Computational Model Research Articles

Numerical analysis and Schlieren visualization of gas flow dynamics inside the plasma arc cut kerf with curved cutting fronts

In plasma arc cutting (PAC), the characteristics of the gas flow exiting the nozzle significantly influence the cut quality. The high-pressure jet in PAC forms complex shockwave structures when it is incident on the metal to be cut. In this study, the flow behavior inside the kerfs of various geometries derived from an actual PAC workpiece is assessed. The shape of the cutting front in the kerf varies with the changing cutting speed. A high cutting speed yields a curved cutting front, resulting in unwanted gas flow behavior and adversely influencing the cutting performance. In this study, a computational fluid dynamics simulation model was used to analyze the effects of a curved cutting front on the gas flow behavior during the PAC process. The gas flow patterns obtained from the numerical simulations were qualitatively compared with the Schlieren experiment results. The analysis results indicated that the curvature of the cutting fronts generated oblique shockwave structures that significantly reduced the flow velocity. In particular, the weak shock structures throughout the curved cutting front gradually decreased the flow velocity. The critical flow velocity was realized in the kerf with a highly curved cutting front, beyond which the vertical penetration of the material was not possible. The shear stress lines concurred with the striation patterns on the kerf walls, thereby validating the numerical analysis results.

Multiscale neural dynamics in sleep transition volatility across age scales: a multimodal EEG-EMG-EOG analysis of temazepam effects.

Recent advances in computational modeling techniques have facilitated a more nuanced understanding of sleep neural dynamics across the lifespan. In this study, we tensorize multiscale multimodal electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals and apply Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to quantify interactions between age scales and the use of pharmacological sleep aids on sleep stage transitions. Our cohort consists of 22 subjects in a crossover design study, where each subject received both a sleep aid and a placebo in different sessions. To understand these effects across the lifespan, three evenly distributed age groups were formed: 18-29, 30-49, and 50-66years. The methodological framework implemented here employs tensor-based machine learning techniques to compute continuous wavelet transform time-frequency features and utilizes a GARCH model to quantify sleep signal volatility across age scales. Support Vector Machines are used for feature ranking, and our analysis captures interactions between signal entropy, age, and sleep aid status across frequency bands, sleep transitions, and sleep stages. GARCH model results reveal statistically significant volatility clustering in EEG, EMG, and EOG signals, particularly during transitions between REM and non-REM sleep. Notably, volatility was higher in the 50-66 age group compared to the 18-29 age group, with marked fluctuations during transitions from deep sleep to REM sleep (standard deviation of 0.35 in the older group vs. 0.30 in the 18-29 age group, p < 0.05). Statistical comparisons of volatility across frequency bands, age scales, and sleep stages highlight significant differences attributable to sleep aid use. Mean conditional volatility parameterization of the GARCH model reveals directional influences, with a causality index of 0.75 from frontal to occipital regions during REM sleep transition periods. Our methodological framework identifies distinct neural behavior patterns across age groups associated with each sleep stage and transition, offering insights into the development of targeted interventions for sleep regularity across the lifespan.

Prefrontal cortex astrocytes in major depressive disorder: exploring pathogenic mechanisms and potential therapeutic targets.

Major depressive disorder (MDD) is a prevalent mental health condition characterized by persistent feelings of sadness and hopelessness, affecting millions globally. The precise molecular mechanisms underlying MDD remain elusive, necessitating comprehensive investigations. Our study integrates transcriptomic analysis, functional assays, and computational modeling to explore the molecular landscape of MDD, focusing on the DLPFC. We identify key genomic alterations and co-expression modules associated with MDD, highlighting potential therapeutic targets. Functional enrichment and protein-protein interaction analyses emphasize the role of astrocytes in MDD progression. Machine learning is employed to develop a predictive model for MDD risk assessment. Single-cell and spatial transcriptomic analyses provide insights into cell type-specific expression patterns, particularly regarding astrocytes. We have identified significant genomic alterations and co-expression modules associated with MDD in the DLPFC. Key genes involved in neuroactive ligand-receptor interaction pathways, notably in astrocytes, have been highlighted. Additionally, we developed a predictive model for MDD risk assessment based on selected key genes. Single-cell and spatial transcriptomic analyses underscored the role of astrocytes in MDD. Virtual screening of compounds targeting GPR37L1, KCNJ10, and PPP1R3C proteins has identified potential therapeutic candidates. In summary, our comprehensive approach enhances the understanding of MDD's molecular underpinnings and offers promising opportunities for advancing therapeutic interventions, ultimately aiming to alleviate the burden of this debilitating mental health condition. KEY MESSAGES: Our investigation furnishes insightful revelations concerning the dysregulation of astrocyte-associated processes in MDD. We have pinpointed specific genes, namely KCNJ10, PPP1R3C, and GPR37L1, as potential candidates warranting further exploration and therapeutic intervention. We incorporate a virtual screening of small molecule compounds targeting KCNJ10, PPP1R3C, and GPR37L1, presenting a promising trajectory for drug discovery in MDD.

COVID-19 vaccine refusal is driven by deliberate ignorance and cognitive distortions

Vaccine hesitancy was a major challenge during the COVID-19 pandemic. A common but sometimes ineffective intervention to reduce vaccine hesitancy involves providing information on vaccine effectiveness, side effects, and related probabilities. Could biased processing of this information contribute to vaccine refusal? We examined the information inspection of 1200 U.S. participants with anti-vaccination, neutral, or pro-vaccination attitudes before they stated their willingness to accept eight different COVID-19 vaccines. All participants—particularly those who were anti-vaccination—frequently ignored some of the information. This deliberate ignorance, especially toward probabilities of extreme side effects, was a stronger predictor of vaccine refusal than typically investigated demographic variables. Computational modeling suggested that vaccine refusals among anti-vaccination participants were driven by ignoring even inspected information. In the neutral and pro-vaccination groups, vaccine refusal was driven by distorted processing of side effects and their probabilities. Our findings highlight the necessity for interventions tailored to individual information-processing tendencies.

Monte Carlo simulation of spatial frequency domain imaging for breast tumors during compression.

Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth. To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression. Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast. When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths. This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression.

Promising organoselenium corrosion inhibitors for C1018-steel in hydrochloric acid environments

BackgroundNovel organoselenium (OSe) corrosion inhibitors, namely 2-(((4-(benzylselanyl)phenyl)imino)methyl)-5-nitrophenol (BSeOH) and its Ni (II) chelate [Ni(BSeO)2(H2O)2] were synthesized in 90 % and 88 % yields, respectively. Their chemical structures were characterized using different spectroscopic tools. MethodsThe corrosion inhibition efficiency was investigated toward C1018-steel in 1.0 M HCl solutions using potentiodynamic polarization, impedance spectroscopy, X-ray photoelectron spectroscopy, density functional theory, and Monte Carlo simulations. Several quantum chemical parameters were calculated using Density Functional Theory at the B3LYP/6–31G* computational model to elucidate the inhibitory activity of the compounds. Significant findingsElectrochemical data showed that [Ni(BSeO)2(H2O)2] has more protection efficiency (96.4 %) compared to its ligand BSeOH (93.4 %) at 15.0 × 10−6 M. In addition, the corrosion current density (icor) decreases (245.96 to 8.96 µAcm−2), the charge transfer resistance (Rct) increases (95.15 to 1926.70 Ω cm2), and the admittance (Y0) decreases (173.87 to 18.07 μΩ−1 sn cm−2) with the inhibitors’ dosage indication the formation of a protective adsorbed layer on the steel surface. Furthermore, the BSeOH and [Ni(BSeO)2(H2O)2] inhibitors were spontaneously adsorbed on the steel surface, adhering to the Langmuir isotherm. Moreover, the electrochemical and theoretical results showed that these inhibitors were effectively adsorbed on the C1018-steel surface as a preventive layer. Collectively, the utilization of OSe agents as corrosion inhibitors is a novel approach that combines several advantageous characteristics, including improved efficiency, unique chemical properties, multiple inhibition mechanisms, and the potential for derivatization. Therefore, OSe inhibitors hold significant promise in the field of corrosion prevention and further studies are highly required to develop a more substantial strategy to fight corrosion by applying OSe compounds.

Callous-unemotional traits moderate the association between inhibitory control and disruptive behavior problems.

The presence of callous-unemotional (CU) traits may not be unique to conduct disorder (CD) but also extend to oppositional defiant disorder (ODD). While a distinct neurocognitive profile characterizes CU traits, it remains unclear whether this CU-related neurocognitive profile differs between youth with CD and ODD. This study investigated whether CU traits moderate the relationship between inhibitory control and CD or ODD symptoms. We leveraged computational modeling to decompose task-based inhibitory control in a sample of 200 children (59.5% boys, 86.5% Caucasian), aged 8 to 15 years (M = 10.10, SD = 1.88), referred to an outpatient child diagnostic clinic focused on externalizing problems. Analyses examined whether CU traits moderated the relationship between inhibitory control and CD or ODD symptoms while controlling for ADHD symptoms and child demographics. The results indicated that the strength of the relationship between inhibitory control and CD and ODD symptoms varies as a function of CU traits. Specifically, CD was linked to a more cautious decision-making style when elevated CU traits were present, whereas ODD was associated with more efficient decision making. These findings suggest distinct neurocognitive profiles based on CU traits, which vary between CD and ODD. Clinically, this underscores the importance of tailoring interventions for CD-CU and ODD-CU, focusing on decision making processes rather than merely addressing impulsivity. This research contributes to a more nuanced understanding of the interaction between neurocognitive processes and disruptive behavior, with significant implications for both theoretical models and treatment approaches.

Investigation through naphtho[2,3-a]pyrene on mutated EGFR mediated autophagy in NSCLC: Cellular model system unleashing therapeutic potential.

Mutant epidermal growth factor receptor (EGFR) signaling has emerged as a key cause of carcinogenesis and therapy resistance in non-small cell lung cancer (NSCLC), which continues to pose a serious threat to world health. In this study, we aimed to elucidate the complex molecular pathways of EGFR-mediated autophagy signaling in NSCLC. We identified naphtho[2,3-a]pyrene, an anthraquinolone derivative, to be a promising investigational drug that targets EGFR-mediated autophagy using a cellular model system. By utilizing systems biology, we developed a computational model that explained the signaling of EGFR-mediated autophagy and identified critical crosstalk sites that could be inhibited therapeutically. As a lead compound, naphtho[2,3-a]pyrene was confirmed by molecular docking experiments. It was found to be cytotoxic to NSCLC cells, impact migration, induce apoptosis, and arrest cell cycle, both on its own and when combined with standard drugs. The anticancer efficacy of naphtho[2,3-a]pyrene was validated in vivo on CDX nude mice. It showed synergistic activity against NSCLC when coupled with gefitinib, chloroquine, and radiation. Altogether, our study highlights naphtho[2,3-a]pyrene's therapeutic promise in NSCLC by focusing on EGFR-mediated autophagy and providing a new strategy to fight drug resistance and tumor survival.

PDE8 Inhibition and Its Impact on ICa,L in Persistent Atrial Fibrillation: Evaluation of PDE8 as a Potential Drug Target.

Atrial fibrillation (AF) poses a significant therapeutic challenge with drug interventions showing only limited success. Phosphodiesterases (PDE) regulate cardiac electrical stability and may represent an interesting target. Recently, PDE8 inhibition was proposed as an antiarrhythmic intervention by increasing L-type Ca2+ current (ICa,L) and action potential duration (APD). However, the effect size of PDE8 inhibition on ICa,L and APD seems discrepant and effects on force are unknown. We investigated the impact of PDE8 inhibition on force using PF-04957325 in right atrial appendages, obtained from patients in sinus rhythm (SR) and with persistent AF (peAF) undergoing cardiac surgery. A computational model was employed to predict the effects of PDE8 inhibition on APD in SR and peAF. Results showed no increase in force after exposure to increasing concentrations of the PDE8 inhibitor PF-04957325 in either SR or peAF tissues. Furthermore, PDE8 inhibition did not affect the potency or efficacy of norepinephrine-induced inotropic effects in either group. Arrhythmic events triggered by norepinephrine were observed in both SR and peAF, but their frequency remained unaffected by PF-04957325 treatment. Computational modeling predicted that the reported increase in ICa,L induced by PDE8 inhibition would lead to substantial APD prolongation at all repolarization states, particularly in peAF. Our findings indicate that PDE8 inhibition does not significantly impact force or arrhythmogenicity in human atrial tissue.

Hibiscus bullseyes reveal mechanisms controlling petal pattern proportions that influence plant-pollinator interactions.

Colorful flower patterns are key signals to attract pollinators. To produce such motifs, plants specify boundaries dividing petals into subdomains where cells develop distinctive pigmentations, shapes, and textures. While some transcription factors and biosynthetic pathways behind these characteristics are well studied, the upstream processes restricting their activities to specific petal regions remain enigmatic. Here, we unveil that the petal surface of Hibiscus trionum, an emerging model featuring a bullseye on its corolla, is prepatterned as the bullseye boundary position is specified long before it becomes visible. Using a computational model, we explore how pattern proportions are maintained while petals experience a 100-fold size increase. Exploiting transgenic lines and natural variants, we show that plants can regulate boundary position during the prepatterning phase or modulate growth on either side of this boundary later in development to vary bullseye proportions. Such modifications are functionally relevant, as buff-tailed bumblebees can reliably identify food sources based on bullseye size and prefer certain pattern proportions.

Stoichiometric reconstruction of the Al2O3(0001) surface.

Macroscopic properties of materials stem from fundamental atomic-scale details, yet for insulators, resolving surface structures remains a challenge. We imaged the basal (0001) plane of α-aluminum oxide (α-Al2O3) using noncontact atomic force microscopy with an atomically defined tip apex. The surface formed a complex ([Formula: see text] × [Formula: see text])R±9° reconstruction. The lateral positions of the individual oxygen and aluminum surface atoms come directly from experiment; we determined with computational modeling how these connect to the underlying crystal bulk. Before the restructuring, the surface Al atoms assume an unfavorable, threefold planar coordination; the reconstruction allows a rehybridization with subsurface O that leads to a substantial energy gain. The reconstructed surface remains stoichiometric, Al2O3.

3 Hands -on 1: Applying system dynamics to develop “Flight Simulators” for sustainable animal production

Abstract Solving complex livestock production problems is a pressing issue for achieving long-term sustainability and profitability and often requires modeling techniques. The use of mathematical models is undoubtedly a daily practice in the livestock industry, especially nutrition, and has improved animal performance, productivity, and environmental sustainability while maintaining or reducing costs. Although many students and professionals use spreadsheets and existing empirical models for nutrition and management [NASEM (2016)], there is still a need to understand the complexity of livestock systems and the utility of flight simulator models. At the same time, more complex models (although robust) may fail to provide new insights for experienced nutritionists due to poor user-friendliness. A systems understanding goes beyond simply obtaining a desired output, such as optimizing a total mixed ration, but instead leads to identifying high-leverage solutions and gaining insight. Further, parameterizing and calibrating variables and equations and testing management scenarios is straightforward. However, developing causal feedback linkages (A to B and B to A) and identifying time delays is less intuitive and more challenging for novices. Model flight simulators grounded in fully documented, calibrated models provide a means to introduce practitioners to a methodology of insight generation because the user designs and runs the model scenarios for themselves, challenging their mental models. Such approaches are generally more impactful (compared with someone telling them) because they have gained insight into the system themselves, and, in the case of open source (white box models), they can explore equations and parameters. Therefore, understanding how to utilize dynamic models in scenario-based simulations is critical in training current and future modelers in animal science. This hands-on model training will cover the basics of System Dynamics modeling and allow participants to run real-time animal production simulations. Finally, participants will be “debriefed” to unpack “ah ha” moments that were unexpected. The debrief will include using models to develop accurate guidelines and recommendations for those who cannot use computer models and developing experimental designs to test hypotheses and “validate” model recommendations (proof in the pudding). Participants will gain knowledge of System Dynamics applications for animal production systems, experience using flight simulators, and their utility in teaching, informing, and guiding their livestock production or that of a client. The “flight simulator” will focus on production and nutrition with species-agnostic principles. Participants will also have a new tool to identify areas for improvement in model development (i.e., what is missing?), research questions, or industry needs. The need for modelers who can turn big data into insight, knowledge, and wisdom using a systems approach is becoming even more critical due to the increasing use of precision livestock farming. Thus, providing the livestock industry with trained systems modelers will help achieve current and future sustainability challenges.

Integrated battery thermal management and CO2 control in electric buses using recirculated air and waste heat recovery

In recent years, electric vehicles (EVs) have significantly progressed due to their environmental protection and energy-saving advantages. However, maintaining indoor thermal comfort and effective battery thermal management are critical considerations for EVs. Unlike conventional vehicles, EVs cannot use waste heat and depend on heat pump systems for winter heating and summer cooling. This research proposes a novel system to reduce the energy consumption of heat pump systems by using recirculated air while addressing the challenges of battery temperature management and maintaining safe CO2 concentrations in the cabin. The primary objectives of this research are threefold: first, to ensure passenger comfort through an integrated approach that includes cabin CO2 concentration safety; second, to recover heat from recirculated cabin air to save energy and control battery temperature; and third, to comprehensively analyze the performance of the Electric Vehicle Thermal Management System (EVTMS) using precise Computational Fluid Dynamics (CFD) analysis and mathematical modeling. This study will significantly enhance the understanding of energy consumption related to CO2 concentration control; an aspect previously overlooked in the literature. It will develop a battery temperature management strategy that leverages bus exhaust heat. These contributions will aid in achieving both passenger comfort and battery stability in electric buses, providing practical insights for the design and optimization of thermal management systems for future electric buses.

Confirmatory Factor Analysis of the Computational Thinking Learning Competency Measurement Model of Students in the Bachelor of Education Program in Computer at Rajabhat University

Confirmatory Factor Analysis of the Computational Thinking Learning Competency Measurement Model of Students in the Bachelor of Education Program in Computer at Rajabhat University

Dronedarone inhibits the proliferation of esophageal squamous cell carcinoma through the CDK4/CDK6-RB1 axis in vitro and in vivo.

Treatment options for patients with esophageal squamous cell carcinoma (ESCC) often result in poor prognosis and declining health-related quality of life. Screening FDA-approved drugs for cancer chemoprevention is a promising and cost-efficient strategy. Here, we found that dronedarone, an antiarrhythmic drug, could inhibit the proliferation of ESCC cells. Moreover, we conducted phosphorylomics analysis to investigate the mechanism of dronedarone-treated ESCC cells. Through computational docking models and pull-down assays, we demonstrated that dronedarone could directly bind to CDK4 and CDK6 kinases. We also proved that dronedarone effectively inhibited ESCC proliferation by targeting CDK4/CDK6 and blocking the G0/G1 phase through RB1 phosphorylation inhibition by in vitro kinase assays and cell cycle assays. Subsequently, we found that knocking out CDK4 and CDK6 decreased the susceptibility of ESCC cells to dronedarone. Furthermore, dronedarone suppressed the growth of ESCC in patient-derived tumor xenograft models in vivo. Thus, our study demonstrated that dronedarone could be repurposed as a CDK4/6 inhibitor for ESCC chemoprevention.

Peptides as modulators of FPPS enzyme: A multifaceted evaluation from the design to the mechanism of action

Bone diseases are medical conditions caused by the loss of bone homeostasis consecutive to increased osteoclast activity and diminished osteoblast activity. The mevalonate pathway (MVA) is crucial for maintaining this balance since it drives the post-translational prenylation of small guanosine triphosphatases (GTPases) proteins. Farnesyl pyrophosphate synthase (FPPS) plays a crucial role in the MVA pathway. Consequently, in the treatment of bone-related diseases, FPPS is the target of FDA-approved nitrogen-containing bisphosphonates (N-BPs), which have tropism mainly for bone tissue due to their poor penetration in soft tissues. The development of inhibitors targeting the FPPS enzyme has garnered significant interest in recent decades due to FPPS's role in the biosynthesis of cholesterol and other isoprenoids, which are implicated in cancer, bone diseases, and other conditions. In this study, we describe a multidisciplinary approach to designing novel FPPS inhibitors, combining computational modeling, biochemical assays, and biophysical techniques. A series of peptides and phosphopeptides were designed, synthesized, and evaluated for their ability to inhibit FPPS activity. Molecular docking was employed to predict the binding modes of these compounds to FPPS, while Surface Plasmon Resonance (SPR) and Nuclear Magnetic Resonance (NMR) spectroscopy experiments – based on Saturation Transfer Difference (STD) and an enzymatic NMR assay – were used to measure their binding affinities and kinetics. The biological activity of the most promising compounds was further assessed in cellular assays using murine colorectal cancer (CRC) cells. Additionally, genomics and metabolomics profiling allowed to unravel the possible mechanisms underlying the activity of the peptides, confirming their involvement in the modulation of the MVA pathway. Our findings demonstrate that the designed peptides and phosphopeptides exhibit significant inhibitory activity against FPPS and possess antiproliferative effects on CRC cells, suggesting their potential as therapeutic agents for cancer.

Myosin-I synergizes with Arp2/3 complex to enhance the pushing forces of branched actin networks.

Class I myosins (myosin-Is) colocalize with Arp2/3 complex-nucleated actin networks at sites of membrane protrusion and invagination, but the mechanisms by which myosin-I motor activity coordinates with branched actin assembly to generate force are unknown. We mimicked the interplay of these proteins using the "comet tail" bead motility assay, where branched actin networks are nucleated by the Arp2/3 complex on the surface of beads coated with myosin-I and nucleation-promoting factor. We observed that myosin-I increased bead movement efficiency by thinning actin networks without affecting growth rates. Myosin-I triggered symmetry breaking and comet tail formation in dense networks resistant to spontaneous fracturing. Even with arrested actin assembly, myosin-I alone could break the network. Computational modeling recapitulated these observations, suggesting myosin-I acts as a repulsive force shaping the network's architecture and boosting its force-generating capacity. We propose that myosin-I leverages its power stroke to amplify the forces generated by Arp2/3 complex-nucleated actin networks.

Revisiting the 1934 Mw 8.2 Bihar Nepal earthquake – Simulation of Broadband ground motions

Summary The 1934 Mw 8.2 Bihar-Nepal earthquake was one of the devastating earthquakes, which made seismologists realize the importance of proper seismic hazard analysis and design aspects in India. The event occurred way before proper seismic networks were implemented and hence there are no recorded ground motions available for this event. The present study, thus aims to generate possible ground motions for the 1934 Mw 8.2 Bihar-Nepal event. The complex geographical features, ambiguous source information, and lack of ground motion data make the simulation and validation of ground motions very difficult. In this regard, the broadband (BB) ground motions are simulated and validated for the most recent well-documented Himalayan event, i.e., the 2015 Mw 7.9 Nepal earthquake in order to calibrate the model and simulation methodology. For this purpose, the computational model presented by Sreejaya et al. (2023) is extended up to a region of 1000 km × 670 km (longitude 80-89 °E and latitude 23-30 °N) in the Indo-Gangetic Basin to simulate the low-frequency (LF) ground motions using spectral element method (Komatitsch and Tromp 1999). These deterministically simulated LF ground motions are combined with stochastically simulated high-frequency (HF) ground motions based on an improved seismological model following Otarola and Ruiz (2016). The seismic moment and dimensions of the rupture plane presented by Pettanati et al. (2017) are used to generate ten samples for the finite fault source model having different slip distribution along the rupture plane as a random field (Mai and Beroza 2000; 2002). The BB ground motions (0.01–25 Hz) are obtained by merging LF and HF ground motions in the time domain by matching them at a frequency of ∼0.3 Hz. Such BB results are simulated at a grid of stations and at locations where Modified Mercalli Intensity (MMI) intensity values are available. The estimated MMI values and the observed MMI values are compared to emphasize the efficacy of the model. The maximum PGA estimated from the simulated ground motions in horizontal and vertical directions are observed to be 0.48 g and 0.4 g. Further, 5% damped response spectra and spectral amplification are analyzed concerning the sediment depth of the Indo-Gangetic Basin. The results from the study can serve as inputs for dynamic analysis and the design of earthquake-resistant structures across different locations in the Indo-Gangetic Basin.

Overcoming Ion Trapping in Chevrel Phase Compounds via Tailored Anion Substitution: An Integrated Study of Theory, Synthesis, and In Operando Techniques for Reversible Aqueous Zn-Ion Batteries.

Sustainable batteries are key for powering electronic devices of the future, with aqueous zinc-ion batteries (AZIBs) standing out for their use of abundant, readily available elements, and safer production processes. Among the various electrode materials studied for AZIBs, the Chevrel Phase, Mo6S8 has shown promise due to its open framework, but issues with zinc ion trapping have limited its practical application. In this work, we employed computational methods to investigate the insertion-deinsertion mechanism in a series of isostructural Mo6S8-xSex (x = 0-8) solid solutions as materials that could balance the gravimetric capacity and reversible cycling for AZIBs. Density functional theory (DFT) calculations revealed that increasing the Se content would reduce the binding energy of Zn within the structures, enabling Zn deinsertion compared to the Mo6S8 structure. Experiments confirmed the formation of Mo6S8-xSex (x = 0-8) solid solutions, and electrochemical testing showed improved reversibility of Zn insertion/deinsertion as the amount of Se increased, consistent with the computational predictions. Furthermore, combined in operando X-ray diffraction and electrochemical studies revealed a continuous, gradual Zn-insertion process into Mo6S4Se4, in stark contrast to the abrupt phase changes observed upon Zn insertion in Mo6S8 and Mo6Se8. DFT calculations attributed the stabilization of Zn0.5Mo6S4Se4 as a prime reason for preventing phase separation, making Se-substituted compounds promising materials for high-performance AZIBs. Overall, this interdisciplinary approach, integrating computational modeling, materials synthesis, and advanced characterization techniques, offers a pathway for fine-tuning anion chemistry that can help create high-performance electrode materials for sustainable energy storage technologies.

Towards emotion-aware intelligent agents by utilizing knowledge graphs of experiences

Because of the increasing presence of intelligent agents in various aspects of human social life, social skills play a vital role in ensuring these systems exhibit acceptable and realistic behavior in social communication. The importance of emotional intelligence in social capabilities is noteworthy, so incorporating emotions into the behaviors of intelligent agents is essential. Therefore, some computational models of emotions have been presented to develop intelligent agents that exhibit emotional human-like behaviors. However, most current computational models of emotions neglect the dynamic learning of the affective meaning of events based on agents’ experiences. Such models evaluate the events in the environment according to emotional aspects without considering the context of the situations. Also, these models capture the emotional states of agents by using predefined rules determined according to psychological theories. Therefore, they disregard the data-driven methods that can obtain the relationships between appraisal variables and emotions based on natural human data with fewer assumptions on the nature of such relationships. To address these issues, we proposed a novel and unified affective-cognitive framework (EIAEC) to facilitate the development of emotion-aware intelligent agents. EIAEC uses appraisal theories to acquire the emotional states of the agent in various situations. This paper contains four main contributions: 1- We have designed an efficient episodic memory that uses events and their conditional contexts to store and retrieve knowledge and experiences. This memory facilitates emotional expressions and decision-making adapted to the situations of the agent. 2- A novel method has been proposed that learns context-dependent affective values associated with events by using the agent’s experiences in various contexts. Subsequently, we acquired appraisal variables using the elements and related meta-data in episodic memory. 3- We have proposed a new data-driven method that maps appraisal variables to emotional states. 4- Moreover, a method has been developed to update the activation values regarding actions by using the emotional states of the agent. This method models the influence of emotions on the agent’s decision-making. Finally, we simulate a driving scenarios in our proposed framework to manifest the generated emotions in different situations and conditions. Moreover, we show how the proposed method learns the affective meaning of events and actions used in appraisal computing.