Worst Situations Research Articles

Power Prediction of VLSI Circuits Using Machine Learning

The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit. A big database is needed to undertake important analytical work like statistical method, heat research, and IR-drop research that results in extended running times. This unit focuses on the assessment of test strength. Because of the enormous number of successful designs for current models and the unnecessary time required for every test, maximum energy ratings with all tests cannot be achieved. Nevertheless, test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip. Generally, effective power assessment is only possible in a limited sample of pre-selected experiments. Thus, a key objective is to find the experiments that might give the worst situations again for testing power. It offers a machine-based circuit power estimation (ML-CPE) system for the selection of exams. Two distinct techniques of predicting are utilized. Firstly, to find testings with power dissipation, it forecasts the behavior of testing. Secondly, the change movement and energy data are linked to the semiconductor design, identifying small problem areas. Several types of algorithms are utilized. In particular, the methods compared. The findings show great accuracy and efficiency in forecasting. That enables such methods suitable for selecting the worst scenario.

ESTIMATING THE WATER POVERTY IN SEMI-ARID DISTRICTS OF PUNJAB, PAKISTAN

An increase in population and water demand, climate change, and changes in land-use patterns are severe problems in Pakistan. Water management in Pakistan has been a provincial matter after the 18th amendment. Each province in Pakistan measures the water on its own intention and indicators. The current study aims to map the water scarcity status through the water poverty index (WPI) using the Multiple Indiactor Cluster Survey (2018) at the semi-arid districts in Punjab, Pakistan. WPI was estimated by using the components named “Resources,” “Access”, “Capacity”, “Use”, and “Environment”. Each component score was calculated using ifferent indicators, assuming equal weights for all components. Layyah (57.6) and RajanPur (48.03) had the best and worst situations among all districts in 2018. The findings showed that the absence of physical water resources in these districts is not the only factor of domestic water shortage but environment and capacity also plays a significant role in achieving the domestic water need. Due to their socioeconomic ineptitude, lack of infrastructure, and restricted access to water, the region is experiencing severe to high levels of household water poverty. This study's results showed that WPI is an operative tool to measure water scarcity and could be used to make an important priority for water management.

Study on Freeway Congestion Propagation in Foggy Environment Based on CA-SIR Model

The visibility in a foggy environment has a significant impact on driver behavior and traffic flow status, especially for whole closed highways with long distances between entrances and exits. Foggy days are very likely to cause congestion and even secondary traffic accidents, which seriously affect the reliability of freeway operation. In order to explore the influence of a fog environment on freeway traffic jams, firstly, this paper was based on the analysis of the impact of visibility on foggy days. Light fog, medium fog and heavy fog were classified as one scenario, while dense foggy weather was set separately as an extreme scenario without considering lane change. Furthermore, it used the SIR model of infectious disease for reference, and combined with the cellular automata (CA) model, the car-following model and lane-changing rules in different scenarios were set based on safe driving distance and speed for two scenarios. Finally, the key parameters of CA-SIR were calibrated, such as congestion propagation, recovery probability, vehicle braking, and lane-changing probability. The simulation analysis showed that with the decrease in visibility and vehicle speed, the phenomenon of congestion propagation was more prominent, but the causes of queuing phenomenon were different. A low speed limit was the main reason for traffic jams in the light fog condition. In the medium fog condition, the frequency of traffic jams was related to the random braking probability of the visibility. In heavy fog conditions, the congestion area gradually moved upstream with the passage of time. Moreover, in the dense fog condition, the congested area gradually moved upstream with the passage of time; however, vehicles were more likely to accompany each other, and the congested area traveled downstream synchronously with the passage of time and did not dissipate easily. Therefore, in a foggy environment, the best speed limit should be better established under different visibilities, the flow of highway traffic should be strictly controlled if necessary, and in worse situations than high-density traffic in low visibility, to avoid the spread of congestion, the intermittent release of different lanes is suggested to be implemented.

Visual assessment of soil structural quality across soil textures and compaction levels – Part II: Examination of profile walls vs Intact soil cores

Visual Evaluation of Soil Structure (VESS) methods receive increasing attention and are gaining popularity. Since in modern agriculture, soil structure degradation becomes very common, it is essential to protect and improve soil structure quality. Methods that enable its straightforward monitoring are therefore needed. Numerous visual soil evaluation techniques that serve this purpose exist, and are varying in objective, evaluation depth (topsoil/subsoil), evaluation method, number of criteria assessed and their final scoring. In this study, we mainly focus on three VESS methods, two of which were employed in the field along a profile wall (Visual Evaluation of Subsoil Structure, SubVESS, and Double Spade VESS method, DSVESS), and one in the laboratory on soil cores (CoreVESS). The main aim of this research was to test and compare these VESS-based methods in terms of their feasibility and repeatability in detecting soil structure degradation in the topsoil and subsoil. Sixteen cropped fields were chosen in Belgium covering six of seven major soil texture classes according to the Belgian soil textural triangle (i.e., sand, loamy sand, light sandy loam, silt loam, clay, and heavy clay). SubVESS and DSVESS were performed in the same pits until 80 cm and 40 cm, respectively. Soil cores for CoreVESS and for the determination of soil quality indicators (SQi) with conventional laboratory methods were taken from three layers, that is, the ploughed topsoil (∼10–∼20 cm, “TOP”), the compacted subsoil (∼30–∼40 cm, “CSUB”), and the deeper subsoil (∼60–∼70 cm, “SUB”), totalling 96 horizons. While SubVESS and CoreVESS were originally developed to evaluate subsoil and topsoil, respectively, they were thus used here beyond those zones. Moreover, all sampling and field evaluation was done at two positions per field - the more compacted headland and the less compacted in-field zone. It was shown that the soil quality (Sq) scores assigned to the cores with CoreVESS were not significantly influenced by operators with different backgrounds but all having received basic VESS training. When comparing field and laboratory results, positive relationships were found between Sq scores of SubVESS, DSVESS and CoreVESS. All VESS methods were able to assign significantly better soil quality (lower scores) at in-field positions (IN-FIELD) as compared to headland positions (HEAD), and significantly lower quality (higher scores) to CSUB layers than to the other two layers. Laboratory-derived SQi values presented a similar trend, with CSUB layers always indicating worse situations. There were good significant relationships between the SQi values and the different VESS-based Sq scores as well, with DSVESS showing the best correlations among the three VESS methods. The study thus confirmed that the different VESS methods all have a potential for monitoring soil structure quality in a fast, cheap, intuitive and practical way, although they all have their own advantages and disadvantages. Additionally, those methods that were employed at depths other than those for which they were developed did perform well.

Basis risk management and randomly scaled uncertainty

This paper proposes a method for quantifying the basis risk present in index-based insurance. It applies when the inherent uncertainty is represented by a randomly scaled variable. This turns out to be a reasonable assumption in a number of practical situations. Several properties of such a variable are first briefly studied. Their order in the s-convex sense is discussed and the associated extreme distributions are obtained to generate the worst situations. In each scenario, the basis risk consequences are then assessed using a penalty function that takes into account the risk tolerances of the protection buyer. Basis risk limits for a fixed budget can also be set. The proposed approach is illustrated by a few simple examples.

AUTOMATED ADAPTIVE LIGHT CONTROL FOR AUTOMOBILES

The purpose of this paper is to discuss the design and operation of self-adaptive lights systems in automobiles. The primary goal of this approach is to present an inexpensive methodology for illuminating blind spots and to make the objects visible in dark while driving and during periods of significant diminished visibility in dark locations, thereby avoiding accidents. The reasons for most road accidents without the control of a driver would be due to high beam lights approaching from the vehicles in the opposite direction and the fog causing invisibility of road depending on weather condition and since the beams are focused in a particular direction, it would be difficult in curves to handle the worst situations in driving. To address this problem, motorized headlights that turn according to the steering wheel of a car with adjustable brightness, and depending on atmospheric moisture, fog light is controlled and automatic dim and dip option is used on the basis of opposite vehicle's beam are being proposed as a solution.

Zika, Nipah and Kala-azar: Emerging lethal infectious diseases amid COVID-19 as an escalating public health threat in South India

As of 6 June 2022, a sum 25,782 of active cases and 524,701 deaths due to Coronavirus disease-19 (COVID-19) have been recorded in India. Stewing in the flares of the pandemic, Kerala is entwined in the wrath of multiple emerging infectious diseases.India, a home to 1.3 billion people, recently faced a devastating second wave of COVID-19 during May of 2021, with a ruckus of chronic shortage of medicine, oxygen supplies, ventilators, besides, being challenged by secondary infections and chronic health ailments. The state of Kerala, alone contributes to 50% COVID-19 caseload, besides, recent simultaneous outbreaks of Zika Virus Disease (ZVD), Nipah Virus Disease (NiVD) and Kala-azar (black fever) on July 8, September 5 and 8, 2021 respectively. Syndemicity and a high case fatality rates of these highly contagious diseases coupled with post infection sequelae, overwhelm the already fragile healthcare system. Thus, these lethal infectious diseases along with an anticipated third wave of COVID-19 pose a serious public health threat in and around South India. With this narrative review, we aim to discuss the challenges that the emergence of intersecting outbreaks of Zika, Nipah, Kala-azar presents with, in the nation, amidst the global pandemic of COVID-19 and provide recommendations so as to help alleviate the situation.The syndemicity of COVID-19 with other infectious diseases, calls for adequate surveillance and monitoring of diseases’ outbreaks. To avoid the worst situations like pandemic, the health ministry, public and private health stakeholders in India should strengthen the public healthcare delivery system and providence of quick medical facilities to control the rate of mortality and morbidity during outbreaks.

Study of Complexity Systems in Public Health for Evaluating the Correlation between Mental Health and Age-Related Demographic Characteristics: A General Health Study.

The main objective of this study is to evaluate the quality of nurses' work lives and mental health during outbreaks. We also use the General Health Questionnaire–28 and Walton's QWL technique to assess the association between these two and their dimensions with demographic variables and each other. First, 165 nurses from COVID-19 medical centers in Iran filled surveys for this research. In an SPSS program, the data were examined. There was a strong link between mental health and age-related demographic factors. There was no evidence of a link between the quality of nurses' work life and their psychological health. However, there was a strong link between somatic symptoms and fair and appropriate compensation, as well as constitutionalism. The worst situations for work life quality were linked to the whole living area dimension. In contrast, the worst conditions for mental health were linked to the somatic symptoms dimension.

Free to escape? Economic freedoms, growth and poverty traps

AbstractNew evidence on the relationships between economic freedoms and poverty traps is provided. Methodologically, a new way to classify countries into clusters is used, which stresses the relative position of economies with respect to each other. The paper investigates whether economic freedoms have any impact on shifts from one cluster to another, towards either better or worse situations. Moreover, it analyzes the contribution of economic freedoms to avoid falling into a poverty trap. The impacts of the five macro‐components of the index are studied separately. The results show that economic freedoms help economies to grow and to avoid poverty traps.

Assessment for Nuclear Fuel Cycle Management in the Aspect of Security Using System Dynamic (SD) Method

The security in the nuclear fuel cycle process has demonstrated that the behaviour of international terrorism is one of the nuclear industrial challenges where the complex algorithm of systems dynamics (SD) is applied. The simulation results are shown by graphs of the security of the nuclear fuel cycle which is related to the security scenarios of nuclear power plant (NPP). The simulation results have higher values, when the security is in worse situations. The importance of the study is the performance of dynamical simulation of the nuclear fuel cycle, the security incorporated with safeguard, and the 10 years’ analysis. Keywords—Security, Terrorism, Nuclear power plants, Safeguard, System dynamics, Simulation, Transmission, Cycle

Significance of the Leading Programming Languages

In today's technology era, programming is more essential than ever. Python and C++ are the most widely used programming languages. Python, the most popular programming language of the twenty-first century, is a higher-level object-oriented language, while C++ (the language that powers most operating systems) is a lowerlevel object-oriented language. Python and C++ are compared in this article. This book covers an introduction to various languages, memory management techniques, and reasons for their program execution speed. Furthermore, this study looked at the execution time and memory requirements of a variety of algorithms in both languages, comparing the best, average, and worst situations in each language. Furthermore, they are compared in terms of their benefits and drawbacks. C++ is faster than Python in terms of execution speed, according to the findings, but Python is a better option for beginners due to its simplicity. Aside from that, the language should be selected depending on the project type in order to get the best results. Programmers play a crucial role in computing. C/C++, Python, and Java are the most common languages. Beginning programming classes often utilize C++ and Java. Python has grown in popularity as an introductory programming language due to a variety of reasons. As an entry-level programming language, C, C++, Java, and Python were compared. More students selected Python over C++ because of its simpler pseudo-code syntax, better learning environment, and greater abstraction.

A Logarithmic Formulation for Anisotropic Behavior Characterization of Bovine Cortical Bone Tissue in Long Bones Undergoing Uniaxial Compression at Different Speeds.

The mechanical properties of bone tissues change significantly within the bone body, since it is considered as a heterogeneous material. The characterization of bone mechanical properties is necessary for many studies, such as in prosthesis design. An experimental uniaxial compression study is carried out in this work on bovine cortical bone tissue in long bones (femur and tibia) at several speeds to characterize its anisotropic behavior. Several samples from different regions are taken, and the result selection is carried out considering the worst situations and failure modes. When considering different displacement rates (from 0.5 to 5 mm/min), three findings are reported: The first finding is that the behavior of bone tissues in radial and tangential directions are almost similar, which allows us to consider the transversal isotropic behavior under static loads as well as under dynamic loads. The second finding is that the failure stress values of the longitudinal direction is much higher than those of the radial and tangential directions at low displacement rates, while there is no big difference at the high displacement rates. The third finding is a new mathematical model that relates the dynamic failure stress with the static one, considering the displacement rates. This model is validated by experimental results. The model can be effectively used in reliability and optimization analysis in prosthesis design, such as hip prosthesis.

The Manifesto of Pharmacoenosis: Merging HIV Pharmacology into Pathocoenosis and Syndemics in Developing Countries.

Pathocoenosis and syndemics theories have emerged in the last decades meeting the frequent need of better understanding interconnections and reciprocal influences that coexistent communicable and non-communicable diseases play in a specific population. Nevertheless, the attention to pharmacokinetic and pharmacodynamics interactions of co-administered drugs for co-present diseases is to date limitedly paid to alert against detrimental pharmacological combos. Low and middle-income countries are plagued by the highest burden of HIV, tuberculosis, malaria, and helminthiasis, and they are experiencing an alarming rise in non-communicable disorders. In these settings, co-infections and comorbidities are common, but no tailored prescribing nor clinical trials are used to assess and exploit existing opportunities for the simultaneous and potentially synergistic treatment of intertwined diseases. Pharmacoenosis is the set of interactions that take place within a host as well as within a population due to the compresence of two or more diseases and their respective treatments. This framework should pilot integrated health programmes and routine clinical practice to face drug–drug interaction issues, avoiding negative co-administrations but also exploiting potential favourable ones to make the best out of the worst situations; still, to date, guiding data on the latter possibility is limited. Therefore, in this narrative review, we have briefly described both detrimental and favourable physiopathological interactions between HIV and other common co-occurring pathologies (malaria, tuberculosis, helminths, and cardiovascular disorders), and we have presented examples of advantageous potential pharmacological interactions among the drugs prescribed for these diseases from a pharmacokinetics, pharmacodynamics, and pharmacogenetics standpoint.

Inertia tensor estimation for a rigid nadir pointing satellite based on star tracker

Accurate inertia properties information is important to reach an optimized estimation of attitude and precise control of a rigid spacecraft. Unfortunately, the satellite is succumbing several influences that can affect the inertia properties, such as fuel consumption and sloshing. Thus, this work inspects the use of star tracker to estimate the attitude, angular velocity and moment of inertia for a rigid nadir pointing satellite by employing extended Kalman filter, without any prior information about the nominal inertia matrix. The proposed estimator is applied in nadir pointing mode and without any constant control torque to avoid the attitude tumbling during the estimation phase, which in turn leads to a catastrophic failure of the satellite mission. The simulation results are compared to three other approaches and validated by Monte Carlo method that elucidates the good performance of the suggested approach and demonstrates its efficiency in satellite inertia tensor and attitude estimation even in worst situations.

Management of Facilities at Public Universities in Africa: Current Challenges and the Way Forward

Abstract This paper provides an overview of the challenges of management of facilities at public universities in Africa. Results reveal that: poor condition and the overcrowding of facilities, limited skills and capabilities of facility management departments, and lack of financial resources are the main challenges which the management of facilities at public universities in Africa faces. In worse situations, these problems have adversely affected the quality of the learning and teaching environment at the institutions and also threatened their existence. Prioritization of funding for the provision and improvement of quality facilities at public universities by governments, employment of well qualified and trained staff in the facility management departments at the public universities, and creation of innovative and smarter public-private partnerships aimed at the improvement of existing facilities and provision of new ones were identified as the key solutions that can be implemented to address the challenges that public universities in Africa are facing in relation to facilities. Findings will support public universities in Africa to address challenges of management of their facilities so that they can continue to achieve their intended objectives of offering high quality higher education, research and community engagement in order to accelerate socio-economic development of their countries on the continent in the future.

Traditional implementing usage of core programming languages in the object oriented programming

In today's technology era, programming is more essential than ever. Python and C++ are the most widely used programming languages. Python, the most popular programming language of the twenty-first century, is a higher-level object-oriented language, while C++ (the language that powers most operating systems) is a lower-level object-oriented language. Python and C++ are compared in this article. This book covers an introduction to various languages, memory management techniques, and reasons for their program execution speed. Furthermore, this study looked at the execution time and memory requirements of a variety of algorithms in both languages, comparing the best, average, and worst situations in each language. Furthermore, they are compared in terms of their benefits and drawbacks. C++ is faster than Python in terms of execution speed, according to the findings, but Python is a better option for beginners due to its simplicity. Aside from that, the language should be selected depending on the project type in order to get the best results. Programmers play a crucial role in computing. C/C++, Python, and Java are the most common languages. Beginning programming classes often utilize C++ and Java. Python has grown in popularity as an introductory programming language due to a variety of reasons. As an entry-level programming language, C, C++, Java, and Python were compared. More students selected Python over C++ because of its better pseudo-code syntax, simpler learning environment, and greated abstraction.

Imputation of Missing Traffic Flow Data Using Denoising Autoencoders

In transportation engineering, spatio-temporal data including traffic flow, speed, and occupancy are collected from different kinds of sensors and used by transportation engineers for analysis. However, the missing data influence the analysis and prediction results significantly. In this paper, Denoising Autoencoders are used to impute the missing traffic flow data. In our earlier research, we focused on a more general situation and used three kinds of Denoising Autoencoders: “Vanilla”, CNN, and Bi-LSTM, to impute the data with a general missing rate of 30%. The Autoencoder models are used to train on data with a high missing rate of about 80% in this paper. We demonstrate that even under extreme loss conditions, and Autoencoder models are very robust. By observing the hyper-parameter tuning process, the changing prediction accuracy is shown and in most cases, the three models maintain the original accuracy even under the worst situations. Moreover, the error patterns and trends concerning different sensor stations and different hours on weekdays and weekends are also visualized and analyzed. Finally, based on these results, we separate the data into weekdays and weekends, train and test the model respectively, and improve the accuracy of the imputation result significantly.

Traditionally Used Natural Products in Preventing Ionizing Radiation-Induced.

In various traditional and folk systems of medicine, plants and plant products have been used since time immemorial for treating various skin ailments. Further, many cosmeceutical creams formulated based on knowledge from ethnomedicinal use are marketed and used to treat various ailments. In the current review, an attempt is made at summarizing the beneficial effects of some plants and plant products in mitigating acute radiation dermatitis in humans undergoing curative radiotherapy. Additionally, emphasis is also placed on the mechanisms responsible for the beneficial effects. The objective of this review is to summarize the clinical observations on the prevention of radiodermatitis by plant products. In this review, the protective effects of Adlay (Coix lachryma-jobi L.) bran extract, Aloe vera, Calendula officinalis, Cucumis sativus, green tea constituent the epigallocatechin-3-gallate, honey, Achillea millefolium, Matricaria chamomilla, olive oil, and some polyherbal creams are addressed by also focusing on the mechanism of action for the beneficial effects. Two authors' data mined for information in Google Scholar, PubMed, Embase, and the Cochrane Library for publications in the field from 1901 up to July 2020. The focus was on acute radiation dermatitis, ionizing radiation, curative radiotherapy, human cancer. The articles were collected and analyzed. For the first time, this review addresses the usefulness of natural products like adlay bran, Aloe vera, Calendula officinalis, Cucumis sativus, green tea constituent the epigallocatechin-3-gallate, honey, Achillea millefolium, Matricaria chamomilla, olive oil, and some experimentally constituted and commercially available polyherbal creams as skincare agents against the deleterious effects of ionizing radiation on the skin. The protective effects are possibly due to the free radical scavenging, antioxidant, anti-inflammatory, wound healing and skin protective effects. The authors suggest that these plants have been used since antiquity as medicinal agents and require in-depth investigation with both clinical and preclinical validated models of study. The results of these studies will be extremely useful to cancer patients requiring curative radiotherapy, the dermatology fraternity, agro-based and pharmaceutical sectors at large.

Fishery migration under the influence of global warming

With the pace of global warming getting faster and faster, the temperature of the sea is gradually rising. Meanwhile, Scotland, located on the east coast of the North Atlantic, is facing a serious problem: how to develop their fishing industry while fish are migrating. In this article, we will use a series of models to analyze the current situation and recommend some ways for the development of small Scottish fishing companies. In Task 1, based on the sufficient global ocean temperature data recorded by the Met Office Hadley Centre, we firstly used the convolutional neural network (CNN) model to learn the ocean temperature change in the waters around Scotland in the past 50 years, to find out the changing trend and make a reasonable prediction on the sea temperature (SST) change in the next 50 years. Then, by using the obtained data in ocean temperature, continental shelves, and ocean currents, the behavior of herring and mackerel was excavated and simulated. Besides, we build a double-objective Linear Programming (DLP) model to predict the location of these two fish species in the next 50 years, based on the distribution patterns of the Scottish fisheries over the past 50 years. What’s more, we also performed the Process of Gridding on the map of Scotland, making all the models easier to calculate. In Task 2, we used the Logistic Growth Model (LGM) to establish a freshness model for captured fish, combined with the speed of a Scottish fishery boat to calculate the company's fishing range. Then we obtained the best and worst situations of the two fish habitats for the company's fishing business in the next 50 years through the fine-tuning of the parameters and sensitivity analysis. Also, we predicted the most likely future time for the company to be unable to continue fishing if maintaining a former strategy. In Task 3, we simulated the model in Task 2 to obtain a fish freshness model with refrigeration equipment, which provides basic elements needed for relocating a fishing port or updating fishing boats. Lastly, through the establishment of the Value-Estimated Model (AVEM), we roughly calculated the value of the two schemes to finally assure that the relocation of the fishing port was more economical. Although we did a lot of research to improve the accuracy of our work, the political decision of the government will change indefinitely, which means that our research still needs some innovational work before the actual adjustments of Scottish fishery.

User grouping and optimization-based pilot scheduling for mitigating the pilot contamination in massive multi cell MIMO systems

Massive multiple-input multiple-output (massive MIMO) is a promising approach in wireless communication systems for providing improved link reliability and spectral efficiency and it helps several users. The main aim is to solve pilot contamination issue in massive MIMO systems; this research paper utilizes two approaches for reducing the contamination. This paper presents the user grouping approach based on sparse fuzzy C-means clustering (sparse FCM), which groups user parameters based on parameters such as large-scale fading factor, SINR, and user distance. Here, same pilot sequences are assigned to center users in which the impact of pilot contamination is limited, while the algorithm assigns orthogonal pilot sequences to the edge users that suffer severely from pilot contamination. Therefore, the proposed user grouping keeps away from the inappropriate grouping of users, enabling effective grouping even under the worst situations of the channel. Secondly, pilot scheduling is done based on elephant spider monkey optimization (ESMO), which is designed by integrating elephant herding optimization (EHO) into spider monkey optimization (SMO). The performance of pilot scheduling based on grouping-based ESMO is evaluated based on achievable rate and SINR. The proposed method achieves maximal achievable rate of 41.29[Formula: see text]bps/Hz and maximal SINR of 124.31[Formula: see text]dB.