Operational Centers Research Articles

A survey of practice patterns for real-time intrafractional motion-management in particle therapy.

Organ motion compromises accurate particle therapy delivery. This study reports on the practice patterns for real-time intrafractional motion-management in particle therapy to evaluate current clinical practice and wishes and barriers to implementation. An institutional questionnaire was distributed to particle therapy centres worldwide (7/2020-6/2021) asking which type(s) of real-time respiratory motion management (RRMM) methods were used, for which treatment sites, and what were the wishes and barriers to implementation. This was followed by a three-round DELPHI consensus analysis (10/2022) to define recommendations on required actions and future vision. With 70 responses from 17 countries, response rate was 100% for Europe (23/23 centres), 96% for Japan (22/23) and 53% for USA (20/38). Of the 68 clinically operational centres, 85% used RRMM, with 41% using both rescanning and active methods. Sixty-four percent used active-RRMM for at least one treatment site, mostly with gating guided by an external marker. Forty-eight percent of active-RRMM users wished to expand or change their RRMM technique. The main barriers were technical limitations and limited resources. From the DELPHI analysis, optimisation of rescanning parameters, improvement of motion models, and pre-treatment 4D evaluation were unanimously considered clinically important future focus. 4D dose calculation was identified as the top requirement for future commercial treatment planning software. A majority of particle therapy centres have implemented RRMM. Still, further development and clinical integration were desired by most centres. Joint industry, clinical and research efforts are needed to translate innovation into efficient workflows for broad-scale implementation.

A survey of practice patterns for adaptive particle therapy for interfractional changes.

Anatomical changes may compromise the planned target coverage and organs-at-risk dose in particle therapy. This study reports on the practice patterns for adaptive particle therapy (APT) to evaluate current clinical practice and wishes and barriers to further implementation. An institutional questionnaire was distributed to PT centres worldwide (7/2020-6/2021) asking which type of APT was used, details of the workflow, and what the wishes and barriers to implementation were. Seventy centres from 17 countries participated. A three-round Delphi consensus analysis (10/2022) among the authors followed to define recommendations on required actions and future vision. Out of the 68 clinically operational centres, 84% were users of APT for at least one treatment site with head and neck being most common. APT was mostly performed offline with only two online APT users (plan-library). No centre used online daily re-planning. Daily 3D imaging was used for APT by 19% of users. Sixty-eight percent of users had plans to increase their use or change their technique for APT. The main barrier was "lack of integrated and efficient workflows". Automation and speed, reliable dose deformation for dose accumulation and higher quality of in-room volumetric imaging were identified as the most urgent task for clinical implementation of online daily APT. Offline APT was implemented by the majority of PT centres. Joint efforts between industry research and clinics are needed to translate innovations into efficient and clinically feasible workflows for broad-scale implementation of online APT.

Dynamical Predictability of Leading Interannual Variability Modes of the Asian-Australian Monsoon in Climate Models

The dynamical prediction of the Asian-Australian monsoon (AAM) has been an important and long-standing issue in climate science. In this study, the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide. Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences, especially for the second mode. The first AAM mode, from various seasonal sequences, coincides with the El Niño phase transition in the eastern-central Pacific. The second mode, initialized from boreal summer and autumn, leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring. Our findings hint that ENSO, as an early signal, is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes. Still, the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features. The multimodel ensemble (MME) mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features. The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes. The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.

A review of recent advances (2018–2021) on tropical cyclone intensity change from operational perspectives, part 2: Forecasts by operational centers

This paper summarizes the progress and activities of tropical cyclone (TC) operational forecast centers during the last four years (2018–2021). It is part II of the review on TC intensity change from the operational perspective in the rapporteur report presented to the 10th International Workshop on TCs (IWTC) held in Bali, Indonesia, from Dec. 5–9, 2022. Part I of the review has focused on the progress of dynamical model forecast guidance. This part discusses the performance of TC intensity and rapid intensification forecasts from several operational centers. It is shown that the TC intensity forecast errors have continued to decrease since the 9th IWTC held in 2018. In particular, the improvement of rapid intensification forecasts has accelerated, compared with years before 2018. Consensus models, operational procedures, tools and techniques, as well as recent challenging cases from 2018 to 2021 identified by operational forecast centers are described. Research needs and recommendations are also discussed.

A review of recent advances (2018–2021) on tropical cyclone intensity change from operational perspectives, part 1: Dynamical model guidance

This review summarizes the rapporteur report on tropical cyclone (TC) intensity change from the operational perspective, as presented to the 10th International Workshop on TCs (IWTC-10) held in Bali, Indonesia, from Dec. 5–9, 2022. The accuracy of TC intensity forecasts issued by operational forecast centers depends on three aspects: real-time observations, TC dynamical model forecast guidance, and techniques and methods used by forecasters. The rapporteur report covers the progress made over the past four years (2018–2021) in all three aspects. This review focuses on the progress of dynamical model forecast guidance. The companion paper (Part II) summarizes the advance from operational centers. The dynamical model forecast guidance continues to be the main factor leading to the improvement of operational TC intensity forecasts. Here, we describe recent advances and developments of major operational regional dynamical TC models and their intensity forecast performance, including HWRF, HMON, COAMPS-TC, Met Office Regional Model, CMA-TYM, and newly developed HAFS. The performance of global dynamical models, including NOAA's GFS, Met Office Global Model (MOGM), JMA's GSM, and IFS (ECMWF), has also been improved in recent years due to their increased horizontal and vertical resolution as well as improved data assimilation systems. Recent challenging cases of rapid intensification are presented and discussed.

Track forecast: Operational capability and new techniques - Summary from the Tenth International Workshop on Tropical Cyclones (IWTC-10)

In this paper, we summarize findings from the Tenth International Workshop on Tropical Cyclones (IWTC-10) subgroup on operational track forecasting techniques and capability.The rate of improvement in the accuracy of official forecast tracks (OFTs) appears to be slowing down, at least for shorter lead times, where we may be approaching theoretical limits. Operational agencies continue to use consensus methods to produce the OFT with most continuing to rely on an unweighted consensus of four to nine NWP models. There continues to be limited use of weighted consensus techniques, which is likely a result of the skills and additional maintenance needed to support this approach. Improvements in the accuracy of ensemble mean tracks is leading to increased use of ensemble means in consensus tracks.Operational agencies are increasingly producing situation-dependent depictions of track uncertainty, rather than relying on a static depiction of track forecast certainty based on accuracy statistics from the preceding 5 years. This trend has been facilitated by the greater availability of ensemble NWP guidance, particularly vortex parameter files, and improved spread in ensembles. Despite improving spread-skill relationships, most ensemble NWP systems remain under spread. Hence many operational centers are looking to leverage “super-ensembles” (ensembles of ensembles) to ensure the full spread of location probability is captured. This is an important area of service development for multi-hazard impact-based warnings as it supports better decision making by emergency managers and the community in the face of uncertainty.

Towards a Feminist Geo-legal Ethic of Caring Within Medical Supply Chains: Lessons from Careless Supply During the COVID-19 Pandemic

The COVID-19 crisis illustrates the fragility of supply chains. Countries with excellent health systems struggled to ensure essential supplies of food, medicines, and personal protective equipment which were vital to a fast and effective response. Using geo-legality, which maps the constitutive relations between law and space, we argue that the failure of supply chains in many western countries during the crisis reveals a fundamental tension between their role as facilitators of care and caring, and the logistic logics by which they operate. While supply chains link the intimate, domestic concerns of providing medical care with the globalised geographical concerns of moving goods across different jurisdictions at the right time, their contemporary organisation and regulation does not reflect the caring relations and public goods they are meant to support. Drawing on analysis of examples from Canada, the United Kingdom, and the United States, this article argues that a reconfiguration of supply chains in accordance with feminist approaches that place care at the centre of supply chain operation and organisation will be important to amendments of both domestic and global health law.

High-impact weather in Italy: a workshop to exchange the experience of weather forecasters and researchers

A workshop on high-impact meteorological events in Italy was jointly organized by the newly formed Italia Meteo Agency (AIM) and the Italian Association of Atmospheric Sciences and Meteorology (AISAM) on September 27, 2022. The aim of the workshop was to promote the sharing of the experiences that all operational and research meteorological centers in Italy have gained on specific types of high-impact events, favoring the establishment of a common know-how of Italian operational meteorology.

Evaluating western North Pacific tropical cyclone forecast in the subseasonal to seasonal prediction project database

The Daily Tropical Cyclone Probability (DTCP), defined as the probability of tropical cyclone occurrence within 500 km of a location in 1 day, is proposed and used in evaluating subseasonal to seasonal (S2S) predictions from the subseasonal to seasonal Prediction Project Database, from May 1 to October 31, 1999 to 2010. The ensemble forecasts are collected from eleven operational centers, the Bureau of Meteorology (BoM), China Meteorological Administration (CMA), Environment and Climate Change Canada (ECCC), European Centre for Medium-Range Weather Forecasts (ECMWF), Hydrometeorological Centre of Russia (HMCR), the Institute of Atmospheric Sciences and Climate of the National Research Council of Italy (ISAC), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), Météo-France/Centre National de Recherche Meteorologiques (METFR), the United States National Centers for Environmental Prediction (NCEP), and the United Kingdom Met Office (UKMO). In both observation and these eleven forecast models, the daily tropical cyclone probability is modulated by the Boreal Summer Intraseasonal Oscillation (BSISO), depicted by the two indices, boreal summer intraseasonal oscillation 1 and boreal summer intraseasonal oscillation 2. During boreal summer intraseasonal oscillation 1 phases 1, 5, 6, 7, and 8, the daily tropical cyclone probability in the western North Pacific region is ∼3.5 times higher. Similarly, during phases 1, 2, 3, 4, and 8 of boreal summer intraseasonal oscillation 2, the daily tropical cyclone probability is ∼2.5 times higher. Among the eleven models, the European Centre for Medium-Range Weather Forecasts model best reproduces the climatological daily tropical cyclone probability and its modulation by the boreal summer intraseasonal oscillation in the western North Pacific region, followed by the United States National Centers for Environmental Prediction, the Korea Meteorological Administration, the Japan Meteorological Agency models. Using the daily tropical cyclone probability metric, the highest debiased Brier Skill Score of the eleven models is from European Centre for Medium-Range Weather Forecasts, which has a slightly less skillful prediction than the reference climatological forecast with lead time 11–30 days. The skill of the eleven models is higher during the non-active phases of tropical cyclone activity than their skill during the active phases.

A hybrid data assimilation system based on machine learning

In the earth sciences, numerical weather prediction (NWP) is the primary method of predicting future weather conditions, and its accuracy is affected by the initial conditions. Data assimilation (DA) can provide high-precision initial conditions for NWP. The hybrid 4DVar-EnKF is currently an advanced DA method used by many operational NWP centres. However, it has two major shortcomings: The complex development and maintenance of the tangent linear and adjoint models and the empirical combination of the results of 4DVar and EnKF. In this paper, a new hybrid DA method based on machine learning (HDA-ML) is presented to overcome these drawbacks. In the new method, the tangent linear and adjoint models in the 4DVar part of the hybrid algorithm can be easily obtained by using a bilinear neural network to replace the forecast model, and a CNN model is adopted to fuse the analysis of 4DVar and EnKF to adaptively obtain the optimal coefficient of combination rather than the empirical coefficient as in the traditional hybrid DA method. The hybrid DA methods are compared with the Lorenz-96 model using the true values as labels. The experimental results show that HDA-ML improves the assimilation performance and significantly reduces the time cost. Furthermore, using observations instead of the true values as labels in the training system is more realistic. The results show comparable assimilation performance to that in the experiments with the true values used as the labels. The experimental results show that the new method has great potential for application to operational NWP systems.

A Statistical Hypothesis Testing Strategy for Adaptively Blending Particle Filters and Ensemble Kalman Filters for Data Assimilation

Abstract Particle filters avoid parametric estimates for Bayesian posterior densities, which alleviates Gaussian assumptions in nonlinear regimes. These methods, however, are more sensitive to sampling errors than Gaussian-based techniques such as ensemble Kalman filters. A recent study by the authors introduced an iterative strategy for particle filters that match posterior moments—where iterations improve the filter’s ability to draw samples from non-Gaussian posterior densities. The iterations follow from a factorization of particle weights, providing a natural framework for combining particle filters with alternative filters to mitigate the impact of sampling errors. The current study introduces a novel approach to forming an adaptive hybrid data assimilation methodology, exploiting the theoretical strengths of nonparametric and parametric filters. At each data assimilation cycle, the iterative particle filter performs a sequence of updates while the prior sample distribution is non-Gaussian, then an ensemble Kalman filter provides the final adjustment when Gaussian distributions for marginal quantities are detected. The method employs the Shapiro–Wilk test to determine when to make the transition between filter algorithms, which has outstanding power for detecting departures from normality. Experiments using low-dimensional models demonstrate that the approach has a significant value, especially for nonhomogeneous observation networks and unknown model process errors. Moreover, hybrid factors are extended to consider marginals of more than one collocated variables using a test for multivariate normality. Findings from this study motivate the use of the proposed method for geophysical problems characterized by diverse observation networks and various dynamic instabilities, such as numerical weather prediction models. Significance Statement Data assimilation statistically processes observation errors and model forecast errors to provide optimal initial conditions for the forecast, playing a critical role in numerical weather forecasting. The ensemble Kalman filter, which has been widely adopted and developed in many operational centers, assumes Gaussianity of the prior distribution and solves a linear system of equations, leading to bias in strong nonlinear regimes. On the other hand, particle filters avoid many of those assumptions but are sensitive to sampling errors and are computationally expensive. We propose an adaptive hybrid strategy that combines their advantages and minimizes the disadvantages of the two methods. The hybrid particle filter–ensemble Kalman filter is achieved with the Shapiro–Wilk test to detect the Gaussianity of the ensemble members and determine the timing of the transition between these filter updates. Demonstrations in this study show that the proposed method is advantageous when observations are heterogeneous and when the model has an unknown bias. Furthermore, by extending the statistical hypothesis test to the test for multivariate normality, we consider marginals of more than one collocated variable. These results encourage further testing for real geophysical problems characterized by various dynamic instabilities, such as real numerical weather prediction models.

Asymmetric alkene-alkene reductive cross-coupling reaction via visible-light photoredox/cobalt dual catalysis.

The first example of asymmetric alkene-alkene reductive coupling is demonstrated via visible-light-fueled photoredox/cobalt dual catalysis. The desymmetrization reaction provided products (>20 examples) with up to five chiral centers in single-step operation in up to 95% yields with very high relative (>99 : 1 dr) and absolute stereochemistry (up to 98 : 2 er) control. The preliminary mechanistic investigations suggested that the critical mechanistic steps involved light-mediated controlled low-valent cobalt complex generation, oxidative ene-ene cyclization, and protonation.

Prerequisites for the development and features of the regulation of the digital financial assets

Introduction. Digital financial assets are innovative tools that have received rapid development in the world relatively recently. The lack of a single, universal approach to determining the essence of digital financial assets is a peculiar manifestation of the evolution of the views of financial market regulators and international financial organizations towards the inner essence of these instruments. The rapid growth of the market of digital financial assets in the world increases the risks of uncontrolled movement of capital both within individual national economies and at the global level. The lack of oversight of this market by central banks or other regulatory authorities increases the opportunities for market participants to use crypto-assets to evade taxes or finance prohibited activities. At the same time, the trend of recent years is a gradual increase in interest in the cryptoasset market on the part of central banks, which, due to the strengthening of currency regulation, are trying to limit the development of the shadow economy. The purpose of the article is to justify the need for special approaches to their regulation on the part of central banks and the state, based on the definition of the essence of digital financial assets, the prerequisites for their emergence and the key factors that contribute to the growth of demand for these instruments in the world. Method (methodology). The work used methods of analysis, synthesis, comparison, generalization and statistical processing of data. The results. The essence, prerequisites for the development of digital financial assets are determined, and the factors contributing to the growth of demand for these instruments from the side of market participants are outlined. The evolution of approaches to the definition of digital assets from the side of international organizations and the potential influence of the crypto-asset market on the development of the shadow economy have been revealed. It was determined that since the market of digital assets has a global nature, and emission centers and centers of settlement operations are outside the influence of individual national regulators of the financial market, the consequence of applying strict approaches to its regulation at the level of a separate state may be the strengthening of the shadowing of the national economy.

Real Time Control System for Metro Railways Using PLC & SCADA

This paper proposes to adopt SCADA and PLC technology for the improvement of the performance of real time signaling & train control systems in metro railways. The main concern of this paper is to minimize the failure in automated metro railways system operator and integrate the information coming from Operational Control Centre (OCC), traction SCADA system, traction power control, and power supply system. This work presents a simulated prototype of an automated metro train system operator that uses PLC and SCADA for the real time monitoring and control of the metro railway systems. Here, SCADA is used for the visualization of an automated process operation and then the whole operation is regulated with the help of PLC. The PLC used in this process is OMRON (NX1P2-9024DT1) and OMRON’s Sysmac studio programming software is used for developing the ladder logic of PLC. The metro railways system has deployed infrastructure based on SCADA from the power supply system, and each station’s traction power control is connected to the OCC remotely which commands all of the stations and has the highest command priority. An alarm is triggered in the event of an emergency or system congestion. This proposed system overcomes the drawbacks of the current centralized automatic train control (CATC) system. This system provides prominent benefits like augmenting services which may enhance a network’s full load capacity and network flexibility, which help in easy modification in the existing program at any time.

Accepting a Borderless Workforce through Change Management: Remote Work Integration within Médecins Sans Frontières Operational Centre Amsterdam (MSF OCA)

Remote working has been explained by numerous academicians as form of employment conducted outside the confines of any organization physical scope and hours. Working without boarders has now become more than ever an important working domain especially following the COVID-19 pandemic. The effect of economic lockdowns led to innovation in working models across all sectors therefore increasing the important realization that work can be conducted in almost any environment apart from being confined to one specific physical space and hours. Humanitarian organizations have also engaged remote workers as part of their human resource. The specific problem is that remote work has been implemented within MSF OCA with no clear standard support systems to ensure its sustainability. The other problem being that there is no focused study on the improvement and integration of remote working within humanitarian organization such as Médecins Sans Frontières (MSF), Operational Centre Amsterdam (OCA) has been conducted. Key to note is that in addition to the general and specific management problem, there is also literature gap that addresses remote work improvement and integration within humanitarian organization. The purpose of this exploratory case study, using a critical incident technique (CIT), was to create a taxonomy of responses to the incidents that are critical for integrating and improving remote working within Médecins Sans Frontières Operational Centre Amsterdam (MSF OCA). Data was collected through in-depth interviews and transcripts from 16 research participants as the sample size. This study was anchored by a central research question, what are the employees and managers perception of remote work integration and how can remote work be improved upon. Data analysis was conducted using a 9 step data analysis process. The results revealed two primary themes and two secondary themes; the primary themes were human resources and onboarding and the secondary themes were employee wellbeing and employee development. Study therefore concludes that for effective remote work improvement and integration within MSF OCA, the use of change management strategies is key. The study comprised of recommendations and limitations. The study recommends that an integrated review of the current existing organizational remote working policy consisting of the actual policy reviews and remote workers consultation be conducted so as to attain a more updated and inclusive standardized remote workers policy.

AIR POLLUTION PREDICTION USING MACHINE LEARNING

The fact that environment tracking is focused largely on the fundamental rights of people, lifestyles, and health makes it so important. As a result, this device tracks the quality of the air using excellent sensor nodes within that check for CO2, NOx, UV light, temperature, and humidity. The gad get is able to categorize automatically if a certain geographic area is going above the established gas emission restrictions thanks to the statistics assessment using device mastering algorithms. In order to choose the most contaminated sectors, the DB SCAN with LR, SVM, and NB set of rules delivered a noteworthy category overall performance. Monitoring air quality is a crucial concern in many commercial and physical areas of the world. In areas with serious difficulties with air pollution, Air Quality Operational Centers (AQOCs) are established specifically for this purpose. The AQOCs are operational units responsible for managing tracking networks, analyzing the gathered data, and eventually disseminating online assessments of air pollutants and their short- and long-term evolution. Up until recently, modelling of air pollution events has been focused mostly on dispersion models, which approximate the complex physicochemical processes at play. Although the intricacy and complexity of these models have increased over time, their application in real-time atmospheric pollution tracking appears to no longer be acceptable in terms of performance, input data requirements, and compliance with the problem's time limitations.

The Effect of Job Satisfaction and Work Motivation on Employee Performance in Terminal Operations at Bandung Husein Sastranegara Airport

The performance of an employee is basically an individual thing because each employee has different levels in doing their job duties. This study aims to determine how much influence job satisfaction has on employee performance at the operational center of the Husein Sastranegara Airport terminal in Bandung. To find out how the influence of work motivation on employee performance at the terminal operation center of Husein Sastranegara Airport Bandung and to find out how the influence of motivation and job satisfaction on employee performance at Husein Sastranegara Airport Bandung, especially at the airport terminal operation center. This research uses a type of quantitative research design. This research is an associative research, where this research was conducted to find out the relationship and influence between variables. Where the sample in this study were all employees at the terminal operation center at Husein Sastranegara Airport Bandung as many as 50 employees using purposive sampling technique. The results of this study indicate that simultaneously job satisfaction and work motivation affect employee performance, with a Tcount of job satisfaction of 2.412 with a significant value of 0.020 0.05, meaning that partially job satisfaction variables affect employee performance at Husein Sastranegara Airport Bandung. As well as the value of Tcount work motivation of 3.249 with a significant value of 0.002 0.05, it shows that simultaneously the variable of work motivation influences employee performance at Husein Sastranegara Airport, Bandung. The effect of job satisfaction and work motivation on employee performance at Husein Sastranegara Airport Bandung is 75.3% while the other 24.7% is influenced by other variables.Keywords: Job Satisfaction, Work Motivation, and Employee Performance

Design and Implementation of Graphene-Based Tunable Microwave Filter for THz Applications.

A reconfigurable Substrate-Integrated Waveguide (SIW) filter operating in the THz region was designed in this work. Two SIW resonators were coupled through a magnetic iris to form a second-order filter with a double-layer substrate. The first substrate was silicon of permittivity 11.9; on top of it, silicon dioxide of permittivity 3.9 was placed. The ground and upper plane were composed of gold plates. Graphene material was then used for the tunability of the filter. A thin graphene sheet was sandwiched between the silicon dioxide substrate and the upper gold plate. An external DC bias voltage was then applied to change the chemical potential of graphene, which, in turn, managed to change the operational center frequency of the filter within the range of 1.289 THz to 1.297 THz, which translated to a bandwidth range of 8 GHz. The second part of this work centered on changing the aspect ratio of the graphene patch to change the center frequency. It was observed that the frequency changed within the range of 1.2908 THz to 1.2929 THz, which gave a bandwidth of 2.1 GHz change.

Encuentro con Enrique Ortiz Flores

On February 5, 2017, the Political Constitution of Mexico City was published in the Official Gazette, a symbolically relevant event loaded with a long political and cultural history to which this issue of the Anuario de Espacios Urbanos (AEU) dedicates a dossier. special. In this framework, the coordination of the AEU presents the meeting held with Enrique Ortiz Flores, a participant in the drafting group of the Constitution and, mainly, a reference in habitat issues in Mexico, Latin America and the world. [Brief profile of the CPCM] Enrique is an architect from the National Autonomous University of Mexico, his career stands out for a permanent fight for socio-spatial justice from various spaces such as civil organization, academia and public service. He was the founder and director of the Housing and Population Operational Center (Copevi), a reference in non-governmental organizations on habitat and housing in Mexico for more than 40 years; collaborated in the Secretariat of Human Settlements and Public Works (SAHOP) and in the National Fund for Popular Housing (Fonhapo). He has also been part of the Habitat International Coalition since its inception and is responsible for the founding of the regional office for Latin America. Among the recognitions he has received are the 2014 National Architecture Award, granted by the National Association of Engineers and Architects of Mexico; in 2017 he was honored by the Human Rights Commission of the Federal District; and in 2020 he received the "Popular Habitat" award from the Pan-American Federation of Architects Associations. For the support we received to achieve this meeting, we extend our thanks to Jerónimo Díaz and Patricia Santiago. We also send greetings and recognition to all who are and have been part of the International Coalition for Habitat, Latin America, for their work and fight for habitat rights and social justice.

Hardware alarms reduction in Radio Base Stations by forecasting Power Supply Units headroom

Power Supply Units (PSUs) are the components that supply Radio Base Stations (RBSs) with electric power, making it an essential unit for mobile communication networks. A power supply malfunction is critical and must be resolved, but immediate troubleshooting could result in additional operational costs. Based on the prediction of PSU usage by an advanced generalized additive model for time series forecasting called Prophet, the PSU power headroom is derived and the maintenance schedule is proposed, which depends on the severity of demand for power. The safety criteria are derived from the critical operational conditions for several consecutive worst-case scenarios. A PSU alarm will only be generated under the most critical circumstances for an RBS, having multiple PSUs, and sent to the Network Operating Center (NOC). This approach not only allows us to better schedule maintenance and keep operational continuity but also to improve the overall energy efficiency and costs. • Proposing a method for alarm handling in power system for Radio Base Stations. • Generating alarms based on a new triggering logic in the severity of the power headroom variation. • Deriving power headroom based on Machine Learning prediction of PSU usage. • Improving the maintenance schedule and overall energy efficiency.