Adjustment Factors Research Articles

High levels of heat stress among sugarcane workers in Thailand.

With continued global warming, the effects of elevated temperatures on the health of agricultural workers are a particular concern. This study characterized the levels of heat stress in Thai sugarcane workers and investigated whether season and harvesting method were associated with it. Three hundred sugarcane workers in Nakhon Sawan Province, Thailand, were recruited, and information on demographics, working conditions, and clothing characteristics was collected from participants during the cooler months (n = 152 participants, mid-January to mid-February) and hotter month (n = 148, March). Heat stress was measured using the Wet Bulb Globe Temperature (WBGT) index, and the WBGT instruments were operated for a full work shift in the sugarcane fields where the participants worked. One-hour time weighted average (TWA) effective WBGT (WBGTeff-1hrTWA) estimates were determined for different times of the day based on the measured WBGT and clothing adjustment factor. The average WBGTeff-1hrTWA in the cooler months ranged from 22.5 °C during the early morning to 31.3 °C during the hottest time of the day, and for the hotter month, it ranged from 25.4 °C to 33.9 °C, respectively. The measured WBGT, natural wet-bulb temperature (Tnwb), dry-bulb temperature (Tdb), globe temperature (Tg), air velocity (Av), and absolute water vapor pressure (ea) were all statistically significantly higher in the hotter month than in the cooler months. Harvesting during the hotter month and harvesting burnt sugarcane were significantly associated with increased effective WBGT. The harvesters' heat stress in both seasons exceeded the American Conference of Governmental Industrial Hygienists - Threshold limit value for 72.7% of the working time in the cooler months and 90.9% in the hotter month. The heat stress in Thai sugarcane workers was high in both seasons, particularly in the hotter month and when harvesting burnt sugarcane. This results in a very high risk of developing heat-related health effects, and measures are needed to reduce heat stress. Heat stress in agricultural and other outdoor work in tropical climates is an immediate and growing problem.

Bayesian Sample Size Calculation in Small n, Sequential Multiple Assignment Randomized Trials (snSMART).

A recent study design for clinical trials with small sample sizes is the small n, sequential, multiple assignment, randomized trial (snSMART). An snSMART design has been previously proposed to compare the efficacy of two dose levels versus placebo. In such a trial, participants are initially randomized to receive either low dose, high dose or placebo in stage 1. In stage 2, participants are re-randomized to either dose level depending on their initial treatment and a dichotomous response. A Bayesian analytic approach borrowing information from both stages was proposed and shown to improve the efficiency of estimation. In this paper, we propose two sample size determination (SSD) methods for the proposed snSMART comparing two dose levels with placebo. Both methods adopt the average coverage criterion (ACC) approach. In the first approach, the sample size is calculated in one step, taking advantage of the explicit posterior variance of the treatment effect. In the other two step approach, we update the sample size needed for a single-stage parallel design with a proposed adjustment factor (AF). Through simulations, we demonstrate that the required sample sizes calculated using the two SSD approaches both provide the desired power. We also provide an applet to allow for convenient and fast sample size calculation in this snSMART setting.

Street-scale black carbon modelling over the West Midlands, United Kingdom: Sensitivity test of traffic emission factor adjustments.

Black carbon is harmful for climate, environment, and human health. Road traffic is one of the major sources for black carbon in urban areas. This study develops a street scale air quality model configuration for the dispersion of black carbon concentrations across the West Midlands, UK, incorporating updated road traffic emission factors. A range of modelling cases accounting for traffic emission factor adjustments was conducted and evaluated against measurements at three monitoring sites within the region. The model overall has good performance. With unadjusted traffic emission factors, the model can capture black carbon annual concentrations at urban background sites. However, an adjustment (factor of 3) for the traffic emission factors is needed to better represent the roadside site (although with a slight overestimation of 3% in annual concentration). There are near-linear relationships between black carbon concentrations and the adjustment ratio for the traffic emission factor. Black carbon concentration maps have been generated at 10m×10m resolution, which were then aggregated into health-related electoral ward and local authority levels.

Total neoadjuvant therapy followed by total mesorectal excision and selective lateral lymph node dissection for locoregionally advanced low rectal cancer: A phase III randomized controlled trial (JCOG2207).

TPS312 Background: For locally advanced low rectal cancer (RC), total neoadjuvant therapy (TNT) followed by total mesorectal excision (TME) confers a survival advantage compared with neoadjuvant chemoradiotherapy (nCRT) in the West, even though the local control rate was similar between TNT and nCRT. The Japan Clinical Oncology Group (JCOG) Colorectal Cancer Study Group has been developing therapies for RC based on recurrence risk according to presence/absence of clinical lateral lymph node metastasis (cLLNM). Our previous study in Japan showed that the standard of care (SOC) for RC is TME with lateral lymph node dissection (LLND) plus adjuvant chemotherapy (CTx). However, the prognosis for cStage III low RC remains poor regardless of cLLNM status. Therefore, new treatment strategies are needed for this population. In cLLNM-positive cStage III low RC patients, previous research suggests that adding nCRT to LLND would reduce local recurrence and improve survival. TNT and LLND may further improve outcomes. In cLLNM-negative cStage III low RC, several reports have suggested that nCRT as an alternative addition to LLND has comparable efficacy in terms of local control. Thus, in these populations, if TNT can replace LLND, adverse events due to LLND can be avoided while improving prognosis. We hypothesize that TNT + TME + selective LLND would be the most promising strategy for cLLNM-positive and -negative cStage III low RC. Methods: Eligibility criteria include low rectal adenocarcinoma or adenosquamous carcinoma, cT2-cT4 tumor depth, clinically positive lymph node metastasis, no distant metastasis, no history of pelvic irradiation or rectal surgery, age 18-75 years, and sufficient organ function. Eligible patients are randomized (1:1) with adjustment factors of institution, sex, and cLLNM status. The experimental treatment arm receives TNT, short-course radiotherapy (25 Gy/5 fractions to whole pelvis), and consolidation chemotherapy consisting of 6 courses of CAPOX (oxaliplatin 130 mg/m2 on day 1 and capecitabine 2000 mg/m2/day on days 1-14). After completing TNT, TME + selective LLND are performed for cLLNM-positive cases or TME alone for cLLNM-negative cases. The SOC arm receives TME + LLND followed by adjuvant chemotherapy with 8 cycles of CAPOX (same as above). The primary endpoint is overall survival (OS) in the intention-to-treat population. To detect an increase in 5-year OS from 76% to 84%, corresponding to a target hazard ratio of 0.64, 408 patients (108 events) would achieve 75% power at a one-sided α of 0.05, an expected accrual period of 4 years, and a follow-up period of 5 years. The total sample size was set at 420 patients to account for patients lost to follow-up. Accrual in JCOG2207 began in October 2023. Clinical trial information: s031230415 .

Mental Health and Stress Management Among College Students: A Critical Analysis

- This study explores the mental health challenges faced by college students and the strategies they employ for stress management. Mental health issues, including anxiety, depression and stress, are prevalent among students due to academic pressures, social adjustment and other personal factors. Stress management techniques such as exercise, social support and mindfulness are commonly used but vary in effectiveness. Through a survey-based approach, this research aims to examine the relationship between mental health and coping mechanisms. The findings reveal a significant correlation between the use of effective stress management strategies and improved mental well-being. However, the study also highlights the challenges students face in accessing mental health resources. The importance of institutional support and peer networks in promoting mental well-being is emphasized. Key Words: Mental Health, Stress Management, College Students, Coping Strategies, Anxiety, Depression, Academic Stress

Bridge seismic design method incorporating seismic importance adjustment factor and uniform traffic loss risk

Bridge seismic design method incorporating seismic importance adjustment factor and uniform traffic loss risk

Research on the Spiral Rolling Gait of High-Voltage Power Line Serpentine Robots Based on Improved Hopf-CPGs Model

The efficiency of helical locomotion in snake-like robots along high-voltage transmission lines is often hindered by low motion efficiency, high joint signal noise, and challenges in traversing obstacles. This study aims to address these issues by proposing a gait generation method that leverages a standardized Central Pattern Generator (CPG). We modify the traditional Hopf-CPG model by incorporating constraint functions and a frequency-tuning mechanism to regulate the oscillator, which allows for the generation of asymmetric waveform signals for deflection joints and facilitates rapid convergence. The method begins by determining initial and obstacle-crossing state parameters, such as deflection angles and helical radii of the snake-like robot, using the backbone curve method and the Frenet–Serret framework. Subsequently, a CPG neural network is constructed based on Hopf oscillators, with a limit cycle convergent speed adjustment factor and amplitude bias signals to establish a fully connected matrix model for calculating multi-joint output signals. Simulation analysis using Simulink–CoppeliaSim evaluates the robot’s obstacle-crossing ability and the optimization of deflection joint signal noise. The results indicate a 55.70% increase in the robot’s average speed during cable traversal, a 57.53% reduction in deflection joint noise disturbance, and successful crossing of the vibration damper. This gait generation method significantly enhances locomotion efficiency and noise suppression in snake-like robots, offering substantial advantages over traditional approaches.

Medicare Risk Arrangement and Use and Outcomes Among Physician Groups

Many physician groups are in 2-sided risk payment arrangements with Medicare Advantage plans (at-risk MA). Analysis of quality and health resource use under such arrangements may inform ongoing Medicare policy concerning payment and service delivery. To compare quality and efficiency measures under 2 payment models: at-risk MA and fee-for-service (FFS) MA. This cross-sectional study used Medicare encounter and enrollment data from 2016 to 2019 covering 17 physician groups, 15 488 physicians, and 35 health insurers to compare quality and health resource use for Medicare beneficiaries within the same physician groups. The data were analyzed between August 4 and October 30, 2024. Care delivered under at-risk MA and FFS MA payment arrangements by the same physicians and medical groups. Twenty quality and efficiency measures across 4 domains of patient care (hospital care, avoidance of the emergency department [ED], avoidance of disease-specific admissions, and outpatient care) were examined using logistic regression analysis. The overall sample comprised 5 278 717 person-years (37.7% at-risk MA and 62.3% FFS MA). The mean (SD) age of beneficiaries was 73.6 (9.2) years in the at-risk MA group (56.8% women) and 71.8 (10.4) years in the FFS MA group (57.4% women). For at-risk MA compared with FFS MA, inpatient admissions and 30-day readmissions per 1000 were 10.03 (95% CI, -10.61 to -9.44) and 1.95 (95% CI, -2.18 to -1.73) lower. ED use measures per 1000 ranged from 2.95 (95% CI, -3.28 to -2.63) lower for avoidable ED visits to 26.02 (95% CI, -26.92 to -25.12) lower for overall ED visits. Avoidance of disease-specific admissions per 1000 ranged from 0.24 (95% CI, -0.35 to -0.13) lower for composite diabetes-related admissions to 2.18 (95% CI, -2.43 to -1.94) lower for the composite of chronic disease-related admissions. High-risk drug use per 1000 was 14.26 (95% CI, -14.85 to -13.67) lower. Overall, compared with FFS MA, at-risk MA was associated with higher quality and efficiency in 18 of 20 measures after adjusting for differences in demographics, Hierarchical Condition Categories Risk Adjustment Factor scores, and other health characteristics. In this cross-sectional study, at-risk MA payment arrangements managed by physician groups were associated with higher quality and efficiency compared with FFS MA managed by the same groups. The population and methods used provide robust evidence that at-risk payment arrangements in MA may improve health care delivery for the MA population.

Association of Admission Nutritional Status Evaluated by Prognostic Nutritional Index with One-Year Walking Independence after Primary Total Hip Arthroplasty for Osteonecrosis of the Femoral Head: A Retrospective Matched Cohort Study Based on 1,152 Patients.

Malnutrition is significantly associated with unfavorable outcomes, but the relevant prognostic assessment is not emphasized in patients who have osteonecrosis of the femoral head (ONFH). This study aimed to assess the association between preoperative nutritional status evaluated by the prognostic nutritional index (PNI) and walking independence at one year postoperatively in patients who have ONFH undergoing primary total hip arthroplasty (THA). This was a retrospective analysis of prospectively collected data from patients admitted to a tertiary referral hospital who had ONFH and underwent primary unilateral THA from October 30, 2014 to April 26, 2019. The restricted cubic spline was used to assess the dose-effect relationship between PNI and recovery of walking independence. Propensity score matching was performed to balance potential preoperative confounders, and multivariable conditional logistic regression analysis was applied to assess the association between malnutrition and losing walking independence (LWI) with perioperative and follow-up factors for further adjustment. Inverse probability treatment weighting and sensitivity analyses were performed to test the robustness of the results. Subgroup analyses were used to determine potential population heterogeneity. We found a negative relationship between admission PNI levels and recovery of walking independence at one year postoperatively, and that low PNI (< 42.2) was independently associated with a 2.35-fold (95% CI [confidence interval], 1.16 to 4.77; P = 0.018) increased risk of LWI. The results were overall robust. Significant heterogeneities were observed in the subgroups of age, labor intensity, American Society of Anesthesiologists class, Charlson's comorbidity index, and Association Research Circulation Osseous stage (P for interaction < 0.05). Preoperative low-level PNI is a significant risk factor for LWI after THA in patients who have ONFH. This finding supports nutritional screening and interventions for surgeons in decision-making for elective surgery and postoperative rehabilitation management.

Overexpressed RPS6KA1 and its potential diagnostic value in head and neck squamous cell carcinoma

RPS6KA1 express disorder is associated with many cancers, but the role in head and neck squamous cell carcinoma (HNSCC) and the specific mechanism is still unclear. We used bioinformatics analysis to explore the role of Ribosomal Protein S6 Kinase A1(RPS6KA1) in HNSCC which were predicted to regulate certain pathway and immune microenvironment to increase the risk in HNSCC. Multiple bioinformatics tools based on the EBI, GEO, TCGA databases and clinical samples were used to analyze the expression of RPS6K1 in HNSCC. Western blot (WB) and PCR results confirmed the upregulation of RPS6KA1 in HNSCC tissues. Flow cytometry was used to validate the relationship between RPS6KA1 and immune cell infiltration in the tumor microenvironment. The correlation of RPS6KA1 with the immune environment was further analyzed, and flow cytometry validation was performed in HNSCC samples. Then, Gene Ontology(GO) and Gene Set Enrichment Analysis(GSEA) analysis were used to explore the pathway which could be regulated by RPS6KA1 in HNSCC. And drug sensitivity analysis was used to the access the relationships between RPS6KA1 and drugs therapeutic effects. EBI, TCGA and GEO databases were used to reveal that RPS6KA1 expression was significantly increased in HNSCC, especially in III + IV HNSCC. And it was related to many types of immune cells and immune adjustment factors and positively correlated with tumor immune score and B cells, but had no significant correlation with CD4+ and CD8+ T cells. Drug sensitivity analysis revealed that RPS6KA1 has certain predictive value. In this research, we indicated that RPS6KA1 is overexpressed and may serve as a potential diagnostic and therapeutic biomarker in HNSCC.

Smart Agricultural Pest Detection Using I-YOLOv10-SC: An Improved Object Detection Framework

Aiming at the problems of insufficient detection accuracy and high false detection rates of traditional pest detection models in the face of small targets and incomplete targets, this study proposes an improved target detection network, I-YOLOv10-SC. The network leverages Space-to-Depth Convolution to enhance its capability in detecting small insect targets. The Convolutional Block Attention Module is employed to improve feature representation and attention focus. Additionally, Shape Weights and Scale Adjustment Factors are introduced to optimize the loss function. The experimental results show that compared with the original YOLOv10, the model generated by the improved algorithm improves the accuracy by 5.88 percentage points, the recall rate by 6.67 percentage points, the balance score by 6.27 percentage points, the mAP value by 4.26 percentage points, the bounding box loss by 18.75%, the classification loss by 27.27%, and the feature point loss by 8%. The model oscillation has also been significantly improved. The enhanced I-YOLOv10-SC network effectively addresses the challenges of detecting small and incomplete insect targets in tea plantations, offering high precision and recall rates, thus providing a solid technical foundation for intelligent pest monitoring and precise prevention in smart tea gardens.

A novel sea surface evaporation scheme assessed by the thermal rotating shallow water model

AbstractIn this study, a novel sea surface evaporation scheme, along with its corresponding bulk aerodynamic formulation, is proposed to estimate sea surface evaporation, columnar humidity, and precipitation distribution within the atmosphere. The scheme is based on three distinct functions, each dependent on a single variable: zonal wind velocity, tropospheric (potential) temperature, and free convection. It is shown that the normalized Clausius–Clapeyron formula requires an adjustable scaling factor for real‐world applications, calibrated using empirical fitness curves. To validate the proposed approach, we employ a model based on the pseudo‐spectral moist‐convective thermal rotating shallow water model, with minimal parameterization over the entire sphere. ECMWF Reanalysis 5th Generation (ERA5) reanalysis data are used to compare the model's results with observations. The model is tested across different seasons to assess its reliability under various weather conditions. The Dedalus algorithm, which handles spin‐weighted spherical harmonics, is employed to address the pseudo‐spectral problem‐solving tasks of the model.

Physiologic insulin resensitization lowers cost in patients with diabetes and kidney disease.

To examine the effect of physiologic insulin resensitization (PIR) on the cost of treating patients with diabetes and chronic kidney disease (CKD). The mean 1-year cost of treating 66Medicare Advantage patients with diabetes and CKD who were receiving PIR was compared with that of treating 1301Medicare Advantage patients with diabetes and CKD not receiving PIR. Differences in disease severity were compared using mean risk adjustment factor scores. Cost comparisons were made for CKD stages 2, 3a, 3b, 4, and 5. The total cost of treating the PIR patients was then compared with the total costs of treating the same number of non-PIR patients to determine cost differences potentially incurred. The mean annual cost of treating PIR patients with stage 2 CKD was $11,251 vs $18,058 for the non-PIR group. For patients with stage 3a CKD, the mean PIR cost was $10,974 vs $18,563 for the non-PIR group. For patients with stage 3b CKD, the mean costs were $19,520 and $18,398, respectively. The mean costs for stages 4/5 CKD were $14,042 vs $22,124, respectively. The costs for an equal number of non-PIR patients at each stage were $345,830 higher than the actual costs of the PIR patients. There were no significant differences in the mean risk adjustment factor scores between the 2 groups. PIR is a possible method of reducing the cost of treating patients with diabetes and CKD. Given the rapidly increasing numbers of patients with diabetes and CKD who are Medicare Advantage beneficiaries, PIR should be considered for use by managed care organizations.

The association between domestic water hardness and kidney stone disease: a prospective cohort study from the UK Biobank.

Kidney stone disease is a common surgical disease and significant public health issue, may be influenced by environmental factors such as domestic water hardness and its related minerals. Previous studies have shown inconsistent and controversial results regarding the impact of domestic water hardness on kidney stone formation. This prospective cohort study analyzed data from 288,041 participants in the UK Biobank with no prior history of kidney stones from 2006-2024. The exposures were domestic water hardness, calcium concentration, calcium carbonate concentration, and magnesium concentration. The main outcomes were the disease status and onset time of kidney stone diseases. The confounding factors of model adjustment included age, sex, ethnicity, economic level, education level, Townsend Deprivation Index, Index of Multiple Deprivation, assessment center, body mass index, drug history influencing the metabolism of calcium and magnesium, and water intake based on the directed acyclic graph of causal hypothesis. The association between domestic water hardness and kidney stone was assessed using the Cox regression models, sensitive analyses, subgroup and interactive analyses. During the follow-up period, 3,298 participants (1.14%) developed kidney stones. In all participants, mean concentration of calcium, calcium carbonate, and magnesium was 52.61mg/L, 135.01mg/L, and 4.66mg/L, respectively. In cox regression models, higher magnesium levels (Q4,>5mg/L) in natural water use can reduce risk of kidney stones [HR and 95%CI: 0.88 (0.80-0.97) in the model 3], but no significant correlation was found in domestic water hardness, calcium concentration, and calcium carbonate concentration in the overall models. Four sensitive analyses further supported the overall results in the overall models. In subgroup analysis, hard water and calcium concentration in domestic water can increase the 18%-34% incidence risk of kidney stones in participants over 60years old and female participants; high magnesium concentration (>5mg/L) in domestic water can decrease the 10%-28% risk of kidney stones in males, participants≤45years old, and participants without renal failure. Magnesium interacted with other minerals, and its protective effects was more significant in the hard water (HR: 0.73, 95%CI: 0.61-0.87), high concentration of CaCO3 (HR: 0.62, 95%CI: 0.50-0.78) and calcium (HR: 0.48, 95%CI: 0.33-0.71) in domestic water. Our findings suggested that magnesium levels in water can decrease kidney stone risk, but in the overall population, domestic water hardness, calcium concentration, and calcium carbonate concentration have no significant impact on the formation of kidney stones. Interestingly, hard water and its calcium concentration can promote the formation risk of kidney stones in participants>60years old and females, while high magnesium concentration in domestic water can reduce the risk in males,≤45years old, and those without renal failure. The protective effects of magnesium interacted with other minerals and were more obvious in the population intake of hard water and high concentrations of CaCO3 and calcium. This study contributes to the complex understanding of environmental factors in kidney stone etiology and suggests a need for focusing on mineral-specific effects in different population and interaction with other minerals, which hope to provide some evidence of water in public health and clinical management of kidney stones.

Factors affecting return to work in breast cancer survivors in Korea: a cross-sectional study.

Return to work (RTW) has been understudied in Asian women with cancer, despite the increasing number of breast cancer survivors (BCS). This study examined RTW among Korean BCS, exploring its associations with cancer-related fatigue, quality of sleep, mental adjustment, and psychosocial factors. This cross-sectional study recruited BCS from a hospital, a breast cancer support group, and an online community in Korea between July and August 2019. We collected data on levels of fatigue, fatigability, quality of sleep, mental adjustment, and quality of working life. The analysis included data from 135 respondents who were employed prior to their cancer diagnosis. Descriptive statistics and multiple logistic regression analyses were conducted. Although all participants were employed prior to diagnosis, only 57% remained employed afterward. Participants who returned to work reported significant levels of subjective fatigue (102.48±39.84), physical fatigability (28.14±11.34), borderline poor sleep quality (8.57±4.11), anxious preoccupation (23.33±4.54), and low satisfaction with quality of working life (39.68±21.51). Marital status (odds ratio [OR], 3.34; p=.027), time since breast cancer diagnosis (OR, 2.85; p=.028), anxious preoccupation (OR, 0.89; p=.021), and quality of working life (OR, 1.04; p=.010) were found to be predictors of RTW, explaining 34% of the variance. It is critical to address RTW-related difficulties in Korean BCS, and future RTW interventions should target cancer-related fatigue, anxious preoccupation, and quality of working life. Physical and psychosocial support is essential for BCS and their successful RTW.

UTILIZING COMMUNITY CONNECTIONS: A SOCIAL DETERMINANTS OF HEALTH SCREENING FOR COMMUNITY-DWELLING OLDER ADULTS

Abstract Community-based aging resources, like Area Agencies on Aging (AAA), are often underutilized. Establishing partnerships with healthcare providers can promote better awareness and use of available services/supports. We evaluated a community partnership between a primary care clinic and an AAA; both community partners were committed to reducing adverse health outcomes. This exploratory intervention study assessed social determinants of health (SDH) needs of Medicare-eligible primary care patients (n = 385) during an annual visit (Mage=72.5; 89.6% White) and connected patients to appropriate supportive aging services. There were 284 participants with identified unmet SDH needs, but only 36 participants accepted referral to AAA resources. We analyzed Medicare’s Risk Adjustment Factor (RAF) score at baseline and participants’ use of advanced care (i.e., ER visits/hospital readmissions/institutionalizations), urgent care, and primary care, over a 6-month period. Descriptive, bivariate, and multivariate analyses revealed positive associations between RAF score with age (p&amp;lt; 0.001) and number of SDH needs (p&amp;lt; 0.001), advanced care with SDH needs (p=0.004) and RAF score (p&amp;lt; 0.001), and primary care visits with SDH needs (p=0.003) and RAF score (p&amp;lt; 0.001). The screening revealed that older adults’ SDH needs are often unmet and underrecognized. Cross-sector partnerships between medical and social models of health can reduce risk and meet needs of community-dwelling older adults. Policy provisions that encourage such partnerships are needed to improve community-dwelling older adults’ wellbeing, independence, and ability to age-in-place.

Cooperative Control of Distributed Drive Electric Vehicles for Handling, Stability, and Energy Efficiency, via ARS and DYC

Distributed drive electric vehicles (DDEV), characterized by their independently drivable wheels, offer significant advantages in terms of vehicle handling, stability, and energy efficiency. These attributes collectively contribute to enhancing driving safety and extending the all-electric range for sustainable transportation. Nonetheless, the challenge persists in designing a control strategy that effectively coordinates the objectives of handling, stability, and energy efficiency under both lateral and longitudinal driving conditions. To this end, this paper proposes a cooperative control strategy for DDEVs, incorporating active rear steering (ARS) and direct yaw moment control (DYC) to enhance handling capabilities, stability, and energy efficiency. A stability boundary is delineated using an analytical expression that correlates with the front wheel steering angle, and an adjustment factor is introduced to quantify vehicle stability based on this input parameter. This factor aids in establishing a coordinated control reference for handling and stability. At the upper-level motion control layer, a model predictive control method is developed to track this reference and implement ARS and DYC for superior performance. Specifically, the rear lateral force serves as the control command for ARS, which is converted into a rear wheel steering angle using a tire inverse model. Meanwhile, the front lateral force is modeled as linear-time-varying to simplify calculations. At the lower-level torque allocation layer, the adjustment factor is utilized to balance tire workload rate and in-wheel motors’ (IWM) energy consumption, enabling efficient switching between energy consumption and driving stability targets, and the torque allocation is conducted to acquire the expected IWMs’ command. Both the upper and lower-level optimization problems are formulated as convex problems, ensuring efficient and effective solutions. Simulations verify the effectiveness of this strategy in improving handling, stability, and energy economy under DLC cases, while maintaining high computational efficiency.

Mediterranean diet and brain functional connectivity in a population without dementia.

Adjustable lifestyle factors, such as diet, are associated with cognitive functions, structural and functional brain measures, but the association between the functional connectivity (FC) and the Mediterranean Diet (Medicine) in population without dementia is yet to be explored. The association between MeDi and brain FC in 105 individuals without dementia aged 63 (SD ± 8.72) years old who underwent brain MRI including resting-state (rs) functional MRI (fMRI) was examined. Dietary intake was evaluated through four 24-h recalls using the multiple-pass method and adherence to the MeDi was estimated using the MedDietScore, with higher values indicating greater adherence to MeDi. Multivariable linear regression models were used to investigate the associations between FC (both positive and negative associations) and MedDietScore. Rs-fMRI analysis revealed significant associations between FC and MedDietScore. The FC between the medial prefrontal cortex and a cluster located in left postcentral gyrus and in the left supramarginal gyrus was positively associated with MedDietScore. On the other hand, the FC between medial visual and right posterior division of both middle and superior temporal gyrus was negatively associated with MedDietScore. Of note, a temporal negative correlation was detected between above-mentioned FC networks. The FC between superior temporal gyrus and occipital regions was associated with participants' attention, executive functions, and memory scores. Furthermore, the associations for attention and executive functions were pronounced in participants with high adherence to MeDi compared to those with low adherence to MeDi. In conclusion, our study documented an association between higher adherence to MeDi and rs-FC in fronto-parietal and temporo-occipital regions, particularly in areas that are involved in cognitive processes altered across normal and pathological aging. From a clinical point of view, our findings support a favorable role of MeDi on FC which may have significant clinical implications in the rapidly aging population. Rs-fMRI is also proposed as a useful tool in the emerging field of nutritional neuroscience and a candidate non-invasive biomarker of brain aging.

A retrospective multi-site examination of chronic kidney disease using longitudinal laboratory results and metadata to identify clinical and financial risk

BackgroundA retrospective observational study was conducted at 3 health care organizations to identify clinical gaps in care for patients with stage 3 or 4 chronic kidney disease (CKD), and financial opportunity from U.S. risk adjustment payment systems. Lack of evaluation for CKD in patients with diabetes was also assessed.MethodsOutpatient longitudinal laboratory results and patient metadata available in the electronic medical record, laboratory information system, and/or laboratory billing or facility claims data for the calendar year 2021 were evaluated. Laboratory results were compared to billing data (ICD-10 codes) and risk adjustment scores including Hierarchical Condition Categories (HCC) to determine if laboratory-identified CKD was coded as a disease condition in the electronic medical record. Adults 18 to 75 years of age were included; inpatient laboratory results and pregnant individuals were excluded.ResultsAt the 3 institutions, 12,478 of 16,063 (78%), 487 of 1511 (32%) and 19,433 of 29,277 (66%) of patients with laboratory evidence of stage 3 or 4 CKD did not have a corresponding ICD-10 or HCC code for CKD in the electronic medical record. For patients at the 3 institutions with diabetes on the basis of an HbA1c value of ≥ 6.5%, 34,384 of 58,278 (59%), 2274 of 2740 (83%) and 40,378 of 52,440 (77%) had not undergone guideline-recommended laboratory testing for CKD during the same 12 months. Using publicly available data for calendar year 2021, an estimated 3246 of 32,398 patients (9.9%) at the 3 institutions with undocumented CKD stages 3–4 would be enrolled in Medicare Advantage or Affordable Care Act Marketplace programs. The imputed lost reimbursement under risk-adjusted payment systems for under-documentation of CKD in this subset of patients was $2.85 M for the three institutions combined, representing lost opportunity for both identification and proactive clinical management of these patients, and financial recovery for the costs of providing that care.ConclusionsClinical laboratories can provide value beyond routine diagnostics, helping to close gaps in care for identification and management of CKD, stratifying subgroups of patients to identify risk, and capturing missed reimbursement through risk adjustment factors.

Real-time traffic flow uncertainty quantification: a seasonal kernel density estimation approach

Uncertainty quantification is important for reliability-oriented transportation operations, and it becomes increasingly evident to complement traffic level forecasting with uncertainty quantification, usually in terms of prediction interval. To this end, the distribution-based approach usually relies on a pre-assumed parametric probability density function, which cannot meet the requirements raised by time-varying or streamlined traffic condition patterns. In addition, the second-order seasonality is pronounced for seasonal traffic condition patterns. Therefore, an online seasonal kernel density estimation approach is proposed by combining the second-order seasonality and nonparametric density estimation in a streamlined fashion. For this approach, four online second-order seasonality adjustment factors are developed, and then four seasonal kernel density estimation models are proposed. Real world data are used to validate the four models, and empirical results show that the proposed models consistently outperform the conventional traffic condition uncertainty quantification models. Future studies are recommended for advancing the investigation of traffic uncertainty quantification.