Extensive Training Research Articles

Psychologists aspiring to leadership positions in public sector health care.

Psychologists are well-positioned to take on leadership roles in health care systems as a result of the broad-based skills included in doctoral level, professional training programs. These include knowledge of evidence-based practice, extensive training in applied research and clinical practice, emphasis on critical thinking in scientific methods and hypothesis testing, teaching, supervision, team consultation, and continuous learning (APA Presidential Task Force on Evidence-Based Practice, 2006; Korman, 1974; McFall, 2007). Formal opportunities to learn how to apply these skills in leadership and organizational management roles are, however, limited during graduate training. There have been recent efforts within the American Psychological Association to foster interest and readiness for leadership roles among psychologists. These efforts have included a leadership development fellowship, on-demand webinars, and online learning for continuing education (American Psychological Association, 2023). The content of these training opportunities is typically general in nature so that it can be applied to all types of organizational settings. Psychologists interested in leadership positions within public sector health care organizations are likely to benefit from information that aligns more specifically with the mission and organizational structures of such systems. This article presents a conceptual framework to prepare psychologists aspiring to leadership positions in public sector health care. Leadership theories and models from organizational management science are outlined which capture the context and organizational goals of such programs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Stochastic image spectroscopy: a discriminative generative approach to hyperspectral image modelling and classification

This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources. Numerous experiments with simulated data and real benchmark scenarios show encouraging image classification performance. These results validate the unique ability of our framework to discriminate complex hyperspectral images, irrespective of the presence of highly discriminative spectral signatures.

Transforming policing in Ghana through technology: a focus on police operations in the Greater Accra region

Integrating technology into policing has the potential to greatly improve the effectiveness, transparency, and community participation of law enforcement agencies. This study examines the profound influence of technology on the Ghana Police Service, with a specific emphasis on the Greater Accra Region. The main aim of this research is to examine how technological advancements might enhance police results and tackle existing difficulties in the context of law enforcement in Ghana. The study adopts a qualitative research approach and collects data from police officers and community members through the use of semi-structured interviews and focus group discussions. The investigation involved the participation of a combined total of twenty-six police officials from the Tema and Accra Regional Headquarters, as well as twenty-two community members. The data gathering procedure sought to gather a wide range of viewpoints on the present condition of technology in law enforcement, its advantages, difficulties, and prospects for future advancement. The results demonstrate that the utilisation of technology, such as mobile data terminals and social media platforms, can greatly improve the effectiveness of police operations, promote community involvement, and increase transparency. Nevertheless, the presence of obstacles such as insufficient infrastructure, cultural opposition, and the necessity for extensive training were also acknowledged. The research enhances the current knowledge by presenting empirical evidence on the benefits and constraints of technology in law enforcement, particularly in the setting of Ghana. This study emphasises the significance of making strategic investments in technology infrastructure and training, as well as the necessity of establishing legal frameworks to tackle ethical and privacy concerns. Additionally, it proposes that future studies should prioritise examining the enduring viability of technological advancements in law enforcement and investigating their effects in various geographical areas. The study suggests that politicians and law enforcement organisations should promote the deployment of technology in a way that is successful and build public trust in the Ghana Police Service.

Challenges in institutionalizing evidence-informed priority setting for health service packages: a qualitative document and interview analysis from Iran

BackgroundSetting and implementing evidence-informed health service packages (HSPs) is crucial for improving health and demonstrating the effective use of evidence in real-world settings. Despite extensive training for large groups on evidence generation and utilization and establishing structures such as evidence-generation entities in many countries, the institutionalization of setting and implementing evidence-informed HSPs remains unachieved. This study aims to review the actions taken to set the HSP in Iran and to identify the challenges of institutionalizing the evidence-informed priority-setting process.MethodsRelevant documents were obtained through website search, Google queries, expert consultations and library manual search. Subsequently, we conducted nine qualitative semi-structured interviews with stakeholders. The participants were purposively sampled to represent diverse backgrounds relevant to health policymaking and financing. These interviews were meticulously audio-recorded, transcribed and reviewed. We employed the framework analysis approach, guided by the Kuchenmüller et al. framework, to interpret data.ResultsEfforts to incorporate evidence-informed process in setting HSP in Iran began in the 1970s in the pilot project of primary health care. These initiatives continued through the Health Transformation Plan in 2015 and targeted disease-specific efforts in 2019 in recent years. However, full institutionalization remains a challenge. The principal challenges encompass legal gaps, methodological diversity, fragile partnerships, leadership changeovers, inadequate financial backing of HSP and the dearth of an accountability culture. These factors impede the seamless integration and enduring sustainability of evidence-informed practices, hindering collaborative decision-making and optimal resource allocation.ConclusionsTechnical aspects of using evidence for policymaking alone will not ensure sustainability unless it achieves the necessary requirements for institutionalization. While addressing all challenges is crucial, the primary focus should be on required transparency and accountability, public participation with an intersectionality lens and making this process resilience to shocks. It is imperative to establish a robust legal framework and a strong and sustainable political commitment to embrace and drive change, ensuring sustainable progress.

Overview of the various methods used to assess walking ability in broiler chickens.

Walking ability is a highly important animal-based indicator of welfare in broilers that warrants monitoring across commercial production. A review of the technical and scientific literature on existing methods for assessing walking ability in broilers was conducted, and the reliability, validity and feasibility of the different methods were assessed when relevant information was available. Visual gait scoring has high validity, minimal equipment needs and is relatively quick to perform; however, this method is subjective and requires extensive training. Due to this, more objective tests, such as the latency to lie test, have been developed, although more validation of these tests is required. Other identified methods, such as obstacle courses, the rotarod test and kinetic and kinematic approaches, require considerable amounts of equipment, making them impractical for use during on-farm inspections. Automated methods that rely on movement or activity data have potential for future development but currently lack the resolution of traditional gait scoring and require equipment such as cameras or accelerometers. Objective, seemingly valid, repeatable and feasible options exist for the assessment of walking ability that would facilitate the monitoring of commercial broiler welfare; however, more validation of these methods is required, especially in commercial farm settings.

Farmer’s Knowledge on Utilization of Chemical Fertilizers as Soil Fertility Enhancement Option in Ruhumuro Sub-County, Bushenyi District

The study focused on assessing the farmer’s knowledge on utilization of chemical fertilizers as soil fertility enhancement option in Ruhumuro sub-county, Bushenyi district. It was limited to; establishing the local farmers’knowledge on the use of chemical fertilizers, investigating the factors that influence farmers’ decisions to utilize chemical fertilizers and establishing the benefits that local farmers achieve from utilizing chemical fertilizers. The study employed a cross-sectional research design and primary data was collected from 200 respondents. the data was analyzed using SPSS version 22.0. The study concluded that farmers had local knowledge on use of chemical fertilizers where they revealed that, fertilizer use is influenced by on agro-climatic conditions and farm characteristics at (p=.027), profitability is the principal incentive to adopt fertilizer use at (p=.021), fertilizer use ensure high productivity and yields at (p=.003). The decisions on fertilizer use has mainly considered credit access and price at (p=.026) Fertilizer use alter the soil properties at p= (.029). Fertilizer applied to farmers’ crops do not provide optimal economic returns to farmers as (p=.312) as non-significant. The study also concluded that there were factors that influence the farmers’ decisions to utilize chemical fertilizers. Such as; ownership of land (p=.022), labour avialability (p=.024), Cost of the fertilizers (p=.004) and un-availability of credit services/financial institutions offering services (p=.028). Non-significant factors included; farming experience and level of education (p=.427, p=.369). The study concluded that there were benefits that local farmers achieve from utilizing chemical fertilizers. Such as; Restoration of soil fertility (<i>x<sup>2</sup></i> = 3.002, p=0.021), Sustainable food production (<i>x<sup>2 </sup></i>= 2.441, p=0.001), Income security (<i>x<sup>2</sup></i> = 0.871, p= 0.010), Improvement of crop yields (<i>x<sup>2</sup></i> = 3.123, p=0.023) and Contribute to greenhouse effect (<i>x<sup>2</sup></i> = 4.211, p=0.141) which was not significant. The study recommends that, there is a need for policymakers and agricultural stakeholders to consider the social, economic, and environmental implications of the widespread use of chemical fertilizers. Constant extension training services on chemical fertilizer use should be done to help farmers use the recommended dosage of fertilizers to avoid environmental degradation due to lack of adequate knowledge on fertilizer utilization. Government should undertake legal reforms to recognize and protect the rights of vulnerable groups to own and use land. They should also put in place mechanisms to ensure that the law is enforced.

An Accurate and Rapidly Calibrating Speech Neuroprosthesis

BackgroundBrain–computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain–computer interfaces has been restricted by extensive training requirements and limited accuracy.MethodsA 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes. We report the results of decoding his cortical neural activity as he attempted to speak in both prompted and unstructured conversational contexts. Decoded words were displayed on a screen and then vocalized with the use of text-to-speech software designed to sound like his pre-ALS voice.ResultsOn the first day of use (25 days after surgery), the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. Calibration of the neuroprosthesis required 30 minutes of cortical recordings while the participant attempted to speak, followed by subsequent processing. On the second day, after 1.4 additional hours of system training, the neuroprosthesis achieved 90.2% accuracy using a 125,000-word vocabulary. With further training data, the neuroprosthesis sustained 97.5% accuracy over a period of 8.4 months after surgical implantation, and the participant used it to communicate in self-paced conversations at a rate of approximately 32 words per minute for more than 248 cumulative hours.ConclusionsIn a person with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore conversational communication after brief training. (Funded by the Office of the Assistant Secretary of Defense for Health Affairs and others; BrainGate2 ClinicalTrials.gov number, NCT00912041.)

GPT-4 as an X data annotator: Unraveling its performance on a stance classification task.

Data annotation in NLP is a costly and time-consuming task, traditionally handled by human experts who require extensive training to enhance the task-related background knowledge. Besides, labeling social media texts is particularly challenging due to their brevity, informality, creativity, and varying human perceptions regarding the sociocultural context of the world. With the emergence of GPT models and their proficiency in various NLP tasks, this study aims to establish a performance baseline for GPT-4 as a social media text annotator. To achieve this, we employ our own dataset of tweets, expertly labeled for stance detection with full inter-rater agreement among three annotators. We experiment with three techniques: Zero-shot, Few-shot, and Zero-shot with Chain-of-Thoughts to create prompts for the labeling task. We utilize four training sets constructed with different label sets, including human labels, to fine-tune transformer-based large language models and various combinations of traditional machine learning models with embeddings for stance classification. Finally, all fine-tuned models undergo evaluation using a common testing set with human-generated labels. We use the results from models trained on human labels as the benchmark to assess GPT-4's potential as an annotator across the three prompting techniques. Based on the experimental findings, GPT-4 achieves comparable results through the Few-shot and Zero-shot Chain-of-Thoughts prompting methods. However, none of these labeling techniques surpass the top three models fine-tuned on human labels. Moreover, we introduce the Zero-shot Chain-of-Thoughts as an effective strategy for aspect-based social media text labeling, which performs better than the standard Zero-shot and yields results similar to the high-performing yet expensive Few-shot approach.

Comparative Analysis of Google Sheets and Microsoft Excel

An emerging landscaping and lawn care service company (GreenLawn) is seeking to transition from manual record-keeping to digital solutions. With significant growth over two years, GreenLawn requires efficient software to manage operations and finances while avoiding complexity and extensive training costs. The company evaluates Microsoft Excel and Google Sheets, weighing features, collaboration capabilities, ease of use, and pricing. GreenLawn's specific needs include service scheduling, workforce management, client information storage, inventory tracking, marketing, expense tracking, and financial modeling. The comparison highlights Excel's advanced features and integration with Microsoft Office versus Google Sheets' real-time collaboration and cost-effective accessibility. Questions address software selection, task management, collaboration strategies, cost-effectiveness, integration with industry tools, scalability, training, and adoption. Ultimately, GreenLawn's decision hinges on aligning software capabilities with its unique requirements and growth objectives.

A critical review on machine learning applications in fiber composites and nanocomposites: Towards a control loop in the chain of processes in industries

Fiber composites must be evaluated to achieve correct use in various fields. Their properties, performance, condition, and integrity can be quickly predicted and optimized by machine learning (ML), after extensive training, compared with experiments and conventional computational simulations. In this document, papers on ML applications in fiber composites were collected and critically reviewed. It was revealed that kind learning environments have been primarily used. Supervised ML has been more frequently used than unsupervised ML, whereas some specific semi–supervised ML (e.g., reinforcement learning) or deep predictive control have been overlooked. Most ML applications have been successful on the laboratory scale and in the short term. Furthermore, the deployment of ML applications has been overlooked. In addition, retroactive feedback from the manufacturing of fiber and polymers to the manufacturing of composite laminates and structures was neglected. Accordingly, a control loop in the chain of manufacturing processes was discussed. Additionally, language processing tools and statistics were used to summarize and analyze the papers. Finally, it was proposed that multiscale modeling using ML and physics is a potential approach to advance predictions for future applications. Therefore, physicochemical interactions (van der Waals or electrostatic) from nanoscale can be included.

A pharmacist's guide to mitigating sleep dysfunction and promoting good sleep in the intensive care unit.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. To review causes, risk factors, and consequences of sleep disruption in critically ill patients; evaluate the role of nonpharmacological and pharmacological therapies for management of sleep in the intensive care unit (ICU); and discuss the role of pharmacists in implementation of sleep bundles. Critically ill patients often have disrupted sleep and circadian rhythm alterations that cause anxiety, stress, and traumatic memories. This can be caused by factors such as critical illness, environmental factors, mechanical ventilation, and medications. Methods to evaluate sleep, including polysomnography and questionnaires, have limitations that should be considered. Multicomponent sleep bundles with a focus on nonpharmacological therapy aiming to reduce nocturnal noise, light, and unnecessary patient care may improve sleep disorders in critically ill patients. While pharmacological agents are often used to facilitate sleep in critically ill patients, evidence supporting their use is often of low quality, which limits use to patients who have sleep disruption refractory to nonpharmacological therapy. Dedicated interprofessional teams are needed for implementation of sleep bundles in the ICU. Extensive pharmacotherapeutic training and participation in daily patient care rounds make pharmacists vital members of the team who can help with all components of the bundle. This narrative review discusses evidence for elements of the multicomponent sleep bundle and provides guidance on how pharmacists can help with implementation of nonpharmacological therapies and management of neuroactive medications to facilitate sleep. Sleep bundles are necessary for patients in the ICU, and dedicated interprofessional teams that include pharmacists are vital for their successful creation and implementation.

Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction

Objective. Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied physical acquisition conditions, constrain their generalizability across new imaging contexts and settings. Approach. To overcome these limitations, we propose an unsupervised approach grounded in the deep image prior framework. Our approach advances beyond the conventional single noise level input by incorporating multi-level linear diffusion noise, significantly mitigating the risk of overfitting. Furthermore, we embed non-local self-similarity as a deep implicit prior within a self-attention network structure, improving the model’s capability to identify and utilize repetitive patterns throughout the image. Additionally, leveraging imaging physics, gradient backpropagation is performed between the image domain and projection data space to optimize network weights. Main Results. Evaluations with both simulated and clinical cases demonstrate our method’s effective zero-shot adaptability across various projection views, highlighting its robustness and flexibility. Additionally, our approach effectively eliminates noise and streak artifacts while significantly restoring intricate image details. Significance. Our method aims to overcome the limitations in current supervised deep learning-based sparse-view CT reconstruction, offering improved generalizability and adaptability without the need for extensive paired training data.

Evaluating large language models for health-related text classification tasks with public social media data.

Large language models (LLMs) have demonstrated remarkable success in natural language processing (NLP) tasks. This study aimed to evaluate their performances on social media-based health-related text classification tasks. We benchmarked 1 Support Vector Machine (SVM), 3 supervised pretrained language models (PLMs), and 2 LLMs-based classifiers across 6 text classification tasks. We developed 3 approaches for leveraging LLMs: employing LLMs as zero-shot classifiers, using LLMs as data annotators, and utilizing LLMs with few-shot examples for data augmentation. Across all tasks, the mean (SD) F1 score differences for RoBERTa, BERTweet, and SocBERT trained on human-annotated data were 0.24 (±0.10), 0.25 (±0.11), and 0.23 (±0.11), respectively, compared to those trained on the data annotated using GPT3.5, and were 0.16 (±0.07), 0.16 (±0.08), and 0.14 (±0.08) using GPT4, respectively. The GPT3.5 and GPT4 zero-shot classifiers outperformed SVMs in a single task and in 5 out of 6 tasks, respectively. When leveraging LLMs for data augmentation, the RoBERTa models trained on GPT4-augmented data demonstrated superior or comparable performance compared to those trained on human-annotated data alone. The results revealed that using LLM-annotated data only for training supervised classification models was ineffective. However, employing the LLM as a zero-shot classifier exhibited the potential to outperform traditional SVM models and achieved a higher recall than the advanced transformer-based model RoBERTa. Additionally, our results indicated that utilizing GPT3.5 for data augmentation could potentially harm model performance. In contrast, data augmentation with GPT4 demonstrated improved model performances, showcasing the potential of LLMs in reducing the need for extensive training data. By leveraging the data augmentation strategy, we can harness the power of LLMs to develop smaller, more effective domain-specific NLP models. Using LLM-annotated data without human guidance for training lightweight supervised classification models is an ineffective strategy. However, LLM, as a zero-shot classifier, shows promise in excluding false negatives and potentially reducing the human effort required for data annotation.

Downstream lingering attention transformer network (DsLATNet) for land use land cover classification: A bicolor deep learning framework

Land Use and Land Cover (LULC) classification is the process of locating and classifying regions of the Earth's surface (land cover) based on their physical attributes and human utilization (land use purpose). It is essential for mapping and monitoring changes in ecosystems, facilitating effective resource management and informed decision-making in land-use planning. Convolutional Neural Network (CNN’s) is incapable of capturing long-range dependencies. The effectiveness of transformers relies on extensive training datasets, yet many satellite datasets have comparatively limited sample sizes. Moreover, due to the numerous factors influencing visual prominence, it is challenging to obtain abundant features from a single-color space. To overcome these limitations, we introduce an innovative bicolor architecture Downstream Lingering Attention Transformer Network (DsLATNet). DsLATNet characterizes remote sensing images on two color spaces viz RGB and HSV by processing the information using Residual Network −50(ResNet-50) backbone network.Extracted features of backbone network are further refined to acquire detailed aware features through Downstream Lingering Feature Pyramid Network (DsLFPN).This is further integrated with a DuoTransformer that utilizes attention mechanisms to obtain long-range dependencies. Eventually, the features are aggregated using residual connections that are dilated in-depth multiple times to produce class labels. The efficacy of the algorithm is evaluated through standard classification accuracy metrics. Experimental results on both the Wuhan Dense Labeling Dataset (WHDLD) and the Gaofen Image Dataset (GID) exhibit that the suggested technique exceeds cutting-edge classification methods. WHDLD achieved an Overall Accuracy (OA) of 86.34 %, Average Accuracy (AA) of 75.62 %, and Kappa coefficient (K) of 80.75, while the GID exhibited superior performance with an OA of 86.43 %, AA of 88.13 %, and K of 81.25.

Edge human activity recognition using federated learning on constrained devices

Human Activity Recognition (HAR) using wearable Internet of Things (IoT) devices represents a well investigated researched field encompassing various application domains. Many current approaches rely on cloud-based methodologies for gathering data from diverse users, resulting in the creation of extensive training datasets. Although this strategy facilitates the application of powerful Machine Learning (ML) techniques, it raises significant privacy concerns, which can become particularly severe given the sensitivity of HAR data. Moreover, the labeling process can be extremely time-consuming and even more challenging for IoT wearable devices due to the absence of efficient input systems. In this paper, we address both aforementioned challenges by designing, implementing, and validating edge-based Human Activity Recognition (HAR) systems that operate on resource-constrained IoT devices, which relies on the utilization of Self-Organizing Maps (SOM) for activity detection. We incorporate a feature selection process before training to reduce data dimensionality and, consequently, the SOM size, aligning with the resource limitations of wearable IoT devices. Additionally, we explore the application of Federated Learning (FL) techniques for HAR tasks, enabling new users to leverage SOM models trained by others on their respective datasets. Our federated Extreme Edge (EE)-aware HAR system is implemented on a wearable IoT device and rigorously tested against state-of-the-art and experimental datasets. The results demonstrate that our C++-based SOM implementation achieves a consistent reduction in model size compared to state-of-the-art approaches. Furthermore, our findings highlight the effectiveness of the FL-based approach in overcoming personalized training challenges, particularly in onboarding scenarios.

Enhancing Image Copy Detection through Dynamic Augmentation and Efficient Sampling with Minimal Data

Social networks have become deeply integrated into our daily lives, leading to an increase in image sharing across different platforms. Simultaneously, the existence of robust and user-friendly media editors not only facilitates artistic innovation, but also raises concerns regarding the ease of creating misleading media. This highlights the need for developing new advanced techniques for the image copy detection task, which involves evaluating whether photos or videos originate from the same source. This research introduces a novel application of the Vision Transformer (ViT) model to the image copy detection task on the DISC21 dataset. Our approach involves innovative strategic sampling of the extensive DISC21 training set using K-means clustering to achieve a representative subset. Additionally, we employ complex augmentation pipelines applied while training with varying intensities. Our methodology follows the instance discrimination concept, where the Vision Transformer model is used as a classifier to map different augmentations of the same image to the same class. Next, the trained ViT model extracts descriptors of original and manipulated images that subsequently underwent post-processing to reduce dimensionality. Our best-achieving model, tested on a refined query set of 10K augmented images from the DISC21 dataset, attained a state-of-the-art micro-average precision of 0.79, demonstrating the effectiveness and innovation of our approach.

Inverse design of TPMS piezoelectric metamaterial based on deep learning

Piezoelectric metamaterials are a class of composite structural materials with a piezoelectric effect that achieve efficient coupling between electrical and mechanical energy through their unique microscopic design. Typically, the tailoring of cellular architectures paves the way for unlocking the potential to achieve unprecedentedly designed materials with specific properties. We have developed three different inverse design frameworks for metamaterials, using deep learning (DL) techniques and underpinned by Bayesian optimisation. These frameworks reveal precise one-to-many correlations between material properties and geometric parameters within the triple periodic minimal surface (TPMS) piezoelectric metamaterial. For the first time, we have introduced the Progressive Latin Hypercube Sampling (PLHS) method to the inverse design process of metamaterials, highlighting the effectiveness of uniform sampling to improve the efficiency of deep learning models. The conditional variational autoencoder (cVAE), the conditional generative adversarial network (cGAN) and the multilayer perceptron (MLP) are studied for comparative analysis. All three models incorporate an inverse design network and a feature predictor to construct a continuous latent representation of a large number of hybrid TPMS (H-TPMS). After extensive training, the models can generate various piezoelectric porous structures that accurately meet the required material property parameters. Our results demonstrate the high accuracy of cVAE in inverse design, with 98.92% agreement for piezoelectric values and 96.44% agreement for mechanical properties. The three inverse design frameworks show different generalisation capabilities, with cVAE performing best. This study thoroughly analyses the strengths and limitations of each model, providing valuable insights for researchers seeking inverse design recommendations.

Advanced Denoising and Meta-Learning Techniques for Enhancing Smart Health Monitoring Using Wearable Sensors

This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. Moreover, collecting extensive training data from wearable sensor experiments is challenging, time-consuming, and expensive. In these cases, meta-learning can be particularly useful. This model can quickly adapt to the nuances of new users or specific applications with minimal data. Therefore, to solve the need for a huge amount of training data and to enable the application of artificial intelligence (AI) in data-scarce scenarios, a meta-learning method is proposed. This meta-learning model has been implemented to forecast diabetes, resolve cross-talk issues, and accurately detect R peaks from overlapping electrocardiogram (ECG) signals affected by movement artifacts, poor electrode contact, electrical interference, or muscle activity. Motion artifacts from body movements, external conditions such as temperature, humidity, and electromagnetic interference, and the inherent quality and calibration of the sensor can all contribute to noise. Contact quality between the sensor and the skin, signal processing errors, power supply variations, user-generated interference from activities like talking or exercising, and the materials used in the wearable device also play significant roles in the overall noise in wearable sensor data and can significantly distort the true signal, leading to erroneous interpretations and potential diagnostic errors. Furthermore, discrete wavelet transform (DWT) was also implemented to improve the quality of the data and enhance the performance of the proposed model. The demonstrated results confirmed that with only a limited amount of target data, the proposed meta-learning and DWT denoising method can adapt more quickly and improve the detection of diabetes compared to the traditional method. Therefore, the proposed system is cost-effective, flexible, faster, and adaptable, reduces the need for training data, and can enhance the accuracy of chronic disease detection such as diabetes for smart health systems.

Analysis of the milling response of an artificial temporal bone developed for otologic surgery in comparison with human cadaveric samples

Temporal-bone milling is a delicate process commonly performed during otologic surgery to gain access to the middle and inner ear structures. Because of the numerous at-risk structures of this anatomic area, extensive surgeon training is required. Artificial temporal bones offer an interesting alternative to cadaveric training. However, the evaluation of such simulators has not been systematic, with an absence of objective validation of their milling response, especially in a surgical context.By measuring the milling forces obtained during the classical steps of otologic surgery on six 3D-printed and three cadaveric temporal bones, this work aims at evaluating the ability of the OTOtwin® synthetic temporal bone to reproduce human bone behavior.A better repeatability was obtained for artificial bones than for cadaveric ones. However, the level of forces recorded during artificial bone milling was close to the one measured with cadaveric samples. The effects of both surgical phase and irrigation on milling force levels were also quantified. The experiments conducted in this study confirmed the suitability of OTOtwin® temporal bone model for both otologic surgery training and research purposes. Valuable insights were also gained from this study regarding the understanding of the otologic milling process.

Affirming the arctic outdoors: reading norrland ranger memoirs of becoming through outdoor training

ABSTRACT This article contributes to literature in IR and critical military studies on embodied experiences, military geographies, and research on rangers. Despite being a common feature of military training, little research exists on how the military uses outdoor training to shape soldiers’ identities, emotions, and embodied dispositions. In contrast to existing scholarship’s emphasis on using and constructing outdoor environments in military training to transmit values and skills to make soldiers tough, I argue that outdoor training should be examined from its wealth of embodied experiences and the becoming it gives rise to. Exploring written memoirs by Swedish Norrland Rangers in annual ranger journals on their embodied experiences of extensive outdoor training in north Sweden, the article traces how these rangers represent outdoor training as an assemblage of challenging and pleasurable experiences of being entangled with the northern geographies. The findings indicate that Norrland rangers actively nurture an affirmative orientation to the outdoors and Arctic geographies through their representations of outdoor training, making the challenging ranger formation a rewarding becoming. By developing how rangers constitute and foster this affirmative orientation through their writings, the article provides a better understanding of how pleasurable feelings and experiences of the outdoors are nurtured and drawn upon to socialize rangers.