Repair Efforts Research Articles

Analisa Derajat Kejenuhan, Perlengkapan Jalan, dan Nilai Kerataan Pada Jalan Raya Kepandean, Kabupaten Tegal

Road safety audits are an effort to improve safety and prevent traffic accidents. The implementation of the audit can be carried out by the survey method directly in the field and can use the Hawkeye car to get the International Roughness Index (IRI) value. The results of this study show that the condition of the road on the Kepandean Highway section with the IRI value of the route from the direction of SMPN 1 Dukuhturi is 6.5 while the IRI value of the route to SMPN 1 Dukuhturi is 6.3 so that it can be concluded that the road is in the medium category (not good) and requires periodic repair and maintenance efforts

Socioecological impacts of pine monocultures on Guaraní territories in Argentina: the hidden costs of modern development

ABSTRACT This study examines the socioenvironmental impacts of pine (Pinus taeda) monocultures established by forestry companies on Guaraní territories in the Province of Misiones, Argentina. Conducted from an interdisciplinary perspective, the research addresses the concerns of a Guaraní community (Puente Quemado II) about the effects on their way of life and the potential toxicological risks resulting from changes to their territory, including large monocultures of pine. By incorporating inputs from ethnography, ethnobiology, biology, ecology, ecotoxicology and toxicology, the study provides a comprehensive analysis of the impacts of the forest monoculture system on the communities. The findings indicate that the implementation of the monoculture system has negative impacts on the communities in terms of cultural and material resources, as well as toxicological risks. Collectively, these factors represent a high risk to their existence within the territory. However, conditions in the area allow rapid ecological restoration, which, with appropriate policies, could serve as a strategy for historical repair efforts and the fulfillmentt of indigenous communities’ rights.

Analyzing Road Images for Pothole Detection through Machine Learning Algorithms: A Comprehensive Review

The road accident prediction system leverages deep learning techniques, specifically YOLO (You Only Look Once), for the detection of potholes in road footage. By employing YOLO, the system can efficiently identify and localize potholes within video frames, enabling rapid and accurate detection. Furthermore, the system incorporates a severity prediction module that utilizes the dimensions and characteristics of detected potholes to assess their severity levels. This predictive capability empowers authorities and road maintenance teams to prioritize repair efforts and allocate resources effectively, ultimately contributing to the reduction of road accidents and ensuring safer road conditions for motorists and pedestrians alike. Through the seamless integration of pothole detection and severity prediction functionalities, the road accident prediction system offers a proactive approach to road maintenance and safety management, enhancing overall road infrastructure resilience and public safety.

Waste Disposal Facilities Raise Awareness through Repair and Reuse Efforts

Waste Disposal Facilities Raise Awareness through Repair and Reuse Efforts

Repairing disrupted care processes as sources of stability, learning and change in a Finnish hospital: An activity-theoretical study.

In high-income countries, it is estimated that one in every 10 patients is harmed while receiving hospital care; 50% of these are preventable. The aim of this study was to deepen our understanding of disruptions of care processes and how the repairing of disruptions can be sources of stability, learning and change in complex health care settings. The organisational interactions associated with disruptions in the standard care processes of 15 surgical patients were followed in a public sector hospital in Finland. The patients and medical professionals were interviewed in situ during the observation of the care processes. An activity-theoretically informed methodological framework was used to identify and analyse disruptions and the associated repair efforts and repair solutions. Disruptions were frequent and found in all 15 care processes. These related to (1) the patient's worsening physiological state, (2) the equipment used in surgical care, (3) the information flow, (4) delays in the care process and (5) the unclear division of labour within the team. The actors carried out three types of repair efforts (technical, cognitive-emotional and extended collaborative) to overcome the disrupted processes, which usually led to repair solutions that restored stability. The different repair efforts required different kinds of collaboration and learning. Extended collaborative repair was most demanding, providing challenges and opportunities for practice change and expansive learning.

Autonomous damage segmentation of post-fire reinforced concrete structural components

The existing methods for detecting fire damage in reinforced concrete (RC) structures rely heavily on manual visual inspection, which is time-consuming and challenging due to poor visibility and safety concerns. To address this, an automatic damage segmentation network at the pixel level is proposed. Using a dataset of 403 high-resolution images of fire-damaged RC components, the study employed MobileNetv3 as the encoder in a UNet model, enhanced with three average pooling layers. Among four submodules tested, SPPF demonstrated the best performance in the MB-SPPF-UNet network, achieving an MIoU of 82%, a 7% improvement over the Baseline-UNet. While OCRNet showed the best performance among ten state-of-the-art networks, its MIoU was still 8% lower than MB-SPPF-UNet. The generalization capabilities of the networks were evaluated using four crack datasets, with MB-SPPF-UNet showing a performance decrease of 5%-10%. However, this is acceptable given its primary application in segmenting post-fire concrete spalling and exposed reinforcement. The study aims to establish an efficient damage segmentation network using deep learning for quick assessment and reference in firefighting, rescue, and structural repair efforts.

A Cost-Effective Earthquake Disaster Assessment Model for Power Systems Based on Nighttime Light Information

The power system is one of the most important urban lifeline engineering systems. Identifying the damage to the power system is an important task in earthquake disaster assessments. Considering the importance of timeliness and accessibility, a hyperparameter optimization model is proposed to address the assessment of disaster losses in power systems on earthquakes. The power system vulnerability on earthquakes, PSVE, is assessed by the hyperparameter optimization model based on nighttime light information. Through the utilization of the computational resources provided by Google Earth Engine, the accuracy of the baseline model has been significantly improved to 87.9%; meanwhile, the cost-effectiveness in the evaluation process is maintained. The PSVE-based damage evaluation has the potential to aid in assessing earthquake damage to cities’ energy supply, power infrastructure, and lighting. Furthermore, the PSVE-based damage evaluation can provide valuable guidance for prioritizing and efficiently allocating resources for rapid repair and reconstruction efforts.

Lived experiences of Sri Lankan Tamil refugees in the UK: Migration and identity.

Sri Lankan Tamil refugees (SLTRs) have lived in the United Kingdom in substantial numbers for about three decades. However, they remain under-represented in academic and public discourse, and little is known about their migration experiences. This study examined first-hand accounts of such experiences, with special attention paid to identity and acculturation. Data were collected through four semi-structured interviews and analysed using interpretative phenomenological analysis (IPA). The results suggest that SLTRs' experience of conflict as an imposed life disruption continues to shape their adaptation, identity, and meaning-making ("Afflicted life"). Changing social identities mediate protection from, as well as risk of, trauma. SLTRs try to remedy the socio-economic and emotional losses suffered in the conflict, but achieve only a partial compensation. Consequently their repair efforts are a source not only of positive emotions but also of dissatisfaction ("Living past"). Finally, participants' sense of belonging and quest for home represent a challenging socio-emotional process in which they continue to engage even decades after migration ("Continuing quest for home"). This nuanced analysis of how the past continues to shape lived experience, contributes to the under-developed literature on qualitative psychological investigations of acculturation, research on forced migration, and the establishment of IPA in social psychology.

Exploring Supply Chain Disruptions at Walmart and the Boeing Company: When Saying “I’m Sorry” Is Not Enough”

This chapter explored the salient themes emerging as disruptions in the supply chain management at Walmart and the Boeing Company. It examined how both companies have responded to highly publicized disruptions in their supply chains, compromising customer satisfaction. Thousands of complaints against Walmart could be classified as: 1) Profiling (allegations of theft, race-based refusal of services, locking up goods); 2) Damaged goods; 3) Billing issues; 4) Delivery problems / Missing goods; 5) Communication barriers due to delayed, inconsistent, or incoherent messages; and 6) Employee complaints. Walmart’s typical response was an apology, a courtesy gift card, or to settle lawsuits. In the case of Boeing, investigations unveiled deception in business practices which resulted in inaccurate information being shared with regulating agencies as well as poor coordination of repair efforts, lack of documentation, ineffective communication, or neglect. Boeing’s problems led to catastrophic failures, for which Boeing has paid billions of dollars in damages and agreed to strict monitoring.

Renovation of the retention bunker lining on the example of the Mysłowice-Wesoła coal mine

The scientific article focuses on the analysis, renovation and modernization of the retention bunker No. 4 at the 465 m level in the Mysłowice-Wesoła coal mine complex. This bunker, a key part of the mining infrastructure, has an important function in the coal mining process, accumulating output before its further transport to the surface. During a regular inspection of the bunker, significant damage was found in the form of defects on the lining, caused by the impacts of the output flow. Repair efforts required a comprehensive approach, including the use of rope access techniques (industrial mountaineering) for precise damage assessment and repair. The research and repair work included the installation of rappelling stations, rope access rappelling, non-destructive testing, bolting, concrete encasement of the damage, and other innovative repair techniques. Careful adaptation of operations to underground conditions and precise planning of logistics and repair work were key to successfully restoring the bunker’s stability and functionality. An analysis of the renovation process reveals the comprehensiveness of the measures, covering both the north and south sides of the bunker. The use of bolting, steel mesh, and innovative approaches allowed for the successful repair of damage. The ultimate success of the renovation helped minimize the risk of further damage and restore the integrity of the No. 4 retention bunker. The article also highlights the often-downplayed role of bunker in mining infrastructure, arguing that despite having ready-made longwalls and roadways, the absence of a functional bunker makes a coal mining system incomplete.

Social media trolls as faux third-party agents of image repair: China’s disinformation campaign and statecraft in the Daryl Morey affair

ABSTRACT This study examines trolls affiliated with the People’s Republic of China (PRC) that targeted conversations around former Houston Rockets’ General Manager Daryl Morey, who tweeted support for Hong Kong protests and apologized in October 2019. Researchers coded 5,800 tweets to inform a machine-learning approach examining a corpus of over 163,000 tweets. Findings suggested that a PRC disinformation campaign was organized as a faux third-party image repair effort that sought to force an apology, deprive Morey of support, discourage future image threats, and shape public understanding. This effort used provocation, bolstering, barnacle, redirection, and attack the accuser. While apologies are generally held to be restorative in image repair and statecraft literature, Morey’s apology was a turning point that rallied opposition to the PRC and support for democracy and Morey. Implications include insight into the prevalence, strategies, and efficacy of PRC trolls, as well as recommendations for social media practitioners.

Proactive exfiltration severity management in sewer networks: A hyperparameter optimization for two-tiered machine learning prediction

Effective management of aging sewer pipelines requires accurate analysis of sewer pipeline exfiltration. Previous research studies have not paid attention to proposing an index to represent the exfiltration severity. To this end, this study introduces a novel Exfiltration Severity Index (ESI) by considering the frequency and severity of defects captured from Closed-Circuit Television (CCTV) reports. To address the need for a proactive tool that automates the exfiltration estimation and reduces dependence on CCTV reports, we leverage Machine Learning (ML) and Deep Learning (DL) models to predict sewer exfiltration occurrence and severity. In this regard, our proposed methodology comprises a series of steps that start by computing the ESI of pipeline segments considering the frequency, type, and severity of defects. After that, physical, environmental, and climatic factors influencing pipeline exfiltration are gathered and aggregated to build predictive models. We compare the performance of six ML models and two DL models, developed in two tiers to predict exfiltration occurrence and severity, respectively. The hyperparameters of each model are optimized using GridSearchCV to enhance prediction accuracy. Among the eight algorithms, the light gradient-boosting machine performs best, with 71% and 85% accuracy in the first and second tiers, respectively. Furthermore, our study investigates the influence of various factors on pipeline exfiltration and reveals that pipe diameter and population have the most significant impact on exfiltration occurrence and severity. Our method provides a valuable tool for managing sewer pipeline exfiltration and can be utilized to prioritize sewer network maintenance and repair efforts.

Revitalizing Pennsylvania's infrastructure: local public-private partnerships as the key to bridging the gap with a case study for local public-private transportation partnerships

Pennsylvania's aging transportation infrastructure has long been a cause for concern, with bridges, highways, and other vital components approaching the end of their serviceable lifespans. The state's infrastructure, one of the nation's oldest, is in dire need of repair and replacement, particularly in the case of bridges, many of which are over 50 years old and in poor condition. However, the financial investment required for this task is substantial, posing challenges within the state's budget constraints. To address this infrastructure crisis, an amendment to Pennsylvania's existing legislation, Act 88, is proposed. This amendment would permit local governments to engage in Public-Private Transportation Partnerships (P3s), leveraging private sector financial resources and expertise. Such local government Transportation P3s have the potential to alleviate the state's infrastructure and financial woes. Currently, Act 88 prohibits municipal governments from participating in these partnerships, hindering the Commonwealth's ability to secure private funding and expertise when public resources are limited. This study aims to address the structural deficiencies in Pennsylvania's transportation infrastructure and its $2.2 billion financial deficit, which hampers repair and improvement efforts. The study employs a descriptive, multi-case analysis to investigate how an amendment to Act 88 could enable local municipalities to engage in P3 transportation projects. These partnerships could provide solutions to budgetary and funding challenges, particularly in the context of locally owned bridge repair and replacement projects. The selected cases are evaluated against various performance criteria, such as value, pricing, budgeting, public accountability, regulatory control, and capacity, offering a comprehensive assessment of the potential benefits of local government participation in P3 transportation projects.

Penyebab Menurunnya Kinerja Mesin Pendingin di MV. Vancouver

A cooling machine or commonly called an Air Conditioner (AC) is a system of auxiliary machines that can create cool temperatures in certain rooms so that workers on ships feel comfortable. Cooling machines are used to regulate and manage air quality which are includes air circulation, air conditioning, air hygiene standards and air purification. There are several main components in the cooling machine, namely the Compressor, Condenser, Expansion Valve, Evaporator. Each of these important components has different functions and ways of operating, but are connected to each other so that the cooling machine can function. The purpose of the author making this final project is to determine the performance of the cooling system, factors that reduce the performance of the cooling machine, as well as repair and maintenance efforts so that the cooling machine can return to normal operation. This research was conducted on the MV. Vancouver by using a qualitative approach. The author conducted direct observations as well as in-depth interviews and documentation studies when conducting research. The results of the author's research found that the main factor causing the decline in the performance of the cooling machine was the appearance of sparks on the evaporator and coil pipe caused by interference with the filter dryer so that it inhibits heat absorption and the evaporator cannot evaporate the refrigerant optimally. The impact caused by the less than optimal operation of the cooling machine in the Engine Room Workshop MV. Vancouver was an unstable room temperature making the crew feel uncomfortable while working or resting.

Knowledge-Intensive Diagnostics Using Case-Based Reasoning and Synthetic Case Generation

Due to commercial pressures, North-American printed circuit-board assembly manufacturers have had to reposition themselves in the more difficult market segment of lower volume, higher complexity products, also called High-Mix, Low-Volume (HMLV). The high per-unit costs of these products justify substantial diagnosis and repair efforts when defects are detected in production. Although automated diagnostics is desirable, the low production volumes impose severe limits on available data. We propose a novel approach based on knowledge modeling and casebased reasoning for automated diagnosis of assembled printed circuit boards in an HMLV production environment. Our hybrid approach can overcome the knowledge-acquisition bottleneck even in a data-poor environment. The proposed approach does not require contributions from product designers or experts and is designed to operate using only information available during manufacturing. Our test results show that our diagnostic system can detect, locate and classify all single faults and multiple faults affecting up to three neighboring nodes with a better success rate than the reference commercial tool. Moreover, case base data, including board layout information and user feedback from previous repairs, is used to feed a recommender system to provide repair suggestions. A production and repair data simulator is described and used to evaluate the recommender system’s effectiveness.

EVOLUTION OF RECORDING METHODS: THE AACHEN CATHEDRAL WORLD HERITAGE SITE DOCUMENTATION PROJECT

Abstract. Modern terrestrial laser scanners and photogrammetric imaging systems can provide highly accurate and objective as-built records of existing architectural, engineering, and industrial sites. This comprehensive digital recording benefits culturally significant places like heritage buildings, monuments, and other vital structures. The collected data can be instrumental in various ways, including aiding in conservation, management, monitoring and repair efforts and serving as an educational resource for scholars and the general public. These technical capabilities are especially well-suited for architecturally complex, ornate buildings like the Aachen Cathedral UNESCO World Heritage site. This paper describes the recent recording efforts at the Aachen Cathedral and is a comparative study of the previous documentation work done at the Cologne Cathedral.The 3D documentation of the Aachen Cathedral UNESCO World Heritage Site is an ongoing collaborative project between the Sapienza Università di Roma, Rome, Italy, Robert Gordon University, Aberdeen, Scotland, and in partnership with RWTH Aachen University, and the Dombauhütte Aachen.

Prediction of Water Leakage in Pipeline Networks Using Graph Convolutional Network Method

This study aims to predict leaks in water-carrying pipelines by monitoring pressure drops. Timely detection of leaks is crucial for prompt intervention and repair efforts. In this research, we represent the network structure of pipelines using graph representations. Consequently, we propose a machine learning model called Graph Convolutional Neural Network (GCN) that leverages graph-type data structures for leak prediction. Conventional machine learning models often overlook the dependencies between nodes and edges in graph structures, which are critical in complex systems like pipelines. GCN offers an advantage in capturing the intricate relationships among connections in pipelines. To assess the predictive performance of our proposed GCN model, we compare it against the Support Vector Machine (SVM) model, a widely used traditional machine learning approach. In this study, we conducted experimental studies to collect the required pressure and flow data to train the GCN and SVM models. The obtained results were visualized and analyzed to evaluate their respective performances. The GCN model achieved a performance rate of 94%, while the SVM model achieved 87%. These results demonstrated the potential of the GCN model in accurately detecting water leaks in pipeline systems. The findings hold significant implications for water resource management and environmental protection. The knowledge acquired from this study can serve as a foundation for predicting leaks in pipelines that transport gas and oil.

Studi Analisa Kerusakan Jalan Menggunakan Metode Bina Marga dan Pavement Conditian Index (Studi Kasus : Ruas Jalan Napal Peranap)

Assessment of road surface conditions is a form of road maintenance and repair efforts. The two methods used in assessing road conditions are the Bina Marga method and the PCI (Pavement Condition Index) method. This study aims to compare the condition values ​​of the Peranap Napal road sections based on the two methods. The types of damage that can be found on the Napal Peranap road section are 7 types of damage consisting of: crocodile skin cracks (1.66%), elongated cracks (0.16%), edge cracks (0.08%), road joint cracks (0.01%), fillings (0.74%), holes (0.02%) and grain discharge (3.62%) with a total damage area of ​​2644.02 m2 or (6.30%) of 42000 m2 the area of ​​the Peranap Napal road studied. The Bina Marga method obtained a Priority Order (UP) value of 4 (included in the periodic maintenance program), while the PCI method obtained a damage level value of 66.16 (in fair condition with yellow color). The results of these two methods turned out to produce relatively the same assessment, namely the condition of the road section still required maintenance and repair, including: providing additional layers, making and repairing drainage, widening and compacting the shoulders, filling gaps with a mixture of asphalt and sand, and dismantling the pavement layer. and then re-coated with the same material.
 

Urgensi Psikologi Kriminil Dalam Penanganan Kejahatan Jalanan

Introduction: Criminal psychology investigates individual behavior, particularly the causes of criminal behavior. 
 Purposes of the Research: Analyze and explain the Urgency of Criminal Psychology in handling street crime. 
 Methods of the Research: This study uses the Juridical Empirical Legal Research method. Sources of data used are primary data, secondary data, and tertiary data by collecting data through filling out questionnaires, interview methods and literature study and then analyzed based on the theoretical concept approach. 
 Results Originality of the Research: The findings of this study demonstrate that by understanding the life of criminal psychology, crime as human behavior can be avoided because criminal psychology is the primary psychological basis for criminals, the causes of crime, and prevention, both preventive and repressive as repair or healing efforts. By taking into account psychological and personal factors as well as the reasons people commit crimes, this can also be strengthened by minimizing and accommodating the factors that lead to someone committing street crimes in order to meet limited needs.

From Catch-and-Harvest to Catch-and-Release: Trout Unlimited and repair-focused deinstitutionalization

Increasingly we are faced with broad societal challenges that encourage us to rethink existing institutions. Yet many people also want to preserve institutions they cherish. This tension points to the need for change that can erode or discontinue unsustainable or problematic aspects of institutions while also maintaining what is sacred and valued. In this paper we ask how can organizations deinstitutionalize taken-for-granted practices while also preserving the institution? We answer this question by exploring how Trout Unlimited deployed visual and discursive tactics to push out unsustainable catch-and-harvest fly fishing practices and insert new catch-and-release practices. Our primary theoretical contribution is a model of repair-focused deinstitutionalization, illustrating how custodians utilize three forms of work to respond to threats—mending, caring, and restoring—all with an eye on deinstitutionalization via repair rather than disruption. Importantly, we show how the construct of repair is multipurpose, not limited to maintenance strategies, but can also be a catalyst for change. In addition, we extend research on deinstitutionalization by presenting a multimodal approach that goes beyond discourse, with particular attention to visuality and show how different modalities present different affordances in longer-term repair efforts.