Traditional Search Methods Research Articles

A four-way patient search method for the retrospective identification of poisoning patients

When studying emergency department (ED) visits, electronic health record systems of hospitals provide a good basis for retrospective studies. However, many intoxication patients presenting to the ED, may not be identified retrospectively if only a single search method is applied. In this study, a new four-way combined patient search method was used to retrospectively identify intoxication patients presenting to the ED. The search included reason for admission to the ED, laboratory results related to intoxication diagnostics, ICD-10 codes, and a novel free word search (FWS) of patient records. After the automated search, the researcher read the medical records of potential substance abuse patients to form comprehensive profiles and remove irrelevant cases. The addition of a free word search identified 36% more substance abuse patients than the combination of the other three methods mentioned above. Patients identified by the FWS search alone were generally admitted to the ED for trauma or mental health problems and were often found to be heavily under the influence of alcohol and/or drugs. The main intoxicants were ethanol and benzodiazepines. The free word search was highly complementary to traditional patient search methods, highlighting the importance of the combined patient search method in retrospective data collection.

Test

Semantic search is the process of retrieving relevant information from a large corpus of texts based on the meaning and context of the query. This paper is introduced in order to explore the use of large language models for semantic search of Quranic texts. The Quran, which is the central religious text of Islam, contains rich and complex linguistic and semantic features that pose challenges for traditional keyword-based search methods. This study investigates a semantic search approach utilizing. Large Language Models (LLM) embedding and assess the performance of LLM embedding in comparison to a baseline embedding-based search method using a set of queries that represent different semantic search levels. In addition, this study will also discuss the limitations and implications of using large language models for semantic search of Quranic texts and suggest directions for future research. A significant finding in this study is the consistent effectiveness of the LLM embedding across varying semantic complexities. This suggests that embedding using LLMs can capture deep semantic connections effectively. On the other hand, as a second finding, the state-of-the-art transformer, AraT5, outperforms LLM embeddings in low-level semantic searches, indicating potential for further LLM fine-tuning on Arabic text corpora

Embedding Search for Quranic Texts based on Large Language Models

Semantic search is the process of retrieving relevant information from a large corpus of texts based on the meaning and context of the query. This paper is introduced in order to explore the use of large language models for semantic search of Quranic texts. The Quran, which is the central religious text of Islam, contains rich and complex linguistic and semantic features that pose challenges for traditional keyword-based search methods. This study investigates a semantic search approach utilizing. Large Language Models (LLM) embedding and assess the performance of LLM embedding in comparison to a baseline embedding-based search method using a set of queries that represent different semantic search levels. In addition, this study will also discuss the limitations and implications of using large language models for semantic search of Quranic texts and suggest directions for future research. A significant finding in this study is the consistent effectiveness of the LLM embedding across varying semantic complexities. This suggests that embedding using LLMs can capture deep semantic connections effectively. On the other hand, as a second finding, the state-of-the-art transformer, AraT5, outperforms LLM embeddings in low-level semantic searches, indicating potential for further LLM fine-tuning on Arabic text corpora.

Насильственное перемещение этнических калмыков г. Таганрога на принудительные работы в Третий рейх: опыт микроисторической эвристики

Introduction. The article deals with one interesting pattern of research heuristics tackled to create a unified empirical database on forced labor relocations of Rostov Oblast-based Soviet citizens to Nazi Germany. Goals. The source study primarily attempts a consistent reconstruction of individual elements to the ethnic stratification among the O starbeiter (‘Eastern workers’) from the selected region, the former to be facilitated by the current identification of ethnic Kalmyks among such displaced individuals. This specific epistemological perspective has never been addressed by the preceding historiographic tradition, which makes the research practice relevant enough. Materials and methods. The integrated use of traditional archival search methods at the Taganrog Office of Rostov Oblast Archive (Russia) and the City Archive of Lüdenscheid (Germany) made it possible to identify an approximate circle of Kalmyk Soviet citizens forcibly displaced from Taganrog. Results. The paper contributes to a consistent understanding of how Nazi Germany’s forced labor civilian deportations from occupied territories took place, and outlines the phenomenon’s dynamics. The undertaken reconstruction of the ethnic stratification among forcibly displaced Soviet citizens clearly illustrates the rigid universalism of the occupation authorities that were seeking to gain ‘living space’ for ‘true Aryans’. The integrated object for Nazi repressive actions was civilian populations of occupied regions viewed as inferior communities, with no special privileges for any certain ethnic group. A particularly valuable result is that the study has yielded preliminary verifications for a number of ethnic Kalmyks that experienced such forced labor relocations to Nazi Germany.

Ground penetrating radar used to detect drowning victims under ice.

Floating bodies are successfully detected under both ice and snow using a commercial ground penetrating radar system with ice depths reaching up to 26 cm in a controlled environment.The differences between using radar systems operating at/around 500 MHz and 1 GHz were not pronounced from the point of view of detection.Future studies should investigate the capabilities for detecting bodies in more realistic settings.

Composing Multiple Online Exams: The Bees Algorithm Solution

Online education has gained increasing importance in recent years due to its flexibility and ability to cater to a diverse range of learners. The COVID-19 pandemic has further emphasized the significance of online education as a means to ensure continuous learning during crisis situations. With the disruption of traditional in-person exams, online examinations have become the new norm for universities worldwide. Among the popular formats for online tests are multiple-choice questions, which are drawn from a large question bank. However, creating online tests often involves meeting specific requirements, such as minimizing the overlap between exams, grouping related questions, and determining the desired difficulty level. The manual selection of questions from a sizable question bank while adhering to numerous constraints can be a laborious task. Additionally, traditional search methods that evaluate all possible solutions are impractical and time-consuming for such a complex problem. Consequently, approximate methods like metaheuristics are commonly employed to achieve satisfactory solutions within a reasonable timeframe. This research proposes the application of the Bees Algorithm (BA), a popular metaheuristic algorithm, to address the problem of generating online exams. The proposed solution entails creating multiple exam forms that align with the desired difficulty level specified by the educator, while considering other identified constraints. Through extensive testing and comparison with four rival methods, the BA demonstrates superior performance in achieving the primary objective of matching the desired difficulty level in most test cases, as required by the educator. Furthermore, the algorithm exhibits robustness, indicated by minimal standard deviation across all experiments, which suggests its ability to generalize, adapt, and be practically applicable in real-world scenarios. However, the algorithm does have limitations related to the number of successful solutions and the achieved overlap percentage. These limitations have also been thoroughly discussed and highlighted in this research.

A search tool based on language modelling developed for The Index of Middle English Prose.

Non-standardised early vernaculars present a problem for search tools due to the high degree of variation. The challenge lies in the variation found in orthography, syntax, and lexicon between titles, incipits, and explicits in manuscript copies of the same work. Traditional search methods relying on exact string matching or regular expressions fail to address these variations comprehensively. This project presents a web-based search tool specifically designed to handle linguistic and textual variation. The software is made available as a part of the Index of Middle English Prose (IMEP). The search tool addresses the issue of variation by utilizing a database of incipits and explicits, character-based n-gram language models (LMs) built with the Stanford Research Institute Language Modelling (SRILM) toolkit, and a fuzzy search script (IMEP: FSS) written in Python. The tool optimizes for recall, retrieving multiple potential matches for a search string, without attempting to identify the 'correct' one. The search process involves looking up exact matches in the database while simultaneously using the fuzzy search script to evaluate the incipits and explicits against a model of the search string, followed by a match of the search string against models of the incipits and explicits. This two-step process shortens the processing time, which would otherwise be unreasonably long, because while using SRILM to match the search string against each incipit or explicit in the IMEP for precision could be time-consuming, running a first step where all texts are matched against a single LM built from the search string allows for faster processing. A web application, built using Django and Docker, combines the results of the direct database lookup and the fuzzy search script, presenting them as a list with exact matches followed by fuzzy matches ordered by increasing model perplexity. The tool is made available Open Access and can be adapted to other datasets.

Utilizing a Two-Stage Taguchi Method and Artificial Neural Network for the Precise Forecasting of Cardiovascular Disease Risk.

The complexity of cardiovascular disease onset emphasizes the vital role of early detection in prevention. This study aims to enhance disease prediction accuracy using personal devices, aligning with point-of-care testing (POCT) objectives. This study introduces a two-stage Taguchi optimization (TSTO) method to boost predictive accuracy in an artificial neural network (ANN) model while minimizing computational costs. In the first stage, optimal hyperparameter levels and trends were identified. The second stage determined the best settings for the ANN model's hyperparameters. In this study, we applied the proposed TSTO method with a personal computer to the Kaggle Cardiovascular Disease dataset. Subsequently, we identified the best setting for the hyperparameters of the ANN model, setting the hidden layer to 4, activation function to tanh, optimizer to SGD, learning rate to 0.25, momentum rate to 0.85, and hidden nodes to 10. This setting led to a state-of-the-art accuracy of 74.14% in predicting the risk of cardiovascular disease. Moreover, the proposed TSTO method significantly reduced the number of experiments by a factor of 40.5 compared to the traditional grid search method. The TSTO method accurately predicts cardiovascular risk and conserves computational resources. It is adaptable for low-power devices, aiding the goal of POCT.

Cooperative search for moving targets with the ability to perceive and evade using multiple UAVs

This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.

Evaluation of inpatient medication guidance from an artificial intelligence chatbot.

To analyze the clinical completeness, correctness, usefulness, and safety of chatbot and medication database responses to everyday inpatient medication-use questions. We evaluated the responses from an artificial intelligence chatbot, a medication database, and clinical pharmacists to 200 real-world medication-use questions. Answer quality was rated by a blinded group of pharmacists, providers, and nurses. Chatbot and medication database responses were deemed "acceptable" if the mean reviewer rating was within 3 points of the mean rating for pharmacists' answers. We used descriptive statistics for reviewer ratings and Kendall's coefficient to evaluate interrater agreement. The medication database generated responses to 194 (97%) questions, with 88% considered acceptable for clinical correctness, 76% considered acceptable for completeness, 83% considered acceptable for safety, and 81% considered acceptable for usefulness compared to pharmacists' answers. The chatbot responded to only 160 (80%) questions, with 85% considered acceptable for clinical correctness, 65% considered acceptable for completeness, 71% considered acceptable for safety, and 68% considered acceptable for usefulness. Traditional search methods using a drug database provide more clinically correct, complete, safe, and useful answers than a chatbot. When the chatbot generated a response, the clinical correctness was similar to that of a drug database; however, it was not rated as favorably for clinical completeness, safety, or usefulness. Our results highlight the need for ongoing training and continued improvements to artificial intelligence chatbots for them to be incorporated reliably into the clinical workflow. With continued improvement in chatbot functionality, chatbots could be a useful pharmacist adjunct, providing healthcare providers with quick and reliable answers to medication-use questions.

Multi-UAV Cooperative Search Based on Reinforcement Learning With a Digital Twin Driven Training Framework

This paper considers the cooperative search for stationary targets by multiple unmanned aerial vehicles (UAVs) with limited sensing range and communication ability in a dynamic threatening environment. The main purpose is to use multiple UAVs to find more unknown targets as soon as possible, increase the coverage rate of the mission area, and more importantly, guide UAVs away from threats. However, traditional search methods are mostly unscalable and perform poorly in dynamic environments. A new multi-agent deep reinforcement learning (MADRL) method, DNQMIX, is proposed in this study to solve the multi-UAV cooperative target search (MCTS) problem. The reward function is also newly designed for the MCTS problem to guide UAVs to explore and exploit the environment information more efficiently. Moreover, this paper proposes a digital twin (DT) driven training framework “centralized training, decentralized execution, and continuous evolution” (CTDECE). It can facilitate the continuous evolution of MADRL models and solve the tradeoff between training speed and environment fidelity when MADRL is applied to real-world multi-UAV systems. Simulation results show that DNQMIX outperforms state-of-art methods in terms of search rate and coverage rate.

The Past, Present, and Future of Psychotherapy Manuals: Protocol for a Scoping Review.

Psychotherapy manuals are critical to the dissemination of psychotherapy treatments. Psychotherapy manuals typically serve several purposes, including, but not limited to, establishing new psychotherapy treatments, training providers, disseminating treatments to those who deliver them, and providing guidelines to deliver treatments with fidelity. Yet, the proliferation of psychotherapy manuals has not been well-understood, and no work has aimed to assess or review the existing landscape of psychotherapy manuals. Little is known about the breadth, scope, and foci of extant psychotherapy manuals. This scoping review aims to identify and explore the landscape of existing book-based psychotherapy manuals. This review aims to specify the defining characteristics (ie, foci, clinical populations, clinical targets, treatment type, treatment modality, and adaptations) of existing book-based psychotherapy manuals. Further, this review will demonstrate how this information, and psychotherapy manuals more broadly, has changed over time. This project aims to make a novel contribution that will have critical implications for current methods of developing, aggregating, synthesizing, and translating knowledge about psychotherapeutic treatments. This scoping review will review book-based psychotherapy manuals published from 1950 to 2022.This scoping review will be informed by guidance from the Joanna Briggs Institute Scoping Review Methodology Group and prior scoping reviews. Traditional search and application programming interface-based search methods will be used with search terms defined a priori to identify relevant results using 3 large book databases: Google Books, WorldCat, and PsycINFO. This review will leverage machine learning methods to enhance and expedite the screening process. Primary screening of results will be conducted by at least 2 authors. Data will be extracted and double-coded by research assistants using an iteratively defined codebook. The search process produced 78,600 results, which were then iteratively deduplicated. Following deduplication, 50,583 results remained. The scoping review is expected to identify common elements of psychotherapy manuals, establish how the foci and content of manuals have changed over time, and illustrate coverage and gaps in the landscape of psychotherapy manuals. Results from this scoping review will be critical for future work focused on developing, aggregating, synthesizing, and disseminating knowledge about psychotherapeutic treatments. This review will provide knowledge about the vast landscape of psychotherapy manuals that exist. Findings from this study will inform future efforts to develop, aggregate, synthesize, and translate knowledge about psychotherapeutic treatments. DERR1-10.2196/47708.

The use of coevolutionary algorithms for optimizing the operating regimes of the roasting conveyor machine

In modern conditions of constant growth in prices for fuel and energy resources, the problem of increasing the energy and resource efficiency of technological processes of industrial enterprises has acquired particular relevance. It is especially acute for energy-intensive industries, which include high-temperature processing of mining and chemical raw materials. To reduce the energy intensity of complex chemical-technological processes, it is proposed to use the possibilities of computer simulation, for example, to optimize the operating regimes of existing equipment. The article has considered the scientific and practical problem of optimizing the charge heating regimes in various zones of the roasting conveyor machine used to produce phosphorite pellets from apatite-nepheline ore waste stored in dumps of mining and processing plants. The specifics of the optimization task (nonlinearity of the objective function, large dimension of the search space, high computational complexity) are significant limitations for the use of traditional deterministic search methods. It led to the choice of population algorithms, which are based on modeling the collective behavior and are distinguished by the possibility of simultaneous processing of several options. The cuckoo search algorithm, which is distinguished by a small number of “free” parameters that affect the convergence, was used to solve the stated optimization task. To select the optimal values of these parameters, it was proposed to use the idea of coevolution, which consists in the parallel launch of several versions of the selected algorithm with different “settings” for each subpopulation. The management of the chemical-technological system for the processing of apatite-nepheline ore waste, taking into account the basis of the results obtained, will minimize the amount of return and ensure an energy-saving operating regime of the roasting conveyor machine.

Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy.

In advanced transportation-management systems, variable speed limits are a crucial application. Deep reinforcement learning methods have been shown to have superior performance in many applications, as they are an effective approach to learning environment dynamics for decision-making and control. However, they face two significant difficulties in traffic-control applications: reward engineering with delayed reward and brittle convergence properties with gradient descent. To address these challenges, evolutionary strategies are well suited as a class of black-box optimization techniques inspired by natural evolution. Additionally, the traditional deep reinforcement learning framework struggles to handle the delayed reward setting. This paper proposes a novel approach using covariance matrix adaptation evolution strategy (CMA-ES), a gradient-free global optimization method, to handle the task of multi-lane differential variable speed limit control. The proposed method uses a deep-learning-based method to dynamically learn optimal and distinct speed limits among lanes. The parameters of the neural network are sampled using a multivariate normal distribution, and the dependencies between the variables are represented by a covariance matrix that is optimized dynamically by CMA-ES based on the freeway's throughput. The proposed approach is tested on a freeway with simulated recurrent bottlenecks, and the experimental results show that it outperforms deep reinforcement learning-based approaches, traditional evolutionary search methods, and the no-control scenario. Our proposed method demonstrates a 23% improvement in average travel time and an average of a 4% improvement in CO, HC, and NOx emission.Furthermore, the proposed method produces explainable speed limits and has desirable generalization power.

Autonomous plume Near-Source search assisted by intermittent visible plume information using finite state Machine and YOLOv3-tiny

Since a plume’s visual characteristics have not been fully considered and utilized, the traditional plume near-source search methods lose a large amount of potentially available information, resulting in the low efficiency and reliability of near-source search. This paper proposes a novel plume near-source search approach assisted by the intermittent visible plume information using finite state machine and YOLOv3-tiny for a robot. A finite state machine is used to realize the autonomous behavioral decision-making of the robot according to the obtained visible plume information, olfactory information, and lidar information. In addition, the YOLOv3-tiny network is used to detect the intermittent visible plume information obtained by a depth camera to capture the color and texture of the plume. In order to improve the search reliability of the robot in the olfactory tracking behavior, a whale optimization algorithm is utilized to enable the robot to simulate the hunting behavior and the social mechanism of the whale group and conduct olfactory tracking of the plume. The experimental results show that this proposed plume near-source search approach achieves the success rates of 91.67% and 83.33% in windless and windy environments respectively, and the average total running time is 113.8 s and 159.6 s in the experimental environment with obstacles, respectively.

Querying Similar Multi-Dimensional Time Series with a Spatial Database

Similar time series search is one of the most important time series mining tasks in our daily life. As recent advances in sensor technologies accumulate abundant multi-dimensional time series data associated with multivariate quantities, it becomes a privilege to adapt similar time series searches for large-scale and multi-dimensional time series data. However, traditional similar time series search methods are mainly designed for one-dimensional time series, while advanced methods applicable for multi-dimensional time series data are largely immature and, more importantly, are not friendly to users from the domain of geography. As an alternative, we propose a novel method to search similar multi-dimensional time series with spatial databases. Compared with traditional methods that often conduct the similarity search based on features of the raw time series data sequence, the proposed method stores multi-dimensional time series as spatial objects in a spatial database, and then searches similar time series based on their spatial features. To demonstrate the validity of the proposed method, we analyzed the correlation between temporal features of the raw time series and spatial features of their corresponding spatial objects theoretically and empirically. Results indicate that the proposed method can not only support similar multi-dimensional time series searches but also markedly improve its efficiency under many specific scenarios. We believe that such a new paradigm will shed further light on the similarity search in large-scale multi-dimensional time series data, and will lower the barrier for users familiar with spatial databases to conduct complex time series mining tasks.

Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials

Grain boundaries (GBs) in two-dimensional (2D) materials are known to dramatically impact material properties ranging from the physical, chemical, mechanical, electronic, and optical, to name a few. Predicting a range of physically realistic GB structures for 2D materials is critical to exercising control over their properties. This, however, is nontrivial given the vast structural and configurational (defect) search space between lateral 2D sheets with varying misfits. Here, in a departure from traditional evolutionary search methods, we introduce a workflow that combines the Graph Neural Network (GNN) and an evolutionary algorithm for the discovery and design of novel 2D lateral interfaces. We use a representative 2D material, blue phosphorene (BP), and identify 2D GB structures to test the efficacy of our GNN model. The GNN was trained with a computationally inexpensive machine learning bond order potential (Tersoff formalism) and density functional theory (DFT). Systematic downsampling of the training data sets indicates that our model can predict structural energy under 0.5% mean absolute error with sparse (<2000) DFT generated energy labels for training. We further couple the GNN model with a multiobjective genetic algorithm (MOGA) and demonstrate strong accuracy in the ability of the GNN to predict GBs. Our method is generalizable, is material agnostic, and is anticipated to accelerate the discovery of 2D GB structures.

High-Performance Channel Estimation for mmWave Wideband Systems With Hybrid Structures

In this paper, a channel estimation problem for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with hybrid structures is studied. Firstly, a beamspace multiple signal classification (MUSIC) algorithm for mmWave wideband channels is proposed to simultaneously estimate the angles of arrival (AOAs), angles of departure (AODs) and transmission delays. Since the traditional spectral peak search method has high complexity, a multi-spectral peak search method is skillfully designed to search for multiple spectral peaks on the MUSIC spatial spectrum more quickly and accurately. Then, the proposed channel estimator is extended to more actual systems equipped with uniform planar arrays (UPAs). Finally, the Cramér–Rao bound (CRB) results of these channel parameters are derived for evaluating the performance of the proposed channel parameter estimator. Simulation results demonstrate that the proposed channel estimator has greatly high channel estimation accuracy.

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics

Posttranslational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry-based proteomics, largely relying on traditional database search methods to identify the resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem mass spectrometry experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new labile mode in the MSFragger search engine that provides the flexibility to tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum identification rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

Three Approaches for Detecting Direct Output Cheating in Program Online Judge Systems

Program online judge (POJ) systems allow students to view questions, submit solution code, and receive scores automatically via the web. Most POJs use test cases for scoring. When a POJ is scored by test case pass rate or a problem that has only one test case, students can usually score by providing the direct output of the test cases (direct output cheating). Currently, there is only one work on detecting such cheating. However, its precision is very low. To solve this problem, three novel approaches are proposed to detect direct output cheating: (i) Line Statistics, which computes the proportion of output calls against other statements; (ii) the control flow graph (CFG) Search computes the maximum similarity between the CFG of a program and that of known samples; (iii) abstract syntax tree (AST) Search identifies cheating by matching rules that are summarized from ASTs of previously detected cheating attempts. A student’s code is marked as cheating if the similarity exceeds a predefined threshold; and a program is detected as cheating if the proportion exceeds a predefined threshold. The proposed approaches and three well-known code plagiarism detection tools (JPlag, Sherlock, and SIM) were evaluated using 100,000 submissions for 1153 problems from a POJ based on the C programming language. The F1 scores of these approaches were determined as 0.9752 (AST Search), 0.9440 (CFG Search), 0.7405 (Line Statistics), 0.6446 (JPlag), 0.1587 (Sherlock), and 0.0076 (SIM), respectively. The result indicates that (i) AST Search is most suitable for the detection of direct output cheating; (ii) traditional code search or plagiarism detection methods based on similarity calculations are not effective for complex cheat detection because these cheats are highly similar to normal code.