Correction Strategy Research Articles

Intraoperative Cavity Local Delivery System with NETs-Specific Drug Release for Post-Breast Cancer Surgery Recurrence Correction.

Postoperative breast cancer recurrence is tricky due to the limited therapeutic options. Transforming growth factors-β (TGF-β) is vital in promoting postoperative tumor recurrence. However, conventional blocking strategies fail to satisfy both bio-safety and sufficient relapse correction. Neutrophil extracellular traps (NETs) are essential for the spatiotemporal dynamics of TGF-β at tumor-resection sites, whose unique mechanism for local TGF-β amplification could remarkably increase the risk of relapse after surgery. Herein, the principle of NETs formation is ingeniously utilized to construct a surgical residual cavity hydrogel that mimics NETs formation. The hydrogel is prepared based on the electrostatic interaction between histidine (His) and sodium alginate (Alg). Then, arginine deiminase 4 (PAD4) protein is released during NETs formation. Simultaneously, the electrical property of His in hydrogel changes automatically, which further lead to promising localized release of anti-TGF-β. The hydrogel system can realize specific and selective drug release at targeted NETs site over a prolonged period while exhibiting excellent biocompatibility. Superior breast cancer recurrence inhibition is achieved by suppressing TGF-β and related indicators, impeding epithelial-mesenchymal transition (EMT) progression, and rectifying the locally exacerbated immunosuppressive environment within NETs. The novel NETs local microenvironment drug release functional hydrogel will provide inspiration for postoperative recurrence correction strategies.

Predicting Tourist Arrivals: A Google Trends-Based Model for Destination Management

In this paper, a review of the scientific literature on the importance and knowledge of the tourist destination for the formation of a correct strategy for its development and promotion is made. The possibility of managing the tourist destination by modeling with Google Trends data is represented. A review of studies using the mentioned tool in the field of tourism is conducted. The potential benefits of using Google Trends data on searches for a city as a keyword in combination with archival data are summarized. The possibilities for modeling the visits of tourists using data from Google Trends are investigated, as a result of which a model is built by applying polynomial regression to predict the overnight stays of foreign citizens in the city of Veliko Tarnovo based on archival data from the National Statistical Institute and Google Trends data. Through data analytics with Google Trends, it is concluded that key value is created for the external and internal environment, influencing the destination. The data creates conditions for the development of management strategies for attracting tourists based on primary and secondary data for analysis. The application of the received data sets through data analysis are also important for tourism organizations and institutional bodies in tourism (macro- and meso-level). The resulting analysis affects issues related to the management and marketing of the destination, and on the other hand, it directly affects the segmentation of the market and the distribution of products and services to the end user (micro level).

Botulinum toxin treatment for hemifacial spasm: harmonising neurological and aesthetic outcomes.

Hemifacial spasm (HFS) represents a challenging cranial movement disorder primarily affecting the facial nerve innervated muscles, with significant prevalence among Asians. Botulinum toxin type A (BoNT/A) injections, established as a primary therapeutic intervention since FDA approval, offer considerable effectiveness in alleviating spasms, albeit accompanied by challenges such as temporary effects and potential adverse events including facial asymmetry. This comprehensive review underscores the crucial need for harmonising neurological benefits and aesthetic outcomes in HFS management. The discussion delves into the interplay between facial aesthetics and neurological objectives in BoNT/A injections, emphasising precise techniques, dosages, and site considerations. Distinct aspects in neurological and aesthetic domains are also examined, including detailing the targeted muscles and injection methodologies for optimal therapeutic and aesthetic results. Importantly, evidence regarding various BoNT/A formulations, recommendations, and reconstitution guidelines in both neurology and aesthetics contexts are provided, along with a schematic approach outlining the stepwise process for BoNT/A injection in HFS treatment, addressing critical areas such as orbicularis oculi muscle sites, eyebrow correction strategies, mid- and lower-face considerations, contralateral injection sites, and post-injection follow-up and complication management. By highlighting the culmination of neurological efficacy and facial esthetics in BoNT/A treatment for HFS patients, this review proposes a holistic paradigm to achieve balanced symptomatic relief and natural aesthetic expression, ultimately enhancing quality of life for individuals grappling with HFS.

Ultradense Array of On-Chip Sensors for High-Throughput Electrochemical Analyses.

High-throughput sensors are valuable tools for enabling massive, fast, and accurate diagnostics. To yield this type of electrochemical device in a simple and low-cost way, high-density arrays of vertical gold thin-film microelectrode-based sensors are demonstrated, leading to the rapid and serial interrogation of dozens of samples (10 μL droplets). Based on 16 working ultramicroelectrodes (UMEs) and 3 quasi-reference electrodes (QREs), a total of 48 sensors were engineered in a 3D crossbar arrangement that devised a low number of conductive lines. By exploiting this design, a compact chip (75 × 35 mm) can enable performing 16 sequential analyses without intersensor interferences by dropping one sample per UME finger. In practice, the electrical connection to the sensors was achieved by simply switching the contact among WE adjacent fingers. Importantly, a short analysis time was ensured by interrogating the UMEs with chronoamperometry or square wave voltammetry using a low-cost and hand-held one-channel potentiostat. As a proof of concept, the detection of Staphylococcus aureus in 15 samples was performed within 14 min (20 min incubation and 225 s reading). Additionally, the implementation of peptide-tethered immunosensors in these chips allowed the screening of COVID-19 from patient serum samples with 100% accuracy. Our experiments also revealed that dispensing additional droplets on the array (in certain patterns) results in the overestimation of the faradaic current signals, a phenomenon referred to as crosstalk. To address this interference, a set of analyses was conducted to design a corrective strategy that boosted the testing capacity by allowing using all on-chip sensors to address subsequent analyses (i.e., 48 samples simultaneously dispensed on the chip). This strategy only required grounding the unused rows of QRE and can be broadly adopted to develop high-throughput UME-based sensors. In practice, we could analyze 48 droplets (with [Fe(CN)6]4-) within ∼8 min using amperometry.

An obstacle avoidance safety detection algorithm for power lines combining binocular vision technology and improved object detection

In this paper, a framework of obstacle avoidance algorithm applied to power line damage safety distance detection is constructed, and its overall architecture and key processes are described in detail. The system design covers three core modules: visual data acquisition and preliminary processing, accurate target recognition and distance measurement, and system error analysis and correction. In the visual data processing chain, we deeply analyze every step from image acquisition to preprocessing to feature extraction, aiming to enhance the adaptability of applications to complex scenes. The target recognition and distance estimation part integrates advanced technology of deep learning to improve the reliability of recognition accuracy and distance estimation. In addition, many common error sources, such as system bias, parallax discontinuity, fluctuation of illumination conditions, etc., are discussed in depth, and corresponding correction strategies are proposed to ensure the accuracy and stability of the system, which provides powerful technical support for achieving efficient and accurate safety monitoring. Specifically, by carefully adjusting the learning rate, convolution kernel size, batch size, pooling layer type, and number of hidden layer nodes, we succeeded in improving the overall accuracy from the initial average of 92.4–95%, and the error rate decreased accordingly.

Newborn face mask ventilation training using a standardised intervention and respiratory function monitor: a before and after manikin study

ObjectiveThe International Liaison Committee on Resuscitation has recommended improvements in training for neonatal resuscitation, highlighting the potential role of respiratory function monitors (RFMs). Our objective was to determine whether a...

Measurement precision bounds on aberrated single-molecule emission patterns

Single-molecule localization microscopy (SMLM) has revolutionized the study of biological phenomena by providing exquisite nanoscale spatial resolution. However, optical aberrations induced by sample and system imperfections distort the single-molecule emission patterns (i.e. PSFs), leading to reduced precision and resolution of SMLM, particularly in three-dimensional (3D) applications. While various methods, both analytical and instrumental, have been employed to mitigate these aberrations, a comprehensive analysis of how different types of commonly encountered aberrations affect single-molecule experiments and their image formation remains missing. In this study, we addressed this gap by conducting a quantitative study of the theoretical precision limit for position and wavefront distortion measurements in the presence of aberrations. Leveraging Fisher information and Cramér-Rao lower bound (CRLB), we quantitively analyzed and compared the effects of different aberration types, including index mismatch aberrations, on localization precision in both biplane and astigmatism 3D modalities as well as 2D SMLM imaging. Furthermore, we studied the achievable wavefront estimation precision from aberrated single-molecule emission patterns, a pivot step for successful adaptive optics in SMLM through thick specimens. This analysis lays a quantitative foundation for the development and application of SMLM in whole-cells, tissues and with a large field of view, providing in-depth insights into the behavior of different aberration types in single-molecule imaging and thus generating theoretical guidelines for developing highly efficient aberration correction strategies and enhancing the precision and reliability of 3D SMLM.

Optimal Correction Strategy of Image Guided Radiation Therapy Including the Paraortic Lymph Node Region in Patients With Cervical Cancers

The clinically accepted planning target volume margin for radiation therapy to the paraortic nodal region in cervical cancer patients is 5 mm. However, the comprehensive alignment and variability from the pelvic bone to all lumbar vertebrae are undetermined. This study aims to quantify the residual setup errors between the pelvic bone and lumbar vertebrae and determine the optimal correction strategy for patients with cervical cancer. Fifteen patients underwent pretreatment mega-voltage computed tomography scans (375 total fractions). Residual setup errors and required margins for each lumbar vertebra were calculated based on registrations accounting for pelvic rotation and translation. The systematic residual errors (1 SD) at L1, L2, L3, L4, and L5 using pelvic bone registration were 6.5, 4.9, 3.1, 1.5, and 0.6 mm in the anterior-posterior (AP) direction, 3.1, 2.3, 1.4, 0.6, and 0.3 mm in the right-left direction, and 2.7, 2.2, 1.7, 1.0, and 0.5 mm in the superior-inferior direction, respectively. The residual setup errors were the largest in the AP direction. Registration based on the pelvic bone required margins in the AP direction of 16.0, 12.1, 7.7, 3.6, and 1.3 mm for L1, L2, L3, L4, and L5, respectively, whereas registration based on L3 required margins of 8.8, 4.8, 4.4, 7.1, and 7.7 mm for L1, L2, L4, L5, and pelvic bone, respectively. Considerable local setup variability was found in patients with cervical cancer. After reviewing the corrective strategies, we determined that L3-based registration effectively minimized the required margins.

A Novel SAR Imaging Method for GEO Satellite–Ground Bistatic SAR System with Severe Azimuth Spectrum Aliasing and 2-D Spatial Variability

The satellite–ground bistatic configuration, which uses geosynchronous synthetic aperture radar (GEO SAR) for illumination and ground equipment for reception, can achieve wide coverage, high revisit, and continuous illumination of interest areas. Based on the analysis of the signal characteristics of GEO satellite–ground bistatic SAR (GEO SG-BiSAR), it is found that the bistatic echo signal has problems of azimuth spectrum aliasing and 2-D spatial variability. Therefore, to overcome those problems, a novel SAR imaging method for a GEO SG-BiSAR system with severe azimuth spectrum aliasing and 2-D spatial variability is proposed. Firstly, based on the geometric configuration of the GEO SG-BiSAR system, the time-domain and frequency-domain expressions of the signal are derived in detail. Secondly, in order to avoid the increasing cost caused by traditional multi-channel reception technology and the processing burden caused by inter-channel errors, the azimuth deramping is executed to solve the azimuth spectrum aliasing of the signal under the special geometric structure of GEO SG-BiSAR. Thirdly, based on the investigation of azimuth and range spatial variability characteristics of GEO SG-BiSAR in the Range Doppler (RD) domain, the azimuth spatial variability correction strategy is proposed. The signal corrected by the correction strategy has the same migration characteristics as monostatic radar. Therefore, the traditional chirp scaling function (CSF) is also modified to solve the range spatial variability of the signal. Finally, the two-dimensional spectrum of GEO SG-BiSAR with modified chirp scaling processing is derived, followed by the SPECAN operation to obtain the focused SAR image. Furthermore, the completed flowchart is also given to display the main composed parts for GEO SG-BiSAR imaging. Both azimuth spectrum aliasing and 2-D spatial variability are taken into account in the imaging method. The simulated data and the real data obtained by the Beidou navigation satellite are used to verify the effectiveness of the proposed method.

Peri-operative Replacement of Exogenous Steroids (PREdS): a national audit of current peri-operative prescribing for patients taking therapeutic steroids.

Approximately 1% of the UK population take oral corticosteroids for ≥ 28 days each year, for broadly two reasons: deficiency in corticosteroid requiring replacement; or therapeutic corticosteroid for inflammatory conditions. Acute deficiency can occur at times of physiological stress (e.g. surgery), potentially leading to major complications. The Association of Anaesthetists' 2020 consensus guideline provides detailed advice for the management of glucocorticoids during the peri-operative period for patients with adrenal insufficiency. This national audit aimed to assess compliance with this guideline. Data were collected from 59 Trusts over 14 consecutive days for all eligible patients undergoing procedures under the care of an anaesthetist. Patients who were prescribed ≥ 5 mg oral prednisolone equivalents pre-operatively, in whom supplementary corticosteroid would be indicated, were compared with those prescribed < 5 mg oral prednisolone equivalents. Operations for 21,731 patients were audited: 277 (1.3%) patients were taking therapeutic corticosteroids. Detailed peri-operative data were collected for all patients receiving therapeutic corticosteroids: 201/277 (73%) were ASA physical status ≥ 3; 184/277 (66%) underwent elective procedures; and 252/277 (91%) were prescribed prednisolone pre-operatively, of whom 219/277 (79%) were prescribed ≥ 5 mg oral prednisolone equivalents. In the patients who were prescribed ≥ 5 mg oral prednisolone equivalents, 186/219 (85%) received pre-operative glucocorticoid supplementation and 97/219 (42%) received it postoperatively; however, only 67/219 (31%) and 43/219 (20%) respectively received glucocorticoid supplementation according to the guidelines. Overall, peri-operative prescribing was compliant in 19/219 (9%) patients. A similar proportion, 30/219 (14%), received no supplementation. In the patients taking < 5 mg oral prednisolone equivalents pre-operatively, 28/58 (48%) received inappropriate supplementation. Despite 125/277 (45%) of anaesthetists reporting Association of Anaesthetists' guidelines use, compliance remained low, with adherence in only 27/125 (22%) patients. Further research is required to identify the correct peri-operative strategy for patients taking therapeutic corticosteroids.

A control strategy considering buildings’ thermal characteristics to mitigate heat supply-demand mismatches in district heating systems

In district heating systems (DHS), periodic heat supply-demand mismatches (HSDM) exacerbate issues such as thermal imbalance and hydraulic instability. To mitigate these issues, this paper proposes a supply water temperature correction strategy for heating stations that considers building's thermal characteristics. Firstly, buildings are classified into energy-saving levels based on comprehensive thermal characteristic Bc and heat load index. Secondly, simulation models are developed for buildings with different thermal characteristics, and response of indoor temperature to supply-demand mismatch rates was analyzed. Then, after determining the regulation sequence and supply water temperature of heating stations, a balanced control strategy is formulated for HSDM conditions. Finally, the proposed strategy was applied to a practical combined heat and power system (CHPS) in severe cold regions. The results showed that the studied heating stations were classified into 4 types. After regulation of both insufficient and excessive heating conditions, the indoor temperature guarantee rate increased by over 20 %; the ranges of thermal imbalance degree and hydraulic imbalance degree were reduced by more than 0.09 and 0.22, respectively; the SD of supply water pressure and supply-return pressure difference were reduced by over 0.17 and 0.11, respectively. The strategy provides a reference for global balanced control of DHS under similar HSDM conditions.

High resolution X-ray phase contrast imaging for non-destructive testing of composite materials

X-ray imaging is widely used in non-destructive testing dedicated to industry, medical or security domain. In aircraft production, the aerospace industry increasingly integrates carbon fibre reinforced polymers (CFRP), which may develop defects from operational wear, like lightning strikes. To robustly characterize density changes resulting from induced lightning damage in CFRP, multilateral shearing interferometry (MLSI) – a phase imaging technique – have been incorporated on a X-ray laboratory bench. Indeed, such method provides higher contrasts than X-ray attenuation imaging for composite materials and brings complementary information of the imaged sample. However, extracting the phase signal requires conditions of the imaging system and algorithmic strategies, which can lead to the emergence of artifacts in phase images. This thesis work involved the characterisation of these artifacts in the context of MLSI applied to X-ray phase imaging, as well as the development of tracking methods and corrective strategies.

Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning

AbstractData assimilation (DA) plays a pivotal role in diverse applications, ranging from weather forecasting to trajectory planning for autonomous vehicles. A prime example is the widely used ensemble Kalman filter (EnKF), which relies on the Kalman filter's linear update equation to correct each of the ensemble forecast member's state with incoming observations. Recent advancements have witnessed the emergence of deep learning approaches in this domain, primarily within a supervised learning framework. However, the adaptability of such models to untrained scenarios remains a challenge. In this study, we introduce a new DA strategy that utilizes reinforcement learning (RL) to apply state corrections using full or partial observations of the state variables. Our investigation focuses on demonstrating this approach to the chaotic Lorenz 63 and 96 systems, where the agent's objective is to maximize the geometric series with terms that are proportional to the negative root‐mean‐squared error (RMSE) between the observations and corresponding forecast states. Consequently, the agent develops a correction strategy, enhancing model forecasts based on available observations. Our strategy employs a stochastic action policy, enabling a Monte Carlo‐based DA framework that relies on randomly sampling the policy to generate an ensemble of assimilated realizations. Numerical results demonstrate that the developed RL algorithm performs favorably when compared to the EnKF. Additionally, we illustrate the agent's capability to assimilate non‐Gaussian observations, addressing one of the limitations of the EnKF.

STRATEGI KOMUNIKASI KONTROL JEJARING KEBIJAKAN DALAM GERAKAN BERSAMA RAKYAT ATASI KAWASAN PADAT KUMUH DAN MISKIN (GEBRAK PAKUMIS) DI KABUPATEN TANGERANG

Housing and settlements have a very fundamental role in the life of the Indonesian people considering the problem that often occurs is the level of housing needs that is not balanced with the level of regional capacity in providing habitable housing. As a result, a residential area will receive a load that exceeds the carrying capacity of its environment and tends to become slum. The problem of settlements is a serious problem because it is feared that it will cause fatal pockets of poverty and then cause various social problems beyond the government's ability to handle and monitor them. The Tangerang Regency government tries to clean up uninhabitable houses through the GEBRAK PAKUMIS Program. The main focus of this program is to improve the water and sanitation sector with a focal point on improving the quality of houses because a poor sanitation system is a major factor in the formation of slum areas. This study aims to analyze and find theoretical answers to the principles of policy networks and obtain new concepts about policy networks. This research uses descriptive qualitative methods, data collection techniques by means of interviews, observation, and documentation. One of the problems that is the basis for the failure of this program is the long procedure. This is something that is feared by residents, because it is considered difficult to be realized. Based on the problems faced by residents as recipients of house renovation assistance, environmental development and community empowerment, the correct communication strategy is a solution to communicate the benefits of the program to the beneficiary residents. Therefore, the communication strategy solution offered in this study is as a stimulant for local government to provide populist socialization to residents in order to bring citizens to ease in taking procedures in this program.

Towards a gapless 1 km fractional snow cover via a data fusion framework

Accurate quantification of snow cover facilitates the prediction of snowmelt runoff, the assessment of freshwater availability, and the analysis of Earth’s energy balance. Existing fractional snow cover (FSC) data, however, often suffer from limitations such as spatial and temporal gaps, compromised accuracy, and coarse spatial resolution. These limitations significantly hinder the ability to monitor snow cover dynamics effectively. To address these formidable challenges, this study introduces a novel data fusion framework specifically designed to generate high-resolution (1 km) daily FSC estimation across vast regions like North America, regardless of weather conditions. It achieved this by effectively integrating the complementary spatiotemporal characteristics of both coarse- and fine-resolution FSC data through a multi-stage processing pipeline. This pipeline incorporates innovative strategies for bias correction, gap filling, and consideration of dynamic characteristics of snow cover, ultimately leading to high accuracy and high spatiotemporal completeness in the fused FSC data. The accuracy of the fused FSC data was thoroughly evaluated over the study period (September 2015 to May 2016), demonstrating excellent consistency with independent datasets, including Landsat-derived FSC (total 24 scenes; RMSE=6.8–18.9 %) and ground-based snow observations (14,350 stations). Notably, the fused data outperforms the widely used Interactive Multi-sensor Snow and Ice Mapping System (IMS) daily snow cover extent data in overall accuracy (0.92 vs. 0.91), F1_score (0.86 vs. 0.83), and Kappa coefficient (0.80 vs. 0.77). Furthermore, the fused FSC data exhibits superior performance in accurately capturing the intricate daily snow cover dynamics compared to IMS data, as confirmed by superior agreement with ground-based observations in four snow-cover phenology metrics. In conclusion, the proposed data fusion framework offers a significant advancement in snow cover monitoring by generating high-accuracy, spatiotemporally complete daily FSC maps that effectively capture the spatial and temporal variability of snow cover. These FSC datasets hold substantial value for climate projections, hydrological studies, and water management at both global and regional scales.

Pregnancy and Diabetes in Turin: Lights and Shadows from Real Life Data.

From 2021, PSDTA for women with pregnancy complicated by diabetes will be active in the ASL city of Turin; given the city's increasing multiculturalism, we decided to evaluate from this point of view the patients who entered this pathway. Data on women from 1/10/2022 to 30/09/2023 were collected from the computerized medical record. Total patients: 304, Type of diabetes: T1D 3%; MODY < 1%; T2D 4% Diabetes manifested in pregnancy (DMIP) 2%, GDM 90%, Foreigners prevalence: GDM: 67%, T2D%, T1D: Foreign 11%, Planned vs. neglected pregnancies: GDM 47% vs 18%, T2D 31% vs 32%, DMIP 28% vs 50%, T1D: 66% vs 11%, Therapy: GDM: insulin 31% (multi-injective <30%), metformin 5%, T2D: insulin 100% (multi-injective 68%, metformin in 20%); continuous glycemic sensor in 48%, DMIP: insulin 50% (multi-injective 50%), T1D: multi-injective therapy 33%; pump and glycemic sensor 33%; integrated sensor-micro-infuser system 33%. In the aspect of ISTAT data indicating that for northern Italy, a foreign origin for 26% of mothers, our population is "unbalanced" between GDM, T2D, and DMIP on one side and T1D on the other. The higher percentage of foreigners in the GDM group could be attributable to the higher share of Italian women opting for private practice, conversely, the "missing" share of foreign women with T1D is more difficult to interpret. Unplanned or even neglected pregnancies are significant in women with GDM and DMIP (who are mostly foreign). If these data are confirmed in other Italian realities, corrective strategies need to be planned.

Asymptomatic Empty Sella Syndrome: A "New" Hypothalamic Pathology or Paraphysiological Variant.

The term empty sella refers to a shrunken or displaced (by a subarachnoid diverticulum) pituitary gland. It can be primary (genetically determined) or secondary (due to trauma/surgery/radiation). It has been reported that 50% of patients are asymptomatic, and others experience symptoms, such as headache, hypertension, or visual field defects. Few cases have an empty sella syndrome, i.e., lacking functional pituitary hormones. Diagnosis is made through NMR or CT. If asymptomatic, this condition requires no treatment; otherwise, empty sella syndrome needs hormonal replacement therapy. We examined the case of asymptomatic empty sella syndrome. A 67-year-old female patient was admitted for dilatative cardiomyopathy. She had a past medical history of arterial hypertension and right ICA endovascular repair. Blood tests demonstrated hypothyroidism, hypoadrenalism, and GH deficiency, without any signs or symptoms. NRM confirmed an empty sella, hence replacement therapy with levothyroxine and cortisone acetate was started. During a follow-up evaluation, we discovered that this biochemical profile of the patient had been known for more than a decade and never treated. Despite being exposed to stress conditions, vascular surgery and angiography, she never developed an adrenal crisis, nor has she ever been symptomatic for severe hypothyroidism. Hormonal replacement therapy was performed. The described clinical scenario is rare, as usually, empty sella syndrome presents with signs of hormone deficiency, even if asymptomatic cases have been described. Some authors suggest considering it as a hypothalamic dysfunction requiring treatment; others identify it as a paraphysiological variant. However, more cases are needed to establish a correct therapeutic strategy for these patients.

Enhancing Ship Maintenance Management in Maritime Engineering Education: Insights from Vocational Internships

This research investigates ship maintenance management within the context of vocational education in maritime engineering, focusing on the experiences of senior students during internships in port and shipping management industries. Utilising qualitative methods, including semi-structured interviews and focus group discussions, the study explores the practical application of theoretical knowledge in preventive, corrective, and proactive maintenance strategies. Seven participants from a shipping vocational school in Jakarta provided insights into methodologies, challenges, and educational outcomes in ship maintenance. Key findings highlight the effectiveness of preventive maintenance in enhancing operational reliability and reducing downtime, despite challenges such as resource constraints and operational disruptions. Recommendations include curriculum enhancements to integrate advanced training in predictive maintenance technologies and promote sustainable practices. The research underscores the importance of vocational education in preparing future maritime engineers for industry demands and advancing sustainable practices in maritime transportation.

Analysis of Oral English Errors and Correction Strategies for Junior Middle School Students

Analysis of Oral English Errors and Correction Strategies for Junior Middle School Students

Artificial intelligence-based joint attenuation and scatter correction strategies for multi-tracer total-body PET

BackgroundLow-dose ungated CT is commonly used for total-body PET attenuation and scatter correction (ASC). However, CT-based ASC (CT-ASC) is limited by radiation dose risks of CT examinations, propagation of CT-based artifacts and potential mismatches between PET and CT. We demonstrate the feasibility of direct ASC for multi-tracer total-body PET in the image domain.MethodsClinical uEXPLORER total-body PET/CT datasets of [18F]FDG (N = 52), [18F]FAPI (N = 46) and [68Ga]FAPI (N = 60) were retrospectively enrolled in this study. We developed an improved 3D conditional generative adversarial network (cGAN) to directly estimate attenuation and scatter-corrected PET images from non-attenuation and scatter-corrected (NASC) PET images. The feasibility of the proposed 3D cGAN-based ASC was validated using four training strategies: (1) Paired 3D NASC and CT-ASC PET images from three tracers were pooled into one centralized server (CZ-ASC). (2) Paired 3D NASC and CT-ASC PET images from each tracer were individually used (DL-ASC). (3) Paired NASC and CT-ASC PET images from one tracer ([18F]FDG) were used to train the networks, while the other two tracers were used for testing without fine-tuning (NFT-ASC). (4) The pre-trained networks of (3) were fine-tuned with two other tracers individually (FT-ASC). We trained all networks in fivefold cross-validation. The performance of all ASC methods was evaluated by qualitative and quantitative metrics using CT-ASC as the reference.ResultsCZ-ASC, DL-ASC and FT-ASC showed comparable visual quality with CT-ASC for all tracers. CZ-ASC and DL-ASC resulted in a normalized mean absolute error (NMAE) of 8.51 ± 7.32% versus 7.36 ± 6.77% (p < 0.05), outperforming NASC (p < 0.0001) in [18F]FDG dataset. CZ-ASC, FT-ASC and DL-ASC led to NMAE of 6.44 ± 7.02%, 6.55 ± 5.89%, and 7.25 ± 6.33% in [18F]FAPI dataset, and NMAE of 5.53 ± 3.99%, 5.60 ± 4.02%, and 5.68 ± 4.12% in [68Ga]FAPI dataset, respectively. CZ-ASC, FT-ASC and DL-ASC were superior to NASC (p < 0.0001) and NFT-ASC (p < 0.0001) in terms of NMAE results.ConclusionsCZ-ASC, DL-ASC and FT-ASC demonstrated the feasibility of providing accurate and robust ASC for multi-tracer total-body PET, thereby reducing the radiation hazards to patients from redundant CT examinations. CZ-ASC and FT-ASC could outperform DL-ASC for cross-tracer total-body PET AC.