Disaster Emergency Research Articles

Assessing dynamic congestion risks of flood-disrupted transportation network systems through time-variant topological analysis and traffic demand dynamics

Amid escalating climate change and extreme weather events, urban road networks face increasingly severe flood risks. Current congestion risk analyses on transportation systems, largely grounded in static network characteristics, fall short of capturing the dynamic impacts of evolving flood events on network topology. A notable gap in integrating dynamic flood models with detailed temporal network property measurements impedes a deep understanding of the cascading mechanisms of traffic disruptions caused by floods. The research introduces an innovative framework for assessing dynamic congestion risks within transportation networks under flooding conditions. This framework comprises four distinct layers. The Data and Context layer collates and contextualizes primary data, encompassing road networks, commuter behaviour patterns and hydrological risks. This layer establishes a baseline for understanding the structure of the transportation system and its environmental vulnerabilities. The Computation layer focuses on calculating context-based betweenness centrality values to capture the importance of road segments within the network, setting a static indicator. Concurrently, this layer examines the dynamic exposure flag under flood conditions, providing a dynamic indicator that accounts for the fluctuating susceptibility of road networks to flooding. The Integration layer bridges the topological analysis and the functional performance of the transportation system. It creates a dynamic series that describes the topological properties of the network, reflecting its vulnerability to disruptions. Simultaneously, the time-variant traffic demand series are tracked to capture the system’s operational response to flood-induced conditions. Finally, the Evaluation layer synthesizes data and analyses from the previous modules. This synthesis involves a novel dual assessment: generating a time-variant vulnerability index series and evaluating the time-variant congestion risk map on the transportation system under flooding. The London case study is developed based on this proposed framework to identify critical areas of congestion risks. This study offers valuable data-driven insights for urban planning and disaster management, aiding policymakers, planners and emergency teams to enhance urban resilience against flood-induced transport disruptions.

Strategi Pemerintah dalam Mitigasi Banjir Kecamatan Dayeuhkolot

Flooding is a disaster that is commonly accepted to occur in Dayeuhkolot due to the frequent floods in that area. The floods are caused by the rapid metropolitan development of the basin in the city of Bandung. This change is evident from the increase in building density and the expansion of impermeable areas, the disposal of various types of waste into rivers causing significant sedimentation in the Citarum River, and the exploitation of groundwater, leading to a yearly decline in land surface. The presence of Bandung Regency Regional Regulation No. 02 of 2013 as an effort in disaster management is expected to be a solution to the flood problems in Bandung Regency. However, since the regulation was enacted 6 years ago, floods still frequently occur to this day. This situation arises due to poor communication between the community and policymakers, as well as inadequate resources for the implementation of the regulation. The research method used is descriptive with a qualitative approach. The researcher employs a qualitative descriptive approach, focusing on the problems and understanding them by gathering information from sources. Based on the discussion results, the implementation of the Flood Disaster Mitigation Policy by BPBD Dayeuhkolot Village, Bandung Regency has not been maximized, and there are still problems and obstacles faced in its execution. This is indicated by issues found in Flood Disaster Prevention, Flood Disaster Emergency Response, and Flood Disaster Rehabilitation and Reconstruction.

Automatic Extraction and Cluster Analysis of Natural Disaster Metadata Based on the Unified Metadata Framework

The development of information technology has led to massive, multidimensional, and heterogeneously sourced disaster data. However, there’s currently no universal metadata standard for managing natural disasters. Common pre-training models for information extraction requiring extensive training data show somewhat limited effectiveness, with limited annotated resources. This study establishes a unified natural disaster metadata standard, utilizes self-trained universal information extraction (UIE) models and Python libraries to extract metadata stored in both structured and unstructured forms, and analyzes the results using the Word2vec-Kmeans cluster algorithm. The results show that (1) the self-trained UIE model, with a learning rate of 3 × 10−4 and a batch_size of 32, significantly improves extraction results for various natural disasters by over 50%. Our optimized UIE model outperforms many other extraction methods in terms of precision, recall, and F1 scores. (2) The quality assessments of consistency, completeness, and accuracy for ten tables all exceed 0.80, with variances between the three dimensions being 0.04, 0.03, and 0.05. The overall evaluation of data items of tables also exceeds 0.80, consistent with the results at the table level. The metadata model framework constructed in this study demonstrates high-quality stability. (3) Taking the flood dataset as an example, clustering reveals five main themes with high similarity within clusters, and the differences between clusters are deemed significant relative to the differences within clusters at a significance level of 0.01. Overall, this experiment supports effective sharing of disaster data resources and enhances natural disaster emergency response efficiency.

Experimental study on the impact of the second dam break heights on the evolution of two-dam failure floods

Dam-break floods, especially cascade dam-break floods, could be catastrophic and warranted further investigation to enhance understanding. This paper systematically investigated the impact of break heights of the second dam on the evolution of two-dam failure floods, comparing it with single-dam outburst floods. The experiments were carried out in an inclined rectangular flume with LiDAR technology to capture the free surface. Results indicated that a computational model for the moving hydraulic jump was developed, showing good agreement with experimental results with a relative error of about 10%. The wave height of dam-break flood increased with the increase of evolution distance. The wave front celerity (for dam spacing: L/H = 26) after the flood passed the second dam increased with the break heights. The peak discharges at the second dam site and the peak flow depth (for longitudinal coordinate: x/H = 59) both showed a linear relationship with the initial flow depth in reservoir and the break heights. An increase in the initial flow depth in reservoir or the break heights led to an increase in peak discharges and peak flow depth. This study offered constructive guidance for the risk assessment of multi-dam failure floods and disaster emergency management.

Does Expenditure Structure Affect Rural Poverty Alleviation in Indonesia? The Role of Village Fund Management

This study aimed to explore public financial management at the village level and identify the types of village fund expenditures that had the most significant relationship in reducing short-term rural poverty in Indonesia. Using the 2019-2021 panel data and the fixed effect method, the authors analyzed the relationship between the five types of village fund spending on poverty in 49,192 village units. The estimation results showed that spending merely on village development negatively and significantly affected the number of low-income rural families in Indonesia. Meanwhile, spending on disaster management, emergencies, and village urgency indicated a positive and significant relationship with the number of low-income families in the village. It implies that the government should focus on spending programs and activities that create job opportunities and increase income for rural poverty reduction. The government also needs to improve data related to uniformity and validity in measuring, recording, and reporting data in ministries, institutions, and regional governments. Thus, in allocating village funds, the government must expand the formula allocation so that village funds can significantly reduce rural poverty.

DISASTER EMERGENCY TRAINING TO INCREASE MIGRANT CAPACITY IN SIDOARJO

Migrants seeking asylum in Indonesia are a vulnerable group who need attention and training regarding disaster emergencies. This is because Indonesia is a maritime country which geographically and geologically allows natural disasters to occur. Although aid supplies are very important in emergency situations, preventive measures and preparedness are also equally important so it is necessary to provide health services in the form of expertise in disaster emergencies for migrants. Disaster emergency management efforts are a system that cannot be divided and the quality of life of patients after a disaster injury depends on pre-hospital stage treatment. The aim of this activity is to increase understanding and empowerment by provide outreach and training for migrants living in Sidoarjo. Before counseling, a 10 minutes pre-test was conducted for the participants. followed by a counseling session, training and discussion session for approximately 60 minutes, and after that a post-test was carried out for 10 minutes. Next, the results of the pre-test and post-test are assessed and data processing and evaluation are carried out. The results of the Paired T-tests show a p-value of 0.00 so the sig value is <0.05 which indicates there is a significant difference regarding the pre-test and post-test data. This can be interpreted as an increase in insight, understanding, empowerment, as well as a change in attitude towards migrants after attending the counseling session. Community service activities regarding disaster emergencies for migrants living in Sidoarjo have had a significant impact in the form of increasing insight and empowerment regarding the topic of disaster emergencies.

Landslide extraction from aerial imagery considering context association characteristics

Accurate extraction of landslide information is crucial for timely disaster emergency response, yet this process faces significant challenges due to the interference of bare land and vegetation cover as well as the variability in landslide scales. Furthermore, the scarcity of comprehensive landslide datasets has hindered the in-depth exploration of deep learning techniques in this domain. Addressing these issues, this study introduces a novel framework for landslide extraction that leverages the characteristics of context association. In the encoding phase, a two-branch multiscale context feature extraction module (TMCFM) is established, which captures the contextual relationships across different scales through an attention mechanism while concurrently extracting context information within the same scale. To refine the interplay between features at different levels, self-attention is utilized, allowing for the effective fusion of context information at both ends of the same scale through feature fusion exploiting static and dynamic context feature modules (FSDC). Building upon this, this study develops a deeply supervised classifier (DSC) that enhances the network’s discriminative capabilities in the prediction phase via six auxiliary branches. Additionally, this study contributes a new aerial image dataset for landslide extraction created through meticulous visual interpretation. The proposed method is thoroughly compared with 17 contemporary deep learning methods, demonstrating an increase in the intersection-over-union (IoU) metric of 0.92%-16.94% over these methods. The robustness and superiority of the proposed approach are further validated through various discussions and analyses.

Health consequences of disasters: Advancing disaster data science.

Understanding the health effects of disasters is critical for effective preparedness, response, recovery, and mitigation. However, research is negatively impacted by both the limited availability of disaster data and the difficulty of identifying and utilizing disaster-specific and health data sources relevant to disaster research and management. In response to numerous requests from disaster researchers, emergency managers, and operational response organizations, 73 distinct data sources at the intersection of disasters and health were compiled and categorized. These data sources generally cover the entire United States, address both disasters and health, and are available to researchers at little or no cost. Data sources are described and characterized to support improved research and guide evidence-based decision making. Current gaps and potential solutions are presented to improve disaster data collection, utilization, and dissemination.

Evaluation and Spatial–Temporal Pattern Evolution of Synergy Degree of Emergency Management for Urban Flood Disasters from the Perspective of Sustainable Development—The Case of Henan, China

The management of urban flood disasters is a systematic engineering project that requires a great amount of manpower, material resources, and financial resources, and the interaction and coordination degrees of various elements in the system deeply affect the efficiency of the final governance. According to the theories of synergy, composite systems, and sustainable development, this research first established an evaluation index system to determine the synergy degree of urban flood disaster emergency management from the four dimensions of prevention and preparation, monitoring and early warning, response and rescue, and recovery and reconstruction. Then, the synergy degree was explored by using the developed composite system synergy degree model on the basis of the panel data of 18 prefecture-level cities in Henan Province from 2013 to 2021, and synergy level change characteristics were analyzed from the perspectives of time and space. Finally, the obstacle degree model was applied to explore the obstacle factors affecting synergy degree development. The results showed that the overall level of the urban flood disaster emergency management coordination degree in Henan Province was relatively low, and there were significant differences in synergy among cities. Among them, 12 cities presented mild synergy, and 6 cities showed mild nonsynergy. The spatial correlation of the synergy degree was not stable, which revealed a lack of mature coordination mechanisms for flood disaster emergency management among cities. The analysis of obstacle factors showed that recovery and reconstruction subsystems were the main obstacle systems that affected the synergy degree.

The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

How to improve smart emergency preparedness for natural disasters? ---- Evidence from the experience of ten pilot provinces in China for smart emergency

The natural disasters faced by modern urban systems are complex, with multiple disaster-causing factors coexisting and secondary disasters occurring concurrently. With emergency management moving towards smart, natural disaster response has shifted from emergency-centered response to pre-disaster prevention. How to improve the government's natural disaster emergency preparedness has become an important issue that needs to be addressed. Based on the TOE (Technology-Organization-Environment) framework, the fsQCA method was used to explore the improvement path of emergency preparedness capacity of 10 pilot units in China to deal with natural disasters in 2020. Analyze the group effects and interrelationships of technology level, simultaneous supporting facilities, organizational construction, financial investment, external pressure, and social repercussions. The results show that: there exist four conditional groupings of high emergency preparedness in two modes. Two modes are organization-environment dual-drive and technology-organization-environment triple-drive, which have multiple concurrencies and follow the principle of consistent results. There are substitution effects in the conditional groupings of high emergency preparedness. There are causal asymmetries in the conditional groupings of high emergency preparedness and non-high emergency preparedness. This study aims to explore the smart emergency preparedness of ten pilot and to provide ideas for the overall development of “smart emergency response” and the improvement of emergency preparedness for natural disasters.

Research on an Object-Oriented Intelligent Extraction Method For Landslide

Landslides are the most important type of geological disaster development in China. Heavy rainfall and earthquakes often cause mass landslides. Rapid extraction of postdisaster landslide information is an important method for realizing scientific and efficient emergency investigations and can provide a key basis for disaster assessment and emergency response decision-making. Rapid extraction of postdisaster landslide information is an important method for realizing scientific and efficient emergency investigations and can provide a key basis for disaster assessment and emergency response decision-making. Using Fujian as an example, based on basic topography, geology and other data, the multiscale segmentation method is used for image segmentation, and different features are selected. The applicability of the rule-based extraction method and support vector machine, random forest and other machine learning methods. The applicability of the rule-based extraction method and support vector machine, random forest and other machine learning methods in small-scale landslide extraction in the study area is compared. The results showed that the extraction accuracy of the rule-based extraction method was 90.86%, the extraction accuracy of the support vector machine was 65.95%, and the extraction accuracy of the random forest was 93.43%. A comparison of the three methods revealed that the random forest method was most suitable for landslide extraction in the study area.

5G-enabled smart hospitals: Innovations in patient care and facility management.

Smart hospitals represent the pinnacle of healthcare innovation, leveraging cutting-edge technologies to transform patient care and facility management. This article addresses the pressing need for effective implementation of 5G technology in smart hospitals, aiming to enhance connectivity, improve patient outcomes, and drive operational efficiency. The methodology employed involves a comprehensive review of existing literature, case studies, and expert insights to analyze the impact of 5G on various aspects of smart hospital operations. The article highlights the significance of 5G technology in enabling real-time data analytics, remote monitoring, and telemedicine, thus revolutionizing healthcare delivery. By providing high-speed, low-latency connectivity, 5G facilitates seamless communication and collaboration among healthcare providers, leading to more efficient diagnosis, treatment, and patient care. Additionally, the adoption of 5G enables smart hospitals to leverage artificial intelligence (AI)-based solutions for predictive analytics, personalized medicine and enhanced patient engagement. Furthermore, the article explores the potential of 5G-enabled smart hospitals in enhancing disaster preparedness and emergency response efforts. Case studies and examples demonstrate how 5G technology can improve situational awareness, coordinate resources, and deliver timely care during natural disasters and pandemics. Overall, this article underscores the transformative impact of 5G technology on smart hospitals and emphasizes the importance of embracing innovation to meet the evolving needs of patients and communities. By adopting 5G technology, smart hospitals can usher in a new era of healthcare delivery characterized by enhanced connectivity, improved patient outcomes, and unparalleled efficiency.

Developing emotional preparedness and mental resilience through high-fidelity simulation: a ‘bridge too far’ for institutions teaching major trauma management and mass-casualty medicine?

BackgroundClinical acumen represents only part of being adequately equipped to attend a major incident. The emotive sights, sounds and smells of these dynamic environments are all-encompassing experiences, and responders must also be armed with the emotional preparedness to perform their clinical or managerial duties effectively, as well as the mental resilience to facilitate professional continuance. Despite this, limited training and a sparsity of evidence exists to guide developments within this domain. Historically, major incident training has focused on clinical theory acquisition, but irrespective of how comprehensive the learning materials, they are of little consequence if tandem steps to cultivate mental resilience and emotional preparedness are absent. High-Fidelity Simulation (HFS) has a growing reputation as an effective means of bridging important gaps between theory and practice. This pilot study aimed to measure student’s self-reported perception of their readiness to respond to a major incident following a large-scale HFS.MethodsQuantitative data was obtained from a sample of 108 students undertaking paramedic science, physician associate studies and adult nursing degree programmes. A bespoke questionnaire was developed to measure self-reported clinical acumen, mental and emotional preparedness.Results91% of students agreed the combination of theoretical training and HFS provided made them feel clinically prepared to attend a real major incident; 86% agreed this experience had developed their mental resilience and 90% agreed that they felt emotionally prepared to attend a major incident.ConclusionWithin this pilot study, the blend of theoretical training and HFS contributed to self-reported clinical acumen, mental and emotional preparation, in learners training to work in disaster environments or emergency medicine settings.

Examining organizational collaboration and resource flows of disaster response system based on a time-dynamic perspective

Increasing the efficiency of organizational collaboration and resource flows is key to improving the emergency response performance. Given that previous studies did not consider the interactions and dynamic characteristics of emergency response organizations and resources, this study divided the emergency response into four stages and analyzed the relationship between organizational collaboration and resource flows by combining the network attributes of emergency response goals at each stage. The 2022 Luding earthquake was selected as a case study, and a multisource mixed data method was used to identify 381 organizations and 2496 organizational relationships. Using the social network analysis, the dynamic evolution characteristics of the emergency response networks (ERNs) and organization-resource two-mode networks were analyzed. The results showed that the ERNs generally exhibited the following trends: a more compact structure, balanced distribution, evolution from decentralization to centralization, and homogenous organizational collaboration. A correlation existed between changes in the organizational status and ERN attributes, with government organizations always at the core and non-profit organizations gradually rising. The evolution of the resources was closely related to the network attributes and organizational relationships. We identified the strengths of China's current emergency management system, such as enhanced performance, improved information infrastructure, and emphasis on the engagement of the military and social organizations. We recommend the following for future practice: enhancing the information technology system, establishing a mechanism for the participation of social forces, and strengthening the management of emergency stockpiles. These will help improve the disaster emergency management performance and reduce casualties and losses.

Multi-temporal Monitoring for Road Slope Collapse by Means of LUTAN-1 SAR Data and High Resolution Optical Data

Abstract. Collapse is one of the most destructive natural disaster, being sudden, frequent, and highly concealed, causing large-scale damage. On August 10, 2023, the slope of 108 national highway in Weinan, Shaanxi Province collapsed. The lower edge of the collapse slope body is the Luohe river, and the collapse body rushes into the river to form a barrier lake. Remote sensing technique can provide multiple dimensional information for disaster emergency and management. Lutan-1 SAR satellites are the first group L-band SAR constellation for multiple applications in China. Owing to the precise orbit control ability and high revisit characteristics for Lutan-1 SAR satellites, surface deformation monitoring with centimeter even millimeter accuracy may be achieved. Based on the multi-temporal pre-disaster and post-disaster Lutan-1 SAR data and high resolution optical data, the collapse information including the pre-disaster and post-disaster were extracted and analysed. From July 11 to 27, 2023, the pre-collapse deformation was obtained with the maximum value of 6 cm, and obvious deformation occurred before the collapse. Lutan-1 monitored results pre-collapse can provide certain information for disaster early identification. From July 27 to August 24, 2023, due to the serious incoherence caused by large deformation and ground changes, effective deformation information cannot be obtained based on the InSAR technique. In addition, the collapse information was clearly extracted by the high resolution optical data acquired pre-collapse and post collapse. After the collapse, significant deformation was extracted from August 24 to September 21 with the maximum value of 6 cm, indicating that obvious deformation still occurred over the collapse area. Through the analysis for the series results obtained by SAR and optical data, it is favourable for disaster emergency and management.

Unique Challenges for WHO Emergency Medical Teams in Ukraine: Field Assessment and Global Recommendations.

The purpose of World Health Organization (WHO) Emergency Medical Teams (EMT) is to provide timely, high-quality health services in the immediate aftermath of disasters and during disease outbreaks and other emergencies, including conflict and insecurity.The war in Ukraine has presented all health-care providers with many unique challenges. This assessment addresses the importance and the complexities of the global spread of the Emergency Medical Team system challenges to meet a wide variety of crises including war, those that are unique to this very complex crisis in Ukraine, and the essential role of educational initiatives, not only in professional development but also in teamwork and cultural integration.

A quantum group decision model for meteorological disaster emergency response based on D-S evidence theory and Choquet integral

In addressing complex and dynamic meteorological disaster decision-making environment, the traditional multi-attribute group decision-making domain model is often unable to effectively deal with the correlation between attributes and the mutual influence of group opinions. To overcome this challenge, this paper proposes a novel quantum framework for group decision-making, which is used to deal with the emergency situation of meteorological disaster. The model initially characterizes attribute correlations using 2-additive Choquet integrals and employs Dempster-Shafer evidence theory to both integrate information and ascertain attribute weights, and decision makers' weights are calculated based on grey relative correlation. On this basis, a quantum-like Bayesian network is developed to capture the interference among decision-makers' opinions. The alternatives are ranked by quantum probabilities computed based on Bayesian principle. Finally, a case study on meteorological disaster emergency scenario assessment is conducted to validate the proposed model's effectiveness and superiority. Additionally, its stability and practicality are confirmed through sensitivity analysis and comparative analysis.

Differences Between Discovery Learning And Case-Based Learning Using Animated Videos On Critical Thinking Skills In Start Triage Among Undergraduate Nursing Students

The 2004 disaster in Aceh underscores the need to strengthen disaster triage preparedness. Triage would be more accurate when considering human resource factors. Students are educated nurse candidates who will serve the community through their ability to think critically. Innovative learning opens opportunities to sharpen critical thinking in students' preparedness for emergency disaster situations. This study analyzes the differences between discovery learning and case-based learning on critical thinking in START Triage among undergraduate nursing students. This quasi-experimental study used a pre-test and post-test design involving 40 undergraduate nursing students selected through purposive sampling and divided into two groups: control and intervention. Data collection utilized the Critical Thinking Disposition Inventory questionnaire. The results of the Paired T-test revealed differences in critical thinking ability in START Triage before and after receiving discovery learning and case-based learning. Furthermore, the independent T-test results indicated a significant influence of case-based and discovery learning on critical thinking ability in START Triage. Case-based learning utilizing animated videos significantly impacted nursing students' critical thinking ability in START Triage. Using animated videos as a basis for case-based learning provides a foundation for educational institutions to integrate into learning activities and disaster simulation exercises to enhance the quality of Triage services.

The implementation of FMEA for analyzing the risk of natural disaster logistic activities (case study: CSER project childfund international)

Indonesia is a country prone to natural disasters due to its location at the convergence of active tectonic plates. During natural disasters, logistics becomes crucial in emergency response. The aim of this study is to analyze the risks involved in logistics activities during natural disaster emergency response, focusing on the case of Central Sulawesi in 2018. The method used in this research is Failure Mode and Effect Analysis (FMEA). This research is descriptive-exploratory in nature based on interviews and questionnaire distribution. The results indicate eighteen risks occurring during emergency response logistics activities conducted by Childfund International. Calculation results show six critical risks in emergency response logistics activities, namely errors in planning required demand, expired goods, expiration dates too close, closure or absence of goods reception, prolonged accountability report preparation process, and incomplete accountability reports