Parameter Manipulation Research Articles

Nonequilibrium control of kagome metals

Abstract Exotic quantum order in kagome metals, i.e., quantum materials with a Fermi liquid parent state of electrons on a kagome lattice, has appeared as a vibrant emerging field of condensed matter physics. Already in a small kagome material subclass such as vanadium-based compounds AV$$_3$$ 3 Sb$$_5$$ 5 ($$A=$$ A = K, Rb, Cs), the first wave of experimental exploration has brought about manifold evidence for hitherto largely elusive phenomena such as high-temperature charge ordering with orbital currents, nematic order, cascades of charge ordering transitions with hierarchies of ordering vectors, and unconventional superconductivity. We argue that kagome metals promise to be a prototypical ground for the nonequilibrium analysis of quantum order through time-dependent parameter control and manipulation. In particular, we propose to investigate the nematic character of kagome quantum order through light and strain pulses, as well as the nature of time-reversal symmetry breaking and chirality through properly polarized laser pulses. Graphical abstract

Development of a Marmoset Apparatus for Automated Pulling to study cooperative behaviors

In recent years, the field of neuroscience has increasingly recognized the importance of studying animal behaviors in naturalistic environments to gain deeper insights into ethologically relevant behavioral processes and neural mechanisms. The common marmoset (Callithrix jacchus), due to its small size, prosocial nature, and genetic proximity to humans, has emerged as a pivotal model toward this effort. However, traditional research methodologies often fail to fully capture the nuances of marmoset social interactions and cooperative behaviors. To address this critical gap, we developed the Marmoset Apparatus for Automated Pulling (MarmoAAP), a novel behavioral apparatus designed for studying cooperative behaviors in common marmosets. MarmoAAP addresses the limitations of traditional behavioral research methods by enabling high-throughput, detailed behavior outputs that can be integrated with video and audio recordings, allowing for more nuanced and comprehensive analyses even in a naturalistic setting. We also highlight the flexibility of MarmoAAP in task parameter manipulation which accommodates a wide range of behaviors and individual animal capabilities. Furthermore, MarmoAAP provides a platform to perform investigations of neural activity underlying naturalistic social behaviors. MarmoAAP is a versatile and robust tool for advancing our understanding of primate behavior and related cognitive processes. This new apparatus bridges the gap between ethologically relevant animal behavior studies and neural investigations, paving the way for future research in cognitive and social neuroscience using marmosets as a model organism.

Development of a Marmoset Apparatus for Automated Pulling to study cooperative behaviors.

In recent years, the field of neuroscience has increasingly recognized the importance of studying animal behaviors in naturalistic environments to gain deeper insights into ethologically relevant behavioral processes and neural mechanisms. The common marmoset (Callithrix jacchus), due to its small size, prosocial nature, and genetic proximity to humans, has emerged as a pivotal model toward this effort. However, traditional research methodologies often fail to fully capture the nuances of marmoset social interactions and cooperative behaviors. To address this critical gap, we developed the Marmoset Apparatus for Automated Pulling (MarmoAAP), a novel behavioral apparatus designed for studying cooperative behaviors in common marmosets. MarmoAAP addresses the limitations of traditional behavioral research methods by enabling high-throughput, detailed behavior outputs that can be integrated with video and audio recordings, allowing for more nuanced and comprehensive analyses even in a naturalistic setting. We also highlight the flexibility of MarmoAAP in task parameter manipulation which accommodates a wide range of behaviors and individual animal capabilities. Furthermore, MarmoAAP provides a platform to perform investigations of neural activity underlying naturalistic social behaviors. MarmoAAP is a versatile and robust tool for advancing our understanding of primate behavior and related cognitive processes. This new apparatus bridges the gap between ethologically relevant animal behavior studies and neural investigations, paving the way for future research in cognitive and social neuroscience using marmosets as a model organism.

Development of a Marmoset Apparatus for Automated Pulling (MarmoAAP) to Study Cooperative Behaviors.

In recent years, the field of neuroscience has increasingly recognized the importance of studying animal behaviors in naturalistic environments to gain deeper insights into ethologically relevant behavioral processes and neural mechanisms. The common marmoset (Callithrix jacchus), due to its small size, prosocial nature, and genetic proximity to humans, has emerged as a pivotal model toward this effort. However, traditional research methodologies often fail to fully capture the nuances of marmoset social interactions and cooperative behaviors. To address this critical gap, we developed the Marmoset Apparatus for Automated Pulling (MarmoAAP), a novel behavioral apparatus designed for studying cooperative behaviors in common marmosets. MarmoAAP addresses the limitations of traditional behavioral research methods by enabling high-throughput, detailed behavior outputs that can be integrated with video and audio recordings, allowing for more nuanced and comprehensive analyses even in a naturalistic setting. We also highlight the flexibility of MarmoAAP in task parameter manipulation which accommodates a wide range of behaviors and individual animal capabilities. Furthermore, MarmoAAP provides a platform to perform investigations of neural activity underlying naturalistic social behaviors. MarmoAAP is a versatile and robust tool for advancing our understanding of primate behavior and related cognitive processes. This new apparatus bridges the gap between ethologically relevant animal behavior studies and neural investigations, paving the way for future research in cognitive and social neuroscience using marmosets as a model organism.

Optimizing micro-EDM with carbon-coated electrodes: A multi-criteria approach

The pursuit of precision machining in engineering materials has ignited an imperative to advance electrical discharge machining (EDM) techniques. This study is a deep dive into the domain of micro-electrical discharge machining ([Formula: see text]EDM) with a specific focus on the utilization of carbon-coated tool electrodes. The primary objective is to elucidate the influence of carbon-coated electrodes on vital machining parameters, including machining depth (Z coordinate), tool wear rate (TWR) and overcut (OC) in [Formula: see text]EDM operations. Moreover, the investigation extends to scrutinizing surface characteristics generated by [Formula: see text]EDM with carbon-coated tool electrodes. To enhance performance, carbon-coated tungsten carbide tool electrodes are employed for machining titanium alloy (Ti–6Al–4V) workpieces. The optimization process seeks to identify the optimal combination of process parameters using a Taguchi-DEAR-based multi-response approach. Experimental findings reveal that integrating carbon-coated electrodes significantly improves the [Formula: see text]EDM process, leading to enhanced surface performance metrics. Notably, this study identifies specific parameter settings that effectively optimize machining quality indicators. By achieving a voltage of 160[Formula: see text]V, a capacitance of 10,000[Formula: see text]pF and a tool rotation of 600[Formula: see text]RPM, the research contributes valuable insights into the intricate realm of [Formula: see text]EDM, highlighting the potential for enhanced surface performance through strategic parameter manipulation.

Microbial Melanin: Recent Developments and Challenges in the Production, Extraction, Purification and Application of Microbial Melanin

A class of biopolymers known by the common name melanin has several potential uses in the medicinal sciences, bioelectronics, cosmeceutical and bioremediation. These pigments wide distribution indicates that they help a variety of life forms- fight biotic and abiotic stressors. Different types of melanin, such as eumelanin, pheomelanin, allomelanin, pyomelanin, and neuromelanin, are produced by the oxidative polymerization of phenolic compounds in fungi and bacteria, primarily via the 1,8-dihydroxynaphthalene [DHN] or 3,4-dihydroxyphenylalanine [DOPA] pathways. The tyrosinase, laccase, and polyketide synthase are the groups of enzymes that are primarily responsible for the production of melanin in many microorganisms. Research has indicated that utilising recombinant technologies in conjunction with culture parameter manipulation can enhance melanin yield for industrial production. Its low solubility has hindered the development of extraction techniques despite tremendous efforts, and its heterogeneous structural complexity has hindered structural elucidation, which limits the full potential of its biotechnological applications. The process of extraction may differ slightly depending on the kind of tissue and where the melanin is located. Melanin pigments from various taxa of life (like Sepia, bacteria, fungi) have been the subject of countless investigations to expand our understanding of them and enable their effective use in a wide range of applications. Due to these studies, there is an immediate need for a thorough review of melanin pigments that have been isolated from microorganisms. A review that covers biosynthesis, bioproduction, characterization, and possible applications will aid researchers from a variety of backgrounds in appreciating the significance of microbial melanins and in organizing their own melanin-related research projects. In order to achieve this, the current study contrasts traditional and cutting-edge concepts for environmentally sustainable melanin extraction processes.

Investigation and Tailoring of Rotating Squares' and Rectangles' Auxetic Structure Behavior through Computational Simulations of 6082T6 Aluminum Alloy Structures.

Auxetic structures, renowned for their unique lateral expansion under longitudinal strain, have attracted significant research interest due to their extraordinary mechanical characteristics, such as enhanced toughness and shear resistance. This study provides a systematic exploration of these structures, constructed from rigid rotating square or rectangular unit cells. Incremental alterations were applied to key geometrical parameters, including the angle (θ) between connected units, the side length (a), the side width (b) of the rotating rigid unit, and the overlap distance (t). This resulted in a broad tunable range of negative Poisson's ratio values from -0.43 to -1.78. Through comprehensive three-dimensional finite-element analyses, the intricate relationships between the geometric variables and the resulting bulk Poisson's ratio of the modeled auxetic structure were elucidated. This analysis affirmed the auxetic behavior of all investigated samples, characterized by lateral expansion under tensile force. The study also revealed potential stress concentration points at interconnections between rotating units, which could impact the material's performance under high load conditions. A detailed investigation of various geometrical parameters yielded fifty unique samples, enabling in-depth observation of the impacts of geometric modifications on the overall behavior of the structures. Notably, an increase in the side width significantly enhanced the Poisson's ratio, while an increase in the overlap distance notably reduced it. The greatest observable change in the Poisson's ratio was a remarkable 202.8%, emphasizing the profound influence of geometric parameter manipulation. A cascaded forward propagation-backpropagation neural network model was deployed to determine the Poisson's ratio for auxetic structures, based on the geometric parameters and material properties of the structure. The model's architecture consisted of five layers with varying numbers of neurons. The model's validity was affirmed by comparing its predictions with FEA simulations, with the maximum error observed in the predicted Poisson's ratio being 8.62%.

Multiresolution Representation of Geoelectric Model and Parameter Operation in Wavelet Domain

In this paper, an algorithm for wavelet domain geoelectric model representation and parameter manipulation is introduced, which can be utilized for multiresolution inversions of geophysical data based on tree structure. With proper wavelet choice, the model can be represented by much less parameters than traditional model representation, expecting to reduce the computational cost for inversion remarkably. The use of tree structure to represent the wavelet coefficients makes the parameter operations (e.g., perturbating model parameter and adding more details in a model) in inversion much simpler. Introducing detail in a model or removing detail from a model can be simply realized by adding nodes to or deleting nodes from the existing tree structure. The model parameter value can be perturbated by changing the tree node values. Our algorithm is general and can be applied to develop any geophysical inversions in wavelet domain with trivial additional work once the forward modeling and inversion algorithm is available.

Making recommender systems forget: Learning and unlearning for erasable recommendation

Regulations now mandate data-driven systems, e.g., recommender systems, to empower users to delete private individual data. This prompts the crucial unlearning of data from machine learning models, spotlighting the understudied machine unlearning problem. Despite the widespread usage of machine learning models in modern-day recommender systems, unlearning in this context lacks attention. Existing unlearning methods fall short in the preservation of collaborative information across users and items. To bridge this gap, we propose LASER: a model-agnostic erasable recommendation framework. LASER partitions data into disjoint and balanced shards using hypergraph-based embeddings. Sequential training on these shards, augmented by curriculum learning, LASER sufficiently preserves collaborative information and refines model utility. To address the inefficiency of sequential training, we integrate early stopping and parameter manipulation. Our theoretical analyses and real-world dataset experiments validate LASER’s effectiveness. It enables efficient unlearning while outperforming state-of-the-art models in preserving model utility.

A Deep Neural Network Attack Simulation against Data Storage of Autonomous Vehicles

<div>In the pursuit of advancing autonomous vehicles (AVs), data-driven algorithms have become pivotal in replacing human perception and decision-making. While deep neural networks (DNNs) hold promise for perception tasks, the potential for catastrophic consequences due to algorithmic flaws is concerning. A well-known incident in 2016, involving a Tesla autopilot misidentifying a white truck as a cloud, underscores the risks and security vulnerabilities. In this article, we present a novel threat model and risk assessment (TARA) analysis on AV data storage, delving into potential threats and damage scenarios. Specifically, we focus on DNN parameter manipulation attacks, evaluating their impact on three distinct algorithms for traffic sign classification and lane assist. Our comprehensive tests and simulations reveal that even a single bit-flip of a DNN parameter can severely degrade classification accuracy to less than 10%, posing significant risks to the overall performance and safety of AVs. Additionally, we identify critical parameters based on bit position, layer position, and bit-flipping direction, offering essential insights for developing robust security measures in autonomous vehicle systems.</div>

MILK: a Python scripting interface to MAUD for automation of Rietveld analysis.

Modern diffraction experiments (e.g. in situ parametric studies) present scientists with many diffraction patterns to analyze. Interactive analyses via graphical user interfaces tend to slow down obtaining quantitative results such as lattice parameters and phase fractions. Furthermore, Rietveld refinement strategies (i.e. the parameter turn-on-off sequences) tend to be instrument specific or even specific to a given dataset, such that selection of strategies can become a bottleneck for efficient data analysis. Managing multi-histogram datasets such as from multi-bank neutron diffractometers or caked 2D synchrotron data presents additional challenges due to the large number of histogram-specific parameters. To overcome these challenges in the Rietveld software Material Analysis Using Diffraction (MAUD), the MAUD Interface Language Kit (MILK) is developed along with an updated text batch interface for MAUD. The open-source software MILK is computer-platform independent and is packaged as a Python library that interfaces with MAUD. Using MILK, model selection (e.g. various texture or peak-broadening models), Rietveld parameter manipulation and distributed parallel batch computing can be performed through a high-level Python interface. A high-level interface enables analysis workflows to be easily programmed, shared and applied to large datasets, and external tools to be integrated with MAUD. Through modification to the MAUD batch interface, plot and data exports have been improved. The resulting hierarchical folders from Rietveld refinements with MILK are compatible with Cinema: Debye-Scherrer, a tool for visualizing and inspecting the results of multi-parameter analyses of large quantities of diffraction data. In this manuscript, the combined Python scripting and visualization capability of MILK is demonstrated with a quantitative texture and phase analysis of data collected at the HIPPO neutron diffractometer.

Assessment of the DCMD performances of poly(vinylidene difluoride) vapor-induced phase separation membranes with adjusted wettability via formation process parameter manipulation

Poly(vinylidene difluoride) (PVDF) membranes for direct contact membrane distillation (DCMD) are commonly modified to decrease their wettability by water. Yet, the vapor-induced phase separation (VIPS) process may be a suitable alternative to avoid complex and expensive modifications. Superhydrophobic membranes with a water contact angle >152° were prepared in one single step by adjusting the polymer concentration, dissolution temperature, relative humidity, and exposure time to vapors to 10 wt%, 40 °C, 80 % and 10 min, respectively with N-Methyl-2-pyrrolidone (NMP) as the solvent. Compared to commercial polytetrafluorethylene (PTFE) membrane, the VIPS PVDF membrane showed higher water vapor flux (38 LMH vs 36 LMH), lower flux decline ratio (22 % vs 39 %) and superior salt rejection (99.99 % vs. 99.90 %) in a 3 h-DCMD process. In wetting tests, the VIPS membrane failed for a sodium dodecyl sulfate concentration of 0.2 mM while 0.1 mM was enough to trigger wetting of the commercial membrane. The energy barrier to heterogeneous crystallization was slightly larger for the VIPS membrane, and a higher rejection was measured during scaling tests (99.8 % vs. 94.2 % for PTFE membrane). Finally, the VIPS membrane outperformed the commercial membrane in a long-term test (100 h). These results evidence the suitability of VIPS PVDF membranes for DCMD.

Global waveshape parameter Rd in signaling focal prominence: Perceptual salience in the absence of f0 variation

This paper explores perceptual salience of voice source parameter manipulation in signaling prominence in the absence off0variation. Synthetic stimuli were generated based on an inverse filtered all-voiced utterance “We were away a year ago.” A global waveshape parameterRdwas manipulated in the stimuli to enhance prominence in the two potentially accentable syllables WAY and YEAR and to provide voice source deaccentuation post-focally. The manipulations were intended to mimic an increase in phonatory tension in the prominent syllable while decreasing it in the post-focal material.f0was kept constant. Two listening tests were conducted in which participants rated the perceived prominence of the potentially accentable syllables in the manipulated utterances on a continuous visual analog scale. The results suggest that perceived focal prominence can be achieved by source variation in the absence off0modulations, although the results were not identical in the two tests. The extent of the enhancement of prominence by source manipulations in our data depended on the location of focal syllable in the intonational phrase and on the length of postfocal material (the effect was stronger for WAY than for YEAR).

Fired Heater Simulation,Modelling and Optimization

In this study a dynamic model Simulation was carried out using ASPEN HYSY for industrial refinery fired heater, it was found that Increasing the number of the tube rows in convection bank from 2 to 3 allows us to recover approximately 5% of the overall efficiency, the duty furnace has increased from 65.9MW to 68.1MW and the fuel flow has increased from 5597kg/h to 5807kg/h moreover Adding more rows has a reverse return as we start to notice increase on the flue gas temperature. Furthermore, sensitivity analysis was conducted with HYSYS to determine one of the most important parameters that affect the performance of the heater based on the data generated from the simulation. MATLAB code was generated for efficiency calculation and for parameter manipulation. well-stirred model was used for mathematical model calculations and for optimization of furnace operation, the models were validated with ASPEN Exchanger Design and Rating

Model-based translation of DNA damage signaling dynamics across cell types

Interindividual variability in DNA damage response (DDR) dynamics may evoke differences in susceptibility to cancer. However, pathway dynamics are often studied in cell lines as alternative to primary cells, disregarding variability. To compare DDR dynamics in the cell line HepG2 with primary human hepatocytes (PHHs), we developed a HepG2-based computational model that describes the dynamics of DDR regulator p53 and targets MDM2, p21 and BTG2. We used this model to generate simulations of virtual PHHs and compared the results to those for PHH donor samples. Correlations between baseline p53 and p21 or BTG2 mRNA expression in the absence and presence of DNA damage for HepG2-derived virtual samples matched the moderately positive correlations observed for 50 PHH donor samples, but not the negative correlations between p53 and its inhibitor MDM2. Model parameter manipulation that affected p53 or MDM2 dynamics was not sufficient to accurately explain the negative correlation between these genes. Thus, extrapolation from HepG2 to PHH can be done for some DDR elements, yet our analysis also reveals a knowledge gap within p53 pathway regulation, which makes such extrapolation inaccurate for the regulator MDM2. This illustrates the relevance of studying pathway dynamics in addition to gene expression comparisons to allow reliable translation of cellular responses from cell lines to primary cells. Overall, with our approach we show that dynamical modeling can be used to improve our understanding of the sources of interindividual variability of pathway dynamics.

Learning Computational Thinking Practices Through Agent-Based Modeling in an Informal Setting

In this qualitative study, I investigated the form of interaction when learners co-constructed computational thinking practices in an informal setting. This study focuses on the roles of five youth, three adult mentors, and a facilitator during a summer camp and documents how learners interacted to exchange ideas, collaborate, and accommodate other perspectives. Participants used questions that promote deep thinking to engage in computational thinking practices during the interactions. Analysis of data shows that learners developed computational thinking practices including interpreting, modeling connections to the real-world, manipulating parameters, discovering ideas to explore models, prompting, conjecturing, and making predictions when working on agent-based modeling and writing codes in NetLogo. These practices are further grouped together as model interpretation and connection to the real world, parameter manipulation and discovery, prompting and exploring, and making predictions/conjectures and generalizing. This study also finds some community-based practices such as practical wisdom, trying something and adjusting, and the use of network of resources relevant during interaction to develop computational thinking practices.

Optimization of Calorific Value of Densified Bush Mango Shell and Palm Pressed Fibre Briquettes

Energy value of biomass materials can be enhanced through composition, densification and process parameter manipulation. In this study, biomass briquettes of bush mango shell (BMS) and palm pressed fibre (PPF) compositions were evaluated and its calorific values optimized. The effects of biomass concentration, dwelling/compaction time and compression pressure on calorific value were investigated for briquette samples in the compositions of BMS: PPF ratios of 100:0, 75:25, 50:50, 25:75, and 0:100) as sample A, B, C, D and E respectively. An empirical prediction model of the combustion property of the briquettes was developed and optimized using response surface methodology. It was observed across the samples that as bush mango shell composition increased, the calorific value improved significantly from 12.4kJ/kg to 18.65kJ/kg. Increase in dwelling time and pressure also showed slight increase in calorific value of the briquette samples. An optimum calorific value of 19.03 kJ/kg for briquette sample B (75:25 biomass ratio) was realized at dwelling time of 40 minutes and pressure of 25MPa as adequately predicted by a reduced second order model. The model prediction accuracy was over 98% (Pred. R² of 0.9858) with Coefficient of Variance of 0.64% and Adeq. Precision value of 63.936. Thus, Sample B briquettes possess improved combustion properties with burning rate of 0.472g/min at optimum conditions hence suitable for adoption by investors in renewable energy sector.

Data-driven pre-stack AVO inversion method based on fast orthogonal dictionary

Regularization techniques are commonly used to mitigate the ill-posed problems for pre-stack AVO (amplitude variation with offset) inversion. In recent years, a data-driven regularization method has successfully been applied to achieve desirable inversion accuracy. In contrast of assuming a certain distribution of the data, this method uses the dictionary learning algorithm to learn the structural characteristics of elastic parameters and then takes this information as a prior constraint in AVO inversion. However, in this method, KSVD (An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation) algorithm is used, which is of low computational efficiency and involves many parameters. In this paper, we propose a novel data-driven pre-stack inversion approach based on the orthogonal dictionary (ORTD) learning method. Because the atoms are orthogonal, the efficiency of dictionary operation is nearly 100 times higher than that of KSVD redundant dictionary. Instead of regulating the sparsity, atomic size and atomic length for the KSVD method, the proposed method depends on two parameters: threshold parameters and dictionary atomic length. Moreover, the parameter manipulation has little impacts on the operation efficiency for this approach. This greatly improves the applicability of the data-driven inversion method. We use model tests and field data applications to verify the performance of the proposed method.

De Co Sim: A method for Collaborative Simulation of Complex Systems

The simulation of complex systems increasingly requires the collaboration of related researchers: domain experts (e.g. experts in the studied domains), modellers and computer scientists. However, the actual methods of modelling and simulation allow to produce only simulators that are used locally and considered as standalone applications. This kind of simulator hampers the exchange of these researchers; especially for the domain experts who are usually geographical distributed and not expert at using support tools. Our research focus on the collaboration among these scientists where many issues need to be addressed, e.g. scenario definition, parameter manipulation and results analysis. In this paper, we thus present a method, namely DeCoSim, supporting the collaboration among different scientists in the simulation of complex systems. A case study related to the modelling and simulation of Intra-Urban Daily Rhythms is presented to show the efficiency of the proposed method.

Solvent dependent 4-aminosalicylic acid-sulfamethazine co-crystal polymorph control

Despite polymorphism of crystalline active pharmaceutical ingredients (APIs) being a common phenomenon, reports on polymorphic co-crystals are limited. As polymorphism can vastly affect API properties, controlling polymorph generation is crucial. Control of the polymorph nucleation through the use of different solvents during solution crystallization has been used to obtain a desirable crystal polymorph. There have been two reported polymorphic forms of the 4-aminosalicylic acid-sulfamethazine co-crystals. These forms were found to have different thermodynamic stabilities. However, the control of co-crystal polymorph generation using preparation parameter manipulation has never been reported. The aim of this study was to establish the effect of different solvent parameters on the formation of different co-crystal polymorphic forms. Selection of the solvents was based on Hansen Solubility Parameters (HSPs) as solvents with different solubility parameters are likely to interact differently with APIs, ultimately affecting co-crystallization. Eight solvents with different HSPs were used to prepare co-crystals by solvent evaporation at two different temperatures. Through characterization of the co-crystals, a new polymorph has been obtained. The hydrogen bond acceptability seemed to affect the co-crystal form obtained more than the hydrogen bond donation ability. Furthermore, the use of HSPs can be utilized as an easy calculation method in screening and design of co-crystals.