Operational Centers Research Articles

Detailed Transfer Line Beam Characteristics at an Operational Proton Therapy Center

The McLaren Proton Therapy Center functions as a multi-room, clinically active cancer treatment center. Beam position, size, and current measurements along the transfer lines have been utilized together with simulations to validate computer models for the Center’s transfer lines and associated beam parameters. Beam profile monitor (BPM) data collected along the beamlines provided detailed beam measurements. A computer tool, utilizing nonlinear constrained optimization programs that are integrated with a beam envelope code, was used to find detailed beam phase space parameters (centroids, sigma matrices) that reproduced the measured data. Quadrupole scan and steerer scan experiments have been used to validate the calibration and alignment of these devices, as well as to independently confirm beam parameters at selected locations. Beam current measurements along the beamlines were used to infer transmission efficiencies and then compared to multi-particle simulations in order to identify beam loss locations. The work resulted in an improved understanding of the beams and beam transfer lines to support further beamline optimization and design activities. The experimental and simulation methodologies, tools, and results and their interpretations should be applicable to many types of operating accelerator facilities.

Development of a Wave Model Component in the First Coupled Global Ensemble Forecast System at NOAA

Abstract We describe the development of the wave component in the first global-scale coupled operational forecast system using the Unified Forecasting System (UFS) at the National Oceanic and Atmospheric Administration (NOAA), part of the U.S. National Weather Service (NWS) operational forecasting suite. The operational implementation of the atmosphere–wave coupled Global Ensemble Forecast System, version 12 (GEFSv12), in September 2020 was a critical step in NOAA’s transition to the broader community-based UFS framework. GEFSv12 represents a significant advancement, extending forecast ranges and empowering the NWS to deliver advanced weather predictions with extended lead times for high-impact events. The integration of a coupled wave component with higher spatial and temporal resolution and optimized physics parameterizations notably enhanced forecast skill and predictability, particularly benefiting winter storm predictions of wave heights and peak wave periods. This successful endeavor encountered challenges that were addressed by the simultaneous development of new features that enhanced wave model forecast skill and product quality and facilitated by a multidisciplinary team collaborating with NOAA’s operational forecasting centers. The GEFSv12 upgrade marks a pivotal shift in NOAA’s global forecasting capabilities, setting a new standard in wave prediction. We also describe the coupled GEFSv12-Wave component impacts on NOAA operational forecasts and ongoing experimental enhancements, which altogether represent a substantial contribution to NOAA’s transition to the fully coupled UFS framework.

Prospects of Predicting the Polar Motion Based on the Results of the Second Earth Orientation Parameters Prediction Comparison Campaign

AbstractGrowing interest in Earth Orientation Parameters (EOP) resulted in various approaches to the EOP prediction algorithms, as well as in the exploitation of distinct input data, including the observed EOP values from various operational data centers and modeled effective angular momentum functions. Considering these developments and recently emerged new methodologies, the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC) was pursued in 2021–2022. The campaign was led by Centrum Badań Kosmicznych Polskiej Akademii Nauk in cooperation with Deutsches GeoForschungsZentrum and under the auspices of the International Earth Rotation and Reference Systems Service. This paper provides the analysis and evaluation of the polar motion predictions submitted during the 2nd EOP PCC with the prediction horizons between 10 and 30 days. Our analysis shows that predictions are highly reliable with only a few occasional discrepancies identified in the submitted files. We demonstrate the accuracy of EOP predictions by (a) calculating the mean absolute error relative to polar motion observations from September 2021 through December 2022 and (b) assessing the stability of the predictions in time. The analysis shows unequal results for the x and y components of polar motion (PMx and PMy, respectively). Predictions of PMy are usually more accurate and have a smaller spread across all submitted files when compared to PMx. We present an analysis of similarity between the participants to indicate what methods and input data give comparable output. We also prepared the ranking of prediction methods for polar motion summarizing the achievements of the campaign.

RainForests: A Machine Learning Approach to Calibrating NWP Precipitation Forecasts

Abstract Probabilistic forecasts derived from ensemble prediction systems (EPSs) have become the standard basis for many products and services produced by modern operational forecasting centers. However, statistical postprocessing is generally required to ensure forecasts have the desired properties expected for probability-based outputs. Precipitation, a core component of any operational forecast, is particularly challenging to calibrate due to its discontinuous nature and the extreme skew in rainfall amounts. A skillful forecasting system must maintain accuracy for low-to-moderate precipitation amounts, but preserve resolvability for high-to-extreme rainfall amounts, which, though rare, are important to forecast accurately in the interest of public safety. Existing statistical and machine learning approaches to rainfall calibration address this problem, but each has drawbacks in design, training approaches, and/or performance. We describe RainForests, a machine learning approach for calibrating ensemble rainfall forecasts using gradient-boosted decision trees. The model is based on the ecPoint system recently developed at ECMWF by Hewson and Pillosu, but uses machine learning models in place of the semisubjective decision trees of ecPoint, along with some other improvements to the model structure. We evaluate RainForests on the Australian domain against some simple benchmarks and show that it outperforms standard calibration approaches both in overall skill and in accurately forecasting high rainfall conditions, while being computationally efficient enough to be used in an operational forecasting system. Significance Statement Weather forecasting typically uses physical models to simulate the atmosphere and produce a set of scenarios, called an ensemble, for each forecast time. Forecast calibration is a set of statistical techniques for using these ensemble forecasts to obtain probability forecasts of weather variables. For example, on a given day, we may predict that there are a 90% chance of at least 1 mm of rain and a 50% chance of at least 3 mm. We introduce RainForests, a new machine learning method for calibrating ensemble forecasts, which uses ensemble forecasts of rainfall and other related variables to produce more accurate probability forecasts of rainfall amounts in operational forecasting settings.

World Meteorological Organization (WMO)-Accredited Infrastructure to Support Operational Climate Prediction

Abstract The World Meteorological Organization (WMO) is a specialized agency of the United Nations (UN) system, with an intergovernmental mandate for coordinating the generation and exchange of weather, climate, and water information across its members. WMO has played a vital role in coordinating production and dissemination of weather forecasts from short to medium range whereby global weather forecasts from large operational centers are made available to all WMO members to serve needs of stakeholders at the local level. In recent decades, there has also been an increasing demand for similar forecasts on longer lead times that include prediction on subseasonal, seasonal, and annual to decadal leads. To address the increasing requirements for forecast services by members, WMO has been actively accrediting and coordinating the essential forecast infrastructure that includes provision of forecasts from WMO designated Global Producing Centers and collection of forecasts by Lead Centers to facilitate the dissemination of information and products to WMO members and relevant nongovernmental organizations. Although the basic ingredients of the infrastructure are now in place, the uptake of the forecast information has been suboptimal. To engage the community in developing solutions to enhance the utilization of available information, this paper summarizes the WMO infrastructure for long-range forecasts, particularly for seasonal time scale, and follows with a discussion of current issues that are hindering their uptake. Finally, a set of proposals to advance the utilization of the available information from the WMO long-lead forecast infrastructure are discussed.

Pipe production cost management model based on graph theory

The pipe production process is a complex dynamical system with a significant number of production operations and interconnected cost localization centres. Purpose. To develop a mathematical model of the process of handling analytical information of a pipe enterprise using graph theory for the needs of cost management at each production stage. Methodology. Technological flow charts of the production job, where the list of mandatory activities and routing of the operating process are indicated, were used to organize cost accounting by operational centres of their localization with the help of computer modelling elements, namely the graph theory. The work used a complex of research methods, including analysis and scientific synthesis of scientific and technical information; theoretical studies; methods of mathematical and computer modelling, engineering developments. Findings. The cost accounting process is represented by a directed hypergraph. Each production operation is associated with a graph vertex. Each vertex is assigned to a series of interrelated marks: type of technological operation; weight factor; the value of direct costs for the operation; the value of indirect costs for the operation; the expense factor. The initial state of the hypergraph is specified by means of its initial marking, which corresponds to the set cipher of the batch of pipes and its weight. Further execution of the marked hypergraph is performed by running the allowed vertices. Marking of the graph ends if all the information from the technological flow charts of pipe products has been used. Originality. A model of cost management by operational centres is proposed which will allow dividing the prime cost of pipes which differ according to the production technology. It is recommended to rank pipe products within each technologically similar group for an economically feasible allocation of costs between batches of pipes. The proposed process model of processing analytical information of the pipe enterprise using graph theory will allow providing data governance as for the prime cost of each individual batch of pipe products, will contribute to the improvement in the order generation procedure taking into account the acquired information, and will allow making the appropriate adjustments when making management decisions. Practical value. Calculation of expenses for production operations allows one to identify the most cost-intensive of them. Relying on the results of the calculation, the enterprise management has the opportunity to manage costs at any stage of the production process. Moreover, the model allows determining the amount of consumed metal for each production operation, which is relevant for pipe enterprises. It is appropriate to focus further scientific developments in this direction on the possibility of managing non-manufacturing cost with the help of mathematical models in economics to optimize the supply and sales activities of the enterprise.

The Role Of International Law In Reducing Ship Piracy In The Waters Of The Horn Of Africa

The Horn of Africa waters are the busiest trade routes and are often passed by thousands of merchant ships worldwide. Merchant ships must overcome the threat of Piracy in the Horn of Africa waters, and the danger comes from Somalia, the center of pirate operations. This article discusses how international law, more precisely the United Nations Convention on the Law of the Sea (UNCLOS), reduces acts of ship piracy in the Horn of Africa waters, either in the form of court sentences or by sending patrol teams and forming military operations under the auspices of the U.N. or operating under UNCLOS law to combat Piracy. Piracy or ship hijacking is a universal problem that the international community must consider because the impacts it causes, of course, have a global impact and disrupt the stability of the world economy of international trade, that is why must be eliminated from their roots, namely the failed Somali government. The novelty in this study is the need for more literature discussing the role of international law in reducing acts of ship piracy in the Horn of Africa waters.

«УЧРЕДИТЬ НА ЮЖНОМ БЕРЕГУ КРЫМСКОГО ПОЛУОСТРОВА В СЕЛЕНИИ ЯЛТАХ ТАМОЖЕННУЮ ЗАСТАВУ…»: К ИСТОРИИ СОЗДАНИЯ И ДЕЯТЕЛЬНОСТИ ЯЛТИНСКОЙ ТАМОЖНИ И ЮЖНОГО ОТРЯДА ТАВРИЧЕСКОЙ БРИГАДЫ ПОГРАНИЧНОЙ СТРАЖИ

The article examines the history of the creation and activities of the Yalta customs outpost (later the Yalta customs), which celebrated its 200th anniversary in 2024. Based on archival documents, some of which are being introduced into scientific circulation for the first time, as well as previously published statistical materials, collections of the customs department and other official publications of the Ministry of Finance of the Russian Empire, the main stages of interaction between customs institutions, border and quarantine guard units operating in the Taurida province are determined in the 19th century. The work of these authorities in peacetime and the features of their functioning during the Crimean (Eastern) War of 1853–1856 and the Russo-Turkish War of 1877–1878 had been analyzed. Measures aimed at optimizing the work of these units in difficult conditions of combat operations and occupation of the Crimean Peninsula by the enemy had been studied. The specifics and sources of formation of personnel of the Yalta customs outpost, units of the Taurida border guard brigade, and quarantine guard units stationed in the province had been identified. It is concluded that the organization of customs, quarantine and border services on the territory of the Taurida province, namely, on the southern coast of Crimea in the 19th century was well thought out and fully consistent with local specifics, since the peninsula was not only the center of external and internal trade operations in the Northern Black Sea region, but also the most important region in geopolitical and strategic terms. Russia, which has repeatedly faced the need to eliminate external threats while simultaneously increasing its economic potential, has taken the necessary steps to ensure the normalization of the activities of customs institutions in the region.

Centro de Pesquisa Operacional do COMGAR - Modelos matemáticos para apoio à decisão

The article discusses the importance of Operational Research (OR) in the Air Command (COMAER), emphasizing the need for mathematical models to support quick and precise decision-making in operational environments. OR is presented as a science that seeks to optimize decisions in complex and uncertain situations, utilizing techniques that include Combat Theory, Network Flow, Game Theory, and Simulation. The text suggests the establishment of an Operational Research Center at COMGAR, which could provide specialized support and training for military personnel, promoting a more rational use of resources. The implementation of such models is seen as essential to improve the efficiency and effectiveness of air operations.

Performance of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) Model in the Northeast Region of Brazil

Study is presented on the potential for using the tools contained in the Google Earth Engine (GEE) geospatial data processing and analysis platform in climatological studies in the Northeast Region of Brazil (NEB), in particular the validation of estimates of total annual rainfall obtained by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) model in seven homogeneous regions of the NEB. This data is made up of various sources of meteorological information, including information from satellites, numerical models and observations at weather stations in operational and research centers across the globe. The performance of the CHIRPS model was assessed using climatological data obtained from the National Meteorological Institute's (INMET) meteorological database, metrics and statistical tests. The results showed that the CHIRPS model has potential for use in six of the seven regions studied, with low errors and strong statistical correlation, and limitations in one of these regions. The conclusion is that this technological tool, which has a low operating cost and is free of charge, can be used to assist in climatological studies, planning and the development of strategies for mitigation, adaptation and coexistence with the dry periods observed in the sub-regions of the NEB, especially in regions that are in a situation of socio-economic vulnerability, as is the case in the semi-arid region of the NEB.

Medium‐range predictability of temperature extremes and biases in Rossby‐wave amplitude

AbstractThis study investigates the medium‐range predictability of warm and cold extremes in the Northern Hemisphere and the role that upper‐tropospheric circulation biases play in this regard. Deterministic ERA5 reforecasts for the period 1979–2019 are evaluated based on the ERA5 reanalysis of the respective period, thus providing a large sample for verification and bias identification. The predictability of temperature extremes at 850 hPa is assessed based on the Gilbert Skill Score and other metrics and is shown to exhibit regional and seasonal variations. Summer is generally characterized by lower forecast skill scores than winter for both warm and cold extremes. Moreover, cold extremes in summer have slightly lower skill scores than warm extremes, while the opposite is true in winter. Biases in the frequency of temperature extremes are, to some extent, consistent with biases in mean temperature and indicate an underestimation in the total amount of extremes for much of the hemisphere in summer. Associated with the latter, biases also emerge in the standard deviation of the daily temperature distribution, with the summer values being largely underestimated over most of the hemisphere. The role of upper‐tropospheric circulation in these biases is then assessed by verifying the representation of Rossby‐wave packet (RWP) properties. It is found that the amplitude of RWPs is systematically underestimated in most of the hemisphere in summer, while it is overestimated in many parts of the midlatitudes in winter. Overall, the results suggest that the underestimation of RWP amplitude in summer hinders the medium‐range predictability of temperature extremes in the explored retrospective and operational forecasts. Although operational European Centre for Medium‐Range Weather Forecasts (ECMWF) forecasts gradually improve between 2013 and 2022 in terms of the 850‐hPa temperature and 300‐hPa RWP amplitude absolute errors, the aforementioned summer biases remain qualitatively similar.

Boredom, vigilance, and repetition during remote control room work for autonomous ships

Abstract Introduction: Remote Operating Centers (ROCs) enable human oversight of remotely operated and autonomous ships. However, the “human-in-the-loop” model of automated control oversight implies prolonged periods of inactivity punctuated by safety-critical action. This paper investigates the extent to which this role is aligned with humans’ innate attentional resources in terms of boredom, vigilance, and repetition. Method: A simulator study was completed with ROC operators (volunteer participants with relevant expertise). One group completed a short scenario (5 minutes; N = 16); the other completed a long scenario (30 minutes; N = 16). The duration marked the onset of a stimulus (an alarm), prompting manual takeover. In a mixed method approach, four sources of data were collected, consisting of pupillometry, questionnaire, interview, and video data. Results: Pupillometry data showed larger pupillary constrictions in the 30-minute group, indicative of decreased cognitive activity (effect size 0.25 mm). The questionnaire showed that operators in the 30-minute scenario were more likely to experience their tasks as unchallenging, understimulating, and lacking in meaning. Finally, a relationship was found between repetitive content and varying perceptions of time duration. Moreover, operators perceived scenarios as longer when they detected an “oddball” in the repetitive series (a gorilla passenger). Conclusion: ROC work as currently imagined is prone to boredom, vigilance decrement, and suppression of repetition. The study adds to the empirical research about boredom and its associated cognitive states in control room work. The study also contributes to design considerations impacting safety and organizational aspects like operator training and retention.

AI-Driven FlightOps: Revolutionizing Airline Operations Control with Predictive Analytics and NLP

Abstract: Flight operations control centers (OCCs) are the nerve centers of airline operations, tasked with managing a wide range of activities such as flight scheduling, crew assignments, maintenance coordination, and dealing with disruptions. While traditional approaches have relied heavily on human decision-making and manual analysis of operational data, the increasing complexity of modern airline operations demands more efficient solutions. This paper presents FlightOps AI, a cutting-edge AIpowered system that integrates advanced natural language processing (NLP), deep neural networks (DNNs), reinforcement learning (RL), and computer vision to provide OCC staff with actionable insights and predictive capabilities. The system processes massive amounts of structured and unstructured data in real time, enabling smarter decision-making and automation of routine tasks. By adopting these technologies, FlightOps AI transforms the operational landscape, optimizing airline performance while minimizing human errors

Dari Eksplorasi Minyak Hingga Membangun Kota: Dumai Kota Perusahaan Caltex 1956-1970-an

This research examines the development of Dumai as a company town, where its growth is closely tied to the role of a multinational oil company, Caltex. The study addresses the question of how Caltex developed and shaped Dumai, beginning with the company's arrival in 1956. Caltex initially operated in Minas, Duri, and Rumbai but needed a strategic port to export crude oil from its fields, leading to the selection of Dumai. Using historical methods and literature review, this research chronicles Caltex’s establishment of Dumai as a key operational and distribution center. The findings reveal that since Caltex's arrival, Dumai has rapidly transformed into a company town, a development largely driven by its oil industry operations. The study concludes that Dumai’s urban growth is a direct result of Caltex's significant role in the city’s management and infrastructure.

Modern aspects of rabies in Cameroon

The article presents the results of an epidemiological study of the incidence of rabies in the population of the Republic of Cameroon in the period from 2014 to 2022. The system of epidemiological surveillance and monitoring of rabies in Cameroon is presented. According to WHO, human and animal rabies is recognized as endemic to Central Africa as a whole, and in Cameroon, in particular, this infectious goiter is classified as the first priority zoonosis within the framework of the National Program for the Prevention and Control of Recurrent and Re-emerging Zoonosis (PNPLZER). The average annual incidence of rabies recorded between 2014 and 2022 was 405.7% (95% CI: 401.9% – 409.5%). Based on the results of a retrospective epidemiological analysis, the dynamics of rabies cases among people in Cameroon is presented in accordance with data recorded at the Operational Center for Public Health Emergencies (CCOUSP). The highest rates were recorded in the period from 2018 (547.4%) to 2019 (276.8%), and the lowest in 2021 (10.7%) to 2022 (3.1%). To identify socio-demographic risk factors, a survey of rabies foci was conducted using specially designed questionnaires (epidemiological information collection cards). Mapping of registered cases of rabies in humans was carried out for 10 administrative regions of the country using the LTR QGIS (quantum GIS) program, which allowed us to show the distribution of cases across the country and dynamics over time. It has been established that the distribution of cases of the disease across the territory does not always depend on the population density in administrative districts, but is associated with specific socio-demographic risk factors such as profession, gender, age, type of animal reservoir of the virus, which affect the activity of the pathogen circulation among natural hosts.

Kinematic analysis, workspace definition, and self-collision avoidance of a quasi-spherical parallel manipulator

Abstract Bilateral teleoperation has witnessed significant development since the mid-20th century, addressing challenges related to human presence in environments with constraints or a lack of skilled professionals. This article presents the kinematic and self-collision analyses of the quasi-spherical parallel manipulator, a three-legged parallel robot used as a haptic master device. The device is designed for remote center of motion-constrained operation in the telesurgical field. Inverse and forward kinematics are thoroughly analyzed to study working modes, singular configurations, and implement a haptic control architecture. The research explores the operative and reachable workspaces of the possible working modes, comparing them to find the most suitable one. Results highlight how the addition of the self-collision phenomenon impacts the working mode choice, drastically reducing most of the modes’ operative workspaces. An anti-collision control algorithm is finally introduced to maintain the architecture within its reachable workspace.

May 11–12 Extreme Space Weather Events Brief and Dose Rate Model Response

In this brief paper, we analyze space weather events that occurred on May 11 and 12, 2024, from the perspective of an operational space weather center that provides advisories for civil aviation. One of the key metrics monitored by the center is the radiation dose rate at operational flight altitudes. A model implemented by the center provides the dose rate in real time. The model showed that dangerous levels were momentarily exceeded just above the usual 30,000 feet level during the events. This paper highlights differences in models used by various space weather centers, emphasizing the need for harmonization.

Establishing remote control centers of a mining operation: strategic prerequisites and implementation stages

Establishing remote control centers of a mining operation: strategic prerequisites and implementation stages

Enhanced regional ocean ensemble data assimilation through atmospheric coupling in the SKRIPS model

We investigate the impact of ocean data assimilation using the Ensemble Adjustment Kalman Filter (EAKF) from the Data Assimilation Research Testbed (DART) on the oceanic and atmospheric states of the Red Sea. Our study extends the ocean data assimilation experiment performed by Sanikommu et al. (2020) by utilizing the SKRIPS model coupling the MITgcm ocean model and the Weather Research and Forecasting (WRF) atmosphere model. Using a 50-member ensemble, we assimilate satellite-derived sea surface temperature and height and in situ temperature and salinity profiles every three days for one year, starting January 01 2011. Atmospheric data are not assimilated in the experiments. To improve the ensemble realism, perturbations are added to the WRF model using several physics options and the stochastic kinetic energy backscatter (SKEB) scheme. Compared with the control experiments using uncoupled MITgcm with ECMWF ensemble forcing, the EAKF ensemble mean oceanic states from the coupled model are better or insignificantly worse (root-mean-square errors are 23% to −1.3% smaller), especially when the atmospheric model uncertainties are accounted for with stochastic perturbations. We hypothesize that the ensemble spreads of the air–sea fluxes are better represented in the downscaled WRF ensembles when uncertainties are well accounted for, leading to improved representation of the ensemble oceanic states from the new experiments with the coupled model. This indicates the ocean model assimilation will be improved with coupled models and may relax the need for operational centers to provide atmospheric ensembles to drive ocean forecasts. Although the feedback from ocean to atmosphere is included in this two-way regional coupled configuration, we find no significant effect of ocean data assimilation on the ensemble mean latent heat flux and 10-m wind speed over the Red Sea. This suggests that the improved skill using the coupled model is not from the two-way coupling, but from downscaling the ensemble atmospheric forcings (one-way coupled) to drive the ocean model.

Peer Influence and Single Parenting as Predictors of Sexual Abuse Among Secondary School Students in Nigeria

This study examined peer influence and single parenting as predictors of sexual abuse among secondary school students in Ovia North East Local Government Area of Edo State, Nigeria. Two research questions were raised and two hypotheses were formulated. The correlational survey research design was used for the study. The population of the study is Eleven thousand nine hundred and eighty-five (11985) senior secondary school students in the public and private schools in Ovia North East Local Government Area. The multistage sampling procedure was employed to select 300 students from six (6) senior secondary schools in Ovia North East Local Government Area. The research instrument was a questionnaire titled " Single Parenting, Peer Influence, and Sexual Abuse Questionnaire Scale " was used for the study. The data was analysed using inferential statics (Linear Regression and t-test). It was shown from the findings that peer influence and single parenting both significantly predict sexual abuse amongst secondary school students (p<0.05) and also established that there is a significant difference between public and private schools in their disposition to sexual abuse among secondary school students in Ovia North East Local Government Area of Edo State (T-cal>1.96 & p<0.05). Given the outcome of this study, it was recommended that seminars should be organized for students due to the dangers of indecent dressing and premarital sex, secondary schools should ensure that they have an operational counselling centre and specialised counsellors who can organise individual and group counselling sessions for victims of sexual abuse.