title: Study of metal scrambling in the trinuclear {Pt₂Se₂M} (M = Pt, Pd, Au) system using an electrospray mass spectrometry (ESMS) directed synthetic methodology; isolation and crystallographic characterization of {Pt₂(µ₃-Se)₂(PPh₃)₄[Pt(cod)]}{PF₆}₂ and {Pt(μ₃-Se)₂(PPh₃)₂[Pt(cod)]₂}{PF₆}₂ (cod = Cyclo-octa-1,5-diene)

authors: Yeo, Jeremy S.L.

subjects: chemistry

abstract: Pt₂(μ-Se)₂(PPh₃)₄ reacts with PtCl₂(cod) to give {Pt₂(μ₃-Se)₂(PPh₃)₄[Pt(cod)]}²⁺ and an unexpected cod-rich product that arises from metal scrambling, viz. {Pt(μ₃-Se)₂(PPh₃)₂[Pt(cod)]₂}²⁺. The formation of these species was detected and followed by electrospray mass spectrometry (ESMS) and subsequently verified by batch synthesis and crystallographic characterization. Other metal-scrambled aggregate products were successfully detected.

text: Study of metal scrambling in the trinuclear {Pt₂Se₂M} (M = Pt, Pd, Au) system using an electrospray mass spectrometry (ESMS) directed synthetic methodology; isolation and crystallographic characterization of {Pt₂(µ₃-Se)₂(PPh₃)₄[Pt(cod)]}{PF₆}₂ and {Pt(μ₃-Se)₂(PPh₃)₂[Pt(cod)]₂}{PF₆}₂ (cod = Cyclo-octa-1,5-diene) Pt₂(μ-Se)₂(PPh₃)₄ reacts with PtCl₂(cod) to give {Pt₂(μ₃-Se)₂(PPh₃)₄[Pt(cod)]}²⁺ and an unexpected cod-rich product that arises from metal scrambling, viz. {Pt(μ₃-Se)₂(PPh₃)₂[Pt(cod)]₂}²⁺. The formation of these species was detected and followed by electrospray mass spectrometry (ESMS) and subsequently verified by batch synthesis and crystallographic characterization. Other metal-scrambled aggregate products were successfully detected.

year: 2002

title: Additive Regression Applied to a Large-Scale Collaborative Filtering Problem

authors: Frank, Eibe

subjects: computer science

abstract: The much-publicized Netflix competition has put the spotlight on the application domain of collaborative filtering and has sparked interest in machine learning algorithms that can be applied to this sort of problem. The demanding nature of the Netflix data has lead to some interesting and ingenious modifications to standard learning methods in the name of efficiency and speed. There are three basic methods that have been applied in most approaches to the Netflix problem so far: stand-alone neighborhood-based methods, latent factor models based on singular-value decomposition, and ensembles consisting of variations of these techniques. In this paper we investigate the application of forward stage-wise additive modeling to the Netflix problem, using two regression schemes as base learners: ensembles of weighted simple linear regressors and k-means clustering—the latter being interpreted as a tool for multi-variate regression in this context. Experimental results show that our methods produce competitive results.

text: Additive Regression Applied to a Large-Scale Collaborative Filtering Problem The much-publicized Netflix competition has put the spotlight on the application domain of collaborative filtering and has sparked interest in machine learning algorithms that can be applied to this sort of problem. The demanding nature of the Netflix data has lead to some interesting and ingenious modifications to standard learning methods in the name of efficiency and speed. There are three basic methods that have been applied in most approaches to the Netflix problem so far: stand-alone neighborhood-based methods, latent factor models based on singular-value decomposition, and ensembles consisting of variations of these techniques. In this paper we investigate the application of forward stage-wise additive modeling to the Netflix problem, using two regression schemes as base learners: ensembles of weighted simple linear regressors and k-means clustering—the latter being interpreted as a tool for multi-variate regression in this context. Experimental results show that our methods produce competitive results.

year: 2008

title: What works for midwives supporting māmā through emotional distress?

authors: Roia, Tiaan Mereana Manurangi

abstract: Pregnancy and the postnatal period are particularly vulnerable times for mothers’ mental health, with an increased risk of women experiencing depression and anxiety during this time. Therefore, understanding the level and type of service midwives provide to māmā who experience emotional distress is vital to know what midwives need in order to provide the right care to their clients.
The aim of this study was to find out what tools and resources community-based midwives find most helpful to support the needs of māmā. Seven semi-structured interviews were conducted, including four Māori midwives, who were able to provide insight on their experiences caring for mothers with mental health needs. Overall, the interviews reflected a strong sense of care and empathy from midwives during their time caring for mothers. Midwives appeared to rely on their interpersonal skills and intuition about emotional distress, and emphasised the importance of connecting with people and utilising support from their communities. Whilst midwives were interested and engaged with providing appropriate care to mothers, they were unbeknown to some of the clinical resources that could be available to their clients. The complexity of referrals and access to mental health services was central to the midwives’ frustration around caring for mothers, as well as a lack of education relating to kaupapa Māori, cultural competency, and mental health processes.
Recommendations based on this study are to provide midwives with education and training on topics like kaupapa Māori, on social and mental health resources and processes for referral to increase their ability to support and care for mothers who experience emotional distress. Improvements to the pathways for referrals and access to mental health services is essential. Future research should focus on understanding why midwives aren’t using a range of online apps and tools, and increase awareness of the available modes of support available.

text: What works for midwives supporting māmā through emotional distress? Pregnancy and the postnatal period are particularly vulnerable times for mothers’ mental health, with an increased risk of women experiencing depression and anxiety during this time. Therefore, understanding the level and type of service midwives provide to māmā who experience emotional distress is vital to know what midwives need in order to provide the right care to their clients.
The aim of this study was to find out what tools and resources community-based midwives find most helpful to support the needs of māmā. Seven semi-structured interviews were conducted, including four Māori midwives, who were able to provide insight on their experiences caring for mothers with mental health needs. Overall, the interviews reflected a strong sense of care and empathy from midwives during their time caring for mothers. Midwives appeared to rely on their interpersonal skills and intuition about emotional distress, and emphasised the importance of connecting with people and utilising support from their communities. Whilst midwives were interested and engaged with providing appropriate care to mothers, they were unbeknown to some of the clinical resources that could be available to their clients. The complexity of referrals and access to mental health services was central to the midwives’ frustration around caring for mothers, as well as a lack of education relating to kaupapa Māori, cultural competency, and mental health processes.
Recommendations based on this study are to provide midwives with education and training on topics like kaupapa Māori, on social and mental health resources and processes for referral to increase their ability to support and care for mothers who experience emotional distress. Improvements to the pathways for referrals and access to mental health services is essential. Future research should focus on understanding why midwives aren’t using a range of online apps and tools, and increase awareness of the available modes of support available.

year: 2023