Center Operators Research Articles

Организационные аспекты мониторинга беременных в Тульской области.

In recent decades, there has been an increasing use of modern technologies and information systems in order to increase the accessibility, effectiveness and quality of medical care. The issue of organizing medical care using digital technologies in the Tula region is relevant. The purpose of the study is to determine the main organizational aspects of the work of the situational center of the Tula Regional Perinatal Center named after V. S. Gumilyovskaya for monitoring pregnant women. Materials and methods. The structure and mechanisms of operation of the situational center of the GUZ «TOPTS named after V. S. Gumilyovskaya» are analyzed. Research methods: empirical, analytical, descriptive. Results. The situation center inside the perinatal center represents a new direction, where the collection, visualization and processing of data from various services play a crucial role in optimizing and improving the level of medical care for pregnant women registered in the regional women’s clinics. One of the main pillars is such a division of the perinatal center as the obstetric remote consultation center. In addition to the ADCC, the situation center receives data on the state of the service from the regional information subsystem for monitoring maternity care in the Tula region (RISAR) and reports from the BARS-Web-Vaults information and analytical system. The main visualization tool obtained from all data sources used by the situation center is a component of the information and analytical system of the Situation Center of the Governor of the Tula Region interactive analytical panels with a graphical interface – dashboards. Conclusion. Creation of a situation center in the State Institution «TOPC named after. V. S. Gumilevskaya» is designed to increase the efficiency of the obstetrics and gynecology service in the region through the prism of modern technologies. The combination of modern technologies and medical knowledge in the situation center makes it a relevant tool for improving medical care for pregnant women and newborns, providing medical institutions with solutions based on advanced information technologies and data analytics. The introduction of a situation center made it possible to reduce the number of errors in routing pregnant women at the regional level.

Impact of Outpatient Care and Hybrid DRG - Impact on Hospitals

Hybrid DRG is a new form of remuneration in the German healthcare system. Selected OPS codes apply to all sectors, which, in combination with a specified ICD, result in a flat fee, regardless of whether the patient is managed as an outpatient or inpatient. This new form of billing has a huge impact on hospitals. The revenue situation is significantly worse than under the previous form of billing and requires adjustments at the locations. If these changes are not made, supply would be demonstrably deficient. Therefore, increased outsourcing to an outpatient setting is necessary. The advantages are the efficient use of staff and a structured selection of patients. Ideally, outpatient surgical units have an advantage here, but outpatient paths can also be created via separate areas in the central operating theatre. Using the example of inguinal hernia, over 50% of patients can be treated on an outpatient basis if selection is optimised. The disadvantage of this form of billing is the necessary financial reserve for innovations, training and the use of expensive operational aids. With the addition of incisional hernias to the hybrid catalogue in 2025, future care will become more cost-oriented.

Optimising picking operations in distribution centres: a simulation and algorithm-based approach for travel distance reduction

Abstract This study examines the pivotal role of the logistics sector in corporations, particularly those with diverse product portfolios. It focuses on the time-intensive “picking” process, driven by various products and material handling requirements for order fulfilment. The research presents a methodology that combines distribution centre modelling for simulation with an optimisation algorithm to enhance operational efficiency. The goal is to determine an optimal route for product retrieval, minimising employee travel distances, using the simulated annealing algorithm. The results showcase a significant 7 % reduction in employee travel distance during collection. This research is relevant to academia and practical applications as it presents an opportunity to reduce operational costs in distribution centres, enhance the efficiency of picking operations, and improve the competitiveness of companies. Furthermore, it contributes to operations research by addressing a complex problem with significant practical implications using a rather unused method to solve this problem of picking operations. The approach is empirically applied in a prominent Brazilian beverage company. This methodology proves valuable for optimising logistics operations in various industrial contexts, highlighting its practical applicability.

Satellite Onboard Transmitter Design With Spread Spectrum MIMO Antenna for 5G Wireless Networks

AbstractThe 5G new era implements standalone satellite communications that support wireless networking systems for future mobile communications by locating multiple satellites in low Earth orbit to provide global coverage of the entire Earth's surface. In this research, a newly found model of a satellite onboard transmitter using a uniform circular array multiple‐input multiple‐output antenna was designed to operate at a carrier frequency of 12 GHz and derived theoretical equations compared to the real‐time scenario. The integration of spread spectrum with multiple‐input multiple‐output antenna provides an advantage for higher capacity. It has a higher percentage of gain amplification on improving the transmission of electromagnetic power to meet the bandwidth requirement of center operating frequency, and this can transmit over a bandwidth of 1.28 GHz. The proposed satellite onboard transmitter model design aims to minimize the components, increase the speed of operations for higher bandwidth, and transmit large amounts of information to a large group of users. The transmitter can operate for the speed of 1.28 Gbps using pseudo‐random code, direct‐sequence spread spectrum, quadrature phase shift keying modulation, bandwidth separated in bands for 64 symbols using 128 Chebyshev‐type bandpass filter for transmission using 128‐element uniform circular array multiple‐input multiple‐output antenna. The satellite transmitter antenna produces a maximum gain of 14.526 dBi, and a maximum directivity of 17.986 dBi, and the efficiency at 12 GHz is 45.1% for the radiated power at 0.93 mW. This satellite transmitter will interconnect 5G wireless networks for the application of mobile communications complement terrestrial‐dependent networks.

Comparative analysis of the dispersion modeling and dose projection results performed under BARCO international project

Radiological assessments on zones to take protective actions in case of a nuclear or radiological emergency involve a series of real-time forecasts of radiological impact on the public at various distances from the release point, using actual weather or forecast data, information on the source term or facility status, and primary radiation monitoring data. This practice is implemented during the operation of emergency centers around the world in order to promptly report the occurrence and possible consequences of radiological accidents in the country and abroad in the event of a possible transboundary impact. Since the Chornobyl disaster, a lot of emergency exercises, research programs and projects, in particular, benchmarking, have served as international platforms for improving modeling capacity in atmospheric dispersion. This activity is carried out both on the basis of past severe accidents with significant atmospheric releases and corresponding radiological consequences, and on the basis of specific conditional (hypothetical) events that are developed in accordance with the purpose of the study. The paper is focused on the comparison results performed under the international project “Benchmarking on Assessment of Radiological COnsequences” (BARCO) conducted in 2020–2021 between five technical support organisations – members of the European Technical Safety Organisations Network (ETSON). The work contains a short overview of relevant international activity conducted in the past, a description of the BARCO project and its objectives, a list of participants, project tasks, initial data (source term, meteorology, list of benchmarking quantities, approach to data exchange, codes used). The study presents some of comparative analysis results obtained via two techniques such as code-to-code analysis (CTCA) and matched-pair analysis (MPA). The results discussion concentrates on the overall recommendations for code users. Conclusions provide the main outputs of the project.

Compact Silicon-Arrayed Waveguide Gratings with Low Nonuniformity.

Array waveguide gratings (AWGs) have been widely used in multi-purpose and multi-functional integrated photonic devices for Microwave photonics (MWP) systems. In this paper, we compare the effect of output waveguide configurations on the performance of AWGs. The AWG with an output waveguide converging on the grating circle had larger crosstalk and lower nonuniformity. We also fabricated a 1 × 8 AWG with an output waveguide converging onto the SOI's grating circle, whose central operation wavelength was around 1550 nm. The fabricated AWG has a chip size of 500 μm × 450 μm. Experimental results show that the adjacent channel crosstalk is -12.68 dB. The center channel insertion loss, as well as 3 dB bandwidth, are 4.18 dB and 1.22 nm at 1550 nm, respectively. The nonuniformity is about 0.494 dB, and the free spectral range is 19.4 nm. The proposed AWG is expected to play an important role in future MWP systems given its good nonuniformity and insertion loss level.

Predicting the Number of Visitors with Artificial Neural Networks to Support Strategic Decision-Making for Science Centers

Accurately predicting visitor attendance has become increasingly vital for science centers to optimize operations, improve visitor experiences, and stay competitive in attracting and engaging global audiences. As the demand for advanced predictive analytics grows, this study explores the use of artificial neural networks (ANNs) to forecast visitor numbers at science centers. In order to achieve this objective, data pertaining to the number of visitors to the Konya Science Centre was utilized. By analyzing a dataset of ten input factors, such as weather conditions and past visitor behavior, the study develops predictive models capable of accurately estimating future attendance patterns. The best-performing model, utilizing Bayesian regularization, 0.91444 for the training set, 0.25119 for the test set, and 0.91342 overall. These findings underscore the transformative potential of predictive analytics in science center management. Leveraging machine learning techniques, the study provides valuable insights into visitor preferences and behavior. This knowledge can empower science centers to make data-driven decisions, optimize resource allocation, and adapt their offerings to meet the evolving needs of their target audience. Ultimately, the study highlights how predictive analytics can enhance the long-term sustainability and global competitiveness of science center operations.

Opening a Donor Management Center.

In the United States alone, more than 100,000 people are waiting for a lifesaving organ transplant. In response to the growing need for viable organs to transplant, donor management centers have opened to provide care to brain-dead organ donors prior to the organ procurement operation. This article describes donor management center operations, details the opening of one such unit, and describes the results and lessons learned. More research is needed on the impact of nursing care on the specialized organ donor population.

Design and research of a total-auxiliary-system (TAS) for temperature and humidity control of underwater data center

ABSTRACT Underwater data centers, with their low energy use, cost, and latency, are seen as the future of data centers. This paper designs a Total Auxiliary System (TAS) for underwater data centers, including temperature and humidity control unit, biofouling prevention, and seawater corrosion prevention. In this paper, a model for the temperature and humidity control unit was developed, and key parameters such as heat load, specific moisture extraction rate (SMER), coefficient of performance (COP), and dehumidification volume were simulated. The effectiveness of the cooling performance was verified using Computational Fluid Dynamics (CFD) simulations. The system design also includes biofouling prevention via electric pulse and seawater corrosion prevention using cathodic protection with plastic coating and applied current. Calculations and verifications show that this system reduces energy consumption and ensures the long-term stable operation of underwater data centers.

Clouding the Future: Innovating Towards Net-Zero Emissions

Purpose: This paper explores the intersection of cloud computing, generative artificial intelligence (AI), and sustainability, focusing on how these technologies can drive the transition towards net-zero emissions. The aim is to assess how integrating cloud-based solutions and AI can enhance energy efficiency and support environmental goals. Methodology: The study employs a comprehensive review of recent literature, industry reports, and case studies from major cloud providers and technology companies. It analyzes the sustainability commitments of leading cloud providers, evaluates the role of generative AI in optimizing resource utilization, and examines the benefits of serverless automation in reducing carbon footprints. Findings: The research finds that cloud providers are making significant strides in sustainability through ambitious climate commitments and energy-efficient technologies. Generative AI is shown to improve decision-making and resource management, while serverless automation optimizes resource use and minimizes energy consumption. These advancements collectively contribute to achieving long-term sustainability goals and enhancing operational efficiency. Unique Contribution to Theory, Practice, and Policy: This paper provides a novel perspective on leveraging generative AI and serverless automation for sustainability. It offers actionable recommendations for organizations to integrate these technologies into their cloud strategies, emphasizing the importance of aligning with ESG criteria and optimizing data center operations. The findings support the development of policies that encourage technological innovation in the pursuit of environmental stewardship and carbon neutrality.

Business process improvement in call center operations at XYZ Malaysia through the implementation of Lean Six Sigma methodologies

Purpose: This study explores the application of Lean Six Sigma methodologies to improve the business process in call center operations at XYZ Malaysia to enhance service quality and increase customer satisfaction. This study emphasizes the importance of adopting Lean Six Sigma. Research Methodology: This study employed a mixed-method approach that utilized both qualitative and quantitative data. Qualitative data explores challenges and customer satisfaction levels, while quantitative data assesses the impact of Lean Six Sigma on specific metrics used by the call center. Results: The study concludes that implementing Lean Six Sigma can significantly improve call center operations by mapping the current process, analyzing the challenges comprehensively, and generating possible root causes and solutions to help call center operations enhance efficiency, reduce AHT, and boost customer satisfaction. The findings suggest that Lean Six Sigma methodologies can provide a structured approach to problem-solving and process enhancement, enabling call centers to achieve higher performance standards. Recommendations for future research include expanding the scope to other languages supported by XYZ Malaysia and exploring the long-term impact of Lean Six Sigma on overall business performance. This study provides valuable insights for call center managers and practitioners seeking to improve their business operations. The findings underscore the potential of Lean Six Sigma to drive continuous improvement, ensuring that call center operations not only meet but also exceed customer expectations. Ultimately, this approach is expected to set new standards for customer care and call center management, particularly in the English-speaking market, contributing to XYZ Malaysia's vision of delivering outstanding customer experiences through exceptional service quality.

Mitigation of the commutation failure problem in the HVDC multi-infeed scenario in Brazil using synchronized phasor measurement

This paper presents the Commutation Failure (CF) problem in the High Voltage Direct Current - Line-Commutated Converter (HVDC-LCC) scenario in Brazil through the monitoring of Short-Circuit Ratio (SCR), CF indexes, Direct Current (DC) link power, and the use of simulated synchrophasor data. The analysis was performed by training a neural network using data from the year 2020, and its robustness was verified using data from the year 2021 of the Brazilian Interconnected Power System (BIPS). Additionally, an analysis of transient instability in the North-South Transmission Corridor (NSC) will be presented right after the occurrence of CF by monitoring the Lyapunov transient instability margin, with the primary objective of bringing subsidies to the control center operation area according to the severity of the CF occurrence, preventing its propagation and mitigating it through real-time predictive actions, such as power redispatch on DC links and, thus, minimizing the occurrence of CF.

Challenges in acoustical modeling of data centers

Data centers are physical infrastructures that house computing machines and related hardware equipment for data storage and processing. Due to the ever-increasing trend of cloud computing and the need for secure data storage, several organizations are involved in the development of large data center facilities. This new industry sector is seeing massive growth in recent years and municipalities and counties across the nation are welcoming the construction of data centers in their areas. Acoustical modeling for a data center can be challenging due to various factors and this article discusses a few such challenges that were experienced during the acoustical assessments of data center operations.

Optimizing execution time and cost while scheduling scientific workflow in edge data center with fault tolerance awareness

Abstract Scheduling scientific workflows is essential for edge data centers operations. Fault tolerance is a crucial focus in workflow scheduling (WS) research. This study proposed fault-tolerant WS in edge data centers using Task Prioritization Adaptive Particle Swarm Optimization (TPAPSO). The aim is to minimize the Makespan, execution costs, and overcoming failures at all workflow processing stages, including when virtual machines are insufficient or tasks fail. The approach proposes three components: initial heuristic list, scheduling tasks with TPAPSO, and implementing performance monitoring with fault tolerance (PMWFT). TPAPSO-PMWFT is simulated using CloudSim 4.0. The experiments indicate that the suggested approach shows superior results compared to existing methods.

Quantum algorithm for solving the advection equation using Hamiltonian simulation

A quantum algorithm for solving the advection equation by embedding the discrete time-marching operator into Hamiltonian simulations is presented. One-dimensional advection can be simulated directly since the central finite-difference operator for first-order derivatives is anti-Hermitian. Here this is extended to industrially relevant multidimensional flows with realistic boundary conditions and arbitrary finite-difference stencils. A single copy of the initial quantum state is required and the circuit depth grows linearly with the required number of time steps, the sparsity of the time-marching operator, and the inverse of the allowable error. State-vector simulations of a scalar transported in a two-dimensional channel flow and lid-driven cavity configuration are presented as a proof of concept of the proposed approach. Published by the American Physical Society 2024

Context-aware reinforcement learning for cooling operation of data centers with an Aquifer Thermal Energy Storage

Data centers are often equipped with multiple cooling units. Here, an aquifer thermal energy storage (ATES) system has shown to be efficient. However, the usage of hot and cold-water wells in the ATES must be balanced for legal and environmental reasons. Reinforcement Learning has been proven to be a useful tool for optimizing the cooling operation at data centers. Nonetheless, since cooling demand changes continuously, balancing the ATES usage on a yearly basis imposes an additional challenge in the form of a delayed reward. To overcome this, we formulate a return decomposition, Cool-RUDDER, which relies on simple domain knowledge and needs no training. We trained a proximal policy optimization agent to keep server temperatures steady while minimizing operational costs. Comparing the Cool-RUDDER reward signal to other ATES-associated rewards, all models kept the server temperatures steady at around 30 °C. An optimal ATES balance was defined to be 0% and a yearly imbalance of −4.9% with a confidence interval of [−6.2, −3.8]% was achieved for the Cool 2.0 reward. This outperformed a baseline ATES-associated reward of 0 at −16.3% with a confidence interval of [−17.1, −15.4]% and all other ATES-associated rewards. However, the improved ATES balance comes with a higher energy consumption cost of 12.5% when comparing the relative cost of the Cool 2.0 reward to the zero reward, resulting in a trade-off. Moreover, the method comes with limited requirements and is applicable to any long-term problem satisfying a linear state-transition system.

Scientific Integration of Operations Research and Machine Learning for Data Centre Optimization

In this study, we explore how to optimize data center operations by combining Operations Research (OR) and Machine Learning (ML) methodologies with Python-based categorization algorithms. Using Scikit-learn and TensorFlow, two Python libraries, we investigate how ML algorithms might be integrated with OR techniques like queuing theory and linear programming to forecast workloads and allocate resources more effectively. Problems with scheduling workloads, allocating resources, and managing energy consumption are at the heart of our research into data center optimization. The goal of this comprehensive framework is to create more effective and environmentally friendly data centre operations by systematically evaluating Python-based categorization models in response to changing workload demands and environmental circumstances. Introduction: The backbone of our digital infrastructure, data centers stand tall in the ever-changing world of contemporary technology. A vast variety of online services, including social media platforms, e-commerce websites, cloud computing, and big data analytics, rely on the servers, storage devices, networking gear, and other essential components housed in these expansive facilities. Meeting the ever-increasing demands for computational resources while simultaneously enhancing performance, efficiency, and cost-effectiveness is a daunting task for data centers, which are already struggling to keep up with the exponential growth in both the amount and complexity of digital data. Objectives: Our goal in writing this article is to delve into the ways in which data center optimization intersects with Operations Research and Machine Learning. Data center optimization presents a wide range of problems, and this course will explore the theory, methods, and best practices for using OR and ML to solve these problems. To develop an integrated framework that combines operations research (OR) and machine learning (ML) techniques to optimize the performance, energy efficiency, and reliability of data centers. Methods: Optimization strategies that improve data center operations in terms of performance, efficiency, and sustainability. These proposed strategies make use of both OL and ML techniques. Data center operators can optimize resource allocation, workload management, temperature control, energy consumption, and anomaly detection in real-time by formally stating the optimization problem in a mathematical framework. This allows for informed decision-making, systematic analysis of trade-offs, and the implementation of adaptive control strategies. Results: The visualization depicts the projected energy usage in terms of bandwidth for both approaches, compared to the actual values. In general, although both methods demonstrate potential, additional refinement and optimization may be necessary to attain superior outcomes in real-life situations. This discussion presents an analysis of the performance of both procedures and offers insights into their respective strengths and shortcomings, which can serve as a foundation for further investigation or improvement of the approaches. Conclusions: By using a comprehensive and multidisciplinary approach, we can optimize data centres in a way that boosts efficiency and performance while simultaneously encouraging innovation, resilience, and sustainability in data centre operations. Also, Data center gains in resource operators can achieve considerable utilization, energy efficiency, and overall system performance by integrating optimization algorithms, predictive analytics, and adaptive control strategies

Blowing against the Wind? a narrative approach to central Bank foreign exchange intervention

Most countries in the world use foreign exchange intervention, but measuring the success of the policy is difficult. By using a narrative approach, I identify interventions when the central bank manages to influence the exchange rate based on pure luck. I separate them from interventions when the central bank actually impacted the exchange rate. Because intervention records are daily aggregates, an intervention might appear to have changed the direction of the exchange rate, when it is more likely to have been caused by market news. This analysis allows to have a better understanding of how successful central bank operations really are. I use new daily data on Bank of England interventions in the 1980 s and 1990 s. Some studies find that interventions work in up to 80 % of cases. Yet, by accounting for intraday market moving news, I find in adverse conditions, the Bank of England managed to influence the exchange rate only in 8 % of cases. I use natural language processing to confirm the validity of the narrative approach. Using lasso and a VAR analysis, I investigate what makes the Bank of England intervene. I find that only movement on the Deutschmark and not US dollar exchange rate made the Bank intervene. Also, I find that interest rate hikes were mostly a tool for currency management and accompanied by large reserve sales.

A cell-centered implicit finite difference scheme to study wave propagation in acoustic media: A numerical modeling

In the present paper, we present a Cell-Centered Implicit Finite Difference (CCIFD) operator-based numerical scheme for the propagation of acoustic waves that is very effective, accurate, and small in size. This scheme requires fewer estimation points than the traditional central difference derivative operator. Any numerical simulation is significantly impacted by the precision of a numerical derivative. Long stencils can deliver excellent accuracy while also minimizing numerical anisotropy error. However, a long stencil requires a lot of computational resources, and as these derivatives get bigger, they could start to look physically unrealistic due to contributions from nodes located extremely far, wherein the derivative is local in nature. Furthermore, using such lengthy stencils at boundary nodes may result in errors. The present article investigates a cell-centered fourth order finite difference scheme to model acoustic wave propagation which utilizes a lesser number of nodes in comparison to the traditional Central Difference (CD) operator. However, in general the implicit derivative operator has high computational cost and therefore despite its significant advantages it is generally avoided to be implemented in applications. This serves as a motivation for the present paper to explore a technique called CCIFD that significantly decreases the computational expense by nearly fifty percent. Additionally, spectral characterization of the CCIFD derivative operator has been analyzed and discussed. Finally, the wave propagation has been numerically simulated in 2-dimensional homogeneous and Marmousi model using CCIFD scheme to validate the applicability and stability of the scheme.

Optimization operation of data Center's distributed air conditioning system based on supply-demand matching

A supply-demand matching optimization control of data center's distributed air conditioning system was investigated. For the demand side, an estimation model of a server's cooling air parameters was proposed based on server’ thermal resistance. Test results showed that thermal resistances of servers with power being 6–11 kW were 5.5–9 °C/kW. With the server's heat resistance, chip's temperature and power being known, cooling air outlet temperature and air flow rate can be calculated for different air supply temperature. For the supply side, a mathematic model of the distributed air conditioning system, that combines free cooling and mechanical cooling, was established to calculated energy consumption. Using the above two models, the optimal cooling air supply temperature, which leads to the least power consumption of the air conditioning system can be calculated. Performances of the optimized control method were compared with the conventional control method. Results showed that energy consumption could be saved by 94.4 %, 47.6 % and 31.3 % under the natural cooling mode, the hybrid cooling mode, and the active cooling mode, respectively. Annual energy saving ratios after using the optimized control method in the data center located in Harbin and Kunming were 62.2 % and 45.7 %, respectively.