Qualitative Metrics Research Articles

Methodology and criteria for assessing the financial efficiency of agricultural clusters

This article undertakes a thorough analysis of the methodology and criteria employed in assessing the financial efficiency of agricultural clusters. The focus extends to crucial metrics, such as the generalizing and economic feasibility coefficient, providing a comprehensive framework for evaluating the economic viability of these collaborative agricultural entities. Additionally, the article goes beyond analysis, offering practical proposals and recommendations aimed at refining the mechanisms for assessing financial efficiency within agricultural clusters. The article likely delves into the methodologies used to assess financial efficiency within agricultural clusters. This could involve an exploration of quantitative and qualitative indicators, financial ratios, and performance metrics relevant to the unique characteristics of agricultural clusters. The criteria used to gauge financial efficiency in agricultural clusters are likely scrutinized. This may encompass factors such as return on investment, cost-effectiveness, resource utilization, and overall economic performance. The article might also consider broader economic and social impacts beyond financial metrics. The specific roles and significance of the generalizing and economic feasibility coefficient in assessing financial efficiency are explored. These coefficients likely serve as key tools for synthesizing diverse financial and economic data to arrive at comprehensive evaluations. By combining theoretical analysis with practical recommendations, the article aims to contribute not only to the academic understanding of financial efficiency in agricultural clusters but also to offer tangible solutions for industry practitioners, policymakers, and stakeholders involved in the development and management of these collaborative agricultural endeavors.

FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics

Federated learning is a distributed learning method that seeks to train a shared global model by aggregating contributions from multiple clients. This method ensures that each client’s local data are not shared with others. However, research has revealed that federated learning is vulnerable to poisoning attacks launched by compromised or malicious clients. Many defense mechanisms have been proposed to mitigate the impact of poisoning attacks, but there are still some limitations and challenges. The defense methods are either performing malicious model removal from the geometric perspective to measure the geometric direction of the model or adding an additional dataset to the server for verifying local models. The former is prone to failure when facing advanced poisoning attacks, while the latter goes against the original intention of federated learning as it requires an independent dataset; thus, both of these defense methods have some limitations. To solve the above problems, we propose a robust federated learning method based on geometric and qualitative metrics (FLGQM). Specifically, FLGQM aims to metricize local models in both geometric and qualitative aspects for comprehensive defense. Firstly, FLGQM evaluates all local models from both direction and size aspects based on similarity calculated by cosine and the Euclidean distance, which we refer to as geometric metrics. Next, we introduce a union client set to assess the quality of all local models by utilizing the union client’s local dataset, referred to as quality metrics. By combining the results of these two metrics, FLGQM is able to use information from multiple views for accurate poisoning attack identification. We conducted experimental evaluations of FLGQM using the MNIST and CIFAR-10 datasets. The experimental results demonstrate that, under different kinds of poisoning attacks, FLGQM can achieve similar performance to FedAvg in non-adversarial environments. Therefore, FLGQM has better robustness and poisoning attack defense performance.

Rapid Electrochemical Diagnosis of Battery Health and Safety from Cells to Modules

Rapid electrochemical diagnosis of battery health and failure is critical for ensuring reliable battery performance and battery safety. Traditional battery health diagnostics such as capacity measurements and DC pulse tests are reliable and well-understood, however, these measurements of battery capacity and resistance do not capture all aspects of battery degradation. Other aspects of degradation, such as electrolyte decomposition, lithium-plating, and particle cracking are difficult to detect electrochemically but are crucial to measure to get a full picture of battery safety and flag out potential failures. In this work, lab- and field-aged commercial lithium-ion batteries and modules of various chemistries and formats are tested using a variety of traditional electrochemical characterization methods as well as using 2-minute pseudo-random DC pulse sequences at rest and during charge/discharge. The electrochemical measurements are compared to physical cell measurements, cell efficiency, drive cycle performance, physical and thermal heterogeneity, and qualitative safety metrics using statistical and machine-learning methods to discover if a comprehensive ‘battery health map’ can be accurately identified using only rapid DC measurements.

Memory support training and lifestyle modifications to promote healthy aging in persons at risk for Alzheimer's disease: a digital application supported intervention (Brain Boosters)

BackgroundEvidence-based interventions to protect against cognitive decline among older adults at risk for Alzheimer’s disease and related dementias (ADRD) are urgently needed. Rehabilitation approaches to support memory and behavioral/lifestyle interventions are recognized as promising strategies for preserving or improving cognitive health, although few previous interventions have combined both approaches. This paper describes the protocol of the Brain Boosters intervention, which synergistically combines training in compensatory and healthy lifestyle behaviors and supports implementation and tracking of new behaviors with a digital application.MethodsThe study utilizes a single-site, single-blinded, randomized controlled design to compare a structured lifestyle and compensatory aid intervention to an education-only self-guided intervention. We plan to enroll 225 community-dwelling adults (25% from underrepresented groups) aged 65 + who endorse subjective cognitive decline (SCD) and low baseline levels of healthy lifestyle behaviors. Both interventions will be administered in group format, consisting of 15 two-hour classes that occur weekly for ten weeks and taper to bi-monthly and monthly, for an intervention duration of 6 months. Participants in both interventions will receive education about a variety of memory support strategies and healthy lifestyle behaviors, focusing on physical and cognitive activity and stress management. The structured intervention will also receive support in adopting new behaviors and tracking set goals aided by the Electronic Memory and Management Aid (EMMA) digital application. Primary outcomes include global cognition (composite of memory, attention, and executive function tests) and everyday function (Everyday Cognition Questionnaire). Data will be collected at baseline and outcome visits, at approximately 6, 12, and 18 months. Qualitative interviews, self-report surveys (e.g., indicators of self-determination, health literacy) and EMMA data metrics will also be used to identify what components of the intervention are most effective and for whom they work.DiscussionSuccessful project completion will provide valuable information about how individuals with SCD respond to a compensation and preventative lifestyle intervention assisted by a digital application, including an understanding of factors that may impact outcomes, treatment uptake, and adherence. The work will also inform development, scaling, and personalization of future interventions that can delay disability in individuals at risk for ADRD.Trial RegistrationClinicalTrials.gov. (NCT05027789, posted 8/30/2021).

The Genesis of Systemic Conflicts in the Period of the USSR Collapse and State Formation in the Post-Soviet Period

The object of the study is the genesis of systemic conflicts in the paradigm of the phenomenology of statehood, with its inherent methodological and methodical approaches to the mechanisms of formation and building of the state task, which presupposes the systems of interacting institutions that determine the structure of public administration. The subject of the study is the main trends of statehood formation in modern history, a systemic crisis of administration in the decline period, and the actual trajectory and mechanics of statehood formation in the post-Soviet space and in the world after the collapse of the USSR. Special attention is paid to the analysis of key causes of the emergence of systemic conflicts as a result of differences in content, understanding, and installation of the structure of state building on an operational level without a qualitative legal metric of legitimacy of state-formation. The genesis of systemic conflicts of constitutional and legal regulation in the conditions of the growing scale of global integration, as well as the critical need for an effective supranational level of legal regulation within the framework of a systemically full-fledged international law. The study concludes that due to systemic conflicts, ensuring common understanding, interpretation and application of the guiding principle of the Rule of Law becomes ambiguous and makes the formation of a common unified global legal space impossible. As a result, as an objective consequence, integration processes, in the absence of a systematically organized legal discipline, go into the mode of "self-determination" where the principle Rule of Law based on a system of legal norms on the long term basis, is replaced with a conventional system of situational agreements oriented on the rules of the short-term basis of the current conjecture.

Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study

PurposeTo evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality. MethodA CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2). CycleGAN algorithm gave DL classifier scores (0–1) for quality quantification and produced enhanced versions of QI1 and QI2 from RAD1 and RAD2, respectively. A subset (n = 20 patients) was selected for a blinded, multi-reader study, where four radiologists rated T2WI on a scale of 1–4 for quality. The multi-reader study presented readers with 60 image pairs (RAD1 vs RAD2, RAD1 vs QI1, and RAD2 vs QI2), allowing for selecting sequence preferences and quantifying the quality changes. ResultsThe DL classifier correctly discerned 71.9 % of quality classes, with 90.6 % (96/106) as poor quality and 48.8 % (42/86) as diagnostic in original sequences (RAD1, RAD2). CycleGAN images (QI1, QI2) demonstrated quantitative improvements, with consistently higher DL classifier scores than original scans (p < 0.001). In the multi-reader analysis, CycleGAN demonstrated no qualitative improvements, with diminished overall quality and motion in QI2 in most patients compared to RAD2, with noise levels remaining similar (8/20). No readers preferred QI2 to RAD2 for diagnosis. ConclusionDespite quantitative enhancements with CycleGAN, there was no qualitative boost in T2WI diagnostic quality, noise, or motion. Expert radiologists didn’t favor CycleGAN images over standard scans, highlighting the divide between quantitative and qualitative metrics.

A comparative study of Augmented Reality rendering techniques for industrial assembly inspection

In the manufacturing industry, Augmented Reality (AR) has shown significant potential in enhancing operators’ capabilities while performing inspection and assembly activities. However, the augmented visualization of virtual models on physical components can present challenges and potential misunderstandings, as the visualization mode greatly influences the perception of components and the amount of information received. This study investigates the impact of rendering techniques on user performance during industrial assembly inspection tasks. In particular, a set of rendering techniques, suitable for industrial AR applications, have been selected and implemented through a dedicated AR tool. The selected AR rendering techniques have been compared, in terms of qualitative and quantitative metrics, in order to test their efficiency and effectiveness for industrial assembly inspection activities. 17 domain experts and 33 representative users have been involved in the experimental study which outcomes reveal that the rendering techniques play a significant role in assisting operators for identifying design discrepancies in industrial products. Furthermore, a correlation has been observed between the two AR rendering techniques best suited to support industrial inspection activities and the types of assembly errors detected.

Asymmetric convolution Swin transformer for medical image super-resolution

Medical Image Super-Resolution plays a pivotal role in enhancing diagnostic accuracy. Transformer-based methods, such as Image Restoration Using Swin Transformer (SwinIR) and Swin transformer for fast Magnetic Resonance Imaging (SwinMR), have shown prowess in this area but also exhibit limitations. Specifically, LayerNorm channel normalization diminishes high-frequency detail, while the Multilayer Perceptron prioritizes global information over local information. Moreover, low-resolution inputs contain substantial low-frequency information, treated uniformly in self-attention, hampering the effectiveness of shifted window-based self-attention. To address these challenges, this study proposes a novel Asymmetric convolution Swin Transformer Layer that leverages global and local information within adjacent windows or pixels. Furthermore, this study presents an innovative joint attention mechanism that integrates channel attention into the Swin Transformer architecture, allowing for the simultaneous capture of local and global information and enhancing the network's ability to adapt and represent information effectively. Based on these components, this study develops a joint Channel Attention and Swin Transformer Residual Network (CSRNet) for medical image super-resolution. To evaluate its performance, this study conducts comprehensive experiments on established datasets consisting of high-resolution medical images. Compared to the state-of-the-art medical image super-resolution methods, the proposed method outperforms in reconstructing medical images across qualitative and quantitative metrics.

Electrogastrography in patients with gastric motility disorders.

Electrogastrography (EGG) is a novel diagnostic modality for assessing the gastrointestinal tract (GI) that generates spontaneous electrical activity and monitors gastric motility. The aim of this study was to compare patients with functional dyspepsia (FD) and diabetic gastroparesis (D-GP) with healthy controls (CT) to use established findings on abnormalities of gastric motility based on EGG characteristics. In this study, 50 patients with FD, 50 D-GP patients, and 50 CT subjects were studied to compare EGG with discrete wavelet transform models (DWT) to extract signal characteristics using a variety of different qualitative and quantitative metrics from pre-prandial and postprandial states. As a result, higher statistically significant (p < 0.05*) were found in the DWT models based on power spectral density (PSD) analysis in both states. We also present that the correlations between EGG metrics and the presence of FD, D-GP, and CT symptoms were inconsistent. This paper represents that EGG assessments of FD and D-GP patients differ from healthy controls in terms of abnormalities of gastric motility. Additionally, we demonstrate that diverse datasets showed adequate gastric motility responses to a meal. We anticipate that our method will provide a comprehensive understanding of gastric motility disorders for better treatment and monitoring in both clinical and research settings. In conclusion, we present potential future opportunities for precise gastrointestinal electrophysiological disorders.

Computational investigation of outflow graft variation impact on hemocompatibility profile in LVADs.

Hemocompatibility-related adverse events (HRAE) occur commonly in patients with left ventricular assist devices (LVADs) and add to morbidity and mortality. It is unclear whether the outflow graft orientation can impact flow conditions leading to HRAE. This study presents a simulation-based approach using exact patient anatomy from medical images to investigate the influence of outflow cannula orientation in modulating flow conditions leading to HRAEs. A 3D model of a proximal aorta and outflow graft was reconstructed from a computed tomography (CT) scan of an LVAD patient and virtually modified to model multiple cannula orientations (n = 10) by varying polar (cranio-caudal) (n = 5) and off-set (anterior-posterior) (n = 2) angles. Time-dependent computational flow simulations were then performed for each anatomical orientation. Qualitative and quantitative hemodynamics metrics of thrombogenicity including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), endothelial cell platelet activation potential (ECAP), particle residence time (PRT), and platelet activation potential (PLAP) were analyzed. Within the simulations performed, endothelial cell activation potential (ECAP) and particle residence time (PRT) were found to be lowest with a polar angle of 85°, regardless of offset angle. However, polar angles that produced parameters at levels least associated with thrombosis varied when the offset angle was changed from 0° to 12°. For offset angles of 0° and 12° respectively, flow shear was lowest at 65° and 75°, time averaged wall shear stress (TAWSS) was highest at 85° and 35°, and platelet activation potential (PLAP) was lowest at 65° and 45°. This study suggests that computational fluid dynamic modeling based on patient-specific anatomy can be a powerful analytical tool when identifying optimal positioning of an LVAD. Contrary to previous work, our findings suggest that there may be an "ideal" outflow cannula for each individual patient based on a CFD-based hemocompatibility profile.

Comparison of a Valveless Trocar System and Conventional Insufflation in Pediatric Urologic Surgery.

Background: Conventional operative insufflation uses a one-way trocar to handle instruments while maintaining pneumoperitoneum. In 2007, the AirSeal® valveless trocar insufflation system was introduced, which maintains stable pneumoperitoneum while continuously evacuating smoke. Although this device has been validated in adult patients, it has not been extensively validated in the pediatric population. Materials and Methods: A retrospective cohort study of pediatric urology patients aged 0 to 21 who underwent laparoscopic pyeloplasty between March 2016 and October 2021 was performed. Intraoperative physiologic parameters, procedure characteristics, postoperative outcomes, and demographics of each patient in whom either AirSeal insufflation system (AIS) or conventional insufflation system (CIS) was utilized were obtained from hospital records. Data were compared across the AIS and CIS cohorts. The primary outcomes were intraoperative anesthetic and physiologic parameters, including end tidal carbon dioxide, oxygen saturation, body temperature, positive inspiratory pressure, systolic blood pressure, and heart rate. Results: There were no significant differences in the anesthetic and physiologic parameters in the AIS and CIS groups. In addition, no differences in demographics, procedural characteristics, or complication rates were found between the cohorts. Conclusion: The AirSeal valveless trocar insufflation system demonstrates comparable intraoperative anesthetic and physiologic outcomes compared to conventional one-way valve insufflation in pediatric laparoscopic pyeloplasty. Certain surgeon-related qualitative metrics are underappreciated in this study, however, including improved visualization with vigorous suctioning and pressure maintenance with frequent instrument exchanges. Surgeon experience may mask the benefits of these characteristics as it pertains to quantitative surgical outcomes such as estimated blood loss, operative time, and perioperative complications.

A Pan‐Arctic Algorithm to Estimate Dissolved Organic Carbon Concentrations From Colored Dissolved Organic Matter Spectral Absorption

AbstractSampling for dissolved organic carbon (DOC) in the Arctic is challenging given the limited access and because it is not yet possible to measure with instruments deployed in situ. Compared to DOC, colored dissolved organic matter (CDOM) absorption spectroscopy is an easy‐to‐measure, relatively quick and cost‐effective approach which is often closely related to DOC concentrations in water samples. Here we present an algorithm based on quantitative and qualitative metrics of CDOM to provide DOC estimates derived from a Pan‐Arctic data set (n = 3,302) spanning rivers to deep ocean, with DOC ranging between 31 and 1,958 μM. The algorithm provided robust DOC estimates (r2 = 0.94; p &lt; 0.0001) and could reproduce DOC profiles and mixing plots across different locations in the Arctic Ocean. Besides its simplicity, this method is capable of capturing the extremely broad range of DOC within the strong gradients observed between Arctic riverine and marine systems.

Understanding stroke survivors’ preferences regarding wearable sensor feedback on functional movement: a mixed-methods study

BackgroundIn stroke rehabilitation, wearable technology can be used as an intervention modality by providing timely, meaningful feedback on motor performance. Stroke survivors’ preferences may offer a unique perspective on what metrics are intuitive, actionable, and meaningful to change behavior. However, few studies have identified feedback preferences from stroke survivors. This project aims to determine the ease of understanding and movement encouragement of feedback based on wearable sensor data (both arm/hand use and mobility) for stroke survivors and to identify preferences for feedback metrics (mode, content, frequency, and timing).MethodsA sample of 30 chronic stroke survivors wore a multi-sensor system in the natural environment over a 1-week monitoring period. The sensor system captured time in active movement of each arm, arm use ratio, step counts and stance time symmetry. Using the data from the monitoring period, participants were presented with a movement report with visual displays of feedback about arm/hand use, step counts and gait symmetry. A survey and qualitative interview were used to assess ease of understanding, actionability and components of feedback that users found most meaningful to drive lasting behavior change.ResultsArm/hand use and mobility sensor-derived feedback metrics were easy to understand and actionable. The preferred metric to encourage arm/hand use was the hourly arm use bar plot, and similarly the preferred metric to encourage mobility was the hourly steps bar plot, which were each ranked as top choice by 40% of participants. Participants perceived that quantitative (i.e., step counts) and qualitative (i.e., stance time symmetry) mobility metrics provided complementary information. Three main themes emerged from the qualitative analysis: (1) Motivation for behavior change, (2) Real-time feedback based on individual goals, and (3) Value of experienced clinicians for prescription and accountability. Participants stressed the importance of having feedback tailored to their own personalized goals and receiving guidance from clinicians on strategies to progress and increase functional movement behavior in the unsupervised home and community setting.ConclusionThe resulting technology has the potential to integrate engineering and personalized rehabilitation to maximize participation in meaningful life activities outside clinical settings in a less structured environment.

High-order relational generative adversarial network for video super-resolution

Video super-resolution can reconstruct a sequence of high-resolution frames with temporally consistent contents from their corresponding low-resolution sequences. The key challenge for this task is how to effectively utilize both inter-frame temporal relations and intra-frame spatial relations. The existing methods for super-resolving the videos commonly estimate optical flows to align the features of multiple frames based on temporal correlations. However, motion estimation is often error-prone and hence largely hinders the recovery of plausible details. Moreover, high-order contextual dependencies in the feature space are rarely exploited for further enhancing the spatio-temporal information fusion. To this end, we propose a novel generative adversarial network to super-resolve low-resolution videos, which makes full use of patch embeddings and is effective in exploring high-order spatio-temporal relations of the feature patches. Specifically, a motion-aware relation module is designed to handle the alignment between neighboring frames and reference ones. Depending on a patch-matching strategy for adaptive selection of multiple most similar patches, the cross-scale graph is constructed to reliably aggregate these patches using a feature pyramid. Based on the structure of multi-scale graph, a context-aware relation module is developed to capture high-order dependencies among resulting warped patches for better leveraging long-range complementary contexts. To further enhance reconstruction ability, the temporal position information of video sequences is also encoded into this module. Dual discriminators with cycle consistent constraints are adopted to provide more informative feedback to the generator while maintaining the global coherence. Extensive experiments have demonstrated the effectiveness of the proposed method in terms of quantitative and qualitative evaluation metrics.

Analytical assessment of a novel RP-HPLC method for the concurrent quantification of selected pharmaceutical drugs levodopa and carbidopa using eight greenness metrics comparing to the lean six sigma approach

BackgroundIntegrating green chemistry principles into analytical processes is vital for sustainability and accuracy. AimThis study is dedicated to finding eco-friendly solutions that replace harmful substances and reduce waste production. The suggested work examined eight tools for assessing the greenness and sustainability of a newly validated RP-HPLC technique that quantitatively determines levodopa (LEV) and carbidopa (CAR) drugs in their bulk and pharmaceutical formulations. We also incorporated the lean six sigma methodology to ensure accuracy and efficiency. MethodThe optimal liquid chromatographic conditions (HPLC) for achieving the highest resolution and results in our method are as follows: employing a mobile phase composition consisting of a mixture of phosphate buffer pH 2.2 and ethanol (90:10 v/v) at a flow rate of 1 mL/min, using a C18 column (150 mm × 4.6 mm) and employing an isocratic mode of elution at a wavelength of 280 nm. ResultsThe calibration curves were established for LEV and CAR in the 3–50 μg/mL range to study the linearity with coefficients of determination (R2 ∼0.999). The study determined that the limits of detection (LOD) and quantification (LOQ) for LEV were 1.95 μg/mL and 5.93 μg/mL, respectively. Meanwhile, the results for CAR were 0.67 μg/mL and 2.04 μg/mL for LOD and LOQ, respectively. The developed method has demonstrated accuracy and validity in the simultaneous determination and quantification of LEV and CAR, with a relative standard deviation (RSD) of less than 2%. The method's accuracy was confirmed through recovery studies, demonstrating significant results within the acceptable range of 98–102%. Specifically, the recovery test values for LEV and CAR were 99.75% and 98.97%, respectively. ConclusionThe specificity and selectivity of the developed method have been revealed through forced degradation studies conducted through The International Council for Harmonization (ICH) criteria. The technique could determine and quantify the two drugs even in the presence of their degradants. Our proposed approach beats the previous HPLC methods in adherence to green chemistry principles, providing reliable and precise outcomes with better qualitative and quantitative metrics. It's the most effective and eco-friendly solution for chemical analysis.

HyperSLICE: HyperBand optimized spiral for low-latency interactive cardiac examination.

Interactive cardiac MRI is used for fast scan planning and MR-guided interventions. However, the requirement for real-time acquisition and near-real-time visualization constrains the achievable spatio-temporal resolution. This study aims to improve interactive imaging resolution through optimization of undersampled spiral sampling and leveraging of deep learning for low-latency reconstruction (deep artifact suppression). A variable density spiral trajectory was parametrized and optimized via HyperBand to provide the best candidate trajectory for rapid deep artifact suppression. Training data consisted of 692 breath-held CINEs. The developed interactive sequence was tested in simulations and prospectively in 13 subjects (10 for image evaluation, 2 during catheterization, 1 during exercise). In the prospective study, the optimized framework-HyperSLICE- was compared with conventional Cartesian real-time and breath-hold CINE imaging in terms quantitative and qualitative image metrics. Statistical differences were tested using Friedman chi-squared tests with post hoc Nemenyi test (p < 0.05). In simulations the normalized RMS error, peak SNR, structural similarity, and Laplacian energy were all statistically significantly higher using optimized spiral compared to radial and uniform spiral sampling, particularly after scan plan changes (structural similarity: 0.71 vs. 0.45 and 0.43). Prospectively, HyperSLICE enabled a higher spatial and temporal resolution than conventional Cartesian real-time imaging. The pipeline was demonstrated in patients during catheter pull back, showing sufficiently fast reconstruction for interactive imaging. HyperSLICE enables high spatial and temporal resolution interactive imaging. Optimizing the spiral sampling enabled better overall image quality and superior handling of image transitions compared with radial and uniform spiral trajectories.

2D sodium MRI of the human calf using half-sinc excitation pulses and compressed sensing.

Sodium MRI can be used to quantify tissue sodium concentration (TSC) in vivo; however, UTE sequences are required to capture the rapidly decaying signal. 2D MRI enables high in-plane resolution but typically has long TEs. Half-sinc excitation may enable UTE; however, twice as many readouts are necessary. Scan time can be minimized by reducing the number of signal averages (NSAs), but at a cost to SNR. We propose using compressed sensing (CS) to accelerate 2D half-sinc acquisitions while maintaining SNR and TSC. Ex vivo and in vivo TSC were compared between 2D spiral sequences with full-sinc (TE = 0.73 ms, scan time ≈ 5 min) and half-sinc excitation (TE = 0.23 ms, scan time ≈ 10 min), with 150 NSAs. Ex vivo, these were compared to a reference 3D sequence (TE = 0.22 ms, scan time ≈ 24 min). To investigate shortening 2D scan times, half-sinc data was retrospectively reconstructed with fewer NSAs, comparing a nonuniform fast Fourier transform to CS. Resultant TSC and image quality were compared to reference 150 NSAs nonuniform fast Fourier transform images. TSC was significantly higher from half-sinc than from full-sinc acquisitions, ex vivo and in vivo. Ex vivo, half-sinc data more closely matched the reference 3D sequence, indicating improved accuracy. In silico modeling confirmed this was due to shorter TEs minimizing bias caused by relaxation differences between phantoms and tissue. CS was successfully applied to in vivo, half-sinc data, maintaining TSC and image quality (estimated SNR, edge sharpness, and qualitative metrics) with ≥50 NSAs. 2D sodium MRI with half-sinc excitation and CS was validated, enabling TSC quantification with 2.25 × 2.25 mm2 resolution and scan times of ≤5 mins.

3D multi-modality Transformer-GAN for high-quality PET reconstruction

Positron emission tomography (PET) scans can reveal abnormal metabolic activities of cells and provide favorable information for clinical patient diagnosis. Generally, standard-dose PET (SPET) images contain more diagnostic information than low-dose PET (LPET) images but higher-dose scans can also bring higher potential radiation risks. To reduce the radiation risk while acquiring high-quality PET images, in this paper, we propose a 3D multi-modality edge-aware Transformer-GAN for high-quality SPET reconstruction using the corresponding LPET images and T1 acquisitions from magnetic resonance imaging (T1-MRI). Specifically, to fully excavate the metabolic distributions in LPET and anatomical structural information in T1-MRI, we first use two separate CNN-based encoders to extract local spatial features from the two modalities, respectively, and design a multimodal feature integration module to effectively integrate the two kinds of features given the diverse contributions of features at different locations. Then, as CNNs can describe local spatial information well but have difficulty in modeling long-range dependencies in images, we further apply a Transformer-based encoder to extract global semantic information in the input images and use a CNN decoder to transform the encoded features into SPET images. Finally, a patch-based discriminator is applied to ensure the similarity of patch-wise data distribution between the reconstructed and real images. Considering the importance of edge information in anatomical structures for clinical disease diagnosis, besides voxel-level estimation error and adversarial loss, we also introduce an edge-aware loss to retain more edge detail information in the reconstructed SPET images. Experiments on the phantom dataset and clinical dataset validate that our proposed method can effectively reconstruct high-quality SPET images and outperform current state-of-the-art methods in terms of qualitative and quantitative metrics.

Using multisensory virtual reality nature immersion as a therapeutic modality for improving HRV and cognitive functions in post-traumatic stress disorder: a pilot-study

Introduction: Immersive virtual reality (VR) applications are burgeoning within healthcare as they promote high levels of engagement. Notwithstanding, existing solutions only stimulate two of our five senses (audio and visual), thus may not be optimal in the sense of promoting immersion and of “being present”. In this paper, we explore the benefits of an immersive multisensory experience as a therapeutic modality for participants suffering from post-traumatic stress disorder (PTSD).Methods: In addition to 360-degree videos and corresponding natural sounds, nature smells are also presented by means of a portable ION 2 scent diffusion device attached to an Oculus Quest 2 VR head-mounted display. A 3-week 12-sessions protocol was applied to a sample of 20 participants diagnosed with PTSD.Results and discussion: We report the outcomes seen from a battery of qualitative metrics, including cognitive functioning tests, psychological symptoms, severity of PTSD, and several self-reported questionnaires and heart rate variability (HRV) metrics. Results are compared not only between pre-and post intervention, but also after a 3-month follow-up period. Results suggest a decrease in the severity of PTSD, as well as improvements in processing speed and sustained attention post-intervention, but also sustained decrease in the severity of PTSD and in dissociative tendencies at the 3-month follow-up. Overall, participants rated the experience as highly immersive and produced very mild to no symptoms of cybersickness, thus corroborating the feasibility and usefulness of the proposed multisensory immersive VR tool for reducing PTSD symptoms.

Visualizing Cybersecurity Coverage using MITRE ATT&amp;CK Framework

This paper presents an innovative approach to visualizing cybersecurity coverage within the Zscaler Risk360 product, leveraging the MITRE ATT&amp;CK framework. Emphasizing a user-centered design methodology, the study integrates the complex ATT&amp;CK framework into a practical, interactive interface tailored for cybersecurity professionals. This interface combines qualitative and quantitative metrics, enabling users to assess the security posture of their organization effectively. The design process is informed by insights from semi-structured interviews with Chief Information Security Officers (CISOs) and subject matter experts, ensuring relevance and usability. The paper discusses key design decisions, including the integration of complex framework tree structures, color-coded coverage mappings, and a three-pane view for detailed analysis. Additionally, it explores extended use cases beyond the immediate application, suggesting potential for broader applications in various cybersecurity and compliance contexts. This work aims to enhance decision-making in cybersecurity management and inspire future research and development in cybersecurity and data visualization tools