Aggregate Performance Research Articles

Study of the Performance of HDPE and PVC Plastic Aggregates in Concrete

Study of the Performance of HDPE and PVC Plastic Aggregates in Concrete

Impact of pre-soaked lime water carbonized recycled fine aggregate on mechanical properties and pore structure of 3D printed mortar

Compared to natural fine aggregate, recycled fine aggregate has disadvantages such as high water absorption and poor strength. Accelerated carbonization is considered a method to enhance recycled aggregate performance. However, the lack of substances available for carbonation reaction in old cement mortar restricts the enhancement of RFA performance. In this study, a pre-soaked lime-water carbonization method was employed to enhance carbonization efficiency and improve cement mortar performance by introducing additional sources of calcium. Therefore, the focus was on investigating the effects of pre-soaked lime-water carbonization-treated recycled fine aggregate with different replacement ratios (0%, 30%, 50%, 70%, 100%) on the mechanical properties of 3D printed mortar. In addition, combining XRD、SEM-EDS, and X-CT microscale testing methods, the study analyzed the influence of carbonized recycled fine aggregate on the pore structure of 3D printed recycled mortar. The purpose was to reveal the reinforcement mechanism of pre-soaked lime water composite carbonization. Experimental results showed that the pre-soaked lime water carbonization method improved the physical properties of recycled fine aggregate, with the most significant decrease in the water absorption ratio, which decreased by 53.89%. Incorporating appropriate carbonized recycled fine aggregate can enhance the mechanical properties of 3D printed mortar, increase interlayer bonding strength, reduce mechanical anisotropy and overall porosity, and improve pore defects. Defects on the aggregate surface are filled with CaCO3 carbonization products, improving the pore structure and strengthening the interfacial transition zone, thus enhancing the resistance of 3D printed recycled mortar to external forces. The findings of the present work not only improve the utilization of recycled sand but also provide methods and ideas for improving the properties of 3D printed recycled mortar.

Carbon pricing and aggregate macroeconomic performance in the Eurozone: a contribution to the climate policy debate using the EU ETS and Macroeconomic Performance Index

This article contributes to the carbon pricing debate by providing new evidence on the aggregate macroeconomic effect of the European Union Emissions Trading Scheme (EU ETS) in the Eurozone. To this end, a novel macroeconomic performance index is created to capture the overall economic performance of the Eurozone countries. The index is a weighted aggregation of key macroeconomic variables—GDP growth rate, inflation rate, employment rate, exchange rate, and long-term inflation rate—for the 19 member countries of the group. The effect of the EU ETS on this macroeconomic performance index is then empirically examined while controlling for the effects of physical capital accumulation, human capital accumulation and regulatory quality. A panel framework covering the period 2005–2022 is set up to achieve this objective and the relationship is examined using panel method of moments quantile regression with fixed effects, as well as fixed and random effects regressions of Driscoll and Kraay. A number of important revelations are made. Firstly, the behavior of the macroeconomic performance index constructed clearly reflects the economic reality of the Eurozone, with downward spikes visible in periods corresponding with the economic crisis of 2007–2009, the Eurozone debt crisis of 2010/2011, the aftermath of the financial crisis of 2016, and periods around the outbreak of the Coronavirus (COVID-19) pandemic. Hence, a case is made for the use of the macroeconomic performance index as a superior aggregate measure of the overall economic performance in empirical research. Secondly, a statistically significant negative effect of the EU ETS on aggregate macroeconomic performance in the Eurozone is confirmed. This shows that there are significant economic costs associated with the use of carbon pricing as a means of lowering pollution. Thirdly, the findings further show that the negative impact gradually decreases (in absolute values) from lower to higher quantiles. Overall, higher carbon prices cause greater economic disruption when macroeconomic performance is relatively poor but have less damaging effect when aggregate economic performance is relatively strong. Policy recommendations based on the study findings are also provided.

Effect of co-combustion of coal with biomass on the morphology of soot

This study performed an experimental investigation to explore the effect of co-combustion of coal with biomass on the morphological parameters and nanostructure of soot. Three types of soot, namely Shenghua coal (SH) soot, Pinewood biomass (PW) soot, and Co-combustion of SH and PW (CC) soot, were collected from the combustion flames of Shenhua coal, Pinewood biomass, and a mixture of coal and biomass. Thereafter, the soot samples were observed and analyzed by means of transmission electron microscope (TEM) and scanning electron microscope (SEM) to obtain their morphological parameters and nanostructure characteristics. The TEM results showed that the size of nascent soot particles generated in co-combustion flame is the largest, rather than between the sizes of PW and SH soot. The nanostructure of the three types of soot primary particles is mainly composed of straight parallel crystal stripes. The separation distance of crystal stripes in SH soot was measured to be larger than that in PW soot, indicating that the crystal structure of soot generated in Pine biomass combustion flame is more compact. It was also observed that both coal-derived primary soot particles and biomass-derived primary soot particles were formed in the co-combustion flame. SEM results indicated that PW soot agglomerates have stronger aggregation performance and more compact structure then SH soot and CC soot. The primary particle size of SH and CC soot agglomerates was mainly concentrated between 50 and 70 nm, while that of PW soot agglomerates was relatively uniform. Furthermore, the number of primary particles in SH soot and CC soot was distributed over a wide range compared to PW soot.

A Finite Element Method for Determining the Mechanical Properties of Electrospun Nanofibrous Mats.

This study focuses on the mechanical properties of electrospun nanofibrous mats, highlighting the importance of the characteristics of single nanofibers in determining the overall mechanical behavior of the mats. Recognizing the significant impacts of the diameter and structural properties of the nanofibers, this research introduces a novel methodology for deriving the effects of the mechanical properties of single nanofibers on the aggregate mechanical performance of electrospun oriented nanofiber mats. For this purpose, a finite element method (FEM) model is developed to simulate the elastoplastic response of the mats, incorporating the influence of structural parameters on mechanical properties. The validation of the FEM model against experimental data from electrospun polyacrylonitrile (PAN) nanofibers with different orientations demonstrates its effectiveness in capturing the elastic-plastic tensile behaviors of the material and confirms its accuracy in terms of reflecting the complex mechanical interactions within the nanofibrous mats. Through a detailed analysis of how nanofiber diameter, orientation of fibers, length-to-width ratio, and porosity affect the mechanical properties of the mats, this research provides valuable insights for the engineering of nanofibrous materials to meet specific mechanical requirements. These findings improve our understanding of nanofibrous mat structures, allowing for better performance in diverse applications as well as highlighting the critical importance of identifying the properties of single nanofibers and their associated impacts on material design.

Decomposition approach for examining effects of logistic performance on exports: A case study of Vietnam

The study evaluates to what extent logistics performance and its components impact Vietnam’s bilateral export value. The augmented Gravity model is applied on panel data in the period from 2010 to 2018. Logistics efficiency is measured by Logistic performance index (LPI) and its sub-indices developed by the World Bank. A variety of diagnostic tests and estimation methods are employed to ensure the stability of the results. The main findings confirm that all explanatory variables demonstrate the expected signs, and aggregate logistics performance and its sub-indices have positive impacts on Vietnam’s export flows, with the magnitude of logistics impacts is greater than other factors in the research model. Among LPI components of Vietnam, Ease of arranging shipments index is the most influential factor on exports, followed by Infrastructure, Timeliness, and Quality of logistics services. These export’s effects are also identified by partners’ LPI indicators namely Quality of logistics services, Customs, Infrastructure, and Tracking and tracing.

New Approach to Equitable Intervention Planning to Improve Engagement and Outcomes in a Digital Health Program: Simulation Study.

Digital health programs provide individualized support to patients with chronic diseases and their effectiveness is measured by the extent to which patients achieve target individual clinical outcomes and the program's ability to sustain patient engagement. However, patient dropout and inequitable intervention delivery strategies, which may unintentionally penalize certain patient subgroups, represent challenges to maximizing effectiveness. Therefore, methodologies that optimize the balance between success factors (achievement of target clinical outcomes and sustained engagement) equitably would be desirable, particularly when there are resource constraints. Our objectives were to propose a model for digital health program resource management that accounts jointly for the interaction between individual clinical outcomes and patient engagement, ensures equitable allocation as well as allows for capacity planning, and conducts extensive simulations using publicly available data on type 2 diabetes, a chronic disease. We propose a restless multiarmed bandit (RMAB) model to plan interventions that jointly optimize long-term engagement and individual clinical outcomes (in this case measured as the achievement of target healthy glucose levels). To mitigate the tendency of RMAB to achieve good aggregate performance by exacerbating disparities between groups, we propose new equitable objectives for RMAB and apply bilevel optimization algorithms to solve them. We formulated a model for the joint evolution of patient engagement and individual clinical outcome trajectory to capture the key dynamics of interest in digital chronic disease management programs. In simulation exercises, our optimized intervention policies lead to up to 10% more patients reaching healthy glucose levels after 12 months, with a 10% reduction in dropout compared to standard-of-care baselines. Further, our new equitable policies reduce the mean absolute difference of engagement and health outcomes across 6 demographic groups by up to 85% compared to the state-of-the-art. Planning digital health interventions with individual clinical outcome objectives and long-term engagement dynamics as considerations can be both feasible and effective. We propose using an RMAB sequential decision-making framework, which may offer additional capabilities in capacity planning as well. The integration of an equitable RMAB algorithm further enhances the potential for reaching equitable solutions. This approach provides program designers with the flexibility to switch between different priorities and balance trade-offs across various objectives according to their preferences.

PROPERTIES OF CONCRETE AND FIBER-REINFORCED CONCRETE FOR BASES OF ROAD CLOTHES BASED ON SECONDARY AGGREGATES WITH HETEROGENEOUS COMPOSITION

The problem of disposal of concrete scrap of dismantled building structures is relevant for most countries of the world. For Ukraine, this problem is even more acute due to the significant amount of destruction caused by hostilities and rocket attacks. In current research the properties of concrete and fibre-reinforced concrete for the bases of road clothes based on natural and secondary aggregates were compared: granite river gravel, secondary crushed stone with a heterogeneous composition, quartz sand and secondary sand from recycled reinforced concrete structures. CEM III/A slag Portland cement with a blast furnace slag content of 65% and a polycarboxylate type superplasticizer were used. Three series of samples were studied: without fibre; with glass fibre ANTI-CRAK HP 12 (length 12 mm, diameter 0.017 mm, equivalent thread diameter 0.3 mm) in the amount of 1 kg/m3; with polypropylene fibre BeneSteel 55 (length 55 mm, equivalent thread diameter 0.48 mm) in the amount of 4 kg/m3. In each series, concrete on granite gravel and quartz sand, concrete on secondary crushed stone and quartz sand, concrete on secondary crushed stone and secondary sand were studied. The workability of all mixtures was equal to S1. Due to the use of different types of aggregates and fibres, the W/C of concrete mixtures differed significantly. Concretes on secondary aggregates had a higher W/C than on natural aggregates. When using the Anti-Crak HP 12 fibre, the mobility of mixtures with equal W/C increased by 5.5 – 6.9 %. When using BeneSteel 55 fibre, W/C increased by 10.6 – 15.5 %. The type of aggregate had a significant effect on the average density of concrete. When using secondary crushed stone and quartz sand, the average density decreased by 3.8 – 4.6 %. When using secondary crushed stone simultaneously with secondary sand, the average density of concrete decreased by 5.2 – 8.5 %. When using Anti-Crak HP 12 fiber, the average density of concrete decreases by 2 %, when using BeneSteel 55 fibre – up to 4.1 %. Concretes on secondary crushed stone with heterogeneous composition and quartz sand had 4 % higher compressive strength and 2 % higher tensile strength in bending than concretes on granite gravel and similar sand (29.8 MPa and 3.18 MPa, respectively). When secondary crushed stone is used simultaneously with secondary sand, the compressive strength of concrete is only 1.1 % lower than the strength of concrete on natural aggregates, and the tensile strength in bending is 10 % lower. This confirms the possibility of effective use of these concretes for arranging of bases of road clothes. The high-quality performance of secondary aggregates in concrete explains due to their better adhesion to the cement-sand matrix. Dispersed fibre reinforcement with Anti-Crak HP 12 has a positive effect on the compressive strength of concrete on all types of aggregate and increases the tensile strength of concrete on natural aggregates. The use of BeneSteel 55 fibre was not effective due to a significant increase in the W/C of the mixture when it was introduced. In general, taking into account the economic factor, dispersion reinforcement of concrete on secondary aggregates with the types of fibres used in the research is not advisable.

Venturing into Risk Management: Examining Credit Risk and Performance in Pakistani Commercial Banks through Panel VAR Analysis from a Business Perspective

In Pakistan's dynamic financial sector, effective credit risk management by commercial banks is critical to maintaining financial stability. In this study, we embarked on a unique and comprehensive journey, adopting the Panel Vector Autoregressive (PVAR) analysis, a novel methodology in this context. This approach allowed us to delve into the intricate relationship between credit risk management strategies and the aggregate business performance of commercial banks. The data used in this study spans from 2010Q1 to 2022Q4, investigating the comprehensive nexus of credit risk and macroeconomic indicators using a large dataset encompassing 18 strategically critical commercial banks of Pakistan. Our study uncovers several profound insights, documenting a remarkable increase in credit risk upon increases in the interest rate. The repercussions of the non-performing loans are observed nearly two years after the successive phases of a stern posture of monetary policy have been implemented. Our research findings carry significant weight for the banking industry. We discovered that the levels of credit risk could be reined in due to the protective shield offered by phases of sudden economic booms. This underscores the reinforcing effect of the improving economic conditions on defaults of loans, providing practical and actionable insights for credit risk management strategies.

The unequal effects of trade and automation across local labor markets

We quantify the joint impact of the China shock and automation of labor, across US commuting zones (CZs) in the period 2000–2007. To this end, we employ a multi-sector gravity model of trade with Roy-Fréchet worker heterogeneity across sectors, where labor input can be automated. Automation and increased import competition from China are both sector-specific; they lead to contractions in a sector’s labor demand and a decline in relative income for CZs more specialized in that sector, amplified by a voluntary reduction in hours worked and an increase in frictional unemployment. The estimated model fits well with the aggregate performance of manufacturing subsectors and with the variation across CZs in changes in average income, the hourly wage, hours worked, the employment rate and employment in manufacturing. By itself, the China shock has stronger distributional effects than automation, but its impact on aggregate gains is less than a third of automation’s impact.

A Statistical Analysis of Vietnamese High School English Test Score Data

This statistical study, perhaps the first of its kind in Vietnamese academia, has tried to understand the patterns in high school students’ pre-college national exam test scores which hold tremendous significance in students’ college or university admissions. English belongs to one of the three mandatory subjects (the other two being Mathematics and Literature) where students must be tested as part of their overall evaluation. However, since English is a foreign language for the learners, many high school students, especially those from rural areas, struggle to attain an acceptable level of proficiency. Meanwhile, a good proportion of students, particularly those in urban areas and with their own substantial financial resources, do attend extra tutorial classes to gain an edge in English proficiency. In this study we have analyzed the provincial mean and median test scores over a period of three years (2019, 2020 and 2021). It has been noted that a province’s aggregate performance depends heavily on - (i) the province’s per capita gross domestic product (PCGDP), which measures a province's economic prosperity; and (ii) the average number of students per high school (ANSHS) which indicates the overall competitive environment the students encounter which helps them sharpen their language proficiency.

Integrating narrow and wide framing disposition effect: A novel approach incorporating perceived risk and realized asset performance

This paper proposes an integrated framing approach to investigate the disposition effect by linking it to regret and prospect theory. We build upon the wide framing hypothesis and extend prior research by including metrics of perceived risk and the number of days stocks are at a gain. Using a sample of 24 million trading operations conducted by nearly 4,000 Italian investors from 2010 to 2018, we demonstrate that investment decisions are influenced not only by portfolio aggregate performance but also by asset trading history and perceived volatility. Our results align with several theoretical frameworks that could explain the disposition effect, such as mental accounting, prospect theory and regret aversion. Furthermore, by incorporating both narrow and wide framing factors, along with their interactions, our approach provides a more comprehensive understanding of the disposition effect.

Classification of geogrid reinforcement in aggregate using machine learning techniques

The present study proposes a novel ML methodology for differentiating between unstabilized aggregate specimens and those stabilized with triangular and rectangular aperture geogrids. This study utilizes the compiled experimental results obtained from stabilized and unstabilized specimens under repeated loading into a balanced, moderate-sized database. The efficacy of five ML models, including tree-ensemble and single-learning algorithms, in accurately identifying each specimen class was explored. Shapley’s additive explanation was used to understand the intricacies of the models and determine global feature importance ranking of the input variables. All the models could identify the unstabilized specimen with an accuracy of at least 0.9. The tree-ensemble models outperformed the single-learning models when all three classes (unstabilized specimens and specimens stabilized by triangular and rectangular aperture geogrids) were considered, with the light gradient boosting machine showing the best performance—an accuracy of 0.94 and an area under the curve score of 0.98. According to Shapley’s additive explanation, the resilient modulus and confining pressure were identified as the most important features across all models. Therefore, the proposed ML methodology may be effectively used to determine the type and presence of geogrid reinforcement in aggregates, based on a few aggregate material properties and performance under repeated loading.

Urine-Based Biomarker Test Uromonitor® in the Detection and Disease Monitoring of Non-Muscle-Invasive Bladder Cancer-A Systematic Review and Meta-Analysis of Diagnostic Test Performance.

Optimal urine-based diagnostic tests (UBDT) minimize unnecessary follow-up cystoscopies in patients with non-muscle-invasive bladder-cancer (NMIBC), while accurately detecting high-grade bladder-cancer without false-negative results. Such UBDTs have not been comprehensively described upon a broad, validated dataset, resulting in cautious guideline recommendations. Uromonitor®, a urine-based DNA-assay detecting hotspot alterations in TERT, FGFR3, and KRAS, shows promising initial results. However, a systematic review merging all available data is lacking. Studies investigating the diagnostic performance of Uromonitor® in NMIBC until November 2023 were identified in PubMed, Embase, Web-of-Science, Cochrane, Scopus, and medRxiv databases. Within aggregated analyses, test performance and area under the curve/AUC were calculated. This project fully implemented the PRISMA statement. Four qualifying studies comprised a total of 1190 urinary tests (bladder-cancer prevalence: 14.9%). Based on comprehensive analyses, sensitivity, specificity, positive-predictive value/PPV, negative-predictive value/NPV, and test accuracy of Uromonitor® were 80.2%, 96.9%, 82.1%, 96.6%, and 94.5%, respectively, with an AUC of 0.886 (95%-CI: 0.851-0.921). In a meta-analysis of two studies comparing test performance with urinary cytology, Uromonitor® significantly outperformed urinary cytology in sensitivity, PPV, and test accuracy, while no significant differences were observed for specificity and NPV. This systematic review supports the use of Uromonitor® considering its favorable diagnostic performance. In a cohort of 1000 patients with a bladder-cancer prevalence of ~15%, this UBDT would avert 825 unnecessary cystoscopies (true-negatives) while missing 30 bladder-cancer cases (false-negatives). Due to currently limited aggregated data from only four studies with heterogeneous quality, confirmatory studies are needed.

Machine learning optimization of bio-sandcrete brick modelling using response surface methodology

In this study, raw grinded groundnut shell (RGGNS) was used as a fine aggregate in the brick industry to reuse agricultural waste in building materials. In this study, an experimental approach was used to examine a new cement brick with raw groundnut shells integrated with compressive strength, water absorption and dry density optimization utilizing response surface methodology (RSM). The raw ground-nut shell content improved the fine aggregate performance of the 40%, 50%, and 60% samples. The 28-day high compressive strength with the raw ground-nut shell was 6.1 N/mm2 maximum, as needed by the technical standard. Samples made from 40%, 50%, and 60% raw groundnut shells yielded densities of 1.7, 2.2, and 1.9 kg/cm3 for groundnut shell (GNS) brick, respectively. A product's mechanical properties meet the IS code standard’s minimum requirements. RSM was then utilized to develop a model for the addition of raw groundnut shell to concrete. R-square and Adeq precision values indicated that the results are highly significant, and equations for predicting compressive strength, water absorption, and dry density have been developed. In addition, optimization was performed on the RSM findings to determine the efficiency optimization of the model. Following the optimization results, experiments were conducted to determine the applicability of the optimized model.

Abstract WP78: Large Vessel Occlusion Triage and Routing Utilized for Long-Distance Stroke Transports by Emergency Medical Services (LVO TRUST EMS)

Introduction: Endovascular treatment (EVT) is the most effective therapy in acute ischemic stroke (AIS) with large vessel occlusion (LVO) but its practice is limited to comprehensive stroke centers (CSC). Previous studies have suggested that trained Emergency Medical Services (EMS) professionals can successfully detect LVO cases using a validated screening tool. For these cases, direct transfer to CSC bypassing community hospitals could lead to better outcomes. Methods: This is a retrospective cohort study from 01/01/2018-12/31/2022 of patients with suspected stroke transported by EMS providers from 14 community agencies (primarily fire paramedics) in Southeastern Massachusetts (EMS Region V). When stroke was suspected, EMS providers applied the Field Assessment Stroke Triage for Emergency Destination (FAST-ED) score to assess severity. Bypass of local community hospitals for direct transport to CSC was generally considered for patients with FAST-ED ≥4 and approval of the Region V EMS Medical Director, with the goal of early intervention for severe strokes. In addition to standard education on the use of the FAST-ED score, the EMS agencies received annual aggregate performance feedback and individual case feedback (when requested) from the CSC. Data were collected from the Electronic Health Record (EHR) on demographics, comorbidities, final diagnoses, FAST-ED scores, NIHSS and acute treatments. Descriptive statistics were performed to describe the accuracy of diagnosis and rates of treatment. Results: Among 166 triaged patients to go directly to CSC, 57.6% (95/166) were diagnosed with AIS, and 61.1% (58/95) of them were found to have LVO. There was a higher proportion of AIS or intracranial hemorrhage (ICH) in cases with higher FAST-ED scores. Only 23.4% (39/166) of the cases were diagnosed with stroke mimics, primarily related to lower FAST-ED scores. In total, 36.9% of patients with AIS received EVT: thrombectomy with standard window tPA (21.1%) or late presenter tPA (LKW ≥ 4.5 h, 1.1%), and without tPA (14.7%). Conclusions: A diverse group of community-based EMS services in Southeastern Massachusetts using a prehospital tool can identify patients with severe stroke, i.e. AIS due to LVO or ICH, with acceptable diagnostic accuracy.

Study on the curing conditions on the physico-mechanical and environmental performance of phosphogypsum-based artificial aggregates

The considerable accumulation of phosphogypsum (PG) not only occupies land space but also causes environmental pollution, leading to threatening human health. Under the background of the current massive demand for construction concrete, the problem of natural aggregate shortage urgently needs to be solved. In this research, the raw materials of PG, slag, and cement were adopted to prepare PG-based artificial aggregates (PBAAs) by compression methods, which aimed to develop the applications of PG to construction concrete materials. The different curing temperatures and solutions (Na2SO4 and CaSO4·2 H2O) were investigated to explore the effects of curing conditions on the physical properties and ion leaching of PBAAs. The performance of PBAAs from the aspects of compressive strength, pH value, bulk density, softening coefficient, water absorption, and carbonization degree was evaluated. The microscopic morphology analysis and phase, as well as quantitative analysis were conducted by scanning electron microscopy (SEM) and X-ray diffraction (XRD), respectively, to study the hydration mechanism of PBAAs under different curing conditions. The Avrami model was used to simulate the PG dissolution rate of PBAAs. The results showed that 40 °C was the most favorable temperature for PBAAs and that the dissolution of PG during immersion curing was mainly controlled by ion reactions. The PBAAs cured in saturated Na2SO4 solution raised the pH value of the curing solution. The curing conditions of CaSO4·2 H2O at 40 °C inhibited the dissolution of PG in raw materials, and provided Ca2+ for the formation of ettringite and C-S-H gel, thereby increasing the bulk density, compressive strength, carbonization resistance, and reducing the water absorption rate of PBAAs. Moreover, PBAAs cured in saturated CaSO4·2 H2O and Na2SO4 solutions at 40 °C effectively solidified heavy metals and P elements in PG. This study contributes to developing the replacement of natural aggregates by PBAAs in construction concrete, which can reduce the consumption of natural aggregates and improve the utilization rate of PG.

Decisionalizing the problem of reliance on expert and machine evidence

Abstract This article analyzes and discusses the problem of reliance on expert and machine evidence, including Artificial Intelligence output, from a decision-analytic point of view. Machine evidence is broadly understood here as the result of computational approaches, with or without a human-in-the-loop, applied to the analysis and the assessment of the probative value of forensic traces such as fingermarks. We treat reliance as a personal decision for the factfinder; specifically, we define it as a function of the congruence between expert output in a given case and ground truth, combined with the decision-maker’s preferences among accurate and inaccurate decision outcomes. The originality of this analysis lies in its divergence from mainstream approaches that rely on standard, aggregate performance metrics for expert and AI systems, such as aggregate accuracy rates, as the defining criteria for reliance. Using fingermark analysis as an example, we show that our decision-theoretic criterion for the reliance on expert and machine output has a dual advantage. On the one hand, it focuses on what is really at stake in reliance on such output and, on the other hand, it has the ability to assist the decision-maker with the fundamentally personal problem of deciding to rely. In essence, our account represents a model- and coherence-based analysis of the practical questions and justificatory burden encountered by anyone required to deal with computational output in forensic science contexts. Our account provides a normative decision structure that is a reference point against which intuitive viewpoints regarding reliance can be compared, which complements standard and essentially data-centered assessment criteria. We argue that these considerations, although primarily a theoretical contribution, are fundamental to the discourses on how to use algorithmic output in areas such as fingerprint analysis.

Central core substitution optimized molecular aggregation and photovoltaic performance in A-DA′D-A type acceptors

With the development of A-DA′D-A (D: electron-donating unit; A: electron-withdrawing unit) type acceptors, the performances of organic solar cells (OSCs) have reached a new stage. The central core of A-DA′D-A molecules exhibits a significant impact on molecular planarity and stacking. In this research, the conjugated central core of A-DA′D-A type molecules was extended to the two-dimensional (2D) direction, and the influence of alkyl chains on the 2D-central cores on molecular structure and device performances was explored. Compared with TPTC6 containing alkyl chains, TPTC0 without alkyl chains showed stronger intermolecular interactions and crystallinity, which realized advantageous phase separation in the blend film, leading to more effective charge separation and transport. Therefore, devices based on TPTC0 exhibited an excellent power conversion efficiency (PCE) of 17.70%. In contrast, the blend film exhibited a high energetic disorder due to the poor miscibility between PM6 and TPTC0, resulting in decreased VOC compared with TPTC6. In this work, the influence of alkyl chains on molecular stacking and crystallinity in 2D-expanding conjugated materials was discussed in detail, which would promote the development of related research.

CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping

Estimating 360∘ depth from multiple cameras has been a challenging problem. However, existing methods often adopt a fixed-step spherical sweeping approach with densely sampled spheres and use numerous 3D convolutions in networks, which limits the speed of algorithms in practice. Additionally, obtaining high-precision depth maps of real scenes poses a challenge for the existing algorithms. In this paper, we design a cascade architecture using a dynamic spherical sweeping method that progressively refines the depth estimation from coarse to fine over multiple stages. The proposed method adaptively adjusts sweeping intervals and ranges based on the predicted depth and the uncertainty from the previous stage, resulting in a more efficient cost aggregation performance. The experimental results demonstrated that our method achieved state-of-the-art accuracy with reduced GPU memory usage and time consumption compared to the other methods. Furthermore, we illustrate that our method achieved satisfactory performance on real-world data, despite being trained on synthetic data, indicating its generalization potential and practical applicability.