Recovery Model Research Articles

A Qualitative Analysis of Trialogues Between People with Lived Experience, Their Relatives, and Mental Health Professionals.

Trialogue meetings, aligned with the Recovery model in mental healthcare, foster the development of a shared language and mutual understanding among persons with lived experience, their relatives, and professionals. This study analysed the first mental health trialogue experience in Barcelona, involving six meetings with thirteen voluntary participants over three months. Using qualitative methods for data collection and reflexive thematic analysis, three main themes emerged: Recovery process, Interaction with mental health services, and Trialogue dynamics. Benefits included open dialogues in a safe space, learning from diverse perspectives, and sharing expertise. The meetings created an environment of empathy and respect while promoting open communication. This approach holds substantial potential for transforming mental healthcare systems, suggesting trialogues can be an effective tool for enhancing communication within community-based mental health initiatives. The findings provide a comprehensive picture of the trialogue process, highlighting its potential to foster mutual understanding among participants.

Neurological Recovery Following Surgical Intervention in Patients with Traumatic Spinal Cord Injury at the University Teaching Hospital in Zambia

Introduction Traumatic spinal cord injuries cause long-term disability and functional impairment. Timely and proper surgical treatment improves neurological recovery and functional outcomes. However, in low-resource setting countries, surgical intervention is usually delayed raising concerns about the recovery of patients. Hence, this study investigated the neurological improvement of patients undergoing surgery for traumatic spinal cord injuries at the University Teaching Hospital in Lusaka, Zambia. Methods This was a retrospective study that included all eligible patients with traumatic spinal cord injury managed surgically at the University Teaching Hospital between 2018 and 2022. The study assessed neurological improvement by comparing the American Spinal Injury Association’s neurological grading before surgery to that at discharge. Data was analysed using Stata 17, and the level of significance was set at 5%. Results Out of the 96 patients in the study, 45.8% showed neurological improvement of at least one grade after surgery. Patients with thoracic injuries showed lower improvement rates (25.0%) than those with cervical (55.0%) or lumbar injuries (65.0%), while those with incomplete injuries showed higher improvement rates (78.7%) (p <0.05). Thoracic injuries were associated with reduced neurological improvement (AOR 0.21; 95% CI: 0.04 – 0.95, p = 0.043), while incomplete injuries were associated with higher neurological improvement (AOR 18.58; 95% CI: 6.11 – 56.51, p < 0.001). Conclusion Neurological improvement was poor for thoracic injuries and complete injuries, highlighting the necessity for further investigation into the specific structural features and mechanisms of injury that contribute to poor neurological improvement in thoracic injuries and the identification of effective recovery approaches for these patients.

Fuzzy Boosted Sooty Tern Optimization (FBSTO): Fault Detection and Node Recovery Approach for WSN

Fuzzy Boosted Sooty Tern Optimization (FBSTO): Fault Detection and Node Recovery Approach for WSN

Establishment of Efficient Method for Evaluation of Heat Stress Tolerance of Herbaceous Plant Species and Selection of Heat-Tolerant Plants

Due to climate change, heat-tolerant species have become increasingly important; however, the selection system for herbaceous plants has yet to be well established. This study aimed to establish a selection system for heat-tolerant plant species and to identify such species. Heat stress at 50 °C for 120 min was applied to 27 plant species using a heat treatment and recovery model. Among them, four herbaceous species—Hemerocallis fulva, Heliopsis longipes, Hosta plantagines, and Scilla scilloides—were selected by measuring their electrolyte leakage index (ELI). Additionally, species with high ELI values (Centaurea cyanus) and low ELI values (S. scilloide) were selected to determine the optimal temperature and time for the heat treatment using linear regression analysis. The selected heat-tolerant herbaceous plants survived under optimal heat stress conditions (120 min at 50 °C). The degree of cell death in the selected plant species was measured using Evans blue staining. The results showed that the cell death indexes of heat-tolerant species were less than 22% after heat treatment, compared to over 57% in heat-sensitive species. Furthermore, the recovery ability at room temperature after heat treatment was assessed using the DAB staining method. The selected heat-tolerant plant species recovered quickly (within 90 min), whereas the heat-sensitive species required over 480 min for recovery. The analytical method developed in this study can more accurately determine the response of various plants to heat stress and has high reproducibility. Therefore, it is considered an excellent method for establishing a heat-tolerant plant selection system.

A sustainable approach for concurrent recovery of metals from H2 reduced bauxite residue (“red mud”): Process optimization

Bauxite residue (BR), a polymetallic waste, offers significant potential for valuable metal recovery. This study introduces a low temperature H2 reduction-alkaline (NaOH) roasting, followed by water leaching - wet magnetic separation, aimed at efficient recovery of Fe, Na, and Al, alongside non-magnetic tailings (Ca, Ti, and Si). To minimize H2 consumption, 5 vol % H2 + 95 vol % N2 with 200 L/kg was sufficient. Utilizing response surface methodology (RSM), the optimal H2 reduction process conditions were determined as a temperature of 600 °C and a time of 120 min with 20 wt% NaOH. Under these conditions, the maximum recoveries achieved were 74.4 % Fe (grade = 63.6 %), 84.6 % Al, and 91.6 % Na, with RSM model predicted Fe, Al, and Na recovery values at optimum conditions closely matching experimental results (<5 % deviation). The proposed sustainable process minimizes waste and offers a possible solution for BR processing.

Sustainable humification of food waste slurry through thermally activated persulfate oxidation

This study introduces a thermoanalytical approach utilizing sodium persulfate (PS) to expedite the humification of food waste (FW) through advanced oxidation processes (AOPs). The method harnesses heat-induced free radicals to break down complex organic compounds into simpler forms, leading to the formation of humic substances. We conducted an analysis of changes in soluble chemical oxygen demand (SCOD), dissolved organic carbon (DOC), ammonia nitrogen, soluble proteins, and sugars, employing advanced techniques such as three-dimensional fluorescence spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, and scanning electron microscopy (SEM). The findings indicate that the activation of PS at 70°C effectively increases SCOD and DOC levels, converts proteins into soluble forms, and enhances NH4+-N content. This study, which focuses on the utilization of thermal methods, affirms that the thermal activation of persulfate (PS) significantly boosts the humification of food waste (FW), presenting a sustainable approach for waste management and resource recovery.

Pattern Guided UV Recovery for Realistic Video Garment Texturing.

The fast growth of E-Commerce creates a global market worth USD 821 billion for online fashion shopping. What unique about fashion presentation is that, the same design can usually be offered with different cloths textures. However, only real video capturing or manual per-frame editing can be used for virtual showcase on the same design with different textures, both of which are heavily labor intensive. In this paper, we present a pattern-based approach for UV and shading recovery from a captured real video so that the garment's texture can be replaced automatically. The core of our approach is a per-pixel UV regression module via blended-weight multilayer perceptrons (MLPs) driven by the detected discrete correspondences from the cloth pattern. We propose a novel loss on the Jacobian of the UV mapping to create pleasant seams around the folding areas and the boundary of occluded regions while avoiding UV distortion. We also adopts the temporal constraint to ensure consistency and accuracy in UV prediction across adjacent frames. We show that our approach is robust to a variety type of clothes, in the wild illuminations and with challenging motions. We show plausible texture replacement results in our experiment, in which the folding and overlapping of the garment can be greatly preserved. We also show clear qualitative and quantitative improvement compared to the baselines as well. With the one-click setup, we look forward to our approach contributing to the growth of fashion E-commerce.

Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models

Amyloid PET imaging plays a crucial role in the diagnosis and research of Alzheimer’s disease (AD), allowing non-invasive detection of amyloid-β plaques in the brain. However, the low spatial resolution of PET scans limits the accurate quantification of amyloid deposition due to partial volume effects (PVE). In this study, we propose a novel approach to addressing PVE using a latent diffusion model for resolution recovery (LDM-RR) of PET imaging. We leverage a synthetic data generation pipeline to create high-resolution PET digital phantoms for model training. The proposed LDM-RR model incorporates a weighted combination of L1, L2, and MS-SSIM losses at both noise and image scales to enhance MRI-guided reconstruction. We evaluated the model’s performance in improving statistical power for detecting longitudinal changes and enhancing agreement between amyloid PET measurements from different tracers. The results demonstrate that the LDM-RR approach significantly improves PET quantification accuracy, reduces inter-tracer variability, and enhances the detection of subtle changes in amyloid deposition over time. We show that deep learning has the potential to improve PET quantification in AD, effectively contributing to the early detection and monitoring of disease progression.

Introduction and Consultation on the Indonesia's Oil and Gas Sharing Contract at CNG Co.

Indonesia's oil and gas sector has traditionally employed Production Sharing Contracts (PSCs) to regulate the sharing of revenues between the government and contractors. Two key PSC models are used: PSC Cost Recovery and PSC Gross Split. The PSC Cost Recovery model allows contractors to recover their exploration and production costs before profits are shared, providing financial protection but reducing long-term profitability. Conversely, the PSC Gross Split model, introduced in 2017, offers a simpler revenue-sharing mechanism, eliminating cost recovery and directly splitting gross revenue between the government and contractors. This study analyzes the financial implications of both models using economic simulations, focusing on key indicators like net cash flow, net present value (NPV), pay-out time, and discounted cash flow (DCF) rate of return. Results show that the Gross Split model generates significantly higher gross revenue ($420.908 million) than Cost Recovery ($46.362 million), but at the cost of greater financial risks for contractors due to higher upfront investments and operating costs. The Gross Split model also provides higher long-term returns, with a net cash flow of $67.138 million compared to $8.252 million in Cost Recovery. However, the pay-out time is longer, and the DCF rate of return is slightly lower (29.95% vs. 31.8%). Ultimately, PSC Gross Split is more suited for contractors with higher risk tolerance and capital resources, while PSC Cost Recovery may be preferable for smaller contractors seeking to minimize financial risks. Both models offer distinct advantages depending on the contractor’s financial capacity and risk appetite.

Agile monitoring dashboard for clinical research studies

BackgroundClinical trial success hinges on efficient participant recruitment and retention. However, slow accrual and attrition frequently hinder progress. To address these challenges, a novel dashboard tool with control charts has been developed to provide investigators on the multi-site study of Delirium and Neuropsychological Recovery among Emergency General Surgery Survivors (DANE study) with timely information to improve trial recruitment.MethodsA quality monitoring Excel dashboard with control chart functionality developed by the principal investigator’s (PI) group and implemented in a department of a large hospital was re-engineered for research study recruitment purposes. The dashboard provides the PIs and other stakeholders with timely, actionable, and unbiased information on the count of participants who have completed each stage or action within the process, the rates of completion and trends, both for the current week and cumulatively.ResultsThe DANE dashboard was prototyped using Microsoft Excel for accessibility and rapid development. The tool integrates with a REDCap database, simplifying data import and analysis. By facilitating informed decision-making throughout the recruitment process, the DANE dashboard has significantly enhanced clinical trial efficiency and led to changes in the eligibility criteria and improvements in the approach and consent processes.ConclusionsThe DANE dashboard for monitoring participant recruitment and attrition in research studies represents a significant step towards enhancing study management and decision-making processes. It can be adapted to other clinical studies and other staged processes with attrition. The generic version, currently under development, holds promise for evolving into a valuable simulator by incorporating a spreadsheet for generating random data and accounting for resource constraints. This enhancement could further be augmented by integrating forecasting capabilities into the control charts.Trial registrationThe Delirium and Neuropsychological Recovery among Emergency General Surgery Survivors (DANE) study (NCT05373017, 1R01AG076489-01) is a multi-site, two-arm, single-blinded randomized controlled clinical trial to evaluate the efficacy of the Emergency General Surgery (EGS) Delirium Recovery Model to improve the cognitive, physical, and psychological recovery of EGS delirium survivors over 65. The DANE study received approval from the University of Wisconsin-Madison/University of Wisconsin Hospitals and Clinics Institutional Review Board (IRB, no. 2022–0545, approval date September 14, 2022), and Indiana University agreed to cede IRB review to University of Wisconsin-Madison/University of Wisconsin Hospital and Clinics (September 29, 2022).

Mathematical model and its solution for water-altering-gas (WAG) injection process incorporating the effect of miscibility, gravity, viscous fingering and permeability heterogeneity

AbstractThe oil recovery from the water-alternating-gas (WAG) injection process is significantly impacted by gravity, viscous fingering, and the permeability heterogeneity of the reservoir. Therefore, the combined effect of these parameters cannot be neglected in the WAG injection process. This article presents the development of a mathematical model of oil recovery and its solution for the WAG injection process that takes into account the combined effects of miscibility change, viscous fingering, gravity, and permeability heterogeneity in an inclined stratified reservoir. First, the governing equations and fractional flow functions were explained in relation to the effects of gravity, permeability heterogeneity, viscous fingering, and miscibility change in an inclined stratified porous medium. Then, a mathematical model was developed using fractional flow functions and conservation equations for both injected water and solvent. The model was generated in the form of a quasi-linear first-order partial differential equation, which was solved analytically in two dimensions (2-D) utilizing vector calculus. Next, this model was solved analytically by applying wave theory to practical constant pressure boundary conditions, which generate distinct waves at different times to provide pressure and saturation at the displacement front location. The total volumetric flux and breakthrough time are calculated from the analytical solution at various times. The presented analytical solutions can be used to predict different parameters for stratified porous media in a fast and efficient way. Finally, the results of analytical solution validated with high-resolution numerical simulation for a wide range of permeability heterogeneity, which shows excellent agreement for breakthrough time, saturation, and pressure versus displacement location of different waves at different times. This analytical solution will save time and money by offering guidance to engineers for analyzing the saturation and pressure distribution at different times and predicting oil recovery. It will also improve the understanding of the physics underlying the multiphase flow WAG injection process in heterogeneous reservoirs.

Coupled Electrolysis–Microfiltration System for Efficient Phosphorus Removal and Recovery in the Form of Iron Phosphate Compounds from Wastewater

Electrochemical technology presents a promising approach for phosphorus recovery from wastewater. Nevertheless, its application in industry is hindered by relatively low phosphorus recovery efficiency, high energy consumption and complex reactor configurations. In this study, a coupled electrolysis and microfiltration system was designed for phosphorus recovery in the shape of iron phosphate compounds with the use of steel pickling wastewater as the iron source. In the electrolysis unit, the anode diffusion layer was extracted from the porous anode surface with the production of an acid effluent and an alkaline effluent. The alkaline effluent was mixed with the stainless steel acid washing wastewater generated from the steel pickling process and then introduced into the microfiltration unit to intercept the iron phosphate crystals. The filtered effluent was finally introduced into the air aeration unit to further reduce the phosphorus content in the water. And the extracted acid solution could be reused in the pickling step of the iron and steel manufacturing process. The experimental results show that the coupled system achieved phosphorus recovery of 42~80% at a current density of 5~20 mA cm−2, accompanying energy consumption of 5.78~9.15 kWh (kg P)−1 and current efficiency of 79~43%, when the phosphorus concentration was 3 mM and the iron–phosphorus molar ratio was 1.5. After the microfiltration treatment, the residual phosphorus could be further reduced to 0.5 mg L−1 within 30 min at an aeration rate of 80 mL min−1, which met the discharge standard. The presence of interfering ions (HCO3− and SiO42−) posed inhibited effects on phosphorus recovery. Generally, this study provides a green and environmentally friendly way to efficiently recover phosphorus resources from wastewater.

UNet-Att: a self-supervised denoising and recovery model for two-photon microscopic image

Two-photon microscopy is indispensable in cell and molecular biology for its high-resolution visualization of cellular and molecular dynamics. However, the inevitable low signal-to-noise conditions significantly degrade image quality, obscuring essential details and complicating morphological analysis. While existing denoising methods such as CNNs, Noise2Noise, and DeepCAD serve broad applications in imaging, they still have limitations in preserving texture structures and fine details in two-photon microscopic images affected by complex noise, particularly in sophisticated structures like neuronal synapses. To improve two-photon microscopy image denoising effectiveness, by experimenting on real two-photon microscopy images, we propose a novel deep learning framework, the UNet-Att model, which integrates a specifically tailored UNet++ architecture with attention mechanisms. Specifically, this approach consists of a sophisticated downsampling module for extracting hierarchical features at varied scales, and an innovative attention module that strategically emphasizes salient features during the integration process. The architecture is completed by an ingenious upsampling pathway that reconstructs the image with high fidelity, ensuring the retention of textural integrity. Additionally, the model supports end-to-end training, optimizing its denoising efficacy. The UNet-Att model proves to surpass mainstream algorithms in the dual objectives of denoising and preserving the textural intricacies of original images, which is evidenced by an increase of 9.42 dB in the high Peak Signal-to-Noise Ratio (PSNR) coupled with an improvement of 0.1131 in the Structural Similarity Index Measurement (SSIM). The ablation experiments reveal the effectiveness of each module. The associated Python packages and datasets of UNet-Att are freely available at https://github.com/ZjjDh/UNet-Att.

Implementing culturally appropriate recovery approaches in mental health services: perspectives from the Irish Traveller community.

This study explores the perspectives of Irish Travellers, an ethnic minority group in Ireland, on the service improvements needed to implement culturally appropriate recovery approaches in mental health services. A community-based participatory research approach was employed. Seventeen Traveller peer researchers conducted thirteen qualitative participatory workshops and five individual interviews across Ireland. Eighty-seven adult members of the Traveller community (83% female, mean age = 33, 56.3% not in education or employment) were recruited through convenience sampling. Participatory thematic analysis was employed. Travellers' ideas about improvements in mental healthcare involve practitioners in creating a trusting environment, increasing representation of Travellers within mental healthcare staff and working in partnership with Traveller service users to co-produce strategies that are owned by the community. Study participants emphasised the importance of Traveller cultural awareness, strengthening trauma-informed care, and embedding an ethos of cultural humility within mainstream services. The findings indicate the need for increased collaboration between mental health service providers and non-mental health agencies, and a reorientation of care towards empowering Traveller service users to achieve their social aspirations and building a positive self-identity. This study demonstrates that implementing a recovery approach for Travellers requires a reorientation of services, in line with Travellers' definition of culturally appropriate mental healthcare. This study calls for an expanded mandate for mental health services beyond individual care and towards intersectoral action in addressing the social determinants of Travellers' mental health. This will help to shape an equitable and inclusive healthcare setting that promotes Travellers' mental health and recovery.

Approaches to Disaster Recovery in Cloud Databases: A Comparative Analysis of Current Mechanisms

Disaster recovery (DR) is a critical aspect of maintaining the availability, integrity, and continuity of cloud databases, which store and manage vast amounts of mission-critical data. With the increasing reliance on cloud platforms for business operations, organizations must implement effective disaster recovery mechanisms to safeguard against data loss, downtime, and system failures. This paper presents a comparative analysis of current disaster recovery approaches in cloud databases, examining the strengths and weaknesses of various strategies, including backup and restore, data replication, and automated failover systems. We evaluate how these mechanisms are applied in different cloud models (public, private, and hybrid), considering factors such as recovery time objectives (RTO), recovery point objectives (RPO), cost, and scalability. The paper also discusses emerging technologies and trends in cloud DR, such as real-time data synchronization, machine learning-based predictive recovery, and multi-cloud disaster recovery solutions. Additionally, the role of cloud service providers’ Service Level Agreements (SLAs) in defining DR expectations is analyzed. By comparing the advantages and limitations of these mechanisms, this paper provides insights into how organizations can select the most appropriate DR strategy based on their specific business needs and risk tolerance. The study concludes by offering recommendations for optimizing disaster recovery plans in cloud environments to ensure business continuity and data protection in the face of unexpected disruptions.

New Insight of Nanosheet Enhanced Oil Recovery Modeling: Structural Disjoining Pressure and Profile Control Technique Simulation

This study presents a novel Enhanced Oil Recovery (EOR) method using Smart Black Nanocards (SLNs) to mitigate the environmental impact of conventional thermal recovery, especially under global warming. Unlike prior studies focusing on wettability alteration via adsorption, this research innovatively models ‘oil film detachment’ in a reservoir simulator to achieve wettability alteration. Using the CMG-STARS (2020) simulator, this study highlights SLNs’ superior performance over traditional chemical EOR and spherical nanoparticles by reducing residual oil saturation and shifting wettability toward water-wet conditions. The structural disjoining pressure (SDP) of SLNs reaches 20.99 × 103 Pa, 16.5 times higher than spherical particles with an 18.5 nm diameter. Supported by the Percus–Yevick (PY) theory, the numerical model achieves high accuracy in production history matching, with oil recovery and water cut fitting within precision error ranges of 0.02 and 0.05, respectively. This research advances chemical EOR technologies and offers an environmentally sustainable, efficient recovery strategy for low-permeability and heavy oil reservoirs, serving as a promising alternative to thermal methods.

Feminist urban futures: Envisioning the future of Ukrainian cities through the lens of the displaced community in Valencia (Spain)

Amidst the context of the ongoing war, Ukraine faces the challenge of reconstruction. Similarly to other post-disaster and post-conflict contexts, the recovery efforts are being guided by the principles of “Build Back Better” (BBB). While these principles can be interpreted as embracing the ideals of sustainability, the challenge lies in how "better" is defined, particularly when the concept is generally interpreted through a lens that prioritizes top-down planning with a focus on infrastructure and economic outputs. The concept of “better” needs to be rethought, particularly from the perspective of the affected populations. However, the viewpoints and experiences of internationally displaced Ukrainians are notably absent from these discussions.This research proposes a participatory approach to envisioning urban recovery from displacement, centring on the experiences of displaced Ukrainians residing in Valencia, Spain. By working with this community, the study aims to redefine “better” in the context of urban recovery in Ukraine. Drawing on feminist urbanism, critical development perspectives and futures studies, the study investigates the intersectional oppressions and insights of displaced individuals, through workshops, interviews, and photovoice methodologies.The findings highlight a collective vision that prioritizes emotional resilience, social cohesion and care, with participants prioritizing community-oriented green spaces, inclusive public services, and sustainable mobility. These insights challenge conventional interpretations of BBB in traditional recovery models, which often focus on measurable economic outcomes and risk-perpetuating "destructive reconstruction". Instead, this research advocates for a more inclusive, sustainable, and socially just recovery process that honours the diverse needs and aspirations of everyone, including those displaced abroad.

FRRL: A reinforcement learning approach for link failure recovery in a hybrid SDN

Network failures, especially link failures, happen frequently in Internet Service Provider (ISP) networks. When link failures occur, the routing policies need to be re-computed and failure recovery usually takes a few minutes, which degrades the network performance to a great extent. Therefore, a proper failure recovery scheme that can realize a fast and timely routing policy computation needs to be designed. In this paper, we propose FRRL, a Reinforcement Learning (RL) approach to intelligently perceive network failures and timely compute the routing policy for improving the network performance when link failure happens. Specifically, to perceive the link failures, we design a Topology Difference Vector (TDV) encoder module in FRRL for encoding the topology structure with link failures. To efficiently compute the routing policy when link failures happen, we integrate the TDV in the agent training for learning the map between the encoded failure topology structure and routing policies. To evaluate the performance of our proposed method, we conduct experiments on three network topologies and the experimental results demonstrate that our proposed method has superior performance when link failures happen compared to other methods.

Overall mass transfer approach for electrodialysis-based acid recovery

The reclamation of low-concentration waste acid (e.g., pH > 0.5) has garnered significant attention due to its prevalence, substantial output, and considerable potential value. Notably, a further concentration of the separated acid is typically required for practical applications, for which electrodialysis (ED) has been widely acknowledged as an effective method. However, various factors, including proton leakage, complicate the application of ED in acid systems compared to salt systems. This study presents a novel modeling approach for ED-based acid recovery utilizing the overall mass transfer (OMT) methodology for the first time. Importantly, regression models incorporating initial concentration and current density as independent variables have been developed to calculate the OMT coefficients and cell resistance following a quantitative analysis of the effects of acid media and electric field on mass transport phenomena, based on a series of carefully designed experiments. Subsequent predictions regarding a typical ED-based acid recovery process were conducted, yielding results that align closely with experimental data. For instance, the relative deviations for concentration and volume evolution were found to be less than ±8.4 % and ±7.9 %, respectively. Additionally, the relative deviations for energy consumption were recorded at 8.2 % and 3.2 % for the overall process and the ED stack, respectively. Furthermore, simulations were performed to evaluate the impacts of operational conditions on ED performance using the derived models. In conclusion, the efficacy of the OMT approach for modeling the ED process in acid systems has been substantiated. It is reasonable to assert that the developed models will be beneficial for production management and the optimization of operating conditions in ED-based acid recovery processes.

Comparative evaluation of commercial DNA isolation approaches for nanopore-only bacterial genome assembly and plasmid recovery

The advent of Oxford Nanopore Technologies has undergone significant improvements in terms of sequencing costs, accuracy, and sequencing read lengths, making it a cost-effective, and readily accessible approach for analyzing microbial genomes. A major challenge for bacterial whole genome sequencing by Nanopore technology is the requirement for a higher quality and quantity of high molecular weight DNA compared to short-read sequencing platforms. In this study, using eight pathogenic bacteria, we evaluated the quality, quantity, and fragmented size distribution of extracted DNA obtained from three different commercial DNA extraction kits, and one automated robotic platform. Our results demonstrated significant variation in DNA yield and purity among the extraction kits. The ZymoBIOMICS DNA Miniprep Kit (ZM) provided a higher purity of DNA compared to other kit-based extractions. All kit-based DNA extractions were successfully performed on all twenty-four samples using a single MinION flow cell, with the Nanobind CBB Big DNA kit (NB) yielding the longest raw reads. The Fire Monkey HMW-DNA Extraction Kit (FM) and the automated Roche MagNaPure 96 platform (RO) outperformed in genome assembly, particularly in gram-negative bacteria. Based on our finding, we recommend a minimum read coverage and raw read N50, obtained from the appropriate DNA extraction kit for each bacterial species, to optimize genome assembly and plasmid recovery. This approach will assist end-users in selecting the most effective kit-based extraction method for bacterial whole-genome assembly using only long-read nanopore sequences.