Support Recovery Research Articles

Voices to be heard: Understanding family perspectives in forensic care trajectories.

De-institutionalization of psychiatric care has greatly increased the role of family members in the recovery pathways of Persons labeled as Not Criminally Responsible (PNCR). However, the role of family members in supporting PNCR in forensic psychiatric care remains understudied. Scarce evidence indicates that PNCR have to deal with stigma and endure specific burdens (i.e., symptom-specific, financial, social, and emotional). Recovery-focused research showed that recovery in both persons with a severe mental illness and family members develop in parallel with each other and are characterized by similar helpful principles (e.g., hope and coping skills). As such, the recovery pathways of PNCR often goes hand in hand with the recovery pathway of their family members. During the family recovery process, family members often experience not being listened to or being empowered by professionals or not being involved in the decision-making process in the care trajectory of their relative. Therefore, the aim of this study is to capture how family members experience the care trajectories of their relatives, more specifically by looking at family recovery aspects and personal advocacy of family members. Semi-structured interviews were conducted with 21 family members of PNCR from 14 families. A thematic analysis confirms that family members suffer from stigma and worry significantly about the future of their relative. Regarding the care trajectory of PNCR, family members experienced barriers in multiple domains while trying to support their relative: involvement in care and information sharing, visiting procedures, transitions between wards, and the psychiatric and judicial reporting by professionals. In addition, family members emphasized the importance of (social) support for themselves during the forensic psychiatric care trajectories and of a shared partnership. These findings tie in with procedural justice theory as a precondition for family support and family recovery within forensic psychiatric care.

Synthesis of UiO-66 series metal–organic framework composites and the adsorption effect on gallium

It is an urgent problem to alleviate the contradiction between the supply and demand of gallium (Ga) resources and improve the extraction and utilization efficiency of Ga resources. The GO/UiO-66-NH2 composite adsorbent was synthesized by hydrothermal synthesis of graphene oxide (GO) and UiO-66-NH2 series metal–organic skeleton, which was employed for efficient recovery and extraction of Ga in solution. The morphology, structure, elemental composition, and chemical properties of the composite adsorbent were studied by a series of batch experiments, respectively. The effects of the amount of adsorbent, adsorption time, initial concentration, and pH value of the solution on the adsorption effect were investigated. Results found that the adsorption mechanism of Ga by 4-GO/UiO-66-NH2 is mainly ion exchange forming chelate, accompanied by electrostatic interaction. The optimized adsorption conditions for reaching the saturated adsorption capacity of 661.67 mg/g Ga were found to be pH 9 and the temperature 313 K. The results of thermodynamic study pointed out the adsorption of Ga is spontaneous and feasible. The aforementioned results demonstrate that GO/UiO-66-NH2 composite adsorbent has strong adsorption performance and application potential for the recovery and exploitation of Ga, offering practical and theoretical support for the future development and utilization of Ga resources.

Construction of 3D printable Pickering emulsion gels using complexes of fiber polysaccharide-protein extracted from Haematococcus pluvialis residues and gelatin for fat replacer

Structural fat replacers that can meet the requirements of various forms of reduced-fat foods are essential for the application of fat replacement, and emulsion gels emerge as a potential alternative for fat replacers. In this study, fiber polysaccharide-protein extracted from Haematococcus pluvialis residues (FPHRs) formed FPHRs-Gelatin complexes with gelatin. Fourier transformed infrared spectroscopy confirmed the formation of hydrogen bonds between FPHRs and gelatin, the secondary structure order of the protein was enhanced. Three-phase contact angle determination showed that the contact angle of FPHRs-Gelatin complexes was 104°–114°. By exploring the effect of FPHRs concentration, gelatin concentration, and oil-water ratio on the emulsion gels, the Pickering emulsion gels were formed when the FPHRs and gelatin concentration were 2 wt% and 3 wt%, respectively. What's more, these emulsion gels had shear thinning phenomenon, low frequency dependence as well as excellent thixotropy when the oil-water ratio was 0.6, which indicated that they had strong structural support and great recovery capacity to be used as 3D printed materials. The 3D objects printed by the FPHRs-Gelatin complexes emulsion gels had superior print resolution and shape fidelity. This study provides a new type of emulsion gel with structure that can be used as an ideal fat replacer in food processing, the construction of a new 3D emulsion gel material is of great significance for broadening the applications of gelatin-based gels and microalgal food.

In her own words: a phenomenological analysis of stories told by female service members and veterans after traumatic brain injury

Purpose Given the majority of Service Members and Veterans (SMV) who have sustained a traumatic brain injury (TBI) are male, the female experience with TBI has not been captured in the general understanding of TBI. To improve understanding of the experience of female SMV after TBI utilizing a qualitative phenomenological approach on stories as told by female SMV. Materials and methods Ten female SMV participated in storytelling workshops and created video stories documenting their personal experience with TBI. Workshops were hosted by the VA Palo Alto Health Care System Polytrauma System of Care (VAPAHCS PSC). A grounded thematic analysis was conducted on the video stories. Results Three common content themes emerged from all the stories: (1) negative psychological and emotional impacts of TBI, (2) acceptance and healing process associated with recovery, and (3) military contexts. Negative psychological and emotional impacts included intrapersonal impacts such as negative emotions, suicidal ideation, and dealing with cognitive and physical challenges, and interpersonal impacts in relationships and loss of independence. Notably, all the stories acknowledged an acceptance and healing process, characterized by several subthemes including motivational factors, TBI education, spirituality, and advocacy work. Lastly, each story mentioned military context, highlighting the unique experience within this population. Conclusions This phenomenological examination adds evidence-based understanding to the experience after TBI among female SMV. Each story uncovered nuanced and multifaceted issues that women experience in their TBI recovery. Our findings provide context to guide future intervention on the care, support, and TBI recovery for the female SMV population.

Nested population structure of threatened boreal caribou revealed by network analysis

Delineating relevant local populations of widely distributed species is a common challenge in conservation ecology. Caribou and reindeer (Rangifer tarandus) are in general decline throughout their global range, despite ongoing conservation efforts. In Canada, recovery actions for the threatened boreal population of woodland caribou (Rangifer tarandus caribou) are stratified by ‘local population units’ (LPUs) on ranges distributed across 2.4 × 10 km2 of the species’ geographic range. To estimate local population dynamics, LPUs are assumed to be geographically closed, though supporting evidence varies widely. We assembled an exceptionally large database of GPS telemetry locations (891,306 telemetry days, 1998–2020) from 1586 adult female caribou across the 19 northwesternmost LPUs. We generated a many-to-many Gaussian Bayesian Network to identify candidate local populations at range-level extents, as well as subpopulations, termed ‘communities’ in network analysis. We detected local population boundaries that in some cases were consistent with accepted LPUs and consistent with the assumption of geographic closure. In other cases, local population boundaries did not map well to currently delineated LPUs. Several communities at smaller spatial extents were consistent with expert and local knowledge of caribou movements and support recovery planning and actions “stepped down” from entire ranges. Evidence consistent with population fragmentation was confirmed along the southern and southwestern boundaries of the species’ geographic range within the study area, as were more continuous distributions confirmed to the north. We suggest that network analysis can help to inform conservation planning for boreal caribou and other wide-ranging species that would benefit from data-driven characterizations of multiscale population spatial structure.

Increases in China’s wind energy production from the recovery of wind speed since 2012

China has realized a 56-fold increase in installed wind capacity, from 5.9 GW in 2007 to 328 GW in 2021. In addition to increasing installed capacity, plans to substantially increase wind energy production for climate change mitigation also depend on future wind speeds, which strongly influences the efficiencies of installed turbines within individual wind farms. A reversal in globally decreasing wind speeds over several decades has been reported previously. However, subsequent studies using other data sources reported only a slight increase or no reversal in China. These uncertainties regarding China’s wind energy production hamper estimates of wind energy production potential. Here, our analysis of quality-controlled wind speed measurements from in-situ stations shows that the wind speed decline in China reversed significantly since 2012 (P < 0.001), but with substantial spatio-temporal variability. We further estimated the capacity factor (CF) growth and the wind power gain solely associated with the changes in wind speed ranges from 31.6 to 56.5 TWh yr−1 based on the 2019 installed capacity. This estimate explains 22.0%–39.3% of the rapid increase in wind generation CF in China during 2012–2019. The result implies that the site selection of wind farms should consider both current wind situation and future wind speed trends. Further studies are needed to understand the driving factor of wind speed recovery in support of the wind energy industry.

On the Support Recovery of Jointly Sparse Gaussian Sources via Sparse Bayesian Learning

In this work, we provide non-asymptotic, probabilistic guarantees for successful recovery of the common nonzero support of jointly sparse Gaussian sources in the multiple measurement vector (MMV) problem. The support recovery problem is formulated as the marginalized maximum likelihood (or type-II ML) estimation of the variance hyperparameters of a joint sparsity inducing Gaussian prior on the source signals. We derive conditions under which the resulting nonconvex constrained optimization perfectly recovers the nonzero support of a joint-sparse Gaussian source ensemble with arbitrarily high probability. The support error probability decays exponentially with the number of MMVs at a rate that depends on the smallest restricted singular value and the nonnegative null space property of the self Khatri-Rao product of the sensing matrix. Our analysis confirms that nonzero supports of size as high as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(m^{2})$ </tex-math></inline-formula> are recoverable from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> measurements per sparse vector. Our derived sufficient conditions for support consistency of the proposed constrained type-II ML solution also guarantee the support consistency of any global solution of the multiple sparse Bayesian learning (M-SBL) optimization whose nonzero coefficients lie inside a bounded interval. For the case of noiseless measurements, we further show that a single MMV is sufficient for perfect recovery of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -sparse support by M-SBL, provided all subsets of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k + 1$ </tex-math></inline-formula> columns of the sensing matrix are linearly independent.

Study Phenomenology Of Wound Care In Diabetic Foot Ulcer (DFU) Patients With Theory Of Planned Behavior Approach

Background: Diabetic Foot Ulcer (DFU) is one of the most common complications in patients with type 2 Diabetes Mellitus, so it is necessary to carry out continuous wound care. The Theory of Planned Behavior (TPB) is a theory of behavior that can be implemented in wound care in the study of attitudes and beliefs, subjective norms, and perceived control. Objective: This study aims to determine the behavior of wound care in DFU patients based on the Theory Of Planned Behavior (TPB). Method: Qualitative study with a phenomenological approach with a total of 14 participants obtained through purposive sampling. Data was collected through in-depth interviews and analyzed using qualitative thematic analysis. Results: The results of the study found that wound care was carried out because they had a positive attitude and belief in wound healing if they were treated regularly according to the program, received emotional support, motivation for healing for the family, family support, and control, as well as convenience and quick treatment in health services. Conclusion: This finding recommends for health professionals that a positive attitude and belief in recovery in patients, support and motivation to recover, family control, and professional health services can change the behavior of patients to carry out DFU treatment regularly.

Probing political paradox: Urban expansion, floods risk vulnerability and social justice in urban Africa

ABSTRACT Urban managers in sub-Saharan Africa have recently come under intense pressure to prepare for and adapt to the footprints of rapid peri-urbanization and increased climate-related risks. Addressing spatial planning integral with the urban expansion is not only because climate variability is becoming more prominent. Further, within peri-urban zones, people most often live and work in physical areas of hazard that are commensurate with their economic stability. This makes the need for adaptation amidst inadequate resources imperative. These concerns find expression at the local level, where stakeholders’ priorities focus on the gap between adaptation needs and existing adaptation efforts. Drawing insights from our study in Accra, which combines the perspectives and experiences of practitioners, academics, and citizens, we show how decisions constructed around flood vulnerabilities, people’s actions, and planning processes are seldom neutral. We infer how prioritizing efforts to adapt to floods may privilege some residents and compromise others’ support, agency, and capacities to recover. We call for increased attention to how city authorities can creatively move urban planning toward more informed, inclusive, and supportive recovery visions in response to the consolidation of urban peripheries and increased climate exacerbated flooding in the quest for social justice for all.

Distributed Decoding From Heterogeneous 1-Bit Compressive Measurements

We develop a communication-efficient distributed estimation for the 1-bit compressive sensing where unknown sparse signals are coded into binary measurements with noises and sign flips. We allow for distinctive sign-flipped probabilities and intensities of noises for measurements collected at different nodes, which raises a heterogeneity issue. We suggest a distributed algorithm through penalized least squares to recover sparse signals. This algorithm is computationally very efficient with only gradient information communicated. The resulting distributed estimate converges after a single iteration even when a lousy initial estimate is provided, and achieves a nearly oracle rate after a constant number of iterations. We prove that, under some mild conditions, with high probability, the distributed estimate approximates the underlying true sparse signal with precision δ after a finite number of iterations, as long as the total sample size N satisfies ( s log p ) / ( δ 2 N ) = O ( 1 ) , where p is the dimension and s is the number of nonzero elements of the underlying true sparse signals. We establish statistical guarantee for support recovery. Extensive experiments are provided to illustrate the effectiveness of our proposed distributed algorithm. Supplementary materials for this article are available online.

Twitter data from the 2019–20 Australian bushfires reveals participatory and temporal variations in social media use for disaster recovery

Social media platforms have proved to be vital sources of information to support disaster response and recovery. A key issue, though, is that social media conversation about disasters tends to tail off after the immediate disaster response phase, potentially limiting the extent to which social media can be relied on to support recovery. This situation motivates the present study of social media usage patterns, including who contributes to social media around disaster recovery, which recovery activities they contribute to, and how well that participation is sustained over time. Utilising Twitter data from the 2019–20 Australian bushfires, we statistically examined the participation of different groups (citizens, emergency agencies, politicians and others) across categories of disaster recovery activity such as donations & financial support or mental health & emotional support, and observed variations over time. The results showed that user groups differed in how much they contributed on Twitter around different recovery activities, and their levels of participation varied with time. Recovery-related topics also varied significantly with time. These findings are valuable because they increase our understanding of which aspects of disaster recovery currently benefit most from social media and which are relatively neglected, indicating where to focus resources and recovery effort.

Supporting COVID-19 elective recovery through scalable wait list modelling: Specialty-level application to all hospitals in England.

The recovery of elective waiting lists represents a major challenge and priority for the health services of many countries. In England’s National Health Service (NHS), the waiting list has increased by 45% in the two years since the COVID-19 pandemic was declared in March 2020. Long waits associate with worse patient outcomes and can deepen inequalities and lead to additional demands on healthcare resources. Modelling the waiting list can be valuable for both estimating future trajectories and considering alternative capacity allocation strategies. However, there is a deficit within the current literature of scalable solutions that can provide managers and clinicians with hospital and specialty level projections on a routine basis. In this paper, a model representing the key dynamics of the waiting list problem is presented alongside its differential equation based solution. Versatility of the model is demonstrated through its calibration to routine publicly available NHS data. The model has since been used to produce regular monthly projections of the waiting list for every hospital trust and specialty in England.Supplementary InformationThe online version contains supplementary material available at10.1007/s10729-022-09615-2.

Machine Learning Empowered Spectrum Sensing Under a Sub-Sampling Framework

Compressive sensing (CS) is a technique frequently adopted in wireless communications. By utilizing CS, a receiver could sense the state of channels with sub-Nyquist analog to digital converters when signals are sparse. Traditional CS methods struggle with non-sparse signals due to their intrinsic sparsity assumption. Therefore, we propose using deep learning (DL) to solve the vector support recovery problem with channels’ high occupancy. The simulation results show that the proposed CS framework powered by DL can perform better than a traditional CS analytical benchmark, both in high and low channel occupation regions. We also observe that the ML can work under a lower sampling rate than traditional CS methods. To process data sampled with high channel numbers, a divide and conquer tactic is implemented.

A sparse additive model for high-dimensional interactions with an exposure variable

A conceptual paradigm for onset of a new disease is often considered to be the result of changes in entire biological networks whose states are affected by a complex interaction of genetic and environmental factors. However, when modeling a relevant phenotype as a function of high dimensional measurements, power to estimate interactions is low, the number of possible interactions could be enormous and their effects may be non-linear. A method called sail for detecting non-linear interactions with a key environmental or exposure variable in high-dimensional settings which respects the strong or weak heredity constraints is proposed. It is proven that asymptotically, sail possesses the oracle property, i.e., it performs as well as if the true model were known in advance. A computationally efficient fitting algorithm with automatic tuning parameter selection, which scales to high-dimensional datasets is proposed. Simulation results show that sail outperforms existing penalized regression methods in terms of prediction accuracy and support recovery when there are non-linear interactions with an exposure variable. sail is applied to detect non-linear interactions between genes and a prenatal psychosocial intervention program on cognitive performance in children at 4 years of age. Results show that individuals who are genetically predisposed to lower educational attainment are those who stand to benefit the most from the intervention. The proposed algorithms are implemented in an R package available on CRAN (https://cran.r-project.org/package=sail).

Cooperative Extension’s Capacity to Address Food Insecurity by Supporting Food Recovery Organizations

Virginia Cooperative Extension (VCE) educators are uniquely positioned to support the food recovery organizations (FROs) which address hunger-related needs resulting from food insecurity. Based on an online survey to measure how VCE educators have engaged with FROs and their experiences, respondents who previously supported FROs did so across multiple programming areas, and those who had not indicated an interest while also experiencing barriers. Respondents also reported the need for context- and audience-specific resources particular to the spectrum of food recovery. Addressing barriers and resource needs through a transdisciplinary eXtension Food Recovery Community of Practice may support educators in doing this work.

Distributed ranking-based resource allocation for sporadic M2M communication

This work proposes a novel scheme for distributed ranking-based and contention-free resource allocation in large-scale machine-to-machine (M2M) communication networks. We partition a network of N devices into disjoint clusters based on service type, and assign to each cluster a cluster-specific signature for active cluster members to indicate their active status. The devices in each cluster are totally ordered in some a priori-known manner, which gives rise to an active ranking of active cluster members. In order to tackle complexity issues in large-scale M2M networks with a massive number of devices, we propose a distributed resource allocation scheme using the framework of compressed sensing (CS), which mainly consists of three phases: (i) In a full-duplex acquisition phase, the devices transmit their cluster-specific signatures simultaneously and the network activation pattern is collected in a distributed manner. (ii) The base station detects the active clusters and the number of active devices per cluster using block sketching, and allocates resources to each active cluster accordingly. (iii) Each active device determines its active ranking in the cluster and accesses a specific resource according to the ranking position. By exploiting the sparsity in the activation pattern of the M2M devices, the proposed scheme is formulated as a CS support recovery problem for a particular binary block-sparse signal xin {mathbb{B}}^N – with block sparsity K_{B} and in-block sparsity K_{I} over block size d. Our analysis shows that the proposed scheme efficiently reduces the signature length to mathcal {O}(max {K_{B}log N, K_{B}K_{I}log d}) and achieves less computational complexity of mathcal {O}(dK_{I}^{2}+frac{N}{d}log N) compared with standard CS algorithms. Moreover, numerical results suggest strong robustness of the proposed scheme under noisy conditions.

A communication-efficient method for ℓ0 regularization linear regression models

We propose a communication-efficient distributed learning algorithm for high-dimensional sparse linear regression models in the scenario that the data are stored across multiple machines. Our approach is a distributed version of the SDAR [Huang J, Jiao Y, Liu Y, et al. A constructive approach to l0 penalized regression. J Mach Learn Res. 2018;19(1):403–439] method for solving the KKT system of the regularized least squares. At each step of the proposed method, the reduced least squares are solved by the steepest descent method, which only needs to calculate the gradient vectors on each node machine and communicate them instead of the data. We refer to this as SD-SDAR for brevity. Under some regular conditions, we obtain the sharp and error bounds for the solution sequences generated by SD-SDAR algorithm. We investigate the computational complexity and show that the number of rounds of communications are bounded by and , respectively, where J is the number of important predictors, R is the relative magnitude of the non-zero target coefficients, N is the total sample size and p is the dimension of covariates. Simulation studies illustrate that SD-SDAR outperforms some existing distributed methods in accuracy, efficiency and support recovery.

Iron removal and titanium dioxide support recovery from spent V2O5-WO3/TiO2 catalyst

Iron seriously affects the quality of TiO2 support recovered from spent selective catalytic reduction (SCR) catalyst. In this paper, the iron removal and TiO2 support recovery from spent SCR catalyst were studied. The experiment results suggested that iron exists on the surface and bulk structure of a spent SCR catalyst. The iron deposited on the surface of the spent SCR catalyst was removed by ultrasonic cleaning. The iron remaining in the bulk structure of the spent SCR catalyst was primarily Fe(III) and was adequately removed by NaOH activation and acid washing. The NaOH activation process considerably increased the specific surface area of the spent SCR catalyst. Subsequently, iron was efficiently dissolved through the synergistic effect of sulfuric acid and ascorbic acid. Sulfuric acid dissolved iron in the form of Fe3+, and ascorbic acid promoted iron dissolution by reducing Fe3+ to Fe2+. After the recovery process, 97.1% of the iron in the spent SCR catalyst was removed, and anatase TiO2 was recovered from the spent SCR catalyst. The recovered anatase TiO2 can be used as support material to produce fresh SCR catalyst.

Predictive Indicators for Complications of Proximal Humerus Fractures Treated with Locking Plate or Intramedullary Nail Fixation.

ObjectiveThe purpose of this study was to evaluate the best placement of calcar screws in proximal humerus fracture surgeries.MethodsThis retrospective cohort study included clinical and radiographic outcomes of 98 patients treated with proximal humerus fracture surgeries between January 2017 and June 2020. Demographic data of patients were obtained from medical records. The surgical and radiographic results were also collected: operation time, blood loss, time to surgery, fibular allograft, disruption of medial region hinge, Neer classification, and recovery of medial support. Patients were allocated into two groups: the locking plate group (n = 65) and the intramedullary nail group (n = 33). In this study, we proposed new predictive indicators, named horizontal ratio (HR) and vertical ratio (VR), to quantify the placement of calcar screws in these two groups. A receiver operating characteristic (ROC) analysis was conducted to display the accuracy of these indicators. Shoulder activity, visual scale analog (VAS) score, and Constant score were performed to evaluate postoperative clinical outcomes at 1 year follow‐up.ResultsIn the multivariate logistic regression analysis, only time to surgery and effective medial support were considered statistically significant factors of postoperative complications (p < 0.05). Significant differences were observed between medial support and postoperative complications both in the locking plate group and the intramedullary nail group (p < 0.05). Only the vertical ratio of locking plate (VRLP) was a statistically significant predictor of postoperative complications (p < 0.05). The area under curve was calculated to assess the predictive value of VRLP, which came to 0.84. In addition, a ROC analysis found quantifiable thresholds of the VR was 0.1713 as measures to avoid postoperative complications in the locking plate fixation.ConclusionIn locking plate fixation, the incidence of postoperative complications increased significantly when the VR of calcar screws greater than 0.1713, which was beneficial to surgeons to place calcar screws.

Valuing subjective well-being benefits from leisure activities: informing post-Covid public funding of arts, culture and sport

ABSTRACT The Covid-19 pandemic has significantly impacted the leisure sector as lockdowns and social distancing resulted in a temporary shutdown. Using large-scale UK social survey data from two waves of Understanding Society, we employ the life satisfaction valuation method to provide estimates of monetary values of the subjective well-being benefits of leisure activities. We find that well-being benefits to life satisfaction from arts events, visiting museums and moderate sports have a significant monetary value. The value of some leisure activities to domains of satisfaction is higher compared with life satisfaction. The value to leisure satisfaction is particularly high, especially arts activities. Well-being benefits of moderate and mild sport are particularly high for health satisfaction. Meanwhile, activities which involve social interaction, including arts events and moderate sport have greater relevance to job satisfaction. These findings evidence the value of leisure activities, informing arguments for public funding to support and aid recovery in the sector.