Operational Methodology Research Articles

Assessing lodging damage of jute crop due to super cyclone Amphan using multi-temporal Sentinel-1 and Sentinel-2 data over parts of West Bengal, India.

The present study is a maiden attempt to assess jute crop lodging due to super cyclone Amphan (20 May 2020) by synergistic use of Sentinel-2 (optical) and Sentinel-1 (SAR) data over part of West Bengal, India. Pre-event Sentinel-2 data (9 April, 14 May) along with the ground information were used to map the jute crop of the affected districts with accuracy of 85%. The cross-polarized backscatter (σ0VH) of Sentinel-1 was found to be sensitive to the sudden change in the canopy structure due to lodging and partial flooding.[Formula: see text](σ0VH_22 May - σ0VH_16 May) indicating post-event damage was > 2.5dB over the affected jute crop and [Formula: see text] (σ0VH_22 May - σ0VH_28 May) representing post-event recovery showed > 1.5dB for recovered crop, depending on the crop vigor/height. Decision matrix was prepared combining [Formula: see text] and [Formula: see text] for NDVI-based crop vigor strata (low, medium, and high) to classify the area into affected, marginally affected and normal. Overall accuracy of the classified map was found to be 84.12% with kappa coefficient of 0.74. Nearly, 12.5% of the jute area, i.e., 38,119 ha was found to be either affected or marginally affected due to Amphan and distributed in the southern part of Murshidabad, north-eastern Nadia, northern 24 Paraganas (N), and middle region of Hooghli district. Geospatial map of block-wise affected jute area was prepared to facilitate informed decision making. The study demonstrated an operational methodology for assessing crop lodging due to natural calamities to support relief management and crop insurance.

A reinforcement learning-based economic model predictive control framework for autonomous operation of chemical reactors

Economic model predictive control (EMPC) is a promising methodology for optimal operation of dynamical processes that has been shown to improve process economics considerably. However, EMPC performance relies heavily on the accuracy of the process model used. As an alternative to model-based control strategies, reinforcement learning (RL) has been investigated as a model-free control methodology, but issues regarding its safety and stability remain an open research challenge. This work presents a novel framework for integrating EMPC and RL for online model parameter estimation of a class of nonlinear systems. In this framework, EMPC optimally operates the closed loop system while maintaining closed loop stability and recursive feasibility. At the same time, to optimize the process, the RL agent continuously compares the measured state of the process with the model’s predictions (nominal states), and modifies model parameters accordingly. The major advantage of this framework is its simplicity; state-of-the-art RL algorithms and EMPC schemes can be employed with minimal modifications. The performance of the proposed framework is illustrated on a network of reactions with challenging dynamics and practical significance. This framework allows control, optimization, and model correction to be performed online and continuously, making autonomous reactor operation more attainable.

An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data

In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).

Knowledge-based approach for dimensionality reduction solving repetitive combinatorial optimization problems

This paper proposes a knowledge-based approach that combines case-based reasoning and operational research methodologies for solving repetitive combinatorial optimization problems. The novel approach makes use of past experience which is generally neglected in solving current optimization problems, and introduces this knowledge into operational research techniques for problem-solving, especially when the dimension of the problem is large and conventional schemes cannot solve it within a reasonable time limit. It greatly reduces the dimension of the problem and provides near-optimal solutions.

Square peg in a round hole: are small private liberal arts higher education institutions prepared to implement lean operational and organizational methodologies to survive?

ABSTRACT The focus of this paper is to promote the awareness of higher-education administration and faculty of the opportunities presented by developing and implementing lean operational models in their institutions. The paper provides a brief account of the author’s observations of small liberal arts colleges that are operating in non-efficient models. It also provides an insight into the author’s experience with orchestrating organisational change which included multiple transformational projects over a significant period of years in corporate America. It also provides the reader with published information suggesting that lean implementation, while difficult to sell, makes sense in the small liberal arts’ higher education environment. The first finding is that many of these institutions are failing and not operating efficiently. Second, lean operational methodologies being deployed by many higher education entities, are successful. Third, operational and organisational change is difficult to sell within a very tradition-based environment, however, it is possibly a necessity to implement for survival. There is a significant opportunity for educational leaders to consider the implementation of lean operational models to promote the success of their organisations. Failure to achieve operational success may cause significant disruption to or even failure of small private liberal arts schools. Efficiently providing quality education is good for future generations by providing venues for potential success, and that is advantageous for society. Research exists considering lean operational methodologies in higher education. However, there is a lack of literature available focusing on the specifics of small private liberal arts colleges, their dilemmas, and their needs. There is a need for these schools to survive, so educational leaders should contemplate these suggestions.

MONITORING AND ASSESSMENT OF AGRI-URBAN LAND CONVERSION USING MULTI-SENSOR REMOTE SENSING AND GIS TECHNIQUES

Abstract. Continuous agricultural land conversion poses threat to food security but this has not been monitored due to ineffectual policies. One of the Philippine provinces with a high rate of conversion is the rice-producing province of Cavite. To assess the spatiotemporal dynamics of agricultural land conversion in Cavite, this study aims to develop an operational methodology to produce Land Use and Land Cover (LULC) change maps using a multi-sensor remote sensing approach for decision making and planning. LULC maps were generated using Random Forest Classification of Landsat 8 and Sentinel-1 image collections. Spectral indices, combinations of radar polarizations (VV, VH), and their principal components were included to improve its accuracy. Conversion maps were generated by taking the bi-annual difference of LULC maps from 2016 to 2019. Accuracy was assessed using visual inspection with Google Earth Pro. Classification was carried out using single-sensor (optical or radar) and multi-sensor (optical and radar) approach in combination with three feature selection algorithms, namely, Sandri and Zuccolotto (2006), Liaw and Wiener (2015), Kursa and Rudnicki (2010). Multi-sensor and single sensor yielded similarly high overall accuracies (OA = 96%) with the exception of single-sensor radar approach (OA = 53%). Multi-sensor approaches exhibit high accuracies (Cumulative Accuracy = 91%) in detecting agricultural to built-up LULC change up to 5,000 square meters unlike single-sensor optical approach (Cumulative Accuracy = 76%). Among the multi-sensor approaches, the method of Liaw and Wiener (2015) remains to be superior as it only uses eight (8) variables.

Состояние нервных волокон роговицы после факохирургии

The question of potential changes in CNF induced by phacosurgery remains poorly understood. The need for research in this direction is dictated by a number of circumstances: the introduction into clinical practice of confocal microscopy of the cornea, the need for phacosurgery against the background of changes in the cornea of various genesis, the possibility of the negative impact of laser radiation in the process of hybrid (femtolaser) phacoemulsification. Purpose. Assessment of changes in CNF after various methods of microinvasive phacosurgery. Material and methods. The studies were conducted in two groups of patients aged 50 to 60 years who underwent standard ultrasound and hybrid PV (30 operations each, respectively). The algorithm for assessing the state of the nerve fibers of the cornea included laser confocal microscopy using original software. To quantify the state of nerves, two coefficients were used: directivity anisotropy (K Δ L) and directivity symmetry (Ksym). Results. Regardless of the operation methodology, a tendency toward a decrease in the directivity anisotropy coefficient and an increase in the directivity symmetry coefficient, conditionally comparable with previously identified age-related changes in nerve fibers, was noted. After hybrid phacoemulsification, a decrease in the directivity anisotropy coefficient 2–2.5 months after the intervention turned out to be statistically less significant compared to the standard ultrasound technique. Further research should be aimed at analyzing the long-term results of both microinvasive phacosurgery techniques and the “classical” extracapsular cataract extraction, which still remains the method of choice in a number of clinical situations. Key words: corneal nerve fibers, phacosurgery, in vivo laser microscopy.

Development of Intelligent Transportation System and Its Applications for an Urban Corridor During COVID-19

Transportation is a driving force behind economic development and well-being of all people around the world. An urban transport system is one of the most critical infrastructures for urban development. Impacts of a poor urban transport are manifested in terms of congestion, delays, pollution, accidents, high-energy consumption, low productivity of resources, community severances and inadequate access to the service. In urban areas, despite the construction of flyovers and roads, the roads continue to face traffic congestion, environmental pollution and lack of mass transportation facility resulting in heavy shift of commuters to intermediate and private vehicle ownership. Therefore, it is necessary to make transport system attractive and user friendly in terms of safety, reliability, travel time and comfort, which can be achieved using intelligent transportation system (ITS). This study has been intended to develop a methodology for development and operation of intelligent transportation system namely Presto ITS mobile application. The Android application, Presto ITS, developed works along with the Urban Traffic Data Management Centre to help reduce the congestion on the road networks by allowing the users to make informed decisions about route choices. This helps in reducing further inflow of vehicles in an already congested road network and thus allowing distribution of vehicles among the different routes resulting in lesser overall congestion. This system when installed on an overall city wide basis will drastically help in reducing the congestion and evenly distributed traffic.

Impacts of Climate Change and the Need to Define an Efficient Operating Methodology for Nangbéto Dam

Climate change is a major threat to industrial development because of its adverse effects on the energy sector, especially hydroelectric power plants. This paper focuses on the analysis of the impact of climate change on the Mono River basin and the implications for the electricity production of Nangbéto Hydroelectric Power Plant. The approach consisted of combining historical hydrological data and physical, technical, and economic information to analyze the extent to which variations in average rainfall and global warming impacted the operation of the Nangbéto Hydroelectric Power plant. Based on the curves obtained, the impacts of climate change on the Mono River and on the operation of the Nangbéto Power Plant were recorded. Also, the management of the Nangbéto dam water retainer by the current operator was analyzed to highlight the strengths relating to the optimization of electricity production and the economic profitability of the plant. Keywords: Climate change, hydropower plant, optimization, hydrological data.

An eco-driving algorithm for trains through distributing energy: A Q-Learning approach

The energy-efficient train operation methodology is the focus of this paper, and a Q-Learning-based eco-driving approach is proposed. Firstly, the core idea of energy-distribution-based method (EDBM) that converts the eco-driving problem to the finite Markov decision process is presented. Secondly, Q-Learning approach is proposed to determine the optimal energy distribution policy. Specifically, two different state definitions, i.e., trip-time-relevant (TT) and energy-distribution-relevant (ED) state definitions, are introduced. Finally, the effectiveness of the proposed approach is verified in a deterministic and a stochastic environment. It is also illustrated that TT-state approach takes about 20 times more computation time compared with ED-state approach while the space complexity of TT-state approach is nearly constant. The hyperparameter sensitivity analysis demonstrates the robustness of the proposed approach.

Modelling and deploying multi-energy flexibility: The energy lattice framework

• New multi-energy lattice framework to model multi-energy systems (MES) flexibility. • Categorization of physical and market/regulatory flexibility features. • Two-step methodology to deploy MES flexibility for short-term operational planning. • Simultaneous participation in energy and frequency control markets. • Multi-energy lattice framework demonstrated on the Milan district heating system. This work proposes a novel modelling framework and an associated optimization methodology for short-term operational planning to deploy multi-energy system (MES) flexibility, with application to district energy systems and participation in energy and frequency control ancillary services (FCAS). In this paper, the proposed flexibility framework, based on the concept of multi-energy lattice , models a MES by several energy layers, each one associated with a specific energy carrier. The identified energy layers are linked by specific conversion nodes associated with coupling devices that operate across several energy carriers. After illustrating in detail the main features of the multi-energy lattice methodology and, particularly, how it enables to clearly describe and quantify how flexibility arises from both single-layer and cross-layer energy balancing, different features of the concept of multi-energy flexibility are defined and discussed. An associated operational optimization methodology to deploy multi-energy flexibility in a market environment is then introduced. This methodology is based on a two-step mixed integer linear programming approach, namely, (i) definition of a multi-energy baseline to cope with the energy demand across multiple energy vectors and (ii) identification of flexibility margins and economic convenience to offer different FCAS by deploying multi-energy flexibility. The multi-energy lattice concept is then demonstrated on the Milan district heating system. This MES plant exploits its flexibility for participation in multiple FCAS markets besides day-ahead energy trading, and it is shown how the proposed methodology is able to optimize the flexibility arising from different devices and optimally combine these contributions across multiple energy layers for business case purposes, in line with the general theory of the multi-energy lattice.

Molecular dynamics investigation of the slip flow liquid–solid interfacial thermal conductance

With the integrated high-power device packaging structure rapidly developing, the embedded heat dissipation architectures are challenged by the local micro-/nanoscale massive heat flux. The slip flow molecular dynamics models were established to explore the liquid–solid interfacial thermal conductance. With stepwise declining shear forces (0.032 pN/200, 0.024 pN/200, and 0.016 pN/200 ps, respectively), the slip flow [the slip shear velocity is Si: (125.43 ± 0.92 m/s), graphite: (142.43 ± 1.92 m/s), and Cu: (180.93 ± 3.42 m/s), respectively] water–solid interfacial thermal conductance of different materials [Si: (8.11 ± 0.1) × 107 W/m2 K, graphite: (10.18 ± 0.1) × 107 W/m2 K, and Cu: (17.97 ± 0.1) × 107 W/m2 K] can be calculated. The rationality of the calculated values can be verified in the literature. The slip flow water–solid interfacial thermal conductance values are about 0.5 times higher than the static ones. It can be significantly affected by the slip shear velocity. The slip shear velocity increasing about five times can enhance the interfacial thermal conductance two times. From the water layer density distribution, it is found that the dependence of interfacial thermal conductance on velocity slip relies more on the dynamical properties than on the fluid structure. This molecular dynamics model provides an operative methodology to investigate the slip flow liquid–solid interfacial heat transfer for the various embedded cooling surfaces.

Disentangling ecosystem services perception by stakeholders: An integrative assessment based on land cover

Understanding where ecosystem services (ES) are and quantifying their supply using stakeholderś information is key for effective sustainable management. This paper describes a participatory methodology for extracting stakeholders’ ES perception for continental Portugal based on land cover using analytical hierarchy process (AHP), matrix-based approach with data visualization techniques, and scenario analysis. Results show that drought regulation was the most valued ES by stakeholders and recreation was considered the least important. Results also show that the “Agricultural areas and “Forests and semi-natural areas” land cover classes provide about two-thirds of the total ES for the country. An “Economic development” scenario will yield negative values for all ES except recreation and food supply, whereas an “Environmental development” scenario will increase all ES, except food supply. Finally, a “Sustainable development” scenario, presents values between the previous two scenarios and is the best for food supply. This operational methodology for extracting information from stakeholders and to report information on the mapping and assessment of ES can be helpful for sustainable planning in Portugal and elsewhere.

An operational methodology for determining relevant DRASTIC factors and their relative weights in the assessment of aquifer vulnerability to contamination

The DRASTIC index used to assess the vulnerability of aquifers to contamination, has been subject to various adjustments to improve its reliability. These adjustments include adding and/or eliminating certain aquifer factors and modifying the factor weights. Nonetheless, there is no consensus about which factors, or their respective weights, are most important for assessing aquifer vulnerability. In the present study, we propose an operational methodology that: (1) identifies the relevant factors for assessing aquifer vulnerability to contamination; and (2) determines the relative importance of the selected factors. We applied this approach to a large data set of granular aquifers from a region in Canada, which includes information for DRASTIC factors, combined with groundwater quality and land-use data. We found that for our study region, topography (terrain-slope) is an irrelevant factor for assessing the vulnerability of aquifers to contamination. On the other hand, the relevant factors ranked according to their relative importance (from highest to lowest), are (1) water table depth; (2) hydraulic conductivity; (3) characteristics of vadose zone materials; and (4) recharge. Our approach can serve as an initial step for identifying the relevant aquifer factors when assessing aquifer vulnerability and determining the relative importance of the relevant factors to validate weights attributed to these factors. Our methodology can help adapt index-based methods of aquifer vulnerability assessment to a range of study regions.

How Can Enterprise Risk Management Help in Evaluating the Operational Risks for a Telecommunications Company?

Operational risk is defined as the potential losses resulting from events caused by inadequate or failed processes, people, equipment, and systems or from external events. One of the most important challenges for the management of the company is to improve its results through its operational risk identification and evaluation. Most of Enterprise Risk Management (ERM) scholarship has roots in the finance/risk management and insurance (RMI) discipline, mainly in the banking sector. This study proposes an innovative operational risk assessment methodology (OpRAM), to evaluate operational risks focused on telecommunications companies (TELCOs), on the basis of an operational risk self-assessment (OpRSA) process and method. The OpRSA process evaluates operational risks through a quantitative analysis of estimates which inputs are the economic impact and the probability of occurrence of events. The OpRSA method is the “engine” for calculating the economic risk impact, applying actuarial techniques, which allow estimation of unexpected losses and expected losses distributions in a TELCO. The results of the analyzed business unit in the field work were compared with standardized ratings (acceptable, manageable, critical, or catastrophic), and contrasted against the company’s managers, proving that the OpRSA framework is a reliable and useful management tool for the business, and leading to more research in other sectors where operational risk management is key for the company success.

Brönsted acid hydrotrope combined catalysis in water: a green approach for the synthesis of indoloquinoxalines and bis-tetronic acids

The present work describes the applications of Bronsted acid hydrotrope combined catalyst (BAHC) as a mild, efficient and reusable catalyst for synthesis of indoloquinoxalines and bis-tetronic acids in water. Using BAHC, we synthesized many indoloquinoxaline derivatives from isatins and o-phenylene diamine using 10 mol% PTSA in 40% aqueous hydrotropic (NaPTS) solution at room temperature with 83–90% yields. On the other hand, the reaction of tetronic acid with the aldehydes/isatins forms bis-tetronic acids with 83–88% yields through Knoevengel condensation-Michael addition pathway in same BHAC. Moreover, the BAHC can be recycled upto 5th cycles with slight decrease in product yields. The extremely simple operational methodology, green solvent, ambient reaction conditions and high yields render this approach extremely appealing for the synthesis of different heterocyclic compounds.

Groundwater Risk Assessment in the Context of an Underground Coal Mine Closure and an Economic Evaluation of Proposed Treatments: A Case Study

Mining companies are responsible for the impacts that result from their mining activities even after the mining period has ended. At the same time, at the European and international levels, there is a lack of a detailed operational methodology comprising environmental risks during and after closure of underground coal mines. The environmental risk aspects that need to be considered when planning the closure of an underground coal mine and post closure in the broader environmental context are the following: modification of water flow scheme, surface instability, mine gas emission on the surface, and water and soil pollution. In this study, we focus on assessing groundwater risk in the context of an underground coal mine closure and evaluating the selected risk mitigation strategies in terms of performance and cost. The results from this study could be used for developing a final closure groundwater assessment plan by selecting the most feasible treatment alternatives for different environmental impacts, together with the transitional monitoring that could guarantee a hazard level in compliance with land reuse and the use of natural resources. Finally, the cost-efficient monitoring and treatment programs are used to estimate the financial provisions needed to mitigate groundwater risks during underground coal mine closure contexts.

Aquatic Ecosystems and Climate Changes: Data Gaps in Relation to Flooding Events

At European level the strategy on adaptation to climate changes promoted adaptation plans at all levels and has recommended the need to bridge the gaps through applications of actions at local level. In the paper has been remarked that there are some gaps in the European legislation that should be solved through the funding of more research projects and pilot studies; for example the use of microbiological monitoring, not foreseen by the water framework directive, can help to identify and prevent effect adverse on human health. In this study we propose an operative methodology to be applied at local level with the aim to support the institutional requirements for risk management before and after flooding events, based on a multi-criteria approach. This integrated monitoring plan could represent a shared methodology with the aim to give a contribution for the management of the risks of extreme weather events such as flooding for human health and ecosystem protection.

Exploring the use of operational excellence methodologies in the era of COVID-19: perspectives from leading academics and practitioners

PurposeThis paper aims to present the results of a study carried out by the authors in the form of research interviews on the topic: “Exploring the use of operational excellence methodologies in the era of COVID-19.”Design/methodology/approachA qualitative interview approach was utilized by interviewing a panel of leading academics and practitioners who are familiar with operation excellence methodologies.FindingsOperational excellence methodologies are proven and can be utilized in pandemic situations to improve efficiency in the healthcare system and preparedness for pandemics.Research limitations/implicationsOne limitation of this research was that most of the interviewees who participated in this study came from Europe, North America, South America and Asia, representing four continents. It would have been better to have different views from other continents such as Australia and Africa. Also, the interviews were short and at a high level. There is an opportunity for further study and analysis.Practical implicationsOperational excellence methodologies are proven and can be utilized in pandemic situations to improve efficiency in the healthcare system and preparedness for pandemics.Originality/valueThe paper provides an excellent resource for those people to get an insight into the value of the application of operations excellence methodologies in pandemic situations to aid healthcare process improvement and aid public and patient safety.

Building with landscape

The multi-dimensionality of BwN calls for the incorporation of ‘designerly ways of knowing and doing’ from other fields involved in this new trans-disciplinary approach. The transition out of a focus on rational design paradigms towards reflective design paradigms such as those employed in the spatial design disciplines may be a first step in this process. By extension, the knowledge base and design methodologies of BwN may be critically expanded by drawing on ways of knowing and doing in spatial design disciplines such as landscape architecture, which elaborates the agency of the term ‘landscape’ as counterpart to the term ‘nature’. Operative perspectives and related methodologies in this discipline such as perception, anamnesis, multi-scalar thinking, and process design resonate with specific themes in the BwN approach such as design of/with natural processes, integration of functions or layers in the territory and the connection of engineering works to human-social contexts. A series of installations realised for the Oerol festival on the island of Terschelling between 2011 and 2018 serve as case studies to elaborate potential transfers and thematic elaborations towards BwN. In these projects inter-disciplinary teams of students, researchers and lecturers developed temporary landscape installations in a coastal landscape setting. Themes emerging from these project include ‘mapping coastal landscapes as complex natures’, ‘mapping as design-generative device’, ‘crowd-mapping’, ‘people-place relationships’, ‘co-creation’, ‘narrating coastal landscapes’, ‘public interaction’ and ‘aesthetic experience’. Specific aspects of these themes relevant to the knowledge base and methodologies of BwN, include integration of sites and their contexts through descriptive and projective mappings, understanding the various spatial and temporal scales of a territory as complex natures, and the integration of collective narratives and aesthetic experiences of coastal infrastructures in the design process, via reflective dialogues.