Loss Of Human Life Research Articles

Cyberbullying: Between the Virtual and the Real

In modern society, the use of information communication technologies and digital services is intensifying. The post-Covid-19 era brings many changes in all social spheres and stands out for its unprecedented reliance on digital technologies. Younger generations, who have had intensive interaction with them since the earliest days, are finding their place in the cyber world and, in this way, are increasingly erasing the boundaries between the real and the digital. Vulnerability of human life is one of the characteristics of cyber security and as a very complex phenomenon that is fully justified to be studied in a multidisciplinary manner. Cyberbullying is a widespread phenomenon, most often expressed among the younger population, which can produce great negative consequences for an individual, and even the loss of human life. In addition to the fact that it is necessary to work on raising awareness and pointing out the unsafe cyberspace in which interaction and communication take place, it is necessary to give an adequate response from the aspect of education and information security culture in order to preserve what is most valuable. This paper aims to present and offer some answers to the mentioned phenomenon.

Recent seismicity in the Messina area, southern Italy, and comparison to the local geology and tectonics

We have investigated the seismicity occurred during 2000-2021 in the area of Messina, the town which suffered greatest loss of human lives over the territory devastated by the magnitude 7.1 earthquake of December 28, 1908. We have found that most of recent activity was located beneath the historical centre of the town, in and near the very peculiar sickle-shaped harbor zone which prompted the Greek colonizers in the VII Century B.C. to give Messina the old name of “Zancle”(“Sickle” in the ancient Greek language). Extracting from the whole dataset (consisting of hundred earthquakes of maximum magnitude 3.8) the data relative to a small sequence of 28 events concentrated in a few days at the end of 2013, and performing high-quality Bayesian hypocenter locations of these events, we have found very clear epi-hypocentral trends suitable for comparison with the local structural scenario. The joint analysis of seismic, geological and geomorphological data including morphobathymetric curves of the sea bottom in the study area, has brought us to propose that the small sequence in question (and probably most of activity recorded during the whole 22-years period) may have been generated by internal dynamics of a local horst/graben system, the position of which (i) appears to correspond to one of the minor horsts documented in the Messina Strait basin area and (ii) is very close to the upper edge of the 1908 earthquake blind source reported in the Database of Individual Seismogenic Sources of the Italian National Institute of Geophysics and Volcanology.

ANALYSIS OF MULTISTORY HIGH-RISE BUILDINGS IN HIGH SEISMIC ZONE

The effects of severe earthquakes on structures are often catastrophic, and thus it is important to consider them. It is especially the case of High Rise Buildings, where the seismic analysis plays a vital part in the structural analysis and in predicting the response of these structures under the seismic loads. The main aim of the optimal seismic design is to withstand seismic loads or at least behave predictably. Also, it allows for minimizing the loss of human lives and damage to infrastructures. In this paper, the attention is focused on the seismic zones of Japan. The study reveals the structural members and their load transfer mechanisms, the types of loads, and the corresponding behavior. The simplified manual calculations for the design of the structural members are presented and compared with more refined results obtained by using the software such as ETABS for the design of the super-and sub-structures.

Research on Landslide Detection in SAR Images Based on Multi-channel Change Detection Fusion

The landslides have resulted in the grave loss of human life and economic damage. Currently, emergency investigations predominantly employ manual field investigations and remote sensing interpretation. Traditional methods for assessing landslides are time-consuming and ineffective for widespread and numerous landslides. In response, many scholars have successfully employed optical remote sensing imagery to extract landslides. Nonetheless, landslide regions are typically accompanied by copious rainfall along with inclement weather conditions, such as cloudy or overcast skies, which limits the observation capabilities of optical remote sensing methods. At this juncture, the advantages of SAR systems that can penetrate clouds, rain, and fog, and work around the clock under any weather conditions are highlighted. This paper proposes a framework using multi-channel SAR images to extract landslides. The framework consists of fused multi-channel change detection images and traditional detection methods, resulting in effective landslide detection. The proposed method was verified in an experiment using landslide data triggered by an earthquake in Milin County, Tibet, on November 17, 2017.

Modeling roadway temperatures for wildfire evacuation and assessment of pavement damage

Wildfires can cause enormous loss of assets and human lives, and thus, timely evacuation from the wildfire zone is critical to saving lives. Understanding evacuation route conditions, including traffic and temperature data, is imperative for safe evacuation. This study modeled the temperature conditions of an evacuation roadway within a wildfire zone, using the 2018 Camp Fire in Butte County, California as an experiment. The numerical modeling program, FlamMap, was used in this study to analyze the temperature conditions of an evacuation route. The results show that the temperature of the evacuation roadway increased quickly in a matter of a few hours. The change of roadways temperatures ranged from 3°C to 297°C within five hours after ignition, depending on the specific location along the roadway and other conditions. The results from this study can be used to understand temperature conditions for effective evacuation and to estimate roadway damage as well.

Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies

Precipitation deficit conditions and temperature anomalies are responsible for the occurrence of various types of natural disasters that cause tremendous loss of human life and economy of the country. Out of all natural disasters, drought is one of the most recurring and complex phenomenons. Prediction of the onset of drought poses significant challenges to societies worldwide. Drought occurrences occur across the world due to a variety of hydro-meteorological causes and anomalies in sea surface temperature. This article aims to provide a comprehensive overview of the fundamental concepts and characteristics of drought, its complex nature, and the various factors that influence drought, drought indicators, and advanced drought prediction models. An extensive survey is presented in the different drought prediction models employed in the literature, ranging from statistical approaches to machine learning and deep learning models. It has been found that advanced techniques like machine learning and deep learning models outperform traditional models by improving drought prediction accuracy. This review article critically examines the advancements in technology that have facilitated improved drought prediction, identifies the key challenges and opportunities in the field of drought prediction, and identifies the key trends and topics that are likely to give new directions to the future of drought prediction research. It explores the integration of remote sensing data, meteorological observations, hydrological modeling, and climate indices for enhanced accuracy. Under the frequently changing climate conditions, this comprehensive review provides a valuable resource for researchers, practitioners, and policymakers engaged in drought prediction and management and fosters a deeper understanding of their capabilities and limitations. This article paves the way for more accurate and effective drought prediction strategies, contributing to improved resilience and sustainable development in drought-prone regions.

“When Everything was Forest”: Aikanã Histories and Environmental Destruction in Southern Amazonia

This article explores how the Aikanã, speakers of an isolate language, who live in the south-eastern Amazon, in the Brazilian State of Rondônia, make sense of the drastic transformations brought about by the colonisation of this region. Through an analysis of Aikanã narratives and life histories, the article highlights how Aikanã social memory gives meaning to experiences of contact, land loss and environmental destruction. It will contemplate the temporal markers, i.e. periodisations that occur in narratives and life histories and that delineate Aikanã historicity. These temporal markers refer predominantly to an experienced past between the beginning of the 20th century and the present, from a distant period of displacement from their traditional territory and the severe loss of human lives to the transformation of the forest into pastures for cattle and soy. In this vein, temporal markers are also anchored in space, unveiling sentiments of nostalgia and ecological grief for a past fertility of social life, interconnected with the fertility of the more-than-human world of the lost forest. Exploring Aikanã narrativity and its operation in the construction of social memory, the article aims to contribute to contemporary debates on Amazonian historicities, as well as to the theoretical and political role of Amazonian socialities in face of the current environmental crisis.

Studying the evolution of Uttarkashi cloudburst event from reanalysis datasets–A case study

In recent years, the frequency and severity of cloudburst considerably increased over southern rim of Himalayas due to hot climate that leads to loss of human lives and damage properties. The observed rainfall data shows that cloudburst events with heavy rainfall ∼ 100–200 mm/day are common over the Himalayan region during the summer monsoon period. It is very necessary to understand the mechanisms associated with such type of short span of high impact localised weather events over the regions where observations are limited. Therefore, one of best way to study the mechanism associated with the formation and development of cloudburst is using the available high resolution reanalysis datasets. An effort is made to understand the role of atmospheric conditions that control the evolution of cloudburst event by considering two reanalyses datasets such as high resolution IMDAA and ERA5 reanalyses. The present study analysed a cloudburst case that occurred on 3rd August 2012 at 10 pm with heavy rainfall of ∼ 100 mm in a very short span of time over the Uttarkashi district. Various dynamic and thermodynamic parameters are calculated from the two datasets with an aim of determining the best representation of severity of the cloudburst event. It is noticed that the evolution of dynamic and thermodynamic variables is well represented in the high resolution IMDAA dataset as compared to the ERA5 dataset. The amount and spatial distribution of rainfall from IMDAA reanalyses are well comparable with satellite estimated rainfall (GPM), having better correlation (0.60) with the observed rainfall as compared to the ERA5 (0.28). The rainfall time bias over the Uttarkashi district is larger in ERA5 reanalyses (∼ 5 h) than in the IMDAA (∼ 3 h). The ERA5 is not able to capture such type of localise high rainfall event due to its low resolution, compared to high resolution reanalyses (12 km) of IMDAA. The observations also indicate that the moisture flux from the Bay of Bengal (BoB) and Arabian Sea interacted with northwesterly dry air over Uttarkashi and the orographic uplifting resulted the cloudburst. Overall, results show that the eveolution and mechanism associated with the cloudburst is better represented in IMDAA than the ERA5. More cases are required to be studied to further support the findings of this study.

Aplicación de Redes Neuronales Artificiales a la modelación de lluvia-escorrentía en la cuenca del río Chancay Lambayeque

Between the months of December to April, regions of northern Peru, including Lambayeque, are affected by maximum extreme events, wreaking havoc on homes, flooding crop fields, collapsing hydraulic works, and the most irreparable loss of human lives. In this line, the objective of this research was to apply Artificial Neural Networks to rain-runoff modeling in a basin in northern Peru, namely, the Chancay Lambayeque river basin belonging to the Pacific slope. For this purpose, records of precipitation and flows of 30 years (hydrological normal) were collected from 12 hydrometeorological stations belonging to the basin and neighboring it. Thus, applying a model of Long and Short Term Memory Networks (LSTM) we proceeded to model the rain, seeking to follow the behavior of the flows observed in the Racarrumi hydrometric station, with 80% of the information the model was trained and with 20% it was validated. In short, it was obtained that in the modeling validation stage, the Nash coefficient was 0.93, corresponding to the qualifier "very good".

Identification of Reasons for Culpable Homicides and Attempted Murders: A Case Study for the Kelaniya Police Division

Culpable Homicides and attempted murders are ultimate crimes that could create ripple effects on a society which could go far beyond the original loss of human life. Owing to the unpredictable nature of such crimes that require complex investigations the objective of this study was to come up with an appropriate model to identify the reason for a culpable homicide or an attempted murder using a statistical approach. This study use data collected from 12 Police stations in Kelaniya Police Division relating to the incidents happened between 2010 and 2020. The Pearson Chi-square test was used in identifying the influential explanatory variables. Out of the 18 variables, 8 predictors including Weapon used, Relationship, Location, Civil Status of the perpetrator were statistically associated with the identified reasons at 5 % level of significance. Multinomial logistic regression followed by four data mining models including classification tree, support vector machine (SVM), k-nearest neighbour (KNN), and probabilistic neural network (PNN) were employed initially with a training and testing set which was randomly selected in the ratio 90:10. The 4 data mining models were then fitted separately by using the bagging technique. The accuracies were compared using the confusion matrixes and rates of misclassifications of the critical classes. Out of the fitted models, the highest accuracy of 93.75 % was shown by the PNN model with a spread of 0.6. The identified model can be used as a decision support tool by crime investigators and relevant authorities for wise decision making.

ACP-Dnnel: anti-coronavirus peptides' prediction based on deep neural network ensemble learning.

The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs' identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides' candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/ . The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew's correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides' prediction and it is available at http://150.158.148.228:5000/ .

Predicting Aging-Related Bugs Using Network Analysis on Aging-Related Dependency Networks

Software aging, a phenomenon that exhibits an increasing failure rate or progressive performance degradation in long-running software systems, has caused serious cost damage or even loss of human lives. To aid aging-related bug (ARB, whose activation can result in software aging) detection and removal before software release, ARB prediction was proposed. Based on the prediction results, software teams can allocate limited testing resources to ARB-prone modules. Previous research has proposed several methods for both within-project and cross-project ARB prediction. However, they are based on the same set of metrics focusing on the contents of a single module, and only six metrics are aging-related. In this paper, we develop aging-related network measures by constructing an aging-related dependency network to model the flow of aging-related information in the software. Our evaluation on three commonly used open-source projects reveals that aging-related network measures show an inconsistent association with ARB-proneness in three projects, and the performance of aging-related network measures varies under different ARB prediction settings.

INTERVENTION SOLUTIONS ON LANDSLIDES IN ROAD AREAS. CASE STUDY

In our country, there is a large variety of foundation soils, some of them having inferior characteristics to those necessary for the proper support of a communication path. This raises some of the most common problems in the field of road infrastructures, namely the sliding of slopes in the area related to road construction. This phenomenon appears more and more frequently, especially after some periods with accentuated atmospheric instability, sometimes leading to the partial inability to use the affected infrastructure sector. In some cases, the development of this phenomenon can lead to the traffic blockage and the isolation of the communities in the area, to the destruction of a well-defined road sector and even to the loss of human lives. In order to maintain the quality requirements imposed by the law and to limit the risk of reaching the limit state of normal operation, respectively of compromising the structure, the administrators and the staff who are directly involved in the maintenance and the expertise of the affected area, achieve direct examination works or investigation by means of specific observation and measurement. In the current practice, these works are part of the technical expertise of the affected area, being made by a specialized team, led by an authorized technical expert in the field.

Diabetes Mellitus Associated with Outcome of COVID-19 Patients.

Introduction: COVID-19 is a highly infectious disease spreading all over the world since its appearance causing enormous sufferings and loss of human life specially among those who have suffered from Diabetes Mellitus. Material & method: This retrospective cohort study was conducted among purposively selected 133 COVID-19 patients in which 39 were exposed to DM and 94 were not-exposed to DM who were admitted in CMH, Dhaka from July 2020 to June 2021 with the objective to find out the association of Diabetes Mellitus with the mortality and morbidity outcome of COVID-19 patients within 90 days of discharge from Hospital. The data were collected by telephonic interview and from medical record. Result: The study revealed that the mean age of the respondents were 48.01 years, 80.5% were male. It was seen from the study that among all the respondents 95.5% were survivor and 4.5% were non-survivor. Among the exposed 87.2% were survivor and among the non-exposed 89.9% were survivor. Among the exposed more than half (51.3%) were ⩾60 years age group,48.7% were military retired personnel, 61.5% had family income TK< 25,000. On 14th and 28th day of admission among exposed 94.9% and 30.8% and among non-exposed 83.0% and 21.3% were not cured respectively. Before attaining 28th day of admission among exposed 12.8% and among non-exposed 1.1% were non-survivor. Regarding ICU support among exposed 30.8% and among non-exposed 10.6% required ICU support during treatment, whereas among exposed 33.3% and among non-exposed 6.4% were critical patients. Among exposed 48.7% required oxygen, 17.9% needed ventilator support whereas among non-exposed 23.4% required oxygen and 4.3% needed ventilator support. Among exposed HTN, CHDs and CKDs were 61.5%,7.7% and 12.8% whereas among non-exposed it was 21.3%, 3.2% and 1.1% respectively. The risk exposure outcome among ⩾60 years age group were higher among exposed (RR:2.05, 95% CI 1.33-3.17). Morbidity outcome among exposed on 14th 28th and 90th day were higher (RR:2.05, 95% CI 1.33-3.173), (RR: 1.485, 95% CI 0.847-2.603) and (RR:1.190, 95% CI 0.371-3.820) respectively. Need of Oxygen were higher among exposed (RR:1.445, 95% CI 1.064-1.961). Association in regards to HTN, CHDs and CKD were also higher among exposed which were (RR:1.87, 95% CI 1.321-2.656), (RR: 1.433, 95% CI 0.639-3.214) and (RR:4.394, 95% CI 0.732-26.376) respectively. Conclusion: Health education measures should be taken to prevent and control the morbidity and mortality outcome of COVID-19 patients with special attention to those who are aged, and suffering from DM, HTN and CKD. However, to draw comprehensive inferences on this study, further research on representative sample size in this field is recommended.

REVIEWING THE LITERATURE ON DAM-BREAK INVESTIGATIONS AND FLOOD MAPPING THROUGH THE USE OF HYDRAULIC MODELS AND GIS

Nowadays, it is essential to store water to meet the demands of human consumption and to mitigate flooding, which reduces the risk of inundation downstream of the river. The failure of dams can result in disastrous consequences in the surrounding areas downstream. Causing loss of human lives and properties valuedfrom millions to billions. Therefore, it is crucial to conduct dam break studies to understand the behavior of floods generated from such incidents and to identify the areas that may become inundated. This information can be utilized to prepare emergency plans for the evacuation of residents and to minimize property damage. While the floods themselves cannot be prevented, proper evacuation methods can help to minimize losses. This paper reviews various literature on dam break studies that have utilized numerical models to create inundation maps, and also discuss some hydraulic models commonly used in such studies.

Goal-Oriented Modeling of an Urban Subway Control System Using KAOS

The extent to which a safety-critical system, such as an urban subway control system, accomplishes its goals is a fundamental metric of its success. Identifying and assessing these goals should thus be one of the primary tasks in safety-critical system development. The breakdown of these systems may result in the loss of human lives and assets. The failure of these systems is caused by insufficient, incomplete, ambiguous, or conflicting requirements. Non-functional requirements are also separated from requirement specifications. Goal-oriented requirements engineering methodologies, such as KAOS, are used to tackle these challenges by providing adequate, complete, unambiguous, and consistent requirements in terms of goals. As a result, the KAOS approach is utilized in this article to construct a goal-oriented model of an urban subway control system. Variability and obstacle concerns are also addressed in this study.

Border control and accident rate of irregular immigration in the route to the Canary Islands (Spain) during the COVID-19 pandemic

Throughout the first decades of the 21st century, the maritime borders of the European Union have witnessed a growing and increasingly complex mobility of an irregular nature. Moreover, the effects of the COVID-19 crisis on many African countries revealed the weakness of the maritime border and its permeability, as well as its increased danger. This research aims to study these irregular migration flows by sea during 2020 and 2021, through the Atlantic border between the Canary Islands and Africa. The so-called “Canary route” is one of the most dangerous maritime routes in the world due to the ocean conditions, the distances that must be covered and the way in which migrants move. Our analysis will therefore focus on one of the central aspects of this crossing, its danger and associated accident rate, which can be measured in terms of the number of shipwrecks, deaths, and missing persons. The objectives of this paper are: i) to show the spatial location of those wrecks; ii) to give an estimate of data on missing persons and deaths; iii) to focus on the study of those persons affected; iv) to delve into the underlying causes; and v) to analyse the border control actions developed during the pandemic. With this aim, information has been compiled from different national and international sources: official records, contrasted data provided by some NGOs and the IOM, and testimonies of the agents involved. This material is used to reflect on the normalization of border control as a factor that inevitably leads to deaths and disappearances. It suggests that this event should be a central element in European migration policy, contributing to designing border control actions that go beyond containment, thereby preventing the loss of human lives.

A Survey Study of Common Security Failures and Mitigations for the Internet of Things (IoT)

The Internet of Things (IoT) is transforming the world on almost a daily basis. There are tens of billions of things interconnected and that number is growing daily. Everywhere the IOT is present security is critical, especially in healthcare applications. Reported frequently are exploitations of security failings within the IoT. The exploitation of IoT security failures can result in dramatic consequences including loss of human life. This makes it essential to create a culture of awareness of the potential security failures within the IoT and their available mitigations. This article begins the engendering of that awareness. Presented is a simplified three-tier architecture of the IoT to serve as a lens to view the commonalities in the diverse IoT ecosystem. While there are multiple security weaknesses in the IoT ecosystem and numerous individual malicious attacks potentially exploiting them the viewpoint presented provides for the distillation of those into a common group that indicates a finite set of mitigation techniques. When applied these mitigations make the entire IoT ecosystem secure against the currently known exploitations.

Learning environments in software testing education: An overview

AbstractNowadays, we heavily depend on software, as we utilize it daily. Bugs in the software can cause significant damage, loss of private data and money, and even loss of human lives. Software testing and quality assurance discipline, which belongs to the software engineering domain, aims to ensure that the software is good enough before its release to the market. Therefore, it is vital to ensure that software engineers are highly trained in this domain. Unfortunately, this discipline is frequently neglected throughout software engineering courses. Students are mostly taught just the theoretical principles of software testing and gain little practical experience. Additionally, the software testing lectures are considered to be dull, especially if they are not followed by practical tasks where the students could apply learned techniques and gain hands‐on experience. Several tools, games, and collaborative learning environments have been proposed to make the lectures more interesting and increase student engagement. The main goal of this article is to provide a systematic survey of such environments, where each environment was evaluated in terms of major software testing topics covered and the approach used to engage the students.

Relevance of Genetic Identification and Kinship Analysis in Human and Natural Catastrophes—A Review

Different types of disasters, whether natural or human in character, lead to the significant loss of human lives. In the latter case, the quick action of identification of corpses and human remains is mandatory. There are a variety of protocols to identify victims; however, genetics is one of the tools that allows an exact identification of the victim. However, several factors may interfere with this identification, from the biological samples’ degradation not allowing the analysis of nuclear information, to failure to dispose of biological samples from family members. Access to certain family members could be a determinant of the proper choice of genetic markers that allow the identification of the victim, or his/her inclusion in a given genetic maternal or paternal lineage. New advances in the field of genetics are soon expected to allow for the identification of victims from disasters with only their biological postmortem samples; it may also be possible to draw a robot portrait of a victim’s most likely physical characteristics. In all cases, genetics is the only modern tool with universal character and can be used in essentially all biological samples, giving and identification of more or less accurate statistical character, depending on whether nuclear or lineage markers are used.