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HaNDLE-on-QoL (Head And Neck Database Listing Evidence on QoL) is a searchable database that comprises abstracts of papers that have used questionnaires to report on quality of life (QoL) in patients with cancer of the head and neck. It can be searched by title, first author, year of publication, words used in the abstract, site of cancer, study design, and questionnaires used. The aim of this paper was to summarise its contents. In May 2017 we searched the website using the criteria above. It contained 1498 papers (including 149 reviews), and the number is increasing each year. Most studies concerned a combination of subsites in the head and neck (n = 871); 180 focused specifically on oral sites, and 109 on the larynx. The commonest topics were swallowing (n = 353), speech (n = 299), pain (n = 292), emotions (n = 226), and depression (n = 193). Nearly all the papers concerned function or predictors of health-related QoL (HRQoL), but 98 were clinical or randomised controlled trials. The site included over 250 questionnaires of which the most common were the European Organisation for Research and Treatment of Cancer C30 (EORTC-C30, n = 369), the EORTC-head and neck 35 (EORTC H&N35, n = 353), and the University of Washington Quality of Life (UWQoL) (n = 276). HaNDLE-on-QoL highlights the complexity of QoL after treatment and the diversity and range of the studies. It is a useful point of reference for those involved in clinical practice or research. [ABSTRACT FROM AUTHOR]

The article focuses on 18 to 65 years aged patients with Multiple Sclerosis (MS) who were referred for 4 weeks of inpatient rehabilitation, with an expanded disability status scale score. It mentions the treatment group received immediate admission for multidisciplinary inpatient rehabilitation; and also mentions multidisciplinary inpatient rehabilitation had small and inconsistent effects on parameters of health-related quality of life in people with MS at 6 months.

Abstract: Improving cancer immunotherapy by targeting T cell network also triggers autoimmunity. We disrupted regulatory T cell (Treg) function to probe the balance between breast cancer vaccination and autoimmune thyroiditis (EAT) in four models, with particular attention to MHC-associated susceptibility, EAT induction with mouse thyroglobulin (mTg) without adjuvant, and tolerance to Her-2/neu in transgenic mice. 1) In EAT-resistant BALB/c mice, Treg depletion enhanced tumor regression, and facilitated mild thyroiditis induction. 2) In Her-2 tolerant C57BL/6 mice expressing HLA-DR3, an EAT-susceptibility allele, Her-2 DNA vaccinations must follow Treg depletion for (Her-2xDR3)F1 mice to resist tumor challenge; thyroiditis incidence was moderated by the EAT-resistant IAb allele. 3) In neu tolerant, EAT-resistant BALB/c mice, implanted neu+ tumor also regressed only after Treg depletion and DNA vaccinations. Tumor immunity was long-term, providing protection from spontaneous tumorigenesis. In all three, immune stimuli from concurrent tumor regression and EAT development have a noticeable, mutually augmenting effect. 4) In Treg-depleted, EAT-susceptible CBA/J mice, strong tumor protection was established by immunization with a cell vaccine. mTg injections led to greater thyroiditis incidence and severity. Combination models with MHC class II diversity should facilitate autoimmunity risk assessment and management while generating tumor immunity. [Copyright &y& Elsevier]

Objectives: A new patient-reported outcome (PRO) instrument to measure fatigue symptoms and impacts in relapsing multiple sclerosis (RMS) was developed in a qualitative stage, followed by psychometric validation and migration from paper to an electronic format.Methods: Adult patients with relapsing-remitting multiple sclerosis (RRMS) were interviewed to elicit fatigue-related symptoms and impacts. A draft questionnaire was debriefed in cognitive interviews with further RRMS patients, and revised. Content confirmation interviews were conducted with patients with progressive-relapsing multiple sclerosis (PRMS) and relapsing secondary-progressive multiple sclerosis (RSPMS). Psychometric analyses used data from adult patients with different RMS subtypes and matched non-RMS controls in a multicenter, observational study. After item reduction, the final instrument was migrated to a smartphone (eDiary) and usability was confirmed in interviews with additional adult RMS patients.Results: The qualitative stage included 37 RRMS, 5 PRMS, and 5 RSPMS patients. Saturation of concepts was reached during concept elicitation. Cognitive interviews confirmed that participants understood the instructions, items, and response options of the instrument-named FSIQ-RMS-as intended. Psychometric validation included 164 RMS and 74 control patients. Internal consistency and test-retest reliability were demonstrated. The symptoms domain discriminated along the RMS symptom-severity continuum and between patients and controls. Patients were able to attribute fatigue-related symptoms to RMS. Usability and conceptual equivalence of the eDiary were confirmed (n = 10 participants).Conclusions: With 7 symptom items and 13 impact items (in 3 impacts subdomains: physical, cognitive and emotional, and coping) after item reduction, the FSIQ-RMS is a comprehensive, valid, and reliable measure of fatigue-related symptoms and impacts in RMS patients. [ABSTRACT FROM AUTHOR]

We examine trends in words/terms published in Health & Place between 1995 and 2018. Terms used have diversified over this period, including a decline in 'health' overall, as well as greater usage of 'diet', 'alcohol' and 'depression'. There was a noticeable increase in using 'neighbourhood' to describe context. Topic modelling reveals that papers are increasingly concerned with specific aspects of place (e.g. the built environment). Our findings emphasise that Health & Place has matured into the leading outlet for examining the geographical dimensions of health with important and enduring insights that continue to inform research and policy. [ABSTRACT FROM AUTHOR]

To improve health and safety outcomes at mass gathering events (MGEs) for young attendees, it is essential to understand the psychosocial factors that may influence behaviour so that the implementation of support strategies before, during and after MGEs can be developed to enhance outcomes. This review identifies the psychosocial outcomes that may occur at MGEs, including social connection, substance use, risky behaviours and psychological distress and examines what interventions have been implemented to target these outcomes. Scoping review. This study examined MGE psychosocial interventions with predominantly youth attendees was conducted using the Preferred Reporting Items of Systematic reviews and Meta-Analysis extension for Scoping Reviews guidelines. Papers were collected from the databases CINAHL, MEDLINE, Embase and PsycINFO. Titles and abstracts were scanned for relevance, followed by a full-text screening. Information relevant to the research question was extracted from papers meeting the inclusion criteria. Twenty-six papers met the inclusion criteria. The psychosocial factors that were most studied included social influence, social interactions and psychological stressors, which lead to behaviours such as excessive alcohol consumption, drug taking, risky casual sex and risk taking amongst psychological factors of young attendees. Effective interventions implemented before or during MGEs, such as alcohol-free zones, antidrinking campaigns, psychoeducation and disapproval from parents with regard to drinking alcohol, showed promise in reducing harms. Psychosocial interventions have the potential to reduce harms and enhance well-being for young people attending MGEs. This review has identified gaps and opportunities in the current literature with regard to psychosocial interventions and strategies to support young people at MGEs and makes recommendations to support the development and refinement of evidence-based interventions aimed at young MGE attendees. [ABSTRACT FROM AUTHOR]

Lithium-ion Batteries (LiB) have a wide range of applications in daily life. However, as they get used over time, battery degradation becomes inevitable, which can lead to a drop in performance and a reduction in the battery's cycle life. The State of Health (SoH) is widely regarded as the health indicator for the battery pack. In Electric Vehicle (EV) applications, the EV user defines the lower limit of SoH when they experience that the battery no longer supports the EV; at that point, the battery is said to be translated from first life to second life. The SoH estimations of Second Life Batteries (SLB) have plenty of uncertainties, such as the availability of battery's previous history, non-uniform degradation in the EV application, variations in chemistry, and charging protocols defined by vehicle manufacturers, making the SoH estimation of SLB a challenging task. This paper discusses the equipment, timelines, computational complexity, health indicators, and list of parameters that need to be considered for the SoH estimation of SLB. The SoH estimation methods are classified into direct and indirect techniques. Direct assessment techniques involve cyclic ageing experiments followed by dismantling the battery for microscopic studies performed by previous researchers that were explained. Indirect assessment techniques include physical and chemical based approach, electrical, and Artificial Intelligence (AI)-based methods that estimate SoH indirectly through incremental, differential approaches and other parameters such as Integrated Voltage (IV) and Probability Density Function (PDF). Health indicator identifications play a vital role in indirect assessment methods to gain critical insights regarding battery degradation. The challenges involved in SoH estimation are categorized into equipment requirements, parameters, SoH accuracy and efforts required to compute SoH, which are discussed. Of all the SoH estimation methods, comparison of such methods in First Life Batteries (FLB) and SLB perspectives are discussed. To estimate the SoH of SLB, this paper explains all aspects, such as computational methods, filtering data, data sampling frequency, and the need for a specific algorithm to post-process the battery test data. Equipment availability and timelines are interrelated with the cost incurred in the SoH estimation of SLB. The efficacy and practicality of SoH estimation methods that are proposed for SLB is discussed. Overall, this paper provides necessary insights into the parameters required for SoH estimation and the computational and experimental methods that can be considered for estimating the SoH of SLB while some of the methods are applicable to FLB as well. • Review of State of Health (SoH) estimation methods for lithium-ion battery pack translating from first life to second life. • Critical analysis of equipment's and test protocols subjected to cyclic ageing. • Classification of SoH estimation methods in the form of physical and chemical based approach, electrical and Artificial Intelligence (AI) based techniques. • Listed the parameters acquired from battery to be considered in second life SoH estimation. • Presented the challenges associated with SoH methods for Second Life Batteries (SLB). [ABSTRACT FROM AUTHOR]

Despite the growing interest on the Russian-Ukrainian war and its repercussion on the children's health, there is no previous systematic review compiling the current evidence on this topic. This study conducted a rapid systematic review to investigate the current findings concerning the impact of the Ukraine war on the social and health aspects of the Ukrainian pediatric population. A rapid systematic review was conducted. PubMed, Scopus, CINAHL, PsycINFO, Web of Science, and Virtual Health Library (BVS Spain) were searched between February and April 2023. In addition, high-impact journals and institutions focused on pediatric health and human rights were also consulted. All relevant original articles, letters, editorials, and policy papers assessing the health and social repercussions of the war on Ukrainian children were included. From 134 publications matching the search criteria, 23 were included. These publications were categorized into three distinct domains: 'Public health challenges for the physical and mental health of children and adolescents', 'Lack of Healthcare resources and initiatives to mitigate suffering', and 'Policies, Government, and Children's rights'. Our findings revealed that the war is seriously impacting the life and the health of Ukrainian children, resulting in worse physical and mental health outcomes and perpetrating a deprived situation. To overcome such problems, several initiatives have been proposed by European and non-European countries, relying mostly on receiving refugees, providing mental health support, complementing lack of resources, and establishing policies to improve health care. It could help health professionals, policy makers, and governments to plan preventive, promotive, and therapeutic strategies for Ukrainian children. [ABSTRACT FROM AUTHOR]

Equipment degradation is ubiquitous in the Chemical Process Industry (CPI), causing significant losses in efficiency, controllability, and plant economy, as well as an increased environmental fingerprint and additional operational safety risks. The case of fouling in heat exchangers, in particular, is well-known and pervasive but still hard to cope with, given the complexity of the underlying mechanisms and the difficulty of assessing its extension in real-time. This problem becomes even more complex in batch processes producing different products, where multiple recipes are used, bringing additional variability and new challenges to the analysis. In this work, we propose a functional data-driven approach for streamlining the analysis and monitoring of the progression of fouling taking place in heat exchangers in multiproduct batch processes. With the approach developed and presented in this paper, process analysis can be efficiently conducted by integrating historical data with engineering knowledge. Furthermore, a surrogate measure of fouling extension in heat exchangers is proposed, that can be readily implemented as an equipment health indicator (EHI) leading to a safer operation of the heat exchanger. [ABSTRACT FROM AUTHOR]

A new method is developed in the paper to estimate the maximum available capacity which is an important basis for indicating the State of Health (SOH) of lithium-ion batteries. Firstly, a data reconstruction approach is proposed to pre-process the acquired data to suppress the influence of measurement noise and reduce the negative impact on estimation precision when measuring equipment adopts different sampling frequencies. Then, the variation trend of the incremental capacity curve obtained based on the reconstructed data with the battery aging is analyzed, and a health indicator (HI) including multi-view features is put forward to characterize the battery degradation more comprehensively. The multi-view features are coming from the capacity increment curve versus voltage and time, including the maximum value of the capacity increment curve, the voltage corresponding to the maximum value, other values surrounding the maximum value and so on. Finally, Support Vector Regression is used to establish a model between the extracted HI and the maximum available capacity, and two types of open source data are used to verify the performance. The experimental results show that the data reconstruction method and multi-view health indicator proposed in the paper can obtain high precision estimation results. [Display omitted] • The problems of ICA in practical application are deeply analyzed. • A data reconstruction method is proposed. • The reconstruction charging voltage curve is monotonically increasing. • The capacity estimation results are not affected by the sampling frequency. • A new HI including multi-view features is put forward. [ABSTRACT FROM AUTHOR]

• We analyze the effect of work disability on the job involvement of older workers. • Job involvement is measured by the intentions to retire from current job. • Work disability measures are based on objective indicators and anchoring vignettes. • Work disability limitations reduce job involvement. • The role of work disability varies with job characteristics. This paper analyzes the effect of work disability on the job involvement of workers aged 50–65 living in Europe. We elicit a measure of job involvement from a question asking respondents to think about their job and declare whether they would like to retire as early as they can. We exploit objective health indicators and anchoring vignettes to enhance the comparability across individuals of work disability self-assessments. Individuals' evaluations of their health-related work limitations are found to be mildly affected by justification bias but to depend on individual heterogeneity in reporting behaviour. Work disability significantly reduces the job involvement of workers. After controlling for individual fixed-effects and an extensive set of time-varying covariates, moving from the first to the third quartile of the work disability distribution is associated with a 8% increase (4 percentage points) in the probability of desiring to retire as soon as possible. The effect is larger for blue-collar workers. Justification bias and heterogeneity in reporting behaviour do not alter the magnitude of these effects. 1 1 We thank the Editor and two anonymous referees for their comments. We are also grateful to Marco Bertoni and the participants at the workshop "The Labour Market with an Ageing Population" (2018) organized by IFAU and Uppsala University and at the ASSET Annual Meeting (2018) for their suggestions. A previous version of this paper circulated under the title "The effect of work disability on the intention to retire of older workers". This paper uses data from SHARE Waves 1 and 2 (DOIs: 10.6103/SHARE.w1.600, 10.6103/SHARE.w2.600), see Börsch-Supan et al. (2013) for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812) and FP7 (SHARE-PREP: N°211909, SHARE-LEAP: N°227822, SHARE M4: N°261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged (see www.share-project.org). We gratefully acknowledge financial support from the European Commission – Horizon 2020 (SHARE-COHESION: GA No870628). D. Cavapozzi thanks the VERA center at the Ca Foscari University of Venice for supporting open access publication. [ABSTRACT FROM AUTHOR]

In recent years, the development of autonomous health management systems received increasing attention from worldwide companies to improve their performances and avoid downtime losses. This can be done, in the first step, by constructing powerful health indicators (HI) from intelligent sensors for system monitoring and for making maintenance decisions. In this context, this paper aims to develop a new methodology that allows automatically choosing the pertinent measurements among various sources and also handling raw data from high-frequency sensors to extract the useful low-level features. Then, it combines these features to create the most appropriate HI following the previously defined multiple evaluation criteria. Thanks to the flexibility of the genetic programming, the proposed methodology does not require any expertise knowledge about system degradation trends but allows easily integrating this information if available. Its performance is then verified on two real application case studies. In addition, an insightful overview on HI evaluation criteria is also discussed in this paper. [ABSTRACT FROM AUTHOR]

• Standardized ear tag data via transformation and cleaning. • Conducted time series analysis on daily pig data. • Analyzed seasonal trends in pig population data. • Classified ear tag data using six methods. The behavioral and physiological patterns of pigs are key indicators for assessing their health status. This study aims to explore the differences in activity level and body temperature variation patterns between normal and abnormal pigs. In view of the characteristics of pig ear tag data, such as high volatility, unclear features and strong randomness, we adopted various processing and analysis methods. The main work of this paper includes: (1) Preprocessing and integrating pig ear tag data to improve data quality; (2) Performing similarity and time series analysis on the daily data of each pig to identify the change patterns of activity level and body temperature; (3) Performing seasonal feature analysis and trend change analysis on the data of the pig population, finding the gap between abnormal pigs and the population pattern, and classifying abnormal and normal pigs. The main results and conclusions of this paper are as follows: (1) Pigs are most active in two time periods: 5:00 to 10:00 in the morning and 14:00 to 18:00 in the afternoon; (2) The activity duration of lame pigs is significantly lower than that of normal pigs, while the activity duration of pigs under other abnormal conditions is not much different; (3) Although the ear tag temperature data is affected by the pig's behavior, abnormal pigs can be found by the characteristic of low temperature trend within a day. (4) Random forest algorithm can effectively classify normal and abnormal pigs, with an accuracy of 0.879. The research results of this paper have important significance and value for realizing the refined management of pigs, improving the breeding efficiency, and ensuring the health and production safety of pigs. [ABSTRACT FROM AUTHOR]

Existing research has documented various determinants of mental health related to individuals' social connections, but less is known about the role of the structural features of interpersonal networks. This is especially true in the case of bridging , which refers to ties to people who are otherwise disconnected from each other. By intersecting theories of social networks and gerontology, this study employs within- and between-person analysis with data from the National Social Life, Health, and Aging Project (NSHAP) to examine the association between social network bridging and depression in later life. The study finds that bridging, particularly between kin and non-kin members in the network, is associated with increased depressive symptoms in later life. This association is contingent on social support and strain respondents experienced, and it exhibits variations within individuals over time, especially among older adults in the youngest age cohort (57–64 years old included in NSHAP in 2005). In closing, the paper discusses the extent to which heterogeneous network structures may be one mechanism that shapes mental health trajectories in the context of later life-course experiences. • The paper discusses how bridging disparate network members produces strain that elevates depression risks in later life. • Older adults' bridging potential in personal networks is classified into three distinct types. • The association between bridging and elevated depressive symptoms is unique to bridging unconnected kin and non-kin. • The effect of bridging is more so for the youngest age cohort than older cohorts. [ABSTRACT FROM AUTHOR]

• Violence against older people is one of the least known forms of violence, despite being significantly represented worldwide. • Nowadays, the most common types of violence are neglect and psychological or emotional abuse. • The keywords "elderly abuse" led to a low number of results, showing low interest in this topic. • This type of abuse is little reported and not adequately addressed. Social and structural changes are of primary importance. • This paper is a call to the scientific community to study epidemiological and medico-legal aspects of older people abuse. The World Health Organization (WHO) describes gender violence as a real global health problem with a major impact not only on the victims' physical and mental health, but also on the economics of the National Health System. Gender-based violence has been also extended to all types of subjects defined as fragile: children, elderly, women, men and disabled people. Older people abuse, more frequent in women, is a far less socially debated issue, with many forms: physical, sexual, psychological, abandonment, neglect, economic-financial, pharmaceutical, discriminatory, institutional. An electronic literature research was carried out search using the keywords "elderly abuse" on various online sources. The research showed low number of results and little attention to this topic in the scientific literature. The paper highlights how the theme of older people abuse is little reported and not adequately addressed in scientific literature and in real life, being a form of violence certainly underestimated by the public. [ABSTRACT FROM AUTHOR]

Background: We examined whether a context and process-sensitive 'intelligent' checklist increases compliance with best practice compared with a paper checklist during intensive care ward rounds.Methods: We conducted a single-centre prospective before-and-after mixed-method trial in a 35 bed medical and surgical ICU. Daily ICU ward rounds were observed during two periods of 8 weeks. We compared paper checklists (control) with a dynamic (digital) clinical checklist (DCC, intervention). The primary outcome was compliance with best clinical practice, measured as the percentages of checked items and unchecked critical items. Secondary outcomes included ICU stay and the usability of digital checklists. Data are presented as median (interquartile range).Results: Clinical characteristics and severity of critical illness were similar during both control and intervention periods of study. A total of 36 clinicians visited 197 patients during 352 ward rounds using the paper checklist, compared with 211 patients during 366 ward rounds using the DCC. Per ICU round, a median of 100% of items (94.4-100.0) were completed by DCC, compared with 75.1% (66.7-86.4) by paper checklist (P=0.03). No critical items remained unchecked by the DCC, compared with 15.4% (8.3-27.3) by the paper checklist (P=0.01). The DCC was associated with reduced ICU stay (1 day [1-3]), compared with the paper checklist (2 days [1-4]; P=0.05). Usability of the DCC was judged by clinicians to require further improvement.Conclusions: A digital checklist improved compliance with best clinical practice, compared with a paper checklist, during ward rounds on a mixed ICU.Clinical Trial Registration: NCT03599856. [ABSTRACT FROM AUTHOR]

Accurate prediction of remaining useful life (RUL) for lithium-ion battery (LIB) plays a key role in increasing the reliability and safety of battery related industries and facilities. In this paper, RUL prediction of LIB is investigated by employing a hybrid data-driven method based support vector regression (SVR) and error compensation (EC). Firstly, two health indicators (HIs) are established by using capacity and discharging voltage difference of equal time interval (DVD), respectively. Secondly, the ensemble empirical mode decomposition (EEMD) is adopted to preprocess the obtained HIs, which is used to reduce the influence of capacity regeneration and noise. Especially, phase space reconstruction (PSR) with C–C technique is introduced to achieve optimal input sequence selection pattern, it has an important influence on the accuracy of SVR prediction. As an important innovation of the paper, the idea of EC is implemented by combining the predictions of both forecast error and RUL prediction with PSR-SVR. Last but not least, the genetic algorithm (GA) is utilized to optimize the key parameters of SVR so as to achieve more accurate RUL prediction. To verify the effectiveness of the proposed approach, the real data set of LIBs from National Aeronautics and Space Administration (NASA) is carried out, and the dominant is emphasized by comparison with other important methods. [ABSTRACT FROM AUTHOR]

The first Transplant Games took place in 1978 in Portsmouth, England. Since then, numerous Olympic-style sporting 'Games' have been established, each aiming to increase public awareness of organ donation, demonstrate the benefits of transplantation, and encourage patient fitness. Despite this, there is limited research exploring the psycho-social and health impacts of the Games. Drawing on qualitative research conducted at the 2022 British Transplant Games and the 2022 European Transplant and Dialysis Games, this paper explores the Games through a therapeutic landscapes framework, a concept that examines the ways in which environments contribute to health and wellbeing. Building upon work that acknowledges the relational and contingent nature of health-in-place, I argue that the Games are a therapeutic landscape of social relations for transplant recipients in three ways: providing a landscape of belonging , a landscape of hope , and a landscape of motivation. Through this therapeutic landscapes perspective, the Transplant Games are presented as a peer-to-peer clinic – a care space where individuals are more actively involved in their health on a reciprocal level than in the traditional hospital clinic. In turn, this paper emphasises the crucial role of affective peer support in producing health-promoting environments. This research seeks to make a practical contribution to the wider transplant community by promoting the Games as an environment which may positively contribute to both physical and mental wellbeing. • The Transplant Games function as a therapeutic landscape for organ recipients. • They provide a peer-to-peer clinic where participants are active in their health. • The Games provide a landscape of belonging, acceptance, understanding and family. • In showing you can still lead a healthy life, the Games become a landscape of hope. • As a landscape of motivation, the Games improve both physical and mental health. [ABSTRACT FROM AUTHOR]

[Display omitted] • This paper proposed a new method for evaluating BCS based on shape analysis. • This paper provided a highly automatic GUI tool to evaluate the BCS of livestock. • The method in this paper is more general and is applicable to multiple species. • The method in this paper can be extended to other shape related research. Body condition has always been an important indicator reflecting the health status of livestock, and new automated and contactless methods for assessing it have also been continuously proposed. In recent years, with the continuous expansion of research on quantitative analysis of three-dimensional shapes in the field of agriculture, amount of studies has shown that quantitative analysis of shapes can promote the development of smart agriculture. This article proposed a method for quantitatively analyzing the local three-dimensional shape of livestock to evaluate their body conditions. This is a universal method that can be applied to various species. It uses the method of point-to-point correspondence in the three-dimensional shape to finely calculate the difference between shapes, and then maps the difference value to the range of the body condition score. The article analyzed the shape of 198 dairy cows, 100 beef cattle, and 201 pigs using body condition scores as test parameters. The accuracy of body condition evaluation within the range of error of 0.5 for dairy cows, 1 for beef cattle, and 0.5 for pigs are 100%, 87.61%, and 92.09%, respectively. Compared to previous methods for assessing body condition, the method proposed in this paper is more accurate and versatile. The dataset used in this study, along with the code implementation have been made publicly available in https://gitee.com/kznd/lshape-analyser/tree/master/MATLAB. [ABSTRACT FROM AUTHOR]

In multilevel converters (MCs), system components such as capacitors and semiconductor switches are very susceptible to damage. Furthermore, utilizing voltage sensors to balance capacitor voltages increases the system's cost and complexity while decreasing reliability. This paper presents methods for voltage balancing of capacitors, capacitance monitoring and open-circuit fault detection in nested neutral point-clamped (NNPC) converter with a reduced number of voltage and current sensors. In the proposed method, converter capacitors' voltage and current sensors are eliminated, and only the output sensors are employed to control the converter. In order to balance the voltage of capacitors, their voltages are estimated in three cases without using individual sensors. Comparing the capacitors' measured and estimated voltage change ratios is used to assess their health in the monitoring method. Thus, the capacitors' status can be checked by considering the system's health indicator during each period. Furthermore, the open-circuit fault detection method can identify defective switches within one processing cycle by comparing the converter's measured and standard output voltages in all switching states. The proposed methods have been evaluated in an experimental study of the system, and the results confirmed the effectiveness of the methods based on the new system configuration. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• Introducing a novel dynamic data-driven degradation method for predicting RUL. • Designing a decision-level fusion approach for the selection and integration of measurement features. • Establishing a rolling HI-RUL mapping through the utilization of measured health indicator curves. • Conducting the run-to-failure experiment across various milling conditions to evaluate system performance. Monitoring tool condition and remaining useful life (RUL) are vital in preventing the occurrence of excessive tool wear. This paper develops a novel dynamic data-driven degradation method for monitoring the RUL of cutting tools. In sensor-data collection, vibration, sound, and power external sensors and built-in data are gathered from the machine tool. In multi-feature selection and dynamic model updating, a decision-level fusion method of spanning multi-domain features is designed to dynamically utilize a global prediction error for selecting and fusion degradation features, which are associated with the cutting tool life in the degradation model. A rolling HI-RUL mapping is established in RUL prediction by employing a historical health indicator curve, which estimates the RUL of cutting tool with a given threshold. The effectiveness of proposed method was assessed through two run-to-failure experiments of cutting tools, showing that an average global prediction error is reduced to 4.07%. [ABSTRACT FROM AUTHOR]

This study explores the significant correlation between frailty and an elevated risk of mortality in COVID-19 patients, suggesting that increased frailty screening could enhance disease management and optimize resource distribution. An analysis of peer-reviewed papers on frailty and cardiovascular diseases (CVD) over a ten-year period reveals a peak of 4480 articles from September 2021 to September 2022. The literature review conducted on frailty and CVD highlights the high prevalence of frailty in older adults with CVD and its role as a predictor of cardiovascular death. The study suggests that frailty can inform treatment decisions, offering more personalized care. However, standardizing frailty assessment in clinical practice and trials is needed. The impact of frailty on coronary artery disease, peripheral artery disease, and atrial fibrillation requires further research. The study also discusses the increasing global burden of CVD among older adults due to aging populations and improved care. It highlights the challenges posed by older age, multiple comorbidities, polypharmacy, frailty, and adverse noncardiovascular outcomes. The review focuses on geriatric conditions that significantly impacted health status, quality of life, and overall prognosis. The study concludes that frailty significantly increases the risk of CVD events and major adverse cardiovascular events in older adults without prior CVD. Screening for frailty could help identify those at higher risk and facilitate targeted preventive measures. [ABSTRACT FROM AUTHOR]

Mothers are commonly blamed for the ill-health of their children, and this is well documented in research. However, few studies have considered gendered patterns of blame for hereditary conditions caused by mutations from both parents through dual, shared genetic inheritance. This paper explores the 'gendering' of blame in the context of an inherited blood disorder known as sickle cell disease. The findings are drawn from 18 focus group discussions with 117 caregivers of children with sickle cell disease in Malawi and Uganda. Although one mutation from each parent is required for the disease to develop, low awareness about their status as healthy carriers of a sickle cell trait complicated the caregivers' recognition and acceptance of their genetic link to the child's condition. This study demonstrates how fathers and other members of the paternal side of the child's family would deflect blame from their own lineage by directing sole 'genetic responsibility' for the child's disease towards mothers. We discuss the implications of gendered blame on household dynamics and healthcare-seeking for children with sickle cell disease in this setting. • Sickle cell disease develops if the child inherits one mutation from each parent. • Low awareness about sickle cell disease led to gendered blame-shifting. • Mothers were attributed sole genetic responsibility for the child's condition. • Invisible inheritance through healthy carriers may cause gendered patterns of blame. [ABSTRACT FROM AUTHOR]

Remaining Useful Life (RUL) prediction is vital for system functionality. Non-end-to-end approaches is an important type of RUL prediction approaches for their important application in industrial scenarios. In non-end-to-end approaches, Health Indicator (HI) construction is a critical aspect. However, existing HI construction approaches ignore First Predicting Time (FPT) detection, leading to increased domain knowledge demand and system health comprehension difficulty. To address this issue, this paper proposes a multi-objective-optimization-based HI construction approach enabling both FPT detection and RUL prediction. A novel metric called the monotonicity strength index is proposed to address the limitation of the conventional monotonicity. The constructed HI possesses the ability to indicate FPT by taking the detectability metric as an optimization objective. The optimization problem is solved by the combination of the multi-objective ant lion optimizer and the entropy weight method. The superiority of this HI is demonstrated through experiments on the widely used IMS bearing dataset and a gearbox dataset. • An health indicator enabling both first predicting time detection and RUL prediction. • The constructed health indicator helps people understand system health status. • A new health indicator assessment metric which can indicate monotonicity and robustness. [ABSTRACT FROM AUTHOR]

Prader-Willi syndrome (PWS) is a rare neurogenetic syndrome that results in significant challenges in the school setting, often requiring a myriad of academic, social-emotional, and behavioral supports to maintain health and safety and academic success. This paper provides a primer for school psychologists, introducing the physiological, educational, and behavioral differences experienced by children with PWS. As the symptoms of PWS are widespread, the impacts of common physical and neurological differences on specific learning abilities, adaptive skills, and overall functioning in the classroom setting are discussed in detail. While PWS is a spectrum, and each child will experience symptoms and challenges differently, this article aims to provide a foundation for the development and facilitation of support services to assist educators in not only maintaining the health and safety of their students with PWS, but also in capitalizing on their strengths, and building a plan to help them succeed. [ABSTRACT FROM AUTHOR]

• Adverse childhood experiences (ACEs) screeners can be verbal or self-administered. • We used 2 contemporaneous surveys to compare prevalence of caregiver-reported ACEs. • The telephone survey had higher ACEs disclosure rates compared to paper/web survey. • Findings support the importance of verbal communication to identify ACEs exposure. Adverse childhood experiences (ACEs) are traumatic experiences that increase people's susceptibility to adverse physical health, mental health, and social consequences in adulthood. Screening for ACEs in primary care settings is complicated by a lack of consensus on appropriate methods for identifying exposure to ACEs. It is unclear whether self-report methods could increase disclosure of ACEs as compared to interview-based methods. This study compares data on the prevalence of ACEs from two publicly available surveys conducted on the same population of children's caregivers: the 2019 Ohio subsample of the web/mail-based National Survey of Children's Health and the telephone-based 2019 Ohio Medicaid Assessment Survey. We find higher disclosure of caregiver-reported child exposure to ACEs in the telephone interview survey, highlighting the importance of the role of verbal communication in developing a safe and trusting relationship in the disclosure of trauma. [ABSTRACT FROM AUTHOR]

As durability of proton exchange membrane fuel cell (PEMFC) remains as the main obstacle for its larger scale commercialization, predicting PEMFC degradation progress is thus an effective way to extend its lifetime. To realize reliable prediction, a novel health indicator (HI) extraction method based on auto-encoder is proposed in this paper, with which PEMFC future voltage can be predicted by long short-term memory network (LSTM). The effectiveness and robustness of proposed approach is investigated with test data simulating vehicle operation conditions, and accurate prediction performance can be observed, with the maximum root mean square error (RMSE) of 0.003513. Moreover, by comparing with two commonly prognostic methods including attention-based gated recurrent unit network and polarization model-LSTM, the proposed method can provide better predictions under various operating conditions. Furthermore, with the proposed method, the degradation mechanism of PEMFC can also be analyzed. Therefore, the proposed prognostic method can predict reliable PEMFC degradation progress and its corresponding degradation mechanisms, which will be beneficial in practical PEMFC systems for taking appropriate strategies to guarantee PEMFC durability. [Display omitted] • A data-driven method is proposed for PEMFC degradation prediction. • An auto-encoder based health indicator extraction method is proposed. • Effectiveness of proposed approach is verified at vehicle operating conditions. • PEMFC degradation mechanism can be analyzed from prediction results. [ABSTRACT FROM AUTHOR]

Bearing is one of the critical components in rotating equipment. Therefore, accurate estimation of the remaining useful life (RUL) of bearings plays a vital role in reducing the costly unplanned maintenance and increasing the reliability of machines. This paper proposes a method for bearing prognostics that uses iteratively updated degradation regression models to capture the degradation trend in bearing's health indicator (HI), and the models are utilized to predict the degradation trajectory of HI and to estimate the RUL of bearings. The importance of determining the time to start prediction by elbow point is explained, which is often overlooked in prognostics. To improve the prognostic performance, an adaptive approach for elbow point detection is designed based on the gradient change of HIs, and a new smooth approach is applied to reduce spurious fluctuations in degradation trajectory. The effectiveness of the proposed method is validated on two publicly available data sets, i.e., IMS and FEMTO bearing prognostics data set, and its prognostic performance is compared with that of three state-of-the-art methods. The obtained results demonstrate that the proposed method can effectively detect elbow point and determine the time to start prediction, and can calibrate the degradation regression model dynamically according to the evolving degradation trend in the HI, which validates its superior prognostic performance. • A remaining useful life prediction method using the dynamic degradation regression models is proposed. • An adaptive approach for elbow point detection based gradient change of health indicator is proposed. • The proposed method is tested and validated on IMS and FEMTO bearing data sets, respectively. • The proposed algorithm shows superiority comparing to other art-of-the-state methods. [ABSTRACT FROM AUTHOR]

Blood pressure (BP) is known as an indicator of human health status, and regular measurement is helpful for early detection of cardiovascular diseases. Traditional techniques for measuring BP are either invasive or cuff-based and thus are not suitable for continuous measurement. Aiming at the deficiencies in existing studies, a novel cuffless BP estimation framework of Receptive Field Parallel Attention Shrinkage Network (RFPASN) and BP range constraint is proposed. Firstly, RFPASN uses the multi-scale large receptive field convolution module to capture the long-term dynamics in the photoplethysmography (PPG) signal without using long short-term memory (LSTM). On this basis, the features acquired by the parallel mixed domain attention module are used as thresholds, and the soft threshold function is used to screen the input features to enhance the discriminability and robustness of features, which can significantly improve the prediction accuracy of diastolic blood pressure (DBP) and systolic blood pressure (SBP). Finally, in order to prevent large fluctuations in the prediction results of RFPASN, RFPASN based on BP range constraint is proposed to make the prediction results of RFPASN more accurate and reasonable. The performance of the proposed method is demonstrated on a publically available MIMIC-II database. The database contains normal, hypertensive and hypotensive people. We have achieved MAE of 1.63/1.59 (DBP) and 2.26/2.15 (SBP) mmHg for BP on total population of 1562 subjects. A comparative study shows that the proposed algorithm is more promising than the state-of-the-art. • In order to effectively extract the information that can represent BP changes in the PPG signal, the BP prediction model based on Receptive Field Parallel Attention Shrinkage Network (RFPASN) is built in this paper. It is an "end-to-end" BP estimation method. • We propose Receptive Field Parallel Attention Shrinkage Block (RFPASB), which can capture the long-term dynamics in the PPG signal. Then, the features with strong discriminability are extracted by parallel mixed domain attention module, which is used as the threshold of soft threshold function, so as to retain important features and eliminate redundant information. • In order to prevent unreasonable prediction results from RFPASN and limit BP prediction results to a reasonable range, we construct abnormal BP discriminative model to limit the range of RFPASN prediction results to further improve the accuracy and reasonableness of BP prediction results. [ABSTRACT FROM AUTHOR]

This paper examines rural-urban inequities in dusty lung (pneumoconiosis) deaths in China from 2002 to 2016 and possible causes for such inequities in the framework of social and spatial justice. The research reveals alarming results that have not been reported before. Dusty lung death rates for rural men increased twice as fast as those among urban men. The rural rates were particularly higher among middle-aged men, but lower in older age groups, as compared to urban rates. There were dramatic increases in death rates from 2011 to 2016 particularly among rural men aged 40–49 years old, who were many times more likely to die from dusty lung than urban men of a similar age. Chinese rural male victims also die at a younger age, compared to men in any of the seven countries or regions included in the study. For example, rural Chinese men aged 35–59 years old were many times more likely to die from dusty lung than their American, German, Polish, and Spanish counterparts. A possible explanation for the high death rates among middle-aged rural Chinese men is the tendency for rural migrant workers to be employed in unhealthy and unsafe working conditions in decent decades. Other explanations for the severe spatial injustice include the Hukou (household registration) system and the development policies that prioritize economic growth and urban development over occupational health and safety. The paper demonstrates the usefulness and weakness of the social and spatial justice concepts in understanding health inequity. Therefore, it proposes new definitions and models of social justice and spatial justice that combine the competing distribution and capabilities approaches and highlight interactions among multiple attributes. • Middle-aged rural men are worst victims of a growing dusty lung crisis in China. • They may be many times more likely to die from dusty lung than urban Chinese men. • Social and spatial injustice is responsible for such health inequity in China. • Urban growth-centered development policies are responsible for such injustice. • A new definition along with models of social justice and spatial justice are proposed. [ABSTRACT FROM AUTHOR]

Productive activities are crucial factors leading to an "active aging" population. With the case of Vietnam, this paper aimed to explore the productive activities among Vietnamese older people by using data from the Vietnam Aging Survey (VNAS), which was conducted in 2011 as the first-ever nationally representative survey on persons aged 50 and over in Vietnam and contained 2789 older people (those aged 60 and over) representing all older people living in 6 ecological regions and urban and rural areas in Vietnam. Productive activities included working and/or taking care of any (great)grandchild(ren) in the past 12 months prior to the survey. Using paired t-tests and probit models, we compared the differences in these productive activities among older people in terms of sex and living area. The results from various t-tests showed that educational attainment, health status and poverty status were key factors differentiating older men and women and rural and urban older people in these productive activities. For the probit estimations in terms of both gender and living location, the results indicated that age, education, health status and supportive children were determinants of working decision, while age, marital status and size of household were consistently important factors of taking care of (great)grandchildren. The paper also discussed policy implications for socio-economic and health protection in promoting older people's productive activities as well as protecting them from a variety of risks and vulnerabilities. • Older people are active in working and taking care of their (great)grandchildren. • Their activities are significant differences, especially in terms of sex and age. • Health is also a key determinant of older people's productive activities. • Adaptive social and health protection policies are needed for "active aging". [ABSTRACT FROM AUTHOR]

Highlights • This paper explores the effects of four dimensions of social exclusion on self-rated health and depression among older people in China. • Lower level of exclusion from social relationships was associated with less likely to report fair or poor SRH. • Lower level of subjective feelings of exclusion was associated with less likely to report fair or poor SRH. • Lower level of subjective feeling of exclusion was associated with less likely to report depression. • Lower level of exclusion from financial products was associated with less likely to report depression. Abstract Background It is well established that social exclusion is a key social determinant of health; however, such association between social exclusion and health outcomes among older people remain a relatively under-researched area. This paper explores the effects of four dimensions of social exclusion on self-rated health and depression among older people in China. Methods This paper includes 8038 individuals aged 60 and over from the first wave national multi-stage probability sample (2014) from the China Longitudinal Aging Social Survey (CLASS). Descriptive univariate information for individual variables and four dimensions of social exclusion are presented. Multinomial and binary logistic regression models are used to examine the associations between social exclusion and self-rated health and depression. Results Older people who were in the lower level of exclusion from social relationships or subjective feelings of exclusion were significantly less likely to report fair or poor self-rated health than people in the higher level of exclusion (lower level of exclusion from social activities was significantly associated with being less likely to report poor SRH only). Older people who were in the lower level of subjective feeling of exclusion or exclusion from financial products were significantly less likely to report depression. Conclusions Different dimensions of social exclusion have different effects on self-rated health and depression. Social policies need to reflect this and efforts of services could usefully be oriented to prevent multi-dimensions of social exclusion. Ultimately, such policies should have the potential to enhance the health of older people in China. [ABSTRACT FROM AUTHOR]

Abstract Small area health data are not always available on a consistent and robust routine basis across nations, necessitating the employment of small area estimation methods to generate local-scale data or the use of proxy measures. Geodemographic indicators are widely marketed as a potential proxy for many health indicators. This paper tests the extent to which the inclusion of geodemographic indicators in small area estimation methodology can enhance small area estimates of limiting long-term illness (LLTI). The paper contributes to international debates on small area estimation methodologies in health research and the relevance of geodemographic indicators to the identification of health care needs. We employ a multilevel methodology to estimate small area LLTI prevalence in England, Scotland and Wales. The estimates were created with a standard geographically-based model and with a cross-classified model of individuals nested separately in both spatial groupings and non-spatial geodemographic clusters. LLTI prevalence was estimated as a function of age, sex and deprivation. Estimates from the cross-classified model additionally incorporated residuals relating to the geodemographic classification. Both sets of estimates were compared against direct estimates from the 2011 Census. Geodemographic clusters remain relevant to understanding LLTI even after controlling for age, sex and deprivation. Incorporating a geodemographic indicator significantly improves concordance between the small area estimates and the Census. Small area estimates are however consistently below the equivalent Census measures, with the LLTI prevalence in urban areas characterised as 'blue collar' and 'struggling families' being markedly lower. We conclude that the inclusion of a geodemographic indicator in small area estimation can improve estimate quality and enhance understanding of health inequalities. We recommend the inclusion of geodemographic indicators in public releases of survey data to facilitate better small area estimation but caution against assumptions that geodemographic indicators can, on their own, provide a proxy measure of health status. Highlights • A novel cross-classified extension to small area estimation methodology. • Small area estimation of health indicators can be enhanced by geodemographic data. • Geodemographics and deprivation independently impact small area health estimates. • Deprived urban communities have much worse health than expected. [ABSTRACT FROM AUTHOR]

Health inequalities continue to grow despite continuous policy intervention. Work, one domain of health inequalities, is often included as a component of social class rather than as a determinant in its own right. Many social class classifications are derived from occupation types, but there are other components within them that mean they may not be useful as proxies for occupation. This paper develops the exposome, a life-course exposure model developed by Wild (2005), into the worksome, allowing for the explicit consideration of both physical and psychosocial exposures and effects derived from work and working conditions. The interactions between and within temporal and geographical scales are strongly emphasised, and the interwoven nature of both psychosocial and physical exposures is highlighted. Individuals within an occupational type can be both affected by and effect upon occupation level characteristics and health measures. By using the worksome, occupation types are separated from value-laden social classifications. This paper will empirically examine whether occupation better predicts health measures from the European Working Conditions Survey (EWCS). Logistic regression models using Bayesian MCMC estimation were run for each classification system, for each health measure. Health measures included, for example, whether the respondent felt their work affected their health, their self-rated health, pain in upper or lower limbs, and headaches. Using the Deviance Information Criterion (DIC), a measure of predictive accuracy penalised for model complexity, the models were assessed against one another. The DIC shows empirically which classification system is most suitable for use in modelling. The 2-digit International Standard Classification of Occupations showed the best predictive accuracy for all measures. Therefore, examining the relationship between health and work should be done with classifications specific to occupation or industry rather than socio-economic class classifications. This justifies the worksome, allowing for a conceptual framework to link many forms of work-health research. • Work is often included as part of social class as a social determinant of health. • This is not always appropriate, as class has an implied hierarchy. • The worksome, a theoretical framework is developed from the exposome. • With Bayesian methods, we show that occupational classifications are more suitable. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes a reliable technique for the health prognosis of rolling element bearings. • The proposed algorithm is tested and validated on the PRONOSTIA dataset. • The proposed method shows the excellent prognostic performance. Abstract Induction motors most often fail due to faults in the rolling element bearings. Such failures can cause long and unscheduled downtime in a production facility, which can result in huge economic losses. The prediction of imminent failures and estimation of a bearing's remaining useful life (RUL) is vital for avoiding abrupt shutdowns and scheduling maintenance. In this paper, a reliable technique for the health prognosis of rolling element bearings is proposed, which infers a bearing's health through a dimensionless health indicator (HI) and estimates its RUL using dynamic regression models. The HI measures the instantaneous vibration level of the bearing with respect to a normal baseline value. The regression models are recursively updated to capture the evolving trend in the bearing's health indicator and are then used to project the future values of the health indicator and estimate the RUL of the bearing. The RUL of a bearing is estimated after determining the time to start prediction (TSP) using a new approach. The proposed algorithm is tested and validated on the PRONOSTIA dataset, and its prognostic performance is compared with two state-of-the-art techniques that are based on the extended Kalman filter and an exponential model that is improved using particle filters. The experimental results demonstrate the excellent prognostic performance of the proposed method due to its ability to determine an appropriate TSP and dynamic calibration of the regression models to adopt to the evolving trend in the bearing health indicator. [ABSTRACT FROM AUTHOR]

Abstract Traditionally in clinics or hospitals, it is the staff (physician, nurses, and so forth) who would check the patient's health status (e.g., blood pressure, height, weight, body temperature, and so forth). However, when mobile apps are used as the point of contact between patients and healthcare providers, the self-monitoring of health status will be exposed to biases due to being done by common people. Therefore 'self-report reliability' becomes an essential factor in the mobile healthcare context. Drawing on 'technology identity' and 'technology self-efficacy' literature, we theorized that perceived mobile technology identity directly affects self-report reliability, and perceived self-efficacy moderates the relationship between the two. A sample of patients from a clinic who completed a survey and self-reported their health status using a mobile health app was collected. The results of the analyses suggest that academic education affects the reliability of self-reports. In addition, patients aged 61 and above were more accurate in reporting their health status. Moreover, we found that self-efficacy improves the accuracy of self-reports and moderates the effect of mobile technology identity on self-report reliability. The findings of this paper contribute to the ongoing research around mobile healthcare application use and issues surrounding this phenomenon. Highlights • This paper contributes to "Quantified Self" and "Mobile Healthcare" literature. • Patients holding academic degree reported themselves with higher accuracy. • Patients aged 61 + reported blood pressure with higher accuracy compared to 31–60. • Patients aged 18–30 reported blood pressure with higher accuracy compared to 31–60. • Self-efficacy moderates the effect of mobile identity on self-report reliability. [ABSTRACT FROM AUTHOR]

Abstract Using Census-derived data for consistent spatial units, this paper explores how the population of Britain in 1991, 2001 and 2011 was spatially structured by self-reported health including exploring the trajectories of change. This paper uses consistent small area units to examine the changing spatial structure of census-derived Limiting, Long-Term Illness (LLTI) in Britain over the twenty year period and utilises the 2011 Office for National Statistics Output Area Classification (OAC) as a geodemographic indicator. The results allow the geography of change to be captured, highlighting how health is inextricably linked to geography, demonstrating quantitatively a complex, yet distinctive, spatial organisation of health inequalities within Britain. Overall decreasing unevenness values, coupled with increased positive spatial association suggests that neighbouring areas have become more similar over time – the distinction between areas characterised by poor health or by good health is decreasing. Highlights • Health inequalities are distinctively spatially organised within Britain. • Britain is now more visibly divided by its geography of long-term illness. • Distinct spatial concentrations of good and poor health correspond with area type. [ABSTRACT FROM AUTHOR]

Highlights • Nutrition-sensitive agricultural interventions often support vulnerable populations. • Evidence shows interventions improve intermediate outcomes like dietary diversity. • Limited evidence that interventions improve long-term outcomes like child growth. • Unintended consequences for women's workload and sustainability need attention. • LANSA has significantly extended range and scope of interventions in South Asia. Abstract Research on the potential impact of interventions in agriculture on nutrition outcomes is of particular relevance in South Asia where agriculture-related activities are a major source of livelihoods for large sections of society and where the population suffers from one of the highest global burdens of malnutrition in all its forms. This systematic review aims to assess the strength of the available evidence that agricultural interventions have an impact on intermediate and final nutrition outcomes in India, Bangladesh, Nepal, Pakistan and Afghanistan. We searched five literature databases and reference lists of previous systematic reviews to identify peer-reviewed studies published between 2012 and 2017, detailing impacts of household- or farm-level agricultural interventions on nutritional outcomes in South Asia. We identified six intervention studies (reported in nine papers) conducted in Bangladesh (two studies), India (two studies) and Nepal (two studies). The majority of studies examined the impact of provision of seed, plants and training to increase home garden fruit and vegetable production with or without integrated poultry provision and training. Other studies evaluated the impact of livestock or aquaculture provision and training. Study designs and quality were mixed; heterogeneity across studies precluded formal meta-analysis. Interventions had a positive impact on intermediate outcomes on the pathway from agricultural intervention to nutritional or health status including dietary quality and dietary diversity of households and individuals (reported in seven papers). The evidence on the impact on final nutritional outcomes was mixed: one paper reported that home gardens with poultry reduced the odds of anaemia but there was no convincing evidence of an impact of agricultural interventions on child anthropometric measurement (reported in four papers). In recent years, the Leveraging Agriculture for Nutrition in South Asia (LANSA) research programme consortium has significantly expanded research on agricultural interventions for nutrition outcomes by conducting and commissioning a suite of formative and feasibility studies that have extended both the range and geographic location of interventions under study. This expanding body of research should, in the future, enable the identification of cost-effective interventions to enhance the impact of agricultural interventions sustainably to improve nutrition outcomes especially in women and children in South Asia. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction is of great significance for improving maintenance efficiency and ensuring the reliability of rotating machinery. In recent years, there are a large number of deep-learning-based methods for RUL prediction of rotating machinery. However, the effect of conventional end-to-end RUL prediction methods relies on the distribution consistency of training data and test data, and conventional health indicator extrapolation RUL prediction methods are susceptible to interference from abnormal fluctuations in the health curve. To overcome the problem, this paper proposes a new RUL prediction method for rotating machinery using health indicators constructed by the residual hybrid network with self-attention mechanism (Res-HSA). First of all, we propose the residual hybrid network combined with self-attention mechanism to extract the high-level degenerate feature. Then, the health assessment model based on Res-HSA is proposed to generate the health indicators of the equipment. To assist in network training, the segmented data labels based on the degradation rule are applied to optimize the labels of training sets. Finally, to address the problem of abnormal fluctuations in the health curve, a fitting interval selection method is used to optimize conventional curve fitting schemes to calculate RUL. Two public datasets, IEEE-PHM-2012-challenge datasets and C-MAPSS datasets, are used to verify the effectiveness of the proposed method. The experiment results on two public datasets show that the RUL prediction method proposed in this paper has good prediction performance. Compared to the state-of-the-art method, the method proposed in this article reaches the most advanced level in some test projects, while the rest of the projects can be very close to the most advanced method. • A novel residual hybrid network with self-attention mechanism is proposed for bearing health assessment construction. • A health assessment model based on Res-HSA is proposed to generate the health indicators of equipment. • The segmented data labels based on the degradation rule are applied to optimize the labels of training sets. • An optimized fitting interval selection scheme is proposed to improve the accuracy of RUL prediction. [ABSTRACT FROM AUTHOR]

Prison populations across the world are increasing. In the United Kingdom, numbers have doubled in the last two decades, and older prisoners now constitute the fastest growing section of the prison population. One key reason for this shifting prisoner demographic is the growing numbers of men convicted of ‘historic’ sexual offences, many of whom are imprisoned for the first time in old age, and housed in prisons not suited to their needs. These demographic changes have profound consequences, including increased demand for health and social care in prison, and rising numbers of anticipated deaths in custody. Using the findings from a recently completed study of palliative care in prison, this paper proposes that older prisoners face a ‘double burden’ when incarcerated. This double burden means that as well as being deprived of their liberty, older people experience additional suffering by not having their health and wellbeing needs met. For some, this double burden includes a ‘de facto life sentence’, whereby because of their advanced age and the likelihood that they will die in prison, they effectively receive a life sentence for a crime that would not normally carry a life sentence. There has been little popular or academic debate concerning the ethical and justice questions that this double burden raises. Drawing on the work of Wacquant and others, the paper proposes that these changes are best understood as unplanned but reasonably foreseeable consequences of neoliberal penal policies. Although the paper focuses on the UK (which by comparison with other European countries has high rates of imprisonment), many of the challenges discussed are emerging in other countries across the world. This paper illustrates starkly how neoliberal policies and discourses have shaped the expansion and composition of the prison population with its consequent implications for health and justice. [ABSTRACT FROM AUTHOR]

Understanding the transition to adulthood has important implications for supporting young adults and understanding the roots of diversity in wellbeing later in life. In South Africa, the end of Apartheid means today's youth are experiencing their transition to adulthood in a changed social and political context which offers opportunities compared to the past but also threats. This paper presents the first national level analysis of the patterning of key transitions (completion of education, entry into the labour force, motherhood and marriage or cohabitation), and the association between the different pathways and health amongst young women. With the use of longitudinal data from the South African National Income Dynamics Study (2008–2015), this paper employs sequence analysis to identify common pathways to adulthood amongst women aged 15–17 years at baseline (n = 429) and logistic regression modelling to examine the association between these pathways and self-rated health. The sequence analysis identified five pathways: 1. ‘ Non-activity commonly followed by motherhood’, 2. ‘Pathway from school, motherhood then work’, 3. ‘Motherhood combined with schooling’, 4. ‘Motherhood after schooling’ , and 5. ‘Schooling to non-activity’ . After controlling for baseline socio-economic and demographic characteristics and health, the regression results show young women who followed pathways characterised by early motherhood and economic inactivity (1, 3 and 4) had poorer self-rated health compared to women whose pathways were characterised by combining motherhood and economic activity (2) and young women who were yet to become economically active or mothers (5). Therefore, policies should seek to prevent adolescent childbearing, support young mothers to continue their educational careers and enable mothers in work and seeking work to balance their work and care responsibilities. Further, the findings highlight the value of taking a holistic approach to health and provide further evidence for the need to consider work-family balance in the development agenda. [ABSTRACT FROM AUTHOR]

This paper investigates the healthcare needs, access to healthcare, and healthcare strategies of Ethiopian migrant domestic workers (MDWs) in Lebanon, drawing on qualitative empirical research. The analysis focusses on four types of health care needs: minor illnesses, pregnancies, serious illnesses (such as cancer, tuberculosis or heart problems), and emergencies (due to accidents, suicide attempts or assaults). Predictably, access to healthcare is distinctly differentiated according to an MDW's status as a documented, freelancer, or undocumented worker. Drawing on the concepts of systemic health inequities and inter-personal racial discrimination, the paper provides evidence for inequitable access to healthcare experienced by Ethiopian women in Lebanon. I identify the specific forms of exclusion they experience and develop a matrix for analysis of systemic inequities in access to healthcare differentiated by migrant status. [ABSTRACT FROM AUTHOR]

Early cervical cancer screening has been shown to be beneficial in reducing cervical cancer related deaths. Despite the benefits of early cervical cancer screening, uptake remains limited, with wide disparities in access and uptake in most developing countries. As part of a larger study, this paper uses a socio-ecological framework to explain the determinants of cervical cancer knowledge and screening among women of reproductive age (15–49 years) in Kenya. We conducted a multilevel analysis of cervical cancer knowledge (n = 11,138) and screening (n = 10,333) using the 2014 Kenya Demographic and Health Survey (KDHS). Results show regional disparities in cancer knowledge and the utilization of cervical cancer screening services; regions with high wealth inequality (OR = 0.70, 95% CI [0.56–0.87]) emerged as vulnerable regions where women were less likely to screen for cervical cancer. Gender equity, health insurance coverage and education level significantly predicted cervical cancer screening rates. Results further revealed regional as well as rural-urban wealth inequalities in cervical cancer screening. We argue that given that Kenyan women are highly exposed to human papilloma virus (HPV) due to the legacy of human immunodeficiency virus (HIV) in the country, cervical cancer may be the next epidemic if integrated measures are not adopted to increase cervical cancer knowledge and overcome the barriers to utilizing early screening services. The paper concludes with policy recommendations and directions for future research. [ABSTRACT FROM AUTHOR]

The construction of effective health indicators is crucial for assessing system degradation, enabling anomaly detection and health condition assessment, which contribute to reducing costs, improving productivity, and enhancing system availability. However, there are notable challenges in the health condition assessment for unlabeled single-sample data. In this paper, a multivariable correlation feature network—the Nested Autoencoder (NAE) network, is proposed, which incorporates inter-variable correlations to constrain the construction of latent space, thereby enhancing the accuracy of health condition assessment. Moreover, the autoencoder parameters calculated with each discrete interval data are leveraged to construct latent feature graphs, enabling component anomaly detection. Furthermore, a comprehensive indicator is introduced to describe the topological changes of the graphs, facilitating the assessment of the health conditions of the system. Finally, the effectiveness of our method is validated on the N-CMAPSS dataset as well as a real satellite dataset. [ABSTRACT FROM AUTHOR]

• The GARCH family models from time series analysis is introduced for the first time into machine condition monitoring. • Health indicators constructed based on GARCH and EGARCH models demonstrate the capability to detect incipient faults in bearings, verified through two experimental cases. • In accelerated degradation tests, it is established that acoustic emission signals are closely associated with damage evolution. • Acoustic emission technology can be employed for quantitative assessment of bearing damage. Recent progress in digital twin (DT) has significantly contributed to the advancement of predictive maintenance. The interaction of data between physical and virtual models is facilitated through carefully designed health indicators (HIs). Conventional condition monitoring HIs are inadequate for early-stage fault detection and lack the capacity to quantitatively assess defects. In light of this, the paper proposes HIs based on the generalised autoregressive conditional heteroskedasticity (GARCH) family time series model to characterise the evolution of bearings dynamic response, specifically the cyclostationarity of the repetitive transients. The verification of the proposed indicators is assessed using a publicly available vibration dataset and acoustic emission signals acquired from accelerated bearing degradation tests. The result shows that the GARCH oriented indicators can determine the incipient failure earlier than traditional statistical HIs and have the ability to quantify defects. Significantly, the improved Exponential GARCH model-based indicator demonstrates heightened stability throughout the monitoring process. [ABSTRACT FROM AUTHOR]

This paper investigates how restriction policies have impacted elderly self-assessed health (SAH) in Europe during the pandemic, and how the Covid-19 infection interacts with policy stringency to modulate the SAH deterioration. Using the Survey of Health, Aging and Retirement in Europe (SHARE) between October 2019 and August 2021, including 9,034 adults aged 50 years and above, alongside with a stringency index from the Oxford's Coronavirus Government Response Tracker (OxCGRT), we design both an adjusted probit model and a recursive bivariate probit model to test for endogeneity of Covid-19 infection. Estimations results show a bell curve between stringency and SAH degradation: a deleterious effect of restrictions at low levels of stringency up to a tipping point after which more stringent policies become protective. Covid-19 infection moderates this association. Depending on individuals' initial health, the effect of restrictions is uneven: highly stringent policies become damaging for individuals most likely to enter a vulnerabilization path, for whom the bell curve is thus inverted. Overall, this study shows clear patterns of association between policy stringency and perceived health among older Europeans, and highlights the potential trade-off between targeting as many people as possible, those in poor health or those on the edge of vulnerability. • The association between policy stringency and SAH degradation is a bell curve. • Strict restrictions only affect the SAH of people likely to become vulnerable. • Covid-19 infection moderates the deterioration of SAH caused by policy stringency. • The recursive bivariate probit model controls for endogeneity in infection. [ABSTRACT FROM AUTHOR]

The association between digitalization and individual health has attracted increasing attention from both scholars and policymakers. Existing research, however, has not agreed on whether digitization can improve health or reduce health inequality. The purpose of this study is to clarify whether and how the development of digitalization may be related to health and health disparities. We rely on China Family Panel Studies (CFPS) surveys from 2012 to 2018 to obtain a sample of 82,471 observations to explore the impact of digitalization on self-rated health and health inequality and its transmission mechanisms. The hypotheses are tested by Ordinary Least Squares Modeling. As expected, digitalization is significantly and positively correlated with self-rated health. Furthermore, the development of digitalization has led to a notable decrease in health inequality. The influencing mechanisms of digitalization include income, healthcare consumption and health behaviors. Both dimensions of digitalization—internet development and digital finance—generate significant effects and the effects of internet development are greater. This study is the first to systematically investigate the impact of digitalization development on health and health inequality. Our findings provide evidence for the health promotion theory by clarifying the benefits of digitalization in improving residents' health and reducing health inequality. Therefore, utilizing the tools of digitalization efficiently could be a focus of policymakers aiming to accomplish the SDGs' health targets. • This paper contributes to assess the health outcomes of digitalization in China. • Developing digitalization has greatly improved self-rated health of residents. • Also, digitalization has reduced health inequality between residents. • Digitalization can affect health through income, health consumption and behaviors. • Digitalization is a useful tool for policymakers to reach the SDGs' health goals. [ABSTRACT FROM AUTHOR]

Preference weights are widely used to score generic health states into utility indexes for estimation of quality adjusted life years (QALYs) and to aid health care funding decisions. To date, health state utilities are predominantly derived using stated preference methods based on decision utility. This paper tests an alternative and generates preference weights using experienced utility for children based on the Child Health Utility 9D (CHU9D) descriptive system. We estimate the relative values of the CHU9D health states with regard to experienced utility, where experienced utility is approximated by self-reported happiness. A nationally-representative longitudinal survey was used including 6090 Australian children aged 12–17 years surveyed over 2014–2018. The derived weights were then applied to calculate the utility decrements for a few common child health conditions. We found that the estimated utility decrements are largely similar to those estimated using the published CHU9D Australian adolescent weights based on decision utility, except for pain and depression. A smaller utility decrement for pain and a larger utility decrement for depression were indicated by experienced utility. We contribute to the literature by showing that using experienced utility methods to generate preference weights for health states is possible, and we discuss some important methodological challenges for future studies such as the impracticability of anchoring to 'dead' when utilizing experienced utility. • Established methods of valuing health states rely on decision utility. • We use experienced utility to generate preference weights for children. • The estimated decrements for health conditions are similar to published weights. • We see smaller impact for pain, larger for depression compared to published weights. • Feasibility is demonstrated using child experienced utility for preference weights. [ABSTRACT FROM AUTHOR]

Globally, there are 2 billion 'informal' workers, who lack access to social protection while facing profound health risks and socioeconomic exclusions. The informal economy has generated most jobs in Low and Middle-Income Countries (LMICs), but few studies have explored informal workers' complex health vulnerabilities, including in the face of climate change. This paper will discuss recent action-research in Indore (India), Harare, and Masvingo (Zimbabwe) with informal workers like vendors, waste-pickers, and urban farmers. We conducted qualitative interviews (N = 110 in India), focus group discussions (N = 207 in Zimbabwe), and a quantitative survey (N = 418 in Zimbabwe). Many informal workers live in informal settlements ('slums'), and we highlight the interrelated health risks at their homes and workplaces. We explore how climate-related threats—including heatwaves, drought, and floods—negatively affect informal workers' health and livelihoods. These challenges often have gender-inequitable impacts. We also analyse workers' individual and collective responses. We propose a comprehensive framework to reveal the drivers of health in the informal economy, and we complement this holistic approach with a new research agenda. Our framework highlights the socioeconomic, environmental, and political determinants of informal workers' health. We argue that informal workers may face difficult trade-offs, due to competing priorities in the face of climate change and other risks. Future interventions will need to recognise informal workers' array of risks and co-develop multifaceted solutions, thereby helping to avoid such impossible choices. We recommend holistic initiatives to foster health and climate resilience, as well as participatory action-research partnerships and qualitative, intersectional data-collection with informal workers. • Climate change is exacerbating many risks to informal workers' health and livelihoods. • WASH access may foster gender equality and decent work, while tackling heat stress. • Policymakers can partner with informal workers to create inclusive, multi-pronged interventions. [ABSTRACT FROM AUTHOR]