Manual Intervention Research Articles

A hybrid meta-heuristic framework with ensemble deep learning for multi-functional simultaneous optimized automatic intensity-modulated radiotherapy planning

Intensity-modulated radiotherapy (IMRT) is one of the main treatments for patients with cancer, and its treatment planning holds significant importance. Compared to manual treatment planning, automatic treatment planning (ATP) is expected to improve plan quality and efficiency, which is of great clinical importance. However, treatment planning is an intractable “black art” that calls for intuition and heuristics from medical dosimetrists. There are two main issues with the current ATP methods: (i) they rely strongly on prior clinical experience and are not sufficiently scalable, and (ii) they use a single-functional optimization concept that ignores delivery accuracy information, which lowers the delivery accuracy of the generated ATP plans. To address these issues, this paper presents a novel multi-functional simultaneous optimized automatic treatment planning (MFSO-ATP) method. First, to address the issues of high reliance and insufficient scalability, an evolutionary strategy (ES) and simulated annealing (SA)-based hybrid meta-heuristic trial-and-error (ESSA-HMTE) framework is designed to automatically adjust optimization parameters without human intervention. Second, to address the issue of delivery accuracy deterioration, an ensemble deep learning-based gamma passing rate (GPR) prediction (EnDL-GPR) framework was constructed and trained to predict the plan delivery accuracy a priori. The ESSA-HMTE framework effectively fuses dosimetric quality information and delivery accuracy a priori information to simultaneously optimize the two, ensuring delivery accuracy while achieving automatic planning. We tested the MFSO-ATP method using multiple diseases with high inter-tumor heterogeneity. The results showed that the proposed method can improve the quality and delivery accuracy of IMRT plans, reduce the percentage of quality assurance failures, is highly scalable, and has good clinical application prospects.

Effect of chemical thinning on fruit growth and fruit quality of ‘Majhoul’ date palm at the end of the ‘Khalal’ stage

The aim of this research work was to study the effect of chemical thinning using NAA (Naphthalene acetic acid) and the manual thinning used by the farmer on the fruit growth and fruit quality of ‘Majhoul’ date palm at the end of the ‘khalal’ stage (color stage and fruit physiologically mature). Trials were carried out on an adult plantation in the Tinejdad region, Tafilalet area. Observations focused on monitoring the fruit growth of different flowering phases and fruit quality at the end of the ‘khalal’ stage. Obtained results showed that the evolution of the fruit size parameters is similar for all flowering phases and all thinning treatments and at the end of the ‘khalal’ stage there is no significant difference (p ˃ 0.05) between flowering phases and between thinning treatments for these parameters. At the end of the ‘khalal’ stage, fruit length is between 4.8 and 4.9 cm and fruit mass is between 25.0 and 25.5 g. The content of sugar in the fruit is higher at the manual thinning treatment than at the NAA treatments, whereas the flowering phases have no effect on this parameter. The pH of the fruit juice is relatively similar for all thinning treatments and all flowering phases.

Comprehensive data security and compliance framework for SMEs

Small and Medium-sized Enterprises (SMEs) are increasingly relying on cloud platforms to support critical business operations, making effective disaster recovery (DR) strategies essential for ensuring business continuity. This review proposes a robust disaster recovery framework tailored for SMEs, designed to minimize downtime and data loss in the event of a system failure, cyberattack, or natural disaster. The framework integrates advanced cloud technologies to create a cost-effective, scalable solution that aligns with the resource constraints of SMEs while providing enterprise-grade resilience. Key components of the disaster recovery framework include cloud-based data replication, automated backup solutions, and geo-redundant storage to ensure that data is continuously available and recoverable. This model employs real-time data synchronization and incremental backups to minimize Recovery Point Objectives (RPO), ensuring that critical data is not lost during an unexpected outage. Additionally, the framework leverages automated failover mechanisms to achieve low Recovery Time Objectives (RTO), allowing businesses to restore operations quickly after an interruption. Cloud orchestration tools such as AWS Elastic Disaster Recovery or Azure Site Recovery are utilized to automate disaster recovery processes, reducing manual intervention and improving the speed of recovery. The framework also incorporates regular testing of disaster recovery plans, using simulation tools to identify weaknesses and optimize response times. For SMEs, cost-effectiveness and ease of management are crucial. The framework emphasizes a pay-as-you-go model for cloud resources, allowing businesses to scale their disaster recovery solutions as they grow without incurring excessive upfront costs. By providing continuous monitoring and proactive threat detection, this disaster recovery framework ensures that SMEs can maintain uninterrupted business operations on cloud platforms, thereby enhancing resilience and mitigating the financial and operational risks associated with data loss and system downtime.

A multi-strategy ontology mapping method based on cost-sensitive SVM

As the core of ontology integration, the task of ontology mapping is to find the semantic relationship between ontologies. Nevertheless, most existing ontology mapping methods only rely ontext information to calculate entity similarity, thereby disregarding semantic nuances and necessitating substantial manual intervention. Therefore, this paper introduces an ontology mapping method. Based on the traditional ontology mapping method, the process employs a deep learning model to mine the semantic information of entity concepts, entity properties and ontology structure to obtain the embedding vector. We use the similarity mechanism to calculate the similarity between different embedding vectors, and combine the similarity values obtained from multiple strategy entities into a similarity matrix. The similarity matrix serves as input to the support vector machine (SVM), and the ontology mapping problem is finally transformed into a binary classification problem. However, since the number of mapped pairs is much larger than the number of non-mapped pairs, the number of positive samples in the data set is much smaller than the number of negative samples. Therefore, based on the traditional SVM, the paper adopts cost-sensitive strategy to deal with the class imbalance problem. In comparative evaluations against contemporary ontology mapping techniques, our method demonstrates a noteworthy 5.0% enhancement in recall and a 3.0% improvement in F1 score when tested on both public benchmark datasets and domain-specific datasets.

A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords

This study presents a comprehensive framework to enhance Wikidata as an open and collaborative knowledge graph by integrating Open Biological and Biomedical Ontologies (OBO) and Medical Subject Headings (MeSH) keywords from PubMed publications. The primary data sources include OBO ontologies and MeSH keywords, which were collected and classified using SPARQL queries for RDF knowledge graphs. The semantic alignment between OBO ontologies and Wikidata was evaluated, revealing significant gaps and distorted representations that necessitate both automated and manual interventions for improvement. We employed pointwise mutual information to extract biomedical relations among the 5000 most common MeSH keywords in PubMed, achieving an accuracy of 89.40 % for superclass-based classification and 75.32 % for relation type-based classification. Additionally, Integrated Gradients were utilized to refine the classification by removing irrelevant MeSH qualifiers, enhancing overall efficiency. The framework also explored the use of MeSH keywords to identify PubMed reviews supporting unsupported Wikidata relations, finding that 45.8 % of these relations were not present in PubMed, indicating potential inconsistencies in Wikidata. The contributions of this study include improved methodologies for enriching Wikidata with biomedical information, validated semantic alignments, and efficient classification processes. This work enhances the interoperability and multilingual capabilities of biomedical ontologies and demonstrates the critical role of MeSH keywords in verifying semantic relations, thereby contributing to the robustness and accuracy of collaborative biomedical knowledge graphs.

Development and implementation of effective Web Scraping methods for automated data collection and processing using Python

With each passing year, the process of digitalization in society is accelerating, leading to a significant increase in demand for processed and analyzed information. In today's world, data has become a valuable resource, and the ability to quickly find and analyze large amounts of information is a key competitive advantage for companies, researchers, and analysts. In this context, web scraping has become an important tool, enabling the efficient collection of data from various online sources for further analysis and informed decision-making. This paper examines the latest advancements in the development and implementation of effective web scraping methods for automatic data collection and processing using Python. The use of the latest Python libraries, such as BeautifulSoup, Selenium, and Scrapy, allows for high-speed and accurate data collection from various web sources, particularly in secondary markets. The proposed algorithms reduce the risk of site blocking, ensuring the stability and reliability of data collection in various situations. Additionally, the paper places great emphasis on automating the data collection process through the development of automated scripts and the implementation of job scheduling programs, such as cron jobs. This ensures continuous database updates and the collection of new information without manual intervention. Special attention is given to the processing and cleaning of collected data, particularly in the development of methods for filtering out unnecessary information, duplicates, and noise, which enhances data quality. The efficient use of the collected data demonstrates its value for market analysis, demand assessment, and quality forecasting, highlighting the importance of the proposed method. The research includes examples of real-world data use cases in various fields such as marketing, economics, and business analysis. A comparative analysis of different data collection methods is also provided, allowing for the assessment of the effectiveness and reliability of the proposed solutions.

Machine learning embedded hybrid MCDM model to mitigate decision uncertainty in transport safety planning for OAS countries

Providing defensible decisions is a prerequisite for methodologies of multi-criteria decision-making (MCDM) activities, and this is especially true for socio-economic analysis in public sector. This study proposes an all-in-one MCDM model with machine learning algorithms. The model integrates the method based on the removal effects of criteria (MEREC), combined compromise solution (CoCoSo), and density-based spatial clustering of applications with noise (DBSCAN), i.e., MEREC–CoCoSo–DBSCAN. In particular, the uniform manifold approximation and projection (UMAP) is implanted in DBSCAN to reduce the data dimensionality, and the k-nearest neighbors (KNN) algorithm is embedded to determine the inflection points (ɛ) and minPts in the data. This counters the inherent model failure of DBSCAN in dealing with high-dimensional data and eliminates the requirement for manual intervention in the model procedure, thereby fully avoiding potential human error and automating the computing process. A case study on benchmarking transport safety systems for member countries of the Organization of American States (OAS) demonstrates the reliability, adaptability, and efficiency of the proposed model. It moreover reflects its feasibility in resolving real-life socio-economic issues by offering valuable insights and potential solutions in economic investment and funding allocation in regard to transport safety strategy. Overall, this study provides government officials, managers, and policymakers with a valuable tool for handling MCDM activities in socio-economic development with considerable practicality and credibility.

Increasing Transgender Acceptance in Religious Families: A Pilot Study of Manualized Strategic Family Therapy

ABSTRACT This pilot study explores strategies to increase transgender acceptance within religious families by applying a brief, manualized treatment approach. The research uniquely integrates Haley’s Model for Effective Family Therapy with principles of cultural humility to address the specific challenges associated with transgender identity in a conservative Baptist context. Employing a clinical case study approach, this research combines qualitative and quantitative methods to evaluate the implementation of manualized therapeutic strategies. The study focuses on adapting Haley’s model to enhance family dynamics and promote acceptance while respecting the family’s cultural and religious context. Quantitative data show significant improvements across both the Closeness Scale and the Brief COPE Scale. Family cohesion, overall coping, problem-focused coping, and emotional regulation strategies substantial increased. Qualitative findings further validated these improvements, highlighting enhanced family dynamics and transgender acceptance. These effects were sustained for at least three months post-intervention, indicating lasting positive impacts on family dynamics and coping. This pilot study provides novel contributions by demonstrating how Haley’s Model, adapted with cultural humility, can effectively enhance transgender acceptance within a religiously conservative family system. It underscores the value of culturally respectful and systematic approaches in therapy and highlights the need for further research to explore the long-term effects and broader applicability of these interventions in diverse contexts.

Automating the Generation of NVD CVE Fixes using Generative AI and RAG

As modern software applications increase, the challenge of addressing security vulnerabilities [1] will increase. Generating fixes for these vulnerabilities rely on manual intervention—a process that can be both timeconsuming and difficult to scale. In this paper, we propose an automated approach to CVE fix generation, leveraging data from the National Vulnerability Database (NVD) [2], a local vector database for efficient storage, and Generative AI to produce relevant fixes based on vulnerability descriptions. Our approach involves first loading all publicly available CVE data into a vector database, indexed by CVE ID and metadata for efficient retrieval. When a query for a CVE fix is made, then using Generative AI using custom prompts to provide fixes based on the vulnerability descriptions stored in the database. The generated solutions are stored back in the database for future use, or for review. In the methodology section, we demonstrate how this system can automate the generation of effective CVE fixes in a matter of seconds, streamlining a traditionally manual and time-consuming process. With the findings suggest that the proposed system can reduce the manual/time consuming effort on the teams while enhancing the speed and accuracy of vulnerability remediation

Robust algorithm for automatic surface-based outlier detection in MBES point clouds

Bathymetric multibeam echosounder systems (MBES) provide high-resolution mapping of underwater topography but are highly susceptible to errors due to harsh environmental conditions and the measurement process. Traditionally, manual post-processing is required to ensure data quality, a time-consuming, expensive, and subjective task. To address this issue, we propose a surface-based algorithm for pre-processing and cleaning MBES data that reduces manual intervention and improves consistency. A surface-based algorithm models the underwater topography as a surface instead of processing individual points. By assuming a continuous surface for underwater geometry, the algorithm easily identifies observations that deviate significantly from this model. The method combines a hierarchical B-spline surface with iterative robust estimation to automate data cleaning. Preliminary results on example datasets show a balanced outlier detection accuracy of 0.99, with manual processing time reduced from 2 days to just 30 min.

RetVes segmentation: A pseudo-labeling and feature knowledge distillation optimization technique for retinal vessel channel enhancement

Recent advancements in retinal vessel segmentation, which employ transformer-based and domain-adaptive approaches, show promise in addressing the complexity of ocular diseases such as diabetic retinopathy. However, current algorithms face challenges in effectively accommodating domain-specific variations and limitations of training datasets, which fail to represent real-world conditions comprehensively. Manual inspection by specialists remains time-consuming despite technological progress in medical imaging, underscoring the pressing need for automated and robust segmentation techniques. Additionally, these methods have deficiencies in handling unlabeled target sets, requiring extra preprocessing steps and manual intervention, which hinders their scalability and practical application in clinical settings. This research introduces a novel framework that employs semi-supervised domain adaptation and contrastive pre-training to address these limitations. The proposed model effectively learns from target data by implementing a novel pseudo-labeling approach and feature-based knowledge distillation within a temporal convolutional network (TCN) and extracts robust, domain-independent features. This approach enhances cross-domain adaptation, significantly enhancing the model's versatility and performance in clinical settings. The semi-supervised domain adaptation component overcomes the challenges posed by domain shifts, while pseudo-labeling utilizes the data's inherent structure for enhanced learning, which is particularly beneficial when labeled data is scarce. Evaluated on the DRIVE and CHASE_DB1 datasets, which contain clinical fundus images, the proposed model achieves outstanding performance, with accuracy, sensitivity, specificity, and AUC values of 0.9792, 0.8640, 0.9901, and 0.9868 on DRIVE, and 0.9830, 0.9058, 0.9888, and 0.9950 on CHASE_DB1, respectively, outperforming current state-of-the-art vessel segmentation methods. The partitioning of datasets into training and testing sets ensures thorough validation, while extensive ablation studies with thorough sensitivity analysis of the model's parameters and different percentages of labeled data further validate its robustness.

MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn.

The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the MolPipeline package, which extends this concept to cheminformatics by wrapping standard RDKit functionality, such as reading and writing SMILES strings or calculating molecular descriptors from a molecule object. We aimed to build an easy-to-use Python package to create completely automated end-to-end pipelines that scale to large data sets. Particular emphasis was put on handling erroneous instances, where resolution would require manual intervention in default pipelines. MolPipeline provides the building blocks to enable seamless integration of common cheminformatics tasks within scikit-learn's pipeline framework, such as scaffold splits and molecular standardization, making pipeline building easily adaptable to diverse project requirements.

Curative Effect of Electroacupuncture and Manual Acupuncture for Knee Osteoarthritis: A Meta-Analysis.

We aimed to examine how electroacupuncture and manual acupuncture affect treatment results, pain levels, and joint function in individuals with knee osteoarthritis. Research was carried out in various databases including PubMed, Medline, Embase, CENTRAL, and CNKI. Following the "Cochrane manual", the risk of bias of included RCTs was assessed. A funnel plot was utilized to evaluate any potential bias in the publications. The impact size was indicated by the average discrepancy along with its 95% confidence interval. The EA group showed a higher effectiveness rate (P = 0.001) and a lower WOMAC pain score (P < 0.00001) compared to the control group. The EA group had a lower WOMAC pain score compared to the SA/exercise group and the group that received manual acupuncture. The WOMAC pain score was significantly lower in the EA group compared to the manual acupuncture group under intense electroacupuncture stimulation (P < 0.0001). The WOMAC pain score was significantly lower in the EA group compared to the manual acupuncture group when weak current acupuncture was applied (P = 0.0001). However, no significant difference in WOMAC function score between EA and control group. Comparison to manual acupuncture, placebo acupuncture, and exercise training, electroacupuncture enhanced the effectiveness of treating KOA and decreased the WOMAC pain score in patients with KOA. The level of pain relief achieved may be linked to the strength of the current stimulation. However, electroacupuncture had no significant effect on WOMAC function score.

An integration methodology of safety and security requirements for autonomous vehicles

Safety and security co-engineering is one of the latest challenge in autonomous vehicle (AV) development. Efficiently integrating safety and security requirements during co-engineering is a new issue. Most functional safety and security analysis methods do not directly derive safety requirements, and improper handling of their relationship can affect system design and timelines. This article aims to use large language models (LLMs) to assist in the collaborative work of functional safety and security analysis. The main contributions are as follows: First, we propose three types of formulations to summarize hazard scenarios and threat scenarios and use LLMs to extract functional safety requirements and security requirements from them. Second, we utilized the three LLMs to perform relationship checks on the extracted functional safety requirements and security requirements. The results showed that the majority of the checks were correct and consistent, with only a small portion requiring manual intervention, significantly reducing human labor. Through these methods, we demonstrate the potential and efficiency of LLMs in the collaborative analysis of functional safety and security.

Sensitivity Analysis of Fatigue Life for Cracked Carbon-Fiber Structures Based on Surrogate Sampling and Kriging Model under Distribution Parameter Uncertainty

The quality and reliability of wind turbine blades, as core components of wind turbines, are crucial for the operational safety of the entire system. Carbon fiber is the primary material for wind turbine blades. However, during the manufacturing process, manual intervention inevitably introduces minor defects, which can lead to crack propagation under complex working conditions. Due to limited understanding and measurement capabilities of the input variables of structural systems, the distribution parameters of these variables often exhibit uncertainty. Therefore, it is essential to assess the impact of distribution parameter uncertainty on the fatigue performance of carbon-fiber structures with initial cracks and quickly identify the key distribution parameters affecting their reliability through global sensitivity analysis. This paper proposes a sensitivity analysis method based on surrogate sampling and the Kriging model to address the computational challenges and engineering application difficulties in distribution parameter sensitivity analysis. First, fatigue tests were conducted on carbon-fiber structures with initial cracks to study the dispersion of their fatigue life under different initial crack lengths. Next, based on the Hashin fatigue failure criterion, a simulation analysis method for the fatigue cumulative damage life of cracked carbon-fiber structures was proposed. By introducing uncertainty parameters into the simulation model, a training sample set was obtained, and a Kriging model describing the relationship between distribution parameters and fatigue life was established. Finally, an efficient input variable sampling method using the surrogate sampling probability density function was introduced, and a Sobol sensitivity analysis method based on surrogate sampling and the Kriging model was proposed. The results show that this method significantly reduces the computational burden of distribution parameter sensitivity analysis while ensuring computational accuracy.

Wheat leaf localization and segmentation for yellow rust disease detection in complex natural backgrounds

Wheat yellow rust disease poses a significant threat to global wheat yield and grain quality. Early detection of this disease will help to minimize the loss caused by its effects. Existing models work well on images taken in a controlled environment, whereas a uniform background is placed behind the leaf, but these models fail to produce good results in natural settings. Previous research also involves manual interventions in the pipeline to achieve good classification results such as cropping the images, using uniform backgrounds, etc. These systems are not practical to use in natural environments where there will be a lot of background noise to the image and manual cropping becomes an extra step for the farmer. Moreover, the unavailability of the dataset in which images of leaves are taken in a natural setting became another challenge. In this research, a dataset is curated and leaves are annotated for object detection, object segmentation further the leaves are classified into 3 classes ie healthy, resistant, and susceptible. A novel unsupervised image rotation algorithm is proposed that takes input from YOLOv8 to align the leave in such a way that maximum background can be removed by a rectangular bounding box . Then the comparison between multiple state-of-the-art segmentation models ie. UNET, Segment-Anything (SAM), Segnet, LinkNet, PSPNet, FPN, Deep-Labv3+ (Xception), and DeepLabv3+ (Mo-bileNet) has shown that UNET has outperformed all the other segmentation models with an IOU score of 0.9563. Lastly for classification, the performance of multiple convolution neural networks ie. VGG16, Resnet 101(v2), Xception, Mo-bileNetV2, and Transformer-based models ie. Swin trans-former and MobileVit have been compared. Swin transformer has outperformed the state-of-the-art CNN models with an accuracy of 95.8%. This paper proposes a complete robust pipeline that can be deployed in natural environment and does not need any manual intervention to produce good results. This research shows that good localization of leaves and removal of unwanted background noise at the earliest stage of the pipeline will assist the segmentation model to effectively segment the leaf from the background which will enable classification models to achieve high classification accuracy, even when dealing with very small datasets.

A Digital Twin System for Adaptive Aligning of Large Cylindrical Components

Most large aerospace cylindrical components still adopt a manual aligning method with low automation, large manual intervention, and heavy dependence on operator workers, resulting in the low quality and efficiency of large component aligning, which seriously prolongs the manufacturing time of aerospace products. To cope with this issue, based on closed-loop adaptive control and digital twin (DT) technologies, an adaptive aligning system for large cylindrical components, i.e., the DT aligning system, is proposed in this study. For the DT aligning system, through the DT multi-dimensional modeling, i.e., geometric modeling, physical modeling, functional modeling, and data modeling, it can be divided into a physical space, a virtue space, and twin data. Note that the association, mapping, and interaction between physical space and virtual space of the aligning system can be realized via the twin data, thereby realizing real-time virtual display, monitoring, and control of the large component aligning. In addition, based on the measured pose data, aligning stress, and predicted aligning error, an adaptive force/position control method for large component aligning is proposed, and it can achieve real-time decision-making and precise execution of the aligning process. Finally, through application validation, the DT process system can realize the real-time status perception and process execution decision during the large component aligning. Finally, through experimental validation, it is found that the proposed system, i.e., the DT aligning system, can improve the quality and efficiency of the large aerospace cylindrical component aligning, as well as the automation and intelligent level of the aligning system.

Spatial resolution improvement method of RDTS assisted by small-scale thermal region length recognition model

The Total Variation Deconvolution (TVD) algorithm plays an important role in signal reconstruction, however, when it is used to improve the spatial resolution of Raman Distributed Temperature Sensor (RDTS), there are certain challenges in parameter settings. This paper proposes to use Fully-Connected Neural Network to identify the length of small-scale thermal regions(SSTR), and based on the recognition results to set the TVD parameters automatically. We constructed training sets based on the periodic changes of SSTR signals in RDTS (which we call Thermal Region Response Modes, TRRM), to verify performance, we conducted comparative experiments between models obtained from a training set containing 100 types of TRRMs and 25 types of TRRMs, the Macro-F1 value of the former one is 0.2749 higher, reaching 0.9087, performed well in SSTR length recognition tasks. the traditional TVD assisted by this model can increase the spatial resolution of RDTS from 1.6 m to 0.4 m without manual intervention, which complements the lack of automation in applications of TVD and has practical value.

R-C-D-F machine learning method to measure for geological structures in 3D point cloud of rock tunnel face

This study introduces an innovative Roughness-CANUPO-Dip-Facet (R-C-D-F) methodology for the measurement of dip angle and direction in geological rock facets. The R-C-D-F method is distinguished by its comprehensive four-step approach, encompassing filtration through roughness analysis, CANUPO analysis, and dip angle filtration, followed by facet segmentation as the measurement step. To achieve precise and efficient results, the method specifically focuses on isolating joint embedment, achieved by systematically filtering out joint bands. This selective filtration process ensures that measurements are conducted exclusively on relevant joint embedment points. The novelty of this methodology lies in its capability to automatically eliminate joint bands while retaining the joint embedment points, facilitating precise measurements without manual intervention. Three site models were evaluated using the R-C-D-F method, alongside four different techniques for measuring dip angle and direction: plane fitting, normal vector conversion, facet segmentation, and compass measurements. The results demonstrated that all methods accurately calculated the dip angle, with an accuracy ranging from 97 % to 99.4 %. The facet segmentation method was selected as the optimal measurement tool due to its automatic nature and capacity to provide accurate results without manual intervention. Furthermore, the optimal local neighbour radius (LNR) for calculating normal vectors was determined, with findings indicating that a larger LNR value enhances accuracy but also increases computational time. A verification was conducted to estimate the dip angle used for filtering and discarding additional points representing joint rock bands, with the optimal value being 45, 30, and 45 degrees for the respective sites.The R-C-D-F method effectively detected and eliminated 100 % of joint band points while retaining 81 % of joint embedment points, and the facet segmentation method provided accurate dip angle and direction measurements for each joint embedment segment. These outcomes underscore the robustness and precision of the R-C-D-F method in geological engineering and rock stability studies.

Implementing peer support into practice in mental health services: a qualitative comparative case study

BackgroundPeer workers are people with personal experience of mental distress, employed within mental health services to support others with similar experiences. Research has identified a range of factors that might facilitate or hinder the introduction of new peer worker roles into mental health services. While there is mixed evidence for the effectiveness of peer worker delivered interventions, there are no studies exploring how implementation might be associated with effect.MethodsThis was a qualitative comparative case study using data from interviews with 20 peer workers and their five supervisors. Peer workers delivered peer support for discharge from inpatient to community mental health care as part of a randomised controlled trial. In the trial, level of participant engagement with peer support was associated with better outcome (hospital readmission). Study sites with higher levels of engagement also had higher scores on a measure of fidelity to peer support principles. We compared data from sites with contrasting levels of engagement and fidelity using an analytical framework derived from implementation theory.ResultsIn high engagement-high fidelity sites, there was regular work with clinical teams preparing for working alongside peer workers, and a positive relationship between staff on inpatient wards and peer workers. The supervisor role was well resourced, and delivery of peer support was highly consistent with the intervention manual. In low engagement-low fidelity sites peer workers were employed in not-for-profit organisations to support people using public mental health services and in rural areas. Supervisors faced constrained resources and experienced barriers to joint working between organisations. In these sites, peer workers could experience challenging relationships with ward staff. Issues of geography and capacity limited opportunities for supervision and team-building, impacting consistency of delivery.ConclusionsThis study provides clear indication that implementation can impact delivery of peer support, with implications for engagement and, potentially, outcomes of peer worker interventions. Resourcing issues can have knock-on effects on consistency of delivery, alongside challenges of access, authority and relationship with clinical teams, especially where peer workers were employed in not-for-profit organisations. Attention needs to be paid to the impact of geography on implementation.Trial registrationISRCTN registry number ISRCTN10043328, registered 28 November 2016.