Tool For Reduction Research Articles

A machine learning approach to simulate flexible body dynamics

Abstract Flexible body dynamics simulations are powerful tools to realistically analyze vehicles, machines, mechanics, etc. However, the inherently nonlinear governing equations often require tailor-made and computationally expense solution strategies. Employing artificial neural networks for forward dynamics analyses of flexible bodies may be not only useful as a model reduction tool, since evaluating a network is frequently faster compared to solving physics-based models, but also to enhance models with experimental data. In this realm four primary strategies have emerged: (i) Incorporating time as an input to the artificial neural network to predict the desired solution variables at that specific time. (ii) Utilizing an entire time series as input, the network generates the corresponding time series of the desired solution variables in a single pass through the artificial neural network. (iii) Employing an artificial neural network to advance one time step into the future using the states as input. (iv) Leveraging the artificial neural network solely for learning the equations of motion or energetic quantities, e.g., Lagrangian/Hamiltonian, coupled with standard techniques from analytical dynamics and time integration. Approaches (iii) and (iv) can be considered advantageous owing to their inherent physics-informed nature, greater flexibility in adopting varying time steps, and ease of integration with classical simulation techniques. This contribution, therefore, presents a method to predict flexible body dynamics one step at a time using two complimentary artificial neural networks. One network predicts the equations of motion relationship between positions, velocities, and accelerations, while a second network or numerical integrator propagates the system states forwards in time. This work demonstrates the feasibility of the developed approach on the testcase of a flexible beam. Various parameter studies and alternative solutions are presented to highlight the performance of the developed approach. The robustness and ability to extrapolate are also explored to determine the practical viability of this solution. The intention of this single-body work is to enable the future embedding in a multibody context.

Semi-field experiments reveal contrasted predation and movement patterns of aquatic macroinvertebrate predators of Anopheles gambiae larvae

BackgroundMembers of the Anopheles gambiae complex are major malaria vectors in sub-Saharan Africa. Their larval stages inhabit a variety of aquatic habitats in which, under natural circumstances, they are preyed upon by different taxa of aquatic macroinvertebrate predators. Understanding the potential impact of predators on malaria vector larval population dynamics is important for enabling integrated local mosquito control programmes with a stronger emphasis on biocontrol approaches. This study experimentally evaluated the predation efficacy and foraging strategy of three common aquatic macroinvertebrate predators of An. gambiae, diving beetles (Coleoptera), backswimmers (Hemiptera), and dragonfly nymphs (Odonata) in a semi-field system in South-Eastern Tanzania.MethodsAn array of alternating small and large basins used as aquatic habitats was created in two compartments of a semi-field system and filled with well water. Field-collected adult diving beetles, backswimmers or dragonfly nymphs were randomly assigned to these habitats and Anopheles arabiensis larvae were added as prey in half of the habitats. The number of mosquito larvae consumed, predator mobility across habitats and mortality were recorded at 24, 48 and 72 h.ResultsThe presence of An. gambiae larvae in habitats significantly increased the survival of backswimmer and dragonfly nymphs, which are not mobile. In contrast, diving beetles survived well under any initial condition by preferentially flying away from habitats without prey to nearby larger habitats with prey. The larval predation rates of predacious diving beetle, backswimmer and dragonfly nymphs were stable over time at a mean of 3.2, 7.0 and 9.6 larvae consumed each day.ConclusionThis study demonstrates that aquatic macroinvertebrate predators display adaptive foraging behaviour in response to prey presence and aquatic habitat size. It also confirms the ability of these predators to significantly reduce An. gambiae larval densities in aquatic habitats, thus their potential for consideration as additional biocontrol tools for mosquito population reduction.

Audio-Visual Distraction- A Non-Pharmacological Approach to Alleviate Pain in Pediatric Vaccine Administration: An Observational Study

Introduction: The routine vaccine injections are one of the most common and painful procedures during childhood specifically in infancy. In order to improve the effectivity of pain reduction during routine vaccination OPD this study was done to analyse the effectiveness of audio-visual gadgets as a distraction tool for pain reduction in infants during the vaccination procedure. Methods: A comparative observational study conducted at a tertiary healthcare center vaccination out patient department. Children aged 1 month to 2 years receiving the vaccine were included in the study. The study group was exposed to audio-visual clip while the control group received the vaccine as per routine vaccination protocol. The Face, Legs, Activity, Cry, Controllability Scale score was used to assess the behavioural reactions to pain which assesses five behavioural areas. The study protocol was approved by the Institute Ethics Committee (Reference number: BJGMC/IEC/1122238-238).Results: The Face, Legs, Activity, Cry, Controllability Scale score were studied among two groups, it was observed that the pain score and every component of the score had a statistically significant lower mean score in audio-visual group as compared to the control group. Mean pain score in the audio-visual group was 6.31±0.79 as compared to 9.57±0.65 in the control group with the p value of 0.001.Conclusions: A lower pain score in response to vaccination with an audio-visual distraction indicates that it can be regularly used to reduce pain during the vaccination procedure and can be used during different painful procedures.

Flying Blind: How Thorough are IRBs when Assessing Scientific Value?

Institutional Review Boards (IRBs) in the United States play a crucial role in ensuring the ethical conduct of clinical trials, including assessing the scientific merit of studies to justify the risks to participants. However, prior research suggests that many IRBs do not systematically evaluate scientific merit, raising concerns about the approval of low-quality trials. To investigate whether IRBs provide adequate guidance on assessing scientific merit in their Standard Operating Procedures (SOPs) and other relevant materials. A systematic pilot investigation of IRB SOPs and related guidance documents from a sample of U.S.-based non-profit institutions. IRB materials from 35 U.S.-based non-profit institutions selected from the FDA's Bioresearch Monitoring Information System database, representing 39.9% of submissions between 2018 and 2021. Additionally, materials from one U.S.-based for-profit IRB were included. Not applicable. The presence of guidance on 15 dimensions of scientific merit, clustered into four PICO (Population, Intervention, Comparator, Outcome) categories, was assessed by reviewing IRB SOPs and related documents. Evidence of guidance was determined by mention of keywords related to each dimension. Most IRB materials mentioned basic study elements such as study design (99%), subject recruitment (90%), and intervention justification (97%). However, critical aspects related to study quality, such as bias reduction (53%) and outcome measurement tools (57%), were less frequently mentioned. The least represented dimension was confounder control (10%). IRB guidance materials vary in their coverage of scientific merit dimensions, with significant gaps in areas critical for assessing study quality. Strengthening guidance materials by including comprehensive instructions for all 15 dimensions could improve IRB assessments of scientific merit, thereby enhancing the ethical oversight of clinical trials.

Deep learning based super-resolution for CBCT dose reduction in radiotherapy.

Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly concerning for pediatric patients. Deep learning super-resolution has shown promising results in enhancing the resolution of photographic and medical images but has not yet been explored in the context of CBCT dose reduction. To facilitate CBCT imaging dose reduction, we propose using an enhanced super-resolution generative adversarial network (ESRGAN) in both the projection and image domains to restore the image quality of low-dose CBCT. An extensive projection database, containing 2997 CBCT scans from head and neck cancer patients, was used to train two different ESRGAN models to generate super-resolution CBCTs. One model operated in the projection domain, using pairs of simulated low-resolution (low-dose) and original high-resolution (high-dose) projections and yielded CBCTSRpro. The other model operated in the image domain, using pairs of axial slices from reconstructed low-resolution and high-resolution CBCTs (CBCTLR and CBCTHR) and resulted in CBCTSRimg. Super-resolution CBCTs were evaluated in terms of image similarity (MAE, ME, PSNR, and SSIM), noise characteristics, spatial resolution, and registration accuracy, using the original CBCT as a reference. To test the perceptual difference between the original and super-resolution CBCT, we performed a visual Turing test. Visually, both super-resolution approaches in the projection and image domains improved the image quality of low-dose CBCTs. This was confirmed by the visual Turing test, that showed low accuracy, sensitivity, and specificity, indicating almost no perceptual difference between CBCTHR and the super-resolution CBCTs. CBCTSRimg (accuracy: 0.55, sensitivity: 0.59, specificity: 0.50) performed slightly better than CBCTSRpro (accuracy: 0.59, sensitivity: 0.61, specificity: 0.57). Image similarity metrics were affected by varying noise levels and did not reflect the visual improvements, with MAE/ME/PSNR/SSIM values of 110.4HU/2.9HU/40.4dB/0.82 for CBCTLR, 136.6HU/-0.4HU/38.6dB/0.77 for CBCTSRpro, and 128.2HU/1.9HU/39.0dB/0.80 for CBCTSRimg. In terms of spatial resolution, quantified by calculating 10% levels of the task transfer function, both CBCTSRpro and CBCTSRimg outperformed CBCTLR and nearly matched the reference CBCTHR (CBCTLR: 0.66lp/mm, CBCTSRpro: 0.88lp/mm, CBCTSRimg: 0.95lp/mm, CBCTHR: 1.01lp/mm). Noise characteristics of CBCTSRimg and CBCTSRpro were comparable to the reference CBCTHR. Registration parameters showed negligible differences for all CBCTs (CBCTLR, CBCTSRpro, CBCTSRimg), with average absolute differences in registration parameters being below 0.4° for rotations and below 0.06mm for translations (CBCTHR as reference). This study demonstrates that deep learning can be a valuable tool for CBCT dose reduction in CBCT-guided radiotherapy by acquiring low-dose CBCTs and restoring the image quality using deep learning super-resolution. The results suggest that higher quality images can be generated when super-resolution is performed in the image domain compared to the projection domain.

Visual Analysis of Anomalous Behavior Patterns in Oil Wells Using Dimensionality Reduction with t-SNE Projection

Anomaly detection in production processes, especially in the oil industry, is of paramount importance due to the complexity and criticality of these systems. Producing oil wells are subject to a series of dynamic and interdependent variables, which can be monitored using sensors installed along the well. Precise analysis of these sensor records is essential for early identification of possible anomalies, as any deviation from the normal pattern can result in serious consequences, from operational failures to environmental and safety risks. Given the multidimensionality and variability of this data, the use of machine learning techniques becomes indispensable for analyzing and classifying the well's situation. However, even for computational software, anomaly identification remains a significant challenge, given the oscillatory behavior and scale disparity of the variables analyzed throughout the well's productive life. To mitigate these challenges, dimensional reduction techniques can be implemented, as they can bring some advantages, such as reducing computational cost, noise reduction, and improving classifier accuracy. A widely used tool for dimensional reduction, especially for visualization, is Distributed Stochastic Neighbor Embedding (t-SNE), which seeks to project high-dimensional data into a low-dimensional space preserving clusters, bringing similar data together by affinity through a t-distribution. For this approach, real records of pressure and temperature sensors from oil wells are used. These sensors are located at eight distinct points in the well, totaling eight dimensions that will be reduced to a two-dimensional plane. Therefore, this study aims to identify behavior patterns in multivariate data from producing oil wells visually using the t-SNE dimensionality reduction method. It is expected that through visualization, it will be possible to identify behavior patterns before and after the occurrence of anomalies and enable the development of appropriate artificial intelligence algorithms for this anomaly detection problem.

Challenges and recommendations for overdose prevention and harm reduction in an era of fentanyl and xylazine: Perspectives of women with opioid use disorder and professionals.

The current qualitative study examines the perspectives of women with opioid use disorder (OUD) and professionals that serve them on barriers to engaging in overdose prevention and harm reduction practices and recommendations for improving engagement. Semistructured interviews (N = 42) were conducted with women with a history of OUD (n = 20), substance use disorder treatment professionals (n = 12), and criminal legal professionals (n = 10). The interviews were inductively coded to identify themes and subthemes regarding experiences with overdose and harm reduction practices. Themes included heightened vulnerability to overdose, harm reduction challenges faced by women with OUD, and recommendations for overdose prevention and harm reduction practices. Heightened vulnerability to overdose included concerns about toxic supply and concerns about women's drug use behaviors. Challenges to women's harm reduction engagement included lack of knowledge and education about harm reduction tools and strategies and continued stigma toward harm reduction practices. Finally, recommendations for improving harm reduction engagement included increasing accessibility of harm reduction tools, expanding harm reduction education, and shifting away from "abstinence-only" paradigms. Finding ways to teach women with OUD about harm reduction, more effectively distribute harm reduction tools to them, and reduce stigma among providers and professionals is essential to reduce overdose risk for women with OUD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Novel insights into the cognitive, emotional, and experiential dimensions of stakeholder acceptance of wildlife management

Understanding wildlife stakeholders is vital in mitigating the risk for inertia in the implementation of management and illegal activities, e.g., poaching. We used a unique set of questionnaire data of stakeholders in Sweden (n = 8728) comprising birdwatchers, hunters, farmers, and the general public, to analyze evaluations of geese, beliefs about goose management at multiple levels, and acceptance of management tools. Also, a hierarchical model incorporating prior wildlife experience and integrating cognitions and emotions for acceptance was tested. The overall attitude towards geese was positive, but ecosystem disservices caused by large populations were acknowledged. The general public and birdwatchers displayed trust in the national, regional, and local levels. In contrast, farmers and hunters trusted mainly the local level. Broad stakeholder acceptance was found for several non-lethal tools for damage reduction and more generally a goose conservation approach including e.g., hunting free zones, but also for hunting during open hunting season. We found support for the proposed model in relation to acceptance of both the conservation and the lethal approach, thereby advancing theory integration. Results suggest that while goose conservation is widely accepted, problems caused by geese and distrust in the higher management levels among some stakeholder groups need to be addressed.

Islamic Economic Mechanisms to Achieve Inclusiveness and Islamic Finance for Sustainability

The present study explores avenues for Islamic economics to achieve financial inclusiveness and Islamic finance to achieve sustainability. The study focuses on the importance of providing financial services to the poor as a tool for development and poverty reduction. The study also reviews impediments to poor people's access to financial services, such as low income, high risks, high costs, and cultural and religious constraints. The present study emphasizes the importance of providing Shariah-compliant financial services to the poor in Islamic countries. The study also addresses the regulatory aspects of Islamic financial institutions and the role of zakat and waqf in achieving financial inclusion and sustainability.

An Innovative Deep-Learning Technique for Fuel Demand Estimation in Maritime Transportation: A Step Toward Sustainable Development and Environmental Impact Mitigation

This study introduces an innovative deep-learning approach for fuel demand estimation in maritime transportation, leveraging a novel convolutional neural network, bidirectional, and long short-term memory attention as a deep learning model. The input variables studied include vessel characteristics, weather conditions, sea states, the number of ships entering the port, and navigation specifics. This study focused on the ports of Jazan in Saudi Arabia and Fujairah in the United Arab Emirates, analyzing daily and monthly data to capture fuel consumption patterns. The proposed model significantly improves prediction accuracy compared with traditional methods, effectively accounting for the complex, nonlinear interactions influencing fuel demand. The results showed that the proposed model has a mean square error of 0.0199 for the daily scale, which is a significantly higher accuracy than the other models. The model could play an important role in port management with a potential reduction in fuel consumption, enhancing port efficiency and minimizing environmental impacts, such as preserving seawater quality. This advancement supports sustainable development in maritime operations, offering a robust tool for operational cost reduction and regulatory compliance.

Influence of Metal Artifact Reduction Tool of Two Cone Beam CT on the Detection of Bone Graft Loss Around Titanium and Zirconium Implants-An ExVivo Diagnostic Accuracy Study.

The objective of this study is to investigate the influence of metal artifact reduction (MAR) on two cone beam computed tomography (CBCT) units in the evaluation of bone graft loss adjacent to titanium (Ti) and zirconium (Zr) implants. Twelve Ti and twelve Zr implants were placed in the posterior region of dry human mandibles. Bone graft was applied to the level of the cover screw. Bone graft loss was simulated in half of the sample (6 Ti and 6 Zr) by removing the graft material up to the third implant thread on the buccal surface. CBCT images were acquired on two units, varying the application of MAR (OP300-off and on; Eagle 3D-standard, intermediate, and extreme). The images were assessed by five evaluators that scored the presence of graft loss according to a 5-point scale. The diagnostic values were calculated and compared by non-parametric tests with a significance level of 5%. Higher diagnostic values were achieved with MAR activated in the OP300 unit, for Ti and Zr (p < 0.05). On the Eagle 3D unit, MAR in extreme mode resulted in lower diagnostic values for both types of implants (p < 0.05). The diagnostic values of Ti implants were higher than Zr implants (p < 0.05). The application of MAR influences the diagnosis of bone graft loss adjacent to Ti and Zr dental implants. However, the extreme mode of MAR in the Eagle 3D unit can impair the diagnostic task in both types of implants and should be avoided.

Flood fragility and vulnerability functions for residential buildings in the Province of Leyte, Philippines

AbstractThe Philippines experiences frequent flooding, but, despite expansive tools for risk reduction, there remain gaps in understanding generalised relationships between flood events and damage to residential structures for regions outside the nation's capital. This gap has limited the ability to model flood risk and damage without robust functions to link hazards and housing vulnerability. This research draws on 394 household surveys to empirically derive a suite of flood fragility and vulnerability functions for residential structures in the Province of Leyte for light material, elevated light material and masonry structures. The results showed that masonry construction was more resilient to floods compared to light material counterparts. Elevated light material structures also exhibited lower damages at low inundations but tend to fail abruptly at flood depths greater than 3 m. By empirically deriving flood damage functions, the findings contribute to a more localised approach to quantifying housing vulnerability and risk that can be used for catastrophe and risk modelling, with applications for government agencies, the insurance industry and disaster risk researchers. This research lays the foundation for future flood risk mapping with growing significance under climate change.

Combining different metal artifact reduction levels with sharpening filters and slice thickness for the visualization of mandibular canals perforated by implants.

To investigate the influence of combining levels of metal artifact reduction (MAR) tool with different filters and slice-thickness in the detection of mandibular canals perforated by implants on CBCT scans and to objectively assess the impact of the aforementioned combinations. Implants were placed above (8 implants) and inside (10 implants) mandibular canals of dried-mandibles. CBCT scans were obtained with the Eagle 3D unit (85 kVp, 8mA, 5 × 5cm FOV, 130μm voxel size, and off/medium/high MAR levels). Examiners evaluated the scans under each MAR level and across different conditions: no filter, Sharpen 1×, Sharpen 2×; 0mm, 1mm, and 2mm slice-thickness. The gray values on axial reconstructions were assessed. Area under the receiver operating characteristic curve (AUC), sensitivity, specificity, mean and standard deviation (SD) for gray values were calculated and compared by multi-way Analysis of Variance. Overall, the AUC and sensitivity decreased with high-level MAR (p < 0.0001), regardless other variables tested. Enabling MAR tool decreased mean gray values (p < 0.01) and increasing MAR levels reduced the SD values on scans with Sharpen 2× (p < 0.012). High-level MAR impairs the visualization of mandibular canals perforated by implants. Moreover, enabling MAR tool decreases the mean gray values. MAR tool, filters, and slice thickness influence the image quality of CBCT scans. Therefore, it is important to evaluate the impact of these parameters on the diagnosis of mandibular canals perforated by implants.

Qualitative study on sustainability of intervention measures in the Shandong Provincial Department's Joint Salt Reduction Project

Objective: To employ qualitative research methods to evaluate the sustainability of the Shandong-Ministry of Health Action on Salt and Hypertension (SMASH) interventions and their influencing factors. Method: In September 2023, interviewees meeting the inclusion criteria were recruited through purposive sampling in Jinan, Shandong Province. A semi-structured interview guide was designed based on the CFIR and RE-AIM frameworks. Personal in-depth interviews and focus group discussions were conducted to gather insights on the feasibility, effectiveness, and sustainability of the project interventions from various stakeholders, including representatives from the government, food manufacturers, restaurants, academia, and residents. Results: A total of 15 individuals participated in in-depth interviews, involving six representatives from food manufacturers, four from restaurants, three from the government, and two from academia. There were four focus group discussions with 30 residents. The study found that at the individual resident level, health awareness and the availability of sodium reduction tools in the market could affect their salt reduction practices. For food manufacturers and restaurants, consumers' preferences, technical challenges in reformulation, and government support were key factors determining the smooth progress of their salt reduction efforts. At the governmental level, multi-sectional coordination, chronic disease management demonstration areas, and the impact of the pandemic were the main factors influencing the implementation of sodium reduction interventions. Conclusion: Future endeavours should strengthen long-term management and optimise the complex influencing factors associated with intervention measures. This will be essential in sustaining and expanding the positive health outcomes achieved through the Shandong population sodium reduction strategy.

Elevating equity: advancing diversity and inclusivity through trialing bias reduction tools in the general internal medicine program resident application process.

Decades of medical data show worse patient outcomes among racial and gender minorities due to implicit, explicit, and structural biases. Increasing representation of marginalized groups among care providers is imperative to help address this. Limited literature exists on bias awareness strategies for interviewers during the selection of applicants to General Internal Medicine (GIM) programs in Canada. This study examines the trial of bias reduction tools to increase interviewers' awareness of implicit biases. The Model of Improvement framework guided the trail of an instructional video, the adapted implicit association test (IAT), and a paper awareness tool (PAT) to increase interviewers' awareness of implicit biases during the University of Alberta's GIM applicant selection. An anonymous online survey was disseminated to physician interviewers. Descriptive statistics (percentages) and a modified sentiment analysis was completed. 10 of 14 interviewers completed the survey. Respondents reported an increased awareness of using bias reduction tools (IAT, 25%; video, 71%; PAT, 67%) to inform them on their implicit biases. The future use of IAT, video, and PAT was supported by 50%, 71%, and 67% of interviewers, respectively. Interviewers prefer the instructional video and PAT over the IAT. Textual responses suggest existing concerns for biases inherent to the interview process yet 70% (7/10) of respondents believe that interviews should have a weighting of 50% towards final ranking of candidates. As many institutions continue to rely on interviews to evaluate candidates, our findings indicate the need for a national study to develop a framework to mitigate inherent biases during interviewing of candidates.

Distributed Estimation of Principal Support Vector Machines for Sufficient Dimension Reduction

The principal support vector machines method (Li et al., 2011) is a powerful tool for sufficient dimension reduction that replaces original predictors with their low-dimensional linear combinations while preserving the information for regression and classification. However, the computational burden of the principal support vector machines method constrains its use for massive data. To address this issue, we propose a naive and a refined distributed estimation algorithms for fast implementation when the sample size is large. Both distributed sufficient dimension reduction estimators exhibit the same statistical efficiency as when all the data is merged together, which provides rigorous statistical guarantees for their application to large-scale datasets, while the refined method requires smaller batch sample sizes and hence is more advantageous when memory limitations exist on distributed machines. The two distributed algorithms are further adapted to principal weighted support vector machines (Shin et al., 2017) for sufficient dimension reduction in binary classification. The statistical accuracy and computational complexity of our proposed methods are examined through comprehensive simulation studies and in a real data application with more than 600000 samples.

Automatic visual recognition for leaf disease based on enhanced attention mechanism.

Recognition methods have made significant strides across various domains, such as image classification, automatic segmentation, and autonomous driving. Efficient identification of leaf diseases through visual recognition is critical for mitigating economic losses. However, recognizing leaf diseases is challenging due to complex backgrounds and environmental factors. These challenges often result in confusion between lesions and backgrounds, limiting information extraction from small lesion targets. To tackle these challenges, this article proposes a visual leaf disease identification method based on an enhanced attention mechanism. By integrating multi-head attention mechanisms, this method accurately identifies small targets of tomato lesions and demonstrates robustness in complex conditions, such as varying illumination. Additionally, the method incorporates Focaler-SIoU to enhance learning capabilities for challenging classification samples. Experimental results showcase that the proposed algorithm enhances average detection accuracy by 10.3% compared to the baseline model, while maintaining a balanced identification speed. This method facilitates rapid and precise identification of tomato diseases, offering a valuable tool for disease prevention and economic loss reduction.

A New Artificial Intelligence-Based System for Optimal Electricity Arbitrage of a Second-Life Battery Station in Day-Ahead Markets

Electric vehicles (EVs) are widely regarded as a crucial tool for carbon reduction due to the gradual increase in their numbers. However, these vehicles are equipped with batteries that have a limited lifespan. It is commonly stated that when the battery capacity falls below 70%, it needs to be replaced, and these discarded batteries are typically sent for recycling. Nevertheless, there is an opportunity to repurpose these worn-out batteries for a second life in electric power systems. This study focuses on the arbitrage situation of a second-life battery (SLB) facility in day-ahead electricity markets. This approach not only contributes to balancing supply and demand in the electric power system but also allows the battery facility to achieve significant gains. We propose an artificial intelligence system that integrates optimized deep learning algorithms for market price predictions with a mixed-integer linear programming (MILP) model for market participation and arbitrage decisions. Our system predicts prices for the next 24 h using Neural Hierarchical Interpolation for Time Series (N-HiTS) and decides when to enter the market using the MILP model and incorporating the predicted data and the statuses of the batteries. We compare the accuracy of our trained deep learning model with other deep learning models such as recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and Neural Basis Expansion Analysis for Interpretable Time-Series Forecasting (N-BEATS). We test the efficiency of the proposed system using real-world Turkish day-ahead market data. According to the results obtained, this study concludes that substantial gains can be achieved with the predicted prices and the optimal operating model. A facility with a total battery energy capacity of 5.133 MWh can generate a profit of USD 539 in one day, showcasing the potential of our study. Our new system’s approach provides proof of concept of new research opportunities for the participation of SLB facilities in day-ahead markets.

Robust Principal Component Analysis: A Median of Means Approach.

Principal component analysis (PCA) is a fundamental tool for data visualization, denoising, and dimensionality reduction. It is widely popular in statistics, machine learning, computer vision, and related fields. However, PCA is well-known to fall prey to outliers and often fails to detect the true underlying low-dimensional structure within the dataset. Following the Median of Means (MoM) philosophy, recent supervised learning methods have shown great success in dealing with outlying observations without much compromise to their large sample theoretical properties. This article proposes a PCA procedure based on the MoM principle. Called the MoMPCA, the proposed method is not only computationally appealing but also achieves optimal convergence rates under minimal assumptions. In particular, we explore the nonasymptotic error bounds of the obtained solution via the aid of the Rademacher complexities while granting absolutely no assumption on the outlying observations. The derived concentration results are not dependent on the dimension because the analysis is conducted in a separable Hilbert space, and the results only depend on the fourth moment of the underlying distribution in the corresponding norm. The proposal's efficacy is also thoroughly showcased through simulations and real data applications.

Weighted Vest Combined With Vibrotactile Stimulations Decrease the Sympathetic Activity: A Repeated Measures Study.

Mental and neurological disorders are a growing global concern, further intensified by the COVID-19 pandemic. Stress management techniques, such as deep pressure therapy, have gained attention, with weighted vests commonly used for anxiety relief. However, there is limited scientific evidence supporting their efficacy. This study aimed to rigorously assess the effectiveness of a weighted vest incorporating vibrotactile stimulation in reducing anxiety by measuring physiological indicators-pulse rate and skin conductance (SC)-as well as subjective comfort ratings. A total of 30 final-semester college students participated in the study. Participants wore a vibrotactile-weighted vest, and both pulse rate and skin conductance (SC) were measured to gauge anxiety levels. Additionally, participants rated their comfort using a Comfort Rating Scale (CRS). Changes in pulse rate and SC were statistically analyzed, and effect sizes (Cohen's d) were calculated to assess the magnitude of the intervention's impact. The weighted vest with vibrotactile stimulation resulted in a significant reduction in both pulse rate (dpulse = 0.23-0.62) and SC (dsc = 0.32-0.66), indicating a small to medium effect size in anxiety reduction. Subjective evaluations of the vest using the CRS revealed low scores on discomfort-related items, with participants rating unfavorable statements between 1.5/10 and 4.6/10, suggesting overall comfort during use. This study provides compelling evidence that vibrotactile-weighted vests effectively reduce anxiety, as indicated by both physiological measures and subjective comfort ratings. The findings support the potential of this intervention as a formal therapeutic tool for stress and anxiety reduction. Further research may explore long-term effects and broader applications in clinical settings.