to the target, become typical and focused, with a narrow spread of the corresponding first-passage times. Conversely, statistically dominant indirect paths exploring the entire system tend to be massively dissimilar. The initial distance to the target particularly impacts gene regulatory or competitive stochastic processes, for which few binding events often determine the regulatory outcome. The proximity effect is independent of details of the transport, highlighting the robust character of the FPT features uncovered here.

Usually, knowledge spillovers (KS) are related to geographic proximity. In the present study, we measure KS on the basis of different proximity matrices, focusing on the relational, social, cognitive and technological preconditions for knowledge diffusion. In the light of previous studies on KS, we examine: (i) which types of proximity enhance or hamper knowledge flows, and (ii) whether local absorptive capacity favour such flows. Our results indicate that KS across European NUTS2 regions measured through geographic, relational, social, cognitive and technological proximity channels increase with local absorptive capacity. This finding points towards the emergence of large clusters of regions (absorptive capacity clubs) where relational, cognitive, social and technological proximity lock-in maximizes the returns to local investment in R&D.

The importance of geographical proximity for interaction and knowledge sharing has been discussed extensively in recent years. There is increasing consensus that geographical proximity is just one out of many types of proximities that might be relevant. We argue that proximity may be a crucial driver for agents to connect and exchange knowledge, but too much proximity between agents on any of the dimensions might harm their innovative performance at the same time. In a study on knowledge networks in the Dutch aviation industry, we test this so-called proximity paradox empirically. We found evidence that the proximity paradox holds to a considerable degree. Our study clearly showed that cognitive, social, organizational and geographical proximity were crucial for explaining the knowledge network of the Dutch aviation industry. However, we found strong evidence that too much cognitive proximity lowered firms’ innovative performance, and organizational proximity did not have an effect.

We present evidence that cultural proximity (shared codes, beliefs, ethnicity) between lenders and borrowers increases the quantity of credit and reduces default. We identify in-group lending using dyadic data on religion and caste for officers and borrowers from an Indian bank, and a rotation policy that induces exogenous matching between them. Having an in-group officer increases credit access and loan size dispersion, reduces collateral requirements, and induces better repayment even after the in-group officer leaves. We consider a range of explanations and suggest that the findings are most easily explained by cultural proximity serving to mitigate information frictions in lending.

Our theory extends the situational considerations explaining firm R&D search intensity beyond the behavioral theory of the firm by including shifts in the focus of attention among bankruptcy, aspirations, and slack. We also allow that search can reflect institutionalized investment patterns within firms and industries. We find stable firm-specific R&D investment patterns (i.e., institutionalized search) and variations in R&D intensity depending on firms' situations--including performance relative to aspirations, proximity to bankruptcy, and slack. Our empirical results evidence shifts in the focus of attention relevant to explaining R&D search intensity for subsamples of firms in different situations.