Suitable Actions Research Articles

Estimation of human error probabilities in marine safety services: The case of lifeboat and davits inspection

Shipboard Operation Human Reliability Analysis (SOHRA) method is recognized as a practical tool to predict human error probability (HEP) of operators engaging marine operations. Identifying the generic task types (GTTs) and marine specific error producing conditions (m-EPCs), the tool successfully derives HEP value distribution of critical operations in maritime environment. However, the real-time applications of SOHRA to prioritize and implement the suitable recovery actions have still open for development due to the limited time and expertise at pre-operation stage. This paper adapts SOHRA into lifeboat and davits inspection process as a critical marine safety service. Considering the tasks conducted by marine safety service engineers in routine and remote support modes, the GTTs and m-EPCs are assigned to estimate HEP values. In this context, a remote assistance system, is alternatively extended to involve standardization, camera tracking, and advisory support to enhance human reliability through inspection stages. The findings spotlight the deviation in HEP between routine ( 8.26E + 00) and remote support (4.04E-01) modes. A set of recovery actions (i.e. instructional materials) to remedy the HEP values in routine mode are suggested while it is not required at the remote mode assistance. The application illustrates that remote supporting to the marine service might reasonably reduce service engineers’ error rates. Consequently, the study is expected to enhance SOHRA applications in inspection period, particularly added value to marine service engineers in duty. The further studies on the proposed remote assistance concept as new generation solution will contribute to the service quality of marine safety companies.

The impact of the social adaptability of different groups on the human capital level in an uncertain environment: Panel data analysis based on 35 urban life satisfaction.

To explore the role of the social adaptability of different groups in human capital in an uncertain environment, this paper empirically tests the relationship between social adaptation and human capital by using a panel quantile regression model (QRPD) with the life satisfaction of 35 cities in China as an indicator. By selecting Nelder Mead and adaptive MCMC estimation methods, this paper finds that (1) the higher the level of social adaptation is, the more obvious the promotion effect on the level of human capital; (2) for eastern cities, social adaptation can promote the improvement of the human capital level; for central or western cities, the relationship between social adaptation and human capital is not obvious and depends on the method selected for estimation. To enhance the social adaptability of individuals, the government leadership should actively create conditions, carry out targeted social adaptation training and active adaptation skills exchange for different social groups in order to strengthen social adaptability and enhance human capital. At the same time, policymakers need to consider the differences between the eastern, central and western regions and choose the most suitable human capital improvement action framework for the region to manage environmental uncertainty and make regional human capital sustainable competitive in the era of uncertainty.

Analysis of medical emergencies in commercial flights and utility of medical resources onboard

Objectives: Civil aviation in India is flourishing and has resulted in a substantial increase in air travel. Hence, the increase in the number of in-flight emergencies is not unanticipated. This study attempts to analyze the incidence of medical emergencies that occurred on board in a single major Indian domestic airline and to assess the adequacy of the medical equipment provided in the in-flight emergency kits. Material and Methods: A retrospective analysis was carried out of the narrative summaries of all medical events reported by the cabin crew of one major airline in the domestic sector. The data represented an average of 39.9% of total domestic passenger volume from January 2015 to December 2017. An assessment of available medical resources onboard and their utility in managing the medical events was carried out. A comparative study was also attempted on the Directorate General of Civil Aviation (DGCA) recommendations on onboard medical resources and those of other regulatory agencies. Results: A total of 221 in-flight emergencies were reported from the sample. Breathlessness (n = 52) was the most common presenting symptom and cardiovascular conditions (n = 68) were the most commonly diagnosed medical illnesses. Four deaths on board were reported. Medical kits recommended by DGCA, India, are comprehensive and carry most of the required medications. Conclusion: Civil aviation in India is growing with more citizens using airlines for regular commutes and, hence, the likely incidence of more in-flight emergencies. A standardized system for reporting all in-flight medical emergencies, including their outcomes, should be made available to have a better understanding of the problem statement and guide suitable mitigating actions.

Enhanced deep learning frame model for an accurate segmentation of cancer affected part in breast

SummaryBreast cancer is one of the primary causes of death in females worldwide. So, recognizing and categorizing breast cancer in the initial stage is necessary for helping the patients to have suitable action. In this research, a novel spider monkey‐based convolution model (SMCM) is developed for detecting breast cancer cells in an early stage. Here, breast magnetic resonance imaging (MRI) is utilized as the dataset trained to the system. Moreover, the developed SMCM function is processed on the breast MRI dataset to primarily detect and segment the affected part of breast cancer. Additionally, segmented images are utilized for tracking in the dataset to identify the possibility of breast cancer. Moreover, the simulation of this approach is done by Python tool, and the parameters of the current research work are evaluated with prevailing works. Hence, the outcomes show that the current research model has improved accuracy by 1.5% compared to existing models.

Regional water footprint assessment for a semi-arid basin in India.

Water footprint assessment enables us to pinpoint the impacts and limitations of the current systems. Identifying vulnerabilities across various regions and times helps us prepare for suitable actions for improving water productivity and promoting sustainable water use. This study aims to provide a comprehensive evaluation of the sector-wise water footprint in the Banas River Basin from 2008–2020. The water footprint of the Banas River Basin was estimated as 20.2 billion cubic meters (BCM)/year from all sectors. The water footprint has increased over the year with the increase in population, the number of industries, and crop production demand. The average annual water footprint of crop production varied from 11.4–23.1 BCM/year (mean 19.3 BCM/year) during the study period. Results indicate that the water footprint has nearly doubled in the past decade. Wheat, bajra, maize, and rapeseed & mustard make up 67.4% of crop production’s total average annual water footprint. Suitable measures should be implemented in the basin to improve water productivity and promote sustainable water use in agriculture, which accounts for nearly 95.5% of the total water footprint (WF) of the Banas basin. The outcomes of the study provide a reference point for further research and planning of appropriate actions to combat water scarcity challenges in the Banas basin.

A Review of Factors Affecting Ganoderma Basal Stem Rot Disease Progress in Oil Palm.

In recent years, oil palm has grown on a major scale as it is a prominent commodity crop that contributes the most to almost every producing country’s gross domestic product (GDP). Nonetheless, existing threats such as the Ganoderma basal stem rot (BSR) disease have been deteriorating the oil palm plantations and suitable actions to overcome the issue are still being investigated. The BSR disease progression in oil palm is being studied using the disease progression through the plant disease triangle idea. This concept looks at all potential elements that could affect the transmission and development of the disease. The elements include pathogenic, with their mode of infection in each studied factor.

Physics aware analytics for accurate state prediction of dynamical systems

It has been successfully demonstrated that synchronisation of physical prior, like conservation laws with a conventional neural network significantly decreases the amount of training necessary to learn the dynamics of non-linear physical systems. Recent research shows how parameterisation of Lagrangian and Hamiltonian using conventional Neural Network’s weights and biases are achieved, and then executing the Euler-Lagrangian and Hamilton’s equation of motion for prediction of future state vectors proved to be more efficient than conventional Neural Networks in predicting non-linear dynamical systems. In classical mechanics, the Lagrangian formalism predicts the trajectory by solving the second-order Euler-Lagrangian equation following all the symmetries of the system while, the Hamiltonian mechanics explicitly uses the Poisson bracket of canonical position and canonical momentum to arrive at two independent first-order equations of motion. For a physical system that follows energy conservation, we obtain the momentum taking the first derivative of the Lagrangian which supplements the energy conservation via Hamiltonian Mechanics. The dynamics of the physical system converges when the generalised coordinates transform to suitable action angle variables using Poisson bracket formalism conserving the energy. In this work, we have demonstrated the prediction by integrating Hamiltonian and Lagrangian neural network leading to faster convergence with the actual dynamics from less number of training epochs. Hence, integration of HNN(Hamiltonian Neural Network) and LNN(Lagrangian Neural Network) outperforms their autonomous implementation. We have tested our proposed model in three conservative non-linear dynamical systems and found promising results.

Failure of the Ryll-Nardzewski theorem on the CAR algebra

Spreadability of a sequence of random variables is a distributional symmetry that is implemented by suitable actions of JZ, the unital semigroup of strictly increasing maps on Z with cofinite range. We show that JZ is left amenable but not right amenable, although it does admit a right Følner sequence. This enables us to prove that on the CAR algebra CAR(Z) there exist spreadable states that fail to be exchangeable. Moreover, we also show that on CAR(Z) there exist stationary states that fail to be spreadable.

Generation Z and cyberviolence: between digital platforms use and risk awareness

ABSTRACT The article focuses on the results of a national empirical research on Generation Z’s perceptions and experiences of cyberviolence. This term refers to aggressive and intentional actions, carried out, through electronic tools by a single person or a group, which deliberately aim to hurt or damage another who cannot defend himself. The main aim of this study was to gather national data on cyberviolence in order to explore better how young people (aged 18–25) understand and experience this phenomenon and what maybe the most suitable actions to limit it. The data analysis explored three main issues: (1) The main platforms used by Generation Z on which cyberviolence can occur; (2) The level of knowledge and awareness that Generation Z has about the different forms of cyberviolence and their dangerousness; (3) The features of social media perceived as most dangerous by the respondents. The article also proposes a reflection on the actual level of awareness of young people about this issue and the need to consider digital literacy programmes that aim to teach young people how to analyse the media critically.

Deep reinforcement learning for gearshift controllers in automatic transmissions

Control design for gearshifts in modern automotive automatic transmissions constitutes a challenging, time consuming task performed by highly trained experts. This is due to the fact that a variety of non-linear and partially observable systems need to be actuated, such that a comfortable shifting behavior is achieved within an sufficiently low shifting time. The presented approach leverages deep reinforcement learning (DRL) to control gear shifts, outperforming current state of the art controller performance. This requires formulating the shifting task as a Markov decision process by designing suitable action and observation spaces as well as a meaningful reward function. Due to the sample complexity of DRL methods, the control agents are trained in simulation and are subsequently transferred to a real transmission on a test bench. To successfully transfer DRL agents from simulation to reality, methods such as domain randomization and domain adaption leveraging evolutionary optimization are applied. To the best of the authors’ knowledge, this work is the first to successfully apply DRL for the closed loop control of a real world automotive automatic transmission of realistic complexity.

Deep Learning Approach for Event Monitoring System

With an increasing number of extreme events and complexity, more alarms are being used to monitor control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It is important to have a rigid surveillance that should guarantee protection from any sought of hazard. Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO algorithm, it divides an image from the video into grid system and each grid detects an object within itself.

Hydration Assessment Using the Bio-Impedance Analysis Method.

Body hydration is considered one of the most important physiological parameters to measure and one of the most challenging. Current methods to assess hydration are invasive and require costly clinical settings. The bio-impedance analysis offers a noninvasive and inexpensive tool to assess hydration, and it can be designed to be used in wearable health devices. The use of wearable electronics in healthcare applications has received increased attention over the last decade. New, emerging medical devices feature continuous patient monitoring and data collection to provide suitable treatment and preventive actions. In this paper, a model of human skin is developed and simulated to be used as a guide to designing a dehydration monitoring system based on a bio-impedance analysis technique. The study investigates the effect of applying different frequencies on the dielectric parameters of the skin and the resulting measured impedance. Two different interdigitated electrode designs are presented, and a comparison of the measurements is presented. The rectangular IDE is printed and tested on subjects to validate the bio-impedance method and study the interpretation of its results. The proposed design offers a classification criterion that can be used to assess dehydration without the need for a complex mathematical model. Further clinical testing and data are needed to refine and finalize the criteria.

A Skeletal Model for 2hbox {d} Conformal AQFTs

A simple model for the localization of the category mathbf {CLoc}_2 of oriented and time-oriented globally hyperbolic conformal Lorentzian 2-manifolds at all Cauchy morphisms is constructed. This provides an equivalent description of 2-dimensional conformal algebraic quantum field theories (AQFTs) satisfying the time-slice axiom in terms of only two algebras, one for the 2-dimensional Minkowski spacetime and one for the flat cylinder, together with a suitable action of two copies of the orientation preserving embeddings of oriented 1-manifolds. The latter result is used to construct adjunctions between the categories of 2-dimensional and chiral conformal AQFTs whose right adjoints formalize and generalize Rehren’s chiral observables.

Using failure mode and Effects Analysis to increase patient safety in cancer chemotherapy.

Medication errors may occur during chemotherapy and can have fatal consequences. Healthcare Failure Mode and Effects Analysis (FMEA) is a method used to detect potential risks and prevent them. Aim of this study was to evaluate the medication process of intravenous tumor therapy in order to guarantee a high standard of patient safety. The main part of the study was performed at the University Hospital of Bonn, Germany. After assembling a multidisciplinary team, the individual steps of prescription, compounding, transport, and administration of chemotherapy were mapped in a flow diagram. The possible failures were identified and analyzed by calculating the risk priority numbers (RPNs). Finally, corrective actions were developed and after hypothetical implementation re-analyzed to measure their effects on the process. Subsequently, a shortened FMEA based on the catalogue failure modes developed in Bonn was carried out at the University Hospital of Cologne in order to evaluate its transferability to another hospital. A total of 52 potential failure modes was identified in Bonn. Relating to the RPNs the most critically steps in the process were associated with the prescription, namely, incorrect information about individual parameters of the patient; non-standardized chemotherapy protocols; and problems related to supportive therapy. A significant risk reduction for most of the failure modes was assessed by implementing suitable corrective actions. The shortened FMEA in Cologne led to a different ranking of failure modes. The implementation of this analysis has not only identified various safety gaps, but also shows how patient safety during chemotherapy can be enhanced. Moreover, it has sensitized the practitioners to failure modes potentially occurring in their work routine.

Risk Factors for the Pap Test Cytological Changes in Iran: A Multicenter Case-Control Study

Background: Pap tests can detect changes inside the cells of the cervix before cancer develops. Objective: To compare the risk factors and determinants for abnormal cytological changes in Pap test results among women with and without atypical cytological findings in their Pap test, and to determine the clinical manifestations associated with those changes. Methods: A multicenter case-control study was conducted in four clinics of gynecology and oncology in Iran. Of a total of 201 women with a result of Pap smear, 51 were cases (atypical cytological findings) and 150 women were control (benign/reactive changes). The data was collected by a researcher-designed tool through the interview, observing medical records and clinical examinations. Results: According to the logistic regression, the women’s age (OR= 1.08), age at the menarche (OR= 0.75), number of deliveries (OR= 1.44), and BMI (OR= 2.60) are the determinants of the Pap test cytological changes in Iran. Taking hormonal contraceptives, using IUD, sexual activity, and having a family history of cancer are not determinants (p>0.05). The clinical manifestations as having an unhealthy cervix, painful sex, low abdominal pain, pelvic pain, and low back pain were significantly higher (P< .001) in the case group than those of the control group. Conclusion: The Pap test cytological changes in Iranian women are associated with age, age at the menarche, number of deliveries, and BMI. This information can help healthcare providers to predict their patients' health situation for taking a suitable and quick action.

Predicting coastal profiles evolution from a diffusion model based on real data

A diffusion model is considered to represent the time evolution of coastal profiles, over the time span of several years ahead. This is important in view of both, natural and anthropic activities, which affect and tend to modify the environment near the sea coastal lines, river’s estuaries, and harbors. The final aim is to make useful predictions for the depth profile evolution, basing on real available data, so that suitable actions might be planned. From the mathematical standpoint, the problem takes on the form of an inverse problem for a parabolic model equation, and thus the minimization of a suitable cost functional was accomplished to calibrate the model. An open source version of a genetic algorithm is used to identify the desired model parameters. To effectively reduce the overall computational cost, the code was parallelized. The computed and the measured depth profiles have been compared estimating the integrated relative error (IRE), using only ten years of measurements, each of them having been taken once a year. A regression analysis was carried out exploiting a machine learningalgorithm, provided by a support vector machine, to predict the evolution of the model parameters over one, two, and three years ahead. Predicted and measured depth profiles were compared in terms of IRE.

Short Term Glucose Prediction in Patients with Type 1 Diabetes Mellitus.

Glucose prediction is used in diabetes self-management as it allows to take suitable actions for proper glycemic regulation of the patient. The aim of this work is the short-term personalized glucose prediction in patients with Type 1 diabetes mellitus (T1DM). In this scope, we compared two different models, an autoregressive moving average (ARMA) model and a long short-term memory (LSTM) model for different prediction horizons. The comparison of two models was performed using the evaluation metrics of root mean square error (RMSE) and mean absolute error (MAE). The models were trained and tested in 29 real patients. The results shown that the LSTM model had better performance than ARMA with RMSE 3.13, 6.41 and 8.81 mg/dL and MAE 1.98, 5.06 and 6.47 mg/dL for 5-, 15- and 30-minutes prediction horizon.

A multitechnique approach for the identification of multiple contamination sources near a polluted industrial site

AbstractDegraded land requires rapid and suitable remedial actions, thus appropriate and fast methodological approaches for estimating the spatial distribution of contaminants are needed. In this context, a methodological approach combining isotopic measurements, heavy metals concentrations mapping, X‐ray diffraction, and cluster analysis, was applied to characterize a polluted industrial site where the contamination due to improper waste disposal could have spread also into the surrounding fields. This approach was applied to a set of representative topsoil (from 15 to 30 cm depth) and subsoil (from 130 to 150 cm depth) samples, selected either inside or outside the contaminated site. The X‐ray diffraction analysis highlighted that only the mineralogy of the subsoil below the buried waste was noticeably altered, while the concentration mapping highlighted that the levels of several heavy metals on the edge of the contaminated site, at both depths, were very similar to those found in the samples from the outside. Based on these findings, Pb was used as a tracer for heavy metal pollution by its stable isotopes' analysis. The cluster analysis of the isotopic measurements revealed that only the contamination detected inside the industrial site could be attributed to the pollutants migration from the buried waste. Conversely, the contaminants concentrations found in the topsoil samples taken outside the site could be ascribed to contamination sources other than the polluted waste present inside the site.

Cruising Under and Post the COVID Pandemic: Toward Realization of the United Nations’ Sustainable Development Goals

Maritime sectors have always dealt with uncertainties and disruptions. The COVID pandemic confronted the cruise industry with profound, wide-ranging, and lasting challenges while disrupting normal operations. Although the cruise industry contributes to the implementation of the United Nations Sustainable Development Goals (UN SDGs), resumption and sustainable cruising requires the industry to adapt to the challenges presented. To this end, the paper suggests adaptive actions for the cruise sector to respond to the pandemic and links the actions to the UN SDGs to highlight their sustainable contributions. A system thinking approach is applied and a literature review is conducted to identify suitable adaptive actions. This paper shows the importance of UN SDGs 3, 4 and, in particular, 17 for sustainable cruising. The results of this paper provide support for informed decision-making to increase the cruise industry’s sustainability. This paper recommends that stakeholders: 1- identify drivers and barriers of sustainable cruising, 2- adapt to changes and embrace the UN SDGs, as they provide a platform for realizing sustainability, and 3- use educational programs to improve and transfer knowledge on sustainable cruising between academia and policymakers.

Walmart Inc. in light of COVID-19 pandemic: the financial assessment

Motivation: The pandemic's trajectory is challenging to be forecast. The COVID-19 pandemic modifications can cause the growth of infected people. Furthermore, the defined financial activity resulting from additional lockdowns may have a meaningful contrary impact on aggregate demand. The paper can motivate further study and examination in the field of financial indicators of the Walmart Inc. and other inspiring research. Aim: The research regards the selected Walmart's financial indicators during the COVID-19 pandemic. The aim of the paper is the assessment of the financial situation of the Walmart Inc. as an American multinational retail corporation in light of COVID-19. Materials and methods: The paper is using of statistical, dynamics, comparative, documentation analysis. Results: The discussion shows that the multinational corporation within the retail sector succeeded in the research period. Walmart implemented the suitable mechanisms, solutions, and actions that mitigated the impact of the COVID-19 pandemic and, in consequence, positioned the world pioneer in multinational corporation retail in the leading rank against the rest of the entities competing in that diversified market.