Successful Search Research Articles

A Reactive Molecular Dynamics Study of Chlorinated Organic Compounds. Part II: A ChemTraYzer Study of Chlorinated Dibenzofuran Formation and Decomposition Processes.

In our two-paper series, we first present the development of ReaxFF CHOCl parameters using the recently published ParAMS parametrization tool. In this second part, we update the reactive Molecular Dynamics - Quantum Mechanics coupling scheme ChemTraYzer and combine it with our new ReaxFF parameters from Part I to study formation and decomposition processes of chlorinated dibenzofurans. We introduce a self-learning method for recovering failed transition-state searches that improves the overall ChemTraYzer transition-state search success rate by 10 percentage points to a total of 48 %. With ChemTraYzer, we automatically find and quantify more than 500 reactions using transition state theory and DFT. Among the discovered chlorinated dibenzofuran reactions are numerous reactions that are new to the literature. In three case studies, we discuss the set of reactions that are most relevant to the dibenzofuran literature: (i) bimolecular reactions of the chlorinated-dibenzofuran precursors phenoxy radical and 1,3,5-trichlorobenzene, (ii) dibenzofuran chlorination and pyrolysis, and (iii) oxidation of chlorinated dibenzofurans.

Ecological landscapes guide the assembly of optimal microbial communities.

Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish the conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia.

CIDO: Chaotically Initialized Dandelion Optimization for Global Optimization

Metaheuristic algorithms are widely used for problems in many fields such as security, health, engineering. No metaheuristic algorithm can achieve the optimum solution for all optimization problems. For this, new metaheuristic methods are constantly being proposed and existing ones are being developed. Dandelion Optimizer, one of the most recent metaheuristic algorithms, is biology-based. Inspired by the wind-dependent long-distance flight of the ripening seed of the dandelion plant. It consists of three phases: ascending phase, descending phase and landing phase. In this study, the chaos-based version of Chaotically Initialized Dandelion Optimizer is proposed for the first time in order to prevent Dandelion Optimizer from getting stuck in local solutions and to increase its success in global search. In this way, it is aimed to increase global convergence and to prevent sticking to a local solution. While creating the initial population of the algorithm, six different Chaotically Initialized Dandelion Optimizer algorithms were presented using the Circle, Singer, Chebyshev, Gauss/Mouse, Iterative and Logistic chaotic maps. Two unimodal (Sphere and Schwefel 2.22), two multimodal (Schwefel and Rastrigin) and two fixed-dimension multimodal (Foxholes and Kowalik) quality test functions were used to compare the performances of the algorithms. When the experimental results were analyzed, it was seen that the Chaotically Initialized Dandelion Optimizer algorithms gave successful results compared to the classical Dandelion Optimizer.

Searching Efficient Model-guided Deep Network for Image Denoising.

Unlike the success of neural architecture search (NAS) in high-level vision tasks, it remains challenging to find computationally efficient and memory-efficient solutions to low-level vision problems such as image restoration through NAS. One of the fundamental barriers to differential NAS-based image restoration is the optimization gap between the super-network and the sub-architectures, causing instability during the searching process. In this paper, we present a novel approach to fill this gap in image denoising application by connecting model-guided design (MoD) with NAS (MoD-NAS). Specifically, we propose to construct a new search space under a model-guided framework and develop more stable and efficient differential search strategies. MoD-NAS employs a highly reusable width search strategy and a densely connected search block to automatically select the operations of each layer as well as network width and depth via gradient descent. During the search process, the proposed MoD-NAS remains stable because of the smoother search space designed under the model-guided framework. Experimental results on several popular datasets show that our MoD-NAS method has achieved at least comparable even better PSNR performance than current state-of-the-art methods with fewer parameters, fewer flops, and less testing time.

A Novel Extreme Value Theory Based approach to Hyperparameter Optimization

Finding the optimal values of hyperparameters is crucial for many machine learning algorithms. Algorithms like deep learning, XGBoost, etc. require us to choose among a large set of hyperparameters, thus increasing the computational requirements for a successful hyperparameter search. Another factor complicating the hyperparameter search process is the variability in performance due to choice of different initial random seeds. This variability might be caused by different initial weight assignments or by different environment interactions in the training process. In this paper an extreme value theory (EVT) based approach for hyperparameter optimization in this noisy setting is proposed. To the best of our knowledge this is the first work based on EVT for hyperparameter optimization. It is demonstrated through extensive experimentation on many real world datasets (including a medical diabetes dataset consisting of several medical predictor variables), that by using only a fraction of observations near optimal hyperparameter values can be obtained using the parameters of a generalized extreme value distribution for the test set performance metric of interest in the algorithm.

The Body Recovery From Water Study: The application of science to missing person search

Abstract Missing person behaviour datasets indicate that a significant proportion of those reported missing are subsequently found deceased in water. Despite this, information about how human bodies move in an aquatic environment is limited. Created by a serving police officer and police search advisor, this study involved the collection and analysis of quantitative and qualitative data relating to deceased human bodies found in inland water. The analysis of this data was further enhanced by experimentation. The research aimed to identify factors that might affect body movement in water and to establish if these variables could determine search parameters more effectively and increase the chances of successful search operations and associated investigations. In this manuscript, the search and investigation approach is introduced and the key findings and implications of the application of natural sciences for police practice are discussed.

On applying the lackadaisical quantum walk algorithm to search for multiple solutions on grids

Quantum computing promises to improve the information processing power to levels unreachable by classical computation. Quantum walks are heading the development of quantum algorithms for searching information on graphs more efficiently than their classical counterparts. A quantum-walk-based algorithm standing out in the literature is the lackadaisical quantum walk. The lackadaisical quantum walk is an algorithm developed to search graph structures whose vertices have a self-loop of weight l. This paper addresses several issues related to applying the lackadaisical quantum walk to search for multiple solutions on grids successfully. Firstly, we show that only one of the two stopping conditions found in the literature is suitable for simulations. In the most discrepant case shown here, a stopping condition is prematurely satisfied at the step T=288 with a success probability Pr=0.593276, while the suitable condition captures the actual amplification that occurred until T=409 with Pr=0.878178. We also demonstrate that the final success probability depends on both the space density of solutions and the relative distance between solutions. For instance, we show here that decreases in the density of solutions can even take a success probability of 0.849178 to 0.961896. In contrast, increases in the relative distances can even take a success probability of 0.871665 to 0.940301. Furthermore, this work generalizes the lackadaisical quantum walk to search for multiple solutions on grids of arbitrary dimensions. In addition, we propose an optimal adjustment of the self-loop weight l for such d-dimensional grids. It turns out other fits of l found in the literature are particular cases. Our experiments demonstrate that successful searches for multiple solutions with higher than two dimensions are possible by achieving success probabilities such as 0.999979, with the value of l proposed here, where it would be 0.637346, with the value of l proposed in previous works. Finally, we observe a two-to-one relation between the steps of the lackadaisical quantum walk and Grover’s algorithm, which requires modifications in the stopping condition. That modified stopping condition can escape intermediary fluctuations that would produce premature stops at T=6 with Pr=0.000878 where the system can evolve until T=354 with Pr=0.99999, as an example that we show here. In conclusion, this work deals with practical issues one should consider when applying the lackadaisical quantum walk, besides expanding the technique to a broader range of search problems.

La infodiversidad, el derecho a la información y el uso ético de la información

This article explores the social and technological relationships that stimulate the creation and use of information that, in turn, reflect the diversity, plurality and multiculturality of the planet. However, we cannot promote one benefit by affecting another. In this sense, we must defend infodiversity from our specialty, but without violating or hindering other rights, such as the right to information, or other laws and agreements that intervene in this process. It also presents the interrelationships between school, family and library that intervene in the formation of our users in order to achieve a successful search for information through critical thinking. In this way, it is necessary to promote an ethical and social conduct in the user, in the librarian, in the author and in every person involved in the edition of information. This work emphasizes the utmost respect that should be granted to the interests and creative works of others. Therefore, each of the actors involved in the creation and use of information must comply with codes of ethics that emphasize objectivity and impartiality.

Does psychological detachment benefit job seekers? A two study weekly investigation.

On a weekly basis, job seekers need to exert effort to successfully navigate their search. Yet, despite the notion that job seeking is likely depleting, there has been little research and discussion to date surrounding whether taking time to recover from job seeking can be restorative and helpful for job seekers. Applying theory from the effort-recovery model (Meijman & Mulder, 1998) and the stressor-detachment model (Sonnentag & Fritz, 2015) to the job search context, we highlight the self-regulatory and job search-related benefits associated with psychologically detaching from the job search each week. Specifically, we theorize that weekly psychological detachment from the job search (at t) helps job seekers feel recovered (at t) and, in turn, more vigorous (at t + 1), prompting subsequent job search effort (at t + 1); further, weekly job search effort is expected to engender an increase in subsequent interviews (at t + 2). We also explore the cross-level moderating effect of implicit theories of depletion, considering whether the beneficial impact of weekly psychological detachment is contingent on how depleting job seekers perceive the search process to be. We tested our model with two weekly experience sampling studies of over 200 new labor market entrants. Across both studies, we found considerable support for our model, suggesting that taking time to psychologically detach from the job search can help job seekers maintain their well-being and obtain job search success. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Dogs trained to recognise a bark beetle pheromone locate recently attacked spruces better than human experts

One of the most promising techniques for rapid detection of bark beetle-infested trees is the use of specially trained dogs. Due to the novelty of using dogs in detecting bark beetle-infested trees, evaluation of success or comparison with the traditional approaches is lacking. Spruces were pre-treated with a synthetic pheromone at several tree positions in six experimental forest stands of 4–12 ha. The tree positions were selected based on their arrangement in a random scheme or in patches considered suitable for bark beetle colonisation. Three dogs of different ages, sex, and levels of experience in detecting Ipstypographus were compared with three experienced human bark beetle specialists. We used GPS positioning of dog tracks (unleashed), handlers, human experts, and detection points during the search under a blind-test procedure for tracking positions. The potential utility of the search methods was estimated with three aspects: 1) search success: detection of infested trees, 2) search effort: length of route, and 3) search efficiency: trees detected / unit time.Dog-handler pairs were overall more successful in detecting trees attacked by bark beetles than human experts. In particular, the success rate of dogs was higher in plots with random arrangement pre-baited trees and search efficiency was four times higher than that of the human experts.The most efficient time for the use of dogs for detection would be during the spring flight period, when detection of first attacks and small hot spots could potentially prevent the development of larger infestations. The main advantage of using the dog search method is to amplify the time-window from about one week to one month from the detection to prompt removal of the infested trees from the forest.

Across-task neural architecture search via meta learning

Adequate labeled data and expensive compute resources are the prerequisites for the success of neural architecture search (NAS). It is challenging to apply NAS in meta-learning scenarios with limited compute resources and data. In this paper, an across-task neural architecture search (AT-NAS) method is proposed to address this problem via combining gradient-based meta-learning with EA-based NAS to learn over the distribution of tasks. The supernet is learned over an entire set of tasks by meta learning. Architecture encodes of subnets sampled from the supernet are iteratively adapted by evolutionary algorithms while simultaneously searching for a task-sensitive meta-network. The searched meta-network can adapt to a novel task via a few learning steps and it only costs a little search time. Empirical results show that AT-NAS achieves excellent performance in few-shot classification. The performance of AT-NAS on classification benchmarks is comparable to that of models searched from scratch, by adapting the architecture in less than an hour from a 5-GPU-day pretrained meta-network.

A Band Subset Selection Approach Based on Sparse Self-Representation and Band Grouping for Hyperspectral Image Classification

Band subset selection (BSS) is one of the ways to implement band selection (BS) for a hyperspectral image (HSI). Different from conventional BS methods, which select bands one by one, BSS selects a band subset each time and preserves the best one from the collection of the band subsets. This paper proposes a BSS method, called band grouping-based sparse self-representation BSS (BG-SSRBSS), for hyperspectral image classification. It formulates BS as a sparse self-representation (SSR) problem in which the entire bands can be represented by a set of informatively complementary bands. The BG-SSRBSS consists of two steps. To tackle the issue of selecting redundant bands, it first applies band grouping (BG) techniques to pre-group the entire bands to form multiple band groups, and then performs band group subset selection (BGSS) to find the optimal band group subset. The corresponding representative bands are taken as the BS result. To efficiently find the nearly global optimal subset among all possible band group subsets, sequential and successive iterative search algorithms are adopted. Land cover classification experiments conducted on three real HSI datasets show that BG-SSRBSS can improve classification accuracy by 4–20% compared to the existing BSS methods and requires less computation time.

A Double Metan-Semantic Search Model Based on Ontology and Semantic Similarity: Asthma Disease

With the exponential and rapid growth of online resources in recent years, there has been a huge increase in the use of search engines; these are also one of the most common ways to navigate the Web content without taking into account, in general, the request meaning by which was successfully added the user’s webpage provides us with a lot of results. This problem has led to the integration of semantics in the search for information on the Web (Semantic Web). The use of semantic tools, such as ontology, WordNet dictionary, semantic similarity measure, etc., has contributed to the semantic search development and more particularly, semantic Metan-search. The success of semantic search is closely linked to the availability of domain ontologies. The objective of this paper is to propose a double model of repetitive semantic search, called Double Metan-Semantic Search Model (2[Formula: see text]-SSM). On the one hand, it is assisted and based on the concepts extracted from the user’s search domain ontology, which will permit the user to choose a concept from this list of concepts and launch their search; on the other hand, it is free, in that the user enters their own concept and launches their search. This is based on WordNet tool, user’s same search domain ontology and the semantic similarity calculation techniques between concepts in the same ontology. The result of this model is a set of URL links. The term Metan indicates that the search is done in depth ([Formula: see text]-SS) via choosing each time a URL result by the user. Its experimentation in the asthma disease field gave very promising results in quantity and quality of information via the URL link results (semantic support).

A Multi-AUV Maritime Target Search Method for Moving and Invisible Objects Based on Multi-Agent Deep Reinforcement Learning.

Target search for moving and invisible objects has always been considered a challenge, as the floating objects drift with the flows. This study focuses on target search by multiple autonomous underwater vehicles (AUV) and investigates a multi-agent target search method (MATSMI) for moving and invisible objects. In the MATSMI algorithm, based on the multi-agent deep deterministic policy gradient (MADDPG) method, we add spatial and temporal information to the reinforcement learning state and set up specialized rewards in conjunction with a maritime target search scenario. Additionally, we construct a simulation environment to simulate a multi-AUV search for the floating object. The simulation results show that the MATSMI method has about 20% higher search success rate and about 70 steps shorter search time than the traditional search method. In addition, the MATSMI method converges faster than the MADDPG method. This paper provides a novel and effective method for solving the maritime target search problem.

Commodity Search Based on the Hybrid Breadth-Depth Algorithm in the Crowd Intelligence Based Transaction Network

Crowd intelligence based transaction network (CIbTN) is a new generation of e-commerce. In a CIbTN, buyers, sellers, and other institutions are all independent and intelligent agents. Each agent stores the commodity information in a local node. The agents interconnect through a circle of friends and construct an unstructured network. To conduct the commodity search task in a network more efficiently and in an energy-saving manner when a buyer presents a commodity demand, a hybrid breadth-depth search algorithm (HBDA) is proposed, which combines the search logic of the breadth-first search algorithm and the depth-first search algorithm. We defined the correlation degree of nodes in a network, optimized the rules of search and forwarding paths using the correlation degree between a node and its neighboring nodes in the circle of friends, and realized the HBDA based on the PeerSim simulation tool and Java. Experimental results show that, in general, the proposed HBDA has a better search success rate, search time, commodity matching degree, and search network consumption over the two blind search algorithms. The HBDA also has good expansibility, thus allowing it to be used for commodity search efficiently with a high success rate in large-scale networks.

Constraints on the electron-hole pair creation energy and Fano factor below 150 eV from Compton scattering in a skipper CCD

Fully depleted thick silicon skipper-charge-coupled devices (skipper CCDs) are an important technology to probe neutrino and light-dark-matter interactions due to their subelectron read-out noise. However, the successful search for rare neutrino or dark-matter events requires the signal and all backgrounds to be fully characterized. In particular, a measurement of the electron-hole pair creation energy below 150 eV and the Fano factor are necessary for characterizing the dark matter and neutrino signals. Moreover, photons from background radiation may Compton scatter in the silicon bulk, producing events that can mimic a dark matter or neutrino signal. We present a measurement of the Compton spectrum using a skipper CCD and a $^{241}\mathrm{Am}$ source. With these data, we estimate the electron-hole pair-creation energy to be $(3.71\ifmmode\pm\else\textpm\fi{}0.08)\text{ }\mathrm{eV}$ at 130 K in the energy range between 99.3 eV and 150 eV. By measuring the widths of the steps at 99.3 eV and 150 eV in the Compton spectrum, we introduce a novel technique to measure the Fano factor, setting an upper limit of 0.31 at 90% C.L. These results prove the potential of skipper CCDs to characterize the Compton spectrum and to measure precisely the Fano factor and electron-hole pair creation energy below 150 eV.

Successful immunomodulators for the treatment of COVID-19 have opened the pathway for comparative trials

Because coronavirus disease 2019 (COVID-19) became a dominant factor in our daily lives, we have learned a tremendous amount about it through both study and shared experience. Therapies for patients hospitalized for COVID-19 have improved from ineffective shots in the dark, through mildly effective antivirals, to mortality-reducing immunomodulatory therapies [1]. The widely accepted view of COVID-19 as a dual-phase illness with a viral component and an inflammatory component has led to the successful search for immunomodulatory therapies to attenuate host response and improve outcomes [2,3].

Fault location method of Distributed Generation Sources in Distribution Networks based on optimal configuration of voltage sag monitoring points

In order to evaluate the fault location performance under the optimal configuration scheme of Power Quality Monitoring System(PQMS) monitoring points with Distributed Generation(DG) in, a fault location method based on successive binary search was proposed. Firstly, the influence of DG on traditional optimal configuration is analyzed, and the optimal configuration improvement strategy for DG access is proposed. The function relation between node voltage and fault distance under DG access is analyzed, and the ratio variation relation caused by voltage variation between monitoring points is used to reflect the characteristics of slight variation of electrical quantity, and the ranging evaluation function is established. In order to avoid the setting of convergence threshold, the disadvantage of setting fixed threshold is eliminated. Based on the mean ranging error, several feasible monitoring point configuration schemes are evaluated for ranging performance and the optimal configuration scheme for fault location performance is obtained. The 4-node system verifies that the ranging method does not need to set fixed convergence threshold. In the improved IEEE14 node system, this feature can be used to evaluate the positioning performance of four different monitoring point configuration schemes, and finally obtain the optimal positioning performance configuration scheme.

PROSPECTS FOR THE CHEMISTRY OF IMIDAZOLE DERIVATIVES (Review)

The problem of creating new effective domestic pharmacological preparations, including the development of the methods for obtaining biologically active substances in compliance with the “green chemistry” principles, are among the priority areas for the development of chemical science. The choice of an initial molecule, which carries the potential of biological activity, is the guarantor of a successful experimental search. Imidazole derivatives occupy a unique place in the medicinal chemistry. An imidazole cycle is part of the natural compounds such as histamine, biotin, some alkaloids and nucleic acids, and is a structural fragment of medicinal preparations. The goalof the present review is to analyze the publications on the chemistry of imidazole derivatives with an emphasis on the methods of obtaining biologically active and other practically useful molecules with an obligatory imidazole cycle. The objects of the study:imidazole derivatives. The examples of the routes of synthesizing imidazole derivatives, as well as the compounds of interest for the medicinal chemistry, agriculture, and other fields, which have been published in the scientific and technical literature since 2000, have been presented. Conclusion.The studies in the field of searching for new highly effective preparations among imidazole derivatives are relevant and promising. The most important stage in this search is the directed synthesis of the substances with the specified, practically useful properties. The range of new practically useful substances in the series of imidazole derivatives has been significantly expanded and replenished thanks to the modern modifications of the classical methods of their obtaining.

Exceptional molecular preservation in the Late Jurassic Claudia palaeo-geothermal field (Deseado Massif, Patagonia, Argentina)

Gas chromatography–mass spectrometry was applied to samples collected from an exceptionally well-preserved Late Jurassic (∼150 Ma) sinter complex of the Claudia palaeo-geothermal field, Deseado Massif geological province, Argentinean Patagonia, which, despite its age, has never been deeply buried. Results indicate that the distal sinter apron has a much higher preservation potential for indigenous organic matter (OM) than the more proximal (vent area) facies of this palaeo-geothermal field. Specifically, homohopane ratios show that the OM of the proximal apron is of mixed thermal maturities and is in low abundance. In contrast, the OM extracted from the distal apron contains highly abundant, thermally immature biomarkers, the presence of which are consistent with the lower original fluid temperatures of the distal spring facies. Moreover, despite indications of the presence of some thermally mature aromatic compounds, hopane and sterane ratios confirm that the distal apron samples are extremely thermally immature and thereby constitute an area of exceptional molecular preservation. From an astrobiological viewpoint, these results suggest that silica sinter can preserve abundant organics over millions of years in palaeoenvironmentally conducive settings, and that sample-site selection within a hot spring facies-model framework may be critical in the successful search for ancient extra-terrestrial life.