Incoming Stream Research Articles

Opportunities for blue carbon restoration projects in degraded agricultural land of the coastal zone in Queensland, Australia

Restoring degraded agricultural lands to their original coastal wetland cover is an approach for enhancing blue carbon storage. This approach enhances carbon sequestration in biomass and soils whilst reducing greenhouse gas emissions and delivering other conservation benefits such as enhancing biodiversity, improving water quality, and protecting coastlines from sea level rise. In Queensland, Australia, tens of thousands of hectares of coastal land have been converted to agriculture since the 1900s, often through drainage. We evaluated the characteristics of degraded agricultural land to identify opportunities for blue carbon restoration projects. Degraded agricultural land was identified through visual inspection of satellite imagery. Our analysis revealed degradation was associated with historical Melaleuca-dominated wetland vegetation and current land uses other than intensive agriculture. Field sampling of a subset of paired degraded and non-degraded sites found that water content, organic carbon, and electrical conductivity were significantly higher at degraded sites. We also observed standing water, drainage structures, and dead trees at degraded sites. From our analyses, we inferred land degradation is likely caused by waterlogging, salinisation, and land management choices. Degraded land historically vegetated by Melaleuca-dominated wetlands could be targeted for blue carbon restoration projects that protect remaining soil carbon and enhance carbon storage, restore ecosystem services, and provide new income streams for landowners. Further characterisation of the distribution of degraded lands may contribute to prioritisation of sites suitable for restoration.

Pre-arranged sequences of micropillars for passive mixing control of water and ethanol

Microfluidic reactors have enabled the continuous conduction of chemical reactions with exceptional control of fluids and operating conditions. To enhance reagent mixing in laminar flow conditions, the effective strategy of adding cylindrical obstacles (i.e., pillars) in the mixing channel is appealing. Indeed, they can passively extend the contact surface of the incoming fluid streams, where diffusion and reaction occur. From our previous work, optimized sequences of pillars in a T-microchannel – that differ in diameter and position – can significantly improve the mixing for single-phase flow (i.e., mixing of miscible fluids of similar density and viscosity). However, real-world mixing applications often require the merging of fluids with significantly different fluid properties before, during, and after mixing. Hence, further investigations are required to unveil efficiency and robustness in these more complex practical mixing conditions. We jointly carried out experiments and numerical simulations on miscible fluids in an optimized sequence of pillars called IAF. Our analysis shows how the fluid velocity (i.e., the Reynolds number, Re) and the pillar configuration affect the formation of horseshoe vortices, which we consider crucial for water mixing when 1<Re<100. Then, we analyzed water–water and water–ethanol mixtures at Re=40 to observe how fluid properties alter the flow passively controlled by the pillar sequence. Eventually, the analysis extends within Re=10–100, showing efficiency and pressure drops of optimized designs that depend on the evolution of physical properties. These findings provide insights into the predictions of confined fluid flow around obstacles and its optimization for engineering processes, e.g., nanoparticle precipitation.

‘Invested’ partnerships as key to high quality apprenticeship programmes as evidenced in on and off the job training

ABSTRACT In England, a new model of apprenticeship was initiated in whose stated intention was to ‘put employers in the driving seat’. Regulation of the new model was focused on the mandatory 20% of apprentices’ work time allocated to off-the-job training offered by colleges and other training providers. Based on case studies of employers and training providers across a range of industry sectors, this paper brings together data from two projects researching the on-the-job and off-the-job training elements of the new model. Three contrasting modes of apprenticeship emerged: developing apprentices to become experts in an industry-wide community of practice; apprenticeship as staff development; and apprenticeship as income stream. These resulted from different forms of employer-provider collaboration, enabled through a marketized landscape of training providers, and flexibility in regulations that requires very little of employers. Utilising Fuller and Unwin’s work on expansive and restrictive environments, we found that that the new model supports a range of apprenticeships some being excellent but also others that are not worthy of the me. The paper concludes that quality apprenticeships tended to be in traditional industries and relied on strong partnerships between employers and training providers that worked against the grain of competitive market relations.

Periodicity, Subscription, and Mass Circulation: Mail-Order Book Culture Reconsidered

Connections between periodical studies and book history run deep. Building on this&amp;nbsp;existing work, this contribution discusses untapped potential at the cross-section of book&amp;nbsp;studies and periodical studies, especially when considering mass-market periodical and&amp;nbsp;book formats. Several popular magazines, among them&amp;nbsp;Psychology Today&amp;nbsp;and&amp;nbsp;Playboy,&amp;nbsp;created book series and even book clubs, expanding their brands and diversifying their&amp;nbsp;income streams. The article offers a case study of the mass-market book club ‘Reader’s&amp;nbsp;Digest Condensed Books’, launched by the magazine&amp;nbsp;Reader’s Digest&amp;nbsp;in the 1950s. This&amp;nbsp;popular club reached millions of households in the second half of the twentieth century&amp;nbsp;and still continues to play a role in the book industry today with the series ‘Reader’s&amp;nbsp;Digest Select Editions’.

Interactive effects of dual short-chain n-alkanes on removal performances and microbial responses of biotrickling filters

Enhanced biotrickling filtration at the presence of surfactants could be competitive in treatment of waste gas streams polluted by hydrophobic volatile organic compounds (VOCs) such as light hydrocarbons, while few such reports are available. Herein, biotrickling filters (BTFs) were applied to investigate the removal performances for binary-component n-alkanes (n ≤ 6) at the presence of sodium dodecyl benzene sulfonate (SDBS), and the microbial responses were also examined. Interestingly, results showed that the existence of shorter chain n-alkanes promoted the removal of longer chain n-alkanes, as the elimination capacities (EC) of BTF for n-hexane increased by 44 ± 1.2 % when there was methane in incoming gas stream. While the presence of longer chain n-alkanes antagonistically affected shorter chain n-alkane removal, as EC for methane decreased by 65 ± 8.1 % at the presence of n-hexane. Additionally, the presence of SDBS improved the mixed n-alkane removal. EC of the BTF with 15 mg/L SDBS for mixed propane and n-hexane reached 55 ± 1.4 and 31 ± 0.5 g/m3·h respectively, which increased by 90 ± 4.6 % and 47 ± 0.9 % compared with those without SDBS. The richness of Bacillus and Sporosarcina was improved in the BTF fed with SDBS. Moreover, the presence of SDBS improved the content of hydrophobic proteins and the activity of fluorescent substances inextracellular polymeric substances. Also, both the activities of ATPase and the antioxidant capacity of microbial cells exhibited higher than that when SDBS was not added. These data could lead to a better understanding and more reasonable design of biotrickling filtration to remove n-alkanes from waste gas streams of refinery wastewater treatment plants.

Skewed Evolving Data Streams Classification with Actionable Knowledge Extraction using Data Approximation and Adaptive Classification Framework

Skewed evolving data stream (SEDS) classification is a challenging research problem for online streaming data applications. The fundamental challenges in streaming data classification are class imbalance and concept drift. However, recently, either independently or together, the two topics have received enough attention; the data redundancy while performing stream data mining and classification remains unexplored. Moreover, the existing solutions for the classification of SEDSs have focused on solving concept drift and/or class imbalance problems using the sliding window mechanism, which leads to higher computational complexity and data redundancy problems. To end this, we propose a novel Adaptive Data Stream Classification (ADSC) framework for solving the concept drift, class imbalance, and data redundancy problems with higher computational and classification efficiency. Data approximation, adaptive clustering, classification, and actionable knowledge extraction are the major phases of ADSC. For the purpose of approximating unique items in the data stream with data pre-processing during the data approximation phase, we develop the Flajolet Martin (FM) algorithm. The periodically approximated tuples are grouped into distinct classes using an adaptive clustering algorithm to address the problem of concept drift and class imbalance. In the classification phase, the supervised classifiers are employed to classify the unknown incoming data streams into either of the classes discovered by the adaptive clustering algorithm. We then extract the actionable knowledge using classified skewed evolved data stream information for the end user decision-making process. The ADSC framework is empirically assessed utilizing two streaming datasets regarding classification and computing efficiency factors. The experimental results shows the better efficiency of the proposed ADSC framework as compared with existing classification methods.

Smallholder Farming during COVID-19: A Systematic Review Concerning Impacts, Adaptations, Barriers, Policy, and Planning for Future Pandemics

Our broad aim was to systematically analyse research on the effect of COVID-19 on smallholder farming during 2019–2021 and to discuss how the research could be beneficial to smallholder farm resilience to future pandemics. The review methods were based on PRISMA guidelines, and 53 articles were included in the final review. The review aims to document the social-economic impacts on different groups, barriers and opportunities of smallholder farmers adapting to COVID, and policy options. Barriers to adaptations were considered in only 15% of journal articles, suggesting a research gap. This review highlights the fact that, among others, technology access to ensure information and crisis communication that specifically targets smallholders, as well as multi-layered diversification, serves as good predictors of smallholder adaptation to COVID-19. Multi-layered diversification includes product diversification, market diversification and income stream diversification. This confirms the established knowledge in disasters and livelihood studies where diversification of livelihoods portfolio serves as the key factor to resilience against shocks and crisis. Finally, we summarised the different policy implications arising from the literature. This implies that governments must develop an effective policy-mix that leaves no smallholder farmers behind in future pandemics.

Design of a request/response buffering application for I/O intensive workloads

The performance of I/O intensive applications is largely determined by the organization of data and the associated insertion/extraction techniques. In this paper we present the design and implementation of an application that is targeted at managing data received (upto ~ 150 Gb/s payload throughput) into host DRAM, buffering data for several seconds, matched with the DRAM size, before being dropped. All data are validated, processed and indexed. The features extracted from the processing are streamed out to subscribers over the network; in addition, while data resides in the buffer, about 0.1 ‰ of them are served to remote clients upon request. Last but not least, the application must be able to locally persist data at full input speed when instructed to do so. The characteristics of the incoming data stream (fixed or variable rate, fixed or variable payload size) heavily influences the choice of implementation of the buffer management system. The application design promotes the separation of interfaces (concepts) and application oriented specializations (models) that makes it possible to generalize most of the workflows and only requires minimal effort to integrate new data sources. After the description of the application design, we will present the hardware platform used for validation and benchmarking of the software, and the performance results obtained.

StreamDFP: A General Stream Mining Framework for Adaptive Disk Failure Prediction

We explore machine learning for accurately predicting imminent disk failures and hence providing proactive fault tolerance for modern large-scale storage systems. Current disk failure prediction approaches are mostly offline and assume that the disk logs required for training learning models are available a priori. However, disk logs are often continuously generated as an evolving data stream, in which the statistical patterns vary over time (also known as concept drift). Such a challenge motivates the need of online techniques that perform training and prediction on the incoming stream of disk logs in real time, while being adaptive to concept drift. We first measure and demonstrate the existence of concept drift on various disk models in production. Motivated by our study, we design <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamDFP</small> , a general stream mining framework for disk failure prediction with concept-drift adaptation based on three key techniques, namely online labeling, concept-drift-aware training, and general prediction, with a primary objective of supporting various machine learning algorithms. We extend <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamDFP</small> to support online transfer learning for minority disk models with concept-drift adaptation. Our evaluation shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StreamDFP</small> improves the prediction accuracy significantly compared to without concept-drift adaptation under various settings, and achieves reasonably high stream processing performance.

Simulations of multi-rotor interaction noise at hovering &amp; forward flight conditions

A high-fidelity simulation of two in-line counter-rotating propellers in hover, and in forward flight conditions are performed. Near field flow and acoustic properties were resolved using Hybrid LES-Unsteady RANS. Far-field sound predictions were performed using Ffowcs-Williams-Hawkings formulation. The two-propeller results in hovering are compared with that of the single propeller. This enabled us to identify the aerodynamic changes resulting from the proximity of the two propellers to each other and to understand the mechanisms causing the changes in the radiated sound. We then considered the forward flight case and compared it with the corresponding hovering case. This enabled us to identify the aerodynamic changes resulting from the incoming stream. By examining the near acoustic field, the far-field spectra, the Spectral Proper Orthogonal Decomposition, and by conducting periodic averaging, we were able to identify the sources of the changes to the observed tonal and broadband noise.

Speech prosody supports speaker selection and auditory stream segregation in a multi-talker situation

To process speech in a multi-talker environment, listeners need to segregate the mixture of incoming speech streams and focus their attention on one of them. Potentially, speech prosody could aid the segregation of different speakers, the selection of the desired speech stream, and detecting targets within the attended stream. For testing these issues, we recorded behavioral responses and extracted event-related potentials and functional brain networks from electroencephalographic signals recorded while participants listened to two concurrent speech streams, performing a lexical detection and a recognition memory task in parallel. Prosody manipulation was applied to the attended speech stream in one group of participants and to the ignored speech stream in another group. Naturally recorded speech stimuli were either intact, synthetically F0-flattened, or prosodically suppressed by the speaker. Results show that prosody – especially the parsing cues mediated by speech rate – facilitates stream selection, while playing a smaller role in auditory stream segmentation and target detection.

A Phased Approach to Increase Human Tolerance in Elephant Corridors to Link Protected Areas in Southern Mozambique

Pathfinding elephants are moving through human dominated landscapes, often across international boundaries, thereby playing a vital role in connecting protected areas. Their movements are a call to action to not only understand their spatial requirements but to urgently work towards innovative ways to make people’s livelihoods compatible with conservation outcomes so that coexistence and connected landscapes can prevail. We discuss the first three phases of a long-term strategy to conserve elephant corridors whilst incorporating the socio-economic needs of the people that share the landscape with them. We present a comprehensive satellite-tracking history of elephants across two transfrontier conservation areas (TFCA), represented by Great Limpopo- and Lubombo TFCAs and involving four countries (South Africa, Zimbabwe, Mozambique and Eswatini) to flag where linking corridors exist. We use innovative cafeteria-style experiments to understand which elephant-unpalatable plants would offer lucrative alternative income streams to farmers living in human–elephant-conflict hotspots. The most suitable unpalatable plants are chosen based not only on whether they are unpalatable to elephants, but also on their life history traits and growth prerequisites. We consider a combination of potential economic values (food, essential oil, medicinal and bee fodder value) to ensure that selected plants would accommodate changing economic markets. Lastly, we highlight the importance of combining food security measures with ensuring people’s safety by means of deploying rapid-response units. By implementing these three phases as part of a longer-term strategy, we draw closer to ensuring the protection of bioregions to achieve biodiversity objectives at a landscape scale.

The Impact of Tertiary Educators’ Financial Literacy on Saving Behaviour in Zimbabwe and the Moderating Effects of Demographic Factors

Financial literacy is a major issue of concern as itaffects both businesses and individuals globally. Being highly literate has been seen to have considerable effect on the management of interest rates on loans, retirement savings,poverty levels, massive debts, andrisk management. Financial illiteracy impedes economic growth, financial system stability, and income streams at a macro level The studyinvestigated the impact of tertiary educators’financial literacy on saving behaviour and the moderating effects of demographic factors (age, gender, level of education, subjecttaught, and length of service) using Mashonaland Central Provinceas a case study. The research objectives were to measure the level of financial literacy of educators, to determine factors influencing the financial literacyof educators, to determine the saving behaviour of educators and to recommend strategies which enhance the financial literacy of educators. The study adopted a positivism philosophy and an explanatoryresearch design. The target population was made up ofeducators from theuniversitiesand collegesin Mashonaland Central. A sample of 384educators was drawn using random sampling basedonBlacks(2005) sample size formula.Questionnaires were used to collect dataand descriptive statistics and regression analysis wereused to analyse the responses.The study found thateducators’knowledge of financial instruments and computation capabilities had a significant effect on saving behaviour.All demographic factors were found to have no significant impact on the educators’saving behaviours.The study recommends that educators should be trainedon financial literacy, that the ministry of higher and tertiary education should provide a room for further improvement of financial literacy of educators. Areas of further studies includestudying the feasibility of introducing financial literacy in elementary school and the effect of financial educators’ literacyon financial inclusion.

Determination of the location of the ground object of interest using the approximation method of recreating the terrain based on the incoming video stream of the camera of a small-sized UAV

Determination of the location of the ground object of interest using the approximation method of recreating the terrain based on the incoming video stream of the camera of a small-sized UAV

Context-Aware Machine Learning for Intelligent Transportation Systems: A Survey

Context awareness adds intelligence to and enriches data for applications, services and systems while enabling underlying algorithms to sense dynamic changes in incoming data streams. Context-aware machine learning is often adopted in intelligent services by endowing meaning to Internet of Things(IoT)/ubiquitous data. Intelligent transportation systems (ITS) are at the forefront of applying context awareness with marked success. In contrast to non-context-aware machine learning models, context-aware machine learning models often perform better in traffic prediction/classification and are capable of supporting complex and more intelligent ITS decision-making. This paper presents a comprehensive review of recent studies in context-aware machine learning for intelligent transportation, especially focusing on road transportation systems. State-of-the-art techniques are discussed from several perspectives, including contextual data (e.g., location, time, weather, road condition and events), applications (i.e., traffic prediction and decision making), modes (i.e., specialised and general), learning methods (e.g., supervised, unsupervised, semi-supervised and transfer learning). Two main frameworks of context-aware machine learning models are summarised. In addition, open challenges and future research directions of developing context-aware machine learning models for ITS are discussed, and a novel context-aware machine learning layered engine (CAMILLE) architecture is proposed as a potential solution to address identified gaps in the studied body of knowledge.

Financial Innovation and Restriction Hypothesis in the Banking Industry: Evidence from ASEAN- 5

This study investigates the financial innovation impact on bank market power in ASEAN banking from 2008 to 2018. It uses income diversification as a representative of financial innovation. The impact of countries’ development of financial innovation on market power is measured by the number of ATM, internet, and cellular phone users. The data panel regression model reveals that diversified banks may enjoy higher market power. This result rejects the banking restriction activity hypothesis, which states that a bank that diversifies its income stream results in increased competition. A higher number of available ATMs and more internet users lowers the percentage disparity of price and marginal cost and consequently increases the market competitiveness. Nevertheless, an increasing number of cellular users in the country increases market power. Conjecturally, more people use the online bank platform on their cellular phones, which creates a greater flow of fees to the bank.

Exploring environmental technologies for optimizing vehicle management in commercial warehouses

This research article focuses on exploring environmental technologies to optimize vehicle management in commercial warehouses, specifically by studying the nature of incoming streams. The paper investigates key aspects related to supply volume determination, delivery frequency, and timing. The authors conduct a thorough analysis of existing methods for assessing the nature of incoming streams and propose their own approach, utilizing mathematical models. By integrating environmental technologies into vehicle management, this study aims to enhance the efficiency and sustainability of transportation processes in commercial warehouses.

Interaction Between Age Pension Means Testing and Innovative Income Streams in Australia

Interaction Between Age Pension Means Testing and Innovative Income Streams in Australia

ASSESSEMENT OF RESIDENTIAL HOUSING INFRASTRUCTURE AND AFFORDABILITY ALONG IDAH ROAD, ANYIGBA, KOGI STATE

The study carried out an Assessment of `Residential Housing Infrastructure and Affordability along Idah Road, Anyigba, Kogi State. The objectives of the study were to identify the type of residential infrastructure, examine the characteristics of the residential housing infrastructure and to assess the affordability of the residential housing infrastructure in the study area. The study used systematic sampling techniques to administer 384 questionnaires. Frequency distribution tables and percentages were used to analyze the data. The study reveals that majority of the residential housing infrastructure 22.9% is room and palour. Also, the residential housing characteristics indicates that majority of residential housing infrastructure nature of floor furnishing is ceramic marble tile 79.4%, nature of walling material is normal bricks 69.2% and nature of roofing material is corrugated metal zinc sheets 63.0%. Furthermore, the study shows that majority of respondents 35.2% pay rents of #81,000 - # 100,000. Likewise, about 84.1% spent above 30% bench mark for housing affordability. Equally, majority of the respondents 48.6% agreed that the residential housing infrastructure is moderate and the general condition of the residential housing infrastructure in the study area is fair with 51.2%. The study recommends that they should be general upgrading of the residential housing infrastructure and that civil servant need to get other stream of income in other to be able to pay their rents as at when due.

THE NEOLIBERAL APPROACH TO CORPORATE SOCIAL RESPONSIBILITY AND ECONOMIC PATRIOTISM

The problem of economic security should be considered as an organized development of the economy and the need to ensure an appropriate level of enterprise efficiency and quality of life from advanced technology, intelligent technology, incoming stream, and the proper flow of information. Neoliberals can make it so that states should implement the no-disturbance principle; this will have the entrepreneurial realization effect. One needs to find out if there is still room for charity and trustees in the global world. Is it only meeting the main comments about the development of individual entrepreneurship, which comes from energy resources?