Indirect Indicators Research Articles

Improving the quality of the steam temperature regulation in the TsKTI-100 boiler thanks to use of fuzzy adjuster

The results of designing and setting up fuzzy logic adjusters for controlling the steam temperature in the CCTI-100 gas-tube boiler at Berezniki Soda Plant JSC are presented. The setting of the adjusters is made by a method based on the use of a geometric progression. The quality of management is evaluated on the grounds of direct and indirect indicators system. Comparison of their values with the corresponding quality indicators for a conventional PID adjuster showed a significant advantage of fuzzy regulation. Keywords theory of automatic control, fuzzy logic, PID adjuster, fuzzy adjuster, simulation, regulation quality indicators

Monitoring groundwater quality with real-time data, stable water isotopes, and microbial community analysis: A comparison with conventional methods

Groundwater provides much of the world's potable water. Nevertheless, groundwater quality monitoring programmes often rely on a sporadic, slow, and narrowly focused combination of periodic manual sampling and laboratory analyses, such that some water quality deficiencies go undetected, or are detected too late to prevent adverse consequences. In an effort to address this shortcoming, we conducted enhanced monitoring of untreated groundwater quality over 12 months (February 2019–February 2020) in four shallow wells supplying potable water in Finland. We supplemented periodic manual sampling and laboratory analyses with (i) real-time online monitoring of physicochemical and hydrological parameters, (ii) analysis of stable water isotopes from groundwater and nearby surface waters, and (iii) microbial community analysis of groundwater via amplicon sequencing of the 16S rRNA gene and 16S rRNA. We also developed an early warning system (EWS) for detecting water quality anomalies by automating real-time online monitoring data collection, transfer, and analysis – using electrical conductivity (EC) and turbidity as indirect water quality indicators. Real-time online monitoring measurements were largely in fair agreement with periodic manual measurements, demonstrating their usefulness for monitoring water quality; and the findings of conventional monitoring, stable water isotopes, and microbial community analysis revealed indications of surface water intrusion and faecal contamination at some of the studied sites. With further advances in technology and affordability expected into the future, the supplementary methods used here could be more widely implemented to enhance groundwater quality monitoring – by contributing new insights and/or corroborating the findings of conventional analyses.

The Ordovician ocean circulation: a modern synthesis based on data and models

Abstract Surface currents constitute an efficient transport agent for (larvae of) marine faunas, while the circulation of water masses in the ocean interior drives nutrient redistribution, ventilates the ocean and contributes to shaping surface biological productivity and the benthic redox landscape. Therefore, a robust understanding of ocean circulation, both shallow and deep, and of its response to climate change, is required to interpret palaeobiogeographic signals, biological productivity patterns and biodiversity trends. This is especially critical during periods of dynamic biological change, such as the Ordovician. Yet, oceanic circulation patterns leave no direct evidence in the geological record and can therefore be reconstructed solely based on indirect indicators, such as the distribution of faunas and geochemical proxies. General circulation models offer independent, physically robust insights onto the coupling between climate change and ocean circulation. Integrated approaches based on the assimilation of geological data in numerical models thus constitute a promising way forward. We here provide a literature review and updated synthesis of the current understanding of the Ordovician ocean circulation, based on data and models.

Towards understanding and mitigating unintended biases in language model-driven conversational recommendation

Conversational Recommendation Systems (CRSs) have recently started to leverage pretrained language models (LM) such as BERT for their ability to semantically interpret a wide range of preference statement variations. However, pretrained LMs are prone to intrinsic biases in their training data, which may be exacerbated by biases embedded in domain-specific language data (e.g., user reviews) used to fine-tune LMs for CRSs. We study a simple LM-driven recommendation backbone (termed LMRec) of a CRS to investigate how unintended bias — i.e., bias due to language variations such as name references or indirect indicators of sexual orientation or location that should not affect recommendations — manifests in substantially shifted price and category distributions of restaurant recommendations. For example, offhand mention of names associated with the black community substantially lowers the price distribution of recommended restaurants, while offhand mentions of common male-associated names lead to an increase in recommended alcohol-serving establishments. While these results raise red flags regarding a range of previously undocumented unintended biases that can occur in LM-driven CRSs, there is fortunately a silver lining: we show that train side masking and test side neutralization of non-preferential entities nullifies the observed biases without significantly impacting recommendation performance.

Wildlife health in environmental impact assessments: are we missing a key metric?

Environmental impact assessments (EIAs) aim to assess the predicted effects of future projects on the environment, human health, and the economic potential of a region. They are an instrumental tool for sustainable development and to reduce the impact of large-scale industrial projects on biodiversity. The accurate assessment of the effects of projects on wildlife populations has a variety of ecological, cultural, and economic implications. However, assessments are commonly done using indirect indicators such as the predicted impact of changes in the quantity and quality of wildlife habitat and (or) predicted changes in nonspecific wildlife population metrics (e.g., relative abundance). In recent decades, the interpretation of wildlife health has moved from the classical dichotomous state of “disease presence/absence” to a broader concept that integrates the interaction of biological, social, and environmental health determinants. We sought to determine how wildlife health metrics are used in EIA processes and propose a framework to characterize, quantify, and monitor wildlife health in future EIAs. First, we performed a targeted review of EIA documents from three jurisdictions in Canada to characterize the relevance and use of “wildlife health” in these documents. Then, we reviewed case studies and research examples to understand wildlife health in different contexts associated with conservation biology to propose a framework to incorporate wildlife health into baseline monitoring and mitigation processes in EIAs. Our targeted review illustrates that while wildlife health and related terminology is often invoked in the main and specific objectives of EIAs, it is rarely tracked and quantified in EIAs and related processes. We identified approaches that can be used to effectively incorporate wildlife health in EIAs, including context-specific wildlife health metrics, participatory epidemiology, community-based sampling, and local ecological knowledge. We illustrate case studies where wildlife health can facilitate the inclusion of communities, Indigenous governments, and local ecological knowledge into the evaluation process of projects and developments and into comanagement practices of wildlife. Our work highlights the critical need to move towards incorporating wildlife health into EIA processes to provide a more direct and holistic perspective on the potential environmental impacts and improve the opportunities for early implementation of mitigation measurements.

Fossilized diatoms as indirect indicators of the origin of carbon stored in intertidal flats

Coastal systems store enormous carbon quantities in their sediment, which originates from various autochthonous and allochthonous sources. Carbon fluxes in coastal ecosystems have a strong effect on the recipient food-webs and carbon emission offsets. Yet, the relative importance of autochthonous vs. allochthonous C inputs to coastal carbon budget is still challenging to identify. Here, we combine diatoms preserved in the sediment with geochemical analyses to identify the sources of carbon stored in Africa’s largest intertidal seagrass beds at Banc d’Arguin, Mauritania. The area lies between an active ocean upwelling and the ‘Sahara-dust hotspot’ systems. The extensive seagrass beds of the area are thus expected to receive C from these neighboring systems in addition to producing C in-situ. Three sediment cores (50 cm) were collected at three intertidal sites with different hydrodynamic regimes, and analyzed for diatom composition, total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), and carbon isotopic signatures (δ13C). Diatom taxa are grouped into three guilds: (1) benthic (epiphyte, epipelon, and epipsammon), (2) planktonic, and (3) freshwater. Benthic diatoms are considered to be autochthonous, while typical oceanic and freshwater diatoms are considered to be allochthonous. Benthic diatoms are the most diverse and abundant group, while allochthonous freshwater (i.e., dust imported) and typical upwelling (i.e., tidal imported) taxa ranked last in both abundance and species’ richness. Structure equation modelling shows that variation in the stored carbon is best explained by the total abundance of diatoms and guild composition. We conclude that the C stored in the intertidal seagrass beds of Banc d’Arguin is predominantly autochthonous. Our method provides an effective way to identify historical carbon sources in coastal systems.

Формирование критериев оптимальности мехатронных систем с использованием аппарата нечеткой логики

Currently, the problem to develop the optimality criterion in terms of parametric synthesis of state regulators of mechatronic systems is truly relevant. The optimality criteria developed earlier are based on the use of indirect quality indicators of automatic control systems. These criteria are mostly focused on providing an analytical solution of the synthesis problem than on the real properties of the systems. To consider all the requirements for the system, it is necessary to transfer from indirect optimality criteria to direct ones. It provides a compromise between the main requirements considering the specific features of the system. In this regard, to optimize electromechanical systems it is proposed to use a complex optimality criterion based on the mathematical apparatus of fuzzy logic. As part of the study, state space methods are used to develop a vector-matrix model of an object and synthesize a state controller. The calculation of the optimality criterion according to the determinable quality indicators is based on the mathematical apparatus of fuzzy logic. Development of a fuzzy model is conducted by means of the modeling tools of the Matlab software package. To find the optimal parameters of the regulator search optimization methods are used. A new optimality criterion for mechatronic systems has been developed and studied. It is based on the use of fuzzy logic apparatus, which makes it possible to more fully consider the set of specified technical requirements for their design. The results of the conducted computational experiments have proved the effectiveness of the developed criterion and optimization algorithms. Designers can effectively use the obtained results for parametric optimization to enhance diverse types of mechatronic systems with state controllers.

Benthic foraminifera as proxy for fossil seagrass from the Lower Pleistocene deposits of the Stirone River (Emilia-Romagna, Italy)

This paper analyses and describes the benthic foraminiferal associations within the Pleistocene deposits of the Stirone River (Emilia-Romagna, Italy), with the purpose of testing foraminifera as Indirect Palaeo-Seagrass Indicators (IPSIs). Our analyses focused on two different biofacies: a Thalassinoides biofacies, characterised by an oligotypic biotic assemblage and a Pinna biofacies representing an infralittoral soft bottom colonised by marine phanerogams and characterized by high biodiversity. To strengthen the analyses, we have compared the Stirone foraminiferal association with the one of Fauglia (Tuscany, Italy), in which a well-preserved, early Pleistocene, fossil Posidonia meadow, is present. The aim of this work is to provide qualitative and quantitative parameters that can be used to recognize past vegetated environments, where fossil seagrass are no longer present. Considering the influence of ecological constraints and diagenetic processes on the foraminiferal assemblages, several indexes such as the IndexEP, the modified FORAM index (FI’), the “long vs. short life-span index” (ILS), and the K/REXT (keeled/rounded morphotypes) have been calculated and a morphotype-based analysis has been provided. Among the tested indexes, the latter proved to be the most reliable IPSI, in association with the presence of morphotype A*, permanently attached, encrusting foraminifera and the abundance of Rosalinidae.

Impact of CPAP Therapy on New Inflammation Biomarkers.

Obstructive sleep apnea (OSA) is a common sleep disorder leading to increased risk of developing cardiovascular diseases (CVDs) by supporting a low-grade chronic inflammation as one of the pathological mechanisms. The continuous positive airway pressure (CPAP) device is used as an effective treatment for moderate and severe OSA. Neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), white blood cell–mean platelet volume ratio (WMR), C-reactive protein–albumin ratio (CAR) and fibrinogen–albumin ratio (FAR) are new potential inflammatory biomarkers that are widely available and were shown to be possibly favorable screening or follow-up tools for moderate- or severe-grade OSA, as well as indirect indicators for cardiovascular risk. Our study evaluated the impact of CPAP therapy in patients with severe OSA and acceptable therapy adherence on NLR, PLR, WMR, FAR and CAR. Of 57 patients who were initially enrolled and had no exclusion criteria, 37 had a satisfactory CPAP adherence (usage of ≥4 h per night) after a minimum of 6 months of therapy. There was a statistically significant difference in NLR (2.122 ± 0.745 before therapy vs. 1.888 ± 0.735 after therapy) and FAR (86.445 ± 18.763 before therapy vs. 77.321 ± 19.133 after therapy) suggesting a positive effect of the CPAP therapy on chronic inflammatory states, thereby possibly reducing cardiovascular risk.

A stakeholder-driven framework for measuring potential change in the health risks of people who inject drugs (PWID) during the COVID-19 pandemic.

BackgroundPeople who inject drugs (PWID) have likely borne disproportionate health consequences of the COVID-19 pandemic. PWID experienced both interruptions and changes to drug supply and delivery modes of harm reduction, treatment, and other medical services, leading to potentially increased risks for HIV, hepatitis C virus (HCV), and overdose. Given surveillance and research disruptions, proximal, indirect indicators of infectious diseases and overdose should be developed for timely measurement of health effects of the pandemic on PWID.MethodsWe used group concept mapping and a systems thinking approach to produce an expert stakeholder-generated, multi-level framework for monitoring changes in PWID health outcomes potentially attributable to COVID-19 in the U.S. This socio-ecological measurement framework elucidates proximal and distal contributors to infectious disease and overdose outcomes, many of which can be measured using existing data sources.ResultsThe framework includes multi-level components including policy considerations, drug supply/distribution systems, the service delivery landscape, network factors, and individual characteristics such as mental and general health status and service utilization. These components are generally mediated by substance use and sexual behavioral factors to cause changes in incidence of HIV, HCV, sexually transmitted infections, wound/skin infections, and overdose.ConclusionThis measurement framework is intended to increase the quality and timeliness of research on the impacts of COVID-19 in the context of the current pandemic and future crises. Next steps include a ranking process to narrow the drivers of change in health risks to a concise set of indicators that adequately represent framework components, can be written as measurable indicators, and are quantifiable using existing data sources, as well as a publicly available web-based platform for summary data contributions.

УПРАВЛЕНИЕ И ИНТЕНСИФИКАЦИЯ ОБЩЕСТВЕННЫХ ФИНАНСОВ

The article includes material on the toolkit for assessing the intensification of the cost indicators of the activities of public entities, developed by the authors to identify the quality of public finance management, serving for state and municipal authorities as a measure of the financial benefits necessary to provide the population with a decent standard of living and social stability of society, residing in a particular country. It is a system of methods built through the interaction of general and particular direct and indirect indicators (change in the result (income) depending on the dynamics of performance (profitability) and change in costs (expenses) depending on the dynamics of the cost (expenditure) of the consumed monetary resources), formed in the process of operations carried out by public entities on the scale of their economic sphere (social reproduction, economic policy and foreign economic relations). Its construction was carried out by using a calculation-constructive method with elements of chain substitutions and differentiation of variables, in particular, the costs of regulating social reproduction, conducting economic policy, and regulating foreign economic relations, which provide accurate information about the analyzed indicators.

Machine learning models for identifying pre-frailty in community dwelling older adults

BackgroundThere is increasing evidence that pre-frailty manifests as early as middle age. Understanding the factors contributing to an early trajectory from good health to pre-frailty in middle aged and older adults is needed to inform timely preventive primary care interventions to mitigate early decline and future frailty.MethodsA cohort of 656 independent community dwelling adults, aged 40–75 years, living in South Australia, undertook a comprehensive health assessment as part of the Inspiring Health cross-sectional observational study. Secondary analysis was completed using machine learning models to identify factors common amongst participants identified as not frail or pre-frail using the Clinical Frailty Scale (CFS) and Fried Frailty Phenotype (FFP). A correlation-based feature selection was used to identify factors associated with pre-frailty classification. Four machine learning models were used to derive the prediction models for classification of not frail and pre-frail. The class discrimination capability of the machine learning algorithms was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, F1-score and accuracy.ResultsTwo stages of feature selection were performed. The first stage included 78 physiologic, anthropometric, environmental, social and lifestyle variables. A follow-up analysis with a narrower set of 63 variables was then conducted with physiologic factors associated with the FFP associated features removed, to uncover indirect indicators connected with pre-frailty. In addition to the expected physiologic measures, a range of anthropometric, environmental, social and lifestyle variables were found to be associated with pre-frailty outcomes for the cohort. With FFP variables removed, machine learning (ML) models found higher BMI and lower muscle mass, poorer grip strength and balance, higher levels of distress, poor quality sleep, shortness of breath and incontinence were associated with being classified as pre-frail. The machine learning models achieved an AUC score up to 0.817 and 0.722 for FFP and CFS respectively for predicting pre-frailty. With feature selection, the performance of ML models improved by up to + 7.4% for FFP and up to + 7.9% for CFS.ConclusionsThe results of this study indicate that machine learning methods are well suited for predicting pre-frailty and indicate a range of factors that may be useful to include in targeted health assessments to identify pre-frailty in middle aged and older adults.

Prediction of Indirect Indicators of a Grass-Based Diet by Milk Fourier Transform Mid-Infrared Spectroscopy to Assess the Feeding Typologies of Dairy Farms.

Simple SummaryThe dairy industry is interested in developing a detection tool for milk produced from pasture to certify the protected designation of origin of certain dairy products. As the cattle’s grazing influences the milk composition, the milk mid-infrared (MIR) spectra will be modified. Therefore, this study aims to develop a predictive model allowing one to use milk MIR spectrometry to classify milk as produced from a cattle diet either based on grass or not. To achieve this objective, the collection of grazing calendars from farms is needed. Unfortunately, this is hardly possible, as this information is rarely recorded. Therefore, the innovation of this research consists of using the usual farming practices developed in the southern part of Belgium combined with a large-scale milk database containing 48 milk composition-related traits. Indeed, in this geographical area, the cows are mainly on pasture between April and September. Therefore, the month of testing can be considered indirect information about their feeding. The developed models were able to distinguish with a precision of around 90% the supposed grass-based diet. Therefore, the probability of belonging to the GRASS class could be used in a tool counting the number of grazing days to confirm the labeling of dairy products.This research aims to develop a predictive model to discriminate milk produced from a cattle diet either based on grass or not using milk mid-infrared spectrometry and the month of testing (an indirect indicator of the feeding ration). The dataset contained 3,377,715 spectra collected between 2011 and 2021 from 2449 farms and 3 grazing traits defined following the month of testing. Records from 30% of the randomly selected farms were kept in the calibration set, and the remaining records were used to validate the models. Around 90% of the records were correctly discriminated. This accuracy is very good, as some records could be erroneously assigned. The probability of belonging to the GRASS modality allowed confirmation of the model’s ability to detect the transition period even if the model was not trained on this data. Indeed, the probability increased from the spring to the summer and then decreased. The discrimination was mainly explained by the changes in the milk fat, mineral, and protein compositions. A hierarchical clustering from the averaged probability per farm and year highlighted 12 groups illustrating different management practices. The probability of belonging to the GRASS class could be used in a tool counting the number of grazing days.

Joint state-of-health and remaining-useful-life prediction based on multi-level long short-term memory model prognostic framework considering cell voltage inconsistency reflected health indicators

Battery-health prognostics for the state-of-health (SOH) and remaining-useful-life (RUL) are necessary to ensure the safety and reliability of system operation. However, the aging of an actual battery pack is related to internal electrochemical reactions in the constituent cells and the inter-cell inconsistency effect, which cannot be measured directly. Thus, reasonable feature selection and an effective prognostic framework are indispensable for achieving feasible and accurate results in actual systems. This paper proposes a prognostic framework for the joint prediction of the SOH and RUL of a battery pack. The proposed method utilizes indirect health indicators (HIs) during the charging stage. Moreover, Pearson correlation analysis and incremental capacity analysis are used to recognize the inconsistency effect and optimize the sampling interval of the HIs that can be adapted to the battery pack. A multi-level long short-term memory (LSTM) model is designed to jointly predict the SOH and RUL for a battery pack. The first-level LSTM model is applied to short-term SOH estimation using the optimized HIs. For the RUL prediction, the second-level LSTM is established iteratively to extend the degradation pattern from the current step to end of life using the SOH estimation result and previous output. The robustness of the extracted features and predictive capability of the multi-level LSTM model are demonstrated by accelerated aging tests for series battery packs. The experimental results confirm that the proposed prognostic framework can provide robust, reliable, and accurate SOH and RUL predictions.

Прагматический потенциал базовых лексико-грамматических категорий в жанре «политическая email-рассылка»

The article examines basic lexico-grammatical categories of modality, temporality and personality contributing pragmatic functions in political direct email genre. There is the lack of research that has explored the language means forming these categories in the new multimedia genre of political discourse. The study aims to identify common to all genres of political media discourse and specific to political direct email genre lexico-grammatical means of expressing modality, temporality and personality. The data of research are lexical and grammatical units used for representing modality, temporality and personality in the political direct email genre by continuous sampling method. This study is based on the methods of contextual and linguo-pragmatic analysis. The main types of modal meanings in political direct email genre are necessity need, should), obligation (must) and possibility (may). The credibility of the statement is ensured by the derivative modal words really, clearly, truly. In the political direct email genre reference to the present and future tenses is expressed both grammatically (the present and future tenses) and lexically (lexical markers ‒ today, now, still). Other lexical means of conveying temporality are direct temporal nominators (time, year, month, week, hour, by age of …, deadline + date ) and indirect indicators of temporality (once, soon). in political direct email genre. Personal pronouns represent a number of pragmatic purposes: create a positive image, express personal opinion, show the significance of being involved in the events, exclusion of the addressee from the mentioned events, indicate special achievements and institutional identity, express ‘other’ opinion.

Dual Transfer Function Approach to Analyze Low Frequency Brake Noise without Comprehending Friction Behavior in Advance

<div class="section abstract"><div class="htmlview paragraph">Analyzing low frequency brake noise (< 300Hz) has been challenging due to the difficulty associated with calculating dynamic friction behavior and its multiple structure-borne noise transfer paths. In theory, it is possible to simulate sound pressure level inside the cabin by calculating a transfer function between friction excitation, which is on the interface between rotor and pads, and cabin acoustic response, and by multiplying dynamic friction force at the rotor-pad interface to that transfer function. However, calculating the dynamic friction forces when brake noise occurs has been one of the most challenging research topics in the brake community. This paper describes a novel concept to simulate sound pressure level inside the cabin without knowing the dynamic friction forces at the rotor-pad interface in advance. It becomes possible by dividing the transfer function into two separate steps: between the dynamic friction excitation at the rotor-pads and acceleration on caliper, and between the dynamic friction excitation at the rotor-pads and pressure response inside the cabin. The acoustic transfer function and the acceleration transfer function cancel out the need of knowing the friction excitation forces. The acceleration on the caliper is what is needed to calculate the sound pressure level along with those two transfer functions, and it is straightforward to collect/calculate the caliper acceleration than calculating/measuring the dynamic friction forces at the rotor-pads interface. Assessing the risk of low frequency brake noise based on actual acoustic response rather than indirect indicators like vibration or instability becomes a reality using this novel approach. The method has been proven that it captures the brake noise below 300Hz well when the noise is relatively pure-tone and quasi-transient.</div></div>

En-garde: Source Evaluations in the Digital Age

Students have difficulty assessing the quality of information. They often rely on content-focused criteria to make reliability assessments and, as a result, may accept inaccurate information. Despite the impact of poor source evaluation skills, educational researchers have not widely examined source evaluation behaviours in authentic environments or tasks. Students’ epistemic cognition, or their thinking about the epistemic properties of specific knowledge claims and sources, is one promising avenue to better understand their source evaluation behaviours. Two studies were conducted to explore students’ epistemic thinking. In Study 1, college students (n = 12) reported their reliability criteria in focus group interviews. Four of these participants (n = 4) also examined the reliability of an online news article. Grounded theory was used to infer students’ epistemic ideals and reliable epistemic processes. In Study 2, students (n = 43) rank-ordered two news articles and justified how they assigned each article’s rank in a written response. Most students were able to accurately rank-order the articles using relevant epistemic processes. Cluster analysis was used to characterize the evaluation criteria used. Surprisingly, more participants who justified their decisions using relevance criteria accurately rank-ordered the articles. The role of direct and indirect indicators of reliability are discussed through the lens of the Apt-AIR framework of epistemic thinking.

Effects of micronutrient and phytochemical supplementation on cardiovascular health in obese and overweight children: a narrative review.

Cardiovascular damage could begin early in life. Our aim was to examine the current state of the art related to micronutrient supplementation on vascular health in obese and overweight children. We considered only the studies performed over the past few years. Vitamin D supplementation in the obese pediatric population with vitamin D deficiency could improve the vascular health of these subjects. The evidence is less clear on supplementation with other micronutrients. Zinc supplementation is currently the most supported by the literature. As of today, we can only speculate that supplementation with other micronutrients could improve the vascular health of obese and overweight children. Strong limitations are the different instrumental methods used to assess vascular health in obese children and adolescents under micronutrients supplementation. Actually, indirect indicators more reliable to evaluate vascular health seem to be lipid profile and insulin sensitivity. Furthermore, there is a particular lack of studies in this area in recent years, especially in the pediatric population. Additional studies performed in this population should be pursued to clarify significant relationships between micronutrients and vascular health.

Spatio-temporal predictions of COVID-19 test positivity in Uppsala County, Sweden: a comparative approach

Previous spatio-temporal COVID-19 prediction models have focused on the prediction of subsequent number of cases, and have shown varying accuracy and lack of high geographical resolution. We aimed to predict trends in COVID-19 test positivity, an important marker for planning local testing capacity and accessibility. We included a full year of information (June 29, 2020–July 4, 2021) with both direct and indirect indicators of transmission, e.g. mobility data, number of calls to the national healthcare advice line and vaccination coverage from Uppsala County, Sweden, as potential predictors. We developed four models for a 1-week-window, based on gradient boosting (GB), random forest (RF), autoregressive integrated moving average (ARIMA) and integrated nested laplace approximations (INLA). Three of the models (GB, RF and INLA) outperformed the naïve baseline model after data from a full pandemic wave became available and demonstrated moderate accuracy. An ensemble model of these three models slightly improved the average root mean square error to 0.039 compared to 0.040 for GB, RF and INLA, 0.055 for ARIMA and 0.046 for the naïve model. Our findings indicate that the collection of a wide variety of data can contribute to spatio-temporal predictions of COVID-19 test positivity.

Generating Women Empowerment Index through Principal Component Analysis

Lack of women’s empowerment is a critical aspect of gender equality but despite all government initiatives, the empowerment barrier exists in many forms and the empowerment level did not reach its anticipated level. This study attempted to generate a women empowerment index from all direct and indirect indicators related to empowerment anchored on the identity empowerment theory using Principal Component Analysis (PCA). Utilizing the data from the United Nations Development Program (UNDP), the 10 independent indicators of 145 countries were analyzed and reduced into two principal components, namely: women development and labor inclusion. Results showed that of the two extracted components, women development has the higher weight of 75% compared to labor inclusion with only 25%. Furthermore, countries that are more developed and have high development index have high empowerment index as well. Results also showed that developing countries have low human development index and low index in women empowerment. The study recommends that initiated programs and all forms of research to improve the empowerment index must put emphasis on the developing countries.