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Objectives Extracting PICO (Populations, Interventions, Comparison, and Outcomes) entities is fundamental to evidence retrieval. We present a novel method, PICOX, to extract overlapping PICO entities. Materials and Methods PICOX first identifies entities by assessing whether a word marks the beginning or conclusion of an entity. Then, it uses a multi-label classifier to assign one or more PICO labels to a span candidate. PICOX was evaluated using 1 of the best-performing baselines, EBM-NLP, and 3 more datasets, ie, PICO-Corpus and randomized controlled trial publications on Alzheimer's Disease (AD) or COVID-19, using entity-level precision, recall, and F1 scores. Results PICOX achieved superior precision, recall, and F1 scores across the board, with the micro F1 score improving from 45.05 to 50.87 (P ≪.01). On the PICO-Corpus, PICOX obtained higher recall and F1 scores than the baseline and improved the micro recall score from 56.66 to 67.33. On the COVID-19 dataset, PICOX also outperformed the baseline and improved the micro F1 score from 77.10 to 80.32. On the AD dataset, PICOX demonstrated comparable F1 scores with higher precision when compared to the baseline. Conclusion PICOX excels in identifying overlapping entities and consistently surpasses a leading baseline across multiple datasets. Ablation studies reveal that its data augmentation strategy effectively minimizes false positives and improves precision. [ABSTRACT FROM AUTHOR]

We consider selecting the top-m alternatives from a finite number of alternatives via Monte Carlo simulation. Under a Bayesian framework, we formulate the sampling decision as a stochastic dynamic programming problem and develop a sequential sampling policy that maximizes a value function approximation one-step look ahead. To show the asymptotic optimality of the proposed procedure, the asymptotically optimal sampling ratios that optimize the large deviations rate of the probability of false selection for selecting the top-m alternatives have been rigorously defined. The proposed sampling policy is not only proved to be consistent but also achieve the asymptotically optimal sampling ratios. Numerical experiments demonstrate superiority of the proposed allocation procedure over existing ones. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72250065, 72293582, 72022001, and 71901003], and the National Science Foundation [Grant DMS-2053489], the major project of the National Natural Science Foundation of China [Grant 72293582], and the China Scholarship Council [Grant CSC202206010152]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0333) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0333). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Seismicity induced by fluid injection including wastewater injection, hydrofracking and enhanced geothermal system (EGS) site production draws public attention. Dense arrays have been deployed to improve monitoring capability. In 2016 March, the PoroTomo experiment deployed an 8.6-km-long fibre-optic cable for distributed acoustic sensing (DAS) in the geothermal field at Brady Hot Springs, Nevada, covering an area of 1.5 by 0.5 km. The goal was to assess an integrated technology for characterizing and monitoring temporal changes in the rock mechanical properties of an EGS reservoir in three dimensions. We applied a neural network designed for earthquake detection called ADE-Net2 to the DAS data set to detect seismic events in continuous records. We were able to detect a total of 90 earthquakes, which included 21 events that had not been reported by a previous template-matching study. Additionally, we were able to successfully detect almost all of the active source signals, with only seven events being missed. We used the STA/LTA (short-/long-term average) method to pick arrivals and a clustering method to remove outliers. We initially tried a standard event location algorithm, but the low signal-to-noise ratio resulted in significant picking uncertainty that is up to ∼0.5 s, leading to large location uncertainty. Therefore, we developed a new location method based on the similarity between the theoretical traveltime curve and picked moveout. A grid search scheme was adopted to find the optimal point at which the traveltime curve is most similar to the picked one. Most newly detected earthquakes locate southwest of the DAS array, where five earthquakes were reported by a local seismic network. The plant began shutting down at 19:15 UTC on the March 13, and most earthquakes occurred on the March 14, indicating a relationship between the seismicity and the pressure changes caused by the shutdown of the plant. The pressure changes at epicentres obtained from a simplified model range from 71 to 157 kPa, exceeding a typical earthquake trigger threshold of 10 kPa. [ABSTRACT FROM AUTHOR]

We present a gradient-based algorithm for solving a class of simulation optimization problems in which the objective function is the quantile of a simulation output random variable. In contrast with existing quantile (quantile derivative) estimation techniques, which aim to eliminate the estimator bias by gradually increasing the simulation sample size, our algorithm incorporates a novel recursive procedure that only requires a single simulation sample at each step to simultaneously obtain quantile and quantile derivative estimators that are asymptotically unbiased. We show that these estimators, when coupled with the standard gradient descent method, lead to a multitime-scale stochastic approximation type of algorithm that converges to an optimal quantile value with probability one. In our numerical experiments, the proposed algorithm is applied to optimal investment portfolio problems, resulting in new solutions that complement those obtained under the classical Markowitz mean-variance framework. History: Accepted by Alice E. Smith, Editor-in-Chief; Bruno Tuffin, Area Editor for Simulation. Funding: The work of Y. Peng was supported in part by the National Natural Science Foundation of China (NSFC) [Grants 72022001, 92146003, and 71901003], and by the Key Research and Development Programof Beijing Municipal Science and Technology Commission. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2022.1214. [ABSTRACT FROM AUTHOR]

[Display omitted] Automated identification of eligible patients is a bottleneck of clinical research. We propose Criteria2Query (C2Q) 3.0, a system that leverages GPT-4 for the semi-automatic transformation of clinical trial eligibility criteria text into executable clinical database queries. C2Q 3.0 integrated three GPT-4 prompts for concept extraction, SQL query generation, and reasoning. Each prompt was designed and evaluated separately. The concept extraction prompt was benchmarked against manual annotations from 20 clinical trials by two evaluators, who later also measured SQL generation accuracy and identified errors in GPT-generated SQL queries from 5 clinical trials. The reasoning prompt was assessed by three evaluators on four metrics: readability, correctness, coherence, and usefulness, using corrected SQL queries and an open-ended feedback questionnaire. Out of 518 concepts from 20 clinical trials, GPT-4 achieved an F1-score of 0.891 in concept extraction. For SQL generation, 29 errors spanning seven categories were detected, with logic errors being the most common (n = 10; 34.48 %). Reasoning evaluations yielded a high coherence rating, with the mean score being 4.70 but relatively lower readability, with a mean of 3.95. Mean scores of correctness and usefulness were identified as 3.97 and 4.37, respectively. GPT-4 significantly improves the accuracy of extracting clinical trial eligibility criteria concepts in C2Q 3.0. Continued research is warranted to ensure the reliability of large language models. [ABSTRACT FROM AUTHOR]

The distributed acoustic sensing (DAS) technology that has emerged in recent years, combined with existing optical cables for earthquake monitoring, has the advantages of low deployment cost and dense sampling capability and has been used in various environments. However, the massive data generated by DAS are difficult to process manually, and there is an urgent need for efficient automatic earthquake detection algorithm. The deep neural networks trained with manually processed earthquake data in the past decade have been introduced. However, available manually processed earthquake DAS data are very limited, which makes it difficult to train neural networks. A convolutional neural network called “APM-DAS-earthquake network (ADE-Net) ” was built and trained with only five earthquakes’ records in Tangshan, China. The neural network detected about 2.56 times earthquakes on a DAS dataset than the permanent seismic network’s catalog with an accuracy rate of 80.4% and a recall rate of 93.18%. Results of tests with synthetic datasets and another real dataset suggest the neural network works well with a variety of earthquake location, magnitude, type, and array geometry. The computational time cost with a PC for 10-s data is less than 2.5-s data, which demonstrates its feasibility on time-sensitive applications, for example, earthquake early warning. [ABSTRACT FROM AUTHOR]

The discovery of drug–drug interactions (DDIs) that have a translational impact among in vitro pharmacokinetics (PK), in vivo PK and clinical outcomes depends largely on the quality of the annotated corpus available for text mining. We have developed a new DDI corpus based on an annotation scheme that builds upon and extends previous ones, where an abstract is fragmented and each fragment is then annotated along eight dimensions, namely, focus, polarity, certainty, evidence, directionality, study type, interaction type and mechanism. The guideline for defining these dimensions has undergone refinement during the annotation process. Our DDI corpus comprises 900 positive DDI abstracts and 750 that are not directly relevant to DDI. The abstracts in corpus are separated into eight categories of DDI or non-DDI evidence: DDI with pharmacokinetic (PK) mechanism, in vivo DDI PK, DDI clinical, drug–nutrition interaction, single drug, not drug related, in vitro pharmacodynamic (PD) and case report. Seven annotators, three annotators with drug–interaction research experience and four annotators with less drug–interaction research experience independently annotated the DDI corpus, where two researchers independently annotated each abstract. After two rounds of annotations with additional training in between, agreement improved from (0.79, 0.96, 0.86, 0.70, 0.91, 0.65, 0.78, 0.90) to (0.93, 0.99, 0.96, 0.94, 0.95, 0.93, 0.96, 0.97) for focus, certainty, evidence, study type, interaction type, mechanisms, polarity and direction, respectively. The novice-level annotators improved from 0.83 to 0.96, while the expert-level annotators stayed in high performance with some improvement, from 0.90 to 0.96. In summary, we achieved 96% agreement among each pair of annotators with regard to the eight dimensions. The annotated corpus is now available to the community for inclusion in their text-mining pipelines. Database URL https://github.com/zha204/DDI-Corpus-Database/tree/master/DDI%20corpus [ABSTRACT FROM AUTHOR]

Distributed acoustic sensing (DAS) is a novel seismic observation system developed in recent years that can realize ultrahigh density observations and has attracted extensive attention in the field of seismology. DAS uses fiber-optic cables as sensing units, which are easy to incorporate with urban telecommunication fiber-optic cables for seismological observations. Compared with seismometers, DAS has the advantages of being rapidly deployed and recyclable, being able to acquire dense observations at low cost, and convenient data collection. In this study, a 5.2 km long telecom fiber-optic internet cable was utilized as a DAS array in an urban area to record ambient noise, and the noise cross-correlation function (NCF) was calculated. There are two different distribution types of ambient noise sources along the cable, regular along-road trucks (Taihe Road) and complex ambient noise, including human activities and traffic sources along and across the Jinniu road. In the first case, we constructed a 2D S-wave velocity model down to 100 m depth and a low-velocity zone was revealed. The S-wave model well explained the traffic signal along the Taihe road and the low-velocity zone is also consistent with the results obtained from co-located geophone arrays. In the second case, due to the complexity of the traffic noise distribution, empirical Green's functions were barely achieved. Therefore, we performed a synthetic test obtaining different NCFs with different source distributions, and two specific cases that dominate the NCF results were matched. Finally, we obtained the traffic noise distribution along the road, which is consistent with the power spectra density of the ambient noise. In conclusion, by combining DAS and urban fiber-optic internet cables with urban traffic noise, we can effectively reveal the traffic activities and image shallow structures with high resolution, which could offer a reference for urban construction and disaster prevention. Article Highlights: DAS turns the urban fiber-optic internet cables into ultra-dense permanent seismic observation arrays We revealed a high-resolution shallow structure using urban fiber-optic internet cables We obtained the distribution of traffic activities along the road [ABSTRACT FROM AUTHOR]

Motivation Biomedical research findings are typically disseminated through publications. To simplify access to domain-specific knowledge while supporting the research community, several biomedical databases devote significant effort to manual curation of the literature—a labor intensive process. The first step toward biocuration requires identifying articles relevant to the specific area on which the database focuses. Thus, automatically identifying publications relevant to a specific topic within a large volume of publications is an important task toward expediting the biocuration process and, in turn, biomedical research. Current methods focus on textual contents, typically extracted from the title-and-abstract. Notably, images and captions are often used in publications to convey pivotal evidence about processes, experiments and results. Results We present a new document classification scheme, using both image and caption information, in addition to titles-and-abstracts. To use the image information, we introduce a new image representation, namely Figure-word , based on class labels of subfigures. We use word embeddings for representing captions and titles-and-abstracts. To utilize all three types of information, we introduce two information integration methods. The first combines Figure-words and textual features obtained from captions and titles-and-abstracts into a single larger vector for document representation; the second employs a meta-classification scheme. Our experiments and results demonstrate the usefulness of the newly proposed Figure-words for representing images. Moreover, the results showcase the value of Figure-words, captions and titles-and-abstracts in providing complementary information for document classification; these three sources of information when combined, lead to an overall improved classification performance. Availability and implementation Source code and the list of PMIDs of the publications in our datasets are available upon request. [ABSTRACT FROM AUTHOR]

Tomographic imaging technology is a geophysical inversion method. According to the ray scanning, this method carries on the inversion calculation to the obtained information, and reconstructs the image of the parameter distribution rule of elastic wave and electromagnetic wave in the measured range, so as to delineate the structure of the geological body. In this paper, teleseismic tomography is applied by using seismic travel time data to constrain layered crustal structure where Fast Marching Methods (FMM) and the subspace method are considered as forward and inverse methods, respectively. Based on the travel time data picked up from seismic waveform data in the study region, the P-wave velocity structure beneath Northeast China down to 750 km is obtained. It can be seen that there are low-velocity anomalies penetrating the mantle transition zone under the Changbai volcano group, Jingpohu Volcano, and Arshan Volcano, and these low-velocity anomalies extend to the shallow part. In this paper, it is suggested that the Cenozoic volcanoes in Northeast China were heated by the heat source provided by the dehydration of the subducted Pacific plate and the upwelling of geothermal matter in the lower mantle. The low-velocity anomaly in the north Songliao basin does not penetrate the mantle transition zone, which may be related to mantle convection and basin delamination. According to the low-velocity anomalies widely distributed in the upper mantle and the low-velocity bodies passing through the mantle transition zone beneath the volcanoes, this study suggests that the Cenozoic volcanoes in Northeast China are kindred and have a common formation mechanism. [ABSTRACT FROM AUTHOR]

Modeling plays an important role in engineering exploration. However, the traditional rectangular mesh cannot meet the requirement because the actual shape of the model may be very complex. Therefore, this paper chooses the Delaunay grid for modeling and applies this method to magnetic numerical simulation. At the same time, we make use of the advantages of the Delaunay grid and choose to use grid encryption technology in the boundary and complex area of the model. Then, the formula for calculating the magnetic anomaly of tetrahedron is deduced in detail, and the magnetic anomaly with surface fluctuation is calculated. For a synthetic data model, Delaunay grids with different densities are used to model and calculate surface anomalies. The two results are compared with the analytical solution of the model. The results show that the method has high accuracy. Then, the method is applied to the actual geological body modeling in the Jinchuan mining area. According to local needs, a three-dimensional model with inhomogeneous grid density is established and the surface magnetic field of the model is calculated. Finally, the simulation data are compared with the measured data. The results show that the Delaunay grid modeling method has strong applicability. [ABSTRACT FROM AUTHOR]

Digital libraries of case studies of analogical design have been popular since their advent in the early 1990s. We consider four benefits of digital libraries of case studies of analogical design in the context of biologically inspired design. First, a digital library affords documentation. The 83 case studies in our work come from 8 years of extended, collaborative design projects in an interdisciplinary class on biologically inspired design. Second, a digital library provides on-demand access to the case studies. We describe a web-based library of case studies of biologically inspired design called the Design Study Library (DSL). Third, a compilation of case studies supports analyses of broader patterns and trends. As an example, an analysis of DSL's case studies found that environmental sustainability was a major factor in about a third of the case studies and an explicit design goal in about a fourth. Fourth, a digital library of case studies can support analogical learning. Preliminary results from an exploratory study indicate that DSL may support novice learning about the processes of biologically inspired design. [ABSTRACT FROM PUBLISHER]

In this paper, we present a high resolution 3-D tomographic model of the upper mantle obtained from a large number of teleseismic travel time data from the ISC in the central Philippines. There are 2921 teleseismic events and 32,224 useful relative travel time residuals picked to compute the velocity structure in the upper mantle, which was recorded by 87 receivers and satisfied the requirements of teleseismic tomography. Crustal correction was conducted to these data before inversion. The fast-marching method (FMM) and a subspace method were adopted in the forward step and inversion step, respectively. The present tomographic model clearly images steeply subducting high velocity anomalies along the Manila trench in the South China Sea (SCS), which reveals a gradual changing of the subduction angle and a gradual shallowing of the subduction depth from the north to the south. It is speculated that the change in its subduction depth and angle indicates the cessation of the SCS spreading from the north to the south, which also implies that the northern part of the SCS opened earlier than the southern part. Subduction of the Philippine Sea (PS) plate is exhibited between 14° N and 9° N, with its subduction direction changing from westward to eastward near 13° N. In the range of 11° N–9° N, the subduction of the Sulu Sea (SS) lies on the west side of PS plate. It is notable that obvious high velocity anomalies are imaged in the mantle transition zone (MTZ) between 14° N and 9° N, which are identified as the proto-SCS (PSCS) slabs and paleo-Pacific (PP) plate. It extends the location of the paleo-suture of PSCS-PP eastward from Borneo to the Philippines, which should be considered in studying the mechanism of the SCS and the tectonic evolution in SE Asia. [ABSTRACT FROM AUTHOR]

Remote sensing is a non-contact, long-distance detection technology. The reflection characteristics of a seismic wave can be used to detect remote and non-contact targets. Based on the reflection characteristics of a seismic wave, the underground structure in Tengchong Volcanic Area is explored. In order to further study the deep structure and magmatic activity of the crust in the volcanic area, we carried out a one-year mobile seismic observation. In this paper, nine broadband seismic stations were set up in the Tengchong Volcanic Area, and 3350 receiver function waveforms were collected. The crustal thickness, average wave velocity ratio, and Poisson's ratio below these stations were calculated by the receiver function method, and the velocity structure near the Moho below these stations was evaluated. Combined with topographic data from SRTM3 (Shuttle Radar Topography Mission 3), this study reveals the dynamic relationship among crustal structure, crustal magmatism, and regional tectonic movement. Mantle upwelling plays an important role on the Moho uplift in the northern Tengchong Volcanic Area, and there are interconnected intracrustal magma chambers in the upper platform. The evaluation results of the Moho transition zone also indicate that the Dayingjiang fault is closely related to the tectonic activity of the Tengchong Volcanic fault. [ABSTRACT FROM AUTHOR]

Published literature is an important source of knowledge supporting biomedical research. Given the large and increasing number of publications, automated document classification plays an important role in biomedical research. Effective biomedical document classifiers are especially needed for bio-databases, in which the information stems from many thousands of biomedical publications that curators must read in detail and annotate. In addition, biomedical document classification often amounts to identifying a small subset of relevant publications within a much larger collection of available documents. As such, addressing class imbalance is essential to a practical classifier. We present here an effective classification scheme for automatically identifying papers among a large pool of biomedical publications that contain information relevant to a specific topic, which the curators are interested in annotating. The proposed scheme is based on a meta-classification framework using cluster-based under-sampling combined with named-entity recognition and statistical feature selection strategies. We examined the performance of our method over a large imbalanced data set that was originally manually curated by the Jackson Laboratory's Gene Expression Database (GXD). The set consists of more than 90 000 PubMed abstracts, of which about 13 000 documents are labeled as relevant to GXD while the others are not relevant. Our results, 0.72 precision, 0.80 recall and 0.75 f-measure, demonstrate that our proposed classification scheme effectively categorizes such a large data set in the face of data imbalance. [ABSTRACT FROM AUTHOR]