Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards an Integrated Approach to Urban Decarbonisation in Practice: The Case of Vitoria-Gasteiz

How can local authorities effectively approach the decarbonisation of urban environments? Recent efforts to redirect cities into a less energy-intensive model have been mostly approached from a sectoral perspective, with specific energy policies and plans being issued without deeply considering their ties with other urban aspects. In this sense, well-established urban planning procedures have not been part of those, with the consequence of barriers in the implementation phase of those energy plans. The Cities4ZERO methodology was developed to guide effective integration between urban planning and energy policies, plans, and practices. It provides a holistic approach to strategic municipal processes for urban decarbonisation in the mid-long term, which includes key local stakeholders’ engagement into integrated energy planning processes, as well as tools for effective energy decarbonisation modelling. This paper analyses the application of the Cities4ZERO decarbonisation methodology on its strategic stage in the development of Vitoria-Gasteiz’s Action Plan for an Integrated Energy Transition 2030 (APIET 2030). It suggests that in order to accelerate urban decarbonisation, it is critical to: (a) foster interdepartmental collaboration; (b) allow for flexibility on the land-use planning regulations; (c) back decisions with detailed urban-energy models; and (d) truly engage key local stakeholders in the planning and implementation processes.
Sanitation Upgrading as Climate Action: Lessons for Local Government from a Community Informal Settlement Project in Cape Town

In light of the increasing call for climate action, there is a growing body of literature studying the ways in which informal settlements in the Global South are adapting to the impacts of climate change. In these particularly vulnerable communities where the existing infrastructural vulnerabilities faced by residents are exacerbated by the hazards of climate change, multi-level approaches involving more inclusive forms of governance are needed for the implementation of climate action. Drawing from the case of a sanitation upgrading project in the informal settlement of Murray, located in Philippi, Cape Town, this paper adds to current understandings of multi-level rapid climate action in informal settlements by endeavouring to address two gaps in this body of literature. Firstly, this paper demonstrates a link between climate change and sanitation which has received little attention by showing that improving sanitation infrastructure makes communities more resilient to extreme weather events associated with climate change. Secondly, the paper addresses how and by whom rapid climate action can be implemented in complex socio-institutional contexts such as informal settlements where the impacts of climate change are felt particularly strongly. This paper identifies what enabled and constrained climate action in the Murray informal settlement in an attempt to provide lessons for local government from the case of the sanitation upgrading project. Bottom-up initiation of multi-level climate action is dependent on fragile partnerships which require the support and involvement of a skilled and dedicated local government. Nevertheless, co-operative and transparent engagements across levels hold the potential to contribute to transformative adaptation through the establishment of new partnerships and forms of governance which recognise community groups as legitimate stakeholders and acknowledge the importance of lived experiences and mentalities.
Quantifying Contributions of External Drivers to the Global Ionospheric State

To advance our understanding of the global response of the ionospheric total electron content (TEC) to various external drivers, we apply entropy‐based information theory to quantify contributions of solar, interplanetary, and lower atmospheric drivers to the global ionospheric state described by the Global Ionospheric Map (GIM). By computing normalized transfer entropy on 18 years of GIM TEC, F10.7, solar wind, and lower atmospheric data, we obtain the predictive information transfer from the present drivers to the global ionospheric state at various future times. We find that the solar extreme ultraviolet (EUV) irradiance dominates the information transfer within 3 days into the future, while the lower atmospheric migrating tidal sources dominate beyond 3 days into the future. The maximum information transfer from individual drivers reveals the maximum contributions from the drivers to the global ionospheric state. Among all drivers considered, the lower atmospheric migrating tidal sources contribute most. The solar EUV irradiance and tropospheric deep convection are secondary drivers with comparable contributions at about half of the contribution from migrating tidal sources. The interplanetary driving contributes the least to the global ionospheric state during both geomagnetic storm and quiet time, even though the contribution from the interplanetary magnetic field can enhance four to six times during geomagnetic storm time than quiet time.
Subjective health, well-being and cognitive capabilities

The existence of the relationship between poverty and poor health has been known for ages, although the actual mechanisms that connect poverty to health have not been well understood. A growing body of literature indicates that unemployment (which often goes hand in hand with poverty) can pose substantial health risks by negatively affecting, in particular, the mental health, psychological well-being and cognitive capabilities of those who experience it (for example, Acevedo et al., 2020; Wahrendorf et al., 2019; Wanberg, 2012; Pelzer et al., 2014; Van der Noordt et al., 2014; Kim and von dem Knesebeck, 2016). Moreover, we know that the psychological, social and economic dimensions of well-being are strongly interconnected. Unemployment usually leads to a lack of financial resources, and financial resources serve as an important determinant of well-being (Paul and Moser, 2009). Unemployment can also decrease life satisfaction and happiness by means other than income, such as reducing social participation (Kunze and Suppa, 2017). From a practical perspective, whether we can govern the complex relationship between poverty and well-being through a policy action is an important question. Unconditional cash transfers (UCT) are one potential group of policy instruments for organising social security. UCTs are regular cash transfers that are paid without screening for eligibility and without income-testing or means-testing. Earlier studies have indicated that the introduction of a UCT policy could improve population health. For example, Forget (2011) reports positive results from a Canadian experiment with a guaranteed annual income. Another body of evidence suggests that UCTs may improve some health outcomes in low and middle-income countries, although the relative effectiveness of UCTs and conditional cash transfer programmes (CCT) remains uncertain (for example, Costello et al., 2003; Davala et al., 2015; Pega et al., 2017).
Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues

Passive systems are fundamental for the safe development of Nuclear Power Plant (NPP) technology. The accurate assessment of their reliability is crucial for their use in the nuclear industry. In this paper, we present a review of the approaches and procedures for the reliability assessment of passive systems. We complete the work by discussing the pending open issues, in particular with respect to the need of novel sensitivity analysis methods, the role of empirical modelling and the integration of passive safety systems assessment in the (static/dynamic) Probabilistic Safety Assessment (PSA) framework.
Assessing digital maturity of schools: framework and instrument

ABSTRACT The European Commission emphasises the need for educational institutions to integrate digital technologies in their teaching, learning and organisational practices. This study contributes to the field of digital transformation of schools by proposing and validating a Framework for Digitally Mature Schools (FDMS) and an instrument for assessing the level of schools’ digital maturity. The FDMS and the instrument recognise five areas divided into 38 elements described across five different levels in the form of a rubric. This assessment instrument enables schools to measure their current level of digital maturity and pinpoint the areas that require improvements. The developed Framework and instrument support educational institutions in the assessment, promotion and integration of digital technologies in their teaching, learning and organisational practices.
The HIV cascade of care among serodiscordant couples in four high HIV prevalence settings in sub-Saharan Africa.

BACKGROUND HIV-serodiscordant couples are at high risk of HIV transmission. In sub-Saharan Africa, HIV-serodiscordant couples contribute ~30% of all new infections in the region. OBJECTIVES To quantify the prevalence of HIV-serodiscordant couples and evaluate steps of the HIV cascade of care among people living with HIV in serodiscordant relationships in four high-prevalence settings in sub-Saharan Africa. METHODS Four HIV prevalence surveys were conducted: in Ndhiwa (Kenya) in 2012, in Chiradzulu (Malawi) in 2013, and in Gutu (Zimbabwe) and Nsanje (Malawi) in 2016. Eligible individuals aged 15 - 59 years were asked to participate in voluntary rapid HIV testing. Viral load and CD4 counts were measured on those who tested HIV-positive. A couple was defined as a man and a woman who reported being married or cohabiting and were living together in the same household. RESULTS Among 4 385 couples, the prevalence of HIV serodiscordancy was 10.9% (95% confidence interval (CI) 10.2 - 11.5) overall, ranging from 6.7% (95% CI 5.6 - 7.9) in Nsanje to 15.8% (95% CI 14.5 - 17.3) in Ndhiwa. Men were the HIV-positive partner in 62.7% of the serodiscordant couples in Ndhiwa, in 60.4% in Gutu, in 48.8% in Chiradzulu and in 50.9% in Nsanje. Status awareness among HIV-positive partners in serodiscordant couples ranged from 45.4% in Ndhiwa to 70.7% in Gutu. Viral load suppression (VLS) ranged from 33.9% in Ndhiwa to 68.5% in Nsanje. VLS was similar by sex in three settings, Ndhiwa (37.8% (men) v. 27.8% (women); p=0.16), Nsanje (60.7% v. 76.9%; p=0.21) and Gutu (48.2% v. 55.6%; p=0.63), and dissimilar by sex in Chiradzulu (44.4% v. 62.7%; p=0.03). CONCLUSIONS Low HIV status awareness and poor VLS among HIV-positive partners are major gaps in preventing transmission among serodiscordant couples. Intensifying programmes that target couples to test for HIV and timely antiretroviral therapy initiation could increase VLS and reduce HIV transmission.
Advantages of Spike and Slab Priors for Detecting Differential Item Functioning Relative to Other Bayesian Regularizing Priors and Frequentist Lasso

ABSTRACT An important step in scale development and assessment is to evaluate differential item functioning (DIF) across segments of the population. Recent approaches use lasso regularization to simultaneously detect DIF in all items and avoid incorrect anchor item assumptions that incur inflated error rates for classical DIF evaluation methods. Although promising, lasso methods cause underestimated standard errors and incorrect p-values. An alternative is Bayesian regularization that provides empirical standard errors. However, we point out that using empirical criteria such as credible intervals for selecting DIF parameters has limited validity. We argue that using a spike-and-slab prior with an inclusion probability criterion provides more theoretically coherent DIF selection and inference over Bayesian regularizing priors with empirical selection rules or frequentist lasso. We demonstrate this by simulation studies with Multi-group Item Response Theory and Moderated Nonlinear Factor Analysis models. Practical utility of the spike-and-slab prior selection criterion is discussed.
Real-Time Control Strategy of Fuel Cell and Battery System for Electric Hybrid Boat Application

In this paper, an effective control strategy is proposed to manage energy distribution from fuel cells and batteries for hybrid electric boat applications. The main objectives of this real-time control are to obtain fast current tracking for the batteries’ system, the DC bus voltage stability by using a fuel cell, and energy load distribution for a hybrid electric boat under varying demand conditions. The proposed control strategy is based on a combination of frequency approach and current/voltage control of interleaved boost converters to reduce the hydrogen consumption by the fuel cell and improve the quality of energy transfer. The frequency approach was dedicated to managing the DC power-sharing between the load, the fuel cell, and the batteries’ storage system by extracting the power references. The closed loop control system utilized to control the energy is based on the DC/DC converters. The performance evaluation of the proposed control strategy has been tested through a real-time experimental test bench based on a dSPACE board (DS1104).
Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research

Along with essential nutrients and trace elements, vegetables provide raw materials for the food processing industry. Despite this, plant diseases and unfavorable weather patterns continue to threaten the delicate balance between vegetable production and consumption. It is critical to utilize machine learning (ML) in this setting because it provides context for decision-making related to breeding goals. Cutting-edge technologies for crop genome sequencing and phenotyping, combined with advances in computer science, are currently fueling a revolution in vegetable science and technology. Additionally, various ML techniques such as prediction, classification, and clustering are frequently used to forecast vegetable crop production in the field. In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly; whereas, in plant disease detection and management, the ML approaches can improve decision-support systems that assist in converting massive amounts of data into valuable recommendations. On similar lines, in vegetable breeding, ML approaches are helpful in predicting treatment results, such as what will happen if a gene is silenced. Furthermore, ML approaches can be a saviour to insufficient coverage and noisy data generated using various omics platforms. This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing.
Atmospheric Contamination of Coastal Cities by the Exhaust Emissions of Docked Marine Vessels: The Case of Tromsø

Docked ships are a source of contamination for the city while they keep their engine working. Plumes emissions from large boats can carry a number of pollutants to the nearby cities causing a detrimental effect on the life quality and health of local citizens and ecosystems. A computational fluid dynamics model of the harbour area of Tromsø has been built in order to model the deposition of CO2 gas emitted by docked vessels within the city. The ground level distribution of the emitted gas has been obtained and the influence of the wind speed and direction, vessel chimney height, ambient temperature and exhaust gas temperature has been studied. The deposition range is found to be the largest when the wind speed is low. At high wind speeds, the deposition of pollutants along the wind direction is enhanced and spots of high pollutant concentration can be created. The simulation model is intended for the detailed study of the contamination in cities near the coast or an industrial pollutant source of any type of gas pollutants and can easily be extended for the study of particulate matter.
Investors’ Responses on SOE’s Liquidity Risk Disclosures: Case of Indonesia

This study aims to examine the value relevance of liquidity risk disclosure of Indonesia listed state owned enterprises after Indonesia Statement of Financial Accounting Standard, Disclosure of Financial Instruments (Revised in 2010 and 2014). This study uses 20 Indonesia listed state owned enterprises during period of 2012-2017 or 115 firm years as final samples. Using panel data analysis, this study shows that liquidity risk disclosure is relevant information for investors in Indonesia stock exchange. Investors response differently on liquidity risk disclosure before and after the announcement windows period of financial reports. The main contribution of this is examining the value relevance of liquidity risk disclosure of Indonesia listed state owned enterprises.
Network analysis of the variables involved in Career Development

Abstract The aim of the study was to identify the relationships between career commitment and the perception of employability, career adaptability, self-reflection and insight of professionals from the southern region of Brazil. Participants were 535 individuals who answered an online questionnaire with sociodemographic questions and scales from career commitment, employability perception, career adaptability, self-reflection and insight, to evaluate the variables of interest in the study. The data were submitted to descriptive and network analysis. The results show a moderate and positive relationship between the variables studied and highlight the centrality and strength of the variables identity, planning and confidence among the analyzed variables. It is concluded that it is essential to encourage professionals to reflect on professional choices and their responsibility with their careers, based on the promotion of self-knowledge. Also, the role of organizations in the development of People Management practices that promote autonomy and relationships of trust stands out.
Magnetic detection of albuminuria using hematite nanorods synthesized via chemical hydrothermal method

Objective(s): Albuminuria is a biomarker in the diagnosis of kidney disease which is due to the presence of high albumin in the urine and is one of the complications of diabetes. In recent years, the methods used to identify albuminuria have been expensive and time-consuming. Furthermore, another problem is the lack of accurate measurement of albuminuria. This problem leads to kidney isolation as well as a decrease in erythropoietin levels. Therefore, the main aim of our work is to design a magnetic nanobiosensor with better sensitivity to detect minimal levels of albuminuria. Materials and Methods: In the present work, we synthesized Hematite Nano Rods (HNRs) using FeCl3, NaOH and Cetyltrimethylammonium bromide (CTAB) precursors via the hydrothermal method. Then, HNRs were characterized using UV-vis spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Transmission Electron Microscopy (TEM) and Vibrating Sample Magnetometer (VSM) techniques. Results: The obtained results from clinical performance of the HNR nanobiosensor show that the magnetization changes of HNR in interaction with the albumin biomarker can determine the presence or absence of protein in biological samples. The accuracy and repeatability of the HNR nanobiosensor from the value of the R2 coefficient in the standard equation is 0.9743. Conclusion: We obtained the standard curve through interaction of the HNRs with albumin protein. The standard equation is obtained by plotting the magnetization curve of a non-interacting sample to interacting samples in terms of protein concentration. The Bland-Altman statistical graph prove that the HNR nanobiosensor is as reliable as experimental methods.
Cybersecurity Analysis of Load Frequency Control in Power Systems: A Survey

Today, power systems have transformed considerably and taken a new shape of geographically distributed systems from the locally centralized systems thereby leading to a new infrastructure in the framework of networked control cyber-physical system (CPS). Among the different important operations to be performed for smooth generation, transmission, and distribution of power, maintaining the scheduled frequency, against any perturbations, is an important one. The load frequency control (LFC) operation actually governs this frequency regulation activity after the primary control. Due to CPS nature, the LFC operation is vulnerable to attacks, both from physical and cyber standpoints. The cyber-attack strategies ranges from a variety of attacks such as jamming the network communication, time-delay attack, and false data injection. Motivated by these perspectives, this paper studies the cybersecurity issues of the power systems during the LFC operation, and a survey is conducted on the security analysis of LFC. Various cyber-attack strategies, their mathematical models, and vulnerability assessments are performed to understand the possible threats and sources causing failure of frequency regulation. The LFC operation of two-area power systems is considered as a tutorial example to quantify the vulnerabilities. Mitigation strategies through control theoretic approaches are then reviewed and highlighted for LFC operation under cyber-attack.