Current Approaches Research Articles

Operational performance and metal droplet formation in pulsed-shielded metal arc underwater welding

Underwater Shielded Metal Arc Welding (SMAW) is highly affected by the welding depth. The arc and molten metal in contact with the surrounding environment cause operational and metallurgical challenges regarding arc stability, metal transfer, gas formation, high-cooling rates, and diffusible hydrogen uptake. The hydrostatic pressure causes losses in consumable fusion efficiency, constricts the arc, increases the number of short-circuit events, and consequently decreases the welded joint’s process quality. In the present study, the novel approach of pulsed welding current is applied to wet shielded metal arc welding and is operational characteristics are evaluated in detail. Automated welding using an arc-voltage control system was used to obtain reproducible results. For the pulse conditions, two values of pulse current combinations, taking 140 A as the mean value, were set (ΔI of 40 A and 80 A). The same pulse and base duration were chosen, giving pulsing frequencies of 2.5 Hz and 25 Hz. The voltage and current signals were acquired and the short-circuit numbers and melting rates were calculated by processing the data stability factors. As a result, it was possible to weld with lower average welding currents through pulsed-current technology. This new approach can improve the stability of the wet SMAW process and contribute to obtaining better-quality welds without any changes for the underwater welder.

Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection

Deepfake technology has given rise to a spectrum of novel and compelling applications. Unfortunately, the widespread proliferation of high-fidelity fake videos has led to pervasive confusion and deception, shattering our faith that seeing is believing. One aspect that has been overlooked so far is that current deepfake detection approaches may easily fall into the trap of overfitting, focusing only on forgery clues within one or a few local regions. Moreover, existing works heavily rely on neural networks to extract forgery features, lacking theoretical constraints guaranteeing that sufficient forgery clues are extracted and superfluous features are eliminated. These deficiencies culminate in unsatisfactory accuracy and limited generalizability in real-life scenarios. In this paper, we try to tackle these challenges through three designs: (1) We present a novel framework to capture broader forgery clues by extracting multiple non-overlapping local representations and fusing them into a global semantic-rich feature. (2) Based on the information bottleneck theory, we derive Local Information Loss to guarantee the orthogonality of local representations while preserving comprehensive task-relevant information. (3) Further, to fuse the local representations and remove task-irrelevant information, we arrive at a Global Information Loss through the theoretical analysis of mutual information. Empirically, our method achieves state-of-the-art performance on five benchmark datasets. Our code is available at https://github.com/QingyuLiu/Exposing-the-Deception, hoping to inspire researchers.

ProAgent: Building Proactive Cooperative Agents with Large Language Models

Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems. Current approaches to developing cooperative agents rely primarily on learning-based methods, whose policy generalization depends heavily on the diversity of teammates they interact with during the training phase. Such reliance, however, constrains the agents' capacity for strategic adaptation when cooperating with unfamiliar teammates, which becomes a significant challenge in zero-shot coordination scenarios. To address this challenge, we propose ProAgent, a novel framework that harnesses large language models (LLMs) to create proactive agents capable of dynamically adapting their behavior to enhance cooperation with teammates. ProAgent can analyze the present state, and infer the intentions of teammates from observations. It then updates its beliefs in alignment with the teammates' subsequent actual behaviors. Moreover, ProAgent exhibits a high degree of modularity and interpretability, making it easily integrated into various of coordination scenarios. Experimental evaluations conducted within the Overcooked-AI environment unveil the remarkable performance superiority of ProAgent, outperforming five methods based on self-play and population-based training when cooperating with AI agents. Furthermore, in partnered with human proxy models, its performance exhibits an average improvement exceeding 10% compared to the current state-of-the-art method. For more information about our project, please visit https://pku-proagent.github.io.

Multi-Modality Affinity Inference for Weakly Supervised 3D Semantic Segmentation

3D point cloud semantic segmentation has a wide range of applications. Recently, weakly supervised point cloud segmentation methods have been proposed, aiming to alleviate the expensive and laborious manual annotation process by leveraging scene-level labels. However, these methods have not effectively exploited the rich geometric information (such as shape and scale) and appearance information (such as color and texture) present in RGB-D scans. Furthermore, current approaches fail to fully leverage the point affinity that can be inferred from the feature extraction network, which is crucial for learning from weak scene-level labels. Additionally, previous work overlooks the detrimental effects of the long-tailed distribution of point cloud data in weakly supervised 3D semantic segmentation. To this end, this paper proposes a simple yet effective scene-level weakly supervised point cloud segmentation method with a newly introduced multi-modality point affinity inference module. The point affinity proposed in this paper is characterized by features from multiple modalities (e.g., point cloud and RGB), and is further refined by normalizing the classifier weights to alleviate the detrimental effects of long-tailed distribution without the need of the prior of category distribution. Extensive experiments on the ScanNet and S3DIS benchmarks verify the effectiveness of our proposed method, which outperforms the state-of-the-art by ~4% to ~ 6% mIoU. Codes are released at https://github.com/Sunny599/AAAI24-3DWSSG-MMA.

Abstract 1038: Identification of diagnostic biomarkers for colon adenocarcinoma by analyzing proteins in human-derived extracellular vesicles and particles

Abstract There is an unmet medical need for circulating biomarkers to improve early cancer detection in colon adenocarcinoma (COAD). We investigated the proteomic profile of extracellular vesicles and particles (EVPs) from the colon tissue explants and plasmas in COAD. The research aims to overcome the limitations of traditional serum Carcinoembryonic Antigen (CEA) testing and invasive procedures like colonoscopy. We analyzed EVPs sourced from tumor and adjacent non-tumor tissues and preoperative and postoperative plasma in COAD patients to identify potential EVP-based tumor diagnostic markers. In the Discovery phase with 50 COAD patients, we conducted a proteomic analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS), identifying approximately 50 markers each from tissue explants and plasma. These markers were validated in an independent cohort of 104 participants, including 84 COAD patients and 20 healthy controls. We utilized ELISA to quantify 11 selected EVP proteins. Notably, seven of these proteins exhibited significant differential expression in preoperative and postoperative plasma, highlighting their diagnostic relevance. These seven proteins also showed marked elevation in preoperative plasma compared to healthy controls, reinforcing their potential as diagnostic markers. The study extended to evaluate the diagnostic accuracy of these seven proteins, referred to as "EV-7 levels", across various stages of COAD. Their performance was compared with serum CEA levels and plasma cfDNA mutation rates, as detected by an FDA-approved liquid biopsy assay. The EV-7 levels demonstrated superior detection rates in the early stages of COAD, particularly in Stages I and II, in contrast to the serum CEA method. Among the 84 patients, 73 (86.9%) exhibited EV-7 levels exceeding the established cut-off. In the validation cohort, EV-7 levels were more accurate across different COAD stages than serum CEA levels and cfDNA mutation rates. CEA levels above 5 ng/ml were 27%, 45%, 42%, and 64% for Stages I through IV. cfDNA somatic mutation rates were 60%, 80%, 75%, and 75%. EV-7 levels exceeding the cut-off were 73%, 100%, 88%, and 84%, respectively, with Stages I and II having the highest detection rate among the three methods. ROC curve analysis for EV-7 levels in Stages I to IV showed statistically significant, with AUC values of 0.913, 1.000, 0.985, and 0.984, respectively. Our findings indicate that the seven EVP proteins are significantly present in the plasma of early-stage COAD patients. Integrating these markers with current diagnostic approaches could improve early cancer detection, showing promise for practical application in clinical settings. Citation Format: Yura Seo, Yoon Dae Han, Linda Bojmar, Kyung-A Kim, Yurin Seo, David Lyden, Han Sang Kim. Identification of diagnostic biomarkers for colon adenocarcinoma by analyzing proteins in human-derived extracellular vesicles and particles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1038.

Current design of rectangular steel silos: limitations and improvement

This study proposes a modification for the current design approach for square and rectangular silos that accounts for silos’ wall flexibility. First, the authors investigated the effect of wall stiffness symbolized by the wall width-to-thickness ratio (a/t) and silo’s dimensions, on the wall-filling pressure using a recently validated 3D finite element model (FEM). The model was then employed to predict the pressures acting on silos’ walls accounting for the stress state in stored granular materials. Most design formulas and guidelines assume silos’ walls to be rigid. This assumption is acceptable for the case of rigid wall concrete silos; however, it is questionable for semi-rigid, flexible wall metal silos. Consequentially, it is crucial to determine the minimum wall stiffness necessary to secure the applicability of the current design rigid wall assumptions and to propose a way to deal with semi-rigid and flexible walls. To this end, several wall pressure distributions that correspond to filling steel silos with varied wall thicknesses were studied. A new adjustment to the Janssen technique was proposed for a better estimate of the wall-filling pressures for square and rectangular silos. In the case of prismatic silos, the Eurocode uses the Janssen equation together with an equivalent radius of a corresponding circular silo (with the same hydraulic radius) to determine the wall pressure. This method predicts pressure values that are practically accurate for rigid-wall silos, but its accuracy decreases for semi-rigid and flexible-wall silos. As an enhancement, the Janssen equation was modified in this research to generate more accurate pressure estimates based on the equivalent volume concept. The finite element results of several developed models with the same granular material were compared to the estimations of the newly established approach to verify the broad range of its applicability.

Cytomegalovirus Treatment in Solid Organ Transplantation: An Update on Current Approaches.

The article reviews the safety and efficacy of treatments for cytomegalovirus (CMV) in solid organ transplantation. A literature review was conducted in PubMed, MEDLINE, and Clinicaltrials.gov from database inception through January 2024, using terms CMV, therapy, and solid organ transplantation. Clinical trials, meta-analyses, cohort studies, case reports, and guidelines were included. Letters to the editor, reviews, and commentaries were excluded. After abstract screening and full-text review of 728 citations for eligibility, 53 were included. Valganciclovir and intravenous ganciclovir are drugs of choice for CMV management and, until recently, the availability of alternative options has been restricted due to toxicity. For instance, foscarnet and cidofovir serve as second-line agents due to potential bone marrow and renal toxicity. In patients with refractory or resistant CMV, maribavir, a novel oral agent, has proven efficacy and a lower adverse effect profile. However, in refractory or resistant CMV, foscarnet and cidofovir are preferred in invasive disease (CMV gastritis, CMV retinitis, and CMV encephalitis), high viral loads, and inability to tolerate oral preparations. Consensus guidelines have not been revised since approval of novel antivirals in solid organ transplantation. Valganciclovir and ganciclovir remain drugs of choice for initial CMV therapy. Foscarnet, cidofovir, and maribavir are treatments for refractory or resistant-CMV. Selection of CMV antiviral treatment should be determined by patient-specific factors, including severity of illness, resistant or refractory disease, dose-limiting adverse effects, and the preferred route of administration.

Machine intelligence-accelerated discovery of all-natural plastic substitutes.

One possible solution against the accumulation of petrochemical plastics in natural environments is to develop biodegradable plastic substitutes using natural components. However, discovering all-natural alternatives that meet specific properties, such as optical transparency, fire retardancy and mechanical resilience, which have made petrochemical plastics successful, remains challenging. Current approaches still rely on iterative optimization experiments. Here we show an integrated workflow that combines robotics and machine learning to accelerate the discovery of all-natural plastic substitutes with programmable optical, thermal and mechanical properties. First, an automated pipetting robot is commanded to prepare 286 nanocomposite films with various properties to train a support-vector machine classifier. Next, through 14 active learning loops with data augmentation, 135 all-natural nanocomposites are fabricated stagewise, establishing an artificial neural network prediction model. We demonstrate that the prediction model can conduct a two-way design task: (1) predicting the physicochemical properties of an all-natural nanocomposite from its composition and (2) automating the inverse design of biodegradable plastic substitutes that fulfils various user-specific requirements. By harnessing the model's prediction capabilities, we prepare several all-natural substitutes, that could replace non-biodegradable counterparts as exhibiting analogous properties. Our methodology integrates robot-assisted experiments, machine intelligence and simulation tools to accelerate the discovery and design of eco-friendly plastic substitutes starting from building blocks taken from the generally-recognized-as-safe database.

Novel therapeutics for Hodgkin\'s lymphoma

Hodgkins LymphomaHL) was first described by Thomas Hodgkin in 1832., is a malignant disorder, a rare B cell lymphoma (mutant lymphocytes) with reasonable outcome due to the fair cure rates achieved by modern chemotherapy and/or radiotherapy. The incidence of HL is 2.6 cases per 100,000 people, accounts for 10% lymphoma cases. The Epstein–Barr virus (EBV) is detected in nearly 45% of HL patients Most of the affected patients are between ages 20 to 40 years. HL is uncommon in children < 5 years of age. The Reed-Sternberg (RS), large cells 50 micrometers in diameter which secrete cytokines to recruit reactive cells that include IL-5 and transforming growth factor-beta (TGF-beta). The RS cells are positive for CD 30 and CD15 & sometimes for CD 20. They are negative for CD 45. The 5-year overall survival (OS) in stage 1 or stage IIa is approximately 90%;, the stage 4 disease has a 5-year OS of approximately 60%. More than 80 percent of all patients diagnosed with Hodgkin lymphoma can be cured by current treatment approaches. The cure rate is higher, approaching 90 percent, in younger patients and those with early-stage Hodgkins lymphoma (ESHL). Pragmatic therapeutic approach includes brief chemotherapy (ABVD- Adriamycin), bleomycin, vinblastine sulfate, and dacarbazine. For 3–4 cycles), For advanced-stage classic (CD30-positive) Hodgkin lymphoma, the combination of doxorubicin, vinblastine, dacarbazine, and brentuximab has emerged as a more effective, Patients with recurrent Hodgkin lymphoma would require high-dose chemotherapy followed by ASCT. Hodgkin lymphoma that recurs after ASCT would be managed by the checkpoint inhibitors nivolumab and pembrolizumab. Radiotherapy plays a role as a single modality in early stage lymphocyte-predominant HL. The role of autologous transplant was established decades ago by two randomized controlled trials demonstrating an improvement in PFS but not OS in patients with relapsed/refractory HL.

Nanoscale Methods for Longitudinal Extraction of Intracellular Contents.

Longitudinal analysis of intracellular contents including gene and protein expression is crucial for deciphering the fundamentally dynamic nature of cells. This offers invaluable insights into complex tissue composition and behavior, and drives progress in disease diagnosis, biomarker discovery, and drug development. Traditional longitudinal analysis workflows, involving the destruction of cells at various timepoints, limit insights to singular moments and fail to account for cellular heterogeneity. Current non-destructive approaches, like temporal modeling with single-cell ribonucleic acid sequencing (RNA-seq) and live-cell fluorescence imaging, either rely on biological assumptions or possess the risk of cellular perturbation. Recent advances in nanoscale technologies for non-destructive intracellular content extraction offer a promising solution to these challenges. These novel methods work at the nanoscale to non-destructively access cellular membranes and can be broadly classified into three mechanisms: tip-facilitated aspiration, membrane-based, and probe-based methods. This perspective focuses on these emerging nanotechnologies for repeated intracellular content extraction. Their potential in longitudinal analysis is discussed, the critical requirements for effective repeated sampling are addressed, and the suitability of each technique for various applications is explored. Furthermore, unresolved challenges in repeated sampling are highlighted to encourage further research in this growing field.

An automated energy management framework for smart homes

Over the last fifty years, societies across the world have experienced multiple periods of energy insufficiency with the most recent one being the 2022 global energy crisis. In addition, the electric power industry has been experiencing a steady increase in electricity consumption since the second industrial revolution because of the widespread usage of electrical appliances and devices. Newer devices are equipped with sensors and actuators, they can collect a large amount of data that could help in power management. However, current energy management approaches are mostly applied to limited types of devices in specific domains and are difficult to implement in other scenarios. They fail when it comes to their level of autonomy, flexibility, and genericity. To address these shortcomings, we present, in this paper, an automated energy management approach for connected environments based on generating power estimation models, representing a formal description of energy-related knowledge, and using reinforcement learning (RL) techniques to accomplish energy-efficient actions. The architecture of this approach is based on three main components: power estimation models, knowledge base, and intelligence module. Furthermore, we develop algorithms that exploit knowledge from both the power estimator and the ontology, to generate the corresponding RL agent and environment. We also present different reward functions based on user preferences and power consumption. We illustrate our proposal in the smart home domain. An implementation of the approach is developed and two validation experiments are conducted. Both case studies are deployed in the context of smart homes: (a) a living room with a variety of devices and (b) a smart home with a heating system. The obtained results show that our approach performs well given the low convergence period, the high level of user preferences satisfaction, and the significant decrease in energy consumption.

Non-Factor Therapies: Reflections on Current Clinical Practice

The 17th Annual Congress of the European Association for Haemophilia and Allied Disorders (EAHAD) took place on 6th–9th February 2024 in Frankfurt, Germany. In a comprehensive and interactive session, healthcare experts and patients gathered to discuss current approaches in clinical practice. The session was chaired by Jan Blatny, University Hospital Brno, Czechia; and Niamh O’Connell, The National Coagulation Centre, St James’s Hospital, Dublin, Ireland.

The refusal to license intellectual property as an antitrust violation in China: how should the current approach be improved?

The holder of intellectual property is entitled to exploit its intellectual property in principle. However, China’s Anti-monopoly Law prohibits the anti-competitive refusal to deal, including the refusal to license intellectual property, which might constitute an abuse of market dominance. Existing anti-monopoly rules related to the refusal to license intellectual property in China are rather general and lack a definite test for the assessment, leaving too much discretion for Chinese competition authorities to interpret the law. As the two main antitrust jurisdictions globally, the US and the EU have different approaches to the refusal to license intellectual property. By reviewing comparative experience, the EU approach is more appropriate for China. It is suggested that China should establish a clear-up test to analyse the refusal to license intellectual property cases under the Anti-monopoly Law, considering the indispensability of intellectual property and its influences on innovation. Meanwhile, Chinese law should include provisions on the remedy of a refusal to license. In this regard, China can make its anti-monopoly enforcement as to the refusal to license more transparent and build up a stable expectation for both domestic and foreign high-tech undertakings.

Transcription Factor MYB as Therapeutic Target: Current Developments.

The MYB protein is a pivotal player in the cellular transcriptional network, influencing major important processes such as cell proliferation, differentiation, and apoptosis. Because of its role in oncogenesis, MYB is now a compelling target for therapeutic interventions in cancer research. This review summarizes its molecular functions and current therapeutic approaches aiming to inhibit its oncogenic activity.

Tricuspid Regurgitation: A Review of Current Interventional Management.

Tricuspid regurgitation is a common yet clinically complex problem, traditionally managed with diuretic therapy with no observable mortality benefit. Older studies on surgical intervention observed poor outcomes; however, this clinical reasoning predates current surgical approaches and novel transcatheter technology. The tricuspid apparatus is a complex structure that poses a technical challenge for surgeons and interventional cardiologists. Recent advances in surgical techniques and transcatheter therapy, particularly edge-to-edge repair devices, have demonstrated promising results for safety, reduction in tricuspid regurgitation, and improving quality of life. We review the clinical, imaging, and hemodynamic findings that characterize patients who should be considered for intervention, alongside the rapidly evolving approaches to interventional management.

Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty.

Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling approaches such as traditional survivorship estimators (such as Kaplan-Meier) or competing risk estimators. Recent advances in machine learning survival analysis might improve decision support tools in this setting. Therefore, this study aimed to assess the performance of machine learning compared with that of conventional modeling to predict revision after arthroplasty. Does machine learning perform better than traditional regression models for estimating the risk of revision for patients undergoing hip or knee arthroplasty? Eleven datasets from published studies from the Dutch Arthroplasty Register reporting on factors associated with revision or survival after partial or total knee and hip arthroplasty between 2018 and 2022 were included in our study. The 11 datasets were observational registry studies, with a sample size ranging from 3038 to 218,214 procedures. We developed a set of time-to-event models for each dataset, leading to 11 comparisons. A set of predictors (factors associated with revision surgery) was identified based on the variables that were selected in the included studies. We assessed the predictive performance of two state-of-the-art statistical time-to-event models for 1-, 2-, and 3-year follow-up: a Fine and Gray model (which models the cumulative incidence of revision) and a cause-specific Cox model (which models the hazard of revision). These were compared with a machine-learning approach (a random survival forest model, which is a decision tree-based machine-learning algorithm for time-to-event analysis). Performance was assessed according to discriminative ability (time-dependent area under the receiver operating curve), calibration (slope and intercept), and overall prediction error (scaled Brier score). Discrimination, known as the area under the receiver operating characteristic curve, measures the model's ability to distinguish patients who achieved the outcomes from those who did not and ranges from 0.5 to 1.0, with 1.0 indicating the highest discrimination score and 0.50 the lowest. Calibration plots the predicted versus the observed probabilities; a perfect plot has an intercept of 0 and a slope of 1. The Brier score calculates a composite of discrimination and calibration, with 0 indicating perfect prediction and 1 the poorest. A scaled version of the Brier score, 1 - (model Brier score/null model Brier score), can be interpreted as the amount of overall prediction error. Using machine learning survivorship analysis, we found no differences between the competing risks estimator and traditional regression models for patients undergoing arthroplasty in terms of discriminative ability (patients who received a revision compared with those who did not). We found no consistent differences between the validated performance (time-dependent area under the receiver operating characteristic curve) of different modeling approaches because these values ranged between -0.04 and 0.03 across the 11 datasets (the time-dependent area under the receiver operating characteristic curve of the models across 11 datasets ranged between 0.52 to 0.68). In addition, the calibration metrics and scaled Brier scores produced comparable estimates, showing no advantage of machine learning over traditional regression models. Machine learning did not outperform traditional regression models. Neither machine learning modeling nor traditional regression methods were sufficiently accurate in order to offer prognostic information when predicting revision arthroplasty. The benefit of these modeling approaches may be limited in this context.

Assessment and Management of Sleep Disturbance in Palliative Care Settings.

Background: Sleep disturbances, including insomnia, sleep-disordered breathing, and circadian rhythm disorders with potential consequences including excessive daytime somnolence and worsening fatigue, are prevalent yet largely under-measured and therefore under-managed problems in people receiving palliative care. This has the potential to negatively affect the person's functioning and quality of life. Objectives: We aimed to review the current practice of assessment and management of sleep disturbances in people with life-limiting illnesses in Australian and New Zealand palliative care settings, and to define areas for improvement in assessment and management of sleep disturbances and further research. Design: A cross-sectional, online survey was conducted with palliative care health professionals (PCHPs) to explore current approaches to routine assessment of sleep disturbances and PCHPs' awareness of, and perceived access to, evidence-based resources for assessing and managing sleep disturbances in their local settings. Results: Fifty-four PCHPs responded to the survey, including allied health professionals (44%), palliative care nurses (26%), and physicians (19%). Over 70% of PCHPs endorsed routine verbal screening of sleep symptoms, and >90% recommended management with basic behavioral strategies. However, none of PCHPs used validated patient-reported outcome measures for sleep, and <10% of PCHPs demonstrated awareness or use of sleep-specific interventions (including medications). Only 40% reported they had access to sleep specialist services for patients. Conclusion: Our findings provide a useful snapshot of current approaches to managing sleep disturbances in palliative care. Gaps in current practice are highlighted, including the lack of structured, clinical assessment, referral pathways, and PCHPs' perceived lack of access to targeted interventions for sleep disturbances.

Measuring CSF shunt flow with MRI using flow enhancement of signal intensity (FENSI).

To develop and validate a noninvasive imaging technique for accurately assessing very slow CSF flow within shunt tubes in pediatric patients with hydrocephalus, aiming to identify obstructions that might impede CSF drainage. A simulation of shunt flow enhancement of signal intensity (shunt-FENSI) signal is used to establish the relationship between signal change and flow rate. The quantification of flow enhancement of signal intensity data involves normalization, curve fitting, and calibration to match simulated data. Additionally, a phase sweep method is introduced to accommodate the impact of magnetic field inhomogeneity on the flow measurement. The method is tested in flow phantoms, healthy adults, intensive care unit patients with external ventricular drains (EVD), and shunt patients. EVDs enable shunt-flow measurements to be acquired with a ground truth measure of CSF drainage. The flow-rate-to-signal simulation establishes signal-flow relationships and takes into account the T1 of draining fluid. The phase sweep method accurately accounts for phase accumulation due to frequency offsets at the shunt. Results in phantom and healthy human participants reveal reliable quantification of flow rates using controlled flows and agreement with the flow simulation. EVD patients display reliable measures of flow rates. Shunt patient results demonstrate feasibility of the method and consistent flow rates for functional shunts. The results demonstrate the technique's applicability, accuracy, and potential for diagnosing and noninvasively monitoring hydrocephalus. Limitations of the current approach include a high sensitivity to motion and strict requirement of imaging slice prescription.

The die-hards, negotiators and migrants: Portraits of doctors' career pathways through specialisation.

Global workforce shortages in medical specialties strain healthcare systems, jeopardising patient outcomes. Enhancing recruitment strategies by supporting professional identity (PI) development may be one way to address this workforce gap-yet little research has explored this topic. The goal of the current study was to explore specialty-specific recruitment through considering PI. As proposed causes of workforce shortages in anatomical pathology (AP) bear similarities to many other specialties, this study uses the field of AP as a model for specialist PI development and asks: (1) why, how and when do doctors choose to pursue AP training and (2) what can be learned from this for recruitment to AP and other specialties? A qualitative research approach was undertaken using narrative inquiry. Interviews with junior doctors interested in AP, AP registrars and AP consultants from Australia and New Zealand were interpreted as stories via 're-storying'. Narrative synthesis of participants' collective stories identified chronological key events (i.e. 'turning points') in choosing AP. Narrative synthesis resulted in identification of three portraits entering medical specialist training: (1) die-hards, deciding upon initial exposure; (2) negotiators, choosing after comparing specialties; and (3) migrants, seeking to move away from non-pathology specialties. The negotiators and migrants cemented their decision to pursue AP as a postgraduate doctor, whereas the die-hards made this decision during medical school. Given the similarities in portrait traits between AP and other specialties across the literature, our results suggest ways to support specialty recruitment using PI development. We propose a medical specialist recruitment framework to support the PI development of doctors with die-hard, negotiator and migrant traits. Use of this framework could enhance current specialty-specific recruitment approaches, particularly in fields challenged by workforce shortages.

Investigating Ayurvedic Strategies: An In-Depth Examination of Managing Diabetes across Different Types.

In light of the escalating global concern surrounding diabetes mellitus, contemporary medical practices predominantly hinge on pharmaceutical interventions, accompanied by inherent side effects and enduring limitations. This investigation accentuates a discernible research void regarding the amalgamation of Ayurvedic principles an age-old traditional medical system with prevalent approaches to diabetes management. Despite Ayurveda's promising potential in furnishing a comprehensive and personalized strategy for diabetes treatment, the imperative for further research and collaboration between Ayurvedic practitioners and contemporary healthcare professionals becomes evident. Existing scholarly works underscore the potential advantages of Ayurveda in delivering holistic diabetes care, encompassing not only glycemic control but also fostering overall well-being. Nevertheless, a closer examination reveals specific limitations, challenges, and gaps in current research, necessitating targeted efforts to enable a more exhaustive exploration of Ayurvedic interventions within diabetes management. This comprehensive review scrutinizes Ayurvedic recommendations pertaining to dietary practices, lifestyle adjustments, and herbal therapeutics, shedding light on their plausible efficacy. It serves as a clarion call for heightened research endeavors, aiming to bridge existing gaps and carve a pathway toward an integrated, patientcentric paradigm in diabetes care. In summary, as diabetes prevalence continues to rise globally, the study underscores the limitations of current pharmaceutical-centric approaches and highlights the need for extensive research and collaboration to unlock the full potential of Ayurvedic principles in providing a more holistic and personalized framework for diabetes management. The review navigates through Ayurvedic recommendations, emphasizing the urgency for intensified research efforts to fill existing gaps and pave the way for a seamlessly integrated, patient-focused approach to diabetes care.