Regular Operation Research Articles

Projective tensor products of approximation spaces associated with positive operators

In this paper the projective tensor products of approximation spaces associated with positive operators in Banach spaces are characterized. We show that the tensor products of approximation spaces can be considered as the interpolation spaces generated by $K$-method of real interpolation. The inequalities that provide a sharp estimates of best approximations by analytic vectors of positive operators on projective tensor products are established. Application to spectral approximations of the regular elliptic operators on projective tensor products of Lebesgue spaces is shown.

Practical Sustainable Software Development in Architectural Flexibility for Energy Efficiency Using the Extended Agile Framework

Many regular business operations are transforming into digital services, increasing advanced multi-platforms, rapid operational alignment, flexibility, and environmental impact through energy consumption, hardware waste, and technology investments. Flexible and sustainable system development models emphasizing energy efficiency can help innovate software development as digital servicing applications shift. This research is motivated by the need to improve energy consumption in early software design and development due to rising technological efficiency and sustainability demands. Although effective in iterative development and stakeholder engagement, traditional Agile methodologies often struggle with long-term sustainability and energy efficiency. Extended Agile, combining Agile, layered architecture, and aspect-oriented frameworks (ALAI), promises to improve system modularity, flexibility, maintainability, and sustainability. This study’s findings are not just theoretical, but also practically relevant, as they explore the energy efficiency of ALAI software development methodologies, using graduate admission information system services (GAISS) as an example. GAISS is a complex system that handles the entire process of graduate admissions, from application submission to final decision. The study quantifies the energy usage of a student-list webpage by analyzing Microsoft IIS server logs from February 2022 to May 2024. Directly applicable findings show that the GAISS based on the ALAI framework reduces energy consumption by 10.7914% compared to traditional Agile software developments. ALAI used 892.80 kWh versus Agile’s 1000.80 kWh during operations, saving energy. These findings demonstrate the benefits of integrating aspect-oriented frameworks and layering approaches into Agile methodologies, contributing to sustainable software development discourse. The study emphasizes the importance of energy-efficient frameworks such as ALAI to reduce software systems’ environmental impact and promote software development sustainability. The findings of this study, with their practical relevance, assist software developers and organizations in choosing software design and development methods that maximize operational efficiency and environmental sustainability.

Development of a new environmentally friendly and efficient centrifugal variable diameter metering device.

The design of the maize metering device involves centrifugal variable diameter pneumatic and cleaning mechanisms, aiming to enhance the performance and power efficiency of pneumatic maize metering devices. Leveraging the impact of changes in centrifugal diameter and the guidance and positioning of airflow, we optimize the hole insert, seeding plate, seed limit board, and integrated front shell. This optimization facilitates the adjustment of both the quantity and posture of seed filling. As a result, seeds can form a uniform flow within the annular cavity, reducing the wind pressure necessary for regular operation and decreasing power consumption. A quadratic regression orthogonal rotation combination experiment is conducted using a self-made experiment bench, considering ground speed, wind pressure, and seeding rate as the experiment factors. Furthermore, a comparative experiment involving a novel centrifugal variable-diameter type metering device. The results indicate optimal seeding performance when the ground speed is 13.2 km/h, the wind pressure is 1.2 kPa, and the feeding rate is 25 seeds/s. Under these conditions, the quality of feed index reaches 95.20%, the multi-index is 3.87%, and the miss index is 0.93%. Findings reveal that the developed seed metering device achieved a quality of feed index exceeding 93.00% across varying speeds of 12~18 km/h, aligning with the production requirements. Moreover, the actual power consumption of Type B and C is about 85.00% and 98.00% lower than Type A, standing at only 32.90 W at 18 km/h. The COP of Type C is about 86 times and 12 times that of Type A and B, respectively, meeting the demands for efficient production of maize seed metering devices. In comparison to traditional design and structural parameter optimization methods for maize seed metering device, this study is helpful to the sustainable development of maize industry and reduce environmental pollution.

O-218 Prospective validation of an artificial intelligence platform for automated follicle counting

Abstract Study question How often are fully automated follicle counts and measurements modified in expert review? Summary answer Automatic follicle annotations provided by an artificial intelligence platform in regular operation of a clinic were edited 3.27% of the time. What is known already Ovarian follicle counting is frequently performed, time-consuming, and subject to noticeable inter-observer variability; as such, it is well-suited to automatizing with artificial intelligence. FOLLISCAN (MIM Fertility) is a software platform that automatically annotates follicles on 2D or 3D ultrasound cine videos, with exact outlines and measurements, without any manual pre-processing. It has undergone several retrospective tests of its precision and recall in detecting ovarian follicles, accuracy of measurements, and time-savings compared to manual counting Study design, size, duration The platform was integrated with a clinic’s existing picture archiving and medical record systems. Ultrasound scans of ovaries were performed as part of regular infertility treatment in two IVF centers. Scans were made by 3 experts in a period from November to December 2023, automatically sent to the platform, analyzed, and then immediately visualized for review by the person performing the ultrasound, in place of manual annotation. Participants/materials, setting, methods The study included 294 cine videos from 147 examinations of 101 patients, with 4347 follicles in total (14.7 per ovary on average, from 2mm). The platform allows users to modify proposed measurements and introduce new follicle annotations. After confirming the review, results were automatically sent back to the clinic’s system to be used in medical decisions just as manual annotations would be. Main results and the role of chance Among all videos the average number of editions (follicle additions, modifications, or deletions) was 0.48 (CI: 0.37-0.61). In total, 142 follicles out of 4347 (3.27%, CI: 2.74-3.80) were edited: 66 follicles were added (1.52%, CI: 1.15-1.88), 26 were modified, (0.60%, CI: 0.37-0.83 ), and 50 were deleted (1.15%, CI: 0.83–1.47). Out of the 151 edited follicles, 13 (9.2%) had 2–5 mm, 29 (20.4%) had 5-10 mm, 51 (35.9%) had 10-15 mm, 42 (29.6%) had 15-20 mm, and 7 (4.9%) had 20 mm or more in diameter. Among follicles of 15 mm or more in size, 49 were edited. Out of 294 videos, 87 (29.6%, 95% Confidence Interval: 24.4-34.6) were edited in review in any way. Limitations, reasons for caution The study was limited in scope and did not track patients until success rates in IVF treatments could be observed. A multi-center randomized control trial could compare pregnancy rates for treatments performed with and without the platform. Wider implications of the findings An integrated platform allows for easy review, while significantly reducing the time spent on follicle counting in a real-world setting. Automated annotations result in consistent, reliable, and quick measurements, with the number of expert modifications smaller than the inter-observer reliability reported in previous studies. Trial registration number Project support was provided by the Polish National Center for Research and Development no. POIR.01.01.01-00-1634/20-00 and ERC Consolidator Grant TUgbOAT no. 772346. This study was conducted following the approval of the research protocol by the review board of the Regional Medical Chamber in Gdańsk (approval no. KB – 51/22).

Potential and Limitations of Anomaly Detection via Tower-Radar Monitoring of Wind Turbine Blades in Regular Operation with Convolutional Networks

The authors have already presented a first analysis of a field study on radar- based structural health monitoring of rotor blades on two operational wind turbines. This analysis will now be extended with measurements from a third operational wind turbine used as a case study for structural monitoring of individual rotor blades over different time intervals and blade modifications. More precisely, a sensor box with a 35 GHz FMCW radar and a highspeed camera was positioned at 90m height on the turbine tower, collecting radar data for structural information and videos for automatic ground truth labelling. After two months of collecting data, each individual rotor blade of that turbine was differently modified and measured again. The radar-facing surface of one rotor blade was partially coated with a microwave absorber foil, while an equivalent foil was applied to the backside of the second rotor blade. The third rotor blade was intentionally left unchanged for the sake of comparability. This study investigates potential limitations like general dataset complexity in radar-based anomaly detection for operational wind turbines. Furthermore, the discriminative ability of a convolutional neural network for classifying moving rotor blades is discussed when encountering feature drift through changing environmental and operational conditions, i.e. metadata like nacelle orientation, rotor blade pitch and rotation speed.

ANALYSIS AND REMEDIATION OF ENDOGENOUS FIRE AT LONGWALL TOTAL COAL THICKNESS MINING OF THE MAIN COAL SEAM IN RASPOTOČJE MINE OF ZENICA BROWN COAL MINES

Mining of the thick coal layers that include roof caving operation can results in residual coal quantities in the gob as a potential threat causing occurence of spontaneous oxidation process, smoldering, and endogenous mine fire that can affect the safety and regular mine operations. Endogeneous fire occurences in Zenica coal mines are directly linked to complex natural conditions reflecting in complex geological conditions, great depth of mining, high methane content in coal seams, and tendency of coal to spontaneous oxidation process. The subject of the paper is the analysis of endogenous fire supression method applyed in conditions of complete coal thickness longwall mining in Raspotočje mine, that has been rehabilitiated upon the endogeneous fire and then reactivated. The following methods were used in fire fighting: passive fire fighting methods (sealing of the area affected by the fire), active method (injection of electrofilter ash) and ventilation methods. Furthermore additional data (position of gob area and sealing objects, air flow regulators, routes of possible air migration, suggested technical solutions, etc) were added in the linear and canonic schemes for the purpose of defining efficient solutions for fire fighting. Key words: endogenous fire, longwall mining, advancing mining, fire fighting.

Real-time vacuum plume flow field reconstruction during lunar landings based on deep learning

In space missions, the vacuum plume generated by rocket engines can negatively impact spacecraft. Therefore, researching the vacuum plume is crucial to guarantee the regular operation of spacecraft. The conventional numerical simulation methodology, the direct simulation Monte Carlo (DSMC) method, is time-consuming and lacks real-time calculation capabilities. Recently, deep learning (DL) methods have emerged in the field of fluid dynamics. In this study, a DL model trained by a convolutional neural network with multiple decoders is introduced to predict the vacuum plume flow field during lunar landings. The network processes shape topology information and boundary conditions as inputs, yielding flow field data including velocity and pressure fields as outputs. Meanwhile, the flow field prediction results under different conditions and training methods are discussed. The results show that the predicted flow field under different lunar surface conditions is in accord with the DSMC results. The maximum mean and standard deviation errors of the data distribution of each flow field do not exceed 9.72% and 9.07%, respectively. Different training methods with flat and inclined lunar surfaces also have an impact on the prediction results. Compared with the DSMC method, the DL method exhibits higher efficiency with a speedup of about four orders of magnitude, indicating that the DL-based flow field reconstruction method has strong application prospects in the real-time computation of vacuum plume flow fields.

Are the smart contracts on Q&A site reliable?

AbstractEthereum, as a leading blockchain platform, has attracted a significant number of practitioners. These practitioners require a platform for communication and collaborative problem‐solving, which led to Ethereum Stack Exchange (ESE), a Q&A site dedicated to Ethereum‐related issues. While the Q&A site facilitates communication among practitioners, it also introduces new challenges. Practitioners adopt code snippets from Q&A sites to address problems encountered. However, the quality of code snippets on ESE remains largely unexplored. Vulnerabilities and gas‐inefficient patterns in ESE may spread to the code in Ethereum and threaten its regular operation. In this article, we conduct an empirical study investigating the distribution of vulnerabilities and gas‐inefficient patterns in ESE. Further, we analyze the potential impact of vulnerabilities and gas‐inefficient patterns from ESE on Ethereum. However, we encounter a problem during the vulnerability and gas‐inefficient pattern detection. Established smart contract analysis tools in the mainstream realm necessitate complete source code files for thorough analysis, while codes on ESE are often incomplete code snippets. To address this, we introduce the AST‐based code clone detection technique to construct detectable files corresponding to code snippets. This enables us to detect vulnerabilities and gas‐inefficient patterns in code snippets. In the end, our findings demonstrate that 11.18% of the contract‐level code snippets and 4.06% of function‐level code snippets in ESE have vulnerabilities. And 27.21% of contract‐level code snippets and 17.89% of function‐level code snippets contain gas‐inefficient patterns. The additional consumption caused by the gas‐inefficient pattern in ESE is approximately $1,695,002. Based on these findings, we provide recommendations for both ESE and its users, aiming to foster collaborative efforts and create a more reliable Q&A site for practitioners.

Spending on Intangibles and The Growth of Public Sector Enterprises In India: A Measure-Specific DEA Analysis

A recent surge of intangible investment to navigatethe productive capability of Central Public Sector Enterprises (CPSEs) resurrected a range of doubt about the trajectory of choices and the actual managerial space that actualizes these choices. Though spending on intangibles and more particularly on R&D contribute substantially to growth parameters but the ambiguous magnitude of spending patterns and treatment of such expenditure from regular business operation to an investment element, in particular, to enhance the valued capability, yet unresolved in the corporate world. Recent studies on the usefulness of intangibleoutlay and capitalization of the same in improving future returns evidenced a gloomy pattern of crisis in the measurement and allocation of intangibles. As evidenced, most of the spending on intangibles by the business units in India is targeted to secure “patent for profit” rather than to achieve “valued capability” to expand real opportunities to choose between alternatives. Based on the last ten years' dataset of Maharatna Enterprises this study critically examines the contribution of decisive one from the container of four inputs namely spending on software, other R&D expenditure, materials, and employee costs on a set of productive activities covering the revenue and net- value- added, as these are more regular in enhancing both profit and production capability with the help of input-oriented VRS weighted measure-specific DEA model. Results show that the multidimensional performances between the enterprises are not uniform throughout the study period and that the efficiency is highly correlated with the size of a few companies like ONGC, BHEL, and GALE and higher spending DMUs on intangibles do not necessarily have higher-ranked performance in terms of returns and net value added. This study provides valuable insights for managers aiming to maximize the benefit of variety in Software and other R&D spending patterns considering the sizes of the companies while minimizing the risk associated with excessive diversity beyond the moderate threshold in intangible investments.

Dual-path lightweight convolutional neural network for automatic sorting of olive fruit based on cultivar and maturity

Identifying olive cultivars and maturity stages is crucial in the olive industry, as these traits significantly impact the nutritional and sensory properties of olive products and extracted oil. For this purpose, this study presents a novel automatic computer vision system that applies state-of-the-art deep learning technology to sort and classify two Iranian olive cultivars, Zard and Roghani, in five maturity stages, resulting in a total of ten distinct classes. The model was developed by evaluating multiple user-defined and standard structures. It was based on a dual-path lightweight convolutional neural network that uses both regular and dilated convolution operators. Dilated convolutions were used to extract more information and capture different properties by providing larger receptive fields. With a significantly lower number of trainable parameters than standard architectures, the lightweight nature of the model would enhance its potential for delivering fast responses in on-the-go applications. Four optimizers (RMSProp, SGD, Adam, and Nadam) were tested on the developed model to enhance its performance, and Nadam exhibited the greatest accuracy. The proposed model achieved a total classification accuracy of 95.79 % and a loss of 0.2214. The proposed model was completely accurate for some classes, and the classification metrics for all categories were high, ranging from 88 % to 100 % for precision, 83–100 % for recall, and 86–100 % for F1-score. The accuracy of classification within the Roghani cultivar classes stood at 98.28 %, while for the Zard cultivar classes, it achieved 97.76 %. The study found that the proposed model can be efficiently incorporated into an olive sorting system, facilitating the identification of olives with different cultivars and varying levels of maturity, thereby enhancing the production of post-harvest products and superior quality of oil.

The R2D2 Deep Neural Network Series Paradigm for Fast Precision Imaging in Radio Astronomy

Radio-interferometric imaging entails solving high-resolution high-dynamic-range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN’s capability. These range from advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) algorithms propelled by learned regularization denoisers, such as AIRI. Optimization and PnP structures are however highly iterative, which hinders their ability to handle the extreme data sizes expected from future instruments. To address this scalability challenge, we introduce a novel deep-learning approach, dubbed “Residual-to-Residual DNN series for high-Dynamic-range imaging” or in short R2D2. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of deep neural networks (DNNs) taking the previous iteration’s image estimate and associated data residual as inputs. It thus takes a hybrid structure between a PnP algorithm and a learned version of the matching pursuit algorithm that underpins CLEAN. We present a comprehensive study of our approach, featuring its multiple incarnations distinguished by their DNN architectures. We provide a detailed description of its training process, targeting a telescope-specific approach. R2D2's capability to deliver high precision is demonstrated in simulation, across a variety of image and observation settings using the Very Large Array. Its reconstruction speed is also demonstrated: with only a few iterations required to clean data residuals at dynamic ranges up to 105, R2D2 opens the door to fast precision imaging. R2D2 codes are available in the BASPLib (https://basp-group.github.io/BASPLib/) library on GitHub.

Technology of regular urban passenger transportation operation improvement

Introduction. The degree of passenger vehicle capacity utilisation affects the economic component of regular urban passenger transport operation, as it depends on the number of passengers transported, which affects the revenue from transportation, as well as on the passenger vehicle capacity, which affects the cost of transportation. One of the tasks formulated in the programme-target documents of the Republic of Belarus is to achieve full selfsufficiency of urban regular passenger transport. One of the ways to ensure this can be maximising the degree of capacity utilisation set by the manufacturer of the passenger vehicle.The aim of the paper is to estimate the degree of passenger vehicle capacity utilisation and dynamics of its change, as well as to develop measures to improve the efficiency of urban regular passenger transport.Materials and methods. When writing the article, the data obtained by a full-scale survey of passenger flows in one of the cities of the Republic of Belarus, conducted in 2019 and in 2024, were used. When writing the article, statistical methods (descriptive statistics, fitting, visualisation, assessment of significance of differences using statistical criteria), analysis of scientific literature and work technology, synthesis of proposals were used.Conclusions. The low degree of utilisation of passenger vehicle capacity is shown. The new technology of the organisation of work of the city passenger transport of regular communication, allowing to increase use of capacity of passenger vehicles and, at the expense of this efficiency of such work is offered.The possibility of further use of the results of scientific work. The received results substantiate necessity of development of scientific toolkit for optimisation of assignment of passenger vehicles to flights taking into account passenger load on them and other restrictions.Practical significance. This article shows the need to develop measures to improve the efficiency of passenger vehicle capacity utilisation for the purposes of increasing the efficiency of urban regular passenger transport.Originality. This paper shows the change in the degree of use of passenger vehicle capacity, shows the factors affecting it, and proposes a new technology for organising the work of urban regular passenger transport. The article will be useful for officials of the management bodies responsible for the organisation of the work of urban passenger transport of regular communication, as well as employees of the enterprises of urban passenger transport, transport scientists.

Fast phase retrieval without unwrapping for 3D measurement based on digital phase difference

Phase unwrapping is still a tough task for fringe projection profilometry (FPP), due to the heavy computation burden and slow rate of the existing phase unwrapping algorithms. To speed up the retrieval of the absolute phase, a fast phase retrieval algorithm without unwrapping for 3D measurement based on digital phase difference is presented in this paper. Firstly, 4-step phase-shifting profilometry is utilized to retrieve a complex-valued map, who contains the complete phase information of the measurement object in its phase part. Instead of extracting the phase map from the complex-valued map directly, a novel complex-valued map containing the differential phase is obtained by the division between the complex-valued map and its pixel-shifted version. Then, the differential phase can be extracted from the novel complex-valued map by regular phase extraction operation easily. Finally, the absolute phase can be retrieved by integrating the differential phase along the inverse pixel-shifted direction. This proposed algorithm can retrieve the absolute phase easily and fast, while the measurement object has continuous surface with the phase variation in the range of (−π, π]. Simulated and experimental results were demonstrated to present that this algorithm has faster phase retrieval rate than existing algorithms and has no obvious jump error, which allows fast and high-quality phase retrieval.

A plug-and-play framework for curvilinear structure segmentation based on a learned reconnecting regularization

Curvilinear structures are present in various fields in image processing such as blood vessels in medical imaging or roads in remote sensing. Their detection is crucial for many applications. In this article, we propose an unsupervised plug-and-play framework for the segmentation of curvilinear structures that focuses on the preservation of their connectivity. This framework includes an algorithm for generating realistic pairs of connected/disconnected curvilinear structures and a reconnecting regularization operator that can be learned from a synthetic dataset. Once learned, this regularization operator can be plugged into a variational segmentation scheme and used to segment curvilinear structure images without requiring annotations. We demonstrate the interest of our approach on the segmentation of vascular images both in 2D and 3D and compare its results with classic unsupervised and deep learning-based approach. Comparative evaluations against unsupervised classic and deep learning-based methods highlight the superior performance of our approach, showcasing remarkable improvements in preserving the connectivity of curvilinear structures (approximately 90% in 2D and 70% in 3D). We finally showcase the good generalizability behavior of our approach on two different applications : road cracks and porcine corneal cells segmentations.

Planning Service Protocols for Extra-Long Trains with Transfers

This paper presents a modeling framework for optimizing operational protocols of extra-long trains (XLTs) in metro systems (i.e., trains longer than station platforms). With the rising travel demand in megacities, metro systems face challenges such as overcrowded stations, delays, and passenger anxieties. XLTs have been proposed as a promising solution to increase metro line capacity without additional infrastructure construction. The study explores the trade-offs between the additional capacity gained through complex protocols, the potential benefits of protocols with inline transfers, and the importance of effective passenger information systems from both passengers’ and operators’ perspectives. Mathematical programs are proposed to optimize protocols for a given demand distribution and to estimate the maximum line capacity of an XLT system. The benefits of implementing XLTs are evaluated in hypothetical and real-world cases with varying demand distributions and network sizes. The results demonstrate significant capacity increases ranging from 24% to 126% as compared with regular train operations, depending on system parameters and demand distribution. These findings demonstrate promise for using such systems to improve metro line capacity in the real world. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT 25 Conference. Funding: The work was supported in part by the University of Illinois, Urbana Champaign [Grant Grainger STII Seed Fund] and the Zhejiang University-University of Illinois Urbana-Champaign Institute [Grant DREMES-202001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0527 .

COMPREHENSIVE DISCUSSION OF THE REPAIRABLE SINGLE SERVER CATASTROPHE AND MULTIPLE VACATION QUEUEING MODEL

The queueing system with two types of vacation policies and disasters occurring in every state are examined in this study. As soon as the system is empty, the server goes on a short-term vacation. If there are no active clients in the system when the server returns from a short-term vacation, then the server goes on a long-term vacation. Upon seeing at least one client in the queue during a short or long-term vacation, the server will turn on right away. Catastrophes can happen during short or long vacations and also when the server is active. All current clients are removed from the system when a disaster occurs. When the system has been fixed, it starts to work properly once again. After repair, the server immediately switches to working mode(model 1) or takes a short-term vacation (model 2). A steady-state solution is determined by using the PGF (Probability-Generating Function). The above two categories of after-repair will be examined in this paper. Additionally, numerical examples related to performance measures, cost models, and the steady analysis of the provided models are discussed. Management for networking disaster recovery gives suggestions for restarting regular operations and network services following a disaster.

DC circuit breaker: A topology with regenerative current breaking capability for DC microgrid applications

The modern DC grid is predominantly inductive due to several factors, including line inductance, and the use of current-limiting inductors to control fault current rising rates. These network inductances store a significant amount of energy during regular operation, which is either dissipated or freewheeled to ensure safe current breaking. This research article proposed a highly efficient bidirectional DC circuit breaker topology that not only provides safe current breaking but also effectively recovers the post-current breaking energy from the network’s inductance instead of dissipation. An extensive simulation investigation is performed in PSIM software to test the breaker capability for a 400V/400A DC system and the laboratory-scale 48V/1A experimental setup has been developed and employed for further validation purposes.

Measuring and shaping the nutritional environment via food sales logs: case studies of campus-wide food choice and a call to action.

Although diets influence health and the environment, measuring and changing nutrition is challenging. Traditional measurement methods face challenges, and designing and conducting behavior-changing interventions is conceptually and logistically complicated. Situated local communities such as university campuses offer unique opportunities to shape the nutritional environment and promote health and sustainability. The present study investigates how passively sensed food purchase logs typically collected as part of regular business operations can be used to monitor and measure on-campus food consumption and understand food choice determinants. First, based on 38 million sales logs collected on a large university campus over eight years, we perform statistical analyses to quantify spatio-temporal determinants of food choice and characterize harmful patterns in dietary behaviors, in a case study of food purchasing at EPFL campus. We identify spatial proximity, food item pairing, and academic schedules (yearly and daily) as important determinants driving the on-campus food choice. The case studies demonstrate the potential of food sales logs for measuring nutrition and highlight the breadth and depth of future possibilities to study individual food-choice determinants. We describe how these insights provide an opportunity for stakeholders, such as campus offices responsible for managing food services, to shape the nutritional environment and improve health and sustainability by designing policies and behavioral interventions. Finally, based on the insights derived through the case study of food purchases at EPFL campus, we identify five future opportunities and offer a call to action for the nutrition research community to contribute to ensuring the health and sustainability of on-campus populations-the very communities to which many researchers belong.

Enhancing PHGMS performance for recovery of ultra-fine ilmenite from tailings

In southwest China, the Panzhihua area annually produces about 80 million tons of tailings with a TiO<sub>2</sub> grade of around 5.0%, which causes serious waste of titanium resources as well as environmental and safety issues. The ilmenite contained in these tailings is ultra-fine in size, so it is difficult to recover under the regular operating conditions of pulsating high gradient magnetic separation (PHGMS). In this study, an SLon-100 PHGMS separator was applied to concentrate an ultra-fine titanium tailing under a wide range of operating conditions. The experimental results indicated that a combination of high pulsating frequency, large pulsating stroke, and low feed velocity was favorable for the highly efficient recovery of ultra-fine ilmenite from the tailings. The TiO<sub>2</sub> grade in the optimal concentrate was enhanced from 4.33% to 13.64%, at a recovery of 66.55% and an enrichment ratio of 3.15 through a one-stage PHGMS process. The size analysis of the optimal concentrate showed that the TiO<sub>2</sub> recovery in -25+18 µm and -18+10 µm fractions exceeded 70%. To further understand this PHGMS performance, the optimal ultra-fine ilmenite and larger-size ilmenite concentration conditions were compared. This study provides a valuable reference in the PHGMS operation for recovering ultra-fine weakly magnetic minerals, including ilmenite.

Performance assessment of a newly developed non-invasive 2D beam profile monitor for high-intensity accelerators

In high-intensity accelerators, minimizing beam loss is paramount to avoid damages and activation of accelerator components. To achieve this, a reliable beam profile monitor is essential for assessing the beam center and spread. Additionally, the beam profile can be used to steer the beam during regular operation, thus reducing beam loss. In cases of irregular events, the monitor can initiate beam profile recording to identify and address any issues promptly. In this manuscript, we present a detailed description of a non-invasive 2D beam profile monitor, focusing on its components, operation, and most importantly, its reliability and failure modes. From operation experience, failure modes of the system and its effects were identified. Fault tree analysis was carried out. Failure rate of sheet generator was estimated using response surface method. Failure rates of components were used to estimate the failure rate of the system. From the target reliability, the required maximum repair time of the system was calculated.