Evaluating complementaries among urban water, energy, infrastructure, and social Sustainable Development Goals in China.

Urban areas' performance in water, energy, infrastructure, and socio-economic sectors is intertwined and measurable through Sustainable Development Goals (SDGs) 6-13. Effective synergy among these is critical for sustainability. This study constructs an indicator framework that reflects progress towards these urban SDGs in China. Findings indicate underperformance in SDGs 8-11, suggesting the need for transformative actions. Through network analysis, the research reveals complementarities among these SDGs. Notably, the SDG space divides into socio-economic and ecological clusters, with SDG 6 (Clean Water and Sanitation) central to both. Additionally, SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure) act as bridges, while greater synergies exist between SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action). An in-depth view at the indicator-level shows a core-periphery structure, emphasizing indicators like SDG 6.2 (Wastewater Treatment Rate) and SDG 6.6 (Recycled Water Production Capacity per capita) as pivotal. This study confirms the urban SDG space's stability and predictiveness, underscoring its value in steering well-aligned policy decisions for sustainable growth.

Dyadic Appraisals of Family Decisions and Health Tasks in Midlife-Older Couples.

Shared appraisals and collaboration within couples play important roles in optimizing health. Less is known about concordance regarding collaboration, factors associated with concordance, and implications for health. Data from 2,761 couples from the Health and Retirement Study (2014/2016 and 2016/2018 waves) were examined to determine within-couple concordance in completion of two tasks (family decisions and medical forms). The majority of couples were concordant regarding who makes family decisions (69.7%) and who completes medical forms (64.4%); 62% agreed they make family decisions collaboratively versus 25.5% completing medical forms collaboratively. Concordance was significantly associated with greater marital support and longer marital duration. Concordance was not significantly associated with depressive symptoms 2 years later, but the link between concordance in making major family decisions and self-rated health differed by age and gender. Future research at the intersection of concordance and collaboration may shed important light on how older couples navigate tasks and decisions.

A comprehensive analysis of real options in solar photovoltaic projects: A cluster-based approach.

Solar photovoltaic (PV) projects are pivotal in addressing climate change and fostering a sustainable energy future. However, the complex landscape of renewable energy investments, characterized by high upfront costs, market uncertainties, and evolving technologies, demands innovative evaluation methods. The Real Options Approach has emerged as a powerful tool, offering strategic flexibility in decision-making under uncertainty. This paper comprehensively analyzes the application of real options for evaluating solar photovoltaic projects in 2008-2023. Analysis of document descriptors (author keywords, index keywords, and noun phrases extracted from titles and abstracts) reveals that the dominant research topics in the last ten years (2014-2023) include investment optimization, strategic analysis, energy policy, optimization of energy generation and investments in wind energy. These descriptors are used to analyze the evolution of research interests on a two-year basis and reveal the yearly evolution of the research topics. Finally, the concept of emergence is used to unveil emerging research trends, providing valuable insights for researchers and practitioners in the renewable energy sector. Ultimately, this work contributes to a deeper understanding of how real options analysis empowers decision-makers to make informed choices in advancing clean and sustainable energy solutions.

Taste or Health: The Impact of Packaging Cues on Consumer Decision-Making in Healthy Foods.

According to the theory of dietary regulation, consumers frequently encounter conflicts between healthiness and tastiness when selecting healthy foods. This study explores how packaging cue that highlight "tasty" versus "healthy" affect consumers' intentions to purchase healthy food. After an Implicit Association Test (IAT) confirmed a perceived lack of tastiness in health foods in the preliminary test, Study 1 analyzed pricing and packaging details of the top 200 most-popular items in each of the ten healthy food categories on a major online shopping platform. Results showed that products with taste-focused cues commanded higher prices, indicating stronger consumer acceptance of healthy foods marketed as delicious. To address the causality limitations of observational studies, Study 2 used an experimental design to directly measure the impact of these cues on purchase intentions and perceptions of energy, healthiness, and tastiness. Findings revealed that taste-focused cues significantly boosted purchase intentions compared to health-focused cues, although they also diminished the perceived healthiness of the products. Moreover, in the control group exposed to unhealthy food options, health-emphasized packaging also increased purchase intentions, indicating that consumers seek a balance between healthiness and tastiness, rather than prioritizing health alone. Study 3 further explored the impact of cognitive load over these cue influences, revealing a heightened inclination among consumers to purchase healthy products with taste-focused cue under high cognitive load state. These insights have direct implications for food packaging design, suggesting that emphasizing a balance of taste and health benefits can effectively enhance consumer engagement. The study, which conducted in China, also opens avenues for future research to explore similar effects, maybe in different cultural contexts, different consumer groups, and under varied cognitive conditions.

Knowledge, skills, and confidence gaps impacting treatment decision making in relapsed/refractory chronic lymphocytic leukemia and mantle cell lymphoma: a quantitative survey study in France, Germany, and the United States.

BACKGROUND: With recent advancements in the treatment of chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL), healthcare specialists may face challenges making treatment and management decisions based on latest evidence for the optimal care of patients with these conditions. This study aimed to identify specific knowledge, skills, and confidence gaps impacting the treatment of CLL and MCL, to inform future educational activities. METHODS: Hematologists and hemato-oncologists (HCPs, n = 224) from France (academic settings), Germany, and the United States (academic and community settings) responded to a 15-minute quantitative needs assessment survey that measured perceived knowledge, skills, and confidence levels regarding different aspects of treatment and management of CLL and MCL patients, as well as clinical case questions. Descriptive statistics (cross tabulations) and Chi-square tests were conducted. RESULTS: Four areas of educational need were identified: (1) sub-optimal knowledge of treatment guidelines; (2) sub-optimal knowledge of molecular testing to inform CLL/MCL treatment decisions; (3) sub-optimal skills when making treatment decisions according to patient profile (co-morbidities, molecular testing results); and (4) challenges balancing the risk of toxicities with benefits of treatment. Over one-third of the respondents reported skill gaps when selecting suitable treatment options and prescribing therapies and reported a lack in confidence to initiate and manage treatment. Larger gaps in knowledge of guidelines and skills in patient assessment were identified in MCL, compared to CLL. CONCLUSIONS: This study suggests the need for continuing medical education specifically to improve knowledge of treatment guidelines, and to assist clinicians in developing skills and confidence when faced with clinical decision-making scenarios of patients with specific comorbidities and/or molecular test results, for example, through case-based learning activities.

Estimating optimal decision trees for treatment assignment: The case of K > 2 treatment alternatives.

For many problems in clinical practice, multiple treatment alternatives are available. Given data from a randomized controlled trial or an observational study, an important challenge is to estimate an optimal decision rule that specifies for each client the most effective treatment alternative, given his or her pattern of pretreatment characteristics. In the present paper we will look for such a rule within the insightful family of classification trees. Unfortunately, however, there is dearth of readily accessible software tools for optimal decision tree estimation in the case of more than two treatment alternatives. Moreover, this primary tree estimation problem is also cursed with two secondary problems: a structural missingness in typical studies on treatment evaluation (because every individual is assigned to a single treatment alternative only), and a major issue of replicability. In this paper we propose solutions for both the primary and the secondary problems at stake. We evaluate the proposed solution in a simulation study, and illustrate with an application on the search for an optimal tree-based treatment regime in a randomized controlled trial on K = 3 different types of aftercare for younger women with early-stage breast cancer. We conclude by arguing that the proposed solutions may have relevance for several other classification problems inside and outside the domain of optimal treatment assignment.

Development and validation of a prognosis risk score model for neonatal mortality in the Amhara region, Ethiopia. A prospective cohort study.

BACKGROUND: A neonatal mortality prediction score can assist clinicians in making timely clinical decisions to save neonates' lives by facilitating earlier admissions where needed. It can also help reduce unnecessary admissions. OBJECTIVE: The study aimed to develop and validate a prognosis risk score for neonatal mortality within 28 days in public hospitals in the Amhara region, Ethiopia. METHODS: The model was developed using a validated neonatal near miss assessment scale and a prospective cohort of 365 near-miss neonates in six hospitals between July 2021 and January 2022. The model's accuracy was assessed using the area under the receiver operating characteristics curve, calibration belt, and the optimism statistic. Internal validation was performed using a 500-repeat bootstrapping technique. Decision curve analysis was used to evaluate the model's clinical utility. RESULTS: In total, 63 of the 365 neonates died, giving a neonatal mortality rate of 17.3% (95% CI: 13.7-21.5). Six potential predictors were identified and included in the model: anemia during pregnancy, pregnancy-induced hypertension, gestational age less than 37 weeks, birth asphyxia, 5 min Apgar score less than 7, and birth weight less than 2500 g. The model's AUC was 84.5% (95% CI: 78.8-90.2). The model's predictive ability while accounting for overfitting via internal validity was 82%. The decision curve analysis showed higher clinical utility performance. CONCLUSION: The neonatal mortality predictive score could aid in early detection, clinical decision-making, and, most importantly, timely interventions for high-risk neonates, ultimately saving lives in Ethiopia.

Main findings: This prognosis risk score for neonatal mortality tested in Ethiopia had high performance accuracy and the decision curve analysis showed increased clinical utility performance.Added knowledge: The tool developed here can aid healthcare providers in identifying high-risk neonates and making timely clinical decisions to save lives.Global health impact for policy and action: The findings have the potential to be applied in local contexts to identify high-risk neonates and make treatment decisions that could improve child survival rates.

Trade-offs and Synergies between Economic and Environmental Cocoa Farm Management Decisions.

Optimizing sustainability among smallholder farms poses challenges due to inherent trade-offs. In the study of organic and conventional cocoa smallholder farming in Ghana, 398 farms are assessed using the Food and Agriculture Organsation of the United Nations (FAO) Sustainability Assessment of Food and Agriculture systems (SAFA) Guidelines and Sustainability Monitoring and Assessment Routine (SMART)-Farm Tool. Organic farming exhibited synergies in environmental aspects (e.g., soil quality, energy efficiency) and between biodiversity conservation and risk management. Conventional farming showed potential vulnerabilities, including trade-offs with long-range investments (e.g., chemical inputs) and species diversity. Both systems demand tailored approaches for short-term economic and environmental sustainability, aligning with community-wide long-term goals. To mitigate trade-offs in conventional farming, smallholders should adopt practices like material reuse, recycling, and recovery within their operations.

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

A single-cell transcriptomic map of the developing Atoh1 lineage identifies neural fate decisions and neuronal diversity in the hindbrain.

Proneural transcription factors establish molecular cascades to orchestrate neuronal diversity. One such transcription factor, Atonal homolog 1 (Atoh1), gives rise to cerebellar excitatory neurons and over 30 distinct nuclei in the brainstem critical for hearing, breathing, and balance. Although Atoh1 lineage neurons have been qualitatively described, the transcriptional programs that drive their fate decisions and the full extent of their diversity remain unknown. Here, we analyzed single-cell RNA sequencing and ATOH1 DNA binding in Atoh1 lineage neurons of the developing mouse hindbrain. This high-resolution dataset identified markers for specific brainstem nuclei and demonstrated that transcriptionally heterogeneous progenitors require ATOH1 for proper migration. Moreover, we identified a sizable population of proliferating unipolar brush cell progenitors in the mouse Atoh1 lineage, previously described in humans as the origin of one medulloblastoma subtype. Collectively, our data provide insights into the developing mouse hindbrain and markers for functional assessment of understudied neuronal populations.

Predicting citations in Dutch case law with natural language processing.

With the ever-growing accessibility of case law online, it has become challenging to manually identify case law relevant to one's legal issue. In the Netherlands, the planned increase in the online publication of case law is expected to exacerbate this challenge. In this paper, we tried to predict whether court decisions are cited by other courts or not after being published, thus in a way distinguishing between more and less authoritative cases. This type of system may be used to process the large amounts of available data by filtering out large quantities of non-authoritative decisions, thus helping legal practitioners and scholars to find relevant decisions more easily, and drastically reducing the time spent on preparation and analysis. For the Dutch Supreme Court, the match between our prediction and the actual data was relatively strong (with a Matthews Correlation Coefficient of 0.60). Our results were less successful for the Council of State and the district courts (MCC scores of 0.26 and 0.17, relatively). We also attempted to identify the most informative characteristics of a decision. We found that a completely explainable model, consisting only of handcrafted metadata features, performs almost as well as a less well-explainable system based on all text of the decision.

"How long is life worth living for the horse?" A focus group study on how Austrian equine stakeholders assess quality of life for chronically ill or old horses.

BACKGROUND: Quality of life (QoL) provides a comprehensive concept underpinning veterinary decision-making that encompasses factors beyond physical health. It becomes particularly pertinent when seeking responsible choices for chronically ill or old horses that emphasise their well-being and a good QoL over the extension of life. How different stakeholders use the concept of QoL is highly relevant when considering the complexity of these decisions in real-life situations. METHODS: Seven focus group discussions (N = 39) were conducted to gain insights into how stakeholders assess and use equine QoL in veterinary care decisions for chronically ill and/or old horses. The discussions included horse owners (n = 17), equine veterinarians (n = 7), veterinary officers (n = 6), farriers (n = 4), and horse caregivers (n = 5). The combination of deductive and inductive qualitative content analysis of the group discussions focused on identifying both similarities and differences in the views of these groups regarding QoL for old and/or chronically ill horses. RESULTS: Findings show agreement about two issues: the importance of the individuality of the horse for assessing QoL and the relevance of QoL in making decisions about veterinary interventions. We identified differences between the groups with respect to three issues: the time required to assess QoL, stakeholders' contributions to QoL assessments, and challenges resulting from those contributions. While owners and caregivers of horses emphasised their knowledge of a horse and the relevance of the time they spend with their horse, the veterinarians in the study focused on the differences between their own QoL assessments and those of horse owners. In response to challenges regarding QoL assessments and decision-making, stakeholders described different strategies such as drawing comparisons to human experiences. CONCLUSIONS: Differences between stakeholders regarding equine QoL assessments contribute to challenges when making decisions about the care of chronically ill or old horses. The results of this study suggest that individual and collaborative reflection about a horse's QoL should be encouraged, for example by developing practicable QoL assessment tools that support relevant stakeholders in this process.

High early mortality after percutaneous liver biopsy in metastatic cancer: national analysis.

OBJECTIVE: The study aimed to assess outcomes in patients undergoing liver biopsy for metastatic cancer, focusing on mortality rates and chemotherapy following their biopsy. METHODS: Hospital Episode Statistics data from 2010 to 2019 identified 30 992 patients with metastatic cancer who underwent percutaneous liver biopsy. Primary outcomes included 14-day and 30-day mortality rates, as well as the proportion receiving chemotherapy within 6 months. RESULTS: 30 992 patients were studied (median age of 69 (IQR 59-74) years, 52% female). 28% underwent inpatient biopsy with 8% dying within 14 days and 26% within 30 days. Outpatient biopsies had lower mortality rates: 2.2% at 14 days and 8.6% at 30 days.30-day mortality was associated with: inpatient biopsy (OR 3.5 (95% CI 3.26 to 3.76)) and increasing comorbidity (Charlson score 1-4: 1.21 (95% CI 1.11 to 1.32)); but negatively with all ages under 70 (eg, for 18-29 years 0.35 (95% CI 0.20 to 0.63)) and biopsy at a radiotherapy centre (0.88 (95% CI 0.82 to 0.95)).46% of patients received chemotherapy within 6 months of biopsy (53% with outpatient biopsies but only 33% with inpatient biopsies). Receiving chemotherapy was associated with: all ages under 70 (eg, 18-29 years 3.3 (95% CI 2.62 to 5.30)), female sex (1.06 (95% CI 1.01 to 1.11)) and medium (1.13 (95% CI 1.04 to 1.22) and high (1.49 (95% CI 1.38 to 1.62)) volume liver biopsy providers; but negatively with inpatient biopsy (0.45 (95% CI 0.43 to 0.48)) and increasing comorbidity (Charlson score 1-4: 0.85 (95% CI 0.79 to 0.91)). CONCLUSIONS: Mortality rates following liver biopsy for metastatic cancer are notably higher among patients undergoing emergency inpatient procedures. Clinicians should carefully weigh the risks and benefits of biopsy in elderly, comorbid or poor performance status patients. Multidisciplinary approaches involving palliative care may aid in decision-making for these patients.

Public value judgments about the criteria for reimbursement of medicines in South Korea.

OBJECTIVES: This study quantified the public value (PV) of the criteria and sub-criteria in the current drug reimbursement systems in South Korea and examined sociodemographic factors that associated with PV. METHODS: The Analytic Hierarchy Process (AHP) was used to quantify the PVs of criteria and sub-criteria. We developed a questionnaire to generate pairwise comparison matrices among criteria and sub-criteria. From 27 March to 1 April 2023, we recruited 1,000 study participants using a quota sampling method stratified by age, sex, and region based on Korean census data. RESULTS: The PVs for the criteria were highest for clinical usefulness (28.5%), followed by cost-effectiveness (27.1%), budget impact (24.3%), and reimbursement in other countries (20.1%). The sociodemographic characteristics of the participants had a significant impact on the PVs of the criteria. Willingness to pay additional premiums for national health insurance was negatively associated with PV for clinical usefulness and cost-effectiveness and positively associated with PV for reimbursement in other countries. CONCLUSIONS: The public prioritized clinical usefulness and cost-effectiveness as the main criteria. However, the PVs of the criteria were divergent and associated with sociodemographic factors. Divergent public interests require an evidence-informed deliberative process for reimbursement decisions.

Introducing the Mellorater-The Five Domains Model in a Welfare Monitoring App for Animal Guardians.

When monitoring an animal's welfare, it helps to have comprehensive and day-to-day information about the animal's life. The goal is to ensure that animal guardians (carers, keepers, and owners) use such information to act in the animals' best interests. This article introduces the Mellorater, an animal welfare monitoring app based on the 2020 Five Domains Model. This framework provides a means of capturing comprehensive information about the world in which individual animals exist. The Mellorater asks animal guardians to rate their agreement with 18 statements covering any focal animal's nutrition, environment, health, and behavioural interactions using a five-point Likert scale. No specialist training is required other than following straightforward instructions on using the app, which are provided. The Mellorater is not proposed as a validated welfare auditing tool because it relies on reflective self-reporting and, thus, is vulnerable to the user's subjectivity. If users' subjectivity is stable over time, then the longitudinal data may be considered useful proxies for trends in quality of life. That said, it has the potential to be used by trained auditors if scientifically validated, species-specific indicators are applied. The Mellorater collects anonymous data and has been approved for a study to explore how the use of such scales may differ among guardians of different species and in different contexts. In this paper, we conduct the following: (1) summarise the app's purposes; (2) clarify its capabilities and limitations; and (3) invite animal welfare scholars, veterinarians, health and welfare professionals, and animal guardians to use it.

Design Decisions for Wearable EEG to Detect Motor Imagery Movements.

The objective of this study was to make informed decisions regarding the design of wearable electroencephalography (wearable EEG) for the detection of motor imagery movements based on testing the critical features for the development of wearable EEG. Three datasets were utilized to determine the optimal acquisition frequency. The brain zones implicated in motor imagery movement were analyzed, with the aim of improving wearable-EEG comfort and portability. Two detection algorithms with different configurations were implemented. The detection output was classified using a tool with various classifiers. The results were categorized into three groups to discern differences between general hand movements and no movement; specific movements and no movement; and specific movements and other specific movements (between five different finger movements and no movement). Testing was conducted on the sampling frequencies, trials, number of electrodes, algorithms, and their parameters. The preferred algorithm was determined to be the FastICACorr algorithm with 20 components. The optimal sampling frequency is 1 kHz to avoid adding excessive noise and to ensure efficient handling. Twenty trials are deemed sufficient for training, and the number of electrodes will range from one to three, depending on the wearable EEG's ability to handle the algorithm parameters with good performance.

Unveiling the adverse effects of artificial intelligence on financial decisions via the AI-IMPACT model.

There is considerable enthusiasm for the potential of artificial intelligence (AI) to improve financial well-being. Despite this enthusiasm, it is important to underscore AI's potential adverse effects on consumers' financial decisions. We introduce the AI-IMPACT model, a unifying theoretical framework for how AI can influence consumers' financial decisions. The model details how AI impacts the marketplace, affecting psychological processes and consumer traits core to financial decision-making (e.g., pain of payment, financial literacy). We use the AI-IMPACT model to illustrate one way AI can reduce financial well-being as its influence on the marketplace (e.g., facilitating biometric payment methods) decreases consumers' pain of payment, increasing spending. Lastly, we use the AI-IMPACT model to identify areas for future research at the intersection of AI and financial decision-making.

Risky effort.

Decision-making involves weighing up the outcome likelihood, potential rewards, and effort needed. Previous research has focused on the trade-offs between risk and reward or between effort and reward. Here we bridge this gap and examine how risk in effort levels influences choice. We focus on how two key properties of choice influence risk preferences for effort: changes in magnitude and probability. Two experiments assessed people's risk attitudes for effort, and an additional experiment provided a control condition using monetary gambles. The extent to which people valued effort was related to their pattern of risk preferences. Unlike with monetary outcomes, however, there was substantial heterogeneity in effort-based risk preferences: People who responded to effort as costly exhibited a "flipped" interaction pattern of risk preferences. The direction of the pattern depended on whether people treated effort as a loss of resources. Most, but not all, people treat effort as a loss and are more willing to take risks to avoid potentially high levels of effort.

Exploring culinary diversity to enhance Mediterranean diet adherence: A randomized controlled trial.

We Investigated how promoting diverse, healthy food options affects long-term dietary choices. We hypothesized that encouraging exploration of nutritious plant-based foods would lead to lasting improvements in diet. Participants (N = 211) were randomly assigned into two groups for a 6-week intervention: The fixed menu group was given the same large menu every week, while the changing menu group received a new small menu each week. At the end of the intervention both groups were exposed to the same menu suggestions. Food diversity evaluation was based on weekly reports collected during the intervention. Self-reported adherence to Mediterranean diet components was assessed using the I-MEDAS screener. The proportion of plant-based foods in participants' diets was estimated using a 0-100% scale based on self-report. Both items were evaluated using online questionnaires given to participants at baseline, at the end of the intervention, as well as three and six months after the intervention concluded. Results mean(SD) demonstrated that participants in fixed menu group explored a significantly wider array of items 26.33(11.64) than those in the changing menus group [19.79(10.29), t(202) = 4.25, p < 0.001, Cohen's d = 0.60]. A repeated measures analysis of covariance rmANCOVA revealed that short-term increase in I-MEDAS and PBD score were noted in both groups; however, only participants with the fixed menu sustained this increase at months follow-up [diff = 1.50, t(132) = 4.50, p < 0.001 Our findings suggest that manipulating the rate of exposure to food suggestions may affect overall dietary variety. It seems that early presentation with options may increase overall dietary variety and may even support longer-term habits. This study contributes to developing effective interventions and highlights the challenge of promoting exploratory behavior in nutrition.