Search Effort Research Articles

Symmetry Detection and Breaking in Linear Cost-Optimal Numeric Planning

One of the main challenges of domain-independent numeric planning is the complexity of the search problem. The exploitation of structural symmetries in a search problem can constitute an effective method of pruning search branches that may lead to exponential improvements in performance. For over a decade, symmetry breaking techniques have been successfully used within both optimal and satisficing classical planning. In this work, we show that symmetry detection methods applied in classical planning with some effort can be modified to detect symmetries in linear numeric planning. The detected symmetry group, thereafter, can be used almost directly in the A*-based symmetry breaking algorithms such as DKS and Orbit Space Search. We empirically validate that symmetry pruning can yield a substantial reduction in the search effort, even if algorithms are equipped with a strong heuristic, such as LM-cut.

Considerations on effort, precision and accuracy for long-term monitoring of African lions (Panthera leo), when using Bayesian spatial explicit capture-recapture models, in fenced protected areas.

Intensive management is frequently required in fenced wildlife areas to reduce deleterious effects of isolation. Decisions on how best to manage such wildlife are ideally informed by regular and reliable estimates of spatiotemporal fluctuations in population size and structure. However, even in small, fenced areas, it is difficult and costly to regularly monitor key species using advanced methods. This is particularly the case for large carnivores, which typically occur at low density and are elusive yet are central to management decision-making due to their top-down effects in ecosystems and attracting tourism. In this study, we aimed to provide robust estimates of population parameters for African lions (Panthera leo) and use the data to inform a resource-efficient long-term monitoring programme. To achieve this, we used unstructured spatial sampling to collect data on lions in Pilanesberg National Park, a small (~550 km2) fenced protected area in South Africa. We used Bayesian spatial capture-recapture models to estimate density, abundance, sex ratio and home range size of lions over the age of 1 year. Finally, to provide guidance on resource requirements for regular monitoring, we rarefied our empirical data set incrementally and analysed the subsets. Lion density was estimated to be 8.8 per 100 km2 (posterior SD = 0.6), which was lower than anticipated by park management. Sex ratio was estimated close to parity (0.9♀:1♂), consistent with emerging evidence in fenced lion populations, yet discordant with unfenced populations, which are usually ~2♀:1♂ in healthy, source populations. Our rarefied data suggest that a minimum of 4000 km search effort needs to be invested in future monitoring to obtain accurate and precise estimates, while assuming similar detection rates. This study demonstrates an important utility of Bayesian spatial explicit capture-recapture methods for obtaining robust estimates of lion densities and other important parameters in fence-protected areas to inform decision-making.

Social search: Retrieving information in Online Social platforms – A survey

Social Search research studies methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users’ preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users’ social profiles and behaviours (as they can be inferred from data available on social platforms) to optimize their information needs further.

Cost Splitting for Multi-Objective Conflict-Based Search

The Multi-Objective Multi-Agent Path Finding (MO-MAPF) problem is the problem of finding the Pareto-optimal frontier of collision-free paths for a team of agents while minimizing multiple cost metrics. Examples of such cost metrics include arrival times, travel distances, and energy consumption. In this paper, we focus on the Multi-Objective Conflict-Based Search (MO-CBS) algorithm, a state-of-the-art MO-MAPF algorithm. We show that the standard splitting strategy used by MO-CBS can lead to duplicate search nodes and hence can duplicate the search effort of MO-CBS. To address this issue, we propose two new splitting strategies for MO-CBS, namely cost splitting and disjoint cost splitting. Our theoretical results show that, when using either splitting strategy, MO-CBS maintains its completeness and optimality guarantees. Our experimental results show that disjoint cost splitting, our best splitting strategy, speeds up MO-CBS by up to two orders of magnitude and substantially improves its success rates in various settings.

Finding Missing Person Using AI

Abstract: The paper focuses on leveraging the power of Artificial Intelligence (AI) algorithms, specifically Convolutional Neural Networks (CNN) for image classification, along with the Python programming language and the Telegram messaging platform for real-time notifications. The objective is to enhance the efficiency and effectiveness of search and rescue operations by utilizing AI techniques to detect missing persons using surveillance cameras. This research aims to harness the potential of AI, particularly in facial recognition, to improve the accuracy and speed of identifying and locating missing individuals, thereby aiding in search and rescue efforts..

Preventive Measures in Problematic Gaming in Asia: A Systematic Literature Review

The rise of problematic gaming (PG) prevalence among the youth is a growing concern globally. PG is a symptom of Gaming Disorder (GD) that has been classified as a mental disorder by the World Health Organization (WHO) in the International Classification of Diseases 11th Revision (ICD-11). Despite the use of Public Service Announcements (PSAs) as a preventive measure in many countries, their effectiveness in increasing the awareness of PG in Asia, particularly in Malaysia is still questionable. The role of PSAs in preventing PG is a topic that deserves closer examination, open to debate and requires a more critical evaluation. The objective of this study is to identify diverse preventive measures employed to prevent PG in Asia, with a specific focus on determining whether PSA has been used or suggested based on empirical support to enhance awareness among Asian youth. A systematic review of the literature between 2015 and 2022 was performed with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, utilizing Scopus, Web of Science, and Google Scholar as the main sources for the search efforts. We systematically analysed 22 PG and GD prevention articles in Asia between 2015 and 2022, which yielded five main themes which were parent and family prevention, clinical practice, self-control, policy and technology restrictions, and school initiatives. Recommendations for future research in this field have been provided to enhance further understanding and effectiveness of PG and GD prevention efforts in Asia. Keywords: Public service announcement, problematic gaming, prevention, youth, systematic review.

Potential Analysis of Bitcoin Cryptocurrency as a Future Investment Asset: A Systematic Literature Review

Despite this promising potential, it is important to remember that investing in Bitcoin also involves risk. The price of Bitcoin is very volatile, and its value can change dramatically in a short amount of time. In addition, regulations regarding cryptocurrencies are still unclear in many countries, and regulatory changes could affect the price and adoption of Bitcoin. Security is also an important factor, as Bitcoin can be vulnerable to cyberattacks and theft. This study aimed to conduct studies related to the potential of bitcoin cryptocurrency as an asset for future investment as well as the potential risks that exist in this asset. Literature search efforts are carried out by searching on search engines using certain keywords. Articles that match the inclusion criteria are reviewed in depth. Bitcoin's potential as a future investment alternative is highly dependent on factors such as value growth, institutional adoption, and developments in blockchain technology. Nonetheless, it is important to be aware of the risks associated with investing in cryptocurrencies and to carry out in-depth research and consulting before making any investment decisions.

Digital Transformation and Its Role in Improving Democracy: A Systematic Literature Review

In order to achieve a more inclusive and robust democracy, it is important to understand the role of digital transformation and how technology can be used as a tool to strengthen public participation, ensure transparency and increase government accountability. By looking at real examples and broader implications of digitalization in the context of democracy, existing opportunities and challenges can be identified, and appropriate policies can be formulated for a better democratic future. This review aimed to describe the digital transformation and its role in improving democracy. Literature search efforts are carried out by searching on search engines using certain keywords. Articles that match the inclusion criteria are reviewed in depth. Digitalization has played an important role in strengthening democracy in an unprecedented way. By expanding public participation, increasing transparency, and strengthening government accountability, digital technology has provided new opportunities for citizens to engage in political processes and effect social change. However, keep in mind that challenges and risks associated with digitization also exist, such as the spread of false information and data privacy.

Recruitment Policies, Job-Filling Rates, and Matching Efficiency

Abstract Recruitment intensity is important for the matching process in the labor market. Using unique linked survey–administrative data, we investigate the relationships between hiring and recruitment policies at the establishment level. Faster hiring goes along with higher search effort, lower hiring standards, and more generous wages. We develop a directed search model that links these patterns to the employment adjustments of heterogeneous firms. The model provides a novel structural decomposition of the matching function that we use to evaluate the relative importance of these recruitment policies at the aggregate level. The calibrated model shows that hiring standards play an important role in explaining differences in matching efficiency across labor markets defined as region/skill cross-products and for the impact of labor market policy, whereas search effort and wage policies play only a minor role.

AI & ML Powered Autonomous Drone System

The use of drone technology is growing in popularity and has many different industrial uses. Drones are used in agriculture for pesticide and fertilizer application, crop monitoring, and mapping. Drones can be used in the building sector to check job sites and track development. In the movie industry, drones are also utilized for aerial photography and in search and rescue efforts to reach inaccessible places. In general, drone technology has the potential to revolutionize a variety of industries and offer fresh approaches to problems that were previously challenging to solve. Our idea is to develop AI ML based autonomous drone system that can be deployed in many areas for multiple applications such as surveillance, warehouse management etc. The layout includes a PI-CAM V2 camera on a quadcopter using GPS and system powered by Robot Operating System, Open CV and other technologies that gives ability to drone to fly without need of pilot autonomously. Key Word: Multipurpose, Robot Operating System, Simulation, Surveillance, Tracking, Quadcopter, OpenCV, Machine Learning, Object Detection.

Changes in movement characteristics in response to private and social information acquisition of socially foraging fish

To overcome the cost of competition resulting from foraging socially, individuals may balance their use of private (i.e. acquired from personal sampling) and social (i.e. acquired by watching other individuals) information to adjust their foraging strategy accordingly. Reliability of private information about environmental characteristics, such as the spatial distribution of prey, is thus likely to affect individual movement and social interactions. We aimed to investigate how movement characteristics of foraging individuals changed as they acquired reliable information about the spatial occurrence of prey in a foraging context. We allowed guppies, Poecilia reticulata, to develop the reliability of their private knowledge about prey spatial occurrence by repeatedly testing shoals in a foraging task under three experimental distributions of prey: (1) aggregated prey forming three patches located in fixed locations, (2) scattered distribution of prey with random locations, or (3) no prey (used as a control). Using individual time series of spatial coordinates, we computed a suite of movement variables reflecting search effort, social proximity and locomotion characteristics during foraging, to examine changes occurring over repeated trials. Over time, individuals foraging on either scattered or aggregated prey travelled greater distances, showed an increasing distance to their closest neighbour and became slightly more stochastic in their acceleration profile, compared to control individuals. We found that behaviour changed as private information increased over time, with a clear behavioural shift and an increase of collective foraging efficiency occurring on the third testing day. Social proximity was the major predictor of foraging success in the absence of prior foraging information, while search effort became the most important predictor of foraging success as information increased. In conclusion, we showed that individuals' movement patterns changed as they acquired private information. Contrary to our predictions, the spatial distribution of prey did not affect any of the movement variables of interest.

Assembling Collaboration Space: Maps in Practice During Search and Rescue Efforts In Northern Norway

Rescue services in Norway are based on collaboration between private, public, and volunteer sectors. Digital mapping platforms serve as a support tool for those involved in a search and rescue (SAR) operation. However, they lack interoperability and pose practical challenges to the responders.This research is situated in New Materialism, which focuses on interactions between heterogenous material-semiotic actors. Therefore, I analyze digital maps as assemblages constituted through practices. I deconstruct the “black box” of maps: investigate how maps are assembled for SAR operations, zooming in on what these assemblages can offer for efficiency of collaboration during an effort, especially when (dis-)connecting different localities. By doing so, I trace the interactions contributing to or disrupting cross-organizational capacity for collaboration during a SAR effort. The database for this study is formed from 15 semi-structured interviews with 13 people who are related to volunteer services, police, JRCC, and map modeling in Northern Norway, and supplemented by informal conversations, observations, and complementary documents analysis.Findings reveal that digital maps can function as assembling or disassembling platforms for a coordinated rescue action while contributing to information sharing, decision making and situational awareness. This article demonstrates that maps are intrinsic to cross-organizational collaboration, and are interlinked with available infrastructure, training procedures, funding, regulations, and other socio-material aspects. Change in one of these nodes can have inadvertent consequences for the operational capacity of rescue services. Having mapped-out constellations can help trace how they are affected when implementing a change in the use of maps during SAR operations.

Larval and adult experience and ecotype affect oviposition behavior in a niche-expanding butterfly

AbstractLocal adaptation, early-developmental and behavioral plasticity have all been suggested to be underlying mechanisms of behavioral variation. They allow organisms to cope with resource heterogeneity in time and space. However, the relative contribution of each of these drivers on niche-expansion success is still unclear. We addressed this issue by studying oviposition behavior in a butterfly (Pararge aegeria) that used to be confined to forest, but recently colonized anthropogenic areas too. We caught females of either forest or agricultural ecotype and allowed their larval offspring to experience early-habitat cues in either open field or woodland conditions. For 110 females of this transplant experiment, we monitored all oviposition-related behaviors (i.e., site selection, searching, motivation, learning) using repeated trials in outdoor flight cages with naturally occurring host grasses. We observed changes in oviposition site preference, with increasing preference for semi-shaded host grass (by vegetation or vertical structures) over consecutive egg-lays. Motivation to oviposit was most affected by larval experience (in interaction with cage environment). We also show evidence for ecotypic differentiation with the agricultural ecotype showing reduced search effort during the first trial compared to the forest ecotype. We argue that females of agricultural landscapes with fragmented resource distributions adopt a more risk-aversive oviposition strategy. Our detailed behavioral tracking under ecologically relevant conditions, stresses the importance of considering different types of behavioral plasticity to unravel a species’ success during niche expansion.

Modified dynamic opposite learning assisted TLBO for solving Time-Cost optimization in generalized construction projects

This study proposes an improved version of teaching learning-based optimization (TLBO) with a modified dynamic-opposition learning (MDOL) strategy, called modified dynamic oppositional learning TLBO (MDOLTLBO) to narrow the gap between research and practice for generalized time–cost optimization. Instead of opposite numbers, this study employs modified dynamic opposite points for the population initialization and generation jumping. This novel feature of the proposed algorithm is intended to diminish the randomness of the initial population and minimize the search effort required to achieve the optimal set of time–cost alternatives in the search space. An iterative-based varying weighting factor for MDOL is introduced to adjust the range of the search space with the aim of balancing the dynamicity and diversity of the algorithm. Hence, the MDOLTLBO algorithm can identify diversified and high-quality solutions based on non-dominated solutions. Four different case studies of real construction projects, ranging from 29 to 290 activities, were investigated to demonstrate the applicability of the proposed model. The experimental findings verify that the proposed algorithm is efficient, effective, and highly satisfying. Hence, the primary contribution of this study is MDOL approach that can successfully achieve high-quality solutions and good convergence speed for large-scale discrete time–cost optimization problems with generalized logical relationships.

Aspects of Axions and ALPs Phenomenology

The physics of axions and axion-like particles (ALPs) is enjoying an incredibly productive period, with many new experimental proposals, theoretical idea, and original astrophysical and cosmological arguments which help the search effort. The large number of experimental proposals is likely to lead to fundamental advances (perhaps, a discovery?) in the coming years. The aim of this article is to provide a very brief overview of some of the recent developments in axions and ALP phenomenology, and to discuss some relevant aspects in this important field. A particular attention is given to the definition of motivated regions in the axion parameters space, which should be the targets of experimental searches.

An Evolutionary Algorithm With Constraint Relaxation Strategy for Highly Constrained Multiobjective Optimization.

Highly constrained multiobjective optimization problems (HCMOPs) refer to constrained multiobjective optimization problems (CMOPs) with complex constraints and small feasible regions, which are commonly encountered in many real-world applications. Current constraint-handling techniques will face two difficulties when dealing with HCMOPs: 1) feasible solution is hard to be found and too much search effort is spent in locating the feasible region and 2) since the total feasible region of an HCMOP can consist of several disconnected subregions, the search process might be stuck in the comparatively larger feasible subregion, which does not contain the whole Pareto front (PF). To address these two issues, an evolutionary algorithm with constraint relaxation strategy based on differential evolution algorithm, that is, CRS-DE, is proposed in this article. In each generation, the CRS-DE relaxes the constraints by dividing the infeasible solutions into two subpopulations based on total constraint violation, that is, the ``semifeasible'' subpopulation (SF) and ``infeasible'' subpopulation (IF), respectively. The SF provides information on the promising regions of finding the feasible solution and is the driving force for convergence toward the PF, while the IF focuses on global exploration for new promising regions. Corresponding reproduction and selection strategies are devised for the SF, IF, and feasible subpopulations, which create a clear division of labor with cooperation to facilitate the search for feasible solutions. To leverage the influence of CRS and prevent the population from premature convergence, a mobility restriction mechanism is developed to restrict the individuals in the SF and IF from entering the feasible subpopulation and enhance the diversity of the whole population. Comprehensive experiments on a series of benchmark test problems and a real-world CMOP demonstrate the competitiveness of our method compared with other representative algorithms in terms of effectiveness and reliability in finding a set of well-distributed optimal solutions for HCMOPs.

Engineering for emergency response: Türkiye and Syria earthquakes

Sakthy Selvakumaran, Mark Scorer and Josh Macabuag of the charity Saraid provide their insights from recently being on the ground as part of the emergency search and rescue efforts after the 2023 earthquakes.

Military Support and Rescue Robot

Abstract: Military support and rescue robots are becoming increasingly important in modern warfare and disaster response efforts. These robots can perform tasks that are too dangerous or difficult for human soldiers or first responders. They can also gather and transmit crucial information in real-time to help commanders make informed decisions. This abstract will discuss the key features and capabilities of military support and rescue robots, as well as their potential applications. One of the primary functions of military support and rescue robots is to assist soldiers and first responders in dangerous situations. These robots can be used to search for and extract wounded soldiers, identify and disarm explosive devices, and provide cover fire for advancing troops. They can also be equipped with sensors and cameras to gather intelligence and provide real-time situational awareness to commanders. In disaster response scenarios, these robots can assist in search and rescue efforts, locate and extract survivors from collapsed buildings, and provide aid and medical assistance to those in need. They can also be used to survey damaged infrastructure and assess the extent of the damage. Military support and rescue robots are typically equipped with advanced sensors and communication systems to enable them to operate in a variety of environments. They can be designed to operate on land, sea, or air, and can be adapted to handle different terrains and weather conditions. Some robots are also capable of autonomous operation, allowing them to navigate and complete tasks without human intervention. Overall, military support and rescue robots have the potential to greatly enhance the effectiveness of soldiers and first responders in dangerous and challenging environments.

Dukungan Keluarga Pada Pasien Penyakit Jantung Koroner (PJK) dalam Pemanfaatan Informasi dan Pelayanan Kesehatan

The morbidity and mortality rate of Coronary Heart Disease (CHD) in Indonesia is increasing every year. The results of the 2019 Basic Health Research showed that 1.5% or 15 out of 1000 Indonesians suffer from CHD. The lack of health literacy of CHD patients and their families related to symptoms, risks, lifestyle, and treatment of CHD will further worsen the patient's condition. The purpose of this study was to analyze family support in coronary heart disease (CHD) patients in the use of information and health services. Using qualitative research methods with a phenomenological approach, data collection techniques through indepth interviews, observations and documentation. There were 8 research informants consisting of one doctor as a key informant, 2 supporting informants and 5 ordinary informants with criteria for a patient's condition that was stable, communicative and willing to be an informant. The results showed that the ability of patients to receive and manage information called health literacy in CHD patients and their families is still low because they lack the ability to receive and manage information from doctors and lack of independent information search efforts. The conclusion of this study is that access to information, receipt and management of information are very important to improve health literacy of CHD and their families.

A needle in a haystack? Catching Population III stars in the epoch of reionization: I. Population III star-forming environments

ABSTRACT Despite extensive search efforts, direct observations of the first [Population III (Pop III)] stars have not yet succeeded. Theoretical studies have suggested that late Pop III star formation is still possible in pristine clouds of high-mass galaxies, co-existing with Pop II stars, down to the epoch of reionization. Here, we re-assess this finding by exploring Pop III star formation in six 50 h−1 cMpc simulations performed with the hydrodynamical code dustyGadget. We find that Pop III star formation (∼10−3.4–10−3.2 M⊙ yr−1 cMpc−3) is still occurring down to z ∼ 6–8, i.e. well within the reach of deep JWST surveys. At these epochs, $\gtrsim\!{10}~{{\ \rm per\ cent}}$ of the rare massive galaxies with M⋆ ≳ 3 × 109 M⊙ are found to host Pop III stars, although with a Pop III/Pop II mass fraction $\lesssim\!0.1~{{\ \rm per\ cent}}$ . Regardless of their mass, Pop III-hosting galaxies are mainly found on the main sequence, at high star-formation rates, probably induced by accretion of pristine gas. This scenario is also supported by their increasing star-formation histories and their preferential location in high-density regions of the cosmic web. Pop III stars are found both in the outskirts of metal-enriched regions and in isolated, pristine clouds. In the latter case, their signal may be less contaminated by Pop IIs, although its detectability will strongly depend on the specific line of sight to the source, due to the complex morphology of the host galaxy and its highly inhomogeneous dust distribution.