Massive Scale Research Articles

Drones for Flood Monitoring, Mapping and Detection: A Bibliometric Review

Floods are one of the most often occurring and damaging natural hazards. They impact the society on a massive scale and result in significant damages. To reduce the impact of floods, society needs to keep benefiting from the latest technological innovations. Drones equipped with sensors and latest algorithms (e.g., computer vision and deep learning) have emerged as a potential platform which may be useful for flood monitoring, mapping and detection activities in a more efficient way than current practice. To better understand the scope and recent trends in the domain of drones for flood management, we performed a detailed bibliometric analysis. The intent of performing the bibliometric analysis waws to highlight the important research trends, co-occurrence relationships and patterns to inform the new researchers in this domain. The bibliometric analysis was performed in terms of performance analysis (i.e., publication statistics, citations statistics, top publishing countries, top publishing journals, top publishing institutions, top publishers and top Web of Science (WoS) categories) and science mapping (i.e., citations by country, citations by journals, keyword co-occurrences, co-authorship, co-citations and bibliographic coupling) for a total of 569 records extracted from WoS for the duration 2000–2022. The VOSviewer open source tool has been used for generating the bibliographic network maps. Subjective discussions of the results explain the obtained trends from the bibliometric analysis. In the end, a detailed review of top 28 most recent publications was performed and subjected to process-driven analysis in the context of flood management. The potential active areas of research were also identified for future research in regard to the use of drones for flood monitoring, mapping and detection activities.

ПІДТРИМАННЯ БОЄЗДАТНОСТІ З’ЄДНАНЬ (ВІЙСЬКОВИХ ЧАСТИН) НАЦІОНАЛЬНОЇ ГВАРДІЇ УКРАЇНИ ПІД ЧАС УЧАСТІ В ОБ’ЄДНАНИХ ОПЕРАЦІЯХ, БОЙОВИХ ДІЯХ

According to the results of scientific research and analysis of guiding documents, according to the authors of the article, the concept of maintaining the combat capability of formations (military units) of the NSU should be understood as the activities of commanders, headquarters, management bodies, moral and psychological support structures regarding their ability to put on alert, conduct hostilities, perform combat missions by maintaining the quality of management to ensure the readiness of units (military units) to perform tasks for purpose, provision of their weapons and military equipment, material means, maintenance of personnel, available forces and means of the MPZ of the moral and psychological state, its mental stability and psychological readiness for an equal level that ensures the fulfillment of the tasks. The improvement of the means of armed struggle and the complication of hostilities led to an increase in combat losses. All this exacerbated the problem of maintaining the combat capability of the troops because the combat capability of formations (military units) should first of all be maintained when put on alert, training and conducting combat operations, and secondly, if necessary, quickly recover to a level that ensures the successful completion of tasks. The experience gained in past wars to maintain the combat capability of troops basically retains value in modern conditions. But at the same time, many new circumstances have arisen that greatly complicate the solution of this problem. This is primarily due to the emergence and widespread development of the means of mass destruction, as well as systems of high-precision weapons, which can be used suddenly on a massive scale both during training and during combat, to simultaneously hit all elements of the combat order ‒ not only the first, but also the second echelons, reserves, rear units and units, regardless of their distance from the front line. Today, the likelihood of instant destruction of certain elements of the combat order of a unit and a unit, violation of the control and communication system, combat, technical, logistics support, as shown by the experience of war in the Persian Gulf, other conflicts, wars in the Middle East, Yugoslavia and especially combat operations in Ukraine, is becoming a reality. Of course, these circumstances make it extremely difficult to implement support measures, and if necessary, restore the combat capability of units, in many respects change their nature and methods of conduct. In the practice of combat activities, they are divided into tactical and special. They are reflected in the decision of the commander to battle, in setting up combat missions for units, organizing interaction and comprehensive support. When maintaining the moral and psychological state of the personnel, their mental stability and psychological readiness at the level that ensures the fulfillment of the tasks set, it is necessary to take into account the fact that in addition to losses from the influence of the enemy, the loss of combat capability is also affected by physical and nervous exhaustion, hunger, cold, etc. For this purpose, if necessary, the rotation of units (military units) should be carried out. At the same time, as the experience of the Israeli army's participation in hostilities shows, it should be borne in mind that the maximum period before the onset of nervous exhaustion for an experienced and motivated unit is about 30 days, and sleep should be no later than 48 hours. It is also necessary to take into account the fact that most of all it demotivates and frightens such stress factors as: light ‒ tracers, flashes of explosions, fire; smells – the smell of burning and burnt meat; noise – explosions, whistles, roars; shouts – commanders, wounded, dying. Therefore, when maintaining combat capability, during preparation for combat operations, it is important for commanders and their deputies to work with personnel to at least partially prepare servicemen for these stress factors (light, smells, noise, screams) so that they are not distracted from their combat missions and work, despite the unnatural and dangerous environment.

LEVIATHAN WAKES: THE EMERGENCE OF EL PILAR AS A MAJOR CENTER AT THE ONSET OF THE LATE PRECLASSIC

The scale and extent of monumental construction at El Pilar – over 150 hectares spread across two kilometers – attest to its position as a dominant Classic-period center in the upper Belize River area. Like several nearby sites in the Belize Valley, El Pilar was founded in the early Middle Preclassic as the focus of a burgeoning agricultural community. Major architectural expansion at El Pilar accompanied population growth across the area during the transition from Middle to Late Preclassic times. This paper explores the transformation of El Pilar from village ceremonial center to political capital at the onset of the Late Preclassic. New radiocarbon dates, stratigraphic reconstructions, and ceramic analyses reveal the massive scale of architectural investment in the ceremonial core of El Pilar – Plaza Copal – during this important transitional phase. We discuss these new data and their implications for understanding the increasingly complex sociopolitical landscape of the Late Preclassic upper Belize River area. The scale of this construction boom at El Pilar demonstrates the deep roots of power its ruling elites continued to wield until the close of the Classic period.

Secure IoMT for Disease Prediction Empowered With Transfer Learning in Healthcare 5.0, the Concept and Case Study

Identifying human diseases remains a difficult process, even in the age of advanced information technology and the smart healthcare industry 5.0. In the smart healthcare industry 5.0, precise prediction of human diseases, particularly lethal cancer diseases, is critical for human well-being. The global Internet of Medical Things sector has advanced at a breakneck pace in recent years, from small wristwatches to large aircraft. The critical aspects of the Internet of Medical Things include security and privacy, owing to the massive scale and deployment of the Internet of Medical Things networks. Transfer learning with a secure IoMT-based approach is considered. The google net deep machine-learning model is used for accurate disease prediction in the smart healthcare industry 5.0. We can easily and reliably anticipate the lethal cancer disease in the human body by using the secure IoMT-based transfer learning approach. Furthermore, the results of the proposed secure IoMT-based Transfer learning techniques are used to validate the best cancer disease prediction in the smart healthcare industry 5.0. The proposed secure IoMT-based transfer learning methodology reached 98.8%, better than the state-of-the-art methodologies used previously for cancer disease prediction in the smart healthcare industry 5.0.

Wealth Destruction on a Massive Scale for Investors? The Wedge between Security and Investor Returns in an Emerging Market

Wealth Destruction on a Massive Scale for Investors? The Wedge between Security and Investor Returns in an Emerging Market

Cultural Aesthetics of Campus Public Space Based on the Concept of Micro-regeneration: A Case Study of Culture Renewal in Shanghai Conservatory of Music

In the new stage of urban development in China, the dominant way of urban regeneration has transformed from the traditional mode of radical reconstruction on massive scale to the organic renewal in gradual process on micro scale. During the practice of organic renewal, the concept of ‘micro-regeneration’ has been developed, aiming at local adjustment and micro-transformation of urban space through small-scale development and renewal, so as to realize space renovation and environment improvement. As a lightweight and practical organic renewal method, ‘micro-renewal’ has been widely discussed and applied in the practice of urban community regeneration in recent years. Campus is a special type of community, but it faces similar problems as urban communities, which is the mismatching of existing environmental quality, functions and actual use demands with the change of development stage. Especially under the construction of socialist spiritual civilization, how to improve the overall cultural atmosphere through the renewal of campus public space is an important issue. Due to the special ownership and use mode of campus space, large-scale renovation is not applicable. While, the concept of ‘micro-regeneration’ provides a possible way for the sustainable development of campus environment. This article takes the regeneration project of the Shanghai Conservatory of Music as an example, applying the theory and method of ‘micro-regeneration’ to carry out the practice of cultural construction of campus public space. In this project, in terms of space renovation, the creation of a cultural atmosphere in the campus public space was achieved through ‘small-scale’ detailed design; in terms of investment costs, the cost control of ‘micro-input’ ensured the balance between social and economic benefits; In terms of the implementation process, the precise deployment of ‘quasi-operations’ avoided the interference of large-scale construction on the daily operation of the campus. In the course of the project, multiple departments and interests were collaborated, which contributed to the whole process control of the ‘microregeneration’ of campus from planning and design, communication and negotiation to construction and operation. The research shows that the concept of ‘micro-regeneration’ provides a suitable path for the reconstruction of the campus cultural, which can realize the continuous improvement of the campus space and cultural quality under the premise of maintaining the normal operation. At the same time, under the concept of ‘micro-regeneration’, the implementation mode of small-scale, micro-investment and quasi-operation is relatively easy for the whole process management from planning to landing, which is conducive to the full realization of the vision of regeneration.

MSR influence on environmental & ecological balance: Mediating effect of environmental regulations & strategies

Considering megaprojects' massive scale and the numerous stakeholders involved, the social responsibility of megaprojects (MSR) is a must. Their undeniable economic importance is accompanied by unavoidable environmental impacts. Today, professionals and researchers from all disciplines recognize that the unavoidable environmental impacts must be mitigated and ecological stability improved, which is an obvious global issue. MSR effects on the environment and ecological balance: mediating effects of environmental regulations are absent from the literature. This study reveals the impact of MSR on environmental and ecological balance (ENV/ES): the mediating effect of environmental regulations and strategies (ER&S) using Partial Least Squares Structural Equation Modeling (PLS-SEM). MSR has a direct minimal effect on environmental and ecological balance. Environmental regulations and strategies partially mediate this relationship. The study also introduced an advanced statistical model and the most up-to-date PLS-SEM, which were not common in the literature on this topic. By its validity and reliability, the conceptual model and the research contribute to the existing knowledge base, assist in reducing the negative environmental impact of mining and megaproject construction, and provide information to environmental agencies, practitioners, and researchers so they can better understand the desired outcomes and improve ecological balance.

Inferring attitudinal spaces in social networks

Ideological scaling methods have shown that behavioral traces in social platforms can be used to mine opinions at a massive scale. Current methods exploit one-dimensional left–right opinion scales, best suited for two-party socio-political systems and binary social divides such as those observed in the US. In this article, we introduce a new method to overcome limitations of existing methods by producing multidimensional network embeddings and align them with referential attitudinal for a few nodes. This allows us to infer a larger set of opinion dimensions from social graphs, embedding users in spaces where dimensions stand for indicators of several social dimensions including (in addition to left–right cleavages) attitudes towards elites, or ecology among many other issues. Our method does not rely on text data and is thus language-independent. We illustrate this approach approach on a Twitter follower network. Finally, we show how our method allows us to analyze the opinions shared within various communities of social networks. Our analyses show that communities of users that have extreme political opinions are also more homogeneous ideologically.

Structure and Raman Spectra of Exotic Carbon Microcrystals from Meteoritic Dust of Chelyabinsk Superbolide.

The Chelyabinsk superbolide, the largest in XXI century, which exploded on 15 February 2013 over snowy fields of Southern Urals was a historic event not just only because of its massive scale and explosive power. High-temperature, high-pressure conditions in the front shock wave caused intense ablation of the asteroid material and formation of huge amount of meteoritic dust dispersed in the atmosphere during the flyby. Massive snowfalls just few days before and after the event conserved precipitated meteoritic dust in thin layer of snow which was collected and studied later. The most intriguing and challenging material discovered in the dust was closed-shell 10-70 µm exotic polygonal graphitic carbon microcrystals of undisclosed nature. Using optical and electron microscopy and Raman spectroscopy the atomic structure of closed-shell microcrystals was thoroughly studied and their graphitic nature was revealed. It was found that some of the particles formed by multilayer graphitic polygonal shells have extensive hollows inside. Comparative microscopic and spectroscopic analysis of meteorite exotic carbon microcrystals with different graphite species, carbon onions, and diamond revealed two distinctively different closed-shell carbon particles. The first type of the particles can be attributed to carbon onions with characteristic graphite nanocrystalline basic structural units (BSU) of 49 nm lateral size and less and, probably, BSU heteroatomic termination necklace with oxygen content comparable to 1.1% and more. It was shown that the second type of unique graphitic carbon particles of a convex shape and perfect hexagonal symmetry with lateral dimensions of 14 µm correspond to multiply twinned closed-shell graphite microcrystals with polyhexacyclooctadecane (-C18-)n core wrapped by multiple layers of carbon honeycombs with low (<1%) content of oxygen termination necklace.

Advancements in Detection Approaches of Severe Acute Respiratory Syndrome Coronavirus 2.

Diagnostic testing to identify individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) plays a key role in selecting appropriate treatments, saving people's lives and preventing the global pandemic of COVID-19. By testing on a massive scale, some countries could successfully contain the disease spread. Since early viral detection may provide the best approach to curb the disease outbreak, the rapid and reliable detection of coronavirus (CoV) is therefore becoming increasingly important. Nucleic acid detection methods, especially real-time reverse transcription polymerase chain reaction (RT-PCR)-based assays are considered the gold standard for COVID-19 diagnostics. Some non-PCR-based molecular methods without thermocycler operation, such as isothermal nucleic acid amplification have been proved promising. Serologic immunoassays are also available. A variety of novel and improved methods based on biosensors, Clustered-Regularly Interspaced Short Palindromic Repeats (CRISPR) technology, lateral flow assay (LFA), microarray, aptamer etc. have also been developed. Several integrated, random-access, point-of-care (POC) molecular devices are rapidly emerging for quick and accurate detection of SARS-CoV-2 that can be used in the local hospitals and clinics. This review intends to summarize the currently available detection approaches of SARS-CoV-2, highlight gaps in existing diagnostic capacity, and propose potential solutions and thus may assist clinicians and researchers develop better technologies for rapid and authentic diagnosis of CoV infection.

Microbial cellulose: an alternate source for plant cellulose

In the contemporary generation, rapid urbanization, industrialization, and declining woodland lead to global weather modifications. The massive scale of deforestation for firewood, constructions, paper products, textile, and plenty of different packages are steadily enforcing a critical poor impact on the surroundings. Inherently, plant cellulose has restrained utility because of the presence of hemicellulose and lignin. Consequently, studies in the discipline of microbial cellulose display many benefits over plant cellulose. It possesses numerous crucial and unique properties compared to plant cellulose, including high purity, better absorptivity, excellent polymerization, crystallization, in-situ mold potential, biodegradability, biocompatibility, and plenty of others. This assessment looks into a potent cellulose producer to develop an economically feasible manner for huge-scale production of microbial cellulose therefore, it may replace some of the requirements where plant cellulose has been currently in use.

Massive production of fibroin nano-fibrous biomaterial by turbulent co-flow

Among the different polymers (proteins, polysaccharides, etc.) that make up natural fibers, fibroin is a protein produced by silk spinning animals, which have developed an optimized system for the conversion of a highly concentrated solution of this protein into high-performance solid fibers. This protein undergoes a self-assembly process in the silk glands that result from chemical gradients and by the application of mechanical stresses during the last step of the process. In the quest for a process that could mimic natural spinning at massive scales, we have discovered that turbulence offers a novel and promising solution: a turbulent liquid jet can be formed by a chemically green and simple coagulating liquid (a diluted solution of acetic acid in etanol) co-flowing with a concentrated solution of fibroin in water by the use of a Flow Blurring nebulizer. In this system, (a) the co-flowing coagulant liquid extracts water from the original protein solution and, simultaneously, (b) the self-assembled proteins are subjected to mechanical actions, including splitting and stretching. Given the non-negligible produced content with the size and appearance of natural silk, the stochastic distribution of those effects in our process should contain the range of natural ones found in animals. The resulting easily functionalizable and tunable one-step material is 100% biocompatible, and our method a perfect candidate to large-scale, low-cost, green and sustainable processing of fibroin for fibres and textiles.

Would the Professional Training Sector Take the Advantage of the Virtual Environment that Was Presented Forcefully by COVID-19?

This applied research looks into the effectiveness of the virtual learning environment in the delivery of corporate and professional courses since the training provider was forced to shift to this mode of delivery since March 2020 due to the COVID-19 circumstances. 
 In literature, the effectiveness of virtual delivery in the basic schooling and higher education sectors is well researched and heavily debated amongst scholars and practitioners, especially during this pandemic. This is not the case, however, in the professional and corporate training field as this sector is often regarded as a voluntary provision that warrants the least priority to invest in during a global crisis; the like of COVID-19. The purpose of the research is to measure the effectiveness of virtual training provision, and how this mode of learning can help in developing skills and supporting learning to achieving their learning objectives. The effectiveness evaluation in this research was measured from the perspective of the end-user (the learner). 
 The research involved a quantitative questionnaire that was answered by 367 respondents amongst trainees who were enrolled in various professional training courses, post COVID-19, in virtual mode. The questionnaire items were purposely designed to gauge the learners’ satisfaction as a measure of effectiveness and quality of the virtual training environment. The questionnaire was distributed and administered electronically via a mass emailing campaign. Upon collecting the responses, the data was quantitatively analysed using descriptive quantitative analysis. 
 The analysis of the data indicates indeed high satisfaction rates amongst learners (more than 72% were satisfied, or higher). The effectiveness of virtual learning in helping trainees achieve their learning objectives and make good progress, however, was lower than the overall satisfaction rate. After months of riding the learning curve of the virtual learning experience, the learners showed mixed picture of reflection on their preferred mode of delivery in the future. Almost a third of the learners opted for blended learning mode, whereas nearly another third preferred virtual, and the remaining liked to go back to physical classes. 
 This research indicates that virtual learning is apparently lending itself to be a feasible mode of delivery of professional training courses. Its effectiveness, as perceived by the beneficiaries, is proven to continue improve as it matures over time. The prospect of this virtual learning methodology looks positive indeed despite the newness of its application on a massive scale post the COVID-19 pandemic.

Resource-Efficient Federated Learning With Non-IID Data: An Auction Theoretic Approach

Federated learning (FL) has gained significant importance for intelligent applications, following data produced on a massive scale by numerous distributed IoT devices. From an FL perspective, the key aspect is that this data is not identically and independently distributed (IID) across different data sources and locations. This distribution-skewness leads to significant quality degradation. Moreover, an intrinsic consequence of using such non-IID data in decentralized learning is increasing costs that would be mitigated if using IID data. As a remedy, we propose a resource-efficient method for training an FL-based application with non-IID data, effectively minimizing cost through an auction approach and mitigating quality degradation through data sharing. In an experimental evaluation, we investigate the FL performance using real-world non-IID data and use the resulting ground-truth outputs to develop functions for estimating the utility of non-IID data, computational resource costs, and data generation costs. These functions are used to optimize the costs of model training, ensuring resource efficiency. It is further demonstrated that using shared-IID data significantly increases the resource efficiency of FL with local non-IID data. This holds true even when the shared IID data size is less than 1% of the size of the local non-IID data. Moreover, this work demonstrates that the profitability of the stakeholders can be maximized using the proposed auction procedure. The integration of the auction procedure and a resource-efficient training strategy allows FL service providers to create practical trading strategies by minimizing the FL clients’ resources and payments in a machine learning marketplace.

Hypertension treatment capacity in India by increased workforce, greater task-sharing, and extended prescription period: a modelling study

The worldwide control rate for hypertension is dismal. An inadequate number of physicians to treat patients with hypertension is one key obstacle. Innovative health system approaches such as delegation of basic tasks to non-physician health workers (task-sharing) might alleviate this problem. Massive scale up of population-wide hypertension management is especially important for low- and middle-income countries such as India. Using constrained optimization models, we estimated the hypertension treatment capacity and salary costs of staff involved in hypertension care within the public health system of India and simulated the potential effects of (1) an increased workforce, (2) greater task-sharing among health workers, and (3) extended average prescription periods that reduce treatment visit frequency (e.g., quarterly instead of monthly). Currently, only an estimated 8% (95% uncertainty interval 7%-10%) of ∼245 million adults with hypertension can be treated by physician-led services in the Indian public health system (assuming the current number of health workers, no greater task-sharing, and monthly visits for prescriptions). Without task-sharing and with continued monthly visits for prescriptions, the least costly workforce expansion to treat 70% of adults with hypertension would require ∼1.6 (1.0-2.5) million additional staff (all non-physicians), with ∼INR 200 billion (≈USD 2.7 billion) in additional annual salary costs. Implementing task-sharing among health workers (without increasing the overall time on hypertension care) or allowing a 3-month prescription period was estimated to allow the current workforce to treat ∼25% of patients. Joint implementation of task-sharing and a longer prescription period could treat ∼70% of patients with hypertension in India. The combination of greater task-sharing and extended prescription periods could substantially increase the hypertension treatment capacity in India without any expansion of the current workforce in the public health system. By contrast, workforce expansion alone would require considerable, additional human and financial resources. Resolve to Save Lives, an initiative of Vital Strategies, was funded by grants from Bloomberg Philanthropies; the Bill and Melinda Gates Foundation; and Gates Philanthropy Partners (funded with support from the Chan Zuckerberg Foundation).

A review of the techniques for treating the landfill leachate

Rapid growth in urbanization and increasing population have accelerated the amount of solid waste generation. Around 70% of this waste generated is disposed of in landfill sites and open dumping grounds. This creates more generation of landfill leachate. Landfill leachate has a wide range of negative environmental effects and contaminates groundwater on a massive scale. This leachate contains highly toxic pollutants such as heavy metals, solids, BOD, COD, ammonia and TOC. This versatile nature of pollutants cannot be treated with a single treatment technique. This paper discusses the landfill leachate treatment techniques such as physicochemical treatment, biological treatment and advanced oxidation treatments. From the literature available, it has been observed that the physicochemical treatment primarily removes solid particles, heavy metals, ammonia, and other contaminants, whereas biological treatment majorly removes organic matter, and the advanced oxidation process is used to reduce COD from the landfill leachate. The efficiency of adsorption for the removal of COD, Ammonia and Heavy metals varies in the range of 91–95%, 19–40% and 70–90% respectively; the efficiency of coagulation for the removal of Turbidity, Colour and COD vary in the range of 93–99.9%, 50–94% and 54–69% respectively. The efficiency of Fenton's process and the Photocatalysis process for the removal of COD varies in the range of 48–91% and 56–84% respectively while the efficiency of SBR and UASB for the removal of COD varies in the range of 55–98.8% and 35–90% respectively. The present paper would be of great use for selecting the appropriate combination of technologies for treating the landfill leachate.

Leveraging a Wildfire Risk Prediction Metric with Spatial Clustering

Fire authorities have started widely using operational fire simulations for effective wildfire management. The aggregation of the simulation outputs on a massive scale creates an opportunity to apply the evolving data-driven approach to closely estimate wildfire risks even without running computationally expensive simulations. In one of our previous works, we validated the application with a probability-based risk metric that gives a series of probability values for a fire starting at a start location under a given weather condition. The probability values indicate how likely it is that a fire will fall into different risk categories. The metric considered each fire start location as a unique entity. Such a provision in the metric could expose the metric to scalability issues when the metric is used for a larger geographic area and consequently make the metric hugely intensive to compute. In this work, in an investigative effort, we investigate whether the spatial clustering of fire start locations based on historical fire areas can address the issue without significantly compromising the accuracy of the metric. Our results show that spatially clustering all fire start locations in Tasmania into three risk clusters could leverage the probability-based risk metric by reducing the computational requirements of the metric by a theoretical factor in thousands with a mere compromise of approximately 5% in accuracy for two risk categories of high and low, thereby validating the possibility of the leverage of the metric with spatial clustering.

Concept and Implementation of Islamic Character Education in Educational Institutions

Education institutions must implement Islamic character education on a massive scale immediately. This study aims to describe the concept and implementation of character education in educational institutions to address student association issues without delay. This investigation is library-based (library research). The obtained data were then analyzed with a hermeneutic approach through content analysis. This study concludes that the concept of character education has been taught in Islam for quite some time. The verses of the Qur'an and hadith explain this. Similar to Surah Luqman verses 17 and 18, as well as the hadith narrated by Imam Bukhari and Muslims from the path of Abdullah bin Amr bin Ash's companions. Implementing Islamic character education in educational institutions can be categorized initially by age range, from early childhood to adolescence. To evaluate the success of Islamic character education in specific educational institutions, class-based, school-based, and community-based Islamic character education must be implemented. The implication for educational institutions is a greater emphasis on Islamic character education beginning at a young age so that students develop Islamic character. This study contributes to advancing and implementing Islamic character education in educational institutions.

High temperature corrosion mechanism of Ni–5W–6B–28Cr–13Al alloy in simulated MSW incineration environment

In this study, the corrosion behavior of a newly designed alloy (Ni–5W–6B–28Cr–13Al) in simulated municipal solid waste (MSW) incineration atmosphere in the presence of HCl, NaCl, and SO2 under an oxidizing humidity condition at 600 °C was conducted. The combined effects of these aggressive species on corrosion mechanism were elucidated by means of XRD, SEM, EDS, and thermodynamic calculations. The results showed that the contribution to the corrosion rate was in the order of NaCl + HCl > NaCl > HCl. The alloy was rarely corroded without NaCl coating, while the alloy experienced enhanced corrosion attack of 12.183 mg/cm2 under NaCl exposure. In the presence of HCl and NaCl, the accelerated corrosion of the tested alloy reached a maximum mass gain of 75.592 mg/cm2. Both experimental and theoretical results suggested that the corrosion occurred via an active oxidation reaction where the NaCl coating was converted to molten phase which degraded the alloy forming a massive unprotective oxide scales. Nonetheless, the synergistic effect of NaCl and HCl was significantly suppressed by the sulfation after SO2 application of nearly 50% less than the mass change before SO2 application.

Undulate: A framework for data-driven software engineering enabling soft computing

Especially web-facing software systems enable the collection of usage data at a massive scale. At the same time, the scale and scope of software processes have grown substantively. Automated tools are needed to increase the speed and quality of controlling software processes. The usage data has great potential as a driver for software processes. However, research still lacks constructs for collecting, refining and utilising usage data in controlling software processes. The objective of this paper is to introduce a framework for data-driven software engineering. The Undulate framework covers generating, collecting and utilising usage data from software processes and business processes supported by the software produced. In addition, we define the concepts and process of extreme continuous experimentation as an exemplar of a software engineering process. We derive requirements for the framework from the research literature, with a focus on papers inspired by practical problems. In addition, we apply a multilevel modelling language to describe the concepts related to extreme continuous experimentation. We introduce the Undulate framework and give requirements and provide an overview of the processes of collecting usage data, augmenting it with additional dimensional data, aggregating the data along the dimensions and computing different metrics based on the data and other metrics. The paper represents significant steps inspired by previous research and practical insight towards standardised processes for data-driven software engineering, enabling the application of soft computing and other methods based on artificial intelligence. • We constructed Undulate, a novel framework for systematically collecting data from software and business processes, storing the data in a dimensional database, thus creating a basis for data-driven software engineering. • We defined the general principles of how soft computing can be used to exercise automated control on and provide decision support for software engineering processes, such as continuous experimentation. • We extended and applied a multilevel modelling language to define key concepts in software experimentation to provide a scientifically rigorous conceptual basis for the Undulate framework.