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Collection of evidence is the first, important component in ensuring the process of proof in criminal proceedings. Its content consists in finding actual data that will allow establishing the circumstances of a criminal offense and ensure objectivity in achieving the objectives of criminal proceedings. Taking into account the specifics of the activity of the authorized subjects involved in the proof, there is a need to form the appropriate theoretical foundations of the outlined activity, which will ensure effective law enforcement. The article analyses the main concepts formed by scholars regarding the definition of «evidence collection», which differ in certain respects. The author provides a linguistic interpretation of such concepts as «collection» and «evidence». It is substantiated that the concept of «collection of evidence» may include such elements as: cognition, finding, perception, demand, seizure, preservation and recording. The author defines their definitions and draws attention to their functions in criminal procedure in the course of evidence collection. The conducted analysis proved that during the formation of the definition of the concept of «gathering evidence» by the parties of criminal proceedings: 1) it is appropriate to use such an element as «finding», which includes such terms as «search», «detection», «receiving», « obtaining» and which individual scientists define them as independent elements of the evidence-gathering process and use them alongside the term «finding»; 2) between such terms as «fixation» and «backing», which are synonyms, it is correct to use «fixation» as an element of the activity of gathering evidence, because it is broader in meaning. It is concluded that the gathering of evidence by the parties to criminal proceedings is a cognitive activity of the subjects authorized to do so, which consists in finding, perceiving, demanding, separation, preserving, fixing actual data that are important for criminal proceedings, in accordance with normative legal acts, decisions of the Constitutional Court of Ukraine, resolutions of the Plenum of the Supreme Court and taking into account the practice of the European Court of Human Rights. [ABSTRACT FROM AUTHOR]

This paper investigates the nonlinear dynamics and control of the deployment and retrieval for a spinning tethered satellite formation system via artificial intelligent method. A dynamic model of the spinning tethered formation system is developed to describe the attitude motions of the system, involving the relative rotations of the tethers to the central main satellite. Considering the system with symmetric and asymmetric configurations, a learning-based control strategy with low time cost is proposed to achieve the stable deployment and retrieval of tethers. In the strategy, a nonlinear model predictive control law accounting for the control constraints and nonlinear dynamics is developed to achieve the control goal. Based on a deep learning method, a dataset including control input and state output obtained offline is trained to form deep neural networks. An online feedback control of the system can be achieved by conducting real-time mapping from the system state to the control input using the neural networks. Finally, numerical simulations for deployment and retrieval of the system with different configurations are presented to demonstrate the computational efficiency and to validate the effectiveness of the control strategy. • Nonlinear dynamics and inherent constraints of the system are accounted for. • Model predictive control law is proposed for deployment/retrieval of the system. • DNNs are developed to achieve control goals with low computational costs. [ABSTRACT FROM AUTHOR]

In the literature on human action control, the binding and retrieval of responses are assumed to shape the coordination of more complex actions. Specifically, the consecutive execution of two responses is assumed to result in their integration into cognitive representations (so-called event files) and can be retrieved from that upon later response repetition, thereby influencing behavior. Against the background of ideomotor theory and more recent theorizing in the binding and retrieval in action control framework (Frings et al., 2020), we investigated whether response execution is necessary for binding and retrieval of responses. We manipulated whether the retrieving response (Experiment 1), as well as the to-be-bound response (Experiment 2), is executed or omitted. The results showed that responses do not need to be executed to retrieve other responses or to be bound to other responses. Apparently, activating the cognitive representation of a response sufficed for this response to trigger event file binding and retrieval. Our results are the first to show that response–response binding is not dependent on executing responses. Together, the results support the core assumptions of ideomotor theory and the binding and retrieval in action control framework, namely a common coding of action and perception. Public Significance Statement: In the literature on action control, it is assumed that human action control relies on the close interconnection of perception and action. According to action control theories, actions are represented by their perceptual effects. Thus, any activation of the effect representation is assumed to trigger the corresponding action representation. Here, we bring together predictions from ideomotor theory with binding approaches in action control. We present first evidence indicating that actions can be represented and retrieved from cognitive representations without necessarily being executed. [ABSTRACT FROM AUTHOR]

Retrievable inferior vena cava filters were designed to provide mechanical prophylaxis from pulmonary embolism with the intent for retrieval once no longer indicated, and have been utilized at a high rate since their introduction. Unfortunately, retrieval rates have historically lagged behind, in part due to significant rates of failed standard retrieval techniques for filters with prolonged dwell time. Refinement in advanced retrieval techniques has now allowed (in experienced centers) for safe removal of filters previously considered irretrievable. An individualized approach is necessary for each potential advanced filter retrieval to determine appropriate course of action. This review will emphasize complex filter retrieval techniques amidst a larger discussion of inferior vena cava filters and their management. [ABSTRACT FROM AUTHOR]

Transformer-based language models (TLMs) have widely been recognized to be a cutting-edge technology for the successful development of deep-learning-based solutions to problems and applications that require natural language processing and understanding. Like for other textual domains, TLMs have indeed pushed the state-of-the-art of AI approaches for many tasks of interest in the legal domain. Despite the first Transformer model being proposed about six years ago, there has been a rapid progress of this technology at an unprecedented rate, whereby BERT and related models represent a major reference, also in the legal domain. This article provides the first systematic overview of TLM-based methods for AI-driven problems and tasks in the legal sphere. A major goal is to highlight research advances in this field so as to understand, on the one hand, how the Transformers have contributed to the success of AI in supporting legal processes, and on the other hand, what are the current limitations and opportunities for further research development. [ABSTRACT FROM AUTHOR]

Retrospective confidence refers to the phenomenological experience of the level of certainty that retrieved information is, in fact, correct. Retrospective confidence judgments are examined across a range of sub-disciplines in psychology from perception to memory research, and in education and legal applications. This paper focuses on retrospective confidence judgments directed at memory. Typically, retrospective confidence judgments are explained by direct-access models. Direct-access models postulate that people have direct access to the strength of the retrieved memory. In contrast, inferential models posit that people use accessible heuristic cues to determine their retrospective confidence judgments. This paper outlines existing models from both the direct-access approach and the inferential approach. I then present the outcomes of studies that support the need to include inferential models in any explanation of retrospective confidence judgments. These heuristics include cue and encoding fluency, retrieval fluency, retrieval of related information, vividness of the retrieval, and self-consistency. I then present an integrative model to account for how retrospective confidence judgments are made. [ABSTRACT FROM AUTHOR]

Leadless pacemakers (LPs) are self-contained pacemakers implanted inside the heart, providing a clinical strategy of pacing without pacemaker leads or a subcutaneous pocket. From an experimental therapy first used clinically in 2012, a decade later this technology is an established treatment option. Because of technologic advances and growing evidence, LPs are increasingly being used. Herein, the experience gained from a decade of leadless pacing is reviewed. We cover the safety and efficacy of single-chamber LPs, including comparisons with transvenous pacemakers and various models, and the initial clinical results of the first dual-chamber LP system. Furthermore, evidence and considerations regarding the optimal replacement strategy will be covered. Finally, we discuss future device developments that may broaden indications, such as LPs communicating with subcutaneous implantable cardiac defibrillators and energy-harvesting LPs. [Display omitted] • LPs have been designed to avoid lead- and pocket-related complications encountered with conventional transvenous pacemakers. • Single-chamber LPs seem to have less overall short- and long-term complications than transvenous pacemakers, but challenges exist. • The adoption of leadless pacing is dependent on clinical experience, evidence, and technologic advances. [ABSTRACT FROM AUTHOR]

True and false memories recruit a number of shared brain regions; however, they are not completely overlapping. Extensive sex differences have been identified in the brain during true memories and, recently, we identified sex differences in the brain during false memories. In the current fMRI study, we sought to determine whether sex differences existed in the location and extent of overlap between true and false memories. True and false memories activated a number of shared brain regions. Compared to females, males produced a greater number of overlapping brain regions (8 versus 2 activations for males and females, respectively) including the prefrontal cortex, parietal cortex, and early/late visual processing cortices (including V1) in males and prefrontal and parietal cortices in females. Males had significantly higher similarity between true and false memory activation maps, revealed by our novel multi-voxel pattern correlation analysis. Moreover, higher similarity between true and false memory activation maps was associated with higher false memory rates. The current results suggest that true and false memories are more similar in males than females. The significant brain–behavior relationship also suggests that males may be more susceptible to false memory errors due to their highly similar true memory–false memory cortical representations. [ABSTRACT FROM AUTHOR]

Traditional disease retrieval and localization for plant leaves typically demand substantial human resources and time. In this study, an intelligent approach utilizing deep hash convolutional neural networks (DHCNN) is presented to address these challenges and enhance retrieval performance. By integrating a collision-resistant hashing technique, this method demonstrates an improved ability to distinguish highly similar disease features, achieving over 98.4% in both precision and true positive rate (TPR) for single-plant disease retrieval on crops like apple, corn and tomato. For multi-plant disease retrieval, the approach further achieves impressive Precision of 99.5%, TPR of 99.6% and F-score of 99.58% on the augmented PlantVillage dataset, confirming its robustness in handling diverse plant diseases. This method ensures precise disease retrieval in demanding conditions, whether for single or multiple plant scenarios. [ABSTRACT FROM AUTHOR]

Background: Intracanal medicament (ICM) eliminates remaining bacteria and their toxins that were not removed by chemomechanical preparation during endodontic treatment. Aim: The aim of this study was to compare and evaluate the removal of ozonated oil-based, silicone oil-based, and distilled water-based ICM with two calcium chelators, i.e., 0.2% chitosan and 17% ethylenediaminetetraacetic acid (EDTA). Materials and Methods: A total of 54 mandibular permanent premolars were included and randomly allocated into 3 groups after cleaning and shaping along with thorough irrigation. Group 1 - ozonized calcium hydroxide group; Group 2 - Metapex group; Group 3 - Ca(OH)2 with distilled water group. Using an irrigant for removal, the teeth in each group were distributed at random to two subgroups – (A) 0.2% chitosan solution; (B) 17% EDTA solution. All the samples were ultrasonically agitated and the volume remaining in each tooth after retrieval was estimated using additional cone-beam computed tomography scans. The result was analyzed using one-way analysis of variance, following post hoc Tukey test. Results: The mean percentage of ozonized Ca(OH)2, aqueous calcium hydroxide, and Metapex removed by 0.2% chitosan had greater values in comparison to 17% EDTA. When eliminating aqueous-based calcium hydroxide, both chelators showed similar effectiveness (P > 0.05). In contrast, 0.2% chitosan outperformed 17% EDTA in retrieving oil-based Ca(OH)2 (P < 0.05). Conclusion: The aqueous-based form of Ca(OH)2 was removed more easily compared to the oil-based form. A combination of 0.2% chitosan and ultrasonics proved to be more effective than 17% EDTA in eliminating oil-based calcium hydroxide. [ABSTRACT FROM AUTHOR]

BackgroundAimsMethods & proceduresOutcomes & resultsConclusionsPersons with aphasia (PWAs) are often impaired in time reference/tense production. It has been suggested that this impairment is due to encoding or/and retrieval deficits. However, to the best of our knowledge, no experimental design that enables teasing apart selective encoding and retrieval deficits has been proposed thus far.This study aims at disentangling time reference-related encoding deficits from time reference-related retrieval deficits in PWAs.Two sentence completion tasks tapping production of time reference and subject-verb agreement (control condition) were administered to eight Greek-speaking PWAs, eight Russian-speaking PWAs, six Italian-speaking PWAs, seven English-speaking PWAs and four groups of language-, age- and education-matched healthy controls. Task 1 tapped encoding and retrieval processes to a similar extent. Task 2 predominantly tapped retrieval processes. Comparisons between each PWA and the corresponding control group, as well as within-participant comparisons were performed.All four control groups performed at ceiling. Twenty-eight out of 29 PWAs were impaired in time reference in at least one of the two completion tasks, and all but three PWAs were impaired in production of subject-verb agreement in at least one of the two tasks. In all language groups, there were PWAs exhibiting between-task dissociations. A double dissociation emerged in the time reference condition, as some Greek-, Russian- and English-speaking PWAs performed better on Task 1 than on Task 2, whereas other Greek- and Italian-speaking PWAs performed worse on Task 1 than on Task 2. In the agreement condition, in each language group, there were PWAs performing better on Task 1 than on Task 2. However, none PWA exhibited the opposite pattern. Based on the results, we identified both PWAs with selective time reference-related encoding deficits and PWAs with selective time reference-related retrieval deficits.The present experimental design provides a sound basis for teasing apart selective time reference-related encoding deficits and time reference-related retrieval deficits. [ABSTRACT FROM AUTHOR]

The co-opinionatedness measure, that is, the similarity of cociting documents in their opinions about their cocited articles, has been recently proposed. The present study uses a wider range of baselines and benchmarks to investigate the measure's effectiveness in retrieval ranking that was previously confirmed in a pilot study. A test collection was built including 30 seed documents and their 4702 cocited articles. Their citances and full-texts were analysed using natural language processing (NLP) and opinion mining techniques. Cocitation values, syntactical similarity and contexts similarity were used as baselines. The distributional semantic similarity and the linear and hierarchical Medical Subject Headings (MeSH) similarities served as benchmarks to evaluate the effect of the co-opinionatedness as a boosting factor on the performance of the baselines. The improvements in the rankings were measured by normalised discounted cumulative gain (nDCG). According to the findings, there existed significant differences between the nDCG mean values obtained before and after weighting the baselines by the co-opinionatedness measures. The results of the generalisability study corroborated the reliability and generalisability of the systems. Accordingly, the similarity in the opinions of the cociting papers towards their cocited articles can explain the cocitation relation in the scientific papers network and can be effectively utilised for improving the results of the cocitation-based retrieval systems. [ABSTRACT FROM AUTHOR]

Theta oscillations support memory formation, but their exact contribution to the communication between prefrontal cortex (PFC) and the hippocampus is unknown. We tested the functional relevance of theta oscillations as a communication link between both areas for memory formation using transcranial alternating current stimulation (tACS). Healthy, young participants learned two lists of Dutch-German word pairs and retrieved them immediately and with a 30-min delay. In the encoding group (N = 30), tACS was applied during the encoding of list 1. List 2 was used to test stimulation aftereffects. In the retrieval group (N = 23), we stimulated during the delayed recall. In both groups, we applied tACS bilaterally at prefrontal and tempo-parietal sites, using either individualized theta frequency or 15 Hz (as control), according to a within-subject design. Stimulation with theta-tACS did not alter overall learning performance. An exploratory analysis revealed that immediate recall improved when word-pairs were learned after theta-tACS (list 2). Applying theta-tACS during retrieval had detrimental effects on memory. No changes in the power of the respective frequency bands were observed. Our results do not support the notion that impacting the communication between PFC and the hippocampus during a task by bilateral tACS improves memory. However, we do find evidence that direct stimulation had a trend for negatively interfering effects during immediate and delayed recall. Hints for beneficial effects on memory only occurred with aftereffects of the stimulation. Future studies need to further examine the effects during and after stimulation on memory formation. [ABSTRACT FROM AUTHOR]

Sketch-based cross-domain visual data retrieval is the process of searching for images or 3D models using sketches as input. Achieving feature alignment is a significantly challenging task due to the high heterogeneity of cross-domain data. However, the alignment process faces significant challenges, such as domain gap, semantic gap, and knowledge gap. The existing methods adopt different ideas for sketch-based image and 3D shape retrieval tasks, one is domain alignment, and the other is semantic alignment. Technically, both tasks verify the accuracy of extracted features. Hence, we propose a method based on the global feature correlation and the feature similarity for multiple sketch-based cross-domain retrieval tasks. Specifically, the data from various modalities are fed into separate feature extractors to generate original features. Then, these features are projected to the shared subspace. Finally, domain consistency learning, semantic consistency learning, feature correlation learning and feature similarity learning are performed jointly to make the projected features modality-invariance. We evaluate our method on multiple benchmark datasets. Where the MAP in Sketchy, TU-Berlin, SHREC 2013 and SHREC 2014 are 0.466, 0.473, 0.860 and 0.816. The extensive experimental results demonstrate the superiority and generalization of the proposed method, compared to the state-of-the-art approaches. The in-depth analyses of various design choices are also provided to gain insight into the effectiveness of the proposed method. The outcomes of this research contribute to advancing the field of sketch-based cross-domain visual data retrieval and are expected to be applied to a variety of applications that require efficient retrieval of cross-domain domain data. [ABSTRACT FROM AUTHOR]

The large and growing amount of digital data creates a pressing need for approaches capable of indexing and retrieving multimedia content. A traditional and fundamental challenge consists of effectively and efficiently performing nearest-neighbor searches. After decades of research, several different methods are available, including trees, hashing, and graph-based approaches. Most of the current methods exploit learning to hash approaches based on deep learning. In spite of effective results and compact codes obtained, such methods often require a significant amount of labeled data for training. Unsupervised approaches also rely on expensive training procedures usually based on a huge amount of data. In this work, we propose an unsupervised data-independent approach for nearest neighbor searches, which can be used with different features, including deep features trained by transfer learning. The method uses a rank-based formulation and exploits a hashing approach for efficient ranked list computation at query time. A comprehensive experimental evaluation was conducted on seven public datasets, considering deep features based on CNNs and Transformers. Both effectiveness and efficiency aspects were evaluated. The proposed approach achieves remarkable results in comparison to traditional and state-of-the-art methods. Hence, it is an attractive and innovative solution, especially when costly training procedures need to be avoided. [ABSTRACT FROM AUTHOR]

Unseen translation forms a central part of assessment and teaching both in the Latin GCSE and A Level. Developing skills for the unseen translation presents several challenges for the classroom teacher; unseens can be introduced using a scaffolded approach, yet pupils must learn to develop independence for the examination. Unseens can often take an entire lesson, or more to translate, precluding the opportunity for meaningful, immediate feedback. Furthermore, classes of mixed- ability students often suffer from staggered completion rates and unequal attention being divided among students. Within the curriculum, unseen classes can additionally suffer from feeling severed from the specification as passages contain unfamiliar material, the content is discrete from the set texts, and lack of an overarching framework for approaching unseens can make them feel irrelevant to pupils. Therefore, striking a balance between productive support for in-class unseen translation practice and nurturing pupil confidence requires a clear strategy. This article investigates the effect of two methods of formative assessment used in preparation for an unseen translation lesson with a year 12 class. Reflecting on the results of the investigation, this article discusses the opportunities different tasks may afford the Latin teacher for developing a vocabulary curriculum that supports long-term retention of vocabulary, increases the efficiency of unseen classes, and allows the unseen passage to be received as part of a wider framework of learning.

AbstractIn two experiments on analogical hypothesis generation, we factorially manipulated the presence of domain and object similarities between a base situation and a target phenomenon, and assessed their effects on the transfer of the source’s explanatory structure before and after an indication to use the base analog as a source for analogical explanations. The absence of any kind of surface similarity led to very low rates of spontaneous transfer. In both experiments, however, either kind of surface similarity sufficed to enhance the spontaneous transfer of the base explanation during the formulation of plausible hypotheses for the target. The transfer advantage of object and domain similarity cannot be attributed to the effect of these variables on post-access processes, since experimental conditions did not differ with regard to the ability to transfer the base explanation upon explicit request. The effect of domain similarity on spontaneous analogical explanation constitutes a relevant finding, especially given the lack of attention received by this dimension of similarity in behavioural studies and computer simulations of analogical retrieval. [ABSTRACT FROM AUTHOR]

Significant Wave Height (SWH) is a crucial parameter in oceanographic research, essential for understanding various marine and atmospheric processes. Traditional methods for obtaining SWH, such as ship-based and buoy measurements, face limitations like limited spatial coverage and high operational costs. With the advancement of Global Navigation Satellite Systems reflectometry (GNSS-R) technology, a new method for retrieving SWH has emerged, demonstrating promising results. This study utilizes Radio occultation sounder (GNOS) data from the FY-3E satellite and incorporates the latest Vision Transformer (ViT) technology to investigate GNSS-R-based SWH retrieval. We designed and evaluated various deep learning models, including ANN-Wave, CNN-Wave, Hybrid-Wave, Trans-Wave, and ViT-Wave. Through comparative training using ERA5 data, the ViT-Wave model was identified as the optimal retrieval model. The ViT-Wave model achieved a Root Mean Square Error (RMSE) accuracy of 0.4052 m and Mean Absolute Error (MAE) accuracy of 0.2700 m, significantly outperforming both traditional methods and newer deep learning approaches utilizing Cyclone Global Navigation Satellite Systems (CYGNSS) data. These results underscore the potential of integrating GNSS-R technology with advanced deep-learning models to enhance SWH retrieval accuracy and reliability in oceanographic research. [ABSTRACT FROM AUTHOR]

Objectives Methods Results Conclusion ‘Dirty adrenaline’ is the informal term used for a rapidly made peripheral dilute adrenaline infusion in the emergency treatment of shock, most commonly 1 mg adrenaline in 1 L 0.9% NaCl. It has long been part of the remote clinician's arsenal despite no supporting scientific literature. Remote clinics in Central Australia can be hours away from critical care support. The region's high prevalence of renal and cardiac disease means that access to early vasopressors and inotropes is a necessity for treating shock. To tackle this, remote clinicians often use ‘dirty adrenaline’. We present a review of ‘dirty adrenaline’ use in this region.Central Australian Retrieval Service's database was screened to identify cases in which a peripheral dilute adrenaline infusion was administered in a remote clinic prior to patient aeromedical retrieval. A retrospective chart review collected: patient demographics; clinical characteristics; infusion details; adverse events; hospital lengths of stay; and mortality outcomes.Fifty‐seven cases were identified. Median patient age was 50 (range: 2–96). Septic shock was the most common clinical indication (40/57). Median infusion duration was 155 min. Median systolic BP from commencement until retrieval increased from 75.5 to 91 mmHg. Survival to hospital discharge was 86% (49/57). No significant adverse events associated with ‘dirty adrenaline’ were recorded.‘Dirty adrenaline’ is safe to administer and appears to considerably improve survival when used to treat fluid‐resistant shock in remote nurse‐led clinics guided by an off‐site critical care physician. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Content-based medical image retrieval (CBMIR) has become an important part of computer-aided diagnostics (CAD) systems. The complex medical semantic information inherent in medical images is the most difficult part to improve the accuracy of image retrieval. Highly expressive feature vectors play a crucial role in the search process. In this paper, we propose an effective deep convolutional neural network (CNN) model to extract concise feature vectors for multiple semantic X-ray medical image retrieval. METHODS: We build a feature pyramid based CNN model with ResNet50V2 backbone to extract multi-level semantic information. And we use the well-known public multiple semantic annotated X-ray medical image data set IRMA to train and test the proposed model. RESULTS: Our method achieves an IRMA error of 32.2, which is the best score compared to the existing literature on this dataset. CONCLUSIONS: The proposed CNN model can effectively extract multi-level semantic information from X-ray medical images. The concise feature vectors can improve the retrieval accuracy of multi-semantic and unevenly distributed X-ray medical images. [ABSTRACT FROM AUTHOR]

This study investigates user opinion, satisfaction level, and challenges towards institutional repositories. The researchers adopted survey method, questionnaire tools, and simple random sampling techniques and collected 548 respondents from the autonomous institutions of the Department of Science and Technology. The mean analysis on opinion towards the benefits of an institutional repository reveals that “It promotes self-archiving 24*7” has the highest mean score, while concerning the satisfaction level of users “I am satisfied with membership facility” has the highest mean score, and vis-à-vis overall problems “Electricity problem” has the highest mean score. The result indicates that age has a significant difference with library facility (p=.001), retrieval (p=.000), searching and browsing facility (p=.001), usability (p=.012), and copyright issue (p=.002). Concerning respondent’s designation, there is a significant difference in the library facility (p=0.027), retrieval (p=0.001), usability (p=0.005), awareness problem (p=0.005), and copyright issues (p=0.001). This study bridged the existing literature gap by determining various factors affecting users’ opinions, satisfaction levels, and overall problems. Further, examine the significant difference between demographic variable and study variables. [ABSTRACT FROM AUTHOR]

A machine learning algorithm combined with measurements obtained by a NILU-UV irradiance meter enables the determination of total ozone column (TOC) amount and cloud optical depth (COD). In the New York City area, a NILU-UV instrument on the rooftop of a Stevens Institute of Technology building (40.74° N, −74.03° E) has been used to collect data for several years. Inspired by a previous study [Opt. Express 22, 19595 (2014)], this research presents an updated neural-network-based method for TOC and COD retrievals. This method provides reliable results under heavy cloud conditions, and a convenient algorithm for the simultaneous retrieval of TOC and COD values. The TOC values are presented for 2014–2023, and both were compared with results obtained using the look-up table (LUT) method and measurements by the Ozone Monitoring Instrument (OMI), deployed on NASA's AURA satellite. COD results are also provided. [ABSTRACT FROM AUTHOR]

CEA has manufactured a series of Fukushima Daiichi fuel debris simulants, either with depleted uranium oxide or with hafnium oxide as a surrogate of UO2. In ex-vessel compositions resulting from an interaction between corium and concrete, the oxidic phase density becomes lighter than that of the metallic phase, which segregates at the bottom. Three of these metallic phases have been mechanically cut at CEA Cadarache with handsaw and with core boring tool in FUJISAN facility. It appeared that two of these metallic blocks were extremely hard to cut (one from a fabrication with uranium oxide, the other from a simulant block) while the last one was more easily cut. The similarities and differences in metallographic analyses (SEM-EDS and XRD) of these three metal blocks will be presented and discussed. This experience provides useful learnings in view of the cutting and retrieval of fuel debris from Fukushima Daiichi. [ABSTRACT FROM AUTHOR]

Double auction provides a cost-effective manner for sellers/buyers in smart grid. Due to concerns about information leakage, the asks/bids from sellers/buyers are sealed, making it challenging to select potential winners. To address this problem, the concept of public key encryption with equality test is deployed in double auction, since it is able to perform information retrieval over secure asks/bids. However, previous solutions suffer from the following two limitations: (i) unable to check inconsistent secure asks/bids due to the lack of tester-verifiable mechanism; (ii) incurring high matching time costs caused by one-to-one secure asks/bids. Therefore, we propose the VerDA, a secure double auction retrieval system with verifiable equality retrieval towards multiple secure asks/bids. Technically, to achieve the property of consistency over secure asks/bids, we develop the tester-verifiable technology by combining the decryption module and test module. To improve the efficiency of retrieval, we introduce secure multi-asks/bids testing function by augmenting the number of inputs in a same retrieval process. Moreover, we implement VerDA based on the PJM dataset in real cloud environment, where the experimental results show practical performance with encryption and test costs amounting to only 57.4% and 18.7% compared to state-of-the-art solution. [ABSTRACT FROM AUTHOR]

Despite the high diversity of elasmobranch fishes in the Balearic Islands, knowledge of their biology and population dynamics is still scarce. A recent mark-and-recapture experiment off the Balearic Islands tagged 3738 individuals of 23 shark and batoid species during MEDITS and CANAL bottom trawl scientific surveys from June 2021 to August 2023. Retrieval was reported for the sharks Scyliorhinus canicula and Mustelus mustelus, revealing relatively small home ranges for these species (0.2–38.5 km and 7.8–15.3 km for S. canicula and M. mustelus, respectively). Recapture efficiency was higher from scientific surveys than from commercial catches, highlighting potential challenges in collaboration with fishermen and recapture reports. Density estimates obtained from the MEDITS bottom trawl survey suggest a much larger population for S. canicula than estimates from the mark–recapture data, indicating MEDITS density estimates for this species may be overestimated due to its scavenger behavior perhaps favoring individuals searching for discards aggregated in the fishing grounds. This study emphasizes the importance of monitoring, collaborative efforts, and improved reporting mechanisms to enhance our understanding of elasmobranch populations and provide support for sustainable management of these vulnerable marine species. [ABSTRACT FROM AUTHOR]

Inferior vena cava (IVC) filters are used to prevent fatal and nonfatal pulmonary embolism in patients who otherwise cannot receive anticoagulation for venous thrombosis. While generally safe and effective, complications can arise, especially after prolonged implantation. Timely retrieval is essential once the indication for insertion has resolved. However, encountering patients with long-standing embedded filters is not uncommon. This case report discusses the successful retrieval of a permanent Greenfield IVC filter after 29 years. [ABSTRACT FROM AUTHOR]

Dual-tasks at the memory encoding stage have been shown to decrease recall performance and impair concurrent task performance. In contrast, studies on the effect of dual-tasks at the memory retrieval stage observed mixed results. Which cognitive mechanisms are underlying this dual-task interference is still an unresolved question. In the present study, we investigated the influence of a concurrent reaction-time task on the performance in a long-term memory task in two experiments. In Experiment 1, participants performed an auditory-verbal free recall memory task and a visual-manual spatial Stroop task in a single or dual-task condition, either at the encoding or retrieval stage of the memory task. In Experiment 2, we examined the influence of processing conflicts in a concurrent RT task on memory encoding. Both experiments showed detrimental effects on recall accuracy and concurrent RT task performance in dual-task conditions at the encoding stage. Dual-task conditions at the retrieval stage led to a slowdown in recall latency and impaired concurrent RT task performance, but recall accuracy was maintained. In addition, we observed larger Stroop congruency effects in the dual-task conditions, indicating an increased processing conflict. However, in Experiment 2, we analyzed the effect of the processing conflict in a time-locked manner and could not find a significant influence on success of memory encoding. These findings suggest that processes in both tasks share the same limited capacity and are slowed down due to parallel processing, but we could not find evidence that this is further influenced by task-specific processing conflicts. [ABSTRACT FROM AUTHOR]

Engaging the retrieval state (Tulving, 1983) impacts processing and behavior (Long and Kuhl, 2019, 2021; Smith et al., 2022), but the extent to which top-down factors--explicit instructions and goals--versus bottom-up factors--stimulus properties such as repetition and similarity--jointly or independently induce the retrieval state is unclear. Identifying the impact of bottom-up and top-down factors on retrieval state engagement is critical for understanding how control of task-relevant versus task-irrelevant brain states influence cognition. We conducted between-subjects recognition memory tasks on male and female human participants in which we varied test phase goals. We recorded scalp electroencephalography and used an independently validated mnemonic state classifier (Long, 2023) to measure retrieval state engagement as a function of top-down task goals (recognize old vs detect new items) and bottom-up stimulus repetition (hits vs correct rejections (CRs)). We find that whereas the retrieval state is engaged for hits regardless of top-down goals, the retrieval state is only engaged during CRs when the top-down goal is to recognize old items. Furthermore, retrieval state engagement is greater for low compared to high confidence hits when the task goal is to recognize old items. Together, these results suggest that top-down demands to recognize old items induce the retrieval state independent from bottom-up factors, potentially reflecting the recruitment of internal attention to enable access of a stored representation. [ABSTRACT FROM AUTHOR]

Passive microwave radiometers onboard satellites rely on the received upwelling radiation to retrieve precipitation, which is a mixed signal from the surface, atmosphere and precipitation hydrometeors. Liquid precipitation droplets increase the upwelling radiation from the surface at lower frequencies, while ice particles cause a decrease in upwelling radiation at higher frequencies. The task of the retrieval algorithm is to identify the precipitation phase and to isolate the signal of precipitation from that of the surface. This study develops a machine learning method to retrieve rainfall and snowfall rates based on observations from the Microwave Hydrometer Sounder and Microwave Temperature Sounder onboard FY‐3E. Self‐organized mapping (SOM) is selected to classify the precipitation and underlying surface types, and an artificial neural network (ANN) is subsequently used to relate the brightness temperature to the precipitation rate for the clusters derived from the SOM. The half‐hour product of the Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) is used to train the ANN. To address the issue that number of heavy precipitation samples are not enough for training, the simulation of radiative transfer for TOVS is used as a supplement to heavy rain samples. The SOM‐ANN algorithm outperforms the IMERG and Goddard profiling algorithm (GPROF) retrieval products in both rainfall and snowfall retrieval. Compared with the hourly observations at ∼4,400 stations during a 2‐year period, the root mean square errors of SOM‐ANN proposed here are 1.06 and 0.34 mm/hr for the rainfall and snowfall rates, which are better than those of IMERG (1.23 and 0.42 mm/hr) and GPROF (1.22 and 0.44 mm/hr). Plain Language Summary: FY‐3E is the first dawn‐dusk orbit meteorological satellite for civil use, and the development of a retrieval algorithm supplements the lack of passive microwave observations in current global precipitation constellation during this period. The Microwave Hydrometer/Temperature Sounder (MWHS/MWTS) onboard FY‐3E can capture radiation information about water vapor, raindrops, and ice crystals, which can be used to retrieve rainfall/snowfall. In particular, the 166 and 183 GHz channels are proved to have advantages in snowfall retrieval. Self‐organized mapping (SOM) is selected to classify the precipitation and underlying surface types, and an artificial neural network (ANN) is subsequently used to relate the brightness temperature to the precipitation rate for the clusters derived from the SOM. The evaluation against gauge observations at ∼4,400 stations over the north hemisphere land during a two year period indicates that the SOM‐ANN method improves the accuracy of rainfall/snowfall retrieval. Key Points: A machine learning method is proposed to retrieve rainfall and snowfall rates from passive microwave observations from the FY‐3E satelliteThe addition of radiative transfer model enhances the retrieval performance in precipitation intensity distributionThe method better handles the impacts of the underlying surface and precipitation phases, improving the accuracy of the retrieval [ABSTRACT FROM AUTHOR]

Objective: To assess whether a new strategy for the choice of inferior vena cava filter placed would improve filter retrieval rates at our institution. Methods: Consecutive patients who underwent retrievable filter placement for temporary embolic protection between January 2021 and January 2023 were considered for study inclusion. Risk factors for nonretrieval of short-term filters were identified in patients receiving filters between January 2021 and January 2022 (prestrategy group). For patients treated between February 2022 and January 2023 (poststrategy group), a long-term filter was recommended for those with these risk factors, and a short-term filter was recommended for those without these risk factors. Results: The study population included 303 patients (prestrategy group, n = 154; poststrategy group, n = 149). Long-term immobilization (odds ratio [OR] = 38.000; 95% confidence interval [CI]: 6.858-210.564), active cancer (OR = 17.643; 95% CI: 5.462–56.993), and venous thromboembolism detected in the intensive care unit (OR = 28.500; 95% CI: 7.419-109.477) were identified as independent risk factors for nonretrieval of short-term filters. The total retrieval rate was significantly higher in the poststrategy group (87.2%) than in the prestrategy group (72.7%; P = 0.002); the short-term filter retrieval rate was also significantly higher in the poststrategy group (84.5%) than in the prestrategy group (68.5%; P < 0.001). Conclusion: The proposed strategy for filter choice based on risk factors for short-term filter nonretrieval can accurately identify patients who need long-term filter placement while also increasing the retrieval rates for both short-term filters retrieval rates and overall retrieval rates. [ABSTRACT FROM AUTHOR]

Recent research suggests that the retrieval of autobiographical memories among cognitively healthy middle‐aged and older adults is sensitive to the Apolipoprotein E ε4 (APOE4) allele, a genetic marker that increases the risk of Alzheimer's disease (AD) dementia. However, whether the APOE4‐associated alteration in autobiographical memory retrieval encompasses rapid (i.e. direct retrieval) or iterative (i.e. generative retrieval) processes remains unclear. In the present study, 39 APOE4 carriers and 45 non‐carriers (ages 60–80) who scored within normal limits on neuropsychological testing were cued to generate specific autobiographical events. We examined group differences in direct and generative retrieval and correlated direct and generative retrieval rates with performance on neuropsychological tests. Direct retrieval rates were lower in the APOE4 carriers compared to non‐carriers. Episodic memory positively correlated with direct retrieval rates across the sample, though this relationship became non‐significant when factoring in age and sex. There were no significant findings related to successful generative retrieval rates and its efficiency. In summary, compared to non‐carriers, cognitively unimpaired middle‐aged to older adult APOE4 carriers demonstrated greater difficulty, rapidly reconstructing specific autobiographical events without the support of semantic memory, suggesting that early autobiographical memory retrieval processes demonstrate vulnerability to AD‐related risk factors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Babylon Center for Humanities Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Psychological Science is the property of Psychological Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aim: To evaluate the effectiveness of electrochemical dissolution of stainless-steel #15 K-files in various irrigating solutions as well as their dissolution time. Materials and Methods: Twenty Dentsply Maillefer single-rooted endoblocks were used. A glide path was made up to a #15 K-file, and then, a ProTaper gold rotary instrument was used for preparation up to S1. Platinum wire electrodes, connecting wires, and a 9 V battery were used to form a circuit. Saline, 3% sodium hypochlorite, 17% EDTA liquid, and acidified hydrogen peroxide (HCl 10 g/L + H2 O2 100 g/L) were all used to submerge the endoblocks. The samples were assessed for dissolution time using a stopwatch, and electrochemical studies were performed for corrosion analysis of different irrigants. Results: The fastest dissolution of the files was observed with sodium hypochlorite (2 min 47 s), followed by acidified hydrogen peroxide (9 min 36 s). The presence of chloride ions in electrolyte solutions has been shown to be a factor directly affecting the rate of dissolution. EDTA demonstrated resilience to corrosion. The electrochemical studies confirmed a corrosion affinity with NaOCl and acidified hydrogen peroxide. Conclusion: The best solutions for dissolving stainless-steel K-files are sodium hypochlorite and acidified hydrogen peroxide. For clinical application and assessment of soft tissue effects, more studies are required. Although electrochemical dissolution has potential, it needs to be further studied before it can be used in therapeutic settings. [ABSTRACT FROM AUTHOR]

ObjectiveTo develop and validate a nomogram for predicting non-retrieval of the short-term retrievable inferior vena cava (IVC) filters.MethodsIn this study, univariate and multivariate logistic regression analyses were performed to identify predictive factors of short-term retrievable filter (Aegisy or OptEase) non-retrieval, and a nomogram was then established based on these factors. The nomogram was created based on data from a training cohort and validated based on data from a validation cohort. The predictive value of the nomogram was estimated using area under the curve (AUC) and calibration curve analysis (Hosmer-Lemeshow test).ResultsA total of 1,321 patients who had undergone placement of short-term retrievable filters (Aegisy or OptEase) were included in the analysis. The overall retrieval rate was 68.7%. Age, proximal and distal deep vein thrombosis (DVT) vs. distal DVT, active cancer, history of long-term immobilization, VTE was detected in the intensive care unit, active/recurrent bleeding, IVC thrombosis, and history of venous thromboembolism were independent predictive risk factors for non-retrieval of filters. Interventional therapy for DVT, acute fracture, and interval of ≥14 days between filter placement and patient discharge were independent protective factors for non-retrieval of filters. The nomogram based on these factors demonstrated good ability to predict the non-retrieval of filters (training cohort AUC = 0.870; validation cohort AUC = 0.813.ConclusionThis nomogram demonstrated strong predictive accuracy and discrimination capability. This model may help clinicians identify patients who are not candidates for short-term retrievable filter placement and help clinicians make timely, individualized decisions in filter choice strategies.

Leaf chlorophyll content (LCC) retrieval from remote sensing imagery is essential for monitoring vegetation growth and stress in the agroforestry industry. Many remote sensing inversion methods for estimating LCC primarily rely on 1D radiative transfer models (RTMs) that abstract canopies into horizontal layers or simple geometric primitives. Yet, this methodology faces challenges when applied to heterogeneous canopies, particularly in fine-scale mapping where each pixel's reflectance is significantly influenced by its surroundings, e.g. crown shadows. While 3D RTMs hold promise for addressing these challenges by explicitly describing complex canopy structures, their computational demands and the complexity involved in parameterizing detailed 3D structures limit the generation of extensive training datasets, requiring simulations across numerous parameter combinations. In this study, we used a semi-empirically accelerated 3D RTM, termed Semi-LESS, with a 1D residual network to accurately retrieve leaf chlorophyll content (LCC) from UAV images and LiDAR data at a 3-m resolution. We first reconstructed structures of forest plots using UAV LiDAR point cloud, based on which, UAV images with varying leaf and soil optical properties are simulated using the Semi-LESS. Subsequently, a training dataset consisting LCC and its corresponding reflectance was generated from the simulated UAV images by focusing on sunlit pixels. A 1D residual network is trained using the training dataset for LCC estimation. For comparison, we also trained an estimation model using a dataset generated from PROSAIL. The results show that estimation model trained with Semi-LESS surpasses PROSAIL in retrieving LCC from both simulation datasets and field measurements of two forest plots. The RMSE of Semi-LESS was 5.40–6.92 µg/cm2 for simulation datasets and 8.21–9.76 µg/cm2 for field measurements, whereas PROSAIL exhibited lower accuracy with an RMSE of 7.76–9.83 µg/cm2 for simulation datasets and 12.76–13.06 µg/cm2 in field measurements. The results demonstrate that Semi-LESS coupled with deep learning is reliable and has great potential for LCC mapping using UAV images, which is particularly useful for fine-scale applications such as crop and orchard monitoring. This approach also highlights the impact of shadows on LCC retrieval.

The Global Precipitation Measurement (GPM) Microwave Imager (GMI) is a microwave (MW) radiometer that has near-global coverage and frequent revisit time. To date, operational total column water vapor (TCWV) data records from the GPM GMI sensor have been exclusively offered over oceanic regions. It is challenging to retrieve TCWV over land from satellite MW measurements because of varying land surface characteristics. In this paper, a novel Light Gradient Boosting Machine-based retrieval algorithm is proposed to derive TCWV over land from GMI-sensed MW brightness temperature (BT) observations. The GMI-observed MW BT at 18.7 GHz and 23.8 GHz, differential BT between 18.7 GHz and 23.8 GHz, latitude, longitude, and month are selected and utilized as the input variables of the retrieval approach, because of their strong correlation with satellite-sensed MW TCWV retrievals. Instead of surface emissivity data or radiative transfer model, we take into account the spatial and temporal elements, namely latitude, longitude, and month. The training of the retrieval method is performed based on ground-based TCWV estimates from worldwide 4,471 Global Navigation Satellite System (GNSS) stations in 2017. The performance of the newly proposed retrieval algorithm is independently validated in a worldwide coverage using reference TCWV from additional 4,341 GNSS stations in 2018–2020 and 605 radiosonde stations in 2017–2020. The newly retrieved TCWV estimates over land have a correlation coefficient of 0.76 and 0.83, a root-mean-square error (RMSE) of 5.82 mm and 6.02 mm, a relative RMSE of 34.91% and 34.36%, and a mean bias of 0.02 mm and −0.42 mm compared to reference TCWV from GNSS and radiosonde, respectively. The performance of the retrieval algorithm is satisfactory when compared to that of land-purpose TCWV of other satellite missions, though we have not used either surface emissivity data or radiative transfer model. This result increases confidence in retrieving TCWV over land from satellite-sensed MW BT measurements based on machine learning using ground-based TCWV observations. The newly developed retrieval algorithm has the potential for integration into operational satellite missions or meteorological services, thereby enhancing weather forecasting, climate modeling, and other relevant applications.

Traditional disease retrieval and localization for plant leaves typically demand substantial human resources and time. In this study, an intelligent approach utilizing deep hash convolutional neural networks (DHCNN) is presented to address these challenges and enhance retrieval performance. By integrating a collision-resistant hashing technique, this method demonstrates an improved ability to distinguish highly similar disease features, achieving over 98.4% in both precision and true positive rate (TPR) for single-plant disease retrieval on crops like apple, corn and tomato. For multi-plant disease retrieval, the approach further achieves impressive Precision of 99.5%, TPR of 99.6% and F-score of 99.58% on the augmented PlantVillage dataset, confirming its robustness in handling diverse plant diseases. This method ensures precise disease retrieval in demanding conditions, whether for single or multiple plant scenarios.

Hardware breakage in the form of guide wire or drill bit is a devastating complication particularly if occurs around hip joint during cephalo-medullary nailing. It should be removed on urgent basis as it can migrate to the pelvic cavity and damages adjacent neuro-vascular bundle and visceral organ immediately as well joint arthritis later on. There are very few cases report available in the literature with retrieval techniques by using disc forceps, pituitary forceps, oversized reamers, arthrotomy with joint dislocation and through ilioinguinal approaches occasionally. We are presenting an interesting and rare case of broken guide wire inside hip joint protruding into the pelvic cavity which got retrieved through lower midline open laparotomy approach. [ABSTRACT FROM AUTHOR]