Actual Damage Research Articles

The Association of Demographic Factors with Pain in Patients After Surgery

Background. Pain is considered a subjective, unpleasant, multidimensional experience, which includes different emotional, affective or cognitive components, related to actual or potential tissue damage. Pain is the most common symptom that patients report after surgical procedures. Studies show that the prevalence of postoperative pain can reach up to 80%. Aim. Evaluation of the level of postoperative pain and its association with socio-demographic characteristics. Materials and Methods. This is a crossectional study carried out at University Hospital Centers, Tirana, during the period 2022 – 2023. American Pain Society Patient Outcome Questionnaire (APS-POQ-R)1995/2010 for “Evaluation of Postoperative Pain management”, revised in 2010 was used for data collection. Pain assessment was conducted 6-24 hours after the surgical procedure. Results. A total of 237 patients with a mea age 49.5 (±18.9) yrs and range 18 to 88 years were interviewed. 45.6 % were females and 54.4% males. The prevalence of POP was 79.3%, 95CI (74.2.-83.7). A significant association of pain with gender, agegroup, level of education was found. Conclusion. Acute pain management continues to be a very serious clinical issue. It was concluded that the age, gender, level of education, civil status and residence of patients were significant factors in POP. In order to achieve a more effective management of POP, other studies should be carried out in the future, to evaluate in a more real way, the association of demographic data with pain after surgery. Received: 2 August 2024 / Accepted: 27 October 2024 / Published: 3 December 2024

Pain management with virtual reality in burn patients: a literature review.

Burns are injuries to the skin or the underlying tissue system caused by heat, radiation, electricity, friction or chemicals. Burns can cause pain and discomfort during dressing changes; a person's pain response varies depending on individual perception. The pain response can indicate tissue damage in the body, as pain causes a person to experience discomfort. Pain can be interpreted as a subjective sensory response and an unpleasant emotional experience associated with actual or potential tissue damage. Pain is usually managed with pharmacological and non-pharmacological therapy. An example of non-pharmacological therapy is the use of distraction techniques, such as virtual reality (VR). VR diverts pain by creating a 3D environment with a screen and VR glasses. It allows the patient to immerse themselves in a virtual world complete with sound and music. The volume can be adjusted as needed and the patient can interact with the virtual world. The purpose of this study is to determine the characteristics and effectiveness of VR pain management in burn patients. The method used in this study is a literature review using the Preferred Reporting Items For Systematic Review (PRISMA). The authors searched for journal articles using the Science Direct, PubMed, and Google Scholar databases, covering the period from 2015-2023. The keywords used were pain management, virtual reality and burn injury. Based on the predetermined inclusion criteria, 10 articles were selected for the review. The results of this study, based on the analysis of 10 articles, indicated that the distraction or transition method (non-pharmacological management using VR) was effective in reducing pain and discomfort during dressing changes in patients with mild to moderate burns, across various age groups from infants and children to adults. Clinicians can leverage VR technology to tailor interventions based on individual patient needs, across all age groups, enhancing patient comfort and engagement during procedures.

Effect of infill walls on the seismic performance of a severely damaged substandard RC building during the February 6, 2023, Kahramanmaras earthquake sequence

On February 6, 2023, two catastrophic earthquakes with moment magnitudes of Mw 7.7 and Mw 7.6 struck southern cities of Türkiye, occurring only nine hours apart. Spectral accelerations obtained from some of the recorded ground motions exceeded the design spectrum specified in the Turkish Building Earthquake Code (TBEC 2018) for the maximum credible earthquake level. This has prompted a review of seismic design practices within the framework of TBEC 2018. To address this concern, this study evaluates the performance of a reinforced concrete (RC) building with a ribbed slab that sustained heavy damage, according to official damage assessments, after the February 6th earthquake sequence. Numerical models were developed both with and without infill walls to assess their influence on the building’s response. Time history analysis and static pushover methods were used for its seismic performance assessment. Time history analyses were conducted under ground motions recorded at three different stations in close proximity to the investigated building. The inclusion of infill walls in the numerical model significantly influences the seismic performance analysis results. Peak floor accelerations of the building are determined to be higher at infilled model compared to the bare frame. The distribution of damaged columns in the actual building aligns well with the analysis results of the numerical model that includes infill walls. Notably, the numerical analysis using the ground motion record of the closest station to the building most accurately represents the actual damage distribution observed after the earthquakes. Finally, while the inclusion of infill walls in the model increases the lateral stiffness of the structure, it was also observed that displacement-dependent results obtained in the nonlinear time history analysis, such as relative story drifts, could increase depending on the selected ground motion record. This highlights the complex interaction between the infill walls, modal properties of the building, and the dynamic characteristics of the earthquake record.

Mapping structural damage to material damage for analysis of urban buildings considering actual damage states

Mapping structural damage to material damage for analysis of urban buildings considering actual damage states

Effects of Raised Heel Insole on Muscle Activity during ankle Sudden Inversion in Normal Adults

Raised heel insoles increase the plantar flexion angle of the ankle and cause ankle inversion sprain. The purpose of this study was to artificially create an ankle sudden inversion situation, which is a mechanical situation of actual ankle joint damage, and investigate the effect of the raised heel insole on ankle joint muscle activity. The subjects of this study were forty subjects with normal adults. The subjects performed sudden ankle inversion on the trapdoor with no raised heel insole, insole heights of 3cm, and insole heights of 7cm. The application of the raised heel insole was conducted randomly. The subjects performed the trapdoor test three times using dominant feet with a 60-second rest period between tests. This study assessed muscle activity during sudden ankle inversion three times. Raised heel insoles showed a significant decrease in Tibialis Anterior, Peroneus Longus, and Peroneus Brevis muscle activity than no raised heel insole (p<.05). Raised heel insoles showed a significant increase in Gastrocnemius muscle activity than no raised heel insole (p<.05). Raised heel insoles increase the risk of ankle sprain injury by reducing tibialis anterior and peroneus muscle activity during sudden ankle inversion.

Potential damage area detection of bridges based on single-temporal point cloud

Abstract With advantages of 3D representation, non-contact measurements and intensive sampling capability, it has been a research hotspot to detect the potential damage area of bridges with point cloud by TLS. However, TLS is commonly used to detect a potential damage area by comparing multi-temporal point cloud data, which limits the timeliness of bridge inspection. Therefore, aiming to accurately detect the potential damage areas of bridges with single-temporal point cloud, this paper proposes a normalized normal vector constrained coordinate transformation method. First, the distribution of sharp features is revealed in a single-temporal point cloud at potential damage areas, and a neighborhood growth method constrained by the normal distance is proposed to eliminate the sharp features in point cloud, which is prone to cause incorrect or missing curvature values from point cloud. Second, a normalized normal vector constrained coordinate transformation method is proposed to construct Gaussian curvature model, which can improve the accuracy of point cloud curvature and accurately detect the potential damage areas in bridges. At last, an evaluation criterion is proposed to quantify the bridge condition by integrating the characteristics of small-span concrete bridges in urban areas with actual damage data from the experimental bridges. The experimental results show that the proposed method can effectively detect the potential damage areas of the measured bridges.

Transfer learning for probabilistic localization of hidden cracks in concrete structures

AbstractThe utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.

Unsupervised Bushfire Burn Severity Mapping Using Aerial and Satellite Imagery

Abstract. It is critical to assess bushfire impact rapidly and accurately because bushfires play a significant role in forest degradation and present a threat to ecosystems and human lives. Over the past decades, several supervised algorithms of burn severity mapping have been proposed, facing the significant drawback of time-consuming labeling. Moreover, there is no robust framework for burn severity mapping through fusing multi-sensor, multi-resolution, and multi-temporal remote sensing imagery from satellite and aerial platforms. Therefore, this paper presents an unsupervised two-step pipeline: processing 2D data followed by 3D data for burn severity mapping, both of which are acquired from either aircraft or satellites. For the 2D data processing, our proposed unsupervised burned area detection (UsBA detection) model enhances burned area mapping accuracy by integrating Ultra-High Resolution (UHR) aerial imagery with bi-temporal medium-resolution PlanetScope imagery, using a Segment Anything Model (SAM)-assisted UNetFormer (pre-trained on the target-style public dataset – LoveDA Rural) for refinement. The model demonstrates superior burned area segmentation, evidenced by improved evaluation metrics calculated from labeled test sites. For the 3D analysis, the burned areas extracted from 2D processing are further assessed using pre- and post-event airborne laser data. We implement a voxel-based workflow, including necessary steps such as ground filtering through Superpoints in RANSAC Planes (SiRP) method and biomass change analysis. The results indicate that the 3D branch provides a reliable lower bound of the actual damage map, because the vegetation growth between two measurements remains, in essence, undetected. The proposed framework offers a more accurate and robust solution for burn severity mapping utilizing combined 2D and 3D data, evaluated on a multi-source dataset from a real bushfire event that occurred in Bushland Park, South Australia.

Prevalence and associated factors of acute postoperative pain in adult surgical patients: A prospective study

BackgroundPain, as defined by the International Association for the Study of Pain, is an unpleasant sensory and emotional experience associated with, or resembling, that is associated with actual or potential tissue damage. In Ethiopia, where healthcare facilities and offerings are expanding to handle countless patients requiring surgical intervention, managing acute postoperative pain is a serious concern. ObjectivesTo assess the prevalence of acute postoperative pain and associated factors after elective surgery among adult patients at Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia, 2023 MethodologyThis is an institution-based cross-sectional study. A structured data collection format was used to collect data from 219 participants. Bivariable and multivariable logistic regression analyses described the association between independent and dependent variables. ResultsOf 219 patients, 180 (82.2 %) had acute postoperative pain. The prevalence of moderate to severe pain was 34.24 %. Preoperative anxiety, the use of intraoperative analgesics, and duration of surgery were the main factors associated with the prevalence of acute postoperative pain. ConclusionOur study revealed that the overall prevalence of postoperative pain was relatively low in the study area. This suggests that the attention given to postoperative pain recognition and management is better than that in other areas. However, the finding of a significant gap in managing postoperative pain underscores the need for further improvements in pain management practices. This should motivate us to commit to change, particularly in the identified areas of concern, such as preoperative anxiety, use of intraoperative analgesics, and duration of surgery.

Study on Fractal Damage of Concrete Cracks Based on U-Net

The damage degree of a reinforced concrete structure is closely related to the generation and expansion of cracks. However, the traditional damage assessment methods of reinforced concrete structures have defects, including low efficiency of crack detection, low accuracy of crack extraction, and dependence on the experience of inspectors to evaluate the damage of structures. Because of the above problems, this paper proposes a damage assessment method for concrete members combining the U-Net convolutional neural network and crack fractal features. Firstly, the collected test crack images are input into U-Net for segmenting and extracting the output cracks. The damage to the concrete structure is then classified into four empirical levels according to the damage index (DI). Subsequently, a linear regression equation is constructed between the fractal dimension (D) of the cracks and the damage index (DI) of the reinforced concrete members. The damage assessment is then performed by predicting the damage index using linear regression. The method was subsequently employed to predict the damage level of a reinforced concrete shear wall–beam combination specimen, which was then compared with the actual damage level. The results demonstrate that the damage assessment method for concrete members proposed in this study is capable of effectively identifying the damage degree of the concrete members, indicating that the method is both robust and generalizable.

A physics-based acoustic emission energy method for mixed-mode impact damage prediction of composite laminates

In-service composite laminates are susceptible to impact-induced damage, which can substantially reduce its integrity and service life. The damage prediction remains a great challenge due to mixed damage modes and varying damage patterns. This study develops a novel acoustic emission (AE) energy method for predicting damage areas under three typical damage modes. Laboratory testing of composite laminate specimens subject to quasi-static indentation is performed in conjunction with in-situ AE monitoring to acquire AE data. By bridging two sets of energy formulations developed, namely, the one that correlates the damage area and the released strain energy of each damage mode and another that relates the released strain energy to the AE energy, an analytical model for predicting damage areas using AE energy components is derived. Proper signal procedure procedures are established to extract the energy components from AE monitoring data, and numerical and testing data are used to calibrate the model parameters. The effectiveness of the proposed model is further validated by comparing the prediction results of the damage areas with the actual damage areas of specimens tested under different indentation depths. The result indicates that the proposed AE energy method can yield reliable predictions of the damage area under mixed damage modes.

Optimising hurricane shelter locations with smart predict-then-optimise framework

ABSTRACT Hurricanes pose an escalating threat to global communities, underscoring the urgent need for robust disaster response strategies. A pivotal component of these strategies involves the establishment of secure shelters. However, the inherent vulnerability of these shelters to hurricane damage frequently undermines their utility. This study introduces a Predict-then-Optimise (PTO) framework designed to support relief agencies in selecting optimal locations for emergency shelters, with an emphasis on minimising potential damage during hurricanes. Employing a two-phase approach, the framework initially predicts potential hurricane-induced damage losses, subsequently utilising these predictions to optimise shelter placement strategies. Nevertheless, conventional PTO methods in shelter planning may lead to suboptimal decisions, primarily because of potential discrepancies between predicted and actual damage losses, given the inherent uncertainties and complexities of hurricane impacts. To address these limitations, our study introduces an advanced smart Predict-then-Optimise (SPO) framework. This SPO framework more cohensively integrates the prediction and optimisation phases, thereby facilitating an adaptive and resilient response to the dynamic challenges posed by hurricanes. We demonstrate the effectiveness of this methodology through a case study in Miami-Dade County, Florida, where the SPO framework successfully identified optimal shelter locations, significantly reducing exposure to high-risk areas.

Incident investigation of hydrogen explosion and fire in a residue desulfurization process

A large hydrogen explosion and fire occurred on October 27, 2022 in a residue desulfurization (RDS) process in a refinery in Kaohsiung, Taiwan. RDS is an important process that use hydrogen to convert the sulfur in the fuel into hydrogen sulfide for producing low sulfur fuel. The incident occurred during pressurization of the RDS reactors and downstream coolers by hydrogen. The incident led to significant damages to the downstream coolers and some part of the RDS reactors. Detailed incident investigation was carried out. A reactor effluent air cooler (REAC) in the downstream of RDS reactors was found to be ruptured in the rectangular header box. With security videos, process DCS data, leak and dispersion modelling, and actual damage on the process, it is possible to reproduce the leak rate, size of the initial fireball and damages done to the process. It is also found that the hydrogen leak was accompanied by prompt ignition which is commonly encountered in high-pressure hydrogen services. Recommendations are made to prevent and mitigate any future incident from the RDS process.

An end-to-end framework for fire following earthquake simulation at regional scale: A case study on the 2024 Japan Noto Peninsula earthquake

Fire following earthquakes remains a significant and threatening hazard to communities in urban regions. Addressing this critical issue requires an effective simulation method that is suitable for widespread adoption. This paper presents a systematic end-to-end framework for simulating fire following earthquakes at a regional scale by integrating models and methods available in existing literature. Relevant theories and models are summarized and incorporated into the calculation workflow. The framework encompasses three key features: (1) GIS-based regional building data management, (2) physics-based fire simulation, and (3) high-fidelity visualization. The implementation of the framework is presented in a sequential and detailed manner, with a focus on its practicality and replicability. Each component of the workflow is designed to be adaptable, allowing for easy customization and improvement to accommodate unique site characteristics or the integration of new models through the introduction of sub-modules. To demonstrate the practicality of the proposed framework, a case study based on the recent 2024 Noto Peninsula earthquake in Japan is presented. The simulation results correspond well with the observed actual damage at the fire following earthquake disaster site, with an accuracy of 87.8 %, which validates the framework's accuracy and reliability in simulating fire following earthquakes.

Effects of human–wildlife conflict on local people's livelihoods and wildlife conservation in and around Alitash National Park, northwest Ethiopia

Human–wildlife conflict has become one of the fundamental aspects of wildlife management. It threatens both wildlife and human welfare. People have injured, abused, and killed wildlife in response to perceived and/or actual damage from wildlife. Such negative interactions are common in adjacent protected areas (PAs) where people and wildlife share the same resources. However, the local contexts of the conflict around the most newly established PAs remain unaddressed. Thus, the aim of this study was to assess the effects of human–wildlife conflict on local people's livelihoods and wildlife conservation in and around Alitash National Park, Ethiopia. The data were collected by interviewing 198 households from 11 villages and conducting 3 focus group discussions and 12 key informant interviews. The survey questions were designed to understand the occurrence of wildlife damage, species involved, losses incurred, and local people's perception and reaction. The results revealed that 59.6% of the surveyed households encountered at least a single type of wildlife‐caused damage. Crop damage was the most prevalent, encountered by more than half (54%) of the households, of which 34.4 and 19.6% lost at most 25 and 75% of their crop fields, respectively. Common warthog, African porcupines, and common baboon were the top‐ranked wildlife species involved in crop‐raiding. The livestock loss from 2016 to 2020 was 287.89 tropical livestock units, which were shared by 47.5% of the respondents. Human attacks were rare, although 18 attacks by lion, spotted hyena, and leopard were reported by the study participants. The responses of the victims were negative and full of resentment to the wildlife. This undermines conservation efforts and the socio‐economic welfare of the local people. Thus, providing alternative livelihood opportunities and creating a context‐based conservation scheme along with continuous conservation education would help to reduce the negative effect of human–wildlife conflict on both wildlife and people.

Comparative Analysis of the Performance and Study of the Effective Anchorage Length of Semi-Grouted and Fully-Grouted Sleeve Connection

Based on the insufficient data on bonding performance and effective anchorage length of sleeve grouting in assembled structure. Combining the existing studies, the sleeve grouting joint test for the static unidirectional tensile test was designed, and the influencing factors are reinforcement diameter and reinforcement anchorage length. Then, the failure mode, load-displacement relationship, energy consumption capacity and bearing capacity of the grouting sleeve connection are analysed, and the stress mechanism of the specimen in the one-way tensile state is expounded. This paper considers the actual damage state of the joint, according to the failure of the reinforcement outside the joint and the sleeve; referring to the reinforcement-concrete bond strength research theory, the effective anchorage length formula is proposed. When the steel bar is pulled out, the bond strength and bearing capacity mainly depend on the effective anchorage length. However, when the specimen breaks the steel bar outside the joint, it depends on the material performance of the steel bar itself. The research results of this paper can lay a theoretical foundation for the application of sleeve grouting joints.

Rapid simulation method for assessing seismic damage to building curtain walls on a regional scale using designed wind load capacity

This study introduces a rapid simulation method for assessing seismic damage to building curtain walls at a regional scale. Although the results are approximate, this approach enables quick evaluations, making it an important instrument for emergency responses during disaster situations. This method’s independence from numerical models is a noteworthy advantage. Unlike conventional approaches, it eliminates the need for structural analysis models when evaluating the seismic capacities of curtain walls regionally. Creating reliable structural analysis models is both time-consuming and labor-intensive, primarily due to the detailed design information they require. In contrast, the presented method leverages the wind load capacities for which curtain walls are designed. It is based on the core premise that most curtain walls, primarily designed for wind resistance, possess wind load capacities that could serve as substitutes for their seismic capacities, even if they are not explicitly designed for such seismic loads. To assess the method’s effectiveness, it was applied to seismic damage assessments across regions experiencing varying wind intensities: weak, moderate, and strong. The results suggest the likelihood of curtain walls sustaining seismic damage in regions with weak wind could be five times higher than in regions with strong wind. This underscores the importance of seismic design considerations for curtain walls. Moreover, the findings closely match the actual seismic damage assessment data from a region with a moderate to strong wind intensity.

A Fault Diagnosis Approach Utilizing Artificial Intelligence for Maritime Power Systems within an Integrated Digital Twin Framework

This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework that continuously observes the induction motor, providing essential support to maintenance personnel. The motor operates under a range of environmental and operational conditions, including temperature fluctuations, rotational speeds, and mechanical loads. These variations can obscure the current time series data, potentially masking signs of actual damage and hindering effective damage detection. To tackle this issue, the proposed framework utilizes artificial intelligence (AI) technology, specifically the well-established autoencoder, in conjunction with the Mahalanobis statistical distance. This approach accounts for the diverse operating conditions during the training phase, allowing it to model complex, non-linear relationships and effectively differentiate between normal and anomalous states. The framework is integrated into a decision support platform designed for real-time operations in maritime settings, offering a sophisticated system architecture that aims to align advanced damage detection methodologies with the maritime industry’s need for real-time, user-friendly solutions.

An In Silico Approach to Exploring the Antinociceptive Biological Activities of Linalool and its Metabolites.

Pain is characterized by the unpleasant sensory and emotional sensation associated with actual or potential tissue damage, whereas nociception refers to the mechanism by which noxious stimuli are transmitted from the periphery to the CNS. The main drugs used to treat pain are nonsteroidal anti-inflammatory drugs (NSAIDs) and opioid analgesics, which have side effects that limit their use. Therefore, in the search for new drugs with potential antinociceptive effects, essential oils have been studied, whose constituents (monoterpenes) are emerging as a new therapeutic possibility. Among them, linalool and its metabolites stand out. The present study aims to investigate the antinociceptive potential of linalool and its metabolites through a screening using an in silico approach. Molecular docking was used to evaluate possible interactions with important targets involved in antinociceptive activity, such as α2-adrenergic, GABAergic, muscarinic, opioid, adenosinergic, transient potential, and glutamatergic receptors. The compounds in the investigated series obtained negative energies for all enzymes, representing satisfactory interactions with the targets and highlighting the multi-target potential of the L4 metabolite. Linalool and its metabolites have a high likelihood of modulatory activity against the targets involved in nociception and are potential candidates for future drugs.

Optimized seismic hazard and structural vulnerability model considering macroseismic intensity measures

Macroseismic intensity measures are fundamental quantitative indicators for evaluating the vulnerability and resilience of regional building clusters. The vulnerability model of building portfolios established can provide a noteworthy reference for the development of large-scale regional earthquake risk distributions. However, there are differences in the quantitative scales of the macroseismic intensity standards used in multiple regions. The positive effects of instrument intensity and probabilistic seismic hazard theory on the vulnerability assessment of regional buildings are rarely considered, resulting in significant differences in their assessment results. This paper comprehensively considers the impact of instrument intensity and seismic hazard models on traditional macroseismic intensity. An updated macroseismic intensity measure (UCMS-20) is proposed for evaluating regional buildings, which is combined with the latest version of China's seismic intensity standards. The actual seismic damage sample dataset (31,649 buildings) of four types of building clusters in three typical earthquakes in China (Wenchuan 2008, Yushu 2010, and Jiuzhaigou 2017) has been expanded and updated. Using the UCMS-20, the European Macroseismic Scale (EMS-98), the Modified Mercalli intensity (MMI), and the Medvedev, Sponheuer, and Karnik Scale (MSK-64), vulnerability assessments are conducted on the extended empirical structural seismic damage dataset, and an empirical disaster matrix model considering updating damage states is established. A nonlinear vulnerability prediction model considering updated macrointensity measures is proposed, and the optimized model is validated and compared against the developed multiscale empirical structure dataset. The multiscale vulnerability index calculation model is improved, and novel optimization curves, point clouds, and matrix probability risk models are established, considering the expansion of empirical structural vulnerability databases.