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Decades of advances in computer architecture, software-intensive applications and system integration have created significant challenges for embedded systems designers and test engineers. Intrusive software instrumentation and breakpoint-based debugging are often viewed as the primary options for observing operational system internals. This narrow sight creates complicated test flows and convoluted debugging procedures. Modern embedded computing systems offer Embedded Trace as the technological answer to the encountered observability conundrum. Although an integral part of virtually all modern processors, it is frequently overlooked. Its technical foundations are little known to application engineers, test engineers, and project managers. This article explains Embedded Trace as an essential technology in the testing and debugging toolbox. It highlights its capabilities, limitations and opportunities.

The Evolution of HCV-Hepatitis C-virus plays the vital cause for liver-related complications in humans, global-wide. But also, specific tools were employed to eliminate the impacts of virus in humans. This phenomena does not rely in the prior stage diagnosis of the virus and in the treatment phases. In this study, the implementation of Ada-Boost algorithm and Random-forest algorithm in the framing the HCV-Hepatitis-C-Virus prediction design prevailing in humans. According to the methodology, Random-forest algorithm were utilized as the weak-learner for choosing the instances of the weight to enhance the stability factors, accuracy-factors and to decrease the outfitting complications. The paper aims to design the secured framework in the improvisation of the user’s privacy. Hence for this purpose, The techniques PPDM-Privacy-Preserving-Data mining approach were developed to keep preserve of the personal data of the userscattered in Distributed-data-mining operational system and also enhancing the security in centralized systems of data-mining as well. The data-set were subjected to the comparison of the ECC-encryption algorithm and the RSA-based encryption along with the blend of SHA-algorithm. The algorithms were evaluated with various sizes of key and proceeded with the decryption process. The Decrypted data, applied with Ada-boost and Random-Forest algorithm in qualitative predictions. Data were partitioned as the test and training sets by the classifiers. The overall-efficiency of the proposed hybrid framework were assessed by the aid of performance-metrics such as accuracy factor, time-factor and specificity-factor. The experimental analysis of the framework, establishes the classifier and various merged classifiers in the predictions of HCV-Stains. The outcomes of the framework exhibited higher accuracy rate in comparison with the other approaches, thus enabling the efficient predictions of HCV.

International audience; In this paper, we propose an operational solution for the yearly classification of crop parcels at national scale (namely France) for Land Parcel Identification System updating, under the Common Agricultural Policy (CAP) open-source framework and fed with both time series of Sentinel-1 radar and Sentinel-2 optical images, with complementary contributions. Three conceivable scenarios are investigated with two sets of nomenclatures (17 and 43 classes): early, on-line, and late classifications. Experiments performed on 2017 show very satisfactory results (82–97%), locally almost on-par with state-of-the-art deep-based methods. We can conclude our framework offers a strong basis for country-scale operational deployment for 2020+CAP.

In the last several decades, the U.S. Health care industry has undergone a massive consolidation process that has resulted in the formation of large delivery networks. However, the integration of these networks into a unified operational system faces several challenges. Strategic problems, such as ensuring access, allocating resources and capacity efficiently, and defining case-mix in a multi-site network, require the correct modeling of network costs, network trade-offs, and operational constraints. Unfortunately, traditional practices related to cost accounting, specifically the allocation of overhead and labor cost to activities as a way to account for the consumption of resources, are not suitable for addressing these challenges; they confound resource allocation and network building capacity decisions. We develop a general methodological optimization-driven framework based on linear programming that allows us to better understand network costs and provide strategic solutions to the aforementioned problems. We work in collaboration with a network of hospitals to demonstrate our framework applicability and important insights derived from it.

During the SARS-CoV-2 pandemic, the self-service restaurant sector, as well as other types of food services, are facing an unprecedented crisis needing to adapt their service to avoid closing their doors. With varied and quick meals, the self-service buffet is one of the most important types of outside services. However, the type of service where the clients follow a line on the buffet and serve their meals has impaired traditional restaurant operation during the SARS-CoV-2 pandemic and, perhaps, after it. In this sense, this study presents an overview of the self-service buffet restaurant operational system in the context of the SARS-CoV-2 pandemic.

Objective: Coronavirus Disease of 2019 (COVID19) as a pandemic could paralyze the operational system of healthcare organizations. However, the crisis management plan of COVID19 implemented in Al-Adan Hospital, Kuwait, has been successful. Therefore, this study aims to present the crisis management plan for COVID19 implemented in our institution. Design: Presenting and framing the prescriptive strategy of the crisis management approach adopted. The literature was further critically reviewed to reflect on the plan implemented to enrich the current experience and formulate future recommendations. Setting: Al-Adan Hospital, Kuwait. Main Outcome Measures: The prescriptive strategy adopted for the crisis management plan consists of three managerial frameworks: COVID19 team, COVID19 management strategy, and strategy for maintaining emergency healthcare services. Results: Balancing between the standardization model of implementing governmental regulations, and the personalization model of placing the patients and the employees at the center of the strategy, could explain the success of the plan. The strategy implemented could be further strengthened by elevating the principles of leaderships and encouraging health digitalization. Conclusion: This study presents a novel framework of crisis management for healthcare systems by raising the concept of balancing between the standardization and personalization models.

This paper aims to present an innovative approach to design a Lean Service System in the India Post service industry using the Simulation method to reduce the system’s complexity. Using logic and systematic building blocks, simulation models have been developed from lean service elements using the arena software and verified in Siemens Tecnomatix plant simulation suite. Simulation is used to replicate the real model, guide the future production system, and determine the optimal parameter values. Simulation studies have been divided into two perspectives, “before LS” and “after LS” through Value Stream Mapping (VSM). It has been found that there is a 9.62 percent enhancement in delivering articles per person. A real case of postal service internal operational performance has been taken for the study. This study is the first to combine Value Stream Mapping - Simulation (VSM-Sim) to simulate and optimize the mailing service operations’ operational performance parameters.

PurposeThis study aims to develop a practical methodology to identify possible areas of improvements as well as exploring how to improve the health-care staff flow within a selected department in a hospital.Design/methodology/approachIt focuses on showing how to properly study and analyze the health-care services and processes practiced at a selected department within a hospital. For this, several techniques like non-value-adding activities, time motion study, spaghetti diagram, layout analysis, etc. are used.FindingsTo test the proposed methodology, a neonatal intensive care unit (NICU) of a hospital in Oman was considered as a case study. The study revealed that this unit has several potential improvements capabilities. Further, this study also discussed possible areas of improvements of this case unit and suggested how such improvements can be implemented.Originality/valueSeveral possible improvements are suggested and are discussed with the hospital authority, which can be clarified as the re-layout of the NICU rooms, reorganization of the store to improve the staff flow, increase the work efficiency, introduction of Help Us Support Healing policy, etc., which can enhance the entire operational system at the studied NICU.

Production equipment that has a high operating time becomes a vital aspect in supporting the operational system, and its maintenance becomes a critical point that must be planned. This research discusses maintenance policies that focus on machine reliability and parts availability to produce proper maintenance actions, optimal maintenance scheduling and good spare-parts management. The research was conducted on a developing manufacturing company engaged the field of agricultural aids in the city of Bekasi. This study focused on hydraulic shear machines because they have the largest downtime value of 8,344.8 minutes. This problem has a negative impact on the company because it can reduce the yield. Based on these conditions, the study was conducted using the Reliability Centered Maintenance method to update the maintenance patterns and Poisson Process to determine the number of component requirements needed for the next one year. The data obtained in the form of historical damage to the machine, component cost, labor cost and production cost. This research produces an optimal preventive replacement time interval that is every 154 hours for the oil seal component, 242 hours for the shear blade component, and 324 hours for the oil hydraulic hose (1/2") component. While the number of critical components required for the next 1 year is 37 units for oil seal component, 10 units for shear blade components, and 7 units for oil hydraulic hose (1/2") component.

The next-generation of satellite-based augmentation system (SBAS) will employ a dual-frequency multi-constellation (DFMC) service providing several advantages over the classical SBAS L1 service. In September 2017, a two-year SBAS test-bed was initiated by Australia and New Zealand in preparation for building an operational system. This system includes DFMC SBAS in addition to the legacy SBAS L1 service and real-time precise point positioning (PPP) service over the Asia-Pacific region. In this paper, we focus on the new DFMC SBAS, and first discuss its positioning and integrity monitoring (IM) algorithms. Next, its performance is tested and analyzed using real DFMC SBAS data, restricting focus to LPV-200 in aviation. Based on real SBAS messages from the geostationary (GEO) satellite Inmarsat-4F1, testing was performed first at two known stations with actual GNSS observations, and next at simulated grids covering the entire Asia-Pacific region. A sensitivity analysis is performed, investigating the contribution of the error sources and the impact of changing the satellite mask angle. Experimental results of 12 consecutive days from September 14 to 25, 2018 of the new SBAS products show that the dual-frequency correction (DFC) residual errors have the dominant contribution in the values of the protection levels needed in IM, whereas other error sources such as the airborne receiver errors, the tropospheric and the ionosphere residual errors have secondary impacts. The accuracy of the positions was found to be at the sub-meter to meter level and was always bounded by the protection levels (PLs) at the known stations. The simulated PLs were less than the alert limit of LPV-200 during the test period in most of the areas of interest, indicating the availability of IM using DFMC SBAS in the main areas of the Asia-pacific region.

The ‘4V’ approach is administered throughout the operational level in an organisation. Expression of empathy among all levels of the hierarchy of the taskforce is the foundation of this ‘4V’approach and need of the hour. The proposed ‘4V’ approach is a continual process; it requires continuous support and initiative of top management for transforming organisational work environment. It is peak time for top management's leadership to instigate and develop a candour relationship between its people and the organisation's vision. The approach addresses and dilutes the psychological distress of the workforce arising from the deadly COVID -19 pandemic. In this research, the '4V' approach is a strategic-compliances deliberation, that is developed based on 'Managerial Good Practices' within the operational system of the organisation. The study also confirms upon offering a pious work environment to every individual in the organisation. Comprehending the detailed analysis of each 'V' of the '4V'approach, it is definite that the '4V'approach is psychologically administered. The uncertainty of COVID-19 is raising without any specific deadline, in this scenario its judicious to accept the new normalness and get our systems ready in parlance with COVID-19 management at the workplace. The need of the hour is to enhance flexibility and adaptability to accept the conscious changes in life along with re-imaging and innovative ideas to restructure the business setbacks s due to the COVID -19 pandemic. Making the employee feel safe, at ease, imperative to the organisation, ought to be the prime concern of this strategic HR approach and ensuring combating-traits among people against the pandemic situation.

The hospital success depends on the human resources. Hospital operational system can run well because of human resources support. Therefore, the hospital needs to empower human resources to achieve the organizational goals. One of the solutions is to initiate the Innovative Work Behavior. This study aims to examine the direct effect of work environment on Innovative Work Behavior and the effect of work environment on Innovative Work Behavior through work motivation. This study uses quantitative research. Data analysis uses the PLS (Partial Least Square). The population was all 200 employees or Malang Public Hospital. The sampling technique uses Simple Random Sampling, so the number of samples used is 133 employees. Data was collected by questionnaire. The study results indicated that work environment has a direct effect on Innovative Work Behavior and work environment has a indirect effect on Innovative Work Behavior through work motivation.

This study aims to determine the importance of management science and company operational standardization that is applied, to know a good and correct company development strategy in accordance with operational standards of company development strategy at UD. AQMARINA. This type of research is descriptive research with a qualitative approach where the data obtained from a UD. AQMARINA in the form of observation, interviews and literature study. The data used is primary data, where the data is processed from interview data of company owners and publications. The results of this study state that the importance of management knowledge and company operational standards for strategy development is very necessary, this isbecause the process of organizing something is done by a group of people organizationto achieve these goals by working together to utilize the available resources by developing strategies. The standardization of company operations that is applied to this company is very standard, therefore it is necessary to have guidelines on the company's operational system so that it can run the company optimally. A good andcorrect company development strategy according to operational standards, this is done to get quality human resources with skills, work abilities, and job loyalty to a company or organization. The company's development strategy is not only skill development but also products produced in the form of product variants which are of course creative and innovative. Keywords: Management Knowledge, Company Operational Standards, Company Development Strategy

Public eagerness to use Transjakarta as a transport mode has been increasing over the past few years as the ridership growth consistently. To keep the growth up, stakeholders need to understand the passenger behavior by creating an effective operational system so that the passenger satisfaction maintained. An understanding of passenger behavior can be obtained by identifying its pattern and trend which recorded as passenger transaction data. This project uses passenger transaction data which contains information about time and space to produce interactive visualization as a means to identify passenger behavior. Interactive visualization created in this project depicts the trend of bus stop occupancy and passenger movement from one stop to another. The visualization consists of a map and diagrams which relate to each other as one integrated analytical dashboard system. The visualization design process combines Tableau 2019.2 and Mapbox GL JS to create powerful visualization supported by a 3D map view that facilitates analytical reasoning work. Geovisual analytics dashboard as a result of this project is intended for PT. Transportasi Jakarta staff to make decisions. Some staff was asked to examine the usability of the dashboard regarding its effectiveness, efficiency, and accessibility aspects. The usability test pointed out that the analytical dashboard can generate new information about the patterns and trends of Transjakarta passengers.

We report a 30-month nationwide deployment and operation study of an indoor arrival detection system based on Bluetooth Low Energy called VALID in 364 Chinese cities. VALID is pilot-studied, deployed, and operated in the wild to infer real-time indoor arrival status of couriers, and improve their status reporting behavior based on the detection. During its full nationwide operation (2018/12- 2021/01), VALID consists of virtual devices at 3 million shops and restaurants, where 530,859 of them are in multi-story malls and markets to infer and influence 1 million couriers' behavior, and assist the scheduling of 3.9 billion orders for 186 million customers. Although indoor arrival detection is straightforward in controlled environments, the scale of our platform makes the cost prohibitively high. In this work, we explore to use merchants' smartphones under their consent as a virtual infrastructure to design, build, deploy, and operate VALID from in-lab conception to nationwide operation in three phases for 30 months. We consider metrics including system evolution, reliability, utility, participation, energy, privacy, monetary benefits, along with couriers' behavior changes. We share three lessons and their implications for similar wireless sensing or communication systems with large geospatial operations.

The paper studies the possibility of using mobile apps for quality control of the urban environment, road networks and transport servicing of the population. We analyze Russian and foreign technical solutions and specify the set of functional user features of the developed application. Based on an online survey in Google Forms, we determine the demand for the application along with the preferable set of functions. iOS is selected as an operational system to run the mobile app. To develop the app based on the iOS platform, Clean Swift architecture is chosen. Marker criteria are developed and selected to evaluate the quality of the urban environment and road networks. The Scrum model is chosen as a life cycle model. The app is developed and tested in the X-Code environment with Swift. The data analysis module is written in Python_3. The app is tested in the TestFlight environment. Guidelines for further development of the developed tools within intelligent transport systems and Smart City are identified.

The increasing number of health-data breaches is creating a complicated environment for medical-data sharing and, consequently, for medical progress. Therefore, the development of new solutions that can reassure clinical sites by enabling privacy-preserving sharing of sensitive medical data in compliance with stringent regulations (e.g., HIPAA, GDPR) is now more urgent than ever. In this work, we introduce MedCo, the first operational system that enables a group of clinical sites to federate and collectively protect their data in order to share them with external investigators without worrying about security and privacy concerns. MedCo uses (a) collective homomorphic encryption to provide trust decentralization and end-to-end confidentiality protection, and (b) obfuscation techniques to achieve formal notions of privacy, such as differential privacy. A critical feature of MedCo is that it is fully integrated within the i2b2 (Informatics for Integrating Biology and the Bedside) framework, currently used in more than 300 hospitals worldwide. Therefore, it is easily adoptable by clinical sites. We demonstrate MedCo’s practicality by testing it on data from The Cancer Genome Atlas in a simulated network of three institutions. Its performance is comparable to the ones of SHRINE (networked i2b2), which, in contrast, does not provide any data protection guarantee.

A decision support system to aid in the risk evaluation of airborne animal diseases was developed for Ireland. The system's primary objective is to assist in risk evaluation of the airborne spread of Foot and Mouth Disease (FMD). The operational system was developed by Met Éireann – the Irish Meteorological Service and CVERA (Centre for Veterinary Epidemiology and Risk Analysis), in co-operation with NOAA-ARL (National Oceanic and Atmospheric Administration – Air Resources Laboratory) and ECMWF (European Centre for Medium-Range Weather Forecasts). The infrastructure largely relies on the HYSPLIT dispersion model driven by both ECMWF meteorological forecasts for longer range simulations, and HARMONIE-AROME meteorological forecasts, a high resolution local area meteorological model, ideal for shorter range national emissions. Following on from previous work by the Bureau of Meteorology, Australia as well as the Australian Department of Agriculture, Fisheries and Forestry, further modifications were made to the HYSPLIT source code to improve the model's characterisation of the Foot and Mouth Disease virus. FMD is a highly infectious disease among cloven hoofed animals that can transmit via airborne means. Biological characteristics related to temperature, humidity, lifespan as well as atmospheric washout were all incorporated either through new or existing functionality of the dispersion model. Combining the model dispersion capabilities of HYSPLIT with a virus emission model and GIS mapping software with farmland zoning, the disease dispersion system becomes a powerful analysis and decision support tool. This airborne animal disease atmospheric dispersion system helps improve emergency preparedness, as well as aid confinement and eradication strategies for relevant Irish authorities, during a disease outbreak.

Purpose The purpose of this paper is to establish the regression model by a simulation method that was obtained by using the system response at unit cost as the response value. The unit availability was maximized, while the unit cost was minimized. Design/methodology/approach In this study, the Monte Carlo simulation method was used to simulate an operational system, and the regression model was obtained by using response surface methodology with the experimental matrix and different levels of experimental combinations. Findings The optimal value of mean time between failure (MTBF) and mean time to repair (MTTR) of each component was then obtained by using the system response at unit cost as the response value. Practical implications Due to the upgrading of industrial technology and the maturity of electronic technology, product development technology has become highly sophisticated with complex designs. Reliability engineering has become a key procedure of high-tech industry. Social implications Based on the system availability of unit cost as the response value, it can maximize the availability to help decision makers to formulate the best selection strategy components and repair strategy. Originality/value Previous works regarding the parameter settings of reliability values never mention the simulation methodology. However, this study aims to achieve the above goals in finding the relationship of MTBF and MTTR simultaneously.

Globalization has led to a rapid increase of container movements in seaports. Risks in seaports need to be appropriately addressed to ensure economic wealth, operational efficiency, and personnel safety. As a result, the safety performance of a Container Terminal Operational System (CTOS) plays a growing role in improving the efficiency of international trade. This paper proposes a novel method to facilitate the application of Failure Mode and Effects Analysis (FMEA) in assessing the safety performance of CTOS. The new approach is developed through incorporating a Fuzzy Rule-Based Bayesian Network (FRBN) with Evidential Reasoning (ER) in a complementary manner. The former provides a realistic and flexible method to describe input failure information for risk estimates of individual hazardous events (HEs) at the bottom level of a risk analysis hierarchy. The latter is used to aggregate HEs safety estimates collectively, allowing dynamic risk-based decision support in CTOS from a systematic perspective. The novel feature of the proposed method, compared to those in traditional port risk analysis lies in a dynamic model capable of dealing with continually changing operational conditions in ports. More importantly, a new sensitivity analysis method is developed and carried out to rank the HEs by taking into account their specific risk estimations (locally) and their Risk Influence (RI) to a port's safety system (globally). Due to its generality, the new approach can be tailored for a wide range of applications in different safety and reliability engineering and management systems, particularly when real time risk ranking is required to measure, predict, and improve the associated system safety performance.

Over the last decade, the beaching of massive quantities of sargassum in the Caribbean has become a major problem. Relatively few studies have focused on beached sargassum which has numerous negative environmental, social and economic impacts. To aid understanding of the problem and develop approaches to effectively manage it, a satellite-based mapping and monitoring service is being developed on the basis of information generated in a set of user needs workshops. Here, we report on the foundations of this mapping and monitoring service. In particular, user needs obtained from a large and diverse set of stakeholders are used to identify appropriate satellite remote sensing systems to use as data sources. The system will use mainly PlanetScope and Sentinel-2 data to meet the spatio-temporal demands of users. It is planned to develop an operational system that may if desired be extended to the Caribbean as a whole and beyond.

This study explored the suitability of simulation tools for accurately predicting fluidized bed gasification in various scenarios without disturbing the operational system, and dedicating time to experimentation, in the aim of benefiting the decision makers and investors of the low-carbon waste-based bioenergy sector, in accelerating circular bioeconomy solutions. More specifically, this study aimed to offer a customized circular bioeconomy solution for a rice processing residue. The objectives were the simulation and economic assessment of an air atmospheric fluidized bed gasification system fueled with rice husk, for combined heat and power generation, by using the tools of Aspen Plus V9, and the Aspen Process Economic Analyzer. The simulation model was based on the Gibbs energy minimization concept. The technological configurations of the SMARt-CHP technology were used. A parametric study was conducted to understand the influence of process variables on product yield, while three different scenarios were compared: (1) air gasification; (2) steam gasification; and (3) oxygen-steam gasification-based scenario. Simulated results show good accuracy for the prediction of H2 in syngas from air gasification, but not for the other gas components, especially regarding CO and CH4 content. It seems that the RGIBBS and Gibbs free minimization concept is far from simulating the operation of a fluidized bed gasifier. The air gasification scenario for a capacity of 25.000 t/y rice husk was assessed for its economic viability. The economic assessment resulted in net annual earnings of EUR 5.1 million and a positive annual revenue of EUR 168/(t/y), an excellent pay out time (POT = 0.21) and return of investment (ROI = 2.8). The results are dependent on the choices and assumptions made.

Islamic commercial banks are highly identic with the profit-sharing as a sharia banking basic operational system. The profit-sharing becomes a specific characteristic of sharia banks as well as a distinction from conventional banks. Hence, this study aims to examine if the profit-sharing characteristic contributes to the performances of Islamic commercial banks in Indonesia. This study employed time series data derived from the Financial Service Authority (OJK) using regression-mixed test and auto-regressive heteroscedasticity (ARCH). The results pointed out that the profit-sharing system for the lending of Musharaka had an impact on the performance of sharia banks; while the profit-sharing for the funding of Mudaraba did not support the hypothesis with negative coefficient. The results suggest that the profit-sharing characteristic provides contribution to the performance of Islamic commercial banks through the lending of Musharaka. These results further indicate that the profit-sharing characteristic performed by Islamic commercial banks is proven to be effective in improving their performances. This study’s results have an implication for Islamic commercial banks to strengthen their profit-sharing characteristics and improve the public trust toward sharia banking system.

In the Mediterranean Sea unique environmental characteristics and sensitive assets coexist with intense maritime traffic that is represented by frequent daily passages of vessels along the main waterways. In order to assess the risk of oil stranding in case of at-sea emergencies and provide key products for environmental agencies or policymakers preparedness, a geographically relocatable, operational numerical system is implemented and tested. The system relies on the application of oceanographic and particle tracking models and is able to provide, on a high-resolution and unstructured computational grid, a 3-days forecast of those variables known as the main drivers of oil slicks at sea. The risk of potential oil stranding is computed through a combination of anthropogenic hazard and shoreline vulnerability. The sources of hazard vary on time and space in relation to local maritime vessel traffic. The shoreline vulnerability is based on the current knowledge of slope, main grain size, geology of rocks, and occurrence of manmade structures at coast. The operational system is enriched by a web graphical user interface and includes automatic and on-demand working modes. Its functionality is demonstrated in the Strait of Bonifacio (western Mediterranean Sea), area with a high potential risk of oil stranding due to an intense maritime traffic. Risk assessment is hence computed for a test year, the 2018. Critical values of risk are found in correspondence of long stretches of littoral while many of them are currently characterized by a low anthropogenic pressure. The results emphasize the geomorphological features of the shorelines as reducing or amplifying factors to any potential impact of oil stranding at coast.

Bengali, is not only a major language in two states West Bengal, and Tripura of India, but it is the main language of an Entire country Bangladesh. Having such large application areas of Bengali language, and with the evolution of automation, machine learning, and trending technological development, it is also high time to make a complete product which can easily and efficiently recognize Bengali handwritten characters. Research in this area is attracting attention day by day, and there are the obvious reasons. Application area of such Bengali handwritten character recognition system is undoubtedly very high, but unfortunately, there is not such success in research, which can produce a complete operational system that can be commercialized or practically implemented in day to day life. The current work is a study of the different tasks to be done along with achievements throughout the last decade for different tasks in the field of handwritten Bengali character recognition.

The new digital business models only intensify the existing competitive forces. The relative bargaining power of suppliers, buyers/customers, substitutes (like 3D printing) and new entrants (like digital forwarders) defines the intensity of competition within freight forwarding. Forwarders have traditionally invested in organic and inorganic growth as well as in upgrades of the operational system for cost reductions. Today, forwarders without digital sales channels lose out on growth. Yet only 1 out of the top 18 forwarders was prepared Q3 2017 to offer the necessary levels of digital access. Forwarders are cooperating with and investing into startups to not solely rely on internal software developments. This will make them more digital over time. Their new digital challengers will become more ‘conventional’ over time as they cannot escape the realities of large companies with large customer requirements. The most likely future will be digital, with both legacy forwarders and new entrants having a role.

History of supply chain management is more than a century when it was used to be highly labour intensive, but simple in practice. With the increase in modern transport technology, the concept of supply chain management has been changed with compressive packaging technology, real-time computational network, and cold logistic system with the facility of instant information on goods under transport process. In this connection, both industrial engineering and operational research play significant role. The concept of operational system was initiated during World War II when scientists demonstrated the value of analytics in the military logistics problems. During the 1940s and 1950s, lots of efforts were made to minimize labour intensive practice in material handling and better warehouse system for the safety and security of materials. In the mid-1950s, the concept of transportation management with the development of intermodal containers together with ship, trains, and trucks to handle these containers was initiated for quickening the process of material transport. Soon after a decade, by the 1960s, industrialists started to prefer freight transportation rather than truck and rail. Earlier to the 1960s, all types of documentation were used to be managed manually. The computerization of this data opened new chapter in supply chain management and logistics planning, from randomized storage in warehouses to optimization of inventory and truck routing. In the late 1970s and early 1980s, many premier institutes like the Georgia Tech of the Production and Distribution Research Centre, the Material Handling Research Centre, and the Computational Optimization Centre were involved in to find out the applied aspects of theoretical model on microchips based supply chain management process.

This chapter lays down the foundation stone for the rest of the book chapters. This chapter analyzes the trends and tendencies occurring in the education industry, especially in the higher education sector given the far-reaching and ever-lasting impacts of global pandemics and epidemics such as COVID-19 and the technological revolutions. The chapter highlights the role of university governance models in addressing diverse challenges that encounter higher education institutions. In essence, the chapter proposes that the university governance model in Latin America and elsewhere must be reconfigured, reshaped, and refocused. Features such as agility, dynamism, and being state-of-the-arts should be at the heart of the future university governance model.

The sample path generated by a stochastic simulation often exhibits significant variability within each replication, revealing periods of good and poor performance alike. As such, traditional summaries of aggregate performance measures overlook the more fine-grained insights into the operational system behavior. In this paper, we take a simulation analytics view of output analysis, turning to machine learning methods to uncover key insights from the dynamic sample path. We present a k nearest neighbors model on system state information to facilitate real-time predictions of a stochastic performance measure. This model is built on the premise of a system-specific measure of similarity between observations of the state, which we inform via metric learning. An evaluation of our approach is provided on a stochastic activity network and a wafer fabrication facility, both of which give us confidence in the ability of metric learning to provide interpretation and improved predictive performance.

To successfully operate in a harsh environment like the Arctic Ocean, one must be able to understand and predict how that environment will evolve over different spatial and temporal scales. This is particularly challenging given the on-going and significant environmental changes that are occurring in the region. Access to the most recent environmental information provides timely knowledge that enables ship-based operations to proceed efficiently, effectively and safely in this difficult arena. Knowledge of the evolving environmental conditions during a field campaign is critical for effective planning, optimal execution of sampling strategies, and to provide a broader context to data collected at specific times and places. We describe the collaborations and processes that enabled an operational system to be developed to provide a remote field-team, located on USCGC Healy in the Beaufort Sea, with near real-time situational awareness information regarding the weather, sea ice conditions, and oceanographic processes. The developed system included the punctual throughput of near real-time products such as satellite imagery, meteorological forecasts, ice charts, model outputs, and up to date locations of key sea ice and ocean-based assets. Science and operational users, as well as onshore personnel, used this system for real-time practical considerations such as ship navigation, and to time scientific operations to ensure the appropriate sea ice and weather conditions prevailed. By presenting the outputs of the system within the context of case studies our results clearly demonstrate the benefits that improved situational awareness brings to ship-based operations in the Arctic Ocean, both today and in the future.

The new generation of the satellite-based augmentation system (SBAS) has been initiated in Australia and New Zealand since 2017 and is anticipated to be a fully operational system by 2023. In addition to the traditional L1 service, the new SBAS also supports the dual-frequency multi-constellation (DFMC) service and the precise point positioning (PPP) service for GPS and Galileo dualfrequency users. Making use of the DFMC SBAS service, horizontal positioning accuracy at sub-meter to meter level can be realized in real-time depending on the measurement environment, which should benefit users of the intelligent transport system (ITS) in road transport. The integrity monitoring is essential for the ITS users to guarantee the reliability of the positioning service. During possible outage of the SBAS messages, the integrity monitoring of the DFMC SBAS positioning becomes difficult due to the missing corrections integrity information that should be updated regularly. Even when relieving these strict time-out windows for land-based applications, the increase of the horizontal protection levels (HPLs) with time is shown to be dramatic. In this contribution, a modified version of the advanced receiver autonomous integrity monitoring (ARAIM) algorithm is used during such SBAS outages. The HPLs are computed based on the ARAIM algorithm with different integrity parameters assumed to investigate their impact on the HPL. Under the expectation that higher precision of the satellite clocks and orbit corrections can be achieved for the new generations of the GPS and Galileo satellites in the future, the HPLs computed with the proposed modified ARAIM algorithm are shown to be close to the level of those based on the DFMC SBAS. In such a case, the ARAIM is expected to be a useful alternative approach for bridging the integrity monitoring of the non-safety-of-life ITS applications during the SBAS outages.

Objective: To develop an educational mobile application as a dynamic platform provided free of charge, including several clinical and laboratory protocols for a wider range of skills and greater knowledge on the strategic axes of child dental care: Pediatric Dentistry and Orthodontics. Material and Methods: The prototype tool was structured as follows: development of clinical and laboratory contents to be addressed, software selection, layout format, inclusion of features, applicability and acceptability tests of the operational system, and indexation on the Play Store ® application store (free of charge) . Results: The mobile application has shown to be a tool capable of storing and providing data through various audiovisual media formats on clinical and laboratory contents of child dental care. Media resources were developed for the following themes: restorative dentistry in pediatric dentistry, radiographic techniques with pediatric patients, pulp therapy in deciduous teeth, traumatic injuries in the primary dentition, and preventive and interceptive orthodontics. The short-duration multimedia content, with different audiovisual formats, proved to be coherent with the peculiarities of each procedure, enabling to better visualize and understand the techniques. Moreover, the rewind, fast forward, and pause features were included in the mobile application, as well as the possibility of saving print screens, as well as highlighting and searching for contents of interest . Conclusion: The OdontoPed-Helpbox ® demonstrated to be able to reach new strategies for the dynamic teaching-learning process, through operational efficiency. It proved to be a useful and instant tool for providing information to support clinical decision making in Dentistry.

This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations.

This paper explores the usefulness of rich pictures as a method in Systemic Lean Intervention (SLI) process. It combines Lean and Systems Thinking analytical tools. Lean emerged with the main objectives of waste identification and removal in an operational system - which is synonymous to cost reduction, and value maximization. The research focuses on the use of rich pictures alongside lean tools such as workshop and interviews in a participatory research process, involving the identified stakeholders who are affected by the operational process. The research applies these tools with the intent to recognise the impacts of the various parts that function together in the operational process. A single case study organisation of a commercial Livestock farm in the Niger Delta region of Nigeria was used. The research found out that the application of rich pictures could be influenced by certain factors like the participants’ interest, power and authority possession of some participants, familiarity with the issues considered, and the participants’ level of background knowledge on the usage of rich pictures. Rich pictures were also found to be time-consuming in its interpretation and application to addressing identified issues.

Previous studies have focused on researching earnings management behavior in nonprofit hospitals in theUnited Kingdomand theUnited States. However, the operational system and environment of hospitals inTaiwanare substantially different from the cases studied, and therefore cannot be considered equivalent. The objective of this study was to determine whether earnings management is practiced in NFP hospitals inTaiwanand to analyze their earnings management behavior. The ordinary least square method was used to examine the relationships inTaiwan’s nonprofit hospitals. Alternative procedures, such as the abnormal bad debt, abnormal non-operating or non-revenue generating activity expenditure, and abnormal net gain on the sale of property models were also developed and tested to measure abnormal real items. Research has indicated that auditor specialization and auditor tenure have a negative relationship with earnings management. The empirical results also showed that the abnormal net gain on the sale of property is ideal for evaluating earnings management inTaiwan’s nonprofit hospitals.

This paper is a preliminary investigation of the provision of enhanced emergency health care at night by North West Air Ambulance (NWAA). A discrete event simulation model of the current NWAA operational system is presented. This model is used to investigate the provision of NWAA services by an air ambulance vs. a rapid response vehicle. More widely, this model will be used within the scope of investigating what NWAA describe to be their ‘optimal service hours’.

Flooding is a rapidly growing concern in West Africa. Several floods have occurred in recent years with severe consequences including loss of lives and damaged infrastructure. Flooding is also projected to increase with climate change. Access to operational forecasts is a critical component in addressing these challenges. This study presents results from our joint efforts to co-design, co-adapt, and co-operate a short- and medium-term operational hydrological forecasting and alert pilot system for West Africa, within the FANFAR project (www.fanfar.eu).The system has been co-developed through a cycle of workshops, training sessions, and expert exchanges involving representatives from hydrological services, emergency management agencies, river basin organisations, and expert agencies in 17 countries in West and Central Africa. Multi-criteria decision analysis was employed to clarify and prioritize system objectives and configurations. We found that the most highly prioritized objectives were: high accuracy, clear flood risk information, reliable access, and timely production and distribution of the information. Our agile development approach also provided ample opportunities to focus development efforts on the most highly prioritized components, and incorporate stakeholder feedback in the development process.The system is built on an ICT cloud platform that employs a daily forecasting chain including meteorological reanalysis and forecasting, data assimilation of gauge observations and satellite altimetry, hydrological initialisation and forecasting, flood alert derivation, and distribution through e-mail, SMS, web visualisation and API. The system is designed to enable multiple configurations and integration of several information sources (e.g. different hydrological models, observations, flood hazard thresholds etc.). We present the system configurations, stakeholder-driven adaptations, challenges, and current forecast performance. To our knowledge, the FANFAR system constitutes a significant advancement toward the vision of achieving efficient flood management in West Africa.

Background The Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) is a consortium that aims to understand complex hydrologic, biogeochemical, and microbial connections within river corridors experiencing perturbations such as dam operations, floods, and droughts. For one ongoing WHONDRS sampling campaign, surface water metabolite and microbiome samples are collected through a global survey to generate knowledge across diverse river corridors. Metabolomics analysis and a suite of geochemical analyses have been performed for collected samples through the Environmental Molecular Sciences Laboratory (EMSL). The obtained knowledge and data package inform mechanistic and data-driven models to enhance predictions of outcomes of hydrologic perturbations and watershed function, one of the most critical components in model-data integration. To support efforts of the multi-domain integration and make the ever-growing data package more accessible for researchers across the world, a Shiny/R Graphical User Interface (GUI) called WHONDRS-GUI was created. Results The web application can be run on any modern web browser without any programming or operational system requirements, thus providing an open, well-structured, discoverable dataset for WHONDRS. Together with a context-aware dynamic user interface, the WHONDRS-GUI has functionality for searching, compiling, integrating, visualizing and exporting different data types that can easily be used by the community. The web application and data package are available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1484811, which enables users to simultaneously obtain access to the data and code and to subsequently run the web app locally. The WHONDRS-GUI is also available for online use at Shiny Server (https://xmlin.shinyapps.io/whondrs/).

The need for house is one of the primary needs for every human being and basic rights for humans. The financing of musyarakah mutanaqisah in the Islamic banking system is one of the bank's involvement in the provision of housing loans. Financing musyarakah syirkatul milk is the combination of capital from two or more people to finance a project / business, profits will be divided based on the proportion of capital; whereas if there is a loss it will be borne together according to the agreement contained in the contract / contract agreement. Islamic banking has obtained formal juridical legitimacy that strengthens sharia principles as a principle that underlies the operational system of profit-sharing banking activities. This is a concrete manifestation of the objective conditions of the Islamic community's need for Islamic institutions, which is believed to be an alternative solution for efforts to optimize the economic potential of the community.

Operations management is an area of management concerned with overseeing, designing, and controlling the process of production and redesigning business operations in the production of goods or services. It involves the responsibility of ensuring that business operations are efficient in terms of using as few resources as needed, and effective in terms of meeting customer requirements. It is concerned with managing the process that converts inputs (in the forms of raw materials, labor, and energy) into outputs (in the form of goods and/or services).The relationship of operations management to senior management in commercial contexts can be compared to the relationship of line producers to highest-level of production process. Management, including operations management, is like engineering in that it blends art with applied science.Operation management (control) of the productive process consists of: operational planning, delivery control, process monitoring and operational registration. In operating system management objectives define the required results (Wz) the production process, given the operational system plans. As which he defines not only objective, but also the development of the manufacturing process to observe the given goals (structure, tasks, and sequence operating plan). Operational management is a multi- level control system,The process of producing new energetic drink L-Carnitine, “Gora” , drinking water in various volumes, juices various volumes and other products.

This article examines Indonesia’s initiatives in combating and preventing drugs abuse which has reached the level of ‘drugs emergency’. Although there are many advantages in this new approach, this article argues that the implementation of the rehabilitation approach still faces various challenges such as policy implementation priority, mindset changing of the executors, un-fully operational system, infrastructure availability, reliable rehabilitation method test, which generate questions on the effectiveness of the decriminalization policy. In the future, continuous evaluation is needed to make sure that the policy is well implemented and provides impact as is expected, which is to decrease the number of drug abusers.

Hydropower makes up nearly half of Sweden's electrical energy production. However, the distribution of the water resources is not aligned with demand, as most of the inflows to the reservoirs occur during the spring flood period. This means that carefully planned reservoir management is required to help redistribute water resources to ensure optimal production and accurate forecasts of the spring flood volume (SFV) is essential for this. The current operational SFV forecasts use a historical ensemble approach where the HBV model is forced with historical observations of precipitation and temperature. In this work we develop and test a multi-model prototype, building on previous work, and evaluate its ability to forecast the SFV in 84 sub-basins in northern Sweden. The hypothesis explored in this work is that a multi-model seasonal forecast system incorporating different modelling approaches is generally more skilful at forecasting the SFV in snow dominated regions than a forecast system that utilises only one approach. The testing is done using cross-validated hindcasts for the period 1981–2015 and the results are evaluated against both climatology and the current system to determine skill. Both the multi-model methods considered showed skill over the reference forecasts. The version that combined the historical modelling chain, dynamical modelling chain, and statistical modelling chain performed better than the other and was chosen for the prototype. The prototype was able to outperform the current operational system 57 % of the time on average and reduce the error in the SFV by ∼ 6 % across all sub-basins and forecast dates.

The paper deals with the control of complex dynamic systems. The main objective is to partition the whole operational system domain in local regions using an incremental neuro-fuzzy classifier in order to achieve multiple neural control strategies for the considered system. In our case, this approach is applied to a greenhouse operating during one day. Therefore, banks of neural controllers and direct neural local models are made from different partitioned greenhouse behaviors and two multiple neural control strategies are proposed to control the greenhouse. The selection of the suitable controller is accomplished by computing the minimal output error between desired and direct neural local models outputs in the case of the first control strategy and from a supervisor block containing the considered neuro-fuzzy classifier in the case of the second control strategy. Simulation results are then carried out to show the efficiency of the two control strategies.

One of the biggest challenges facing leaders today is the need to position and enable organizations and people for adaptability in the face of increasingly dynamic and demanding environments. Despite this we know surprisingly little about this topic. In this paper we provide a theoretical synthesis and integrative review of research from strategy, organization theory, innovation, networks, and complexity to provide a framework of leadership for organizational adaptability. Our review shows that leadership for organizational adaptability is different from traditional leadership or leading change. It involves enabling the adaptive process by creating space for ideas advanced by entrepreneurial leaders to engage in tension with the operational system and generate innovations that scale into the system to meet the adaptive needs of the organization and its environment. Leadership for organizational adaptability calls for scholars and practitioners to recognize organizational adaptability as an important organizational outcome, and enabling leadership (i.e., enabling the adaptive process through adaptive space) as a critical form of leadership for adaptive organizations.

Condition monitoring of mining conveyor is a highly essential task to ensure minimum disruption to the mining operational system. Failure of one or more conveyor components can result in significant operational downtime, economic loss, and safety risks. The current monitoring method still involves subjective measure from maintenance engineers, where at some cases, fault can be left undetected and leads into site incident. Therefore, there is a high demand for real-time condition monitoring technology to detect early fault on conveyor. In this study, the effective application of distributed optical fibre sensor (DOFS) was explored for long distance real-time condition monitoring of mining conveyor. The fault detection framework was developed by integrating and modifying the Isolation Forest algorithm to analyse optical signals for effective detection of defective idlers. Further, the optical signal was analysed to extract the damage progression of defective idler with time and space. The results were used to classify various levels of damage and to set appropriate damage thresholds. Also, software interface, that can be used to set the sensing parameters, to collect, analyse, and visualise the signal in real-time, was developed. Finally, the developed condition monitoring system was used to monitor a 1.6 km long section of a conveyor structure in Western Australia for a period of 10 months. The results and findings from the field monitoring were presented together with automated fault detection framework for condition monitoring of mining conveyor.

The Arctic Ocean has drawn major attention in recent years due to its rich natural resources and shorter navigational routes. Arctic development and transportation involve significant risk caused by the unique features of this region, such as ice, severe operating conditions, unpredictable climatic changes, and remoteness. Considering the high degree of uncertainty in the performance of vessel operating systems and humans, robust risk analysis and management tools are required to provide decision-support to prevent accidents and ensure safety at sea. This paper proposes an Object-Oriented Bayesian Network model to dynamically predict ship-ice collision probability based on navigational and operational system states, weather and ice conditions, and human error. The model, when integrated with potential consequences, may help estimate risk. A case study related to oil tanker navigation on the Northern Sea Route (NSR) is used to show the application of the proposed model to predict oil tanker collision with sea ice.

Integrated Islamic Primary School (SDIT) is an educational institution that is increasingly in demand by society because of its good quality. The quality of educational institutions is certainly influenced by the management of human resources. The purpose of this research is to analyze human resource management in SDIT. The location of this research is at SDIT Insan Permata in Malang City. The method used in this research is descriptive qualitative method. Data were collected by observation methods, interviews and by reviewing relevant documents. The results showed that SDIT Insan Permata in general has a good human resource management system, ranging from recruitment system, competency development, performance appraisal to employee compensation. Nevertheless, there are still some that need to be improved, such as the operational system procedures need to be documented and controlled well, the recruitment path that needs to be improved and the high turn over of employees that need to be anticipated

Purpose Expert profiling plays an important role in expert finding for collaborative innovation in research social networking platforms. Dynamic changes in scientific knowledge have posed significant challenges on expert profiling. Current approaches mostly rely on knowledge of other experts, contents of static web pages or their behavior and thus overlook the insight of big social data generated through crowdsourcing in research social networks and scientific data sources. In light of this deficiency, this research proposes a big data-based approach that harnesses collective intelligence of crowd in (research) social networking platforms and scientific databases for expert profiling. Design/methodology/approach A big data analytics approach which uses crowdsourcing is designed and developed for expert profiling. The proposed approach interconnects big data sources covering publication data, project data and data from social networks (i.e. posts, updates and endorsements collected through the crowdsourcing). Large volume of structured data representing scientific knowledge is available in Web of Science, Scopus, CNKI and ACM digital library; they are considered as publication data in this research context. Project data are located at the databases hosted by funding agencies. The authors follow the Map-Reduce strategy to extract real-time data from all these sources. Two main steps, features mining and profile consolidation (the details of which are outlined in the manuscript), are followed to generate comprehensive user profiles. The major tasks included in features mining are processing of big data sources to extract representational features of profiles, entity-profile generation and social-profile generation through crowd-opinion mining. At the profile consolidation, two profiles, namely, entity-profile and social-profile, are conflated. Findings (1) The integration of crowdsourcing techniques with big research data analytics has improved high graded relevance of the constructed profiles. (2) A system to construct experts’ profiles based on proposed methods has been incorporated into an operational system called ScholarMate (www.scholarmate.com). Research limitations One shortcoming is currently we have conducted experiments using sampling strategy. In the future we will perform controlled experiments of large scale and field tests to validate and comprehensively evaluate our design artifacts. Practical implications The business implication of this research work is that the developed methods and the system can be applied to streamline human capital management in organizations. Originality/value The proposed approach interconnects opinions of crowds on one’s expertise with corresponding expertise demonstrated in scientific knowledge bases to construct comprehensive profiles. This is a novel approach which alleviates problems associated with existing methods. The authors’ team has developed an expert profiling system operational in ScholarMate research social network (www.scholarmate.com), which is a professional research social network that connects people to research with the aim of “innovating smarter” and was launched in 2007.

Egypt was one of the first developing countries in Africa that used earth observation and remote sensing in various applications since 1970s. It has grown up in the last decades to build its own capacity in space science and technology that ended up by launching earth observation satellites. At the same time Egypt continued to develop the capacity in EO applications and contribute to the national development plans. In this domain NARSS, the governmental research institute that lead the EO and space applications has completed many research and development projects in EO applications in mineral resources exploration, coastal and marine resources, air quality, water resources management, food security, etc. This was via operational projects with the stakeholders and users to ensure sustainability and operation of the services. For example, NARSS has developed an operational system to monitor the national crop rice using EO information that capable to provide the actual land planted with rice and predict the yield. The system has enabled to provide recommendations for other plots of land that suitable for rice plantation. In the area of environmental hazards, many projects on the flash floods and the vulnerability to flash flood hazards were developed providing decision makers with vulnerability maps and Atlases on national level. Further details on the EO activities and future plans at NARSS, Egypt will be presented in this paper.

Purpose Material conscious and information network (MCIN) is a kind of cyber physics social system. This paper aims to study the MCIN modeling method and design the MCIN-based architecture of smart agriculture (MCIN-ASA) which is different from current vertical architecture and involves production, management and commerce. Architecture is composed of three MCIN-ASA participants which are MCIN-ASA enterprises, individuals and commodity. Design/methodology/approach Architecture uses enterprises and individuals personalized portals as the carriers which are linked precisely with each other through a peer-to-peer network called six-degrees-of-separation block-chain. The authors want to establish a self-organization, open and ecological operational system which includes active, personalized consumption, direct, centralized distribution, distributed and smart production. Findings The paper models three main MCIN-ASA participants, namely, design the smart supply, demand and management functions, which show the feasibility innovation and high efficiency of implementing MCIN on agriculture. At the same time, the paper presents a prototype system based on the architecture. Originality/value The authors think that MCIN-ASA improves current agriculture greatly and inspires a lot in production-marketing-combined electronic commerce.