Effect of Cu Film Thickness on Cu Bonding Quality and Bonding Mechanism.

In the hybrid bonding process, the final stage of chemical mechanical polishing plays a critical role. It is essential to ensure that the copper surface is recessed slightly from the oxide surface. However, this recess can lead to the occurrence of interfacial voids between the bonded copper interfaces. To examine the effects of copper film thickness on bonding quality and bonding mechanisms in this study, artificial voids were intentionally introduced at the bonded interfaces at temperatures of 250 °C and 300 °C. The results revealed that as the thickness of the copper film increases, there is an increase in the bonding fraction and a decrease in the void fraction. The variations in void height with different copper film thicknesses were influenced by the bonding mechanism and bonding fraction.

Difference of Cerebrospinal Fluid Biomarkers and Neuropsychiatric Symptoms Profiles among Normal Cognition, Mild Cognitive Impairment, and Dementia Patient.

The delineation of biomarkers and neuropsychiatric symptoms across normal cognition, mild cognitive impairment (MCI), and dementia stages holds significant promise for early diagnosis and intervention strategies. This research investigates the association of neuropsychiatric symptoms, evaluated via the Neuropsychiatric Inventory (NPI), with cerebrospinal fluid (CSF) biomarkers (Amyloid-ß42, P-tau, T-tau) across a spectrum of cognitive states to enhance diagnostic accuracy and treatment approaches. Drawing from the National Alzheimer's Coordinating Center's Uniform Data Set Version 3, comprising 977 individuals with normal cognition, 270 with MCI, and 649 with dementia. To assess neuropsychiatric symptoms, we employed the NPI to understand the behavioral and psychological symptoms associated with each cognitive category. For the analysis of CSF biomarkers, we measured levels of Amyloid-ß42, P-tau, and T-tau using the enzyme-linked immunosorbent assay (ELISA) and Luminex multiplex xMAP assay protocols. These biomarkers are critical in understanding the pathophysiological underpinnings of Alzheimer's disease and its progression, with specific patterns indicative of disease stage and severity. This study cohort consists of 1896 participants, which is composed of 977 individuals with normal cognition, 270 with MCI, and 649 with dementia. Dementia is characterized by significantly higher NPI scores, which are largely reflective of mood-related symptoms (p < 0.001). In terms of biomarkers, normal cognition shows median Amyloid-ß at 656.0 pg/mL, MCI at 300.6 pg/mL, and dementia at 298.8 pg/mL (p < 0.001). Median P-tau levels are 36.00 pg/mL in normal cognition, 49.12 pg/mL in MCI, and 58.29 pg/mL in dementia (p < 0.001). Median T-tau levels are 241.0 pg/mL in normal cognition, 140.6 pg/mL in MCI, and 298.3 pg/mL in dementia (p < 0.001). Furthermore, the T-tau/Aß-42 ratio increases progressively from 0.058 in the normal cognition group to 0.144 in the MCI group, and to 0.209 in the dementia group (p < 0.001). Similarly, the P-tau/Aß-42 ratio also escalates from 0.305 in individuals with normal cognition to 0.560 in MCI, and to 0.941 in dementia (p < 0.001). The notable disparities in NPI and CSF biomarkers among normal, MCI and Alzheimer's patients underscore their diagnostic potential. Their combined assessment could greatly improve early detection and precise diagnosis of MCI and dementia, facilitating more effective and timely treatment strategies.

Development of End-to-End Artificial Intelligence Models for Surgical Planning in Transforaminal Lumbar Interbody Fusion.

Transforaminal lumbar interbody fusion (TLIF) is a commonly used technique for treating lumbar degenerative diseases. In this study, we developed a fully computer-supported pipeline to predict both the cage height and the degree of lumbar lordosis subtraction from the pelvic incidence (PI-LL) after TLIF surgery, utilizing preoperative X-ray images. The automated pipeline comprised two primary stages. First, the pretrained BiLuNet deep learning model was employed to extract essential features from X-ray images. Subsequently, five machine learning algorithms were trained using a five-fold cross-validation technique on a dataset of 311 patients to identify the optimal models to predict interbody cage height and postoperative PI-LL. LASSO regression and support vector regression demonstrated superior performance in predicting interbody cage height and postoperative PI-LL, respectively. For cage height prediction, the root mean square error (RMSE) was calculated as 1.01, and the model achieved the highest accuracy at a height of 12 mm, with exact prediction achieved in 54.43% (43/79) of cases. In most of the remaining cases, the prediction error of the model was within 1 mm. Additionally, the model demonstrated satisfactory performance in predicting PI-LL, with an RMSE of 5.19 and an accuracy of 0.81 for PI-LL stratification. In conclusion, our results indicate that machine learning models can reliably predict interbody cage height and postoperative PI-LL.

Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP).

OBJECTIVES: To investigate whether utilizing a convolutional neural network (CNN)-based arterial input function (AIF) improves the volumetric estimation of core and penumbra in association with clinical measures in stroke patients. METHODS: The study included 160 acute ischemic stroke patients (male = 87, female = 73, median age = 73 years) with approval from the institutional review board. The patients had undergone CTP imaging, NIHSS and ASPECTS grading. convolutional neural network (CNN) model was trained to fit a raw AIF curve to a gamma variate function. CNN AIF was utilized to estimate the core and penumbra volumes which were further validated with clinical scores. RESULTS: Penumbra estimated by CNN AIF correlated positively with the NIHSS score (r = 0.69; p < 0.001) and negatively with the ASPECTS (r = - 0.43; p < 0.001). The CNN AIF estimated penumbra and core volume matching the patient symptoms, typically in patients with higher NIHSS (> 20) and lower ASPECT score (< 5). In group analysis, the median CBF < 20%, CBF < 30%, rCBF < 38%, Tmax > 10 s, Tmax > 10 s volumes were statistically significantly higher (p < .05). CONCLUSIONS: With inclusion of the CNN AIF in perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke. CRITICAL RELEVANCE STATEMENT: With CNN AIF perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke.

Biomechanical Investigation of Hallux Valgus Deformity Treated with Different Osteotomy Methods and Kirschner Wire Fixation Strategies Using the Finite Element Method.

The aim of this study was to propose a finite element method based numerical approach for evaluating various hallux valgus treatment strategies. We developed three-dimensional hallux valgus deformity models, with different metatarsal osteotomy methods and Kirschner wire fixation strategies, under two types of standing postures. Ten Kirschner wire fixations were analyzed and compared. The fixation stability, bone stress, implant stress, and contact pressure on the osteotomy surface were calculated as the biomechanical indexes. The results showed that the biomechanical indexes of the osteotomy and Kirschner wire fixations for hallux valgus deformity could be effectively analyzed and fairly evaluated. The distal metatarsal osteotomy method provided better biomechanical indexes compared to the proximal metatarsal osteotomy method. This study proposed a finite element method based numerical approach for evaluating various osteotomy and Kirschner wire fixations for hallux valgus deformity before surgery.

Natural resource volatility and financial development during Covid-19: Implications for economic recovery.

Demand for natural resources is constant, while the prices of natural resources increase day-by-day, which has a significant impact on financial development and economic activity. Thus, the study intends to test the association of natural resource volatility and financial development, in order to recommend policies for economic recovery. The study acquires and analyses data for the N11 economies. The findings reveal that natural resource volatility is linked to global economic growth and governmental governance in pre-pandemic era as well as during pandemic. Results exposed that natural resource volatility has a large detrimental impact on global economic growth and plays a prominent part in economic recovery. The findings are robust and reveal that natural gas, oil, and the quality of public administration all contribute to N11 financial development. The study suggests that policymakers address the challenges raised through the solutions discussed.

Effect of Porosity and Heat Treatment on Mechanical Properties of Additive Manufactured CoCrMo Alloys.

To minimize the stress shielding effect of metallic biomaterials in mimicking bone, the body-centered cubic (bcc) unit cell-based porous CoCrMo alloys with different, designed volume porosities of 20, 40, 60, and 80% were produced via a selective laser melting (SLM) process. A heat treatment process consisting of solution annealing and aging was applied to increase the volume fraction of an ε-hexagonal close-packed (hcp) structure for better mechanical response and stability. In the present study, we investigated the impact of different, designed volume porosities on the compressive mechanical properties in as-built and heat-treated CoCrMo alloys. The elastic modulus and yield strength in both conditions were dramatically decreased with increasing designed volume porosity. The elastic modulus and yield strength of the CoCrMo alloys with a designed volume porosity of 80% exhibited the closest match to those of bone tissue. Different strengthening mechanisms were quantified to determine their contributing roles to the measured yield strength in both conditions. The experimental results of the relative elastic modulus and yield strength were compared to the analytical and simulation modeling analyses. The Gibson-Ashby theoretical model was established to predict the deformation behaviors of the lattice CoCrMo structures.

The impact of high economic growth and technology advancement on extensive energy production in China: evidence using NARDL model.

High energy production is the global requirement that is the demand of high economic growth in the country and needs regulators and recent researchers' emphasis. Therefore, the current study examines the impact of economic factors such as gross domestic product (GDP), national income, employment rate, foreign direct investment (FDI), and inflation and technological advancement on energy production in China. The present article has used the secondary data extracted from World Development Indicators (WDIs) from 1976 to 2020. The present research has employed the nonlinear autoregressive distributed lagged (NARDL) model to explore the association among the understudy constricts. The findings revealed that all the economic factors such as GDP, national income, employment rate, FDI, inflation, and technological advancement have a significant and positive association with energy production in China. This article guides the relevant authorities and policymakers in developing and implementing the policies related to generating high energy production using foremost economic factors.

Investigating Medical Cost and Mortality Among Psychiatric Patients Involuntary Admissions: A Nationwide Propensity Score-Matched Study.

OBJECTIVE: Involuntary admission to psychiatric inpatient care can protect both patients with severe mental illnesses and individuals around them. This study analyzed annual healthcare costs per person for involuntary psychiatric admission and examined categories of mental disorders and other factors associated with mortality. METHODS: This retrospective cohort study collected 1 million randomly sampled beneficiaries from the National Health Insurance Database for 2002-2013. It identified and matched 181 patients with involuntary psychiatric admissions (research group) with 724 patients with voluntary psychiatric admissions (control group) through 1:4 propensity-score matching for sex, age, comorbidities, mental disorder category, and index year of diagnosis. RESULTS: Mean life expectancy of patients with involuntary psychiatric admissions was 33.13 years less than the general population. Average annual healthcare costs per person for involuntary psychiatric admissions were 3.94 times higher compared with voluntary admissions. The general linear model demonstrated that average annual medical costs per person per compulsory hospitalization were 5.8 times that of voluntary hospitalization. Survival analysis using the Cox proportional hazards model found no significant association between type of psychiatric admission (involuntary or voluntary) and death. CONCLUSION: This study revealed no significant difference in mortality between involuntary and voluntary psychiatric admissions, indicating involuntary treatment's effectiveness.

Unlocking the role of energy poverty and its impacts on financial growth of household: is there any economic concern.

The major purpose of this study is to assess racial disparity and energy poverty index by measuring energy poverty index by using data envelopment analysis and regression equation from South Asia (2001-2018). An energy poverty index is quantifying the size and scope of energy poverty, and DEA is used to investigate the relevance of socioeconomic position to multidimensional energy poverty. In multidimensional energy poverty, location, house ownership position, number of dependents, and the age of the main caregiver have an important positive impact. Our research has shown that Bhutan is the most susceptible nation with an energy poverty index of (0.02), Maldives (0.03), and Bangladesh (0.11), while India (0.86) and Pakistan (0.49) are the least likely to be energy poor as regards energy poverty. Of the total energy production, 78% is based on traditional fuels, followed by 12% based on petroleum products. The Gini index indicates a positive association with the energy poverty index at a 5% significance level. This signifies that these socioeconomic indicators positively contribute to the energy poverty index level. This study developed more synchronized policies to eradicate energy poverty and can provide a way forward for policymakers to develop strategies to implement them suitably in the regional power sector.

Assessing the impact of green fiscal policies and energy poverty on energy efficiency.

This article estimates the ties between green fiscal policies and energy efficiency in COVID-19 era. For this purpose, data envelopment analysis (DEA) approach is considered and applied. The study findings show that green fiscal policies, such as public supports and tax rebates, have significant role in reducing energy poverty of different international countries by advancing energy efficiency. Therefore, a panel data ranging from 2010 to 2020 is used. Our findings indicate that the aggregate degree of green fiscal policies help to decline energy poverty. Renewable energy companies had larger series of net fiscal competence and size efficiency, and their levels of energy efficiency were greater than 0.457%, with the 16% effect of current public supports and 11% effect of taxation rebates supported to diminish energy poverty with 29.7% in different international economies. This is a positive effect by green fiscal policies. The study also presented policy implications suggesting effectively implementing green fiscal policies for more efficient carbon reduction and making climate change supportive for peoples in post COVID-19 period.

Financing for energy efficiency solutions to mitigate opportunity cost of coal consumption: An empirical analysis of Chinese industries.

This study measures the energy rebound effects of Chinese energy and coal power use in Chinese energy-intensive industries by using latent class stochastic frontier models like LMDI, and other various econometric estimation approach for coal-supplying regions in China ranging between 1992 and 2018. The findings reveals that China's coal sector's average capacity consumption is 0.81%, with a pattern of first increasing and then decreasing, falling to 0.68% in 2016 specifically. The coal capacity operation rate concerning low as well as depleted regions is generally strong, with limited space for expansion. In 2015 and 2016, the utilization rate of coal production potential in moderate-producing areas fell about 42%. Economic development variables affect the capacity utilization levels of moderate, weak, and depleted generating regions. At the same time, the price volatility cannot induce a practical improvement in the ability utilization rate, which means that China's coal industry is mainly un-marketized. China's energy efficiency increased about 19.98% among 2000 and 2016, while the rapidest expansion pattern has been noted in the eastern province at 39.86%, next to central (11.71%) and western regions (9.59%). The take back impact via the renewable energy and renewable productivity channels is estimated as 12.34% and 25.40%, respectively. Therefore, the take back impact is of significant importance regarding energy preservation, as China's cumulative renewable energy use is equal to China's aggregate energy use. On such findings, recent research also contributed by presenting novel policy implications for key stakeholders.

The role of solar energy and eco-innovation in reducing environmental degradation in China: Evidence from QARDL approach.

In the past decade, researchers have shifted their interests to explore different ways to mitigate environmental degradation. In that context, the present study explores the role of solar energy and eco-innovation in reducing environmental degradation in China. The study utilized data for the period 1990-2018 and applied the latest available econometric technique, a quantile autoregressive distributed lag model, to determine the impacts of solar energy and eco-innovation on improving China's environmental quality. According to the empirical results, in the long term, solar energy is negatively and significantly associated with CO2 emissions at higher quantiles. Eco-innovation has proven to be the most important channel to mitigate CO2 emissions in China. Eco-innovation is exerting a negative and significant influence on CO2 emissions at all quantiles in the long term. In addition, the population size is causing CO2 emissions to surge significantly at lower quantiles. The empirical analysis reveals that per capita income (PI) is positively associated with CO2 emissions at all quantiles, but it is significant only at higher quantiles in China. We found evidence of unidirectional causality for eco-innovation to CO2 emissions and solar energy to CO2 emissions. However, for population and CO2 emissions, per capita income, and CO2 emissions, we found bidirectional causality. As indicated by our empirical results, solar energy and eco-innovation are the two most effective channels to control CO2 emissions in China. Therefore, policies based on the promotion of eco-innovation and the initiation of new solar energy projects can control emissions and improve environmental quality in China. Integr Environ Assess Manag 2022;18:555-571. © 2021 SETAC.

The Dynamic Impact Response of 3D-Printed Polymeric Sandwich Structures with Lattice Cores: Numerical and Experimental Investigation.

This paper proposes a dynamic drop weight impact simulation to predict the impact response of 3D printed polymeric sandwich structures using an explicit finite element (FE) approach. The lattice cores of sandwich structures were based on two unit cells, a body-centred cubic (BCC) and an edge-centred cubic (ECC). The deformation and the peak acceleration, referred to as the g-max score, were calculated to quantify their shock absorption characteristic. For the FE results verification, a falling mass impact test was conducted. The FE results were in good agreement with experimental measurements. The results suggested that the strut diameter, strut length, number and orientation, and the apparent material stiffness of the lattice cores had a significant effect on their deformation behavior and shock absorption capability. In addition, the BCC lattice core with a thinner strut diameter and low structural height might lead to poor shock absorption capability caused by structure collapse and border effect, which could be improved by increasing its apparent material stiffness. This dynamic drop impact simulation process could be applied across numerous industries such as footwear, sporting goods, personal protective equipment, packaging, or biomechanical implants.