Current System Research Articles

Minimum work associated with separating nitrogen from air: An exergy analysis

Background Nitrogen is essential for a variety of industries, including heat treatment, laser cutting, fire protection, and food packaging. Many companies in these industries obtain nitrogen via on-premises air separation processes. The three main processes for separating nitrogen from ambient air are cryogenic distillation, membrane separation, and pressure-swing adsorption (PSA). Improvements to these processes will likely focus on increasing efficiency, resulting in reduced environmental impact owing to less electrical power demand and opportunities for economic incentives. Regardless of the process utilized, a minimum theoretical amount of work input is required to obtain nitrogen gas at different pressures and concentrations compared to ambient conditions. Methods An equation was derived to evaluate the total exergy (including thermo-mechanical and chemical exergy) of product and exhaust mixtures resulting from air separation, indicating the minimum theoretical work input as a function of the product pressure, purity, and process recovery rate. This analysis considered an air separation system as a black box, with the input, output, and exhaust assumed to be ideal gas mixtures of nitrogen and oxygen at 15°C. The analysis applies to cryogenic distillation if the product and exhaust mixtures return to the gas phase. Results In general, the minimum required work input increases with product purity and recovery rate. Plots of minimum theoretical work versus product purity and recovery rate were made for two product pressures (atmospheric and 800 kPa) to show the behavior of the derived equation. Conclusions The analysis allows for direct efficiency (based on the second law of thermodynamics) comparisons between existing processes and future technological innovations in the field of air separation. Actual air separation systems have low efficiencies compared to ideal systems; actual PSA systems were estimated to have second law efficiencies of 5.5–11.2%. Therefore, there is great potential for improvements to current air separation systems.

H∞ Controller Design for Networked Systems With Two-Channel Packet Dropouts and FDI Attacks.

In this article, the stochastic analysis and H∞ controller design problems of networked systems with packet dropouts and false data injection attacks are investigated. Different from the existing literature, we focus on the linear networked systems with external disturbances and both sensor-controller channel and controller-actuator channel are studied. First, we present a discrete-time modeling framework that leads to a stochastic closed-loop system with randomly varying parameters. To facilitate the analysis and H∞ control of resulting discrete-time stochastic closed-loop system, an equivalent yet analyzable stochastic augmented model is further constructed by matrix exponential computation. Based on this model, a stability condition is derived in the form of linear matrix inequality (LMI) with the aid of a reduced-order confluent Vandermonde matrix, Kronecker product operation, and law of total expectation. Specifically, the dimension of the LMI obtained in this article does not increase as the upper bound of consecutive packet dropouts does, which is also different from the existing literature. Subsequently, a desired H∞ controller is obtained such that the original discrete-time stochastic closed-loop system is exponentially mean-square stable with a prescribed H∞ performance. Finally, a numerical example and a direct current motor system are exploited to substantiate the effectiveness and practicability of the designed strategy.

Subsurface acoustic ducts in the Northern California current system.

This study investigates the subsurface sound channel or acoustic duct that appears seasonally along the U.S. Pacific Northwest coast below the surface mixed layer. The duct has a significant impact on sound propagation at mid-frequencies by trapping sound energy and reducing transmission loss within the channel. A survey of the sound-speed profiles obtained from archived mooring and glider observations reveals that the duct is more prevalent in summer to fall than in winter to spring and offshore of the shelf break than over the shelf. The occurrence of the subsurface duct is typically associated with the presence of a strong halocline and a reduced thermocline or temperature inversion. Furthermore, the duct observed over the shelf slope corresponds to a vertically sheared along-slope velocity profile, characterized by equatorward near-surface flow overlaying poleward subsurface flow. Two potential duct formation mechanisms are examined in this study, which are seasonal surface heat exchange and baroclinic advection of distinct water masses. The former mechanism regulates the formation of a downward-refracting sound-speed gradient that caps the duct near the sea surface, while the latter contributes to the formation of an upward-refracting sound-speed gradient that defines the duct's lower boundary.

Frequency Derivatives of Eddy Current System Including Electric Scalar Potential

Frequency Derivatives of Eddy Current System Including Electric Scalar Potential

Why Is the Westward Rossby Wave Propagation from the California Coast “Too Fast”?

Abstract Past work has shown that interannual California coastal sea level variability is mostly of equatorial origin, and decades of satellite sea surface height (SSH) and in situ dynamic height observations indicate that this interannual signal propagates westward from the California coast as nondispersive Rossby waves (RWs). These observations agree with standard linear vertical mode theory except that even when mean flow and bottom topography are considered, the fastest baroclinic vertical mode RW in each case is always much slower (1.6–2.3 cm s−1) than the observed 4.2 cm s−1. This order-1 disagreement is only resolved if the standard bottom boundary condition that the vertical velocity w′ = 0 is replaced by perturbation pressure p′ = 0. Zero p′ is an appropriate bottom boundary condition because south of San Francisco the northeastern Pacific Ocean boundary acts approximately like an impermeable vertical wall to the interannual equatorial wave signal, and therefore equatorial quasigeostrophic p′ is horizontally constant along the boundary. Thus, if equatorial p′ = 0 at the bottom, then this condition also applies off California. The large-scale equatorial ocean boundary signal is due to wind-forced eastward group velocity equatorial Kelvin waves, which at interannual and lower frequencies propagate at such a shallow angle to the horizontal that none of the baroclinic equatorial Kelvin wave signal reaches the ocean floor before striking the eastern Pacific boundary. Off California this signal can thus be approximated by a first baroclinic mode with p′ = 0 at the bottom, and hence the long RW speed there agrees with that observed (both approximately 4.2 cm s−1). Significance Statement The California Current System is one of the most biologically rich and best-documented coastal regions in the world. In this region coastal sea level propagates westward from the coast at about 110 km month−1, slow enough to enable us to make large-scale ocean climate forecasts of the California Current ecosystem using coastal sea level. Although the westward speed seems slow, theoretically it is about double what we would expect. Offered here is an explanation of why this speed is “too fast” by linking the California wave signal to the equator, El Niño, and the shallow equatorial ocean response to the wind.

Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning

The splendid technological inventions supersede many traditional agricultural monitoring systems. In the last decade, a variety of new techniques and tools are proposed to monitor storage areas, which provide more safe and secure storage for different crops. The term storage area monitoring is supposed to check and avoid fire hazards, whereas numerous other hazards also need attention. One such hazard to cotton storage is spontaneous combustion, a process by which an element having comparatively low ignition temperature (hay, straw, peat, etc.) starts to relieve heat. In the presence of spontaneous combustion and lack of oxygen, if cotton catches any sparks from bales or physicochemical heat to ignite, the combustion can convert in to smoldering, and it can last up to several days without being discovered. Consequently, the actual fire occurs, cotton silently smoldering which not only affects cotton quality but also became the reason of big fire event. Many researchers propose valuable tools and techniques based on laboratory methods and modern techniques as well for detection and prevention of security hazards in storages. However, there is no standalone efficient tool/technique to monitor the storage area for spontaneous combustion. In current research, we propose an efficient wireless sensor network (WSN) and machine learning- (ML-) based storage area monitoring system for early prediction of spontaneous combustion in the cotton storage area. The WSN is used to collect real-time values from storage field by different combinations of sensors and send this over the network, where data is processed to identify spontaneous combustion and distribute the prediction results to the end user. The real-time data collection and ML-based analysis make the system efficient and reliable. The efficiency of the current system is verified by presenting two groups of cotton stored with different conditions. The results showed that the proposed system is able to detect spontaneous combustion well in time with a 95% accuracy rate.

Influence of varying carbon oxides concentrations on the selectivity of an electrical sensor utilizing graphene nanoribbons

We have considered a graphene nano ribbon-based two probe-device working on the principle of alteration of current-voltage (I–V) characteristic, upon interactions of its channel material with the adsorbed gas molecules. The analysis of sensing parameters alongside the electron transport mechanism was carried out by using density functional theory (DFT) formulated on the Hohenberg-Kohen paradigm in conjunction with the non-equilibrium green functions (NEGF) method. Electronic transport through similar work reported earlier reveals that the combination of these two techniques coordinates extremely well and yields convincing electronic results. The nanoribbon-based sensing device was employed to evaluate its capability to detect Carbon dioxide (CO2) and Carbon monoxide (CO) molecules. Analysis of the computed result reveals significant modifications in the electronic property of the device upon adsorption of carbon dioxide and carbon monoxide entities on the channel surface. One such significant and dramatic change in the characteristic that we observed is the nature of the channel that transforms from metallic to semi-metallic. Another observed change is the signal amplification capability relative to the pristine device. The signal gets amplified on increasing number of molecular entities from one to six.The transformation in sensor performance is additionally supported by examining variations in multiple parameters, including adsorption energy, charge transfer, differential resistance, and the figure of merit, also known as the peak-to-valley ratio (PVR). The investigation unveiled that the currently designed zigzag graphene nanoribbon (ZGNR) model exhibits high sensitivity to both CO2 and CO gases. This phenomenon is substantiated by the elevated adsorption energy, increased charge transfer, elevated peak-to-valley ratio (PVR), and heightened sensor response. The substantial sensor response, registering at a magnitude of 8.3, underscores the efficacy of the current system in detecting both low and high concentrations of CO and CO2 molecules. The current study emphasizes the notion that understanding the electronic and transport behaviour of zigzag graphene nanoribbons (ZGNR) and analogous materials could offer valuable insights for addressing the threat of environmental pollution.

Primary pancreatic lymphoma: Histopathological pattern of 8 cases.

To report the histopathological pattern of primary pancreatic lymphoma (PPL) in 2 tertiary hospitals. The pathology slides and reports of all the cases diagnosed in pathology departments in 2 referral hospitals were reviewed. An additional immunohistochemistry study was done to reclassify lymphomas according to the current system. Eight patients with PPL have been identified. The ages ranged from 36 to 71 years. Clinical presentation includes abdominal pain, weight loss, jaundice, abdominal mass, nausea, and vomiting. Pathological evaluation revealed 5 diffuse large B-cell lymphomas, one high-grade B-cell lymphoma, one MALT lymphoma, and one follicular lymphoma. Primary pancreatic lymphoma is a very rare tumor without specific clinical, laboratory tests, or radiological findings. Abdominal pain is the most common clinical presentation. Diffuse large b-cell lymphoma is the most common pathological subtype. Primary pancreatic lymphoma should be taken into consideration when evaluating pancreatic mass to avoid unnecessary surgical resection.

ASSESSMENT OF SIGNALIZED INTERSECTION: A CASE STUDY IN BAGHDAD City by USING SIDRA SOFTWARE

Over the world, signalized intersections and road networks with heavy traffic have posed severe issues. So, it is crucial to employ cutting-edge software tools to ensure the current system can be managed and assessed. Therefore; this paper aims to analyze and evaluate the traffic flow performance at AL-Masbah signalized intersections in Baghdad city during peak hours by utilizing SIDRA INTERSECTION 5. For purposes of evaluation, the Level of Service LOS, average delay time, capacity, degree of saturation, queue for Intersection, fuel consumption, and carbon dioxide emission have been considered. In this study, video count was used to collect data. The main input data included peak hour factor (PHF), numbers and width of lanes, percent of heavy vehicles for each direction and movement, median, phasing, and timing. The results of the analysis indicated that the intersection is operated at LOS ( F) with a backlog queue of 166.1 which is the queue for any lane used by movement (vehicles)and a 1262m queue for any lane used by movement (meters) m, while fuel consumption was at a rate of 849.9 liters per hour. Furthermore, the results revealed that carbon dioxide (CO2) emission was increased by 2124.8 Kg per hour. So, It can be concluded that the intersection needs to improve its level of service by changing its geometry or by implementing an Intelligent Transportation System (ITS).

Substorm‐Time Ground dB/dt Variations Controlled by Interplanetary Shock Impact Angles: A Statistical Study

AbstractIn this study, we investigate the effects caused by interplanetary (IP) shock impact angles on the subsequent ground dB/dt variations during substorms. IP shock impact angles have been revealed as a major factor controlling the subsequent geomagnetic activity, meaning that shocks with small inclinations with the Sun‐Earth line are more likely to trigger higher geomagnetic activity resulting from nearly symmetric magnetospheric compressions. Such field variations are linked to the generation of geomagnetically induced currents (GICs), which couple to artificial conductors on the ground leading to deleterious consequences. We use a sub‐set of a shock data base with 237 events observed in the solar wind at L1 upstream of the Earth, and large arrays of ground magnetometers at stations located in North America and Greenland. The spherical elementary current system methodology is applied to the geomagnetic field data, and field‐aligned‐like currents in the ionosphere are derived. Then, such currents are inverted back to the ground and dB/dt variations are computed. Geographic maps are built with these field variations as a function of shock impact angles. The main findings of this investigation are: (a) typical dB/dt variations (5–10 nT/s) are caused by shocks with moderate inclinations; (b) the more frontal the shock impact, the more intense and the more spatially defined the ionospheric current amplitudes; and (c) nearly frontal shocks trigger more intense dB/dt variations with larger equatorward latitudinal expansions. Therefore, the findings of this work provide new insights for GIC forecasting focusing on nearly frontal shock impacts on the magnetosphere.

DTformer: An Efficient Digital Twin Model for Loss Measurement in UHVDC Transmission Systems

Ultra-high Voltage Direct Current system (UHVDC) can realize long distance transmission, and its loss measurement has great significance for loss reduction and fault diagnosis. Currently, loss measurements can be performed by model-driven and data-driven approaches. However, poor operating conditions can lead to system sensing errors, which can significantly reduce the accuracy and speed of measurement. To deal with this problem, we propose a digital twin architecture and constructs a data-driven learning model. The model, DTformer, is embedded in the digital twin architecture. And it has three remarkable processes: encoder-decoder based random feature extraction, loss measurement transfer learning and knowledge distillation. These processes extract parameter information and topology features from the data of UHVDC. The model has good accuracy, response time and resistance to the missing data. The comparison experiment shows that DTformer outperforms popular feature extraction models with an average decrease of 38.80% in RMSE and 42.82% in MAE, and the best R2 value is closest to 1. The analyses of lightweight and missingness show that the DTformer also has significant response time improvement and missing resistance. Finally, we also implement the visualization of digital twin to enable operators to perform monitoring, computation, analysis and management of UHVDC.

Communication-Efficient and Resilient Distributed Q-Learning.

This article investigates the problem of communication-efficient and resilient multiagent reinforcement learning (MARL). Specifically, we consider a setting where a set of agents are interconnected over a given network, and can only exchange information with their neighbors. Each agent observes a common Markov Decision Process and has a local cost which is a function of the current system state and the applied control action. The goal of MARL is for all agents to learn a policy that optimizes the infinite horizon discounted average of all their costs. Within this general setting, we consider two extensions to existing MARL algorithms. First, we provide an event-triggered learning rule where agents only exchange information with their neighbors if a certain triggering condition is satisfied. We show that this enables learning while reducing the amount of communication. Next, we consider the scenario where some of the agents can be adversarial (as captured by the Byzantine attack model), and arbitrarily deviate from the prescribed learning algorithm. We establish a fundamental trade-off between optimality and resilience when Byzantine agents are present. We then create a resilient algorithm and show almost sure convergence of all reliable agents' value functions to the neighborhood of the optimal value function of all reliable agents, under certain conditions on the network topology. When the optimal Q -values are sufficiently separated for different actions, we show that all reliable agents can learn the optimal policy under our algorithm.

Resource Organizer

There are a lot of problems with the current system because everything is done manually. The present system's biggest flaw is the enormous physical volume of data, which makes searching for and retrieving specific pieces of information a pain. Papers containing information are susceptible to harm. It takes a long time to find out whether documents arrived or not when you have to handle everything by hand. At the time of product application, it will be rejected if any document is missing. It is really time-consuming to reapply for the product process. All these issues with the current method led them to believe that a computerised system would be better. All of these issues will be resolved by the automated system. There is a good probability that more people's time will be wasted in this communication scenario. It is quick, adaptable, and easy to use. Analysing a system is looking closely at all the different things it does and how they relate to each other and the world around them. Analysing a system involves doing things like drawing lines around it and checking to see if a potential system has to take other systems into account. Decision points and transactions processed by the current system are the subjects of data acquired during analysis. The project's backend was built in SQL Server and frontend was built in Asp.Net. A large number of managers are in charge of each and every one of an organization's operations. Manufacturing, production, marketing, and other facets of organisational management are overseen by these managers. An important problem with management is that, as time went on, they shifted their focus to human capital. Now, every company has a hierarchical structure, and each structure has its own unique purpose and character. Given that their people are their most valuable asset, larger companies in this situation must devote significant resources to human resource management. Organisational data evolved over time away from its core functions in this way. To sum up, a resource organiser is a piece of software that helps with the management and coordination of different resources. Assets can be either concrete, like machinery and buildings, or intangible, like human capital and time.

How can Vedic Education Ideals Save Our Current Education System and Help in Its Improvement?

Our current education system is not going well. It is not producing quality education and capable citizens. It is sick at all levels whether its primary, secondary or higher educational level. It has many serious problems which need to be solved as soon as possible for a better education system. It has many issues like - lack of discipline, low concentration power in students, lack of dedication in teachers, high fee charges, corrupt practices, rote learning, theoretical curriculum, boring teaching methods, no personality or character development, pen-paper based evaluation system, no scope for creativity or vocational training, etc. etc. If order to make our modern education system better, more relevant and efficient we need to look at our past. Our ancient historical time period has all the solutions for our modern day problems. We should revive our vedic period’s Gurukula education system. Gurukuls were based on “simple living, high thinking” principle. If we revive some ideals of our Gurukula education system, our whole education system will witness a revolution. This new revolution will be so impactful that it can remove all the current shortcomings in our education system. This article will focus on the need of revivaling the Gurukula system. It will also try to suggest some possible ways to implement Vedic ideals in current education system. Vedic educational ideals are the only hope to save our education system by degrading. They will make our education more practical and valuable.

학부모의 교권 침해 상황에서 나타난 학교장 리더십 분석 -서이초 교사 사건 이후 신문기사와 FGI를 중심으로-

To investigate principals’leadership role and its relevance in school situation in conflict, this study analyzes news articles regarding school principals and interviews with a group of experienced teachers, and identifies some concrete contents of leaderships working on the school sites. Firstly, this study reviews increasing school conflicts by parents’demand for and dissatisfaction with teachers, the leadership roles by principals, and current principal selection institution and training programs and confirms that within current system, it is difficult to form effective and capable leadership to solve conflict democratically and efficiently in school situations. Secondly, this study search news articles for two months from July 18 to september 17 using the Korea Press Foundation's Big Kinds (www.bigkinds.com) service and find 217 articles related to the terms 'teacher rights' and 'principal'. After discussion, 14 articles and 22 cases were extracted and analyzed and a few distinctive leadership types are found such as avoiding responsibility, scolding teacher involved for responsibility, decisions pending, positive conflict mediation type, and proactive communication type. Thirdly, through focus group interview, we check how these kinds of leadership work on school-sites. Based on this analysis, we conclude that further researches and public discourse on leadership formation and principals’ selection and training are required.

Факторы социального доверия в Европе и России

The article analyzes the factors of generalized social trust in the modern states. The authors rely on the “up bottom” approach, which as sumes that trust in political institutions has a significant impact on social trust, while also accounting for socio-economic factors. The authors examine the case of Russia separately due to the specifics of political trust in personalist presidential regimes, Russia’s longer exposure to communism, as well as the modern increase in the level of political trust, which radically exceeds the level of social trust. Based on the comparative study of sociological trends and factors of social trust in the country, they document the gradual development of a situation where social trust is inversely correlated with political trust. They tend to explain this phenomenon by the exaggerated importance of the personal and public security agenda, which negatively affects social trust, while simultaneously increases the demand for political trust. Socio-economic factors in Russia play a limited role, but can resume their influence during periods of reduced social tensions. According to the authors’ conclusion, given the current political system, the potential for increase in social trust in the Russian Federation is limited.

대학 교원이 인식한 학생 강의평가의 실태 분석과 개선 방안 탐색

This study aims to analyze the awareness of instructors regarding course evaluations conducted by students and explore the current state and implications of course evaluations based on their perceptions. To achieve this, a survey was conducted to 394 instructors from universities and colleges nationwide. The key research findings are as follows: Firstly, university instructors generally expressed satisfaction with current course evaluation systems and held a positive perception of their purposes. Secondly, a significant issue identified in course evaluations is the potential reduction in accuracy due to the insincere responses of some students. Thirdly, for improvements of course evaluations, conducting course evaluations twice per semester, spanning mid-term and final exams, with flexible timing between two weeks before final exams and before grade disclosure were suggested. The composition of evaluation items should encompass both objective and subjective questions. Consideration should be given to limiting the application of evaluations from students with low academic achievement. Evaluation items should be designed with consideration for the characteristics of the courses, and the results should be utilized for faculty performance assessments and instructor selection. It is suggested that lower-ranked instructors in course evaluations should be mandated to undergo coaching or training related to teaching methods. Lastly, beyond student evaluations, it is requred to explore various evaluation methods such as self-evaluation, teaching portfolios, and peer evaluations.

TAX INCENTIVE SCHEMES AND BALANCED REGIONAL ECONOMIC GROWTH IN TURKEY

This study delves into the impact of investment incentives on regional development and economic growth in Turkey. It provides a comprehensive overview of the investment incentive system introduced in 2012, categorizing regions based on development levels and offering various incentives such as tax exemptions, reductions, and social security support. The research investigates the relationship between investment incentives, public expenditures, regional differences, and economic growth. Through a thorough examination, the study aims to understand how the current incentive system addresses regional disparities and promotes balanced economic growth. The literature review explores international perspectives, including China and Italy, providing insights into the effectiveness of tax incentives on R&D investment and macroeconomic performance. The study also analyzes the European Commission's report on tax incentives for venture capital and business angels. The results and discussion section outlines the investment incentive framework in Turkey, emphasizing regional, priority, large-scale, and strategic schemes. The research considers twelve investment categories, each supported by specific incentive measures. The conclusion emphasizes the importance of aligning incentives with regional needs, fostering equitable development, and promoting strategic sectors. It highlights the significance of quasi-tax supports, VAT exemptions, and interest subsidies in encouraging investments. The employment and income tax withholding supports, especially in the 6th region, are recognized for their contribution to reducing unemployment. The study concludes by underscoring the need for continuous evaluation and adaptation of incentive schemes to address evolving economic challenges.

OPTIma: simplifying calorimetry for proton computed tomography in high proton flux environments

Objective. Proton computed tomography (pCT) offers a potential route to reducing range uncertainties for proton therapy treatment planning, however the current trend towards high current spot scanning treatment systems leads to high proton fluxes which are challenging for existing systems. Here we demonstrate a novel approach to energy reconstruction, referred to as ‘de-averaging’, which allows individual proton energies to be recovered using only a measurement of their integrated energy without the need for spatial information from the calorimeter. Approach. The method is evaluated in the context of the Optimising Proton Therapy through Imaging (OPTIma) system which uses a simple, relatively inexpensive, scintillator-based calorimeter that reports only the integrated energy deposited by all protons within a cyclotron period, alongside a silicon strip based tracking system capable of reconstructing individual protons in a high flux environment. GEANT4 simulations have been performed to examine the performance of such a system at a modern commercial cyclotron facility using a σ ≈ 10 mm beam for currents in the range 10–50 pA at the nozzle. Main results. Apart from low-density lung tissue, a discrepancy of less than 1% on the Relative Stopping Power is found for all other considered tissues when embedded within a 150 mm spherical Perspex phantom in the 10–30 pA current range, and for some tissues even up to 50 pA. Significance. By removing the need for the calorimeter system to provide spatial information, it is hoped that the de-averaging approach can facilitate clinically relevant, cost effective and less complex calorimeter systems for performing high current pCTs.

Artificial Neural Networks for Long-Bone Fracture Detection Based on X-Ray Image Contour Features

Abstract: Two contour-based fracture detection strategies are presented in the ensuing research. The programs are designed to help medical practitioners identify human long bone fractures from X-ray pictures. The line-based fracture detection systems suggested in [1] served as the foundation for the creation of the contour-based fracture. Convolutional Neural Networks (CNN) are widely used in current Computer Aided Diagnosis (CAD) systems to classify broken X-ray images. Even while the current CAD systems achieve excellent accuracy, doing so comes at a cost: a large volume of training data. By using identified contours in X-ray pictures, the suggested techniques aim to achieve high classification accuracy with little training data. Two methods exist for contour-based fracture detection. The first is the enhanced CHFB scheme, while the second is the Standard Contour Histogram Feature-Based (CHFB). The modified CHFB scheme differs from the other one in that it removes the detected flesh contours surrounding the leg region.