COPE Health Solutions

• Evaluation of physics-based machine learning-aided (PBMLA) model developed for oil retention (OR) prediction. • PBMLA investigated for horizontal and vertical lines of different diameters, refrigerants, and flow conditions. • PBMLA demonstrated superiority over purely standalone machine learning (ML) models. • Sensitivity analysis demonstrated relative improvement of PBMLA model to address variations in the feature inputs compared to physics-based (PB) model. • PBMLA demonstrated relatively higher accuracy than a purely PB model in extrapolation predictions with different refrigerants and oil types. With the deployment of variable-speed compressors in unitary air conditioning (AC) systems as well as the future implementation of newer HFO refrigerants, there is a need to upgrade line sizing guidelines to account for the effects of oil retention (OR). These new guidelines can be facilitated through developing and applying a predictive model for OR in vapor lines for commonly used refrigerant-lubricant combinations in the heating, ventilation, air conditioning, and refrigeration (HVAC&R) industry. This work aims to evaluate the prediction accuracy of machine learning (ML), physics-based (PB), and hybrid models trained using OR data obtained for horizontal and vertical lines of different diameters (11, 17, and 20 mm). The data include different refrigerants (R134a, R410A, R32, and R1234ze(E)) used with POE32 lubricant under various flow conditions. The ML models were trained and tested using data obtained from over 230 experimental tests. The input parameters for each model were refrigerant conditions (type, temperature, pressure, and mass flow rate), pipeline dimensions and orientation, injected oil mass flow rate, and oil viscosity, with OR as the predicted output. Various model types were investigated and compared, including a purely physics-based (PB) model, two standalone ML models, and two physics-based machine learning-aided (PBMLA) algorithms. A sensitivity analysis was performed to assess the effect of input parameters on the prediction errors. In addition, an extrapolation study was conducted using different refrigerants and various oil grades and types to evaluate the models’ ability to predict OR with acceptable accuracy. The results showed that the standalone ML algorithms exhibited lower accuracy in predicting OR compared to the PB model. Furthermore, the PBMLA models demonstrated a modest improvement in OR prediction accuracy over the purely PB model (improved R 2 by up to 10.8 %, and reduced RMSE by up to 4.5 %). Moreover, the parametric analysis revealed that the PBMLA models could address variations of the feature inputs relatively better than the PB model, leading to their higher prediction accuracy compared to all other models. Finally, the extrapolation analysis showed that both the PB and PBMLA models were not limited to specific oil types or grades, suggesting their potential for general applicability in OR prediction and system design.

Este trabalho objetivou qualificar o manejo conservacionista do solo em lavouras sob sistema plantio direto, por meio da adaptação de um índice. O estudo foi conduzido a campo, em 12 talhões agrícolas em Latossolos, Nitossolos e Cambissolos com caráter alumínico. O solo foi coletado em minitrincheiras nas camadas de 0-10cm e 10-20cm, em triplicata. Foram analisados a reação do solo (pH-H2O), os teores de fósforo (P) e potássio (K) trocáveis e a espessura da camada com estrutura granular. Além disso foram levantadas informações qualitativas relacionadas à diversificação de culturas, à conservação do solo e água e ao uso rentável da terra. O índice de qualidade do manejo conservacionista do solo (IQMCS) foi obtido pela soma de cinco subíndices, em cada talhão agrícola, ponderado e multiplicado pelos seus respectivos pesos e classificado em quatro faixas: A, B, C e D, com notas variando de 0 a 10. O IQMCS predominante foi a faixa D com notas <7,00, ocorrendo em 75% das áreas; 17% das áreas na faixa C com notas de 7 a 7,99; 8% das áreas na faixa B, com notas de 8 a 8,99. Não foram encontrados talhões na faixa A. Isso indica que existe a necessidade de aprimoramento dos modelos de produção com destaque para o manejo do solo.

Introduction: The PIECES approach has been utilized for over 25 years across various Canadian healthcare settings, including long-term care (LTC). PIECES fosters a team-based, person-centred approach to addressing responsive behaviors—such as yelling and restlessness—often linked to unmet personal needs. Objective: This study aimed to explore, with implementation of the virtual version of PIECES: (a) the experiences of LTC staff, focusing on challenges, facilitators, and recommendations; and (b) resilience and interprofessional collaboration among LTC staff. Methods: A convergent mixed method approach used focus groups with registered practical nurses (RPNs), managers, PIECES-trained RPN champions and PIECES mentors to gather their experiences. Surveys at baseline and post-intervention assessed individual and workplace resilience, and team collaboration. Results: Themes identified through reflexive thematic analysis included increased team collaboration and efficacy to manage responsive behaviors through PIECES care planning. Formalized processes supported by leadership with input from family/care partners enhanced nurses’ ability to provide the needed care for responsive behaviors. Primary challenges to PIECES implementation were unfamiliarity with technology and staff shortage. Recommendations included embedding technology in usual care, ongoing support with referral process and continued virtual PIECES training. Standard outcome measures revealed reduced personal and workplace resilience, and team collaboration. Discussion and Conclusion: The RPN-led referral algorithm for the virtual PIECES approach invested the LTC staff together during the difficult COVID period and challenged their existing skills and knowledge of technology.

Dans cet entretien avec la directrice du Copes, il est question de l’organisation et de la pratique d’un stage qui a cours depuis un quart de siècle sur l’enfant de parents avec maladie mentale. Le choix a été fait de travailler le lien de l’enfant avec les parents, mais aussi la nature des liens que le professionnel va tisser avec l’enfant et les parents, à l’occasion de la prise en charge thérapeutique et psychique.

The New York Delivery System Reform Incentive Payment (DSRIP) waiver was viewed as a prototype for Medicaid and safety net redesign waivers in the Affordable Care Act (ACA) era. After the insurance expansions of the ACA were implemented, it was apparent that accountability, value, and quality improvement would be priorities in future waivers in many states. Despite New York's distinct provider relationships, previous coverage expansions, and local and state politics, it is important to understand the key characteristics of the waiver so that other states can learn how to better incorporate value-based arrangements into future waivers or attempts to limit spending under proposed Medicaid per-capita caps or block grants. In this article, we examine the New York DSRIP waiver by drawing on its design, early experiences, and evolution to inform recommendations for the future renewal, implementation, and expansion of redesigned or transformational Medicaid waivers.

Recommandations de la Conférence des sociétés cantonales de médecine (CCM) et de la Société Suisse des médecins-conseils et médecins d'assurances (SSMC) La communication entre médecin traitant et médecin-conseil Pour le médecin-conseil et le médecin traitant, la priorité est donnée à un traitement médical adéquat.L'appréciation médicale pour la décision de prise en charge par l'assureur d'une prestation à fournir ou fournie sert le plus souvent des intérêts communs.Les médecins traitants (MT) dénoncent la qualité souvent insuffisante des décisions négatives des assureurs et l'accessibilité difficile du médecin-conseil (MC) compétent pour un échange professionnel direct.Alors que le travail des médecins-conseils (MC) peut être compliqué par des demandes de garantie de prise en charge des coûts lacunaires des MT, ces derniers se plaignent de questions inutiles et insuffisamment compétentes des assureurs.Afin d'améliorer la compréhension mutuelle, les comités de direction de la SSMC et de la CCM ont adopté les recommandations ci-dessous en février 2017.Ces dernières devraient avoir un effet favorable sur le travail des MT et des MC. Demandes de rapport des assureursLes assureurs demandent des rapports aux médecins traitants, en général via leurs services prestations.Les assureurs sont autonomes en la matière.Ils décident dans quels cas ils lancent des demandes, afin de satisfaire à leur obligation de vérification des prestations.Les rapports demandés sont souvent ressentis comme inutiles, voire chicaniers, par les MT.Les demandes de rapport ne sont pas rédigées par le MC, mais habituellement par le service prestations de l'assureur, et contiennent souvent des questions standardisées. Conclusion:Si le médecin traitant estime que la demande de rapport reçue est inopportune, il doit donc s'adresser exclusivement au service demandeur de l'assurance et non au service du médecin-conseil (SMC).

A trace gas sensor, based on quartz-enhanced photoacoustic spectroscopy (QEPAS), consisting of two acoustically coupled micro-resonators (mR) with an off-axis 20 kHz quartz tuning fork (QTF) is demonstrated. The complete acoustically coupled mR system is optimized based on finite element simulations and experimentally verified. The QEPAS sensor is pumped resonantly by a nanosecond pulsed single-mode mid-infrared optical parametric oscillator (MIR OPO). The sensor is used for spectroscopic measurements on methane in the 3.1 um to 3.5 um wavelength region with a resolution bandwidth of 1 cm^-1 and a detection limit of 0.8 ppm. An Allan deviation analysis shows that the detection limit at optimum integration time for the QEPAS sensor is 32 ppbv@190s and that the background noise is solely due to the thermal noise of the QTF.