Catastrophic Phenomenon Research Articles

Red Spot on the European Green Map: Will the Extra Catastrophic Phenomenon Take the Polish Poaching-Pressured Ospreys to the Brink of Extinction?

Simple SummaryScientific research is an integral part of species protection. Thanks to the use of camera monitoring during a conservation project targeting a threatened Polish population of Osprey, we discovered that the influence of natural causes of brood losses is stronger than in previous years. This would be a kind of catastrophe for the species in the whole country. We conclude that only active protection and stopping the anthropogenic causes of Osprey mortality (e.g., poaching) could stop the decline in the population and give Poland a chance to not to be red spot on the European green map of Osprey.Poland is the only European country where the Osprey population is declining due to the mortality of adult birds from poaching, which impacts not only single breeding attempts but also the Lifetime Reproductive Success (LRS) of specimens. However, what if there came an extra mortality factor in the moment of the lowest numbers of Osprey, already vulnerable in the country? In the years 2018–2020, we installed 22 trail cameras and five digital cameras (live online video feeds) on the nests. The total failure level observed in cameras (18.5%) was high. We observed, using these cameras, the extra mortality of chicks (10.7% of potentially fledged chicks) and even adult birds by unexpected predation by Northern Goshawk and White-tailed Eagle. This phenomenon is also common in the national population, as we found a total of ten cases of total losses by predators (eight or nine of them were birds of prey), including nests not covered by camera monitoring. The extra adult-predation by Goshawks means an extra drop in LRS. Those adult and chick predations are an example of exceptional catastrophic phenomena, which have been described as the direct cause of the extinction of animal populations throughout history. Only active conservation and stop poaching of the Polish population could stop the decline and save the Polish Ospreys.

Study on Failure Characteristics and Rock Burst Mechanism of Roadway Roof under Cyclic Dynamic Load

Rock burst is a catastrophic phenomenon that often occurs in underground rock mass engineering. In order to reveal the essence of rock burst of a hard roof in the process of roadway excavation, the particle discrete element method is used to establish a roadway model and simulate the disturbance of harmonic dynamic load based on the analysis of a rock burst accident in a deep mine. The crack field, stress field, displacement field, and kinetic energy of roadway surrounding rock disturbed by cyclic dynamic load were analyzed, and the disaster mechanism of roadway impacting roof instability was discussed. The results show that, compared with the roadway support structure under static load that can give full play to its control function of surrounding rock, the roadway surrounding rock will collapse and lose stability in a large area under the roof cyclic dynamic load, and the ordinary supporting structure cannot give full play to its control function of surrounding rock, resulting in the surrounding rock destruction and supporting structure failure. In addition, the essence of rock burst in a hard thick roof is due to the instantaneous superposition of static stress and dynamic load, leading to the instantaneous instability and collapse of roadway roof in a large area. The research is of great significance to further understand the deformation and failure mechanism of roadway surrounding rock under strong impact load, to guide the safe production and prevent the occurrence of rock burst hazard in underground rock mass engineering.

Living under threat: Will one of the last Pinna nobilis populations be able to survive?

Abstract The critically endangered fan mussel Pinna nobilis, endemic to the Mediterranean Sea, faces a drastic population decline all over the Mediterranean Sea owing to an infectious disease. Coastal lagoons represent one of this species’ last reservoirs; thus, knowledge of the population conservation status in these ecosystems is essential. An extensive monitoring programme was conducted during 2014, 2017 and 2019 in the Mar Menor lagoon, a highly eutrophicated lagoon from Spain. On average, 32,535 m2 have been surveyed each year to determine population densities, distributions and size structures. The cumulative impacts produced by intensive agriculture activity around the lagoon, together with a catastrophic meteorological phenomenon that occurred in 2019, have triggered a significant decline in fan mussel population densities and in population mean shell sizes over the years. Additionally, some fan mussels were infected by the protozoan Haplosporidium pinnae. Therefore, the current P. nobilis population from the Mar Menor is mainly composed of young individuals in specific areas that are facing an extreme situation. Urgent management and conservation actions focused on restoring the ecosystem and protecting P. nobilis individuals are urgently needed, together with reinforcement projects to increase the species’ stock in one of its last reservoirs.

Distribution of Flood Risk Area in Bodri Watershed of Kendal Regency

Flooding is a catastrophic phenomenon that can occur due to various factors, such as uncontrolled landuse changes, climate change, and weather anomalies, and drainage infrastructure damage. The Bodri watershed in Kendal Regency is one of the watersheds in Central Java, which is categorized as critical based on Decree No.328/Menhut-II/2009. Some of the problems in the Bodri watershed include land use that is not suitable for its designation, flooding, erosion, and landslides. This study aims to conduct spatial modeling to create flood hazard maps and flood risk level maps in the Bodri watershed. The method used is hydrograph analysis, flood modeling, potential flood hazards, and flood risk levels. Analysis of the potential for flood hazards from the spatial modeling inundation map with the input of the flood peak return period of 2 years (Q2), 5 years (Q5), and 50 years (Q50). Vulnerability analysis based on land use maps of flood hazard areas. The distribution of flood-prone areas in the Bodri watershed is in Pidodo Kulon Village, Pidodo Wetan Village, and Bangunsari Village.

Transmission frequency of COVID-19 through pre-symptomatic and asymptomatic patients in AJK: a report of 201 cases

BackgroundThe COVID-19 pandemic is a catastrophic global phenomenon, affecting human life in a way unseen since the 1918 influenza pandemic. Effective management of this threat requires halting transmission, a strategy requiring accurate knowledge of SARS-CoV-2 transmission patterns.MethodsThis was a retrospective contact study aiming to estimate the transmission rate of COVID-19 by tracing contacts in symptomatic, pre-symptomatic, and asymptomatic patients. History of patients’ contacts during 24 h before appearance of symptoms or infection confirmation was traced for disease transmission.ResultsOverall, a total of 201 COVID-19 patients had contact with 7168 people in 24 h with an average of 35.66 contacts per patient, ranging from a minimum of 4 to maximum of 87 contacts (meetings). Out of 7168 persons met, infection was detected in 64 (0.89%). For the 155 symptomatic patients, a total of 5611 contacted persons were traced before appearance of symptoms (pre-symptomatic) in last 24 h with an average of 36.20 meetings per patient. The infection was transmitted in 63 (1.12%) people with 5548 (98.88%) remaining uninfected. Out of the 63 transmissions, 62 (98.4%) were traced within 6 h before symptom onset, while only 1 was identified in the 6–12 h timeframe before symptoms. A total of 1557 persons were traced having meeting/contacts with asymptomatic cases in last 24 h before infection confirmation. Out of these 1557 persons, only 1 was found to be infected and the infection rate was calculated to be 0.06%. Statistically, the transmission rate by pre-symptomatic patients was found to be significantly higher than the transmission rate by asymptomatic individuals (P < 0.05).ConclusionIn the studied population, the risk of pre-symptomatic and asymptomatic transmission of COVID-19 was low, with transmission risks of 1.12% and 0.06% respectively. Pre-symptomatic infection becomes very rare in contacts made longer than 6 h before onset of symptoms. The infection transmission is traced as long as about 9 h before the appearance of clear symptoms in the patients, but the incidence rate was as low as about 0.02% of the total contacts in that period.

I am a Survivor: Experiences, Impacts and Coping Mechanisms of Filipino Victims of Sexual Violence

Sexual violence is a catastrophic phenomenon that most women encounter worldwide. However, the stigma surrounding the victims of sexual violence often leads to a culture of silence, causing the number of such cases to be underreported, leading to limited sexual violence-related studies. With this, the goal of this study is to contribute additional information on the experiences of Filipino victims with sexual violence, its impacts, and their coping mechanisms. Ten Filipino women, who were victims of sexual violence, were interviewed in this study. Thematic analysis was used to analyze the gathered data. Themes on their experiences, the effects of sexual violence, and their coping mechanisms were formulated and presented in this study. The study showed that the most common type of sexual violence experienced by the participants was rape. They also reported feelings of fear during and after the abuse. Feelings of shame and guilt were also experienced, which kept them silent about their negative experiences. Experiencing sexual violence can have negative impacts on an individual's total well–being. To cope with these adverse experiences, they sought support from their families and friends. Some confronted their problems and even employed positive reappraisal, while some used avoidance coping.

Bio-Inspired Origami Metamaterials With Metastable Phases Through Mechanical Phase Transitions

Abstract Structural instability, once a catastrophic phenomenon to be avoided in engineering applications, is being harnessed to improve functionality of structures and materials, and has been a catalyst of substantial research in the field. One important application is to create functional metamaterials that deform their internal structure to adjust performance, resembling phase transformations in natural materials. In this paper, we propose a novel origami pattern, named the Shrimp pattern, with application to multi-phase architected metamaterials whose phase transition is achieved mechanically by snap-through. The Shrimp pattern consists of units that can be easily tessellated in two dimensions, either periodically with homogeneous local geometry or non-periodically with heterogeneous local geometries. We can use a few design parameters to program the unit cell to become either monostable or tune the energy barrier between the bistable states. By tessellating these unit cells into an architected metamaterial, we can create complex yet navigable energy landscapes, leading to multiple metastable phases of the material. As each phase has different geometries, the metamaterial can switch between different mechanical properties and shapes. The geometric origin of the multi-stable behavior implies that, conceptually, our designs are scale-independent, making them candidates for a variety of innovative applications, including reprogramable materials, reconfigurable acoustic waveguides, and microelectronic mechanical systems and energy storage systems.

Magneto-active elastic shells with tunable buckling strength

Shell buckling is central in many biological structures and advanced functional materials, even if, traditionally, this elastic instability has been regarded as a catastrophic phenomenon to be avoided for engineering structures. Either way, predicting critical buckling conditions remains a long-standing challenge. The subcritical nature of shell buckling imparts extreme sensitivity to material and geometric imperfections. Consequently, measured critical loads are inevitably lower than classic theoretical predictions. Here, we present a robust mechanism to dynamically tune the buckling strength of shells, exploiting the coupling between mechanics and magnetism. Our experiments on pressurized spherical shells made of a hard-magnetic elastomer demonstrate the tunability of their buckling pressure via magnetic actuation. We develop a theoretical model for thin magnetic elastic shells, which rationalizes the underlying mechanism, in excellent agreement with experiments. A dimensionless magneto-elastic buckling number is recognized as the key governing parameter, combining the geometric, mechanical, and magnetic properties of the system.

Spatial Memory in a Spiking Neural Network with Robot Embodiment.

Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.

Assessment, Monitoring and Prediction of Forest Fire Danger Using Atmospheric Soil Measuring Complex

Large-scale combustion of forest fuels stops when the entire forest area is burned out, when a natural obstacle is encountered on the path of a forest fire front (channel of a large river, mountain range) or at the beginning of the rainy season. In this situation, the problem can be solved only in anticipation of a catastrophic phenomenon, which is possible only when developing a methodology for assessing, monitoring, and predicting forest fire dangers. The purpose of this article is to develop a methodology for assessing, monitoring, and predicting forest fire dangers using an atmospheric-soil measuring complex, which provides information on the meteorological parameters and some data on soil in the control zone of a forest fire situation. The structure of the instrumental network for monitoring environmental parameters in order to forestall forest fires is proposed. Criteria for assessing forest fire dangers have been developed using data from the atmospheric-soil measuring complex located at one of the stations in the Republic of Buryatia. Typical results of assessment, monitoring, and predicting forest fire danger using these data are presented. Recommendations are proposed for the implementation of the methodology under consideration in the practice of forest fire protection.

Nonlinear integrated dynamic analysis of drill strings under stick-slip vibration

Stick-slip torsional vibration raising from bit-formation contact is a catastrophic dynamic phenomenon occurring in deep-water oil and gas drilling systems. The torsional vibrations can result in premature fatigue failure of drilling equipment and reduce drilling efficiency. Thus, studying the dynamics of the system is a vital key to identify the roots of the vibration and establish appropriate mitigation and remediation methods. In this paper, an efficient approach for finite element (FE) modeling of stick-slip vibrations of the full drill strings was proposed. The model was developed based on a rate-dependent formulation of bit-rock interaction, in which the cutting process was integrated through the frictional contact. The nonlinear effects of the large rotations and the geometrically nonlinear axial-torsional coupling were taken into account. The effect of energy dissipation due to the presence of drill mud along the drill pipes and drill collars was incorporated through Rayleigh viscous damping. Modal analysis was conducted to determine the natural frequencies and the mode shapes of the drill string. Furthermore, a five degree-of-freedom lumped parameter model was developed. The performance of the developed models was verified through comparisons with example stick-slip results from a field test. The study showed that the self-excited torsional vibrations may also occur at fundamental frequencies lower than the first natural torsional frequency of the drill string, depending on operational parameters. The conducted research work resulted in a robust and practical integrated FE model to simulate the entire drill string system dynamics under torsional vibrations.

Progressive collapse of regular- and irregular-plan concrete structures in an earthquake

Progressive collapse, in which local failure of an element causes significant deformations leading to the collapse of a structure, is a catastrophic structural phenomenon that can occur due to natural disasters or human negligence. Common practices in progressive collapse analysis generally prevent this phenomenon due to unusual gravity loads or explosions; however, progressive collapse behaviour due to earthquakes is less evaluated. An experimental methodology was used to study the progressive collapse of concrete structures with an intermediate moment frame both in regular and irregular plan using the alternative load path method and guidelines issued by the US General Services Administration and the US Department of Defense considering three cases of column removal using OpenSees. The investigation studied the function of the structure regarding the rotation of plastic hinges and its robustness. The pushover method was used to cover non-linear static analysis and the time history method was used to perform non-linear dynamic analysis for three-, six- and ten-storey structures in three dimensions. The results showed that structures with irregular plan and low heights are more vulnerable to progressive collapse.

Assessment of Coastal Vulnerability to Climate Change Impacts using GIS and Remote Sensing: A Case Study of Al-Alamein New City

Recently, climate change has become a catastrophic phenomenon in the whole world. It has not only devastating effects on the environment but also on the social and economic aspects of societies, especially those seeking to achieve sustainable development. Coastal areas are classified as one of the areas that are particularly exposed to current and projected risks connected to climate change. Selection of suitable climate adaptation means requires an integrated evaluation of climate change impacts and detects the vulnerability degree of various areas exposed to climate change. Therefore, this paper uses Remote Sensing (RS) and Geographic Information System (GIS) techniques to develop a GIS data model (Climate-Coastal Model) for evaluating the long-term impacts of climate changes and delineating the most vulnerable areas in coastal zones. In this regard, specific indicators are needed to detect the vulnerable areas. Four main effective parameters of climate change impacts were selected, which are meteorological parameter, topographical structures parameter (Earth Shape), engineering geology parameter, and shoreline parameter. The paper considered that all indicators in the four parameters have equal weights, where the results depended on changing the values of model indicators during the study period, which is 30 years. Al-Alamein New City in Egypt is presented as a case study to demonstrate the practical features of the proposed model. The results of a developed model according to 30 years study period reveal that the affected areas from climate change impacts are those located along the shoreline. Vulnerability along the shoreline has divided into three categories. The first one represents the least vulnerable areas, which located in Al-Alamein city and constitute about 0.00154 km2 from the total area (227.65 km2). This is due to being affected by low values of meteorological indicators and minor erosion and accretion processes comparing to the other sections. The second one represents the moderate vulnerable areas, which scattered throughout all sections of the case study: Al-Alamein City, Tel Al-Eis, and Sidi Abd El-Rahman. It constitutes about 0.9941 km2 from the total area. The last one represents the most vulnerable areas, which located also in all sections of the study area and forms about 0.72092 km2 from the total area which have low elevations below the average mean of sea water level that means it is most vulnerable to any climate change scenarios. It is also affected by high values of coastal erosion, costal accretion, and wind speed as well as a high percentage of dew point. The proposed model is considered as a decision support tool, which helps the decision-makers to detect the vulnerability degree of any areas exposed to climate-change impacts based on multi-criteria and parameter to give the priority for such areas.

Prediction of Flood Severity Level via Processing IoT Sensor Data Using a Data Science Approach

River flooding is deemed a catastrophic phenomenon caused by extreme climate change and other ecological factors (e.g., amount of sunlight), which are difficult to predict and monitor. However, the use of the Internet of Things (IoT), various types of sensing including social sensing, 5G wireless communication, and big data analysis has devised advanced tools for early prediction and management of distrust events. To this end, this article amalgamates machine learning models and data analytics approaches along-with IoT sensor data to investigate attribute importance for the prediction of risk levels in flooding. The article presents three river levels: normal, medium, and high-risk river levels for machine learning models. Performance is evaluated with varying configurations and evaluations set up including training and testing of support vector machine and random forest using a principal-components-analysis-based dimension reduced dataset. In addition, we investigate the use of synthetic minority oversampling technique to balance the class representations within a dataset. As expected, the results indicate that a “balanced” representation of data samples achieved high accuracy (nearly 93 percent) when benchmarked with “imbalanced” data samples using random forest classifier 10-fold cross-validations.

Social Support Attainment of Older Adults Living in a Flood-Prone Community

Introduction: A flood, a catastrophic phenomenon often experienced by many, brings destruction to property and livelihood. This kind of event renders access to basic services difficult. During times of floods, older adults need additional social support, from family, friends, relatives, or significant others. The aim of this study was to investigate the level of attainment of social support of older adults during periods of floods and periods without floods.Methods: A descriptive quantitative research design was used. Purposive sampling was utilized to reach the population sample of 126 elderly respondents. They were interviewed face-to-face in their homes using a modified questionnaire. To analyze the data, descriptive statistics and mean scores were used.Results: 126 older adults participated in the research study. Most older adults strongly agreed that they received social support during periods of floods (with a mean score of 4.40) and without floods (with a mean score of 4.39).Discussion: Social support was extended to older adults both during times of floods and without floods. The support they received from people who were special to them maintained their health and well-being.

Unique determination of cyclic instability state in flow liquefaction of sand

Flow liquefaction is a catastrophic failure phenomenon of loose sand when subjected to wave or seismic load, and an instability state can be regarded as the onset of flow liquefaction. Most of the previous studies focused mainly on the static instability state, while the investigations on cyclic instability state (CIS) are very limited and unsystematic. The characteristics of CIS are complex and there is no clear consensus about the determination of CIS. Therefore, this study aims to investigate the characteristics of CIS in flow liquefaction via a series of undrained triaxial tests performed on saturated loose sand. The testing program covers a broad range of initial deviatoric stresses qs , cyclic stress amplitudes qcyc , and relative densities Dr . The test results indicate that CIS will occur at either compressive side or extensive side, depending uniquely on the ratio of qs /qcyc . The slopes of CIS lines increase with an increase in the relative density. Under the same qcyc , the mean effective stress at CIS first decreases and then increases as qs increases. Finally, CIS is predicted by a useful method, which is able to monitor CIS both in situ and in laboratory tests.

Glacial Lakes Outburst Floods (GLOFs) modelling of Thulagi and Lower Barun Glacial Lakes of Nepalese Himalaya

Melting of glaciers is one of the major effects of recent atmospheric warming in the Himalayas which results in the development of supraglacial lakes that coalesce to form proglacial lakes. Large volumes of water in these lakes are considered dangerous due to its unstable surrounding geomorphology. Most of the proglacial lakes are moraine-dammed and breaching of such moraine due to some triggering events may give rise to rapid release of water and sediment, often termed as Glacial Lake Outburst Floods (GLOFs). GLOFs represent catastrophic phenomenon of the Himalaya and pose a risk to downstream communities and infrastructure. Several GLOF events have been recorded in Nepal in the past few years. Since 1990s, the lake area of Thulagi has increased from 0.72 to 0.94 km2 while that of Lower Barun has increased from 0.64 to 2 km2 and both glacial lakes rank No. I in the Potentially Dangerous Glacial Lakes (PDGLs) in Nepal. In this study, NWS-BREACH and HEC-RAS model are used to access the impact of GLOF on downstream areas. Different dam breach scenarios of 5, 10, 15 and 20 m is evaluated by NWS-BREACH model to estimate the breach hydrograph. The peak flow at the time of breach ranges from 1400 to 5400 m3/s for Thulagi and 2600 to 7900 m3/s for Lower Barun in low and high breach scenarios respectively. The peak flow estimated from NWS-BREACH is used for the flood routing in HEC-RAS to simulate peak discharge and flood height at different distances in the downstream from the lake outlet. The impact on downstream regions of the two glacial lakes is then evaluated by flood inundation map to access the land exposed to GLOF. Nepal consists of large number of glacial lakes and only a few have been studied. Furthermore, detail field survey of the lakes and downstream areas need to be incorporated for better prediction of GLOF events.

A macroscopic theory for predicting catastrophic phenomena in both biological and mechanical chemical reactions

A possibility for predicting the time-dependent pattern of sickness of human beings, i.e., biological catastrophe, has been shown by proposal of a nonlinear ordinary differential equation, describing temporal features of six macroscopic molecular groups chemically interacting in living beings, (Naitoh in Jpn J Ind Appl Math 28:15–26, 2011; Naitoh and Inoue in J Artif Life Robot 18:127–132, 2013). Logically, we also find that, six is minimum and essential as the number of macroscopic molecular groups for describing living systems. Then, along with the number theory applied for the differential equation, we derive critical mathematical conditions for predicting the premonition just before sickness (discrepancy from a healthy condition), which agree with an important knowledge revealed by the linear analysis proposed by Chen (Dynamical network biomarkers for identifying early-warning signals of complex diseases, Beppu, Oita Japan, 2015). In the present report, we first show that computational several time-histories of sickness obtained by solving the nonlinear differential equation with various parameters describing polymorphism agree well with actual time-dependent patterns of sickness for human beings, which is further evidence of usefulness of the nonlinear differential equation and its critical mathematical conditions. Thus, to examine the boundary between biological and abiological chemical reaction systems, which is related to the origin of living systems, we next check whether or not the nonlinear equation can also predict such abiological catastrophic phenomenon as misfire in artifacts including mechanical combustion engines.

Technical Standard Analysis and Optimal Monitoring Technology to Prevent Ground Subsidence

As ground subsidence has recently been gaining public attention, the government (the Ministry of Land, Infrastructure and Transport) as well as local autonomous bodies have recommended preemptive monitoring and have announced a variety of preventive measures against this catastrophic phenomenon. Furthermore, risk factors associated with ground subsidence were identified in a bid to raise awareness regarding the importance of developing preemptive and energy-response technologies (for checking and monitoring underground facilities). Despite the ongoing efforts to reliably detect and identify the condition of buried pipes at home and abroad, very little has been accomplished when it comes to developing technologies for preventing ground subsidence. Therefore, developing new and innovative technologies to (1) inspect and explore water pipes, sewage pipes, and ducts, and (2) to prevent ground subsidence (which has recently become a major threat) in urban areas is crucial. To this end, this study aims to conduct an in-depth analysis on the measurement and exploration criteria and on technical specifications, prior to developing measurement and exploration technologies (as preventive measures against ground subsidence due to damage to water and sewage pipelines). Based on these, the applicability, efficiency, and feasibility of the technologies will be evaluated, and optimal technical specifications will be suggested.

Pathological Worrying and Artificial Neural Networks

Worrying is a cognitive process that focuses on potential future negative events, where the outcome is often uncertain. Worries can arise in chains with one worry leading to another, often without solution. This may give rise to an uncontrollable worrying that may be associated with psychiatric disorders such as anxiety and depression. The generation of progressively more negative chains of worries can lead to a catastrophic phenomenon of pathological worrying. In this article we show that catastrophic worrying can be simulated by using a cascade-correlation algorithm for artificial neural networks.