Incoming Stream Research Articles

The moral economy of land: from land reform to ownership society, 1880–2018

Abstract This article offers a comparative-historical perspective on the moral economy of land. We reconstruct the moral economy of the popular land reform movement that opposed the illegitimate income streams of rentiers and speculators in the early 20th century, tracing the movement’s legacy through a long-run analysis of political party platforms since 1880 in the USA, the UK, Germany and Sweden. We find that the land reformers’ conceptualization of land as a moral good was a key topic in early 20th-century party politics. Parties across the political spectrum called for wide-ranging interventions in unregulated land markets. But despite the movement’s relative success, the new ideal of the ownership society soon gained ground as an alternative to the more radical politics of land decommodification. We find growing multipartisan support for small property owners over time, culminating in the rise of a new moral conceptualization of land as capital. With the recent comeback of the land question, both rural and urban, we conclude that an understanding of historical land reform debates should inform future research toward a much-needed sociology of land.

Analysis of the Relationship Between Fiscal Deficits and Debt Accumulation in Zambia

Zambia has experienced structural deficits for decades and these have been viewed as the main cause of debt accumulation. Despite heavy investment in infrastructure and other sectors, the country faces low revenue relative to rising cost of debt. Objective: to establish whether deficit financing is the cause of high debt in Zambia. Method: Using secondary data and an archival strategy, data was analyzed through a mixed research choice which required both qualitative and quantitative data to be presented together. This involved using descriptive and analytical approaches. Findings: It was found that Zambia cannot survive without borrowing and this is the main cause for debt accumulation. The reason is that investments do not adequately contribute towards covering debt servicing costs. The country’s reliance on copper exports is the main reason for continuous deficits as the country fails to diversify the economy in order to increase income streams. In addition, Zambia generates growth which is not sufficient to cover the debt servicing burden. Conclusions and Recommendations: The study concluded that Zambia needs to control it’s key resource, mining, to ensure that proceeds are maximized and invested into agriculture and in boosting the manufacturing sector. The country needs to focus on generating the fiscal surplus of 1.32 of GDP to make the debt ratio of 60 percent to be sustainable.

Detecting and Real Time Threat Analysis in Smart Grid Networks

The increasing adoption of smart grid systems has revolutionized the power distribution landscape, providing enhanced efficiency, reliability, and sustainability. However, the integration of modern technologies and communication networks has also introduced cybersecurity challenges, exposing the smart grid to potential threats and attacks. To address this critical issue, we propose a novel approach for detecting threats in smart grid systems using a sequential algorithm based on Long Short-Term Memory (LSTM) networks. Our research focuses on leveraging the temporal nature of smart grid data, which is inherently sequential in nature due to the continuous flow of sensor readings and telemetry information. The proposed LSTM-based sequential algorithm is designed to analyze time-series data, allowing it to capture complex patterns and dependencies characteristic of both normal grid behavior and potential security breaches. The methodology involves collecting labeled data from various smart grid sources, including power meters, sensors, communication devices, and control systems. The data undergoes preprocessing to handle missing values, normalization, and feature extraction, enabling effective model training. The LSTM model is then trained on the labeled data, optimizing its architecture and hyperparameters to achieve accurate threat detection. To ensure real-time threat monitoring, the trained model is deployed in the smart grid servers, where it continuously ingests and analyzes incoming data streams. The real-time threat detection system enhances the security and resilience of smart grid networks, safeguarding critical infrastructure and ensuring a stable and reliable power supply. Keywords: Threat detection, Threat analysis, Sequential algorithm, Real time threat monitoring.

Investigating the relationship between research income and research excellence in education: Evidence from the REF2021‐UoA23 data

AbstractThis paper presents and discusses the relationship between the generation of research income and the REF2021 results for universities who submitted a return to Unit of Assessment 23—Education. Research Excellence Framework exercises tend to be time intensive and come with income streams of Government Quality Research funding as well as reputational gains and risks. Based on REF2021 research environment research income data, freely available on the REF2021 website, analyses have been undertaken to investigate the strength of relationships between different types of fundings streams and grade point averages. The results demonstrate strong variation in funding capture across the different universities with Russell Group members tending to generate more income from prestigious funders such as UK Research and Innovation. However, several post‐1992 universities managed to capture significant income from funders like UK and EU governments, without this necessarily translating into higher grade point averages or 4* scores. While generating research income is perceived as an important part of academic life, the paper concludes that achieving research excellence seems more complicated than just following the money.

Anchored and Lifted Diffusion Flames Supported by Symmetric and Asymmetric Edge Flames

Numerous combustion applications are concerned with the stabilization of diffusion flames formed by injecting gaseous fuels into a co-flowing stream containing an oxidizer. The smooth operation of these devices depends on the attachment and lift-off characteristics of the edge flame at the base of the diffusion flame. In this paper, we address fundamental issues pertinent to the structure and dynamics of edge flames, which have attributes of both premixed and diffusion flames. The adopted configuration is the mixing layer established in the wake of a splitter plate where two streams, one containing fuel and the other oxidizer, merge. The analysis employs a diffusive-thermal model which, although it excludes effects of gas expansion, systematically includes the influences of the overall flow rate, unequal strain rates in the incoming streams, stoichiometry, differential and preferential diffusion, heat loss and gas–solid thermal interaction, and their effect on the edge structure, speed, and temperature. Conditions when the edge flame is anchored to the plate, lifted-off and stabilized in the flow, or blown-off, are identified. Two stable modes of stabilization are observed for lifted flames; the edge flame either remains stationary at a specified location or undergoes spontaneous oscillations along a direction that coincides with the trailing diffusion flame.

Automated Seizure Detection in Neonatal EEG using Signal Processing Algorithms

The crucial challenge of identifying seizures in newborn electroencephalogram (EEG) data is the subject of this research. (ElectroEncephaloGram) EEG analysis has traditionally been labor-intensive and subjective due to the heavy reliance on human inspection by skilled electrophysiologists and neurologists. The authors suggest a novel online automatic detection approach to get over these restrictions. This approach intends to open the door for effective and reliable early seizure detection, delivering a potential development in neonatal care and neurological research. It does this by utilizing the capabilities of signal processing and brain-inspired modeling’s suggested machine-learning method makes effective use of real-time EEG signal processing methods. Its processing of incoming EEG data streams makes it appropriate for neonates to be closely tracked in critical care situations. Automation of the detection method minimizes the need for manual inspection by highly specialized experts, allowing for more rapid responses and possibly increasing seizure management's overall results. Using metal electrodes, the electroencephalogram (EEG) analyses the electrical activity of the brain. European Data Format (EDF) BROWSER and EEG STUDIO are used to analyses EEG signals. Root Mean Square (RMS) and signal samples are performed by EDF BROWSER, as well as mean, frequency, and frequency from filtered output using a band-pass filter. The standard deviation and average frequency are examined by EEG STUDIO. Spike detection, frequency domain analysis, and nonlinear techniques are used in seizure prediction. EOG, EKG, and EMG artifacts produced by the eyes, the heart, and the muscles, respectively may be present in EEG readings.

Effect of Different Levels of Inorganic Fertilizers on Growth and Yield of Beetroot under Poplar Based Agroforestry System

Agroforestry play crucial role in satisfying the need for industrial round wood and reaching the national aim of bringing one-third of total country’s land under forest/tree cover. The risk of losing money on crops due to poor monsoon conditions is mitigated, and farmers receive a steady stream of additional income as a result. Beet root is a highly productive, popular root vegetable grown mainly for its fleshy, enlarged roots prefers moderately cool condition. Furthermore, it could make a significant impact on the national initiative to double farmers' income growing agricultural crops under with spatial and temporal management. Present experiment was laid out to assess the effect of different levels of inorganic fertilizers on growth and yield of Beetroot under Poplar based Agroforestry in Randomized Block Design (RBD) with ten treatments and three replications viz. T0 – (70:110:70 kg NPK / ha), T1 –(100:110:70 kg NPK / ha), T2– (130:110:70 kg NPK / ha), T3–(160:110:70 kg NPK / ha), T4 – (70:130:70 kg NPK / ha), T5 –(70:150:70 kg NPK / ha), T6– (70:180:70 kg NPK / ha), T7– (70:110:80 kg NPK / ha), T8– (70:110:90 kg NPK/ha), T9–(70:110:100 kg NPK / ha). Findings of experiment show that fertilizer Treatment has a significant influence on the growth and yield of Beetroot. The yield parameters of beetroot were significantly affected due to different inorganic nutrient management and comparatively higher tuber yield (307.67 q/ha) was recorded in Treatment T6 (70:180:70kg NPK/ha).

Exclusion by design: a case study of an Indian urban housing subsidy scheme

Abstract The Pradhan Mantri Awas Yojana (Urban)—Housing for All mission (PMAY-U), a flagship mission of the Government of India, aims to address the need for affordable housing in urban areas through five different schemes. One of these schemes is a housing subsidy scheme, the Credit Linked Subsidy Scheme (CLSS), which has significantly contributed to the success of PMAY-U. However, the design of the CLSS scheme favors households considered creditworthy, with stable and secure income streams. This article examines the gap between the policy design and practice of the CLSS scheme to explore how biases get embedded into the policy, resulting in the exclusion of economically vulnerable households. Schneider and Ingram’s Social Construction of Target Population (SCTP) framework is used to identify the target groups involved in the CLSS policy chain. These target groups and policymakers were interviewed to understand their interpretations of the concept of affordable housing. Using a relational lens, these interpretations are compared to know how the meanings of affordable housing get represented within CLSS policy documents. The analysis presents two key insights. First, the power and interests of the target groups predict their representation in policy design and policymaking. Second, privatized implementation design of the subsidy scheme embeds negative selectivism creating exclusionary tendencies in the CLSS design. Lastly, given the shrinking of the welfare state across the globe, this study raises the critical question of “who benefits and who loses?” while challenging the normative aspects of the policy goal of affordable housing.

Children with developmental dyslexia have equivalent audiovisual speech perception performance but their perceptual weights differ.

As reading is inherently a multisensory, audiovisual (AV) process where visual symbols (i.e., letters) are connected to speech sounds, the question has been raised whether individuals with reading difficulties, like children with developmental dyslexia (DD), have broader impairments in multisensory processing. This question has been posed before, yet it remains unanswered due to (a) the complexity and contentious etiology of DD along with (b) lack of consensus on developmentally appropriate AV processing tasks. We created an ecologically valid task for measuring multisensory AV processing by leveraging the natural phenomenon that speech perception improves when listeners are provided visual information from mouth movements (particularly when the auditory signal is degraded). We designed this AV processing task with low cognitive and linguistic demands such that children with and without DD would have equal unimodal (auditory and visual) performance. We then collected data in a group of 135 children (age 6.5-15) with an AV speech perception task to answer the following questions: (1) How do AV speech perception benefits manifest in children, with and without DD? (2) Do children all use the same perceptual weights to create AV speech perception benefits, and (3) what is the role of phonological processing in AV speech perception? We show that children with and without DD have equal AV speech perception benefits on this task, but that children with DD rely less on auditory processing in more difficult listening situations to create these benefits and weigh both incoming information streams differently. Lastly, any reported differences in speech perception in children with DD might be better explained by differences in phonological processing than differences in reading skills. RESEARCH HIGHLIGHTS: Children with versus without developmental dyslexia have equal audiovisual speech perception benefits, regardless of their phonological awareness or reading skills. Children with developmental dyslexia rely less on auditory performance to create audiovisual speech perception benefits. Individual differences in speech perception in children might be better explained by differences in phonological processing than differences in reading skills.

Compensation Shifting from Salary to Dividends

This paper examines whether owner-managers of small firms use their compensation strategically to change reported earnings. We identify an institutional setting, Denmark, in which the owner-manager has the discretion to shift compensation from salary to dividends and hence increase reported earnings at almost no direct cost due to approximate tax neutrality between the two income streams. Three findings emerge. First, owner-managers are twice as likely to decrease their salary when doing so can result in meeting or beating the zero earnings benchmark. Second, those decreasing their salaries to beat the benchmark are 45% more likely to increase dividends simultaneously than those who can beat the benchmark but do not. This indicates that reporting incentives shape compensation shifting. Third, owner-managed firms enjoy about 6% (EUR 1070) lower interest rates (interest expenses), than firms reporting losses, when they beat the benchmark by simultaneously decreasing salaries and increasing dividends. Our results highlight that owner-managers can manage reported earnings by altering their own compensation, which has economic consequences for the firm. This has implications for users of owner-managed firms’ financial reports because reported earnings would seem a poor contracting signal for these firms.

Extensive representation of sensory deviance in the responses to auditory gaps in unanesthetized rats.

Unexpected changes in incoming sensory streams are associated with large errors in predicting the deviant stimulus relative to a memory trace of past stimuli. Mismatch negativity (MMN) in human studies and the release from stimulus-specific adaptation (SSA) in animal models correlate with prediction errors and deviance detection.1 In human studies, violation of expectations elicited by an unexpected stimulus omission resulted in an omission MMN.2,3,4,5 These responses are evoked after the expected occurrence time of the omitted stimulus, implying that they reflect the violation of a temporal expectancy.6 Because they are often time locked to the end of the omitted stimulus,4,6,7 they resemble off responses. Indeed, suppression of cortical activity after the termination of the gap disrupts gap detection, suggesting an essential role for offset responses.8 Here, we demonstrate that brief gaps in short noise bursts in the auditory cortex of unanesthetized rats frequently evoke offset responses. Importantly, we show that omission responses are elicited when these gaps are expected but are omitted. These omission responses, together with the release from SSA of both onset and offset responses to rare gaps, form a rich and varied representation of prediction-related signals in the auditory cortex of unanesthetized rats, extending substantially and refining the representations described previously in anesthetized rats.

Digital social security accounts for platform workers: The case of Estonia’s entrepreneur account

AbstractAdvancements in technology enable new opportunities for creating digital social security accounts, but the effectiveness of these to solve the accessibility and eligibility issues facing platform workers has not been assessed fully in the literature. The potential of digital social security accounts lies in their ability to consider the possible different streams of income of atypical workers and to improve the effective access of these workers to social security. Tax and social security offices can now exchange information on the income of platform workers in real time, which offers the promise of formalizing the previously informal casual work relationships of the self‐employed. This article explores the case of the Estonian entrepreneur account as a digital hybrid solution for improving the effective access to social security of platform workers. Digital portable accounts create the conditions for the structural improvement required to respond adequately to meet the changing social security needs of atypical workers. However, this also requires that the policy design be thought through carefully, to avoid digital portable accounts being simply a digital facilitator of outdated solutions.

Proposal for calculating regulatory capital requirements for reverse mortgages

In the current context in which many people worry about the sustainability of pension systems, reverse mortgages are gaining popularity because they are a way to supplement elderly people's incomes. However, it is necessary to provide banks with an adequate risk measurement and management procedure for reverse mortgages to increase the commercialization of these products, which will result in greater well-being for the retirement age population. In this paper, we propose a method to measure risk and estimate the regulatory capital requirements for a portfolio of reverse mortgages owned by a financial institution according to Basel II and III. The method considers house price risk, mortality risk and interest rate risk; consequently, regulatory capital requirements need to be computed using a Monte Carlo simulation procedure. The proposed method is general and can accommodate several scenarios for reverse mortgage specifications, including fixed or variable mortgage rates and different income stream schemes (with the lump sum as a particular case). The results for the U.K. show that reverse mortgage providers face higher risk when the lender initially advances a higher amount, with the lump-sum case indicating the highest risk, for relatively younger borrowers, the female population, higher interest rates and floating mortgage rates.

Stream Aggregation with Compressed Sliding Windows

High performance stream aggregation is critical for many emerging applications that analyze massive volumes of data. Incoming data needs to be stored in a sliding window during processing, in case the aggregation functions cannot be computed incrementally. Updating the window with new incoming values and reading it to feed the aggregation functions are the two primary steps in stream aggregation. Although window updates can be supported efficiently using multi-level queues, frequent window aggregations remain a performance bottleneck as they put tremendous pressure on the memory bandwidth and capacity. This article addresses this problem by enhancing StreamZip, a dataflow stream aggregation engine that is able to compress the sliding windows. StreamZip deals with a number of data and control dependency challenges to integrate a compressor in the stream aggregation pipeline and alleviate the memory pressure posed by frequent aggregations. In addition, StreamZip incorporates a caching mechanism for dealing with skewed-key distributions in the incoming data stream. In doing so, StreamZip offers higher throughput as well as larger effective window capacity to support larger problems. StreamZip supports diverse compression algorithms offering both lossless and lossy compression to integers as well as floating-point numbers. Compared to designs without compression, StreamZip lossless and lossy designs achieve up to 7.5× and 22× higher throughput, while improving the effective memory capacity by up to 5× and 23×, respectively.

Financial Viability of Business Models For Engineered Vertical Hydroponics Systems For Sustainable Onion Production in The Philippines

This study aimed to explore the financial, socio-economic, and environmental benefits of sustainable onion production using vertical farming and hydroponic systems, and to identify key factors affecting the viability of these business models. Data were collected through a survey of onion farmers and producers in the Philippines, and analyzed using descriptive and inferential statistics. Results showed that sustainable onion production using vertical farming and hydroponic systems has the potential to generate higher income for farmers, increase employment opportunities, improve food security, enhance market competitiveness, and promote environmental sustainability. Key financial factors affecting viability included production costs, market prices, yield, labor costs, energy costs, capital costs, financing costs, taxes and regulatory costs, and maintenance and repair costs. Recommendations for optimizing financial viability include reducing production costs, diversifying income streams, and improving market competitiveness. Overall, this study suggests that sustainable onion production using vertical farming and hydroponic systems is a viable and promising approach to achieving socio-economic and environmental sustainability in the agricultural sector.

Why confidence leads to swifter tertiary education choices: A qualitative study of the international tertiary education industry of Australia

This paper investigates international students' confidence as a base for imminent tertiary education decisions. International students are a highly sought-after commodity, particularly during and after a global pandemic when the income streams of tertiary education providers are limited. In-depth interviews were conducted with students who sought guidance towards an international study experience to explore the guiding research questions, (1) how does confidence impact tertiary education decisions of international students and (2) what is the relationship between confidence and time to make a tertiary education decision? Set within the context of the international tertiary education industry of Australia, the original contribution stems from identifying that guidance towards an international study experience is affected by confidence in; guidance counselors, the brand name of the university, and the tertiary education decision itself. The identified confidence characteristics in this study have an inverse relationship with the length of time taken for the students' decision-making process. This leads to swifter finalizations of tertiary education decisions by students increasing returns on education providers’ admission activities.

Environmental Co‐Benefits of Maintaining Native Vegetation With Solar Photovoltaic Infrastructure

AbstractCo‐locating solar photovoltaics with vegetation could provide a sustainable solution to meeting growing food and energy demands. However, studies quantifying multiple co‐benefits resulting from maintaining vegetation at utility‐scale solar power plants are limited. We monitored the microclimate, soil moisture, panel temperature, electricity generation and soil properties at a utility‐scale solar facility in a continental climate with different site management practices. The compounding effect of photovoltaic arrays and vegetation may homogenize soil moisture distribution and provide greater soil temperature buffer against extreme temperatures. The vegetated solar areas had significantly higher soil moisture, carbon, and other nutrients compared to bare solar areas. Agrivoltaics in agricultural areas with carbon debt can be an effective climate mitigation strategy along with revitalizing agricultural soils, generating income streams from fallow land, and providing pollinator habitats. However, the benefits of vegetation cooling effects on electricity generation are rather site‐specific and depend on the background climate and soil properties. Overall, our findings provide foundational data for site preservation along with targeting site‐specific co‐benefits, and for developing climate resilient and resource conserving agrivoltaic systems.

IoT-based intrusion detection system for healthcare using RNNBiLSTM deep learning strategy with custom features

The need for security in healthcare environments has become increasingly important due to the rise of cyber-attacks and data breaches. In order to address this issue, this paper proposes an Internet of Things (IoT)-based intrusion detection system for healthcare using a deep learning strategy with custom features. This paper proposes an IoT-based intrusion detection system for healthcare using a deep learning strategy with custom features. The proposed system utilizes the IoT technology to gather real-time data from various medical devices and sensors deployed in a healthcare environment. The system incorporates a recurrent neural network (RNN) and a bidirectional long short-term memory (BiLSTM) algorithm to detect and classify intrusion attempts. The custom features are extracted from the incoming data streams and used to train the deep learning models. The proposed system is evaluated on a dataset comprising different types of intrusion scenarios, and it achieves an accuracy of 99.16%, error rate of 0.008371%, sensitivity ratio of 99.89% and specificity ratio of 98.203% for IoTID20 with custom features using RNNBiLSTM. The results demonstrate the effectiveness of the proposed system in detecting and mitigating security threats in a healthcare environment. The system has the potential to improve patient privacy and security, ultimately leading to better healthcare outcomes.

MONEY FOR NOTHING?: CAN NFTS SOLVE MUSICIANS’ MONETIZATION PROBLEM?

Despite the immeasurable value music provides society, finding ways to monetize their music is often an elusive and challenging prospect for musicians. The music industry has evolved into a consolidated “hits market” in which profits are highly concentrated in a small set of intermediaries and relatively few superstars. This “hits market” not only makes it incredibly difficult for most musicians to make a living with their music, it also fails to capture and compensate musicians who aren’t extremely popular for the significant value they create. In the face of this deadweight loss, non-fungible tokens (NFTs) could be a means of disrupting the economic status quo and creating a superior set of economic incentives for musicians. This Article is the first in the legal literature dedicated to evaluating the viability of NFTs as an additional income stream for musicians. After detailing the economics of the traditional music industry and providing a framework for understanding NFTs’ asserted value, this Article considers constraints imposed by contractual obligations and copyright law to analyze NFTs’ potential to transform music monetization. Ultimately, this Article concludes that, notwithstanding their limitations, NFTs are likely to be an important new source of revenue for musicians who have been left behind by the popularity-driven economic incentives of the traditional music industry.

Strategies for the Growth of Radio and Television in China amid Media Convergence

Chinese radio and television have evolved as a consequence of the government's efforts to censor content and tighten its grip on the media industry. Independent media company expansion has been constrained as a result, strengthening governmental control over the media industry. The growth of Chinese radio and television has also been influenced by media convergence and the blending of new media technologies with established media platforms. However, attempts by the government to censor information have stunted the expansion of independent media outlets and strengthened the government's hold on the media industry. Through this integration, established media businesses like SMG have been able to reach a wider and more varied audience and develop new income streams from internet services and e-commerce. Additionally, by minimizing duplication of effort and facilitating resource sharing, media convergence has increased the effectiveness of traditional media firms. This article explained the principles of the Chinese government has implemented strict regulations and policies to control and shape the media landscape, which has resulted in a high degree of consolidation and centralization. For instance, the government has limited the number of licenses issued to television and radio stations, with many stations being owned or controlled by state-owned enterprises. This has led to a high degree of control over the content that is broadcast, with the government heavily censoring programming that is deemed to be politically sensitive or that contradicts the official narrative.