Principal Features of Industry-Funded Trials that Posted Informed Consent Forms on ClinicalTrials.gov: a Cross-Sectional Analysis.

We aim to characterize industry-funded trials that have posted the informed consent forms (ICFs), and to assess whether the role played by industry as 'sponsor' or 'collaborator' could impact several relevant variables. A cross-sectional study was conducted on ClinicalTrials.gov on all industry-funded trials registered on or before 25 February 2023. We registered types of intervention, current recruitment status, design, enrollment, and countries involved. For trials with special interest to potential participants and investigators and/or clinicians an analysis of the role played by industry as 'sponsor' or 'collaborator' was performed. Of 116,281 industry-funded trials registered, 741 (0.6%) had posted ICFs. Most of these trials were categorized as 'completed' (n = 408) or 'terminated' (n = 107). The review of a sample of 359 trials showed that most were on drugs and/or biologics (59%), were randomized (51%), conducted exclusively in the USA (72%), and had posted results (79%), protocols (92%), and statistical analysis plans (SAPs) (89%). Trials in which industry participated as 'collaborator' were significantly more likely to post ICFs when trials were in the 'active, not recruiting' phase (OR 4.70, 99.71% CI 1.59-13.9, p < 0.001) than industry-sponsored trials. This was also the case when assessing drugs/biologics (OR 2.64, 99.71% CI 1.25-5.58, p < 0.001). Conversely, companies acting as 'sponsors' were significantly more likely to post ICFs with trials assessing devices, radiation interventions and/or diagnostic tests (OR 0.37, 99.71% CI 0.17-0.79, p < 0.001) than when participating as 'collaborators'. While industry-funded trials rarely post ICFs, when they do, they are highly compliant with transparency requirements. Regulations and ethics codes should consider requiring posting of protocols, SAPs, and ICFs for all clinical trials, regardless the type of sponsor.

The efficiency of Raphia hookeri adsorbent in indigo carmine dye removal: Economy depth via chemometrics.

The remediation of dye pollutants remains a concern in contemporary water management practices. Hence, the need for efficient and cost-effective techniques for dye removal from wastewater. In this study, the epicarp of Raphia hookeri fruits was treated with orthophosphoric acid for enhanced porosity and efficiency in the uptake of Indigo carmine dye (ICD). Treated Raphia hookeri fruit waste (RHPW) presented morphologically distributed pores as well as high porosity with Branneur-Emmet-Teller (BET) surface area of 945.43 m2/g. RHPW displayed functional groups suitable for adsorption. The maximum ICD uptake was observed at pH 5 while the maximum uptake (qmax) was 20.41 mg/g in the concentration range of 2-10 mg/L. Freundlich isotherm and Pseudo-second order kinetics well-described equilibrium and kinetics data respectively. This indicated a multilayered adsorption. The Dubinin-Radushkecich model energy value was 40.82 kJ/mol, indicating chemical adsorption. The ridge regression, the Lasso and the Elastic net statistical models were used to establish a positive relationship between the various adsorption operational parameters studied. Lasso provided the best result based on the estimated mean squared error. The RHPW-ICD adsorption system was more favorable at room temperature, as the removal efficiency decreased with temperature rise. The findings established Raphia hookeri fruit epicarp as an economical and sustainable precursor for the preparation of potent adsorbent for Indigo carmine dye removal. This can find possible application in wastewater treatment.

Socioeconomic and sociodemographic correlations to COVID-19 variability in the United States in 2020.

The COVID-19 pandemic provided an additional spotlight on the longstanding socioeconomic/health impacts of redlining and has added to the myriad of environmental justice issues, which has caused significant loss of life, health, and productive work. The Centers for Disease Control and Prevention (CDC) reports that a person with any selected underlying health conditions is more likely to experience severe COVID-19 symptoms, with more than 81% of COVID-19-related deaths among people aged 65 years and older. The effects of COVID-19 are not homogeneous across populations, varying by socioeconomic status, PM2.5 exposure, and geographic location. This variability is supported by analysis of existing data as a function of the number of cases and deaths per capita/1,00,000 persons. We investigate the degree of correlation between these parameters, excluding health conditions and age. We found that socioeconomic variables alone contribute to ~40% of COVID-19 variability, while socioeconomic parameters, combined with political affiliation, geographic location, and PM2.5 exposure levels, can explain ~60% of COVID-19 variability per capita when using an OLS regression model; socioeconomic factors contribute ~28% to COVID-19-related deaths. Using spatial coordinates in a Random Forest (RF) regressor model significantly improves prediction accuracy by ~120%. Data visualization products reinforce the fact that the number of COVID-19 deaths represents 1% of COVID-19 cases in the US and globally. A larger number of democratic voters, larger per-capita income, and age >65 years is negatively correlated (associated with a decrease) with the number of COVID cases per capita. Several distinct regions of negative and positive correlations are apparent, which are dominated by two major regions of anticorrelation: (1) the West Coast, which exhibits high PM2.5 concentrations and fewer COVID-19 cases; and (2) the middle portion of the US, showing mostly high number of COVID-19 cases and low PM2.5 concentrations. This paper underscores the importance of exercising caution and prudence when making definitive causal statements about the contribution of air quality constituents (such as PM2.5) and socioeconomic factors to COVID-19 mortality rates. It also highlights the importance of implementing better health/lifestyle practices and examines the impact of COVID-19 on vulnerable populations, particularly regarding preexisting health conditions and age. Although PM2.5 contributes comparable deaths (~7M) per year, globally as smoking cigarettes (~8.5M), quantifying any causal contribution toward COVID-19 is non-trivial, given the primary causes of COVID-19 death and confounding factors. This becomes more complicated as air pollution was reduced significantly during the lockdowns, especially during 2020. This statistical analysis provides a modular framework, that can be further expanded with the context of multilevel analysis (MLA). This study highlights the need to address socioeconomic and environmental disparities to better prepare for future pandemics. By understanding how factors such as socioeconomic status, political affiliation, geographic location, and PM2.5 exposure contribute to the variability in COVID-19 outcomes, policymakers and public health officials can develop targeted strategies to protect vulnerable populations. Implementing improved health and lifestyle practices and mitigating environmental hazards will be essential in reducing the impact of future public health crises on marginalized communities. These insights can guide the development of more resilient and equitable health systems capable of responding effectively to similar future scenarios.

Statistical analysis plan for the TRANSLATE (TRANSrectal biopsy versus Local Anaesthetic Transperineal biopsy Evaluation of potentially clinically significant prostate cancer) multicentre randomised controlled trial.

BACKGROUND: The TRANSLATE (TRANSrectal biopsy versus Local Anaesthetic Transperineal biopsy Evaluation) trial assesses the clinical and cost-effectiveness of two biopsy procedures in terms of detection of clinically significant prostate cancer (PCa). This article describes the statistical analysis plan (SAP) for the TRANSLATE randomised controlled trial (RCT). METHODS/DESIGN: TRANSLATE is a parallel, superiority, multicentre RCT. Biopsy-naïve men aged ≥ 18 years requiring a prostate biopsy for suspicion of possible PCa are randomised (computer-generated 1:1 allocation ratio) to one of two biopsy procedures: transrectal (TRUS) or local anaesthetic transperineal (LATP) biopsy. The primary outcome is the difference in detection rates of clinically significant PCa (defined as Gleason Grade Group ≥ 2, i.e. any Gleason pattern ≥ 4 disease) between the two biopsy procedures. Secondary outcome measures are th eProBE questionnaire (Perception Part and General Symptoms) and International Index of Erectile Function (IIEF, Domain A) scores, International Prostate Symptom Score (IPSS) values, EQ-5D-5L scores, resource use, infection rates, complications, and serious adverse events. We describe in detail the sample size calculation, statistical models used for the analysis, handling of missing data, and planned sensitivity and subgroup analyses. This SAP was pre-specified, written and submitted without prior knowledge of the trial results. DISCUSSION: Publication of the TRANSLATE trial SAP aims to increase the transparency of the data analysis and reduce the risk of outcome reporting bias. Any deviations from the current SAP will be described and justified in the final study report and results publication. TRIAL REGISTRATION: International Standard Randomised Controlled Trial Number ISRCTN98159689, registered on 28 January 2021 and registered on the ClinicalTrials.gov (NCT05179694) trials registry.

Quality evaluation and health risk assessment of karst groundwater in Southwest China.

Groundwater in karst regions is of immense value due to its vital support for regional ecosystems and residents' livelihoods. However, it is simultaneously threatened by multi-source pollution from agricultural non-point sources, industrial and domestic point sources, and mining activities. This study focuses on the Guangxi of China, which features typical karst topography, aiming to thoroughly assess the groundwater quality and related health risks in Guangxi, especially identifying the impacts of various key pollution sources on the groundwater environment. A total of 1912 groundwater samples were collected, covering an area of approximately 237,600 km2. The spatial distribution of pollutants was analysed using the Nemeroww index method and Kriging interpolation, while multivariate statistical and cluster analysis methods were employed to identify the main types of pollution sources. Furthermore, based on the human health risk assessment model of the U.S. Environmental Protection Agency (US EPA), a risk assessment was conducted for key pollutants. The results revealed widespread heavy metal contamination in Guangxi's groundwater, particularly with concentrations of Mn, As, Al, Pb reaching up to 9.4 mg/L, 2.483 mg/L, 37.95 mg/L, 4.761 mg/L, respectively, significantly exceeding China's national Class III groundwater quality standards. Cluster analysis indicated that mining and industrial activities are the primary sources of pollution. The health risk assessment demonstrated that these activities pose a significant risk to public health. The aim of this study is to provide a scientific basis for the protection of the groundwater environment in Guangxi and other karst areas, the formulation of pollution prevention and control strategies, and the optimization of urban and industrial land use layouts. Future research should focus on advanced isotopic and molecular biological techniques to trace pollution sources more precisely and evaluate the effectiveness of pollution control measures.

Origin and comprehensive risk assessment of heavy metals in surface sediments along the Caspian Sea.

Metal concentrations (Cu, Zn, Cr, Fe, As, Pb, Ni, V, and Co) in surface sediments were determined in the southern Caspian Sea. Sediment pollution was investigated using geochemical index such as Enrichment Factor (EF) and ecological risk indices like modified Hazard Quotient (mHQ) and Toxic Risk Index (TRI). The highest calculated geochemical index was observed in sediments near the cities of Chalus and Ramsar, which are highly polluted due to human activities. The ecological potential indices indicated that Ni contents in all the studied stations would impose harmful effects on aquatic organisms, while other metals had a low risk. Principal component and cluster analyses suggested that As and Pb emanated from anthropogenic sources, and other metals probably originated from lithogenic sources.

Comparative assessment of differently randomized accelerated particle swarm optimization and squirrel search algorithms for selective harmonics elimination problem.

A random initialization of the search particles is a strong argument in favor of the deployment of nature-inspired metaheuristic algorithms when the knowledge of a good initial guess is lacked. This article analyses the impact of the type of randomization on the working of algorithms and the acquired solutions. In this study, five different types of randomizations are applied to the Accelerated Particle Swarm Optimization (APSO) and Squirrel Search Algorithm (SSA) during the initializations and proceedings of the search particles for selective harmonics elimination (SHE). The types of randomizations include exponential, normal, Rayleigh, uniform, and Weibull characteristics. The statistical analysis shows that the type of randomization does impact the working of optimization algorithms and the fittest value of the objective function.

Artificial-intelligence-model to optimize biocide dosing in seawater-cooled industrial process applications considering environmental, technical, energetic, and economic aspects.

This research introduces an Artificial Intelligence (AI) based model designed to concurrently optimize energy supply management, biocide dosing, and maintenance scheduling for heat exchangers. This optimization considers energetic, technical, economic, and environmental considerations. The impact of biofilm on heat exchangers is assessed, revealing a 41% reduction in thermal efficiency and a 113% increase in flow frictional resistance of the fluid compared to the initial state. Consequently, the pump's power consumption, required to maintain hydraulic conditions, rises by 9%. The newly developed AI model detects the point at which the heat exchanger's performance begins to decline due to accumulating dirt, marking day 44 of experimentation as the threshold to commence the antifouling biocide dosing. Leveraging this AI model to monitor heat exchanger efficiency represents an innovative approach to optimizing antifouling biocide dosing and reduce the environmental impact stemming from industrial plants.

Forecasting the 20-Year Incidence of Dementia by Socioeconomic Status, Race/Ethnicity, and Region Based on Mid-Life Risk Factors in a U.S. Nationally Representative Sample.

Background: Alzheimer's disease and related dementias (ADRD) incidence varies based on demographics, but mid-life risk factor contribution to this variability requires more research. Objective: The purpose of this study is to forecast the 20-year incidence of dementia in the U.S. overall and stratified by race/ethnicity, socioeconomic status (SES), and U.S. geographic region given prior mid-life risk factor prevalence and to examine the extent to which risk factor differences 20 years ago may explain current SES, race/ethnicity, or regional disparities in dementia incidence. Methods: We applied the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) prediction model to the 2006 wave of the Health and Retirement Study (HRS) in participants aged 45 to 64 to estimate the 20-year risk of incident ADRD. Results: The 20-year risk of dementia among middle-aged Americans was 3.3% (95% CI: 3.2%, 3.4%). Dementia incidence was forecast to be 1.51 (95% CI: 1.32, 1.71) and 1.27 (95% CI: 1.14, 1.44) times that in Hispanic and Non-Hispanic Black individuals respectively compared statistically to Non-Hispanic White individuals given mid-life risk factors. There was a progressive increase in dementia risk from the lowest versus highest SES quintile. For geographic region, dementia incidence was forecast to be 1.17 (95% CI: 1.06, 1.30) and 1.27 (95% CI: 1.14, 1.43) times that in Midwestern and Southern individuals respectively compared statistically to Western individuals. Conclusions: Some disparities in dementia incidence could be explained by differences in mid-life risk factors and may point toward policy interventions designed to lessen the ADRD disease burden through early prevention.

Assessment of natural radioactivity levels and hazard indicators in Tarakeshwor Municipality, Nepal through in-situ technique and multivariate analysis.

The evaluation of primordial radionuclide concentrations in rapidly urbanized and concrete-laden areas through the importation of construction materials from various regions of Nepal is both important and essential. This study utilized a portable gamma-ray spectrometer (PGIS 2) to analyze the distribution of three natural radionuclides: uranium (238U), thorium (232Th), and potassium (40K) in Tarakeshwor Municipality, Kathmandu, Nepal. The measured dose rates ranged from 70.22 nSv hr-1 to 163.66 nSv hr-1, with an average of 124.65±20.29 nSv hr-1, surpassing the global average of 59 nSv hr-1. The activity concentrations of 40K, 238U, and 232Th exceeded global averages, indicating relatively higher natural radioactivity concentrations in the region. Specifically, the average values for 40K, 238U, and 232Th were 935.26±172.30 Bq kg-1, 80.47±15.53 Bq kg-1, and 80.44±18.58 Bq kg-1, respectively. The calculated radium equivalent (Raeq) ranged from 132.26 to 351.22 Bq kg-1, and the annual gonadal equivalent dose (AGED) varied from 372.61 to 1028.81 µSv yr-1. The annual effective dose rates for indoor and outdoor environments were 0.54±0.09 mSv yr-1 and 0.15±0.03 mSv yr-1, respectively, both exceeding the global average. The representative level index (RLI) within the study area averaged 1.96±0.32, indicating an elevated radiation risk. The excess lifetime cancer risk (ELCR) values for outdoor and indoor environments were 0.52×10-3 ±0.09 ×10-3 and 1.87 ×10-3 ±0.31×10-3, respectively, surpassing the world average. Additionally, external hazard indices (Hex) ranged from 0.36 to 0.59, while internal hazard indices (Hin) ranged from 0.38 to 1.20, both indicating values higher than UNSCEAR recommendations. These findings underscore the necessity for further experimental analysis employing ex-situ equipment. The data generated in this study can provide a valuable baseline for future assessments and interventions in radiation risk management guidelines within the country.

Statistical learning models to measure the impact of COVID-19 on financial fragility.

This paper investigates the effects of the economic shock produced by the COVID-19 outbreak and diffusion on households'. Through a survey administered to Italian households, without loss of generality, we investigate changes in financial and economic decisions and the households' ability to cope with daily purchases, repay their debt obligations and face unexpected expenses. The paper also applies a statistical learning model through a synthetic indicator for the financial vulnerability of households, integrating the relevant information on the financial literacy and education of the surveyed individuals.

The Cost of a Locum: A Simulation to Determine When You Are Paying Too Much for Your Anesthesia Locum Tenens Coverage.

BACKGROUND: Current research on locum tenens physicians has primarily focused on their safety, reliability, and patient outcomes, leaving a significant gap in understanding the financial implications of their employment in health systems. Amidst a persistent shortage of physicians across specialties, healthcare organizations have increasingly relied on locum tenens to meet the rising demand for clinical services. This study aims to bridge the knowledge gap by evaluating the financial feasibility of employing locum tenens physicians compared to full-time anesthesiologists, given the context of growing physician shortages and increasing healthcare demands. METHODS:  We developed a Python simulation model to compare the costs of hiring locum tenens versus full-time anesthesiologists. The model inputs included hourly rates for both locum tenens and full-time anesthesiologists and the upfront hiring costs for full-time physicians. By plotting these costs against each other, the model identifies the breakeven point: the number of working hours at which the cost of employing a locum tenens physician equals that of hiring a full-time physician. Utilizing Monte Carlo simulations with data from the Northeastern United States, we assessed the variability and determined an average breakeven point across different scenarios. RESULTS:  The Monte Carlo simulation, based on 10,000 iterations, revealed an average breakeven point of 665 hours, corresponding to just over 11 weeks of 60-hour workweeks. This suggests that for any locum tenens engagement exceeding this duration, hiring a full-time anesthesiologist becomes more cost-effective for the healthcare institution. The simulation also showed that 28% of scenarios had a breakeven point below 60 days, highlighting the financial dynamics and decision-making complexities in employing locum tenens versus full-time physicians. CONCLUSIONS:  The findings indicate that employing locum tenens physicians for durations shorter than 665 hours remains financially viable compared to the option of hiring full-time anesthesiologists. However, the significant variability observed in the simulations underscores the importance of context in making staffing decisions. Healthcare organizations must consider the specific needs and circumstances of their operations when deciding between hiring locum tenens and full-time physicians, especially for longer-term coverage requirements.

Heavy metal analysis of water and sediments of the Kaptai Lake in Bangladesh: Contamination and concomitant health risk assessment.

In this study, concentrations of 9 heavy metals (Cr, Fe, Co, Ni, Cu, Zn, As, Cd, and Pb) in water and sediments of the Kaptai Lake were determined by neutron activation analysis and atomic absorption spectrometry techniques to study their distribution and contamination in the lake. Average concentrations of Cr and Co in sediments, and Fe and Pb in water were higher than those of some international guideline values. Different environmental pollution indexes (individual and synergistic) suggested that the sediments of Kaptai Lake are minorly enriched by As and Zn, and have low severity of contamination at most of the sampling sites. For residential receptors exposed to the heavy metals in lake water, both non-carcinogenic and carcinogenic hazards were assessed which indicated that there is no carcinogenic risk for As while Cr shows a slightly carcinogenic risk. Moreover, estimated potential ecological risks and different SQGs suggested low ecotoxicological risks in the sediments of Kaptai Lake. Multivariate statistical analyses revealed the correlation among the studied heavy metals and indicated that the origin of most of the metals is mainly lithogenic and a small number of metals (Cu and Pb) from anthropogenic sources. The results of this study will be helpful in developing a pollution control strategy for the lake.

Determination of threshold values and heavy metal pollution assessment of soils in an industrial area in Ghana.

Industrial activities have the potential to pollute soils with a wide variety of heavy metals (HMs). In Ghana, however, assessment of HM pollution of soils in industrial areas remains limited. Accordingly, HM soil pollution in one of the industrial areas in Accra, Ghana was assessed. Soil samples were taken and analysed for HMs, including Fe, Zr, Zn, Ti, Sr, Rb, Mn, Pb, Cu, and Co, using X-Ray Fluorescence (XRF). HM geochemical threshold values (GTVs) were determined to establish soil HM pollution levels and identify areas needing remediation. Furthermore, risk assessments were conducted to evaluate the potential ecological and human health risks associated with these metals. The mean concentrations of Fe, Zn, Rb, Sr, Zr, Ti, Mn, Co, Cu, and Pb in the soils were: 27133.83, 147.72, 16.30, 95.95, 307.11, 4663.66, 289.85, 418.54, 44.97, and 112.88 mg/kg, respectively. Generally, the concentrations of HMs decreased with depth, although some lower layers exhibited elevated HM levels. Soil pollution levels were categorized as low for Fe, Rb, Zr, Ti, Mn, Co, and Cu; moderate for Sr and Zn; and considerable for Pb. Notably, the northwestern part of the study area displayed a considerable to very high degree of HM contamination. While HMs in the soils posed low ecological risk, the human health risk assessment indicated potential health effects from Co, particularly in children. The presence of HMs in the soils was noted to originate from both natural geological phenomena and human activities, including industrial operations, agricultural practices, landfill activities, and vehicular emissions.

Comprehensive assessment of global spinal sagittal alignment and related normal spinal loads in a healthy population.

Abnormal postoperative global sagittal alignment (GSA) is associated with an increased risk of mechanical complications after spinal surgery. Typical assessment of sagittal alignment relies on a few selected measures, disregarding global complexity and variability of the sagittal curvature. The normative range of spinal loads associated with GSA has not yet been considered in clinical evaluation. The study objectives were to develop a new GSA assessment method that holistically describes the inherent relationships within GSA and to estimate the related spinal loads. Vertebral endplates were annotated on radiographs of 85 non-pathological subjects. A Principal Component Analysis (PCA) was performed to derive a Statistical Shape Model (SSM). Associations between identified GSA variability modes and conventional alignment measures were assessed. Simulations of respective Shape Modes (SMs) were performed using an established musculoskeletal AnyBody model to estimate normal variation in cervico-thoraco-lumbar loads. The first six principal components explained 97.96% of GSA variance. The SSM provides the normative range of GSA and a visual representation of the main variability modes. Normal variation relative to the population mean in identified alignment features was found to influence spinal loads, e.g. the lower bound of the second shape mode (SM2-2σ) corresponds to an increase in L4L5-compression by 378.64 N (67.86%). Six unique alignment features were sufficient to describe GSA almost entirely, demonstrating the value of the proposed method for an objective and comprehensive analysis of GSA. The influence of these features on spinal loads provides a normative biomechanical reference, eventually guiding surgical planning of deformity correction in the future.

A critical analysis of parameter choices in water quality assessment.

The determination of water quality heavily depends on the selection of parameters recorded from water samples for the water quality index (WQI). Data-driven methods, including machine learning models and statistical approaches, are frequently used to refine the parameter set for four main reasons: reducing cost and uncertainty, addressing the eclipsing problem, and enhancing the performance of models predicting the WQI. Despite their widespread use, there is a noticeable gap in comprehensive reviews that systematically examine previous studies in this area. Such reviews are essential to assess the validity of these objectives and to demonstrate the effectiveness of data-driven methods in achieving these goals. This paper sets out with two primary aims: first, to provide a review of the existing literature on methods for selecting parameters. Second, it seeks to delineate and evaluate the four principal motivations for parameter selection identified in the literature. This manuscript categorizes existing studies into two methodological groups for refining parameters: one focuses on preserving information within the dataset, and another ensures consistent prediction using the full set of parameters. It characterizes each group and evaluates how effectively each approach meets the four predefined objectives. The study presents that the minimal WQI approach, common to both categories, is the only approach that has successfully reduced recording costs. Nonetheless, it notes that simply reducing the number of parameters does not guarantee cost savings. Furthermore, the group of studies classified as preserving information within the dataset has demonstrated potential to decrease the eclipsing problem, whereas studies in the consistent prediction group have not been able to mitigate this issue. Additionally, since data-driven approaches still rely on the initial parameters chosen by experts, they do not eliminate the need for expert judgment. The study further points out that the WQI formula is a straightforward and expedient tool for assessing water quality. Consequently, the paper argues that employing machine learning solely to reduce the number of parameters to enhance WQI prediction is not a standalone solution. Rather, this objective should be integrated with a more comprehensive set of research goals. The critical analysis of research objectives and the characterization of previous studies lay the groundwork for future research. This groundwork will enable subsequent studies to evaluate how their proposed methods can effectively achieve these objectives.

Circular And Sustainable: Evaluating Lithium-Ion Battery Recycling using a Combined Statistical Entropy and Life Cycle Assessment Methodology.

While there has been a growing interest on the concept of Circular Economy (CE), its correlation with sustainability remains controversial. In this work, the combination of Statistical Entropy Analysis (SEA) and Life Cycle Assessment (LCA) is proposed as a new methodology to evaluate recycling processes from the perspective of materials circularity and environmental impacts using a Li-ion battery recycling process as a case study. This work addresses the need of quantitative circularity indicators, as SEA evaluates the concentration of materials at a systems level, while LCA measures the environmental impact of recycling processes in comparison with virgin raw materials production. It was found that process optimization points can be found by simultaneously accounting for materials recovery and the LCA categories of global warming potential, ozone depletion and mineral resource scarcity. Furthermore, a strong correlation was found for the first time between the recovery of critical elements and the environmental impact of raw materials production. The proposed methodology thus offers a robust analysis of a product lifecycle that aids in its design and optimization from the CE perspective.

A statistical shape analysis for the assessment of the main geometrical features of the distal femoral medullary canal.

Statistical Shape Models (SSMs) are widely used in orthopedics to extract the main shape features from bone regions (e.g., femur). This study aims to develop an SSM of the femoral medullary canal, investigate its anatomical variability, and assess variations depending on canal length. The canals were isolated from 72 CT femur scans, through a threshold-based segmentation. A region of interest (ROI) was selected; sixteen segments were extracted from the ROI, ranging from 25% of the full length down to the most distal segment. An SSM was developed to identify the main modes of variation for each segment. The number of Principal Components (PCs) needed to explain at least 90% of the shape variance were three/four based on the length of the canal segment. The study examined the relationship between the identified PCs and geometric parameters like length, radius of curvature, ellipticity, mean diameter, and conicity, reporting range and percentage variation of these parameters for each segment. The SSMs provide insights into the anatomical variability of the femoral canal, emphasizing the importance of considering different segments to capture shape variations at various canal length. These findings can contribute for the design of personalized orthopedic implants involving the distal femur.

Value per Statistical Life at the Sub-National Level as a Tool for Assessing Public Health and Environmental Problems.

This article aims to estimate the Value per Statistical Life (VSL) and Value per Statistical Life Year (VSLY) at the sub-national level, which can be used to calculate the economic impact of health and environmental problems. We estimate the value of life for Mexico and its 32 states, grouped into 5 regions for 2021. We used the OECD's guidelines on "Mortality Risk Valuation in Environment, Health and Transport Policies," which applies the measure of Willingness to Pay (WTP) and Cost-Benefit Analysis (CBA). Mexico's overall VSL of $2 000 000 USD in 2021 showcases the value placed on human life. The variation in VSL across the 32 states, with Chiapas having the lowest VSL of $400 000 USD and Mexico City boasting the highest VSL of $3 300 000 USD highlights the different levels of regional development and people's willingness to pay to reduce the risk of mortality. Our estimates of VSL and VSLY have the potential to contribute to the evaluation of public policies in the fields of health and the environment. Monetizing human life through these estimates can offer valuable insights to policymakers at both the national and sub-national levels. By quantifying the economic value placed on human life, this paper helps decision-makers prioritize investments, assess the cost-effectiveness of interventions, and allocate resources to maximize societal well-being.

Monitoring multistage healthcare processes using state space models and a machine learning based framework.

Monitoring healthcare processes, such as surgical outcomes, with a keen focus on detecting changes and unnatural conditions at an early stage is crucial for healthcare professionals and administrators. In line with this goal, control charts, which are the most popular tool in the field of Statistical Process Monitoring, are widely employed to monitor therapeutic processes. Healthcare processes are often characterized by a multistage structure in which several components, states or stages form the final products or outcomes. In such complex scenarios, Multistage Process Monitoring (MPM) techniques become invaluable for monitoring distinct states of the process over time. However, the healthcare sector has seen limited studies employing MPM. This study aims to fill this gap by developing an MPM control chart tailored for healthcare data to promote early detection, confirmation, and patient safety. As it is important to detect unnatural conditions in healthcare processes at an early stage, the statistical control charts are combined with machine learning techniques (i.e., we deal with Intelligent Control Charting, ICC) to enhance detection ability. Through Monte Carlo simulations, our method demonstrates better performance compared to its statistical counterparts. To underline the practical application of the proposed ICC framework, real data from a two-stage thyroid cancer surgery is utilized. This real-world case serves as a compelling illustration of the effectiveness of the developed MPM control chart in a healthcare setting.