Gap Setting Research Articles

Study on mixed traffic characteristics around highway on-ramp bottleneck using a microscopic simulation model

In the early mixed traffic environment, connected and automated vehicles (CAVs) tend to follow simple control rules for the sake of traffic safety and stability. Comprehensive analysis of overall mixed traffic performance and vehicle to vehicle interactions around highway on-ramp bottleneck is a basis for further traffic management and control. And yet CAVs in existing researches are mainly in the form of cooperative vehicular platoon, and mixed traffic scenarios within simulation software are not applicable for in-depth investigation. The purpose of this paper is to develop a microscopic simulation model to study in detail lane-based mixed traffic performance around the highway on-ramp. This model firstly considers vehicle characteristics of individual human vehicles (HVs) and CAVs by combining different microscopic traffic models. After the hypothetical on-ramp bottleneck structure is constructed, various simulation scenarios are developed under varying traffic volumes and changing market penetration rates (MPRs) of CAVs. Simulation results show that mixed traffic flow is improved in terms of congestion patterns and traffic capacity. For oscillating and nearly homogeneous congested traffic, congestion degrees are greatly reduced when CAV MPR above 30 % and 50 % respectively. Mixed traffic capacity is increased by 19.3 % when a smaller desired time gap set for CAVs, while it shows no significant change in a larger value. In the HV-HV interactions, traffic conflicts are slightly influenced by CAVs. Comparatively, conflict situations in HV-CAV interactions get worse, the proportion of time to collision (TTC) greater than 2 s monotonically declining with the rise in CAV MPR. It is concluded that traffic characteristics overall show a good momentum when MPR of CAV and the desired time gap of CAVs in mixed traffic are appropriate.

In-pack radio frequency heating of liquid whole egg: Effect of agitation on heating rate and uniformity

In-pack radio frequency (RF) heating of liquid whole egg (LWE) was performed using a 27.12 MHz parallel-plate radio frequency (RF) system. To do this, 600 mL of LWE was subjected to RF heating in a RF-transparent plastic bag (140 mm×180 mm). Experiments were conducted at two different electrode gap settings (98 and 103 mm) with and without orbital movement in the horizontal plane. The effect of agitation on heating rate and uniformity was investigated for varying rpm’s (120 and 140 rpm) for a constant orbit diameter (35 mm). An orbital shaker was specially designed and coupled with the RF system for this purpose. Fiber optic probes were used to continuously measure the temperature of LWE at four different internal locations. Temperature uniformity index (TUI) was calculated to evaluate the uniformity of heating. Coagulation of LWE around the headspace was inevitable when the package was stationary during the treatment. Agitation brought about by orbital movement prevented the coagulation. Drastic improvement in heating rate and uniformity was observed upon orbital movement of LWE at all conditions.

Spin parameter optimization for spin-polarized extended tight-binding methods.

We present an optimization strategy for atom-specific spin-polarization constants within the spin-polarized GFN2-xTB framework, aiming to enhance the accuracy of molecular simulations. We compare a sequential and global optimization of spin parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine. Sensitivity analysis using Sobol indices guides the identification of the most influential parameters for a given reference dataset, allowing for a nuanced understanding of their impact on diverse molecular properties. In the case of the W4-11 dataset, substantial error reduction was achieved, demonstrating the potential of the optimization. Transferability of the optimized spin-polarization constants over different properties, however, is limited, as we demonstrate by applying the optimized parameters on a set of singlet-triplet gaps in carbenes. Further studies on ionization potentials and electron affinities highlight some inherent limitations of current extended tight-binding methods that can not be resolved by simple parameter optimization. We conclude that the significantly improved accuracy strongly encourages the present re-optimization of the spin-polarization constants, whereas the limited transferability motivates a property-specific optimization strategy.

Gap setting control strategy for connected and automated vehicles in freeway lane‐drop bottlenecks

Gap setting control strategy for connected and automated vehicles in freeway lane‐drop bottlenecks

Near-field radiative thermal rectification assisted by Bi2Se3 sheet

The ability to control heat flux at the nanoscale opens up numerous exciting possibilities in modern electronics and the field of information processing. In this research, we propose a design with the focus on achieving efficient thermal rectification at moderate gap and relatively low temperature. This study centers on near-field thermal radiation between temperature dependent indium antimonide (InSb) and silicon carbide (3C-SiC) coated with bismuth selenide (Bi2Se3). Our investigation sheds light on the critical role played by the Bi2Se3 layer in enhancing various key parameters, including the net radiative flux, and thermal rectification efficiency (η). We achieved a substantial improvement in the η of a near-field radiative thermal rectifier (NFRTR) due to the presence of the Bi2Se3 sheet. This enhancement is contingent on factors such as the Fermi energy (Ef) of Bi2Se3, emitter temperature, and the vacuum gap (d). Our study culminated in the identification of an optimal design, achieving an impressive η of 75% at an emitter temperature (TH) of 350 K, with vacuum gap (d) set to 20 nm. Furthermore, increasing TH to 500 K resulted in even more promising outcomes, with the highest η reaching 93%. The need for operating the optimized device at moderate temperatures is to strike a balance between efficiency, safety, cost-effectiveness, and material compatibility. These findings represent a significant step forward in the development of efficient Bi2Se3-based NFRTRs, paving the way for future applications in thermal management, energy conversion systems, and thermal logic gates.

Influence of the thermal shrinkage-induced volume contraction on the yielding behavior of waxy oil

The present article investigates the effect of thermal shrinkage on the yielding behavior of waxy oil gel. The paper compares constant gap and constant normal force measurement protocols to examine the effect of measurement protocol on the rheological behavior of thermoreversible gel. The findings of this study reveal that the extent of stress overshoot and yield stress in a constant shear-rate start-up flow shows a lower magnitude in a constant gap protocol compared to a constant normal force protocol. The decrease in gel strength in the former protocol is mainly attributed to the formation of voids. These voids cause localized fractures within the crystal network. In contrast, in the constant normal force measurement protocol, gel contraction is compensated by utilizing a variable gap setting. Variable gaps compensate for the lower specific volume of the gel after crystallization, minimizing void formation and subsequent rupture of the crystal network. Hence, the gel network formed using the constant normal force protocol is more homogeneous, eliminating the uncertainties in yield stress measurement. Finally, the effect of thermal history, wax content, and aging period on the yield stress values highlights notable findings. Contrary to the conventionally accepted results, the aging period is found to impact the yield stress negatively, and a nonmonotonic relationship between the cooling rate and yield stress is noticed under the constant gap protocol. Thus, the results obtained under the constant normal force protocol are more reliable and can help in developing a fundamental understanding of the yielding behavior.

High-Quality Data Enabling Universality of Band Gap Descriptor and Discovery of Photovoltaic Perovskites.

Extensive machine-learning-assisted research has been dedicated to predicting band gaps for perovskites, driven by their immense potential in photovoltaics. Yet, the effectiveness is often hampered by the lack of high-quality band gap data sets, particularly for perovskites involving d orbitals. In this work, we consistently calculate a large data set of band gaps with a high level of accuracy, which is rigorously validated by experimental and state-of-the-art GW band gaps. Leveraging this achievement, our machine-learning-derived descriptor exhibits exceptional universality and robustness, proving effectiveness not only for single and double, halide and oxide perovskites regardless of the underlying atomic structures but also for hybrid organic-inorganic perovskites. With this approach, we comprehensively explore up to 15,659 materials, unveiling 14 unreported lead-free perovskites with suitable band gaps for photovoltaics. Notably, MASnBr3, FA2SnGeBr6, MA2AuAuBr6, FA2AuAuBr6, FA2InBiCl6, FA2InBiBr6, and Ba2InBiO6 stand out with direct band gaps, small effective masses, low exciton binding energies, and high stabilities.

Use of natural language processing to uncover racial bias in obstetrical documentation

Natural Language Processing (NLP), a form of Artificial Intelligence, allows free-text based clinical documentation to be integrated in ways that facilitate data analysis, data interpretation and formation of individualized medical and obstetrical care. In this cross-sectional study, we identified all births during the study period carrying the radiology-confirmed diagnosis of fibroid uterus in pregnancy (defined as size of largest diameter of >5 cm) by using an NLP platform and compared it to non-NLP derived data using ICD10 codes of the same diagnosis. We then compared the two sets of data and stratified documentation gaps by race. Using fibroid uterus in pregnancy as a marker, we found that Black patients were more likely to have the diagnosis entered late into the patient's chart or had missing documentation of the diagnosis.With appropriate algorithm definitions, cross referencing and thorough validation steps, NLP can contribute to identifying areas of documentation gaps and improve quality of care.

Threshold Graphs with an Arbitrary Large Gap Set

Threshold Graphs with an Arbitrary Large Gap Set

Numerical simulation based performance enhancement approach for an inorganic BaZrS3/CuO heterojunction solar cell

One of the main components of the worldwide transition to sustainable energy is solar cells, usually referred to as photovoltaics. By converting sunlight into power, they lessen their reliance on fossil fuels and the release of greenhouse gases. Because solar cells are decentralized, distributed energy systems may be developed, which increases the efficiency of the cells. Chalcogenide perovskites have drawn interest due to their potential in solar energy conversion since they provide distinctive optoelectronic characteristics and stability. But high temperatures and lengthy reaction periods make it difficult to synthesise and process them. Therefore, we present the inaugural numerical simulation using SCAPS-1D for emerging inorganic BaZrS3/CuO heterojunction solar cells. This study delves into the behaviour of diverse parameters in photovoltaic devices, encompassing efficiency (η) values, short-circuit current density (Jsc), fill factor (FF), and open-circuit voltage (Voc). Additionally, we thoroughly examine the impact of window and absorber layer thickness, carrier concentration, and bandgap on the fundamental characteristics of solar cells. Our findings showcase the attainment of the highest efficiency (η) values, reaching 27.3% for our modelled devices, accompanied by Jsc values of 40.5 mA/cm2, Voc value of 0.79 V, and FF value of 85.2. The efficiency (η) values are chiefly influenced by the combined effects of Voc, Jsc, and FF values. This optimal efficiency was achieved with CuO thickness, band gap, and carrier concentration set at 5 µm, 1.05 eV, and above 1019 cm−3, respectively. In comparison, the optimal parameters for BaZrS3 include a thickness of 1 µm, a carrier concentration below 1020 cm−3, and a band gap less than 1.6 eV. Therefore, in the near future, the present simulation will simultaneously provide up an entirely novel field for the less defective perovskite solar cell.

Consumer knowledge in marketing: a critical review and research agenda

Purpose The purpose of this study is to critically review consumer knowledge in marketing and propose a future research agenda. Despite the many works that have examined this variable, given its strong influence on behaviour, it has generally been studied in association with other constructs, and no studies have focused on it in a specific way. Its definition, measurement and approaches to its role and usefulness are superficial and underdeveloped. After structuring and analysing the existing literature, the authors establish, (I) which aspects are of little use to the discipline, and (II) which research lines have the most potential and should be developed and studied in greater depth, to advance and complete the existing consumer knowledge framework. Design/methodology/approach A search was undertaken for documents in the main databases in which the term “consumer knowledge” appears in a marketing or consumer context, and a critical and reflexive approach was taken to analyse the main contributions and to structure them by content blocks. Findings Five main content blocks were identified. A set of research gaps were detected, mainly related to the lax conceptualisation of the topic, measurement problems and the scarcity of more useful works connected with business management, and several research lines are proposed that complement the existing framework to make it more complete and operational. Originality/value This paper offers a critical review and proposes a research agenda for one of the most used but little studied variables in the field of marketing, which may help academics and professionals in the discipline to continue developing useful theories and models.

Data stewardship in FTLD research: Investigator and research participant views.

Federal policies and guidelines have expanded the return of individual results to participants and expectations for data sharing between investigators and through repositories. Here, we report investigators' and study participants' views and experiences with data stewardship practices within frontotemporal lobal degeneration (FTLD) research, which reveal unique ethical challenges. Semi-structured interviews with (1) investigators conducting FTLD research that includes genetic data collection and/or analysis and (2) participants enrolled in a single site longitudinal FTLD study. Analysis of the interviews identified three meta themes: perspectives on data sharing, experiences with enrollment and participation, and data management and security as mechanisms for participant protections. This study identified a set of preliminary gaps and needs regarding data stewardship within FTLD research. The results offer initial insights on ethical challenges to data stewardship aimed at informing future guidelines and policies.

Automorphic spectra and the conformal bootstrap

We describe a new method for constraining Laplacian spectra of hyperbolic surfaces and 2-orbifolds. The main ingredient is consistency of the spectral decomposition of integrals of products of four automorphic forms. Using a combination of representation theory of P S L 2 ( R ) \mathrm {PSL}_2(\mathbb {R}) and semidefinite programming, the method yields rigorous upper bounds on the Laplacian spectral gap. In several examples, the bound is nearly sharp. For instance, our bound on all genus-2 surfaces is λ 1 ≤ 3.8388976481 \lambda _1\leq 3.8388976481 , while the Bolza surface has λ 1 ≈ 3.838887258 \lambda _1\approx 3.838887258 . The bounds also allow us to determine the set of spectral gaps attained by all hyperbolic 2-orbifolds. Our methods can be generalized to higher-dimensional hyperbolic manifolds and to yield stronger bounds in the two-dimensional case. The ideas were closely inspired by modern conformal bootstrap.

Variational Pair-Density Functional Theory: Dealing with Strong Correlation at the Protein Scale.

Multiconfigurational pair-density functional theory (MC-PDFT) offers a promising solution to the challenges faced by traditional density functional theory (DFT) in addressing molecular systems containing transition metals, open-shells, or strong correlations in general. By utilizing both the density and on-top pair-density, MC-PDFT can make use of a more flexible multiconfigurational wave function to capture the necessary static correlation, while the pair-density functional also includes the effect of dynamic correlation. So far, MC-PDFT has been used after a multiconfigurational self-consistent field (MCSCF) step, using the orbitals and configuration interaction coefficients from the converged MCSCF wave function to compute PDFT energies and properties. Here, instead, we propose to perform a direct optimization of the wave function using the pair-density functionals, resulting in a variational formulation of MC-PDFT. We derive the expressions for the wave function gradient and illustrate their similarity to standard MCSCF equations. Furthermore, we illustrate the accuracy on a set of singlet-triplet gaps as well as dissociation curves. Our findings highlight one of MC-PDFT's standout features: a reduced dependency on the active space size compared to conventional multiconfigurational wave function methodologies. Additionally, we show that the computational cost of MC-PDFT is potentially lower than MCSCF and often on-par with standard Kohn-Sham DFT, which is demonstrated by performing a MC-PDFT calculation of the entire ferredoxin protein with 1447 atoms and nearly 12000 basis functions.

Investigating the effects of trade openness on inclusive growth: evidence from the top five performing SADC members states

This study originates from the notion that trade openness promotes inclusive growth as it promotes economic growth. Hence, this study investigates the effects of trade openness on inclusive growth focusing on the selected SADC countries, aiming to inveterate the flow of inclusive growth. The study findings reveal that selected SADC countries require utilitarian policy to be formed in a country context to return responsive inclusive growth standards. The panel autoregressive distributed lag approach was employed from 2000 to 2020. This study has uncovered a shortage of inclusive principles within SADC, to promote inclusive growth. This study confirms that SADC-inclusive channels should be mended. This study contributes by narrowing the inadequacy gap set between SADC trade openness and inclusive growth. Besides its contribution to the field, the study is envisaged to offer insights into selected SADC member states policymakers to reconsider and prioritize benefits that are tied to inclusive growth through trade openness. The findings of this study provide an important scientific basis to the understanding of the relationship between trade openness and inclusive growth in the SADC region. The study plays an important supporting role by sensitizing policymakers and researchers interested in promoting equitable economic development through inclusive growth.

Gap-free 16-year (2005–2020) sub-diurnal surface meteorological observations across Florida

The sub-tropical, flat, peninsular region of Florida is subject to a unique climate with extreme weather events that impact agriculture, public health, and management of natural resources. Meteorological data at high temporal resolutions especially in tropical latitudes are essential to understand diurnal and semi-diurnal variations of climate, which are considered as the fundamental modes of climate variations of our Earth system. However, many meteorological datasets contain gaps that limit their use for validation of models and further detailed observational analysis. The objective of this paper is to apply a set of data gap filling strategies to develop a gap-free dataset with 15-minute observations for the sub-tropical region of Florida. Using data from the Florida Automated Weather Network (FAWN), methods of linear interpolation, trend continuation, reference to external sources, and nearest station substitution were applied to fill the data gaps depending on the extent of the gap. The outcome of this study provides continuous, publicly accessible surface meteorological observations for 30 FAWN stations at 15-minute intervals for years 2005–2020.

Knowledge Advancing Shopping Mall Living Labs and Customer Value Co-Creation, with a Focus on Social Integration

This paper aims at a broad scan of needs for knowledge advancing living labs and broader customer value co-creation in shopping malls. Special attention is given to the meeting and social integration function and metaverse development. Living labs are understood as a methodology of co-creative and open experimentation that is customer-led in a real-life environment. Emerging metaverse development tends to change opportunities and limitations, about which almost nothing is known. The paper uses a qualitative approach in two steps. The first step explores relevant theory on systems, like retail systems, customer-value co-creation (marketing) and capability and personal traits in behavioural change (individuals), while distinguishing between three living lab stages, namely, anticipation, processes on the way, and ex-post evaluation. A few central themes that are becoming increasingly important are the emerging metaverse, if and how traditional malls can continue serving as physical meeting places, and the possibility they will change course towards new combinations of physical and virtual activity. In the second step, a broad array of knowledge-advancing needs is discussed, and this results in a smaller set of knowledge gaps on the basis of urgency, like interests of new ICT stakeholders in shopping malls, impacts from metaverse on site-specificity, and benefits and barriers of disabled persons. The paper also forwards the need for more attention to the ex-post evaluation of co-creation projects, which calls for standard protocols and AI-supported data systems. The paper closes with contributions, management implications, and ideas about future research.

Smart controllable wave dispersion in acoustic metamaterials using magnetorheological elastomers

The success in the flexible design of smart acoustic metamaterials is crucially contingent upon the degree of control over the parameters that uniquely define the spectrum of band structure and morphology of dispersion surfaces. In this work, we have studied the driving physical mechanisms, the control of which makes possible operating, in real-time, on the set of band gaps formed in 3D metamaterials based on magneto elastomers. In such acoustic structures, the stiffness of the medium in which unit cells are immersed, as well as the stiffness of the shells surrounding multi-particle cores depend on the induction, B(t), of an external magnetic field. The results obtained are systematized through the qualitative analysis of diversе scenarios for the evolution of the frequency characteristics of the metamaterial and summarized in the following complete physical picture of their dynamics. Variation of the stiffness of the medium/shells changes the wavelength of the shell's surface waves at characteristic frequencies of the core vibrations and, as a result, the level of coupling between the vibration modes of the flexible shells and cores. Consequently, with an increase/decrease in the stiffnesses of the shells/medium, the dispersion surfaces of the entire acoustic system shift up/down along the frequency axis with noticeably different ‘mobilities’ that reversibly lead either to the formation of the band gaps in initially dense frequency spectrum or to the transformation of the band gaps formed into pass bands. The tuning of the set of dispersion surfaces depending on the range of changes in the magnetic field induction can be carried out in dynamic, quasi-stationary, and over-critical regimes when some of the dispersion surfaces degenerate into planes. In anisotropic metamaterials, simultaneously with creating full band gaps, it is possible to create tunable directional band gaps with adjustable frequency and angular widths. The results obtained, within the framework of the idealized discrete mass-spring model, are in good agreement with the data presented in the available experimental works and can be useful in the design of acoustic systems with desirable properties.

PSIV-21 Comparing the Nutritional Value and the Trade-Off Between Sugars and Starch of Male-Sterile with Non-Sterile Sorghum Hybrids

Abstract The objective of this study was to assess the effect male-sterile versus non-sterile sorghum hybrids on nutritional value with emphasis on carbohydrates. The experiment was conducted on a dairy farm in West Texas under center pivot irrigation. Two male-sterile (Non-BMR, F465; and BMR12, F430) and three non-sterile sorghum hybrids (non-BMR F10; BMR12, F382, and F431; Richardson Seeds) were used in a split-plot design to reduce cross-pollination. Because of westerly prevailing winds, male-sterile hybrids were allocated to the west and non-sterile hybrids to the east, plots were blocked by irrigation section, and replicated 3 times. Plots were harvested 148 days after seeding with non-sterile hybrids after the hard dough stage and male-sterile hybrids 7 weeks after boot stage and harvested with a self-propelled forage harvester with a theoretical length of cut of 12 mm and roll gap setting at 2 mm. Duplicate samples of freshly chopped forage were obtained from each plot and immediately placed into a cooler with ice until harvest was completed. Samples were then frozen until shipped to Rock River laboratory where near-infrared spectroscopy analysis was performed. In addition, water soluble carbohydrates (WSC) and NDFD at 30h were analyzed with wet chemistry methods. The statistical analysis was performed using mixed linear regression models (GLIMMIX procedure of SAS). Type (Sterile and non-Sterile), trait (BMR12 and non-BMR) and its interaction were included as predictor variables in all models. Normal distribution of residuals was assessed using Shapiro-Wilk test (P > 0.05) and visual diagnostic of residual plots. A summary of the results is presented in Table 1. WSC was 2.5-fold greater for the sterile hybrids when compared with non-sterile, whereas starch was 2.2-fold greater for non-sterile when compared with sterile hybrids (P < 0.0001). Non-fiber carbohydrates were greater for non-sterile over sterile sorghum hybrids (P < 0.0001); however, non-sterile hybrids were past the optimal stage of harvest (soft dough), which would result in poor digestibility of grain starch and protein. In addition, crude protein (CP) was less for sterile than non-sterile (8.29% and 10.2% respectively; P < 0.0001). Compared with non-sterile hybrids, male sterile hybrids DM was decreased (28.4% and 40.8%, respectively; P < 0.0001) due to its proportionally greater vegetative plant parts compared with reproductive plant parts. The trade-off between WSC and starch in male-sterile sorghum hybrids is because sugars are not translocated to the grain where starch is developed as in non-sterile hybrids. This research indicates that concerns about low sorghum starch digestibility can be overcome by using male-sterile sorghum hybrids that will, at least partially, compensate for the reduced starch yield by increasing water-soluble carbohydrates storage.

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement.

With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.