Conventional Analysis Research Articles

Protein-Induced DNA Dumbbell Amplification (PINDA) and its Applications to Food Hazards Detection

Quantification of trace amounts of proteins is technically challenging because proteins cannot be directly amplified like nucleic acids. To improve the analytical sensitivity and to complement conventional protein analysis methods, we developed a highly sensitive and homogeneous detection strategy called Protein-Induced DNA Dumbbell Amplification (PINDA). PINDA combines protein recognition with exponential nucleic acid amplification by using protein binding probes made of DNA strands conjugated to protein affinity ligands. When a pair of probes bind to the same target protein, complementary nucleic acid sequences that are conjugated to each probe are brought into close proximity. The increased local concentration of the probes results in the formation of a stable dumbbell structure of the nucleic acids. The DNA dumbbell is readily amplifiable exponentially using techniques such as loop-mediated isothermal amplification. The PINDA assay eliminates the need for washing or separation steps, and is suitable for on-site applications. Detection of the model protein, thrombin, has a linear range of 10 fM-100 pM and detection limit of 10 fM. The PINDA technique is successfully applied to the analysis of dairy sample for the detection of β-lactoglobulin, a common food allergen, and Salmonella enteritidis, a foodborne pathogenic bacterium. The PINDA assay can be easily modified to detect other targets by changing the affinity ligands used to bind to the specific targets.

A self-adaptive model for sensing total phosphorus in natural water bodies using Fourier transform mid-infrared attenuated total reflectance spectroscopy

As the most economically developed area in China, the environmental water quality of Guangdong-Hong Kong-Macao Greater Bay Area has received extensive attention, and the spatial variations of total phosphorus (TP) in the Greater Bay were significant, conventional laboratory analysis is difficult to meet the requirements of TP monitoring due to time and cost consuming, and Fourier transform infrared attenuated total reflectance spectroscopy (FTIR-ATR) technology, combined with the self-adaptive partial least squares (SA-PLS) model, was used to determine the TP concentration. The results showed that P-O vibrations were observed in the wavenumber range of 1200–900 cm−1, and prediction models were established by using this range. For the conventional partial least squares (PLS) model, the R2, RMSE and RPD were 0.817, 0.022 mg L−1 and 2.335, respectively, while for the SA-PLS model the prediction was improved with the values of 0.965, 0.010 mg L−1, 5.296, respectively, and the SA-PLS mode prediction was satisfied when the TP content in water was more than 0.05 mg L−1. It was found that the TP determination was interfered by sulfate, when the sulfate content was < 100 mg L−1, both SA-PLS and the conventional PLS model could be used for quantitative analysis of TP; when sulfate content was > 100 mg L−1, PLS model could not be used while the SA-PLS model still achieved an excellent prediction. Therefore, FTIR-ATR combined with SA-PLS model can rapidly determine TP in water, providing an alternative strategy for monitoring TP in natural water bodies.

Correlated 4D-STEM and EDS for the classification of fine Beta-precipitates in aluminum alloy AA 6063-T6

Tuning the properties of aluminum alloys AA 6063-T6 involves artificial aging to induce precipitate formation, particularly β’’ and β’ phases. Previous characterization challenges due to their similar appearance are addressed here by correlating 4D scanning transmission electron microscopy (4DSTEM) and energy-dispersive spectroscopy (EDS) mapping. This approach allows us to analyze the structure and composition of precipitates individually, overcoming limitations of conventional imaging and structural analysis techniques when the precipitates appear simultaneously, as is often the case. We present detailed characterizations of needle-shaped Beta precipitates, revealing distinct diffraction patterns (DPs) and compositional differences. The method's applicability extends beyond aluminum alloys, offering a promising strategy for complex composite material characterization with multimodal scanning transmission electron microscopy (STEM) techniques.

Computer Simulation for Effective Pharmaceutical Kinetics and Dynamics: A Review.

Computer-based modelling and simulation are developing as effective tools for supplementing biological data processing and interpretation. It helps to accelerate the creation of dosage forms at a lower cost and with the less human effort required to conduct the work. This paper aims to provide a comprehensive description of the different computer simulation models for various drugs along with their outcomes. The data used are taken from different sources, including review papers from Science Direct, Elsevier, NCBI, and Web of Science from 1995-2020. Keywords like - pharmacokinetic, pharmacodynamics, computer simulation, whole-cell model, and cell simulation, were used for the search process. The use of computer simulation helps speed up the creation of new dosage forms at a lower cost and less human effort required to complete the work. It is also widely used as a technique for researching the structure and dynamics of lipids and proteins found in membranes. It also facilitates both the diagnosis and prevention of illness. Conventional data analysis methods cannot assess and comprehend the huge amount, size, and complexity of data collected by in vitro, in vivo</i>, and ex vivo</i> experiments. As a result, numerous in silico</i> computational e-resources, databases, and simulation software are employed to determine pharmacokinetic (PK) and pharmacodynamic (PD) parameters for illness management. These techniques aid in the provision of multiscale representations of biological processes, beginning with proteins and genes and progressing through cells, isolated tissues and organs, and the whole organism.

Circular practices in the hospitality sector regarding food waste

In recent years, there has been an increased focus on researching food waste in the hospitality industry, which is crucial for helping industry to meet the Sustainable Development Goal (SDG) 12.3, which aims to halve food waste by 2030. This study identifies circular practices as a potential pathway to mitigate food waste, as these have a regenerative aim to prevent and reduce waste, preserve resources and protect the environment. A systematic literature review was conducted to identify, analyse, and summarise research on circular practices in the hospitality sector regarding food waste. The review identified 51 published articles. The study examined various aspects of the studies, including publication year, journals, geographical scope, type of establishment studied, research design, and research type. The review identified eight themes through conventional content analysis, which provided an overview of the current state of research on circular practices in the hospitality sector regarding food waste. The findings show that using different circular practices reduces food waste and encourages sustainable behaviours among customers. Further research is necessary to comprehend what drives and hinders customer engagement with circular practices related to food waste in the hospitality sector. To guide future research, we outline a research agenda highlighting opportunities and challenges in waste prevention in the hospitality sector. We also present implications for practice and research.

Assessing Local Public Health Governance in North Carolina Across Organizational and Governance Configurations

Background Every county in North Carolina must include a board of health (BOH) with specific prescribed duties and powers. It is unclear how BOHs in North Carolina are currently exercising their governance ability. In 2012, the North Carolina General Assembly provided coun­ties with additional flexibility to select among different configurations for their local health department (LHD). The impact of this flexibility on the governance and service delivery of LHDs is yet to be explored. Methods We conducted semi-structured interviews with LHD directors and BOH members to assess the strengths and weaknesses of BOHs within different local public health configurations across North Carolina. We employed conventional content analysis to derive themes from the interview transcripts. Results BOHs were largely described as an underutilized institution, with few BOHs noted to be active beyond satisfying their required legal duties. Strong BOHs were noted to fulfill three identities on behalf of the LHD: an advocate, a bridge, and an advisor. The majority of interviewees desired to work in a standalone county health department (as opposed to a consolidated human services agency) with an appointed (versus elected) board of health. This configuration was preferred because, according to participants, it is more likely to enable a structural focus on public health initiatives. Limitations Our sample frame did not control for the length of time an interviewee had been in the office nor the professional background of each BOH member. Conclusions Wide variations exist in the exercise of BOHs across the state, partially due to how different LHD configurations structurally focus resources and attention on public health.

A Generative adversarial network model for estimating temporal frequency variation of vehicle-bridge interaction using modified Stockwell transform

The presence of massive and directional vehicles moving on a railroad bridge causes temporal fluctuations in the fundamental frequencies of both bridge and vehicle systems, making traditional structural inspections difficult to employ effectively. Thus, this paper proposes an image-to-image Generative Adversarial Network (GAN) that estimates temporal frequency variation for the vehicle-bridge interaction system. To address the challenges associated with conventional approaches, eigenvalue analysis, numerical substitution, and Fourier series approximations are examined using a simple degree of freedom and a more complex model. Thus, the GAN model is proposed to overcome those limitations. In the framework, based on the properties of a real bridge and vehicle model, the modified Stockwell transform of bridge acceleration from the dynamic simulation is used as input data with the paired data from the numerical substitution approach. Then, the model is validated through quantitative measures and laboratory scale experiments. Results showed that the model has strong performances across the various scenarios, demonstrating the potential for bridge condition assessment under operational conditions.

Challenges and possibilities of the manual metallographic serial sectioning process using the example of a quantitative microstructural analysis of graphite in cast iron

Challenges and possibilities of the manual metallographic serial sectioning process using the example of a quantitative microstructural analysis of graphite in cast iron

Dynamic risk assessment of Uncertain Random System considering operator's simple emergency-stop action in short time window

Human-machine systems can be considered as typical Uncertain Random Systems (URS). In human-machine systems, operator's simple emergency-stop actions act as a vital soft barrier. Conventional Human Reliability Analyses face challenges in evaluating operator's simple emergency-stop action due to their static characteristics, incompatibility with short time windows, and problems posed by small samples. This paper provides a novel solution to risk assessment of human-machine systems involving operator's simple emergency-stop mechanism. Specifically, an evaluation method of operator's simple emergency-stop action is established, where human cognition is decomposed according to its temporal and logical dimensions, key indicators of human sub-cognition are evaluated, and uncertainty theory is used to integrate these indicators. The output of proposed method is uncertainty of operator's behavior (UOB), which can offer more information than traditional Human Error Probability (HEP) and can be readily converted into uncertainty of human error. Further, a precise simulation method for dynamic risk assessment of Uncertain Random Systems is developed, in which the integration of the UOB is achieved. A case study demonstrates the effectiveness of the proposed methods, and its results show that the proposed methods can clearly reflect the differences in system risk under short time window conditions that have minor distinctions.

All Good Without Anything Good. Beyond Survival: Understanding the Psychosocial Experiences of Individuals With Chronic Kidney Disease and Their Caregivers in Sri Lanka.

This study aims to explore the experiences of individuals with chronic kidney disease (CKD) undergoing haemodialysis and their caregivers, focusing on the disease's impact and the treatment process. In Sri Lanka, CKD is a growing health concern, particularly affecting farming communities and contributing to the strain on the biomedical healthcare system. Despite increasing awareness of CKD's physical implications, its psychosocial impact remains underexplored. This study seeks to fill this gap, aiming to inform culturally sensitive interventions and improve the healthcare system's responsiveness to the unique needs of Sinhala Buddhist individuals with CKD and their caregivers. An exploratory qualitative study. Semistructured interviews were conducted with 10 individuals undergoing haemodialysis and 5 caregivers at a dialysis unit. The interviews were audio-recorded, transcribed and analysed using conventional qualitative content analysis. The analysis revealed three interrelated main themes: (1) impact on standard of living (quality of life), (2) coping strategies and (3) medical experience, with a notable influence of traditional beliefs and practices. The findings highlight the need for a holistic approach to CKD management that integrates physical, emotional, psychological and social aspects, considering the significant role of traditional influences. Further research is essential to develop effective interventions that can enhance the quality of life for CKD. The lived experiences of Sinhala Buddhist individuals with CKD and their caregivers served as a cornerstone, providing profound insights into the impact of the condition on their lives. Throughout the study, these participants played an instrumental role in refining the research's cultural sensitivity and relevance. Their engagement extended beyond the data collection phase to encompass feedback sessions, where they actively shared their perspectives. This ongoing collaboration ensured the study's depth and applicability to real-world experiences. By actively involving those directly affected by CKD, this collaborative approach safeguards that the study remains rooted in their voices and addresses their unique needs. This study adhered to relevant EQUATOR guidelines (the COREQ checklist). This study is not a clinical trial, and thus, registration is not applicable.

The optimization of a low-temperature geothermal-driven combined cooling and power system: thermoeconomic and advanced exergy assessments

Switching from fossil fuels to geothermal energy can provide a solution to environmental issues and the energy crisis caused by power and cooling generation systems. In this regard, this study examines an innovative cogeneration system that utilizes low-temperature geothermal energy to generate power and cool the built environment simultaneously. The proposed system incorporates a Kalina-34 cycle, complemented by an integrated absorption refrigeration cycle. System analysis encompasses thermodynamic and exergoeconomic assessments, as well as parametric investigations to gauge the effects of critical parameters on system performance. Following a parametric study, the TOPSIS multicriteria decision-making technique is applied to optimize turbine inlet pressure, vapor generator temperature difference, and ammonia concentration. Finally, the main source of irreversibility is compared by conventional and advanced exergy analyses under optimal conditions. Results show that under optimal conditions, the energy utilization factor (EUF), exergy efficiency, and unit co-generation cost (UCGC) are 11.15% (30.5% improvement), 45.54% (20.1% improvement), and 6.04 $/GJ (1.63% reduction), respectively. Furthermore, while conventional exergy analysis prioritizes the improvement of the condenser-1, vapor generator, and turbine in that order, advanced exergy analysis suggests a different priority order, with condenser-1, turbine, and vapor generator being the main objects of improvement, respectively. Implementing this study will significantly improve and optimize the proposed system.

Beyond Jacobson and Truax: Estimation of Clinical Significance Trajectories in the Coping Power Intervention Using Measurement Error-Corrected Multilevel Modeling

Coping Power (CP) is a preventive intervention that focuses on reducing child externalizing problems. Although it is typically delivered in a group format (GCP), individually-delivered CP (ICP) has produced greater mean reductions in externalizing problems. However, standard analysis of randomized trials loses individual-level information regarding which youth improve, fail to improve, or get worse, whereas clinically significant change (CSC) metrics capture information on individual change. The present study is a secondary analysis of an ICP/GCP trial (N=360) that examines differences in CSC-based individual-level inferences on externalizing. A novel method for assessing CSC under measurement error-corrected multilevel modeling was used, overcoming three limitations of traditional CSC methods: a) restriction to two timepoints, b) use of total scores, and c) assumption of constant reliability across time and participants. Because of concerns about Type II errors with all CSC methods, an individual-level effect size metric for CSC was also developed. Based on individualized Cohen’s d estimates, individual-level improvements in externalizing from 4th through 11th grades of d ≥ .5 were significantly greater in ICP (73%) versus GCP (45%). Further, GCP saw significantly higher percentages of youth with worsening of externalizing, underscoring concerns about diminished effects for GCP. Half of the sample had improvement that was not statistically significant but exceeded d ≥ .5, highlighting the susceptibility to Type II errors of CSCs results based on statistical significance. An examination of ICP/GCP differences under advanced CSC analysis gives more nuanced information than conventional RCT analysis and greater precision in estimating individual-level outcomes than standard CSC methodologies.

Healthcare provider's perceptions of bleeding in patients with acute leukaemia undergoing induction chemotherapy: A qualitative study.

Bleeding is a primary outcome for many transfusion-related trials in acute leukaemia (AL) patients, typically graded using the World Health Organisation (WHO) bleeding scale (clinically significant bleed (CSB) is ≥grade 2). This composite outcome fails to differentiate minor bleeds that may not be significant, poorly represents the total burden of bleeding and lacks input from healthcare providers (HCPs) and patients. As part of a multi-step project to create a better bleeding tool for trials, our objective was to identify HCPs' perspectives on the components of CSB in AL patients. Using qualitative description, we interviewed 19 physicians and nurses who care for AL patients undergoing induction chemotherapy. Participants were recruited from professional organisations, networks and social media. An inductive approach to conventional content analysis was used. HCPs identified features of CSB as the anatomical site of bleeding, amount of bleeding, need for intervention and changes in vital signs. Using these characteristics, bleeding events were categorised into three groups: clinically significant, could evolve into a CSB and not clinically significant. HCPs considered the patient's condition, bleeding history and clinical intuitions when deciding whether a bleed could escalate into serious bleeding. Using data from HCPs, we categorised bleeds as clinically significant, could evolve into a CSB, and not significant. A study of patients' perspectives on the importance of different kinds of bleeding is the next step to creating a bleeding definition that is informed by evidence, clinicians and patients.

Linking petrophysical heterogeneity and reservoir rock-typing of the post-rift shallow marine siliciclastics to their depositional setting: The Upper Cretaceous Bahariya reservoirs, north Western Desert, Egypt

The upper Cretaceous clastic facies of the Bahariya Formation host the main reservoir intervals in the north Western Desert (NWD) of Egypt. These clastics were deposited in a parallic depositional environment characterized by different scales of reservoir heterogeneities. However, the link between these heterogeneities and the depositional setting of the reservoir facies is still blurred. In this study, we investigate a highly-heterogeneous reservoir facies of Bahariya Formation in Yasser Field wells, NWD in order to construct a predictive framework for the distribution of the best reservoir rock types (RRTs) and flow zones. Seismic stratigraphy was integrated with core sedimentology to understand the depositional architecture of the reservoir facies. Moreover, conventional core analysis and wireline logs were interpreted to evaluate the scales of lithological and petrophysical heterogeneities in the different RRTs.In the studied Yasser Field, the Bahariya Formation consists of tidal facies deposited in a wide range of tidally-influenced conditions. The seismic facies varies greatly with variation in the tidal regime. Best reservoir rock types RRTs are associated with tidal channels and amalgamated tidal bars (RRTI). RRTI rocks represent the main fluid flow conduits in the studied Bahariya reservoir. Lithological and petrophysical heterogeneities are more prominent in the tidal mud flat and mixed tidal flat facies which form RRTIII rocks. Linking seismic with sedimentary facies enabled us to predict the distribution of the best reservoir flow zones in the study region. The present results establish a framework for predicting the optimum reservoir quality facies based on integrating seismic, sedimentary and petrofacies. This framework could be applied in analogous tidally-influenced reservoir facies with high depositionally-controlled pore system heterogeneity.

Nurses’ perception of uncertainty in clinical decision-making: A qualitative study

BackgroundUncertainty is a common challenge for nurses in clinical decision-making, which can compromise patient care quality and safety. To address this issue, it is essential to understand how nurses perceive and cope with uncertainty in their practice. AimThis study aimed to explore nurses’ perceptions of uncertainty in clinical decision-making using a qualitative approach. MethodsThis study was conducted with a qualitative approach and conventional content analysis in 2020. Participants consisted of 17 nurses from different wards of teaching hospitals in Northwestern Iran, recruited using the purposive sampling method. Data were collected through semi-structured interviews and analyzed simultaneously with data collection (June to December 2020). The data were analyzed using the content analysis approach suggested by Wildemuth. Data were managed with MAXQDA10 software. The analysis revealed four main themes and ten subthemes that described the nurses' experiences of uncertainty in clinical decision-making. ResultsThe main themes were: difficult choice, difficult situation, insufficient judgment, and emotional burden. ConclusionsThe study participants defined uncertainty in clinical decision-making as a difficult choice that occurs in difficult situations, which influenced their clinical judgment and emotional well-being. These findings provide valuable insights for developing interventions to help nurses manage uncertainty and improve their decision-making skills and safety.

High-velocity compressible gas flow modelling through porous media considering the quadratic pressure gradient: Part 2. experimental investigations and numerical analysis

This research investigates reliability of existing methods for analysis of transient Pressure Pulse Decay (PPD) test results. Analysis of experimental data reported by previous studies using existing conventional analysis methods revealed significant discrepancies, in some cases with more than 300% error between different experiments on the same sample. New experiments (Knudsen number ranged from 0.22 to 0.34) were designed and conducted in our laboratory, reducing the uncertainty by eliminating slip flow and net stress effects under low and high-pressure conditions. These experimental results aligned much better with the recently proposed analytical solution for improved interpretation of PPD experimental results. The differences are around 25% between different pulses, confirming the reliability of the conducted experiments and newly proposed methodology.Numerical simulations using a commercial software package were conducted to complement experimental and analytical findings. The simulator, which demonstrated good agreement with the proposed analytical solution, facilitated conducting sensitivity analyses on permeability variations and compressive storage capacity effects. These simulations validated the findings of the proposed analytical solution, which accounts for both pressure difference and absolute pressure values, and explains the discrepancies observed in lower pressure scenarios using conventional methods.Overall, this study presents a comprehensive framework integrating experimental, analytical, and numerical approaches to enhance our understanding and better analysis of transient PPD test results for compressible gas flows. The findings also offer a more robust approach for analysing gas flow behaviour in porous media.

Effect of graphite oxide on the mechanical, thermal, structural and hydrophilicity proprieties of Polypropylene / Poly(Propylene-Glycol) Acrylate / Glycidyl Methacrylate PP/PPGA/GMA composite

This research aims to investigate the synergistic effects of grafting flexible fractions and incorporating reinforcing fillers in polypropylene (PP) matrix. The primary objective is to establish an experimental methodology for preparing and characterizing Polypropylene / Poly(Propylene-Glycol) Acrylate / Glycidyl Methacrylate (PP/PPGA/GMA) composites loaded with graphite oxide (GtO).The process of oxidizing natural graphite powder using a mixture of sulfuric acid and nitric acid was validated through various analytical techniques including IRTF, AFM, DRX, XRF and ATG. The grafting reaction of PPGA and GMA on polypropylene and the incorporation of graphite oxide with varying levels were carried out in the melt state in a Brabender internal mixer; Dicumyl peroxide (DCP) was used as a radical initiator. A comprehensive assessment of the obtained materials was conducted through conventional analyses encompassing mechanical properties (tensile and impact), thermal behaviors (ATG and DSC), morphological characteristics (SEM) and the chemical state of film surfaces (Contact angle).The investigation revealed two key outcomes. Firstly, it showcased the grafting effect of PP on the polymer's morphology and properties. Secondly, it underscored the consequential impact of incorporating graphite oxide on the overall behavior of produced materials.

On the convergence of Fourier representations and Schwartz distributions

While Fourier theory remains a cornerstone for analyzing and interpreting the spectral content of diverse signals, its limitations often surface in practical applications. Notably, popular signals like sinusoids, Dirac deltas, signums, and unit steps lack convergent Fourier representations within the conventional framework. This discrepancy necessitates distribution theory to build and interpret suitable representations for such signals. However, existing literature in signal processing and communication engineering often glosses over these intricacies, leaving researchers grappling with obscure concepts regarding the very existence of Fourier representations for non-conforming signals. This work bridges this critical gap by offering a comprehensive exploration of the conditions guaranteeing the existence of Fourier representations. We introduce a novel linear space – the Gauss-Schwartz (GS) function space – and its corresponding class of tempered superexponential (TSE) distributions. We demonstrate that the Fourier transform (FT) acts as an isomorphism on the GS space of test functions and, by duality, on TSE distributions. Crucially, the GS space proves to be minimal in the sense that its dual, encompassing TSE distributions, represents the largest possible linear space over which the FT can be defined through duality. This theoretical advancement signifies a twofold contribution. Firstly, it clarifies the existence and interpretation of Fourier representations for prevalent signals that evade conventional analysis. Secondly, the introduction of the GS-TSE framework expands the reach of the FT to encompass a broader spectrum of functions, enabling novel applications in diverse fields. Ultimately, this work paves the way for a more robust and accessible understanding of Fourier analysis, empowering researchers and practitioners to leverage its full potential in exploring and processing a wider range of signals.

Tree knowledge structure for better insight: Capturing biomedical science-technology knowledge linkage with MeSH

Measuring the knowledge linkage between science and technology (S&T) is crucial for understanding the interactions between S&T and assisting decision-makers in strategizing research and development investments. Conventional analyses of S&T knowledge linkage have frequently overlooked the semantic structure of knowledge elements thereby introducing biases in the measurements. To address this issue, this study introduces a novel method predicated on the tree semantic structure, which quantifies the S&T linkage by considering the hierarchy and category of knowledge elements within an ontological framework. In this method, knowledge trees are constructed to represent the core knowledge of S&T literature, incorporating hierarchically organized MeSH descriptors. These knowledge trees are subsequently utilized to measure the knowledge linkage between S&T by integrating intra-branch knowledge similarity and inter-branch knowledge distribution. An empirical analysis was conducted on a substantial corpus of scientific publications and patents within the biomedicine sector. The findings predominantly revealed a stronger knowledge linkage between S&T in recent years, relative to the early 2000 s. It was also observed that patents are more inclined to include broader concepts in their titles and abstracts, in contract to the more specific concepts found in scientific publications. S&T literatures have increasingly focused on knowledge related to diseases, equipment, and health care. To verify the reliability of the proposed method, validation was performed with alternative measurements of knowledge linkage. In comparison to single-feature-based linkage measurements and network-based approaches, our proposed method demonstrates superior adaptability in capturing S&T linkage, especially when there is a marked disparity in the sample sizes of S&T literature. This study not only enriches the measurements of S&T knowledge linkage, but also furnishes empirical insights into the evolving patterns of S&T linkage within the biomedical domain.

Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach

Accurate seismic ground response analysis is crucial for the design and safety of civil infrastructure and establishing effective mitigation measures against seismic risks and hazards. This is a complex process due to the nonlinear soil properties and complicated underground geometries. As a simplified approach, the one-dimensional wave propagation model, which assumes that seismic waves travel vertically through a horizontally layered medium, is widely adopted for its reasonable performance in many practical applications. This study explores the potential of sequence deep learning models, specifically 1D convolutional neural networks (1D-CNNs), long short-term memory (LSTM) networks, and transformers, as an alternative for seismic ground response modeling. Utilizing ground motion data from the Kiban Kyoshin Network (KiK-net), we train these models to predict ground surface acceleration response spectra based on bedrock motions. The performance of the data-driven models is compared with the conventional equivalent-linear analysis model, SHAKE2000. The results demonstrate that the deep learning models outperform the physics-based model across various sites, with the transformer model exhibiting the smallest average prediction error due to its ability to capture long-range dependencies. The 1D-CNN model also shows a promising performance, albeit with occasional higher errors than the other models. All the data-driven models exhibit efficient computation times of less than 0.4 s for estimation. These findings highlight the potential of sequence deep learning approaches for seismic ground response modeling.