Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of the performance indicators of night-time ventilation

Night-time ventilation is a natural cooling technology, in which cold ambient air is used to cool indoor spaces. This literature review analyses how recent studies have defined the effectiveness or efficiency of night-time ventilation. Most studies used the similar indicators related to heat removal, energy saving, cooling demand reduction, and thermal comfort. However, there were significant differences between the definitions of performance of night-time ventilation, both in terms of criteria of judgement and methods of analysis. This review shows that the assessment of night-time ventilation effectiveness or efficiency should be determined according to the task of the ventilation system, such as removal of heat, provide better thermal comfort. The objective of this article is to review the scientific literature on night-time ventilation indicators to identify and categorize the most suitable indicators for performance assessment. The analysis results form a basic framework regarding the application and operation of night-time ventilation.
Generalized Bayesian quadrature with spectral kernels

Bayesian probabilistic integration, or Bayesian quadrature (BQ), has arisen as a popular means of numerical integral estimation with quantified uncertainty for problems where computational cost limits data availability. BQ leverages flexible Gaussian processes (GPs) to model an integrand which can be subsequently analytically integrated through properties of Gaussian distributions. How-ever, BQ is inherently limited by the fact that the method relies on the use of a strict set of kernels for use in the GP model of the integrand, reducing the flexibility of the method in modeling varied integrand types. In this paper, we present spectral Bayesian quadrature, a form of Bayesian quadrature that allows for the use of any shift-invariant kernel in the integrand GP model while still maintaining the analytical tractability of the integral posterior, increasing the flexibility of BQ methods to address varied problem settings. Additionally our method enables integration with respect to a uniform expectation, effectively computing definite integrals of challenging integrands. We derive the theory and error bounds for this model, as well as demonstrate GBQ’s improved accuracy, flexibility, and data efficiency, compared to traditional BQ and other numerical integration methods, on a variety of quadrature problems.
Short-Term Prediction of Energy Consumption in Buildings Based on Artificial Neural Networks and Nonlinear Time Series Analysis

This paper introduces a new approach for the prediction of hourly energy consumption in buildings. The proposed method uses nonlinear timeseries analysis techniques for the reconstruction of energy consumption timeseries and the estimation of the dynamic invariants, and artificial neural networks as a nonlinear modeling tool. Among the several neural network modeling factors that affect time-series prediction, the most important are the window-size and the sampling lags for the data. Relevant theoretical results related to the reconstruction of a dynamical system are analyzed and the relationship between a correct embedding dimension and network performance is investigated. The problem is examined initially for the univariate case and is extended to include additional calendar parameters, in the process of estimating the optimum model. Different network topologies are considered, as well as existing approaches for solving multi-step ahead prediction problems. The performance of short-term predictors is also examined with regard to prediction horizon. The performance of the predictors is evaluated using measured data from real scale buildings, showing promising results for the development of accurate prediction tools.
The Mediating Role of Employee Engagement in the Relationship between Emotional Intelligence and Change Management

: The research paper aims to examine the mediating role of employee engagement in the relationship between emotional intelligence and change management. It focused on organizational leaders at a water utility company on the Copperbelt province of the Republic of Zambia. The study was driven by five research questions on what the relationship between employee engagement and emotional intelligence was; what the relationship between employee engagement and change management was; what the relationship between emotional intelligence and change management was; what the mediating role of employee engagement in the relationship between emotional intelligence and change management was; and, what the implication of the study was in the water sector in the Republic of Zambia. Four hypotheses were developed and tested using well established scales. The research employed both quantitative and qualitative methodology in its approach. A sample of 31 respondents was taken and data collected through a questionnaire with variables of interest being employee engagement as a mediator variable, emotional intelligence as an independent variable and change management as a dependent variable. The outcomes of the study indicated that employee engagement partially mediated the relationship between emotional intelligence and change management. The results also indicated that besides emotional intelligence and employee engagement being significantly related, they were also significantly related to change management. Implications of the results and opportunities for further research were then advanced.
Uni-leg and -shape thermoelectric cells

The thermoelectric generator (TEG) is a promising device for changing wasted heat energy into electricity, however the device is currently inefficient at generating power in this way. Exploration and development of the device’s efficiency is necessary for cell fabrication. In this work, comparisons are made between the power generation of uni-leg p-Ca 3 Co 4 O 9 , uni-leg n-Ca 0.97 Bi 0.03 MnO 3 and p-n junction (  -shape) of p-Ca 3 Co 4 O 9 , n-Ca 0.97 Bi 0.03 MnO 3 . The dimensions of the thermoelectric material were a cross-sectioned area of 78.5 mm 2 with a height of 10 mm. The size of the alumina substrate was 25×25 mm 2 . Thermoelectric cell fabrication used copper wire, series connected thermoelectric materials for the uni-legs cell and thin copper plate for the  -shape thermoelectric cell. The voltage, current, power output and conversion efficiencies were measured by the steady stat method. It was found that, in the 3 samples, uni-leg n-Ca 0.97 Bi 0.03 MnO 3 had the highest maximum voltage of 76.8 mV, with electrical current around 70 mA, electrical power of 5 mW and a conversion efficiency of 0.187% at a temperature difference of 200 K, which makes this fabricated thermoelectric device feasible.
Value creation under circular economy practices in the production of viche

In recent years, progress in developing the circular economy approach in the agri-food sector has encouraged the formulation of management practices that foster efficient use of resources, thereby creating greater economic, environmental, and social value for the companies that participate in closed-production cycles markets. Under such a perspective, our analysis’s central objective is to establish what type of circular economy management practices in the food industry have furthered the creation of economic and environmental value in the production of viche in the Colombian Pacific. To that end, the methodological development is based on a review of the scientific literature on the matter, identifying the intellectual structure, core categories, and evolution of the main topics and a Latent Semantic Analysis (ASL) of the management practices identified in the literature. Among the main findings are the practices focused on bioconversion, bioactive polyphenol compounds, organic waste, non-biodegradable fractions, waste, biofuels from food waste, and resources recovery through leachates. The results lead to conclude the broad potential for designing and adopting circular economy-approach management practices to create value for viche producers in the Colombian Pacific, given the characteristics of the production process.
MARATHON STRIDE RATE DYNAMICS: A CASE STUDY

The purpose of this study was to investigate stride rate (SR) dynamics of a recreational runner participating in his debut marathon. Tibial accelerometry data obtained during a half marathon (R1) and marathon (R2) were utilised. SR data were extracted utilising novel computational methods and descriptive statistics were utilised for analysis of R2, and comparison of the first half of the marathon (R2half) to R1. Results indicate that the participant employed comparable SR strategy in R1 and R2half. For R2 a combined decreasing trend in SR and increased variance in SR from 30 km (R2 =0.0238) was observed. Results indicate that the participant had the ability to maintain SR strategy for the first half of the marathon, however as fatigue onset occurred this ability decreased. Running strategies on SR during fatigue may be of future use to recreational runners.
Application of silica solubilizing bacteria to improve the water use efficiency of maize

Global climate change will result in decreased rainfall and increased evaporation. Thus, it is estimated that the frequency and severity of drought stress will get worse. Silica increases plant drought resistance by improving water use efficiency in plants. Despite its abundant availability in soil, most silica sources are not available to plants due to their low solubility. Silica solubilizing bacteria (SSB) have an important role in increasing the available silica. This study aims to observe the silica solubilizing activity of three SSB isolates collections of PPBBI on insoluble silica sources, including magnesium trisilicate, quartz, and feldspar, and see their effects on increasing water use efficiency in corn plants via drought experiments. SSB activity was measured using the modified standard method of 4500-SiO2 D Heteropoly blue. Drought control in the greenhouse follows the Snow and Tingey system. The experimental design used a completely randomized design factorial with irrigation conditions and SSB species as variables. Water use efficiency is measured in real-time with a sap flow meter. The results showed that SSB Pseudomonas fluorescens-B41 had the highest silica dissolving activity 81.93 ppm on Mg-trisilicate. The application of SSB can reduce maize transpiration rate and increase water use efficiency up to 84% under moderate drought stress and 46% under normal irrigation, but in severe drought stress, where the nutrient solution was maintained at 25 cm from plant root, water use efficiency was not significant. This is suspected due to the extreme drought conditions in the potting soil so that the applied SSB cannot maintain its activities.
Uncertainty calibrated deep tissue classiﬁcation in histopathology

Deep learning based methods have been successfully employed for a variety of tasks in computational pathology, including delineation of tissue types. However, if models are to be deployed into the ﬁeld, it is essential they recognize when presented out-of-distribution data. We investigated the use of Spectral-normalized Neural Gaussian Process (SNGP) for detection of out-of-distribution data when classifying histological tissue sections. The SNGP approach assigned on average lower conﬁdence values to out-of-distribution data compared to correctly predicted in-distribution data. By discarding tiles with low con-ﬁdence, almost 20 percentage points more out-of-distribution data can be rejected using SNGP than with the corresponding uncalibrated standard model.
An overview of the applications of hazards analysis and critical control point (HACCP) system to mycotoxins

Mycotoxins are toxic metabolites produced by fungal species that commonly contaminate staple foods and feeds. The worldwide mycotoxin contamination of food and feed is a problem of public health as it is shown in the total mycotoxin notifications reported by Rapid Alert System for Food and Feed in the European Union. The inclusion of mycotoxin control in food and feed manufacturing hazard analysis critical control point (HACCP) system is widely used as prevention of mycotoxin occurrence. Several food commodities are mainly involved in control mycotoxins as cereals, grapes, coffee, nuts, milk and wine manufacturers. Moreover, feed is particularly vulnerable to mycotoxin contamination because typically lower quality ingredients are used and HACCP is used to quality control. Aflatoxins and ochratoxins are the most determined mycotoxins for controlling the quality of the products by rapid tests and liquid chromatography methods. Despite many years of research, and the introduction of good practices in the food production, storage and distribution chain, mycotoxins continue to be a concern in food safety.
Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the East–Taylor subbasin (western United States)

Abstract. High-resolution gridded datasets of meteorological variables are needed in order to resolve fine-scale hydrological gradients in complex mountainous terrain. Across the United States, the highest available spatial resolution of gridded datasets of daily meteorological records is approximately 800 m. This work presents gridded datasets of daily precipitation and mean temperature for the East–Taylor subbasin (in the western United States) covering a 12-year period (2008–2019) at a high spatial resolution (400 m). The datasets are generated using a downscaling framework that uses data-driven models to learn relationships between climate variables and topography. We observe that downscaled datasets of precipitation and mean temperature exhibit smoother spatial gradients (while preserving the spatial variability) when compared to their coarser counterparts. Additionally, we also observe that when downscaled datasets are upscaled to the original resolution (800 m), the mean residual error is almost zero, ensuring no bias when compared with the original data. Furthermore, the downscaled datasets are observed to be linearly related to elevation, which is consistent with the methodology underlying the original 800 m product. Finally, we validate the spatial patterns exhibited by downscaled datasets via an example use case that models lidar-derived estimates of snowpack. The presented dataset constitutes a valuable resource to resolve fine-scale hydrological gradients in the mountainous terrain of the East–Taylor subbasin, which is an important study area in the context of water security for the southwestern United States and Mexico. The dataset is publicly available at https://doi.org/10.15485/1822259 (Mital et al., 2021).
Book Reviews

This section reviews those books whose content and level reflect the general editorial policy of Technometrics. Publishers should send books for review to Ejaz Ahmed, Department of Mathematics and Sciences, Brock University, St. Catharines, ON L2S 3A1 (dean.fms@brocku.ca). The opinions expressed in this section are those of the reviewers. These opinions do not represent positions of the reviewer’s organization and may not reflect those of the editors or the sponsoring societies. Listed prices reflect information provided by the publisher and may not be current. The book purchase programs of the American Society for Quality can provide some of these books at reduced prices for members. For information, contact the American Society for Quality at 1-800-248-1946.
Project-based innovation: The world after Woodward

This chapter develops a conceptual framework to help us position and understand the increasing importance of project-based innovation for industrial organization in the 21st century. It builds on and extends Joan Woodward's (1958 and 1965) pioneering research, which classifies industrial organizations according to the complexity of production technology and volume of output. We suggest that a radical revision of Woodward's framework is required to account for the extensive use of project-based organizations to gain competitive advantage through accelerated innovation and growth in new technologies and markets.
MundoFuzz: Hypervisor Fuzzing with Statistical Coverage Testing and Grammar Inference

A hypervisor is system software, managing and running virtual machines. Since the hypervisor is placed at the lowest-level in the typical systems software stack, it has critical security implications. Once compromised, the entire software components running on top of the hypervisor (including all guest virtual machines and applications running within each guest virtual machine) are compromised as well, as the hypervisor has all the privileges to access those. This paper proposes M UNDO F UZZ , a hypervisor fuzzer to enable both coverage-guided and grammar-aware fuzzing. We find that the coverage measurement in hypervisors suffers from noises due to the hypervisor’s asynchronous system event handling. In order to filter out such noises, M UNDO F UZZ develops a statistical differential coverage measurement methods, allowing M UNDO F UZZ to capture the clean coverage information for hypervisor inputs. Moreover, we observe that hypervisor inputs have complex input grammars because it supports many different devices and each device has its own input format. Thus, M UNDO F UZZ learns the input grammar through inspecting the coverage characteristics of the given hypervisor input, which is based on the idea that the hypervisor behaves in a different way if the grammatically correct (or incorrect) input is given. We evaluated M UNDO F UZZ with popular hypervisors, QEMU and Bhyve, and M UNDO F UZZ outperformed other state-of-the-art hypervisor fuzzers ranging from 4.91% to 6.60% in terms of coverage. More importantly, M UNDO F UZZ identified 40 pre-viously unknown bugs (including 9 CVEs), demonstrating its strong practical effectiveness in finding real-world hypervisor
WOMEN’S OVERLOAD DURING THE PANDEMIC: UNPAID CARE WORK, FINANCIAL WELL-BEING, AND STRESS

This study examines the relationship betwe en unpaid care work, financial well-being, and stress levels among women during the COVID-19 pandemic. The pandemic caused downturns in the economies of countries worldwide and led to uncertainties and changes that created conditi ons for increased stress levels and consequently impaired well-being. In addition to social distan cing and lockdown measures to cope with the vi -rus, working from home, home schooling, caring for sick and older family members, and financial problems have added burdens and have proven to be additional personal and family stressors. The findings are based on an online survey conduc ted among women during the second lockdown in Croatia. Additional unpaid care work and lower financial well-being affect the increased stress felt by women during the pandemic. This is espe cially pronounced among women who are more involved in paid work. During the pandemic, additional unpaid care work, increased invol vement in paid work, and jeopardized financial well-being were stressors for women. Our results indicate the importance of women’s free time for their own and their families’ well-being.
Microalgal Proteins and Bioactives for Food, Feed, and Other Applications

Featured Application: This review paper details current production of microalgae globally and isolated microalgal proteins and peptides and their associated health beneﬁts, as well as details concerning microalgal lipids, vitamins and minerals. The use of microalgae in feed is discussed, along with potential uses in other applications such as cosmetics and functional foods. Abstract: Microalgae are a known source of proteins, prebiotics, lipids, small molecules, anti-oxidants and bioactives with health beneﬁts that can be harnessed for the development of functional foods, feeds, cosmeceuticals and pharmaceuticals. This review collates information on the supply, processing costs, target markets and value of microalgae, as well as microalgal proteins, lipids, vitamins and minerals. It discusses the potential impact that microalgae could have on global food and feed supply and highlights gaps that exist with regards to the use of microalgal proteins and ingredients as foods and supplements.
Darwin: A Data Platform for Schema Evolution Management and Data Migration

During the development of NoSQL-backed software, the database schema evolves alongside the application code. Especially in agile development, new application releases are frequently deployed. This leads to heterogeneous data in the database and thus to new challenges for application development. To handle such heterogeneous data, we have developed various algorithms, implemented and evaluated them in a data platform for schema evolution management and data migration called Darwin . We provide an overview of Darwin , the concepts, algorithms, their implementations and the possible usage of Darwin in this paper.
O BJECT R EPRESENTATIONS AS E QUILIBRIA : T RAINING I TERATIVE I NFERENCE A LGORITHMS WITH I MPLICIT D IFFERENTIATION

Deep generative models, particularly those that aim to factorize the observations into discrete entities (such as objects), must often use iterative inference procedures that break symmetries among equally plausible explanations for the data. Such inference procedures include variants of the expectation-maximization algorithm and structurally resemble clustering algorithms in a latent space. However, combining such methods with deep neural networks necessitates differentiating through the inference process, which can make optimization exceptionally challenging. In this work, we observe that such iterative inference methods can be made differentiable by means of the implicit function theorem, and develop an implicit differentiation approach that improves the stability and tractability of training such models by decoupling the forward and backward passes. This connection enables us to apply recent advances in optimizing implicit layers to not only improve the stability and optimization of the slot attention module in SLATE, a state-of-the-art method for learning entity representations, but do so with constant space and time complexity in backpropagation and only one additional line of code.
Propagating integrity bounds in nonlinear state estimation

Future safety-critical GNSS applications will require state-estimation techniques that ensure integrity. Currently-existing state estimators are not suitable for many high-integrity applications because of the way they propagate random variables through nonlinear system dynamics. The major difficulty in nonlinear stateestimation problems dealing with integrity lies in accounting for the nonlinear distortions to the error distribution. This thesis proposes a state-estimation method that employs a Gaussian overbound with integrity ensured by an additional unsigned bias. For this reason we refer to the method as the biased-Gaussian filter, though the focus of this thesis remains on the prediction step of Bayesian estimation. The resulting predictor resembles a Kalman filter that propagates mean, covariance, and one additional term: the unsigned bias. This solution has the benefit of being relatively computationally efficient while still maintaining rigorous integrity. Integrity performance is assessed by both a Monte Carlo simulation and an experiment for a vehicle model with nonlinear kinematics. In each case, the method provided conservative bounds and remained robust to both nonlinear error distortions and non-Gaussian experimental uncertainty.