General Approach Research Articles

The Efficiency Aspect of Taxation in Pakistan: A Computable General Equilibrium Approach

The Efficiency Aspect of Taxation in Pakistan: A Computable General Equilibrium Approach

Electric-Field Catalysis on Carbon Nanotubes in Electromicrofluidic Reactors: Monoterpene Cyclizations.

The control over the movement of electrons during chemical reactions with oriented external electric fields (OEEFs) has been predicted to offer a general approach to catalysis. Recently, we suggested that many problems to realize electric-field catalysis in practice under scalable bulk conditions could possibly be solved on multiwalled carbon nanotubes in electromicrofluidic reactors. Here, we selected monoterpene cyclizations to assess the scope of our system in organic synthesis. We report that electric-field catalysis can function by stabilizing both anionic and cationic transition states, depending on the orientation of the applied field. Moreover, electric-field catalysis can promote reactions which are barely accessible by general Brønsted and Lewis acids and field-free anion-π and cation-π interactions, and drive chemoselectivity toward intrinsically disfavored products without the need for pyrene interfacers attached to the substrate to prolong binding to the carbon nanotubes. Finally, interfacing with chiral organocatalysts is explored and evidence against contributions from redox chemistry is provided.

Novel Pseudo-Two-Dimensional 19F NMR Spectroscopy for Rapid Simultaneous Detection of Amines in Complex Mixture.

Rapid detection of amines in complex mixtures presents a significant challenge. Here, we introduce a novel nuclear magnetic resonance (NMR) method for amine detection utilizing a probe with two fluorine atoms in distinct chemical environments. Upon interaction with an amine, the probe generates two atomic resonance peaks, which are used to create coordinates, revealing fluorine chemical shifts on the 19F NMR spectroscopy. This innovative approach allows for the clear distinction of amine signals in a two-dimensional plane. This method has been effectively employed in analyzing amines in pharmaceuticals and amino acids in Ophiopogon japonicus and dry white wine, providing a robust and general approach for amine analysis.

Comparison of random forest methods for conditional average treatment effect estimation with a continuous treatment.

We are addressing the problem of estimating conditional average treatment effects with a continuous treatment and a continuous response, using random forests. We explore two general approaches: building trees with a split rule that seeks to increase the heterogeneity of the treatment effect estimation and building trees to predict as a proxy target variable. We conduct a simulation study to investigate several aspects including the presence or absence of confounding and colliding effects and the merits of locally centering the treatment and/or the response. Our study incorporates both existing and new implementations of random forests. The results indicate that locally centering both the response and treatment variables is generally the best strategy, and both general approaches are viable. Additionally, we provide an illustration using data from the 1987 National Medical Expenditure Survey.

The Identification of Network Intrusions with Generative Artificial Intelligence Approach for Cybersecurity

Generative Artificial Intelligence (Generative AI) offers a paradigm shift to the way robots perceive and interact with data. Generative AI approaches aim to produce new data samples that closely match the original dataset, in contrast to standard AI models that concentrate on tasks such as categorisation or prediction. This ability has broad implications for a variety of fields, including network security. New generation of network intrusion defense brought forth by Generative AI is revolution using the area of network security. In this work, investigates the revolutionary potential of Generative AI in network intrusion detection, with the goal of developing a proactive and adaptable cyber protection mechanism. In this work the use of Generative AI use for identifying network intrusions, using the NSL-KDD dataset. The work begins with meticulous data preprocessing using handling missing values, encoding, feature scaling, and feature extraction to enhance the dataset and then focuses on using Generative AI for intrusion detection. In this work many models have been used like SVM, AE, DAE, and GANs, and in this GANs has the best accuracy with 91.12% compared with other models.

An analysis and solution of ill-conditioning in physics-informed neural networks

Physics-informed neural networks (PINNs) have recently emerged as a novel and popular approach for solving forward and inverse problems involving partial differential equations (PDEs). However, ensuring stable training and obtaining accurate results remain challenging in many scenarios, often attributed to the ill-conditioning of PINNs. Despite this, a deeper analysis is still lacking, which hampers progress and application of PINNs in complex engineering problems. Drawing inspiration from the ill-conditioning analysis in traditional numerical methods, we establish a strong connection between the ill-conditioning of PINNs and the Jacobian matrix of the PDE system. Specifically, for any given PDE system, we construct a controlled system that allows for the adjustment of the Jacobian matrix's condition number while retaining the same solution as the original system. Our numerical experiments show that as the condition number of the Jacobian matrix decreases, PINNs exhibit faster convergence and higher accuracy. Building upon this principle and the extension of controlled systems, we propose a general approach to mitigate the ill-conditioning in PINNs, leading to successful simulations of three-dimensional flow around the M6 wing at a Reynolds number of 5,000. To the best of our knowledge, this is the first time that PINNs have successfully simulated such complex systems, offering a promising new technique for addressing industrial complexity problems. Our findings also provide valuable insights to guide the future development of PINNs.

Optimized Method for the Synthesis of Alkyne-Modified 2'-Deoxynucleoside Triphosphates.

A general approach is presented for synthesizing alkyne-modified nucleoside triphosphates via the Sonogashira cross-coupling reaction of unprotected halogenated 2'-deoxynucleoside, followed by monophosphorylation and the reaction of the corresponding phosphoromorpholidate with tributylammonium pyrophosphate. A highly efficient approach for the milligram-scale synthesis of base-modified nucleoside triphosphates with an amino acid-like side chain was developed. The present chemical method outweighs the other reported methods of a base-modified nucleoside triphosphates synthesis in terms of it being a protection-free strategy, the shortening of reaction steps, and increased yields (about 70%). The resulting 8-alkynylated dATP was tested as a substrate for DNA polymerases in a primer extension reaction.

A Koopman-based residual modeling approach for the control of a soft robot arm

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Efficient mapping of phase diagrams with conditional Boltzmann Generators

Abstract The accurate prediction of phase diagrams is of central importance for both the fundamental understanding of materials as well as for technological applications in material sciences. However, the computational prediction of the relative stability between phases based on their free energy is a daunting task, as traditional free energy estimators require a large amount of simulation data to obtain uncorrelated equilibrium samples over a grid of thermodynamic states. In this work, we develop deep generative machine learning models based on the Boltzmann Generator approach for entire phase diagrams, employing normalizing flows conditioned on the thermodynamic states, e.g., temperature and pressure, that they map to. By training a single normalizing flow to transform the equilibrium distribution sampled at only one reference thermodynamic state to a wide range of target temperatures and pressures, we can efficiently generate equilibrium samples across the entire phase diagram. Using a permutation-equivariant architecture allows us, thereby, to treat solid and liquid phases on the same footing. We demonstrate our approach by predicting the solid-liquid coexistence line for a Lennard-Jones system in excellent agreement with state-of-the-art free energy methods while significantly reducing the number of energy evaluations needed.

Meaning of work for South African women during the pandemic

Orientation: The coronavirus disease 2019 (COVID-19) pandemic disrupted workplaces worldwide, prompting a re-evaluation of the meaning and significance of work, particularly among women who were uniquely impacted by the crisis.Research purpose: This study aimed to understand the meaning that South African women attributed to their work during the pandemic. Additionally, the research aligned with the Sustainable Development Goals (SDGs 3, 5 and 8), contributing to efforts to improve well-being, promote gender equality and ensure decent work.Motivation for the study: Understanding how the pandemic altered women’s perceptions and experiences of work can provide valuable insights for human resource professionals, helping to create more inclusive and supportive work environments.Research approach/design and method: A generic qualitative approach was employed, involving 10 semi-structured interviews with South African women who were formally employed both before and during the pandemic. Thematic analysis was used to interpret the data collected.Main findings: The study revealed that women had individual motivations for their chosen work, with the meaning of work varying among participants. Factors such as organisational and personal challenges, as well as the support received, significantly shaped these meanings.Practical/managerial implications: Organisations need to consider the unique challenges and support needs of women in the workplace, especially in the context of recovery from the pandemic.Contribution/value-add: The study enriches the body of literature on the meaning of work, particularly focussing on South African women during the COVID-19 pandemic, and informs organisational practices that can better support employees during crises and beyond.

Catalytic N-Alkylation of (Hetero)Aromatic Amines and Tandem Annulation Reactions.

A general and practical approach for N-alkylation of heteroaromatic amines with heteroaromatic alcohols is always challenging and rarely reported. Here, we designed and synthesized phosphine-free, robust, and efficient N,N-bidentate-Co(II) complexes for a universal N-alkylation of amines strategy. This present catalytic methodology can be applied to a wide range of substrates by varying alcohols, including aryl, aliphatic, acyclic, and cyclic groups, with heteroaromatic amines such as aminopyridine, 2-aminopyrimidine, and aminoquinoline to provide diverse monoalkylated organonitrogen compounds in good to excellent yields (108 examples). In addition, the utility of the developed catalytic protocol was also extended successfully for the dehydrogenative synthesis of biologically important quinoline derivatives (11 examples). Particularly, 8-aminoquinoline reacted differently with tandem N-alkylated-transfer hydrogenative byproduct (N-benzyl-1,2,3,4-tetrahydroquinolin-8-amine) was obtained, revealing the catalytic activity of the complex I. The reaction proceeded under environmentally benign conditions, which liberates water as the sole byproduct. Notably, a concise synthesis of acetylcholinesterase inhibitors (AChEIs) scaffolds as potential cognition enhancers illustrated the utility of the present protocol. Interestingly, various control and deuterium-labeled experiments were performed, suggesting that the reaction proceeds via the borrowing hydrogen pathway.

Creative employment as a modern resource of socio-economic recovery of Ukraine

The article's main idea is to substantiate the importance of creative employment for Ukraine's socio-economic recovery. Human capital, its creative element, can accelerate the transformation of a raw economy with its export orientation into a new economy dominated by technology and technologies inherent in Industry 4.0. The use of methods of dialectical analysis, synthesis, generalization, systemic, strategic, and process approaches, and techniques of abstraction made it possible to reveal the high potential of creative employment for preserving existing jobs and generating new jobs, making it attractive to investors. The systematization of the thoughts and work of domestic and foreign scientists regarding the content of creative employment is of theoretical and practical importance. In accordance with the defined goals and objectives, the conceptual principles of "narrow" and "broad" approaches to the content of creative employment are summarized and deepened. It was found that, according to the "narrow" approach, creative workers are mainly people of art and creative professions, people employed in the media industry, and specialists in the field of information systems and technologies, for which individual work is characteristic. According to the author's position, in accordance with the "broad" approach, creative employment takes place in all branches and spheres that produce material, cultural, and spiritual goods, as well as in management activities. This concept not only brings new knowledge to socioeconomics but also has practical significance for determining the scope of creative employment and evaluating state social and economic policy. The study concluded that creative employment is not an artificial phenomenon detached from technical and social progress. Its development is connected primarily with the increase in the economy's and the social and humanitarian sphere's need for innovative developments, which are subsequently commercialized. According to the author, new industrial relations are formed on this basis, which are gradually transformed into partnership ones.

Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi-Sensor System with Foundation Models.

Combining chemical sensor arrays with machine learning enables designing intelligent systems to perform complex sensing tasks and unveil properties that are not directly accessible through conventional analytical chemistry. However, personalized and portable sensor systems are typically unsuitable for the generation of extensive data sets, thereby limiting the ability to train large models in the chemical sensing realm. Foundation models have demonstrated unprecedented zero-shot learning capabilities on various data structures and modalities, in particular for language and vision. Transfer learning from such models is explored by providing a framework to create effective data representations for chemical sensors and ultimately describe a novel, generalizable approach for AI-assisted chemical sensing. The translation of signals produced by remarkably simple and portable multi-sensor systems into visual fingerprints of liquid samples under test is demonstrated, and it is illustrated that how a pipeline incorporating pretrained vision models yields average classification accuracy in four unrelated chemical sensing tasks with limited domain-specific training measurements. This approach matches or outperforms expert-curated sensor signal features, thereby providing a generalization of data processing for ultimate ease-of-use and broad applicability to enable interpretation of multi-signal outputs for generic sensingapplications.

An Automated Cell-Free Workflow for Transcription Factor Engineering.

The design and optimization of metabolic pathways, genetic systems, and engineered proteins rely on high-throughput assays to streamline design-build-test-learn cycles. However, assay development is a time-consuming and laborious process. Here, we create a generalizable approach for the tailored optimization of automated cell-free gene expression (CFE)-based workflows, which offers distinct advantages over in vivo assays in reaction flexibility, control, and time to data. Centered around designing highly accurate and precise transfers on the Echo Acoustic Liquid Handler, we introduce pilot assays and validation strategies for each stage of protocol development. We then demonstrate the efficacy of our platform by engineering transcription factor-based biosensors. As a model, we rapidly generate and assay libraries of 127 MerR and 134 CadR transcription factor variants in 3682 unique CFE reactions in less than 48 h to improve limit of detection, selectivity, and dynamic range for mercury and cadmium detection. This was achieved by assessing a panel of ligand conditions for sensitivity (to 0.1, 1, 10 μM Hg and 0, 1, 10, 100 μM Cd for MerR and CadR, respectively) and selectivity (against Ag, As, Cd, Co, Cu, Hg, Ni, Pb, and Zn). We anticipate that our Echo-based, cell-free approach can be used to accelerate multiple design workflows in synthetic biology.

Organophotocatalytic Selective Deuteration of Metabolically Labile Heteroatom Adjacent C-H Bonds via H/D Exchange with D2O.

We report a general approach for efficient deuteration of the metabolically labile α-C-H bonds of widespread amides and amines. Temporarily masking the secondary amine group as a carbamate allows an unprecedented photoredox hydrogen atom transfer-promoted α-carbamyl radical formation for efficient H/D exchange with D2O. The mild protocol delivers structurally diverse α-deuterated secondary amines including "privileged" piperidine and piperazine structures highly regioselectively with excellent levels of deuterium incorporation (≤100%). Furthermore, we successfully implemented the strategy for α-deuteration of amides, lactams, and ureas with high regioselectivity and high levels of D incorporation. Finally, the observed efficient deuteration of secondary alcohol moieties in late-stage modification of complex amine-containing pharmaceuticals allows for the development of a viable method for efficient α-deuteration of the important functionality.

A Molten Alkali Approach to Tailor Hydroxyl Groups of Hexaazatrinaphthalene Toward High-Capacity and Low-Potential Anode of Aqueous Proton Batteries.

Hexaazatrinaphthalene (HATN) has attracted a lot of attention in aqueous proton batteries (APBs). However, its redox potential as an anode is insufficiently negative. The introduction of electron-donating substituent groups, such as hydroxyl groups, is considered as a good approach to reduce the redox potential of HATN. Nevertheless, manufacturing hydroxyl-substituted HATN (HATN-OH) requires either expensive precursors or multi-step process, limiting their research. Herein, a straightforward strategy is proposed to synthesize HATN-OH based on the nucleophilic substitution reaction of halogenated HATN in a molten alkali. The redox potential of 1,2,7,8,13,14-hexahydroxy-5,6,11,12,17,18-hexaazatrinaphthalene (34-HATN-6OH) electrode may be lowered by 0.15V in comparison to HATN, and exhibits a high specific capacity, low redox potential, remarkable rate capability, and outstanding long-term cycling performance. The electrochemical redox kinetics is significantly enhanced owing to the formation of rapid proton transport channels created by intermolecular hydrogen bond network. The assembled MnO2||34-HATN-6OH full battery delivers a high discharge voltage (1.16V) and cycling stability (74% capacity retention after 5000 cycles). This study provides a general cost-effective molten alkali approach for the synthesis of hydroxyl-substituted conjugated small molecules from their halogenated counterparts and further enriches the regulation means of electro-chemical performances of organic electrodes for enabling high-capacity and high-voltage APBs.

Lump type solutions: Bäcklund transformation and spectral properties

There are various different ways to obtain traveling waves of lump type(higher order lumps) for the KP-I equation. We propose a general and simple approach to derive them via a Bäcklund transformation. This enables us to establish an explicit connection between those lower energy solutions and higher energy ones. Based on this construction, spectral analysis of the degree 6 solutions is then carried out in details. The analysis of higher energy ones can be done in an inductive way.

Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection.

The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector's hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively.

A novel freemium code SAND (v1.0) for generation of randomly packed beds

The main current approaches for generation of the packed bed models are based on rigid body dynamics (RBD) and Newton's laws (discrete element methods - DEM). This paper deals with the development and analysis of a novel code based on analytical geometry approach for the packed bed generation. The architecture and main algorithms of the novel code are described and clarified. The parameters of the packed bed generated via the novel code are compared with experimental data and packed beds generated via Blender (RBD), Yade (DEM). The novel code demonstrates many advantages, such as good correlation with experimental data, no overlaps between pellets in the packed bed, and a low computational time for packed bed generation. The packed bed model can be directly exported in .step format. Other advantages are also demonstrated and clarified. The novel code is attached to this paper and can be freely used by engineers and scientists.

On the selection of control structures using process operability analysis

This work aims to develop a generalizable framework for control structure selection using process operability analysis. Current approaches for selecting controlled variables in chemical processes are limited to assessing system attributes individually, focusing on controller performance or the economic impact based on a constant setpoint policy. However, the competitive industrial manufacturing market requires a holistic approach for control structure selection in large-scale plants that takes into account multiple factors. In particular, process operability can help to enable a generalizable approach that is able to select control structures that are operable considering economic and performance factors simultaneously. To achieve this goal, a framework that uses the Operability Index (OI) as a metric for ranking the achievability of the control objectives for the selected control structures is developed. To test the framework, a depropanizer distillation column is investigated as a case study associated with large-scale energy systems. This work thus introduces novel formulations and algorithms for the control structure selection problem, enhancing the design, operations, and synthesis of existing and future industrial systems.