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Laser microdissection technology is favored by biomedical researchers for its ability to rapidly and accurately isolate target cells and tissues. However, the precision cutting capabilities of existing laser microdissection systems are hindered by limitations in overall mechanical movement accuracy, resulting in suboptimal cutting quality. Additionally, the use of current laser microdissection systems for target acquisition may lead to tissue burns and reduced acquisition rates due to inherent flaws in the capture methods. To address these challenges and achieve precise and efficient separation and capture of cellular tissues, we integrated a digital micromirror device (DMD) into the existing system optics to modulate spatial light. This allows the system to not only implement the traditional point scanning cutting method but also utilize the projection cutting method.We have successfully cut various patterns on commonly used laser microdissection materials such as PET films and mouse tissues. Under projection cutting mode, we were able to achieve precise cutting of special shapes with a diameter of 7.5 micrometers in a single pass, which improved cutting precision and efficiency. Furthermore, we employed a negative pressure adsorption method to efficiently collect target substances. This approach not only resulted in a single-pass capture rate exceeding 90% for targets of different sizes but also enabled simultaneous capture of multiple targets, overcoming the limitations of traditional single-target capture and enhancing target capture efficiency, and avoiding potential tissue damage from lasers.In summary, the integration of the digital micromirror device into laser microdissection systems significantly enhances cutting precision and efficiency, overcoming limitations of traditional systems. This advancement demonstrates the accuracy and effectiveness of laser microdissection systems in isolating and capturing biological tissues, highlighting their potential in medical applications. [ABSTRACT FROM AUTHOR]

The calculation of target distribution area is a prerequisite for anti-ship missile tactical decision-making and target acquisition probability calculation. On the basis of studying the target pursuit model of anti-ship missile, based on the relaxation variable method, the calculation equation for the target distribution is established, sufficient and necessary conditions are proved, the analytical model of target distribution area is obtained. Under the assumption that the target movement direction is arbitrary, and the calculation form under general conditions is given. Through the simulation example, the variation of distribution area under different conditions is compared, which can provide intuitive reference and estimation basis for the decision-making and use of the battle commanders. [ABSTRACT FROM AUTHOR]

The Terminal Tracking Camera (TTCam) imaging system on the NASA Lucy Discovery mission consists of a pair of cameras that are being used mainly as a navigation and target acquisition system for the mission's asteroid encounters. However, a secondary science‐focused function of the TTCam system is to provide wide‐angle broadband images over a large range of phase angles around close approach during each asteroid flyby. The scientific data acquired by TTCam can be used for shape modeling and topographic and geologic analyses. This paper describes the pre‐flight and initial in‐flight calibration and characterization of the TTCams, including the development of a radiometric calibration pipeline to convert raw TTCam images into radiance and radiance factor (I/F) images, along with their uncertainties. Details are also provided here on the specific calibration algorithms, the origin and archived location of the required ancillary calibration files, and the archived sources of the raw calibration and flight data used in this analysis. Key Points: We detail the pre‐flight and initial in‐flight characterization and calibration of the NASA Lucy mission's Terminal Tracking Camera systemPre‐flight results primarily include sensor and system characterization (gain, dark current, linearity, flat field, bad pixels, and radiometry estimates)We describe the calibration pipeline as well as initial in‐flight optical assessment and refined radiometry results from Earth, Moon and star field imaging [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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.)

Within the majority of English-speaking settings, children learning English as an additional language (CLEAL) underperform academically relative to their monolingual, English-speaking peers. This systematic review (preregistration: https://archive.org/details/osf-registrations-zgmha-v1) identifies the elements of the English oral language where these children experience difficulty. We focused on research measuring an English oral language outcome for CLEAL between the ages of 3 and 7 and their age-matched, monolingual English-speaking peers. We searched PsycINFO, Web of Science, British Education Index and Linguistics and Language Behaviour Abstracts, for studies published between 2015 and 2024, written in English. Of the 2125 records identified, 20 were eligible for inclusion, with a low-to-moderate risk of bias. Overall, our target group performed significantly worse in tests of English receptive and expressive vocabulary, syntactic knowledge, listening comprehension, embedding, collocational knowledge and areas of narrative skill and phonological production. However, these children were comparable with their peers in other areas of narrative skill, lexical and pragmatic understanding, elements of morphological production and areas of phonological production and processing. Overall, while the picture is mixed, highlighting the need for further study of oral English in CLEAL, this review strengthens existing arguments for vocabulary and syntax acquisition as appropriate targets for intervention. [ABSTRACT FROM AUTHOR]

Context. The GRAVITY+ upgrade implies a complete renewal of its adaptive optics (AO) systems. Its complex design, featuring moving components between the deformable mirrors and the wavefront sensors, requires the monitoring and auto-calibrating of the lateral mis-registrations of the system while in operation. Aims. For preset and target acquisition, large lateral registration errors must be assessed in open loop to bring the system to a state where the AO loop closes. In closed loop, these errors must be monitored and corrected, without impacting the science. Methods. With respect to the first requirement, our method is perturbative, with two-dimensional modes intentionally applied to the system and correlated to a reference interaction matrix. For the second requirement, we applied a non-perturbative approach that searches for specific patterns in temporal correlations in the closed loop telemetry. This signal is produced by the noise propagation through the AO loop. Results. Our methods were validated through simulations and on the GRAVITY+ development bench. The first method robustly estimates the lateral mis-registrations, in a single fit and with a sub-subaperture resolution while in an open loop. The second method is not absolute, but it does successfully bring the system towards a negligible mis-registration error, with a limited turbulence bias. Both methods proved to robustly work on a system still under development and not fully characterised. Conclusions. Tested with Shack-Hartmann wavefront sensors, the proposed methods are versatile and easily adaptable to other AO instruments, such as the pyramid, which stands as a baseline for all future AO systems. The non-perturbative method, not relying on an interaction matrix model and being sparse in the Fourier domain, is particularly suitable to the next generation of AO systems for extremely large telescopes that will present an unprecedented level of complexity and numbers of actuators. [ABSTRACT FROM AUTHOR]

The idea of target expansion was proposed two decades ago for manual target acquisition, but it is not feasible to implement this idea in traditional user interfaces as the interactive system cannot know exactly which target is the desired one and should be expanded among several candidates. With the increasing maturity of eye tracking technology, gaze input has moved from an academically promising technique to an input method with built-in support in Windows 10; and target expansion has already become very feasible in the context of gaze input, as the user's eye gaze is inherently an indicator of the desired target due to the natural eye-hand coordination in everyday tasks. However, a comprehensive evaluation is still lacking. In this study, two experiments were conducted, each with a different group of subjects, to investigate the effects of target expansion under different expansion feedback styles (visible vs. invisible), expansion factors, as well as different target appearances (i.e. circular vs. rectangular). The experimental results indicated that (1) the index of difficulty in eye pointing tasks (ID eye) does not depend on the initial size of the target, but on its final size, and that the corresponding human performance can be accurately predicted using the ID eye model instead of Fitts' law; and that (2) the visible expansion style could disrupt the user's fixations, making the measured human performance less efficient to some extent, but overall the theoretical predictions using the ID eye model were almost the same as the baselines. Following the experimental results, this study also provided some practical suggestions for UI design. [ABSTRACT FROM AUTHOR]

The calculation of target distribution area is a prerequisite for anti-ship missile tactical decision-making and target acquisition probability calculation. On the basis of studying the target pursuit model of anti-ship missile, based on the relaxation variable method, the calculation equation for the target distribution is established, sufficient and necessary conditions are proved, the analytical model of target distribution area is obtained. Under the assumption that the target movement direction is arbitrary, and the calculation form under general conditions is given. Through the simulation example, the variation of distribution area under different conditions is compared, which can provide intuitive reference and estimation basis for the decision-making and use of the battle commanders.

Deep learning network has the advantages of strong learning ability, strong adaptability, and good portability. Therefore, synthetic aperture radar (SAR) automatic target recognition (ATR) based on deep network is widely used in both military and civilian fields. However, due to the imaging conditions, radar angle, imaging distance, and other reasons, it is difficult to obtain efficient and usable SAR image datasets. SAR images’ recognition under small sample conditions is still a challenging problem. In this paper, a SAR target recognition method based on multi-view differential feature fusion network is proposed to address this problem. Considering the correspondence between RCS and target features, the network extracts dissimilarities between features from SAR images of different angles of the same target and fuses them with the original features of one angle to form new features, which enriches the available training data. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) public dataset show that the proposed method has a higher target recognition rate than other deep network methods, as well as single angle input recognition methods. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica 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.)

The new era of counterforce in the contemporary world could unfold itself as a new era of preemption in South Asia. It is characterised by increasing vulnerabilities in nuclear arsenals and delivery systems, however, prompting a vigorous development of countermeasures to mitigate these strategic vulnerabilities. India is increasing its counterforce capabilities considerably. Apart from improving its capabilities of target acquisition, accuracy of its delivery systems and reducing their time to hit the target, India is likely to make necessary shifts in its nuclear doctrine. These factors will provide India with the incentives for preemption against Pakistan in the future. The possibility of a counterforce strike can create instability in the region. To mitigate the threat posed by India's advancing counterforce capabilities, Pakistan should enhance its ISR capabilities, develop ASAT capabilities, improve missile accuracy and speed, and bolster survivability. [ABSTRACT FROM AUTHOR]

This study examines whether the pre‐deal target‐bidder firm governance gap affects the bidder's postdeal change in governance quality. We estimate cross‐sectional regressions using mergers and acquisitions from 2004 to 2016. We find that the bidder's firm‐level governance improves for acquisitions where the target's governance quality is better than that of the bidder preacquisition. We attribute the results to reverse portability, suggesting that the predeal governance gap creates space for governance transfer, and bidders can adopt better governance of targets after the acquisition. Board independence, audit committee independence, CEO‐Chairman separation, stock compensation, and equal treatment of minority shareholders serve as potential channels to demonstrate the bidder's higher governance after the acquisition. Our findings also reveal that bidders with governance improvement are also associated with higher operating performance. We extend the portability theory of Ellis et al. (2017) and suggest that governance can also travel from targets to bidders through mergers and acquisitions. [ABSTRACT FROM AUTHOR]

Purpose: To quantitatively map the myelin lipid-protein bilayer in the live human brain. Methods: This goalwas pursued by integrating a multi-TE acquisition approach targeting ultrashort T2 signals with voxel-wise fitting to a three-component signal model. Imaging was performed at 3 T in two healthy volunteers using high-performance RF and gradient hardware and the HYFI sequence. The design of a suitable imaging protocol faced substantial constraints concerning SNR, imaging volume, scan time, and RF power deposition. Model fitting to data acquired using the proposed protocol was made feasible through simulation-based optimization, and filteringwas used to condition noise presentation and overall depiction fidelity. Results: A multi-TE protocol (11 TEs of 20-780 µs) for in vivo brain imaging was developed in adherence with applicable safety regulations and practical scan time limits. Data acquired using this protocol produced accurate model fitting results, validating the suitability of the protocol for this purpose. Structured, grainy texture of myelin bilayer mapswas observed and determined to be a manifestation of correlated image noise resulting from the employed acquisition strategy. Map quality was significantly improved by filtering to uniformize the k-space noise distribution and simultaneously extending the k-space support. The finalmyelin bilayermaps provided selective depiction of myelin, reconciling competitive resolution (1.4mm) with adequate SNR and benign noise texture. Conclusion: Using the proposed technique, quantitative maps of the myelin bilayer can be obtained in vivo. These maps offer unique information content with potential applications in basic research, diagnosis, diseasemonitoring, and drug development. [ABSTRACT FROM AUTHOR]

SYNOPSIS: We empirically examine the relevance of the disclosure of internal control weaknesses (ICWs) by target firms for acquirers in making their merger-and-acquisition (M&A) decisions. We find that acquirers offer lower premiums for targets that disclose ICWs (ICW targets) before acquisition in comparison with targets that disclose effective internal controls (non-ICW targets). We also find that acquirers offer less cash (versus stock) for ICW targets in comparison with non-ICW targets. Overall, results indicate that ICW disclosure by targets is informative to acquirers. Practically, we contribute to the literature by identifying the relevance of ICW disclosure as a reliable public source of information that can help mitigate the negative impact on acquirers acquiring target firms with poor financial reporting quality. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G34; D81; M41; M4. [ABSTRACT FROM AUTHOR]

Objective: The effects of asset degradation on trust in human-swarm interaction were investigated through the lens of system-wide trust theory. Background: Researchers have begun investigating contextual features that shape human interactions with robotic swarms—systems comprising assets that coordinate behavior based on their nearest neighbors. Recent work has begun investigating how human trust toward swarms is affected by asset degradation through the lens of system-wide trust theory, but these studies have been marked by several limitations. Method: In an online study, the current work manipulated asset degradation and measured trust-relevant criteria in a within-subjects design and addressed the limitations of past work. Results: Controlling for swarm performance (i.e., target acquisition), asset degradation and trust (i.e., reliance intentions) in swarms were negatively related. In addition, as degradation increased, perceptions of swarm cohesion, obstacle avoidance, target acquisition, and terrain exploration efficiency decreased, the latter two of which (coupled with the reliance intentions criterion) support the tenets of system-wide trust theory as well as replicate and extend past work on the effects of asset degradation on trust in swarms. Conclusion: Human-swarm interaction is a context in which system-wide trust is relevant, and future work ought to investigate how to calibrate human trust toward swarm systems. Applications: Based on these findings, design professionals should prioritize ways to depict swarm performance and system health such that humans do not abandon trust in systems that are still functional yet not over-trust those systems which are indeed performing poorly. [ABSTRACT FROM AUTHOR]

Despite its rich history of success in controlling powered prostheses and emerging commercial interests in ubiquitous computing, myoelectric control continues to suffer from a lack of robustness. In particular, EMG-based systems often degrade over prolonged use resulting in tedious recalibration sessions, user frustration, and device abandonment. Unsupervised adaptation is one proposed solution that updates a model's parameters over time based on its own predictions during real-time use to maintain robustness without requiring additional user input or dedicated recalibration. However, these strategies can actually accelerate performance deterioration when they begin to classify (and thus adapt) incorrectly, defeating their own purpose. To overcome these limitations, we propose a novel adaptive learning strategy, Context-Informed Incremental Learning (CIIL), that leverages in situ context to better inform the prediction of pseudo-labels. In this work, we evaluate these CIIL strategies in an online target acquisition task for two use cases: (1) when there is a lack of training data and (2) when a drastic and enduring alteration in the input space has occurred. A total of 32 participants were evaluated across the two experiments. The results show that the CIIL strategies significantly outperform the current state-of-the-art unsupervised high-confidence adaptation and outperform models trained with the conventional screen-guided training approach, even after a 45-degree electrode shift (p < 0.05). Consequently, CIIL has substantial implications for the future of myoelectric control, potentially reducing the training burden while bolstering model robustness, and leading to improved real-time control. [ABSTRACT FROM AUTHOR]

Copyright of Zarqa Journal for Research & Studies in Humanities is the property of Zarqa Journal for Research & Studies in Humanities 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.)

Accurately detecting ground armored weapons is crucial for achieving initiative advantages in military operations. Generally, satellite or airborne synthetic aperture radar (SAR) systems face limitations due to their revisit cycles and fixed flight trajectories, resulting in single-view imaging of targets, thereby hampering the recognition of small SAR ground targets. In contrast, MiniSAR possesses the capability to capture the multi-view of a target by acquiring images from different azimuth angles. In this research, our team utilizes a self-developed MiniSAR system to generate multi-view SAR images of real ground armored targets and recognize targets. However, the recognition of small targets in SAR images encounters two significant difficulties. First, small targets in SAR images are prone to interference from background noise. Second, SAR target deformation arises from variations in depression angles and imaging processes. To tackle these difficulties, this paper proposes a novel SAR ground deformation target recognition approach based on a joint multi-view transformer model. The method first preprocesses SAR images based on a low-frequency priori SAR image denoising method. Next, it obtains multi-view joint information through a self-attentive mechanism, inputs joint features to the transformer structure. The outputs are jointly updated by a multi-way averaging adaptive loss function to improve the recognition accuracy of deformed targets. The experimental results demonstrate the superiority of the proposed method in SAR ground deformation target recognition, outperforming other representative approaches such as information fusion of target and shadow (IFTS) and Vision Transformer (ViT). It is concluded that the proposed method has high recognition accuracies of 98.37% and 93.86 % on the moving and stationary target acquisition and recognition (Mstar) and our SAR images dataset, respectively, in the field of SAR ground deformation target recognition. We have included links to the code and data in the abstract of this paper for ease of access. The source code and sample dataset are available at https://github.com/Lvjiming/MJT. [ABSTRACT FROM AUTHOR]

The Synthetic Aperture Radar (SAR) image classification has become an essential task in numerous applications. Despite its significance, SAR image classification remains challenging due to the inherent complexity and speckle noise characteristics of SAR data. To address this, an innovative approach is proposed in this study, combining the power of ensemble learning and the efficiency of MobileNetV2 architecture to achieve robust and accurate SAR image classification. The proposed deep learning model consists of two main components: an ensemble learning approach that combines multiple binary classifiers, and the employment of MobilenetV2 architecture with self-attention mechanism as a binary classifier. The ensemble approach uses a max-value selector for predicting final class from the individual classifier output. The MobileNetV2 architecture is finetuned by freezing weights and removing the last four layers. The inclusion of the attention layer within the architecture aids in refining the learning process by focusing on crucial features while disregarding irrelevant background elements in the images.. The effectiveness of the model is evaluated using Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark dataset with accuracy of classification as performance metric.The model is trained and tested with images captured at various aspect angles. This approach enhances the robustness of the results, making them more applicable to real-world scenarios when compared to prior methods. This study also compares the performance of the ensemble method using MobileNetV2 with state-of-art individual classifiers and different CNN architectures. The proposed ensemble method OVA-MbNN achieves an impressive classification accuracy of 76.63%, surpassing the performance of state-of-the-art classifiers while maintaining robustness. [ABSTRACT FROM AUTHOR]

In modern surveillance, automatic target recognition (ATR) is a critical challenge, necessitating rapid and precise object identification, especially in military and disaster response scenarios. This research presents a comprehensive framework for ground target classification, focusing on Synthetic Aperture Radar (SAR) imagery. Harnessing the Moving and Stationary Target Acquisition and Recognition (MSTAR) Mixed Targets dataset, this study integrates Convolutional Neural Networks (CNNs) into SAR image analysis. Transfer learning emerges as a key strategy, adapting insights from pre-trained CNNs to the intricacies of target recognition in SAR imagery. This approach outperforms traditional methods, enhancing efficiency amid dataset annotation challenges. Noteworthy CNN architectures including DarkNet-19, DenseNet-121, InceptionV4, ResNet-152, and VGG19 are explored. The ResNet-152 model demonstrates exceptional performance, emerging as a leading contender with a remarkable testing accuracy of 98.56% when trained from scratch. Furthermore, by employing transfer learning, the model's accuracy may be further enhanced to reach 98.81% on the testing dataset. This research signifies a transformative SAR imagery path guided by pre-trained CNNs and transfer learning. It reshapes ATR's accuracy and efficiency, pointing to a future where innovation surmounts challenges previously deemed insurmountable. [ABSTRACT FROM AUTHOR]

Mergers and acquisitions (M&A) is a standard tool business use to expand into new markets and increase shareholder value. Companies should carefully analyze their acquisition candidates since M&A agreements sometimes pan out. Finding and evaluating high-value possibilities is made more accessible with accounting expertise, which aids in the success of M&A. This article addresses tactics and models for finding and appraising high-value targets and highlights the importance of accounting expertise in M&A deals. We also explore non-financial target evaluation components, best practices, and lessons acquired to maximize M&A performance. The review models of M&A transactions and related literature and case studies. Our past M&A work bolsters our knowledge. The findings suggest that accountants might reduce the likelihood of overpaying for acquisition targets and post-merger financial issues by identifying and evaluating high-value targets. We stress the importance of doing homework and considering factors other than money when evaluating a goal. Implementing best practices and avoiding common pitfalls may boost M&A success and shareholder value. In conclusion, M&A businesses should apply accounting skills and suitable models to locate and appraise high-value acquisitions. Examining financial and non-financial components and following best practices may help M&As succeed and accomplish their strategic goals. [ABSTRACT FROM AUTHOR]

This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background, where the similarity is further used to determine the degree of clutter in the image. Four new clutter metrics based on image quality assessment are introduced, among which the Haar wavelet-based perceptual similarity index, known as HaarPSI, provides the best target acquisition prediction results. It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used. In future work, through subjective tests, it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance. Similarity values are compared with the results of subjective tests of the well-known target Search_2 database, where the degree of agreement between objective and subjective scores, measured through linear correlation, reached a value of 90%. [ABSTRACT FROM AUTHOR]

How to acquire the most valuable consumers to grow your recommender system? We propose a dynamic consumer acquisition model to enable value-driven acquisition decisions. We build a model of consumer acquisition that takes into account the value that a consumer contributes to the recommender system, the cost of their participation (e.g., privacy loss), and the value of their participation to other consumers (via network externality). We also propose data-driven procedures to estimate this model to enable informed, value-driven acquisition decisions. On three different data sets, we perform comprehensive simulation-based evaluations to demonstrate the performance of this dynamic consumer acquisition model. We find nuanced relationships between the firm's choice of incentive strategies and acquisition outcomes. Neither a constant pricing strategy nor a greedy pricing strategy may be optimal. Instead, under a moderately greedy strategy, where the firm only partially extracts the network externality from consumers, the dynamic acquisition sequence can outperform random acquisition sequences on firm utility, recommender system performance, and consumer surplus simultaneously. Our work contributes a novel theoretical framework, practical insights, and design artifacts to facilitate effective consumer acquisition in recommender systems. We consider a marketplace where a recommender system provider (the firm) offers incentives to acquire prospective consumers by leveraging information that a market intermediary collects about these consumers. We investigate a model of consumer acquisition that incorporates several factors affecting acquisition decisions, including the value that a consumer contributes to the recommender system, the cost of participation to the consumer (e.g., privacy loss), and the value that a consumer can derive from the system due to network externality created by existing consumers. Our model is dynamic in nature, where the firm iteratively decides the next acquisition target based on previously realized acquisition outcomes. We propose flexible data-driven procedures to estimate some of the key parameters in the model using consumers' data collected by the market intermediary, for example their historical consumption data or the consumption data of other similar consumers. We also design an algorithm to compute the dynamic acquisition sequence and the corresponding incentives to offer. We conduct simulation-based empirical evaluations on two canonical recommendation tasks: movie recommendation based on numerical ratings and product offer recommendation based on browsing (clicking) behaviors and benchmark our acquisition model with random acquisition sequences with respect to (i) firm utility, (ii) recommender system performance, and (iii) consumer surplus. We find nuanced relationships between the firm's choice of incentive strategies and acquisition outcomes. Specifically, neither a constant strategy (setting the same incentive for all consumers) nor a fully greedy strategy (extracting all cumulative network externality) is optimal on all acquisition outcomes. Under a moderately greedy strategy, where the firm only partially extracts the cumulative network externality from consumers, the dynamic acquisition sequence can outperform random sequences on three acquisition outcomes simultaneously. Our work contributes a novel theoretical framework, practical insights, and design artifacts to facilitate effective consumer acquisition in recommender systems. History: Giri Kumar Tayi, Senior Editor; Maytal Saar-Tsechansky, Associate Editor. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2023.1229. [ABSTRACT FROM AUTHOR]

The cognitive radar framework presents a closed-loop adaptive processing paradigm that ensures the efficient acquisition of target information while exploring the environment and enhancing overall sensing performance. In this study, instead of mutual information, we employed the squared Pearson correlation coefficient (SPCC) to measure the target information in observations specifically considering only linear dependency. A waveform design method is proposed that simultaneously maximizes target information and minimizes the integrated sidelobe level (ISL) under the constant modulus constraint (CMC). To enhance computational efficiency, we reformulated the constrained problem as an unconstrained optimization problem by leveraging the inherent geometric property of CMC. Additionally, we present two conditional equivalences associated with waveform design in relation to target information. The simulation results validate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Frequency‐domain electromagnetic (FDEM) instruments are effective tools for detecting underground electrical abnormal targets. However, due to the influence of various noises in the detection process, would lead to l poor signal‐to‐noise ratio of the system and would ultimately affect the detection accuracy. And FDEM detection systems have a defined measuring range. Excessive noise will lead to saturation of the system acquisition and distortion of the measurement. Because of the above problems, an acquisition technique for the FDEM receiving system is proposed to achieve noise suppression in the detection process. The scheme proposes a multi‐frequency LC filtering circuit to optimize the analog signal acquisition module of the receiver system. The circuit converts the measured voltage signal into a current signal, improving the system's immunity to interference. At the same time, the noise of non‐target frequencies in the measured signal is suppressed, avoiding saturation of the receiving system acquisition, and realizing high signal‐to‐noise acquisition of multiple target frequencies. First, the principle of circuit design is analyzed, and the design flow of circuit parameters is proposed. Further, the validity of the design is verified via simulation results. Finally, the optimization scheme is validated with an actual circuit. The results show that for the measurement environment set in this paper, this optimization scheme can suppress the noise of non‐target frequency in the measured signal, so that the noise amplitude can be reduced by more than 90%, and the signal‐to‐noise ratio of the filtered signal can be improved by 23.35 dB. This study helps to facilitate FDEM instruments to achieve high signal‐to‐noise ratio detection. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The detection of small objects in UAV (Unmanned Aerial Vehicle) target acquisition is a persistent difficulty, mainly because of the abundance of small targets, significant mutual obstruction, limited feature statistics, and the intricate background in the target area. These factors collectively result in low detection accuracy. In order to tackle this challenge, we provide a small target recognition model dubbed YOLOv7X+ (You Only Look Once v7X+), which aims to improve the capability of detecting small targets by UAVs while also ensuring its operational effectiveness. The model effectively addresses the issue of significant mutual occlusion among small objects by integrating the Conv2Former module, which enhances the extraction of spatial information features with more precision. Concurrently, we present a Bi-level routing attention technique that is based on cavity convolution. This mechanism regulates the process of extracting and transmitting features at various levels, hence expanding the range of perception. It improves the model's ability to understand and recognize the connections between closely grouped small targets by analyzing individual pixels. This makes the model more resistant to recognizing these targets within complex and changing scenarios. Moreover, it saves crucial contextual details to enhance the precision of differentiation. Finally, the model incorporates multiple dimensions of input data obtained from beneath the UAV by dynamically modulating the size of the convolution kernel. This improves the algorithm's ability to adjust to the specific demands of real-world scenes. The YOLOv7X+ model attains a mean average precision (mAP50) of 60.3% with a validation test Intersection over Union (IoU) criterion of 0.5. The mean average precision (mAP) of the enhanced YOLOv7X model has been increased by 4.9%. and surpasses five other modern sophisticated detection methods. The data from experiments clearly shows that the model effectively handles the identification of small targets in complicated circumstances, achieving a good compromise between accuracy and efficiency. [ABSTRACT FROM AUTHOR]

It has been argued that the principles constraining first language acquisition also constrain second language acquisition; however, neuroscientific evidence for this is scant, and even less for third and subsequent languages. We conducted fMRI experiments to evaluate this claim by focusing on the building of complex sentence structures in Kazakh, a new language for participants having acquired at least two languages. The participants performed grammaticality judgment and subject-verb matching tasks with spoken sentences. We divided the participants into two groups based on the performance levels attained in one of the experimental tasks: High in Group I and Low in Group II. A direct comparison of the two groups, which examined those participants who parsed the structures, indicated significantly stronger activations for Group I in the dorsal left inferior frontal gyrus (L. IFG). Focusing on Group I, we tested the contrast between the initial and final phases in our testing, which examined when the structures were parsed, as well as the contrast which examined what structures were parsed. These analyses further demonstrated focal activations in the dorsal L. IFG alone. Among the individual participants, stronger activation in the dorsal L. IFG, measured during the sentence presentations, predicted higher accuracy rates and shorter response times for executing the tasks that followed. These results cannot be explained by task difficulty or memory loads, and they, instead, indicate a critical and consistent role of the dorsal L. IFG during the initial to intermediate stages of grammar acquisition in a new target language. Such functional specificity of the dorsal L. IFG provides neuroscientific evidence consistent with the claims made by the Cumulative-Enhancement model in investigating language acquisition beyond target second and third languages. [ABSTRACT FROM AUTHOR]

To improve the hit probability of tank at high speed, a prediction method of projectile-target intersection based on adaptive robust constraint-following control and interval uncertainty analysis is proposed. The method proposed provides a novel way to predict the impact point of projectile for moving tank. First, bidirectional stability constraints and stability constraint-following error are constructed using the Udwadia-Kalaba theory, and an adaptive robust constraint-following controller is designed considering uncertainties. Second, the exterior ballistic ordinary differential equation with uncertainties is integrated into the controller, and the pointing control of stability system is extended to the impact-point control of projectile. Third, based on the interval uncertainty analysis method combining Chebyshev polynomial expansion and affine arithmetic, a prediction method of projectile-target intersection is proposed. Finally, the co-simulation experiment is performed by establishing the multi-body system dynamic model of tank and mathematical model of control system. The results demonstrate that the prediction method of projectile-target intersection based on uncertainty analysis can effectively decrease the uncertainties of system, improve the prediction accuracy, and increase the hit probability. The adaptive robust constraint-following control can effectively restrain the uncertainties caused by road excitation and model error. [ABSTRACT FROM AUTHOR]

The diversity of functional applications of atomic force microscopes is the key to the development of nanotechnology. However, the single probe configuration of the traditional atomic force microscope restricts the realization of different application requirements for the same target area of a single sample, and the replacement of the working probe will lead to the loss of the target area. Here, the design, simulation, fabrication, and application of a unique atomic force microscope dual-probe are presented, which consists of a pair of parallel cantilevers with a narrow gap and a U-shaped hinged probe base. The Integrated Hinged Dual-Probe (IHDP) is developed specifically for fast switching of probes working in limited space and independent and precise manipulation of each probe. The deflection signal sensing of two cantilevers is achieved simultaneously by a single laser beam, and the decoupled independent cantilever deflection signals do not interfere with each other. The switching of the working probe is achieved by a piezoelectric ceramic with a 2 µm stroke and U-shaped hinge structure, which is fast and does not require tedious and repetitive spatial position calibration. By measuring standard grid samples, IHDP exhibits excellent measurement and characterization capabilities. Finally, a working probe switching imaging experiment was conducted on solidified rat cardiomyocytes, and the experimental process and imaging results demonstrated the superiority of IHDP in switching probe scanning imaging of the same target area of a single sample. The two probes of IHDP can undergo arbitrary functionalization modifications, which helps achieve multidimensional information acquisition for a single target. [ABSTRACT FROM AUTHOR]

Large display interaction has undergone considerable growth, whereas distant pointing poses challenges due to distance limitations and input devices. Smart devices with superior computing power and rich input capabilities aim to surmount these restrictions. However, pointing interactions in single-layer have limited accuracy in large display scenarios. We investigate a hierarchical pointing approach that couples multiple control modes of rotation attitude, touch operation, and pressure simulation with different cursor strategies. Specifically, it covers the dual-layer cursor with the hierarchical structure, the pressure-dependent multistage gain cursor, and the cursor projection technique based on the target acquisition mechanism. We contrast seven distant pointing techniques in two groups of experiments. Our results indicate that the hierarchical interaction strategy improves user performance, and cursor projection achieves an optimal trade-off between velocity and accuracy for high Fitts' index of difficulty (ID) contexts. [ABSTRACT FROM AUTHOR]

This paper is the first to examine both technical efficiency and scale elasticity of the forest sector across different European (EU) regions in an uncertain environment. In doing so, a dataset from 29 EU forest sectors have been collected; where the input and output data have been assumed to be random while their prices (or importance weights) were known. A chance-constrained data envelopment analysis approach was derived from several directional value-based models aimed at evaluating technical efficiency in the first stage. The results indicated that the number of efficient EU forest sectors were reduced substantially with extremely weak efficiency dispersion (about 18%), and the total average technical efficiency scores were moderately reduced by 0.74 as well. However, in the second stage, the findings of the modified cost/revenue-based scale elasticity model interestingly revealed that almost 7% of the EU forest sectors are in increasing returns to scale regions, which means they are still attractive acquisition targets and have the opportunity to increase the level of outputs and shift to the fully efficient border and finally increase their overall efficiency. As the major conclusion, all of our applied stochastic data envelopment analysis models (specially the cost/revenue-based approach) have high discriminating power in order to clearly distinguish the efficiency of the EU forest sector. [ABSTRACT FROM AUTHOR]

The small number of pixels in the current linear mode avalanche photodiode (LM-APD) array limits its three-dimensional (3D) imaging resolution. We use an optical phased array-based beam array subdivided pixel method to improve the 3D imaging resolution, using an optical phased array to generate a beam array with the same number of pixels as the LM-APD array and matching positions and controlling each sub-beam in the beam array to scan in the field of view of the corresponding pixel. The sub-beam divergence angle in the beam array is smaller than the instantaneous field of view angle of a single pixel in the LM-APD array. The sub-beam scanning in a single pixel's field of view realizes the multiple acquisition of the target 3D information by the LM-APD array, thus improving the resolution of the LM-APD array. The distribution of the beam array in the far field is simulated, and the main performance parameters of 3D imaging are analyzed. Finally, a liquid crystal phase modulator is used as an optical phased array device to conduct experiments on a target 20 m away, and the results prove that our method can improve the resolution from 4 × 4 to 8 × 8, which can be improved at least four times. [ABSTRACT FROM AUTHOR]

The latest conflicts in Ukraine, Nagorno-Karabakh, Syria, and Libya demonstrated the versatility of unmanned aircraft systems (UAS), which were heavily used by all sides involved. UAS provided accurate intelligence, electronic warfare (EW) capabilities, communications, precise target acquisition (TA), close air support (CAS), air interdiction, and accurate post-strike battle damage assessment (BDA). The proliferation of UAS is constantly increasing. Nowadays, this technology is not exclusive to wealthier countries anymore since "many developing countries, unable to man aviation because of the high financial burden, rely on the UAS as an off-the-shelf air force" (Jovanov 2022, 5). UAS has low procurement and operational costs, longer endurance, and lower training costs for operators compared to modern, technologically advanced aircraft. This paper aims to give an overview of how the employment of a versatile UAS, could facilitate the commander's execution of operations and accomplish the assigned mission, thus improving the unit's capabilities through all warfighting functions. [ABSTRACT FROM AUTHOR]

Although brain–machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodents has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguishes control types at the go cue and target acquisition, respectively, while M1 best predicts target-direction at both task events. We also find effective connectivity from DLPFC → M1 throughout both control types and Cd → M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control. [ABSTRACT FROM AUTHOR]

To solve the problem of multi-target hunting by an unmanned surface vehicle (USV) fleet, a hunting algorithm based on multi-agent reinforcement learning is proposed. Firstly, the hunting environment and kinematic model without boundary constraints are built, and the criteria for successful target capture are given. Then, the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process (Dec-POMDP), and a distributed partially observable multi-target hunting Proximal Policy Optimization (DPOMH-PPO) algorithm applicable to USVs is proposed. In addition, an observation model, a reward function and the action space applicable to multi-target hunting tasks are designed. To deal with the dynamic change of observational feature dimension input by partially observable systems, a feature embedding block is proposed. By combining the two feature compression methods of column-wise max pooling (CMP) and column-wise average-pooling (CAP), observational feature encoding is established. Finally, the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy. Each USV in the fleet shares the same policy and perform actions independently. Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs. Moreover, the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance, migration effect in task scenarios and self-organization capability after being damaged, the potential deployment and application of DPOMH-PPO in the real environment is verified. [ABSTRACT FROM AUTHOR]

Adult Amblyomma sculptum and Amblyomma aureolatum ticks are partially refractory to Rickettsia rickettsii when fed on infected hosts, hindering the functional characterization of potentially protective targets in the bacterial acquisition. In the current study, we used the anal pore route to infect adult A. sculptum and A. aureolatum ticks with R. rickettsii and to assess the effects of the knockdown of microplusin in infection control. The anal pore route was efficient to infect both species, resulting in a prevalence of around 100% of infected ticks. Higher loads of R. rickettsii were detected in microplusin-silenced A. aureolatum in relation to the control, as previously obtained when microplusin-silenced ticks were fed on R. rickettsii-infected rabbits. This is the first report showing R. rickettsii infection through the anal pore in Amblyomma ticks, highlighting this route as a powerful tool to assess the role played by additional targets in the control of pathogens. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) sensor often produces a shadow in pairs with the target due to its slant-viewing imaging. As a result, shadows in SAR images can provide critical discriminative features for classifiers, such as target contours and relative positions. However, shadows possess unique properties that differ from targets, such as low intensity and sensitivity to depression angles, making it challenging to extract depth features from shadows directly using convolutional neural networks (CNN). In this paper, we propose a new SAR image-classification framework to utilize target and shadow information comprehensively. First, we design a SAR image segmentation method to extract target regions and shadow masks. Second, based on SAR projection geometry, we propose a data-augmentation method to compensate for the geometric distortion of shadows due to differences in depression angles. Finally, we introduce a feature-enhancement module (FEM) based on depthwise separable convolution (DSC) and convolutional block attention module (CBAM), enabling deep networks to fuse target and shadow features adaptively. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that when only using target and shadow information, the published deep-learning models can still achieve state-of-the-art performance after embedding the FEM. [ABSTRACT FROM AUTHOR]

Recently, we have developed a localized adaptive waveform inversion (LAWI) method to tackle the cycle-skipping issue in velocity reconstruction through seismic waveform inversion. The LAWI method employs a local matching filter, computed using Gabor deconvolution, to measure the instantaneous time-shift between observed and calculated data. Unlike the adaptive waveform inversion (AWI) approach, the LAWI method can take the non-stationarity between observed and calculated data into account. In this work, we investigate two types of regularization based on prior information about the expected filter, which could be a minimum-norm filter or a delta-shape filter, with regard to their effects on the robustness and resolution of inversion. We demonstrate on synthetic data the advantages and disadvantages of these two types of prior information, where the delta-type LAWI may handle multiple observed phases not initially predicted by the starting velocity model. Therefore, we apply the delta-type LAWI to a high-quality 3-D field data set in the North Sea, eliminating the need for data-windowing tuning, which can be tedious and time-consuming for 3-D data. Under different workflows with varying reliable initial models and frequency bands of the pressure data considered, we show that the LAWI approach is robust, effective and efficient for reconstructing the P -wave velocity, while other approaches such as AWI and graph-space optimal-transport method may require meticulous data-tuning strategies to converge to the correct model. Well logs and data fits, primarily from early arrivals, give us confidence that this LAWI approach could be applied to various acquisitions and subsurface targets, thanks to its phase-driven principle. [ABSTRACT FROM AUTHOR]

Positioned in the study of firm heterogeneity in industrial districts (IDs), this study analyses how mergers and acquisitions (M&A) differ between local and non-local acquirers, impacting district evolution. Focusing on the Castellon ceramic tile district transformation, from acquisitions over 2012–2022, M&A and their implication for the local district are discussed, cross-fertilizing strategy and IDs literatures. Findings, from using mixed-methods, reveal that in IDs, the M&A process remarkably differs between local and non-local firms. While local firms tend to specialize in the related focal business, targeting local acquisitions to achieve internal synergies from a better valuation and integration of acquisitions, foreign firms seek access to tacit knowledge through relational and network synergies through diversification. Effects on the territory from M&A are also discussed. [ABSTRACT FROM AUTHOR]

In severe low-visibility environments full of smoke, because of the performance degeneration of the near-infrared (NIR) collimation system of quantum drones communication networks, the improved dual-threshold method based on trend line analysis for long-wave infrared (LWIR) quantum cascade lasers (QCLs) is proposed, to achieve target acquisition. The simulation results show that smoke-scattering noise is a steeply varying medium–high-frequency modulation. At particle sizes less than 4 μ m, the traditional dual-threshold method can effectively distinguish the target information from the smoke noise, which is the advantage of the LWIR laser compared to the NIR laser. For detecting lasers with high signal-to-noise ratios (SNRs), the method can achieve good target acquisition, by setting reasonable conventional thresholds, such as 0.7 times the peak intensity and 0.8 times the peak rising velocity. At low SNRs and steep intensity variation, the method can also achieve good target acquisition, by adaptively resetting new thresholds after filtering the detecting laser, such as 0.6 times the peak intensity and 0.6 times the peak rising velocity. The results of this paper will provide a reference for the performance improvement and refinement of the collimation system for wireless quantum communication networks in low visibility. [ABSTRACT FROM AUTHOR]

Purpose: To optimize the choice of the flip angles of magnetization‐prepared gradient‐echo sequences for improved accuracy, precision, and speed of 3D‐T1ρ mapping. Methods: We propose a new optimization approach for finding variable flip‐angle values that improve magnetization‐prepared gradient‐echo sequences used for 3D‐T1ρ mapping. This new approach can improve the accuracy and SNR, while reducing filtering effects. We demonstrate the concept in the three different versions of the magnetization‐prepared gradient‐echo sequences that are typically used for 3D‐T1ρ mapping and evaluate their performance in model agarose phantoms (n = 4) and healthy volunteers (n = 5) for knee joint imaging. We also tested the optimization with sequence parameters targeting faster acquisitions. Results: Our results show that optimized variable flip angle can improve the accuracy and the precision of the sequences, seen as a reduction of the mean of normalized absolute difference from about 5%–6% to 3%–4% in model phantoms and from 15%–16% to 11%–13% in the knee joint, and improving SNR from about 12–28 to 22–32 in agarose phantoms and about 7–14 to 13–17 in healthy volunteers. The optimization can also compensate for the loss in quality caused by making the sequence faster. This results in sequence configurations that acquire more data per unit of time with SNR and mean of normalized absolute difference measurements close to its slower versions. Conclusion: The optimization of the variable flip angle can be used to increase accuracy and precision, and to improve the speed of the typical imaging sequences used for quantitative 3D‐T1ρ mapping of the knee joint. [ABSTRACT FROM AUTHOR]

Special purpose acquisition companies (SPACs) exploded in popularity in the recent past, luring both adventurous retail investors and sophisticated institutional investors. In a SPAC, a publicly traded shell corporation acquires a private target, thereby taking it public in a manner that circumvents the rigors of a traditional initial public offering (IPO). Proponents vaunt SPACs' ability to simplify the process of accessing the public markets and democratize capitalism, but in their current form, they pose risks to retail investors and to the market as a whole. Using a hand-collected dataset spanning 2010-2021, this Article fills a gap in the literature by providing new empirical data regarding a critical feature of SPACs--the redemption right. SPACs allow their shareholders to vote for an acquisition target while simultaneously pulling their money out--a species of empty voting, where a vote is decoupled from any economic substance. We document a disturbing level of empty voting in SPACs and demonstrate an inverse correlation with stock performance: SPACs with more empty voting perform worse. Backed by this empirical support, we propose a tailored reform that we believe could make SPACs a viable and valuable alternative to traditional IPOs. [ABSTRACT FROM AUTHOR]

Introduction/purpose: Measuring target acquisition performance in imaging systems with human-in-the-loop plays an essential role in military applications. This paper presents an extended review on the application of image clutter metrics for target acquisition, with the aim of using objective measures to predict the detection probability, false alarm probability and mean search time of the target in the image. Methods: To determine the degree of clutter, simple features on the global (picture-wise) and local (target-wise) level were used as well as contrastbased clutter metrics, target size and metrics derived from image quality assessment measures. Along with the standard ones, the features derived from the distribution of mean subtracted contrast normalized coefficients were also used. To compare the results of the objective scores and the experimental results obtained on the publicly available Search_2 dataset, regression laws accepted in the literature were applied. Linear correlations and rank correlations were used as quantitative measures of agreement. Results: It is shown that the best agreement with target acquisition indicators is obtained by applying clutter metrics derived from image quality assessment measures. The correlation with the results of subjective tests is up to 90%, which indicates the need for further research. A special contribution of the paper is the analysis of the target acquisition prediction performance using simple features at the global and local level, where it is shown that the prediction performance can be improved by determining the features around the target. Furthermore, it was shown that the false alarm probability and the probability of detection can be predicted based on the mean target search time in the image with a probability higher than 90%. Conclusion: In addition to obtaining a high degree of agreement between the objective metrics of clutter and the results of subjective tests (up to 90%), there is a need to improve the existing and develop new metrics as well as to conduct new subjective tests.

The aspects relating to unmanned aircraft and their target acquisition systems are continuously being developed. The subject of the study is a target acquisition system with a thermal camera. A control system is presented consisting of three subsystems. Open- and closed-loop control systems are used. Experimental results unambiguously show that this is a promising line of research and form the basis for further efforts on the topic.

Two experiments were conducted to assess the impact of induced paresthesia on movement parameters of goal-directed aiming movements to determine how visual and auditory feedback may enhance performance when somatosensory feedback is disrupted. In both experiments, neurotypical adults performed the goal-directed aiming task in four conditions: (i) paresthesia—full vision; (ii) paresthesia—no vision; (iii) no paresthesia—full vision; (iv) no paresthesia—no vision. Targets appeared on a computer screen, vision was obscured using visual occlusion spectacles, and paresthesia was induced with a constant current stimulator. The first and last 20% of trials (early and late performance) were compared to assess adaptability to altered somatosensory input. Experiment 2 added an auditory tone that confirmed successful target acquisitions. When compared to early performance in the no-paresthesia and no-vision conditions, induced paresthesia and no vision led to significantly larger endpoint error toward the body midline in both early and late performance. This finding reveals the importance of proprioceptive input for movement accuracy in the absence of visual feedback. The kinematic results indicated that vision could not fully compensate for the disrupted proprioceptive input when participants experienced induced paresthesia. However, when auditory feedback confirmed successful aiming movements in Experiment 2, participants were able to improve their endpoint variability when experiencing induced paresthesia through changes in movement preparation. [ABSTRACT FROM AUTHOR]

There is a growing recognition of the importance of effective instruction in the STEM subjects, including science. The movement towards increased requirements and expectations in science presents a challenge to both students and teachers as many students with Learning Disabilities (LD) often struggle, particularly in their science classes. The purpose of this study was to investigate the use of a technology intervention targeting the acquisition of science vocabulary for adolescents with LD in a general education secondary biology classroom. Additionally, a purpose was to examine the usability and feasibility of a tech-based science vocabulary intervention for secondary science teachers. Participants for this study included three secondary students with LD who were enrolled in a biology class. A multiple treatment reversal design was used to compare the effects of a keyword mnemonic vocabulary intervention via index cards or iPod touch on student vocabulary acquisition. All students acquired the content vocabulary equally well during both conditions. It was clear that the students had a strong interest in using technology to increase their grades and engagement; however, the teachers had little access and training on using technology to support their students with disabilities. [ABSTRACT FROM AUTHOR]