Graphical Approach Research Articles

A Graphical Approach to Automated Congestion Ranking for Signalized Intersections Using High-Resolution Traffic Signal Event Data

A Graphical Approach to Automated Congestion Ranking for Signalized Intersections Using High-Resolution Traffic Signal Event Data

The structure of psychosocial factors in academic success: A gaussian graphical model approach

The structure of psychosocial factors in academic success: A gaussian graphical model approach

Graphical Design Approach for UWB Stacked CG LNA Using Inversion Coefficient

The design of ultra-wide-band (UWB) low-noise amplifiers (LNAs) entails a large number of design challenges and tradeoffs, which include sustaining good input matching over a wide bandwidth along with finding a proper compromise between various LNA performance metrics, such as gain, bandwidth, noise figure, power, and linearity. This paper presents a design approach for UWB LNAs based on the inversion coefficient (IC). The proposed approach is a graphical design approach where the proper operating point is chosen based on predefined constraints. A complete systematic solution is presented for the problem of UWB input matching with a high degree of analytical accuracy. The design approach is illustrated through the design of two UWB stacked common-gate LNAs in 65 nm technology. The post-layout simulation results show very good agreement with analytical expectations. The first LNA achieves an S11 better than −8.2 dB over a 27.6 GHz frequency range, a gain of 12.4 dB over a 16.5 GHz bandwidth, a minimum noise-figure, NF, of 4.5 dB, and an IIP3 of −5.2 dBm while consuming only 530 μW. The second LNA achieves an S11 better than −15 dB over an 8.8 GHz frequency range, a gain of 12.5 dB over a 6.8 GHz bandwidth, a minimum NF of 4 dB, and an IIP3 of −4.3 dBm while consuming only 550 μW.

PID Control Assessment Using L-Moment Ratio Diagrams

This paper presents an application of L-moments and respective L-moment ratio diagrams (LMRD) to the task of control performance assessment (CPA). An L-moment ratio diagram is a graphical approach to the visualization of statistical properties for a given time series. Moreover, it enables comparing various data, showing their similarities and homogeneity. Simultaneously, CPA aims at measuring the control loop quality, supporting decision-making about their tuning and maintenance. This paper shows that control system quality can be efficiently visualized using LMRDs. The method was analyzed using simulations and further validated at a real chemical engineering industrial ammonia synthesis plant.

Was Robert Gibrat right? A test based on the graphical model methodology

AbstractUsing both regression analysis and an unsupervised graphical model approach (never applied before to this issue), we confirm the rejection of Gibrat’s Law (stating that a firm’s growth is independent of that firm’s initial size) when our firm-level data are considered over the entire investigated period, while the opposite is true when we allow for market selection; indeed, the growth behavior of the surviving most efficient firms is in line with Gibrat’s Law. This evidence reconciles early and current literature and may have interesting implications in terms of both theoretical research and policy suggestions regarding subsidies to small firms, which do not necessarily grow faster than their larger counterparts.

Research on synthesis method of weakly coupled three-translation parallel mechanism with large bearing capacity

Aiming at the problem of poor bearing capacity in the direction of decoupled DOF of weakly coupled three-translation parallel mechanism, a class of new configurations of large load-bearing capacity weakly coupled three-translation parallel mechanisms with closed-loop unit has been proposed in this paper. Based on the screw theory, configuration evolution method, and graphical approach method, the synthesis method of these kinds of mechanisms has been proposed, and the decoupled criterion of this mechanism and the addition conditions of the kinematic pair diagram are given. The synthesis process has been developed to synthesize the single degree of freedom translational decoupled branch chain and 2T coupled branch chain. To better apply the weakly coupled mechanism to large load occasions, a closed-loop unit construction method has been proposed. Based on the theory of singular value decomposition, a theoretical formula for increasing the load-bearing capacity of a mechanism by constructing a closed-loop unit with redundant drives has been derived for the first time. The closed-loop unit is added to the 2T coupled branch chain and the single DOF decoupled branch chain to construct the large load-bearing capacity single DOF decoupled three-translation parallel mechanism. Kinematic analysis and static analysis have been carried out on the synthesized mechanism. The Jacobian matrix of the synthesized mechanism is a block diagonal matrix, and the bearing capacity is significantly stronger than that of the decoupled mechanism, even higher than that of the coupled parallel mechanism. The synthesized weak coupling three translation parallel mechanism with large load bearing characteristics has a good application prospect in the field of high precision and large load bearing.

Magnetohydrodynamic flow of carbon nanotubes and heat transfer over a moving thin Needle: A numerical and research surface methodology

A steady flow of carbon nanotubes (CNTs) nanofluids and heat transfer past a horizontally moving thin needle are investigated under the influence of magnetohydrodynamic (MHD). Single-walled CNTs (SWCNTs) and multi-walled CNTs (MWCNTs) are the two main nanoparticles that represent CNTs. The slender needle moves relative to the flow with parallel velocity in either the same or opposite direction. Using the similarity method, a system of partial differential equations (PDEs) subject to boundary conditions is converted into nondimensional ordinary equations (ODEs). The ODEs are then reduced to a first-order ODEs system and solved using the MATLAB R2022b bvp4c solver. On a numerical scale, the impacts of varying potential parameters, such as the magnetic, CNTs’ volume fraction, and moving parameters, on the velocity and temperature profiles, the skin friction, and heat transfer coefficients are investigated. Utilising response surface methodology (RSM), the optimisation of the response based on numerical experimentation of physical quantities is performed. The outcomes are depicted using tables and a graphical approach. Results indicate the existence of dual solutions when the needle travels in the opposite direction. Moreover, the increase in the magnetic parameter by 100% in the flow will increase both the skin friction and heat transfer coefficients by nearly 30% and 4%, respectively. Furthermore, when the value of the CNTs volume fraction increases by 100%, the heat transfer rate increases substantially by almost 33%.However, doubling the size of the thin needle cansignificantly reduce the skin friction coefficient by nearly 32%. RSM results demonstrate that the maximal heat transfer coefficient is generated at the highest values of the magnetic and CNTs’ volume friction parameters and the lowest value of the needle size parameter. Findings also show that SWCTNs are superior to MWCNTs both in the skin friction and heat transfer coefficients. When comparing the performance of water and kerosene, we find that water is less effective as a base fluid than kerosene.

Current state, internal reserves and ways to increase the competitiveness of livestock farming in the Republic of Kazakhstan

Relevance - the livestock industry occupies a strategic position in the economy of all countries because it supplies the population with affordable food products, thereby ensuring food security of states and regions. Purpose –livestock products market in Kazakhstan and its impact on the state of agro-industrial production are considered; monitoring of agricultural producers by the number of farm animals; analysis of volumes and comparative advantages of livestock products for various categories of farms; identifying the main problems and prospects for diversification industries. When conducting the research, methods of analysis and synthesis, graphical, economicstatistical, and systems approaches were used. The main sources of information when conducting the study include data from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan for 2019-2023, and the works of scientists on the development of livestock segment. Results - the authors note that the greatest increase in the number of cattle is observed in personal subsidiary plots and peasant (private) farms, which are the main producers and suppliers of livestock products, providing the food market of the republic. The main problems that make it difficult to increase the volume of livestock products are identified: the predominance of small-scale farms; low level of breeding; undeveloped food supply; poor labor motivation among agricultural workers. Conclusions – the expansion of market niche of livestock products as one of the priority areas of agricultural sector of economy depends on the further expansion of the network of production cooperatives – the most acceptable and effective form of farming in rural areas; increasing productivity of farm animals; improvement of food supply; attracting domestic and foreign investments; in the future, the use of information systems for storing data on feed consumption and payment, the physiological state of animals, economic activities and development of livestock development plans.

Approaches for identifying and managing publication bias in meta-analysis

The consequences of publication bias in meta-analysis pose significant risks, potentially leading to erroneous conclusions within the meta-analytic framework. The objective of this article was to explore the methodologies for identifying publication bias and approaches for mitigating its effects. The techniques employed to detect publication bias can generally be distinguished into two major categories: graphical and statistical methodologies. Graphical approaches utilize techniques such as funnel plots and meta-plots, which visually depict the distribution of effect sizes and standard errors across studies. Statistical methods encompass various computations, including Fail-Safe N, rank correlation, Egger regression, tests for excess significance (TES), and selection models tailored for evaluating publication bias through quantitative analyses. The combination of these methods is recommended for a more comprehensive assessment, rather than relying on individual approaches. Methods for addressing publication bias include the trim and fill (T&F) method, Publication Error and True Effect Size Estimation (PET-PEESE) method, and the Weight-Function Model, each offering unique strategies for adjusting effect size estimates. The selection of these methods should consider the specific characteristics of the meta-analysis under consideration, ensuring the most appropriate approach is employed. Publication bias poses a significant risk in the field of meta-analysis, and selecting methods for its identification and mitigation requires comprehensive consideration

Robust Visual Recognition with Class-Imbalanced Open-World Noisy Data

Learning from open-world noisy data, where both closed-set and open-set noise co-exist in the dataset, is a realistic but underexplored setting. Only recently, several efforts have been initialized to tackle this problem. However, these works assume the classes are balanced when dealing with open-world noisy data. This assumption often violates the nature of real-world large-scale datasets, where the label distributions are generally long-tailed, i.e. class-imbalanced. In this paper, we study the problem of robust visual recognition with class-imbalanced open-world noisy data. We propose a probabilistic graphical model-based approach: iMRF to achieve label noise correction that is robust to class imbalance via an efficient iterative inference of a Markov Random Field (MRF) in each training mini-batch. Furthermore, we design an agreement-based thresholding strategy to adaptively collect clean samples from all classes that includes corrected closed-set noisy samples while rejecting open-set noisy samples. We also introduce a noise-aware balanced cross-entropy loss to explicitly eliminate the bias caused by class-imbalanced data. Extensive experiments on several benchmark datasets including synthetic and real-world noisy datasets demonstrate the superior performance robustness of our method over existing methods. Our code is available at https://github.com/Na-Z/LIOND.

Abstract 7167: Impact of patient specific factors on exposures of tarlatamab, a half-life extended DLL3-directed bispecific T-cell engager in patients with advanced small cell lung cancer

Abstract Background: Tarlatamab is a first in class, half-life extended delta-like ligand (DLL3) targeted bispecific T-cell engager (BiTE) immunotherapy that has shown antitumor activity with durable objective responses and promising survival outcomes in the ongoing studies in patients with previously treated small-cell lung cancer (NCT03319940, NCT05060016) (Ahn et al, 2023; Paz-Ares et al, 2023). Here we describe the effects of patient specific factors [age, sex, bodyweight, ethnicity, estimates of renal and hepatic function, prior lines of therapy, baseline disease status, emergence of anti-drug antibodies (ADA)] on tarlatamab exposures. Methods: Tarlatamab serum concentrations were available from 420 subjects (8509 samples) pooled across the ongoing Phase 1 DeLLphi-300 study (dose range 0.003 mg to 100 mg Q2W and 200 mg Q3W) and Phase 2 DeLLphi-301 study (10 mg and 100 mg Q2W). The data were analyzed using a nonlinear-mixed effects modeling approach implemented in NONMEM (v7.5) software. Model selection and evaluation was guided by standard statistical and graphical approaches. Impact of key baseline demographic variables on tarlatamab clearance and volume of distribution were evaluated. Results: Tarlatamab exposures increased in a dose-proportional manner and were not impacted by age, sex, ethnicity, estimates of renal and hepatic function, prior lines of therapy or baseline disease status. Asian race resulted in 14% higher peak serum concentrations relative to Caucasian subjects. Higher body weight (99 kg; 90th percentile) was associated with a 24% decrease in Day 28 pre-dose concentration (Ctrough) relative to median bodyweight (73 kg). Lower body weight (54 kg; 10th percentile) was associated with a 27% increase in Ctrough relative to median bodyweight. Positive ADA status resulted in a 24% decrease in Ctrough. However, at the recommended dose of 10 mg Q2W, neither body weight nor ADA status had a clinically relevant impact on tarlatamab efficacy or safety measures (Ahn et al, 2023). Conclusion: Overall, based on these analyses, no dose adjustment for tarlatamab is required based on age, sex, bodyweight, ethnicity, estimates of renal and hepatic function, prior lines of therapy, baseline disease status or emergence of ADA. Citation Format: Stephanie M. Kong, Mukul Minocha, Po-Wei Chen, Pablo Martinez, Erik S. Anderson, Amanda Parkes, Brett E. Houk, Chih-Wei Lin. Impact of patient specific factors on exposures of tarlatamab, a half-life extended DLL3-directed bispecific T-cell engager in patients with advanced small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7167.

A novel class of integral inequalities with graphical approach and diverse applications

ABSTRACT In this paper, we investigate a novel class of Hermite-Hadamard inequalities applicable to functions with h-convex absolute derivatives. Graphical representations are provided to bolster the validity of our key findings. Some limiting results of our main findings are discussed as corollaries. Furthermore, we establish error estimates in terms of trapezoid formulae for differences between generalized means.

A Graphical Approach to Visualize and Interpret Biochemically Coupled Biomechanical Models.

The last decade has seen the emergence of progressively more complex mechanobiological models, often coupling biochemical and biomechanical components. The complexity of these models makes interpretation difficult, and although computational tools can solve model equations, there is considerable potential value in a simple method to explore the interplay between different model components. Pump and system performance curves, long utilized in centrifugal pump selection and design, inspire the development of a graphical technique to depict visually the performance of biochemically-coupled mechanical models. Our approach is based on a biochemical performance curve (analogous to the classical pump curve) and a biomechanical performance curve (analogous to the system curve). Upon construction of the two curves, their intersection, or lack thereof, describes the coupled model's equilibrium state(s). One can also observe graphically how an applied perturbation shifts one or both curves, and thus how the other component will respond, without rerunning the full model. While the upfront cost of generating the performance curve graphic varies with the efficiency of the model components, the easily interpretable visual depiction of what would otherwise be nonintuitive model behavior is valuable. Herein, we outline how performance curves can be constructed and interpreted for biochemically-coupled biomechanical models and apply the technique to two independent models in the cardiovascular space. The performance curve approach can illustrate and help identify weaknesses in model construction, inform user-applied perturbations and fitting procedures to generate intended behaviors, and improve the efficiency of the model generation and application process.

SimpleNMR: An interactive graph network approach to aid constitutional isomer verification using standard 1D and 2D NMR experiments.

Despite progress in computer automated solutions, constitutional isomer verification by NMR using one- and two-dimensional data sets is still, in the main, a manual, user-intensive activity that is challenging for a number of reasons. These include the problem of simultaneously keeping track of the information from a number of separate NMR experiments and the difficulty of another researcher subsequently verifying the assignments made without having to independently repeat the whole analysis. This paper describes a graphical interactive approach that overcomes some of these problems. By using concepts used to visualise graph networks, we have been able to represent the NMR data in a manner that highlights directly the link between the different NMR experiments and the molecule of interest. Furthermore, by making the graph networks interactive, a user can easily validate and correct the assignment and understand the decisions made in arriving at the solution. We have developed a usable proof-of-concept computer program, 'simpleNMR', written in Python to illustrate the ideas and approach.

GUGA-based MRCI approach with core-valence separation approximation (CVS) for the calculation of the core-excited states of molecules.

We develop and demonstrate how to use the Graphical Unitary Group Approach (GUGA)-based MRCISD with Core-Valence Separation (CVS) approximation to compute the core-excited states. First, perform a normal Self-Consistent-Field (SCF) or valence MCSCF calculation to optimize the molecular orbitals. Second, rotate the optimized target core orbitals and append to the active space, form an extended CVS active space, and perform a CVS-MCSCF calculation for core-excited states. Finally, construct the CVS-MRCISD expansion space and perform a CVS-MRCISD calculation to optimize the CI coefficients based on the variational method. The CVS approximation with GUGA-based methods can be implemented by flexible truncation of the Distinct Row Table. Eliminating the valence-excited configurations from the CVS-MRCISD expansion space can prevent variational collapse in the Davidson iteration diagonalization. The accuracy of the CVS-MRCISD scheme was investigated for excitation energies and compared with that of the CVS-MCSCF and CVS-CASPT2 methods using the same active space. The results show that CVS-MRCISD is capable of reproducing well-matched vertical core excitation energies that are consistent with experiments by combining large basis sets and a rational reference space. The calculation results also highlight the fact that the dynamic correlation between electrons makes an undeniable contribution in core-excited states.

Sustainability Evaluation of the Al-Qasab Playa by Integrating Hydrogeochemical and Graphical, ArcGIS Watershed, and Thermodynamic Geochemical Modeling Approaches

Drawing development plans requires evaluating the available resources and assessing their sustainable and subsequent utilization-driven environmental impacts. The current work is concerned with evaluating the sustainability of the halite harvesting process from Al-Qasab Playa, Shaqra, Central Saudi Arabia. The authors integrated, conceptually and quantitatively, ArcGIS-processed SRTM-DEM (Shuttle Radar Topography Mission-Digital Elevation Model) data, hydrogeochemical and thermodynamic-based geochemical modelling, and graphical approaches to achieve the ultimate aims of the study. The watershed is identified as a nonmarine closed basin with a drainage area of 1290 km2, with the slope controlling recharge to the Playa. The Chadha plot including the rainwater exhibits linear regression, with an R2 value of 0.9947, confirming the rainwater origin of the Playa water. The hardness-forming ions are primarily removed in pond 3, eliminating the need for costly and power-consuming steps of softening with ion exchange resins or nanofiltration as it can be used directly as a readily available feed for the chlor-alkali process for producing NaOH, Cl2, and H2 gases through electrolysis. XRD (X-ray diffraction) analysis and the SEM-EDS (Scanning electron microscopy-energy dispersive X-ray spectroscopy) of the harvested halite confirmed its purity. An improved design of the current folkloric harvesting process has been proposed based on the saturation indices calculated thermodynamically to provide a readily available feed intake for the electrolysis chlor-alkali process with or without minimal pretreatment to produce higher value chemicals. The methodological aspects presented here are deemed robust and valid for applications in other study areas, including the assessment of the exploitation of the rejected brine from the desalination plants to achieve the zero liquid discharge approach, as well as other types of sabkhas, regardless of their geographical location.

The Energy of Two Inverted Oppositely Moving Wave Pulses: A Graphical Approach

The Energy of Two Inverted Oppositely Moving Wave Pulses: A Graphical Approach

Soil CO2 fluxes measured in the Acoculco Geothermal System, Mexico: Baseline emissions from a long-term prospection programme

The Acoculco Caldera Complex is considered a promissory hidden high-temperature geothermal system in Mexico. To support the geothermal prospection of this anomalous area, a comprehensive programme of soil CO2 flux measurements was performed. A long-term measurement programme was conducted to determine the baseline of natural soil CO2 effluxes. Significant efforts were devoted both to measuring the CO2 fluxes between 2015 and 2022 and interpreting their origin. Eighteen soil gas surveys of CO2 were carried out by using the accumulation chamber method. >1200 diffuse CO2 fluxes were measured in six different areas of the Acoculco Caldera. Two areas (Los Azufres and Alcaparrosa) exhibited cold degassing sites, acid-sulphate springs, and gas bubbling in surface water bodies. The soil CO2 fluxes ranged from 1 to 26,000 g m−2 d−1, whereas lower fluxes <29 g m−2 d−1 were determined as the degassing baseline. A total CO2 output of 492 t d−1 km−1 was estimated using an integrated SGS-GSA approach, where the highest total soil CO2 fluxes were obtained for Alcaparrosa (299 t d−1 km−2) compared with Los Azufres (164 t d−1 km−2), and Surroundings (29 t d−1 km−2). Such results agree well with those values measured in other worldwide volcanic and active geothermal ecosystems. The range of CO2 isotopic composition values from −28.83 ‰ to −3.11 ‰, together with their statistical distribution, suggests multiple CO2 production sources feeding soil degassing. The combined interpretation of flux and isotopic data allowed us to identify two distinct gas sources: endogenous and biogenic. The present study highlights the importance of using soil CO2 monitoring to determine baseline emissions at the early exploration stage of geothermal systems.

Energy consumption prediction of industrial HVAC systems using Bayesian Networks

Predicting energy consumption has become a critical issue for energy-intensive industrial contexts. A significant contribution to their overall energy load is due to the Heating Ventilation and Air Conditioning (HVAC) systems. This work, therefore, aims to validate the applicability of a probabilistic graphical approach, the Bayesian Network, in predicting the HVAC systems' energy consumption. As a data-driven approach, it is compared with more common AI-based models like Support Vector Machine, Artificial Neural Networks and Random Forest. The graphical approach ensures a better interpretation of the main factors determining the energy consumption and the relationships underlying these dependences. After an initial contextualisation and an analysis of the state of the art, the design methodology of a Bayesian network is investigated in detail, deepening in the various solutions for each step and evaluating their performance through the application on two industrial case studies. The results show that Bayesian networks, despite not always providing the best results, are a valid solution, trading off between simplicity, flexibility, and performance. Moreover, the possibility to provide a physical interpretation of the results is one of its main strengths. The critical aspect encountered, instead, is the need for discretisation, which strongly influences the quality of the results.

Integrated decision-making approach for sectoral low-carbon technology solutions

The contributions of different sectors to greenhouse gas (GHG) emissions are at the heart of the climate debate. In the coming decades, the implementation of low-carbon technology (LCT) solutions in key industries will play a pivotal role in mitigating climate change. This study develops a graphical approach that integrates carbon emissions pinch analysis (CEPA), the best-worst method (BWM), and cost-benefit analysis (CBA) to assess LCTs in key industries. CEPA is used to identify LCTs that meet both economic and GHG reduction constraints, then the LCTs selected by CEPA are subjected to CBA, while BWM is applied to assess the weighted performance of LCTs in terms of their environmental, economic, social, and technological aspects. A comparison of the implementation priority sequence of LCTs was made between the dual constraints of carbon and economic considerations and a multi-factor perspective (environmental impact, economic benefits, social impact, and technological aspects). Finally, by incorporating the marginal abatement cost curve (MACC), the optimal low-carbon and economically viable technological pathway can be determined. This study analyzed 85 LCTs from global industries, including the power, food, agriculture, and land use (FAL), industry, transportation, and construction industries. The results for the global scenario from 2020 to 2050 indicate that 38 LCTs have been screened by CEPA to meet the dual constraints of carbon and economic performance, resulting in a GHG emissions reduction of 328.0 Gt CO2-e. Among them, the power industry was the major contributor, with a cumulative reduction of 197.3 Gt CO2-e, followed by the construction industry (97.9 Gt CO2-e), industry (25.4 Gt CO2-e), transportation (4.9 Gt CO2-e), and FAL (2.5 Gt CO2-e); an industry scenario analysis was conducted and listed low-carbon lists in key industries that meet the dual constraints of carbon and economy. This study can contribute to the rational planning and formulation of climate actions at the technological level to effectively enable deep decarbonization.