Future Operations Research Articles

Developments of a centimeter-level precise muometric wireless navigation system (MuWNS-V) and its first demonstration using directional information from tracking detectors

Various positioning techniques such as Wi-Fi positioning system have been proposed to use in situations where satellite navigation is unavailable. One such system, the muometric positioning system (muPS), was invented for navigation which operates in locations where even radio waves cannot reach such as underwater or underground. muPS takes advantage of a key feature of its probe, cosmic-ray muons, which travel straightforwardly at almost a speed of light in vacuum regardless of the matter they traverse. Similar to other positioning techniques, muPS is a technique to determine the position of a client’s muPS receiver within the coordinate defined by reference detectors. This can be achieved either by using time-of-flight (ToF) or angle of arrival (AoA) measurements. The latter configuration (AoA), called the Vector-muPS has recently been invented and the present paper describes the developments of the first prototype of a vector muometric wireless navigation system (MuWNS-V) with this new vector-muPS concept and its demonstration. With MuWNS-V, the reference tracker and the receiver ran wirelessly with fully independent readout systems, and a positioning accuracy of 3.9 cm (RMS) has been achieved. We also evaluated the outcome of measuring continuous indoor localization of a moving receiver with this prototype. Our results indicated that further improvements in positioning accuracy will be attainable by acquiring higher angular resolution of the reference trackers. It is anticipated that “sub-cm level” navigation will be possible for muPS which could be applied to many situations such as future autonomous mobile robot operations.

The effect of conditioning pH behaviour on spodumene flotation with fatty acids

ABSTRACT Spodumene - the primary lithium mineral - is often concentrated by flotation with fatty acid collectors. However, producing high-grade concentrates at high recoveries is challenging and a risk for future operations. Poor spodumene selectivity is largely driven by the sensitive properties and poor solubility of fatty acids and requires high-density conditioning for desired flotation performance. This study related pH behaviour and operating parameters during fatty acid conditioning to flotation performance. The best pH characteristics for >85% lithium recovery at >5.25% Li2O were a minimum pH 6.5–6.8, final pH 6.7–7.1, and a pH change rate of 0.07 ΔpH/min. Regression analysis predicted optimizated conditions of initial pH 8.2, 30 W/L power density, 8 min conditioning and 550 g/t collector. Follow-up testing confirmed a higher W/L improved grade, and recovery was maintained when combined with decreased residence time and pH. Better defining these relationships can improve process control n developing industrial spodumene flotation circuits.

Changes in Microscale Liquid Formation in Lump and Sinter Mixed Burden Softening and Melting Tests with the Addition of Hydrogen

With the movement toward hydrogen-enriched blast furnace operation to lower greenhouse gas emissions, ferrous burden design must be reconsidered to optimize furnace permeability. Increasing the ratio of direct charge lump ore in the ferrous burden also presents an opportunity to lessen the emissions associated with the production of sinter and pellets. Under traditional blast furnace conditions, lump ore usage is improved by mixing it with the sinter in the burden to promote their favorable high-temperature interactions (both chemical and physical). As such, mechanistic changes to the interaction must be understood to optimize burden design, including for future operations with hydrogen addition. In this study, liquid formation in both the metallic and oxide components of ferrous burdens is microscopically investigated. Oxide liquid and solid phase stability at the interfaces of dissimilar burdens are visualized using a novel mapping technique, and metallic iron is etched to reveal microstructures indicative of carbon. Results indicate that the inclusion of hydrogen promotes the gas carburization of metallic iron in sinter, but not lump. It was concluded that mixed burden softening and melting performance with hydrogen addition were improved through the addition of lump in two ways: the highly metallic lump particles provide structural support for the collapsing sinter bed and also suppress the formation of early liquid slag from the sinter.Graphical

Changes in the Arctic Traffic Occupancy and Their Connection to Sea Ice Conditions from 2015 to 2020

Arctic shipping activities are increasing in the context of sea ice decline. However, research gaps persist in studying recent Arctic shipping activities across various vessel types and their connection with sea ice conditions. Utilizing Automatic Identification System (AIS) data and sea ice satellite observations between 2015 and 2020, these matters are delved into this study. A discernible overall growth trend in Arctic traffic occupancy occurs from 2015 to 2020 during summer and autumn. Excluding passenger ships, the traffic occupancy trend for each ship type closely parallels that for all ships. Variations in traffic occupancy along the Northeast Passage dominate that in the entire Arctic. As sea ice diminishes, both Arctic traffic occupancy and its variability noticeably increase. Further examination of the relationship between shipping activities and ice conditions reveals that increased traffic occupancy corresponds significantly to diminishing sea ice extent, and the constraint imposed by sea ice on Arctic traffic occupancy weakens, while the 6-year AIS data could lead to uncertainties. In summary, as the Arctic sea ice declines continuously, not only sea ice but also additional social, military, and environmental factors constraining marine activities should be considered in the future operation of Arctic shipping.

Forecasting individual bids in real electricity markets through machine learning framework

With the increasing uncertainty caused by the complexity of the world’s energy environment and the increasing penetration rate of renewable energy, it is significant to estimate the future operation of power markets in advance. Forecasting individual bids in spot electricity markets is a promising new method for achieving so, but it has not been fully studied due to the difficulty of forecasting a bid function. The idealization of existing optimization-based models decreases their practical effects in real markets. Thus, we propose a scalable forecasting framework that incorporates several customized state-of-art machine learning methods according to the characteristics of the bidding data. First, several low-rank approximation algorithms are customized to encode the high-dimensional bidding curves into low-dimensional feature spaces and reconstruct them from the predicted feature space. Second, a transformer-based multidimensional time series prediction algorithm is proposed to predict the bidding feature based on both related factors and historical bidding records. To appropriately evaluate the performances of the forecasting methods, we introduce a dynamic criterion based on the economic implications of bids. The comprehensive framework is tested based on actual market data from the Australian national electricity market, and in the empirical example, the feasibility and effectiveness of the proposed framework are demonstrated.

Ecological Factors in Public Procurement and Corporate Sustainability Policies

European Institutions and scholars are reflecting upon the nature of the requirements that are or can be used to implement sustainability through Public Procurement. As far as public organisations are concerned, the Portuguese government's actions are in the same wavelength as the European guidelines on the matter. Public procurement involves economic operators. Thus, it is important to try to understand if the European Union proposal for a directive on "Corporate Sustainability Due Diligence" is an interesting tool in the discussion. Recently, the Portuguese Council of Ministers issued Resolution no. 132/2023, of 25 October, defining the ecological criteria applicable to certain kinds of contracts and products, goods and services concerning some administrative bodies, a Resolution which, nevertheless, is prone to criticism. May the shift of the European Union Public Procurement paradigm help implement Sustainable Public Procurement through mandatory requirements? Can the future Directive help economic operators align with contracting authorities to implement Sustainable Public Procurement? Might the obligation to include environmental factors help fulfil the Directive's due diligence obligations? And, lastly, has Portugal taken the right step with the issuance of Resolution no. 132/2023?

Volume prediction of young improved Sitka spruce trees in Great Britain through Bayesian model averaging

Abstract More and earlier thinning operations are expected in Sitka spruce planted forests in Great Britain as a result of an increased demand for biomass and faster growth driven by breeding. It is however unknown whether the current models, which were designed to predict volume in adult trees, can provide unbiased volume predictions for the young individuals that are likely to be harvested in future thinning operations. The primary objective of this study was to answer this question. To do this, we used retrospective data from a destructive experiment originally aimed at assessing timber properties to reconstruct the taper and volume of 12 improved Sitka spruce trees at different ages. These volumes were then compared against the predictions from the current methods, which were found to be from moderately to strongly biased. The second objective was to provide proof of concept that a combination of existing volume models and other theoretical volume models could yield less biased predictions. We successfully addressed this objective through the Bayesian model averaging approach. The method, albeit tested with limited data, proved to be a promising alternative until new volume models are released. Further data from other available destructive experiments can be used to refine our calibration.

Mutual role of dodecylbenzene sulfonic acid on enhanced oil recovery and asphaltene inhibition in low salinity medium: Experimental and XDLVO study

Understanding the molecular interactions during oil recovery processes can shed light on screening new chemicals for future operations. Of particular interest are chemicals capable of providing the mutual role of asphaltene inhibition and interfacial tension reduction at liquid–liquid and rock-liquid interfaces. This study delves into the interfacial interactions between low-salinity brines, model oils containing dodecylbenzene sulfonic acid, and dolomite rock surfaces. In the experimental part, the asphaltene precipitation inhibition efficacy of dodecylbenzene sulfonic acid was evaluated through filtration and precipitation test, and its oil recovery performance was examined from interfacial tension reduction and wettability alteration viewpoints. The theoretical part was then followed by the implementation of the surface energy concept based on the XDLVO theory, where acid-base and Lifshitz-van der Waals interaction energies, as well as the work of cohesion/adhesion between interacting bodies in asphaltene and oil recovery systems, were considered. The results obtained from this study revealed that dodecylbenzene sulfonic acid, owing to its acidic nature and the long alkyl chain in its structure, could simultaneously perform the role of asphaltene inhibitor and oil recovery agents. In particular, asphaltene particles showed less tendency to aggregate and form macrostructures in the presence of low-salinity brines formulated with magnesium chloride. The total interaction energy between asphaltene particles was almost halved by the addition of dodecylbenzene sulfonic acid. The low interfacial tension of 0.16 mN/m between model oil containing 1 wt% of DBSA and low-salinity brine containing 5000 mg/L magnesium chloride and the wettability alteration of the dolomite sample toward water-wetness were also confirmed by theoretical calculations using the concept of work of adhesion.

A STUDY ON MARUTI SUZUKI COMPANY FINANCIAL REVIEW

Every business organization, whether manufacturing oriented or service oriented, needs finance, i.e., money for carrying on its activities. Though business organization gets sufficient money for carrying its activities, success of the business depends on how well the organization manages them. That is, its depends on how well a business organization funds its capital and how efficiently it operates out of the invested capital to generate profit. While the success of the of a business also a subjective measure how well a firm can finance its assets and make use of the assetsto generate revenues, the business can be stable and healthy if it is financial performance consistently yields profit. These measures often determine whether or notthat level of performance is considered adequate. Further, a business organization is considered to be inefficient, if the performance level is often found to be low, even if itis making profit. Financial management is that part of management which deals with raising of funds in the most economical and suitable manner, using the funds as profitability as possible, planning future operations, inspections, controlling current performance and future development through financial accounting and other means. No business can plan its activities without considering its financial resources The business functions ofa finance department typically include planning, organizing, accounting andcontrolling the company’s finance and to ensure intensive and economic use of capitalresources of the organization. Since business firms are profit seeking organizations, their functions are to maximize the company’s wealth. Asset management, costing, budgeting, credit management, debit management are the other functions of the finance department. Finance in essence is considered with the acquisition and use of funds by a business firm. The main objective of financial management is to control required funds for meeting short term and long-term needs of business enterprise and to maximize the value of firm to its equity share holders.To have a clear understanding of the profitability and financial position ofbusiness, the financial statements have to be analyzed and interpreted. Financial Title: Suzuki cars

Do vocal cues matter in information disclosure? Evidence from IPO online roadshows in the SSE STAR market

In this study, we investigate whether vocal cues expressed by management in IPO online roadshows in the SSE STAR Market play an information role. Using deep learning techniques, we classify the emotional tendencies of vocal cues in IPO online roadshows and construct quantitative proxy variables. After a series of empirical analyses, we find that vocal emotions are positively associated with first-day stock returns after IPO. In addition, we find that vocal emotions have predictive power for future operating performance after IPO. Our findings still hold after a series of robustness checks. Through additional analyses, we provide evidence that information content in the prospectus and investor sentiment play moderating and mediating roles, respectively, in the relationship between vocal emotions and the first-day stock returns after IPO. Moreover, we find that the vocal emotions expressed in the presentation section and the management tone in the Q&A section develop synergistic effects. Finally, we find that the information role played by vocal emotions exhibits timeliness. Our findings elucidate the information role of vocal cues and highlight the importance of IPO online roadshows in the SSE STAR Market.

Ultra-short-term operation situation prediction method of active distribution network based on convolutional neural network long short term memory

The large-scale and high-permeability access of distributed generations (DGs) is making the distribution networks develop into active distribution networks (ADNs). The increasing complexity of the structure of ADNs also increases the vulnerabilities for cyberattacks. To accurately predict the operation situation of ADNs no matter whether cyberattacks occurred, this paper proposes an ultra-short-term operation situation prediction method of ADNs based on CNN-LSTM. Firstly, an ADNs situation prediction index system is established, which can comprehensively evaluate the future operation situation of ADNs from different perspectives. Secondly, a multi-feature data reconstruction method based on the time sliding window algorithm is proposed, which is utilized for constructing 2-dimensional matrix and as input datasets for multiple independent CNN-LSTM models to predict each situation prediction index. Then, there are 8 independent CNN-LSTM models shared same input matrix to predict 8 ADNs situation prediction index value without interfering with each other. Among them, CNN is used to extract the spatial correlation feature vectors of ADNs historical operation data and meteorological factor data in ultra-short term, which are reconstructed in time series and used as the input data of LSTM. LSTM is used to extract long-term historical temporal relationship to obtain ADNs operation situation prediction index value. Finally, we utilize the fuzzy analytic hierarchy process to calculate the future operation situation of ultra-short-term ADNs. Taking a real ADNs with 33-node as an example, we compared our proposed methodology to other existing excellent machine learning-based models, the result demonstrates that our proposed prediction method exhibits outstanding performance in time consumption and prediction accuracy.

Data-driven approach for the detection of faults in district heating networks

At present, District Heating Networks (DHNs) are required to operate more and more reliably and efficiently in order to further save primary energy and reduce environmental impact. Thus, monitoring and diagnostic approaches are necessary to identify the typical faults that affect this type of systems (e.g., anomalous heat and pressure losses), with the final goal of optimizing DHN operation and management. To this aim, this paper presents a data-driven diagnostic methodology that exploits NARX (nonlinear autoregressive network with exogenous inputs) neural networks to simulate DHN healthy operation and a threshold-based criterion for fault detection and identification. The novel diagnostic methodology is tested for evaluating the health state of the DHN of the campus of the University of Parma (Italy), based on the availability of time series of measurable variables (mass flow rate, pressure, and temperature) for both the power plant and the end-users. Both single and multiple faults of anomalous heat losses and anomalous pressure losses, with different magnitudes and location, were artificially implanted in DHN pipes. The main novel contribution of this paper with respect to state-of-the-art literature relies on the development of a real-time simulation approach aimed to predict DHN future operation and detect abnormal deviations from normal operation. The methodology proves to correctly detect and identify all simulated faults related to heat and pressure losses, by also correctly estimating their magnitude even in the most challenging scenarios.

Sex Differences in Postinjury Health Profiles Among U.S. Military Personnel Following Deployment-Related Concussion.

Background: Women in the U.S. military are now authorized to serve in direct combat occupations. This may increase their risk of combat injuries, such as concussion, in future conflicts. Knowledge of sex differences in health profiles after concussion is paramount for military medical planning efforts. The purpose of this study was to assess sex-related differences in health profiles among U.S. military personnel following deployment-related concussion. Materials and Methods: We conducted a retrospective study of service members who sustained a concussion during combat deployment between 2004 and 2013. Postinjury diagnoses were abstracted from outpatient encounters in electronic health records for 24 months after concussion. We used hierarchical clustering to identify clusters, termed "health profiles," and logistic regression to determine whether sex predicted membership in the health profiles. Results: The study sample included 346 women and 4536 men with deployment-related concussion. Five postinjury health profiles were identified and classified as no morbidity, back pain, tinnitus/memory loss, posttraumatic stress disorder/postconcussion syndrome, and multimorbidity. Women relative to men had higher odds of membership in the back pain (odds ratio [OR] = 1.32; 95% confidence interval [CI] = 1.05-1.67) and multimorbidity profiles (OR = 1.44; 95% CI = 1.03-2.00) and lower odds than men in the tinnitus/memory loss profile (OR = 0.62; 95% CI = 0.42-0.91). Conclusions: Postinjury health profiles among U.S. service members differ by sex following deployment-related concussion, particularly with a higher burden of multimorbidity among women than men, which may require interdisciplinary care. Women also had higher odds of membership in the back pain profile and lower odds in the tinnitus/memory loss profile than men. To prepare for future military operations where women may have greater exposure to combat, continued research elucidating health-related sex differences after deployment-related concussion is imperative.

Ultrafast atomic-scale scanning tunnelling spectroscopy of a single vacancy in a monolayer crystal

Defects in atomically thin semiconductors and their moiré heterostructures have emerged as a unique testbed for quantum science. Strong light–matter coupling, large spin–orbit interaction and enhanced Coulomb correlations facilitate a spin–photon interface for future qubit operations and efficient single-photon quantum emitters. Yet, directly observing the relevant interplay of the electronic structure of a single defect with other microscopic elementary excitations on their intrinsic length, time and energy scales remained a long-held dream. Here we directly resolve in space, time and energy how a spin–orbit-split energy level of an isolated selenium vacancy in a moiré-distorted WSe2 monolayer evolves under the controlled excitation of lattice vibrations, using lightwave scanning tunnelling microscopy and spectroscopy. By locally launching a phonon oscillation and taking ultrafast energy-resolved snapshots of the vacancy’s states faster than the vibration period, we directly measure the impact of electron–phonon coupling in an isolated single-atom defect. The combination of atomic spatial, sub-picosecond temporal and millielectronvolt energy resolution marks a disruptive development towards a comprehensive understanding of complex quantum materials, where the key microscopic elementary interactions can now be disentangled, one by one.

Examining the Contributions of Various Source Layers and Transport Pathways to Dose Rates Exterior to a Low- and Intermediate-Level Radioactive Waste Storage Facility Using ADVANTG/MCNP

An in-depth shielding analysis of a large indoor facility designed for storing low- and intermediate-level radioactive waste generated from nuclear power plant decommissioning was performed. The facility has a storage capacity of 1260 box-type containers, which are stacked in five layers. The characteristics of the radiation field around the facility were investigated comprehensively through efficient ADVANTG/MCNP simulations. The contribution of each source layer to the dose rates exterior to the facility was determined. Furthermore, dose components from various transport pathways (e.g., direct, streaming, and skyshine) were isolated using algebraic manipulation of the outputs obtained from additional simulations. The combined results provided valuable information for shielding optimization during the design phase and could facilitate the future operation of the storage facility.

Utjecaj posmične brzine i dodatka za obradu drva na hrapavost površine jelovine, topolovine i bukovine obrađenih blanjanjem

The roughness of the machined surface is a crucial factor in the woodworking process because it influences the quality of future operations like gluing, sanding, pressing, surface treatment and protection, and assembly. The quality of the machined surface is determined by a number of machining process parameters as well as material properties, and their proper selection and optimization will yield the best results. The purpose of this article is to demonstrate how specific parameters and wood species affect surface roughness. In the experiment, three species of wood: beech (Fagus sylvatica), fir (Abies alba), and poplar (Populus alba) with the same moisture content were used, and combinations of feed speed (5 and 8 m/min) and cutting depth (2 and 4 mm) were created. The processing was done on a wood planer (thickener) machine of the SD-B-510 series manufactured by RoblandMachines Belgium. Following that, roughness measurements of Ra, Rz, Rt, and Rq were taken with a focus on the mean deviation of the profile Ra, and an analysis of the results was presented, revealing that different roughness values are obtained with the same processing parameters depending on the wood species. The difference between the greatest (5.36 μm) and lowest (2.41μm) roughness values (Ra) for beech is 2.95 μm, 1.25 μm for poplar, and 1.34 μm for fir.

False Data Injection Attack and Corresponding Countermeasure in Multienergy Systems

Worldwide ambitions to combat climate change have expedited the penetration of renewables and decarbonization. Multienergy systems (MES), where different energy systems are optimally coordinated, have been recognized as a key element in future low-carbon energy operations. However, MES operations require intense exchanges of information and control signals, which inevitably intensify the risk of cyberattacks. Existing cybersecurity research mainly focuses on a single-energy system, with only a few pioneering cybersecurity analyses for MES focusing on uncoordinated cyberattacks. A lack of detailed discussion and analysis on the optimally coordinated cyberattack targeting MES prevents operators from accurately evaluating the potential damages of cyberattack in MES operations. Therefore, this paper first proposes an optimally coordinated false data injection attack (OC-FDIA) against MES, where attacks from different energy systems are coordinated to disturb the MES operation. Then, we show that the OC-FDIA can cause synergetic effects leading to much more severe damage than single-system FDIAs and uncoordinated FDIAs. Further, an effective countermeasure is developed to mitigate the OC-FDIA based on deep learning (DL). Eventually, the proposed OC-FDIA and its countermeasures are demonstrated through integrated electricity and gas test systems.

Operational and Environmental Assessment for Night Markets under the Informal Economy Impacts: Insights from the Case Study of Selangor, Malaysia

As an expression of the informal economy, the night market is not only a place that could attract locals to buy goods but also a place where tourists can experience the local characteristics. While the night market brought economic vitality to the city's economy, it also brought some issues. A comprehensive literature review of night markets revealed that existing studies paid less attention to outdoor night markets' spatial operations and waste management. Grounded in the connection between sustainability and night markets, the present study reflected on existing research findings by adopting different periods of fieldwork to conduct a case study of the night market in Jalan 17/1A, Selangor, Malaysia. The study focused on three aspects of night markets: spatial operations, waste, and waste recycling. It found that night market operations brought localized regional economic benefits while causing more severe pollution issues, significantly affecting the region's environmental health and sustainable development. The findings revealed that local government regulation of night market businesses in Malaysia is weak (the issuance of business licenses is informed of the operational guidelines), and there is also no relevant monitoring and penalties by the local government to intervene in the misbehavior of night market consumers. Based on the results of the field survey and combined with the concept of sustainable development, the present study recommended suggestions for the future spatial operation and waste management of night markets. Innovative and novel perspectives of the study combined qualitative observations and data analysis to comprehensively examine how spatial considerations and waste management strategies intersect in the unique context of night markets—aiming to fill the gaps in the sustainability of night market operations and to protect the ecology of the surrounding territorial environment, contributing to the discourse on sustainable development.

Autonomous Dynamic Hitch-Hiking Control of a Bionic Robotic Remora

Remora suckerfish and its hitch-hiking behavior bring enormous inspiration into the engineering field. In this paper, a bioinspired hitch-hiking behavior is accomplished automatically by a robotic remora, which creates possibilities for prolonging its endurance and multi-robot cooperation. First, the definition of the hitch-hiking task and the mechatronic design of robotic remora are introduced. Then, aiming at the hitch-hiking task, the LED-marker-based underwater visual localization method and planar state synchronization controller are developed. The localization method includes a complete framework from LED detection to marker pose optimization, which considers the refraction correction to improve the localization precision in water. The synchronization controller is decomposed into the lateral and longitudinal sub-controllers to overcome the challenges caused by underactuated dynamic. Besides, a finite state machine is designed to model the state and action transition during the hitch-hiking task. Extensive experimental results demonstrate the effectiveness of the proposed method. The autonomous hitch-hiking task toward a moving host is successfully implemented by a robotic fish for the first time. Such results may offer valuable insights into the future autonomous operation of underwater robots.

Bayesian Dual-Input-Channel LSTM-Based Prognostics: Toward Uncertainty Quantification Under Varying Future Operations

Bayesian Dual-Input-Channel LSTM-Based Prognostics: Toward Uncertainty Quantification Under Varying Future Operations