bluuebunny/crossref_metadata_2025 huggingface dataset:
Blockchain enabled trusted task offloading scheme for fog computing: A deep reinforcement learning approach
Authors: Jain Vibha , Kumar Bijendra
Published: June 2022

With the recent advancements in the Internet of Things, cloud computing has emerged as an important industrial technology that assists in various data analysis operations. However, the remote locality of cloud servers and scalability issues of cloud computing make it unsuitable for real‐time computing‐intensive applications. Fog computing strives to support cloud computing in meeting scalability demands by providing location‐sensitive services closer to end devices. With decentralized heterogeneous resource capabilities, fog architecture can handle several computation‐intensive and delay‐sensitive user requests. Although deploying service providers in an untrustworthy environment makes it challenging to assess the trustworthy acquired services. Conspicuously, in this article, we present a trusted task offloading and resource allocation using blockchain technology. To start with, we analyze direct and indirect trust with a subjective logical aggregation approach using a distributed trust assessment approach. Additionally, we examined the various quality of service parameters and constructed a smart contract that utilizes the state‐of‐the‐art deep reinforcement learning algorithm, namely Deep Deterministic Policy Gradient, to maximize fog revenue while serving as many user requests as possible. The entire process from task generation to results calculation is assisted by blockchain and offloading task transactions are stored in the secure, immutable, and tamper‐resistant ledger. To assess the effectiveness of our proposed scheme, we compared the simulation results with other baseline schemes over different performance metrics in terms of reward, service latency, energy consumption, task drop ratio, and transaction success rate. The results suggest that enabling trust computation improves transaction success by 21%.
A new PM2.5 concentration forecasting system based on AdaBoost‐ensemble system with deep learning approach
Authors: Li Zhongfei , Gan Kai , Sun Shaolong , Wang Shouyang
Published: June 2022

A reliable and efficient forecasting system can be used to warn the general public against the increasing PM2.5 concentration. This paper proposes a novel AdaBoost‐ensemble technique based on a hybrid data preprocessing‐analysis strategy, with the following contributions: (i) a new decomposition strategy is proposed based on the hybrid data preprocessing‐analysis strategy, which combines the merits of two popular decomposition algorithms and has been proven to be a promising decomposition strategy; (ii) the long short‐term memory (LSTM), as a powerful deep learning forecasting algorithm, is applied to individually forecast the decomposed components, which can effectively capture the long‐short patterns of complex time series; and (iii) a novel AdaBoost‐LSTM ensemble technique is then developed to integrate the individual forecasting results into the final forecasting results, which provides significant improvement to the forecasting performance. To evaluate the proposed model, a comprehensive and scientific assessment system with several evaluation criteria, comparison models, and experiments is designed. The experimental results indicate that our developed hybrid model considerably surpasses the compared models in terms of forecasting precision and statistical testing and that its excellent forecasting performance can guide in developing effective control measures to decrease environmental contamination and prevent the health issues caused by a high PM2.5 concentration.
Job loss and psychological distress during the COVID‐19 pandemic: Longitudinal Analysis from residents in nine predominantly African American low‐income neighborhoods
Authors: Baird Matthew D. , Cantor Jonathan , Troxel Wendy M. , Dubowitz Tamara
Published: June 2022

While psychological distress is a common sequelae of job loss, how that relationship continued during the COVID‐19 pandemic is unclear, for example, given higher health risk to working due to disease exposure. This paper examines changes in psychological distress depending on job loss among a cohort of randomly selected residents living in nine predominantly African American low‐income neighborhoods in Pittsburgh PA across four waves between 2013 and 2020. Between 2013 and 2016, we found an increase in psychological distress after job loss in line with the literature. In contrast, between 2018 and 2020 we found change in psychological distress did not differ by employment loss. However, residents who had financial concerns and lost their jobs had the largest increases in psychological distress, while residents who did not have serious financial concerns—potentially due to public assistance—but experienced job loss had no increase in distress, a better outcome even than those that retained their jobs. Using partial identification, we find job loss during the pandemic decreased psychological distress for those without serious financial concerns. This has important policy implications for how high‐risk persons within low‐income communities are identified and supported, as well as what type of public assistance may help.
Are European commercial banks more profitable than cooperative banks? Evidence from a low interest rate environment
Authors: Kuc Matěj , Teplý Petr
Published: June 2022

Our aim is to compare empirically the profitability of European cooperative and commercial banks in a low interest rate environment. We employ dynamic panel data methods on a data set of nearly 1000 banks from 11 European countries where both ownership structures compete on the same market. We observed different banking strategies during the 2009–2015 period, when market interest rates reached an all‐time low. Our analysis produced four main conclusions. First, we find that commercial banks maintain superior profitability over cooperative banks in an environment of low interest rates. Second, commercial banks unjustifiably decreased their loan loss provisioning in a low interest rate environment in order to maintain their profitability. This trend is not present in cooperative banks. Third, decreased provisioning is present mainly in smaller institutions. Fourth, cooperative banks are significantly more stable than commercial banks in terms of their Z‐score in a low interest rate environment. This finding is in line with existing findings during normal interest rates. In addition, the Z‐score of cooperative banks increased during the observation period, whereas the Z‐score of commercial banks remained stable. Our findings survive a battery of robustness checks. Our results also show differences in the strategies and priorities of both competing ownership types in a low interest rate environment: commercial banks focus on maintaining their profitability by decreasing loan loss provisions, whereas cooperative banks seek to increase their stability. Therefore, regulators pursuing stability in a low interest rate environment should focus on the credit risk management of small commercial banks.
Children's awareness of ethnic outgroup symbols: Piloting a task in the Republic of Ireland
Authors: Counihan Dearbháile , Carol Sarah , Taylor Laura K.
Published: June 2022

Exploring children's awareness of social categories could uncover the foundation of intergroup attitudes and behaviours. Indigenous to Ireland, Travellers are an ethnic minority marked by a tradition of nomadism, only formally recognised as a distinct ethnic group in 2017. This brief report analyses data from 148 children aged 6–12 (55% female) in the Republic of Ireland. A quantitative task was adapted and applied to assess children's awareness of symbols associated with the Traveller community. We found that primary school children could accurately categorise the symbols which relied on perceptually obvious markers of Traveller identity, and that this remained stable across middle childhood. However, children did not correctly categorise symbols related to less observable elements (e.g., language, trade, religion). To the best of our knowledge, these findings are the first to identify specific symbols which are salient in children's awareness about this marginalised group. Implications for school‐based interventions are discussed.
Repairing attachment in families with depressed adolescents: A task analysis
Authors: Stern Richard S. , King Ashley A. , Diamond Guy
Published: June 2022

Aims

This exploratory pilot study (N = 3 sessions, 793 speech units) used task analysis to refine an early model of the attachment task in attachment‐based family therapy (ABFT). The attachment task aims to repair long‐standing parent–adolescent relational ruptures that inhibit parents from being a resource for adolescents recovering from depression and/or suicidality.

Methods

Video recordings of three attachment task sessions with strong adherence to the model were selected for intensive study. Two sessions were rated as successful (i.e., attachment was repaired) and one as unsuccessful (i.e., attachment repair did not occur). Sessions were then coded using Structural Analysis of Social Behavior (SASB).

Results

The current SASB analysis provided empirical support for our previous clinical impression that the task involves three parts: (I) adolescent disclosure of attachment rupture, (II) parent disclosure, and (III) a more mutual conversation. SASB also provided insights into more subtle elements of the model.

Conclusion

Successful attachment sessions were associated with high parent affiliation and autonomy—both while affirming the adolescent's story and when disclosing their own experience. Unsuccessful task attempts were associated with parental enmeshment and hostile belittling, blaming, and distancing. An enhanced model of ideal parent behaviors during the attachment task is offered, including space for parent disclosure—perhaps even apology—that is both warm and independent, leading to a more mutual parent–adolescent conversation. Clinically, the findings support the importance of the therapist focusing on the process and quality of parent–child interactions to facilitate attachment repair.
Envisioning the manureshed: Toward comprehensive integration of modern crop and animal production
Authors: Kleinman Peter J. A. , Spiegal Sheri A. , Silviera Maria L. , Baker John M. , Dell Curtis J. , Bittman Shabtai , Cibin Raj , Vadas Peter A. , Buser Michael D. , Tsegaye Teferi
Published: June 2022

The specialization and intensification of agriculture have produced incredible gains in productivity, quality, and availability of agricultural commodities but have resulted in the separation of crop and animal production. A by‐product of this separation has been the accumulation of manure regions where animal production is concentrated. Enter the “manureshed,” an organizing framework for integrating animal and crop production where budgeting of manure nutrients is used to strategically guide their recycling and reuse in agricultural production systems where manure resources are of highest value. To move beyond regional nutrient balance analyses into the transformational realm required to mitigate “wicked” manure problems, manureshed management requires recognition of the challenges to systematically reorganizing resource flows. In better integrating crop and livestock systems, manureshed management must account for the unique nature of managing manure nutrients within individual livestock industries, anticipate trade‐offs in substituting manure for commercial fertilizer, promote technologies to refine manure, and engage extensive social networks across scales that range from the farmgate to nation and beyond.
Modulation of western North Pacific tropical cyclone formation by central Pacific El Niño–Southern Oscillation on decadal and interannual timescales
Authors: Song Jinjie , Klotzbach Philip J. , Wei Na , Duan Yihong
Published: June 2022

This study investigates the impact of central Pacific (CP) El Niño–Southern Oscillation (ENSO) on tropical cyclone (TC) frequency over the western North Pacific (WNP) on decadal and interannual timescales. The CP ENSO‐TC frequency relationship is strong and significant only on decadal timescales, with enhanced TC formation over most of the WNP associated with warm CP ENSO phases. TC formation changes, driven by CP ENSO, exhibit a southeast–northwest dipole pattern on interannual timescales, similar to the typical pattern induced by eastern Pacific ENSO. Associated with warm CP ENSO phases on decadal timescales are increases in TC formation east of 125°E, while increases in TC formation east of 140°E are observed with warm CP ENSO phases on interannual timescales. These increases in TC activity are primarily caused by favourable dynamic conditions, including increased 850‐hPa relative vorticity and 200‐hPa divergence and decreased 850–200‐hPa vertical wind shear. The main TC formation difference on decadal and interannual timescales is concentrated over the longitudinal band of 125°–140°E, where reduced TC formation is observed on interannual timescales during CP El Niño years, predominately due to suppressed thermodynamic factors, for example, reduced maximum potential intensity and 700–500‐hPa relative humidity. We find insignificant (significant) changes in thermodynamic conditions between 125°E and 140°E due to weak (strong) descending motion in the western cell of the anomalous Walker circulation on decadal (interannual) timescales. This can be explained by different patterns of CP ENSO‐induced sea surface temperature anomalies on different timescales.
Effects of multi‐temporal environmental variables on SOC spatial prediction models in coastal wetlands of a Chinese delta
Authors: Xu Yiming , Li Bin , Bai Junhong , Zhang Guangliang , Wang Xin , Smith Scot E. , Du Shudong
Published: June 2022

Mapping soil organic carbon (SOC) distributions in coastal wetlands plays an important role in assessing ecosystem services and investigating the global carbon cycle. Little research has explored the effects of environmental variables with seasonal variations on digital soil mapping (DSM). Our research utilized machine learning methods and established multiple prediction models of SOC based on multi‐temporal data from dry and wet seasons, and mono‐temporal data from April. The results showed that the relationships between SOC and environmental variables in different months varied significantly in coastal wetlands of the Yellow River Delta (YRD). In general, the environmental variables in the wet season showed stronger correlations and higher importance scores with SOC compared with those in the dry season. In addition, SOC prediction models in wet season and April had stronger prediction performance compared with those in the dry season. As a result, data fusion of multi‐temporal data did not necessarily contribute to the model performance enhancement. Relative homogenous soil‐landscape attributes and spectral characteristics in dry season could not accurately explain the strong spatial variation of SOC in this area, and it might be the major reason that caused the stronger model performance of soil prediction models in wet season than those in dry season. Therefore, the accurate spatial prediction of soil properties requires the characterization of the seasonal dynamics of soil‐landscape relationships. In general, the findings of this research demonstrated that seasonal variation of environmental variables should be considered in the establishment of a DSM model in coastal wetland.
Development of a microstructured tissue phantom with adaptable optical properties for use with microscopes and fluorescence lifetime imaging systems
Authors: Freymüller Christian , Ströbl Stephan , Aumiller Maximilian , Eisel Maximilian , Sroka Ronald , Rühm Adrian
Published: June 2022

Objectives

For the development and validation of diagnostic procedures based on microscopic methods, knowledge about the imaging depth and achievable resolution in tissue is crucial. This poses the challenge to develop a microscopic artificial phantom focused on the microscopic instead of the macroscopic optical tissue characteristics.

Methods

As existing artificial tissue phantoms designed for image forming systems are primarily targeted at wide field applications, they are unsuited for reaching the formulated objective. Therefore, a microscopy‐ and microendoscopy‐suited artificial tissue phantom was developed and characterized. It is based on a microstructured glass surface coated with fluorescent beads at known depths covered by a scattering agent with modifiable optical properties. The phantom was examined with different kinds of microscopy systems in order to characterize its quality and stability and to demonstrate its usefulness for instrument comparison, for example, regarding structural as well as fluorescence lifetime analysis.

Results

The analysis of the manufactured microstructured glass surfaces showed high regularity in their physical dimensions in accordance with the specifications. Measurements of the optical parameters of the scattering medium were consistent with simulations. The fluorescent beads coating proved to be stable for a respectable period of time (about a week). The developed artificial tissue phantom was successfully used to detect differences in image quality between a research microscope and an endoscopy based system. Plausible causes for the observed differences could be derived based on the well known microstructure of the phantom.

Conclusions

The artificial tissue phantom is well suited for the intended use with microscopic and microendoscopic systems. Due to its configurable design, it can be adapted to a wide range of applications. It is especially targeted at the characterization and calibration of clinical imaging systems that often lack extensive positioning capabilities such as an intrinsic z‐stage.
Solving the Dichotomy between Self‐Healing and Mechanical Properties in Rubber Composites by Combining Reinforcing and Sustainable Fillers
Authors: Araujo‐Morera Javier , Utrera‐Barrios Saul , Doral Olivares Raúl , Reyes Verdugo Manzanares María de los , López‐Manchado Miguel Ángel , Verdejo Raquel , Hernández Santana Marianella
Published: June 2022

The dichotomy between mechanical performance and repairability is a well‐known fact in the self‐healing field. One alternative to overcome this trade‐off is the inclusion of reinforcing fillers. In this research, hybrid reinforced self‐healing styrene‐butadiene rubber (SBR) composites are developed by combining mechano‐chemically modified ground tire rubber (mGTR) with carbon black (CB). The SBR matrix is systematically analyzed by means of dynamic mechanical analysis and broadband dielectric spectroscopy. The SBR composite reinforced with 20 phr mGTR and 20 phr CB maintains the healing efficiency of the unfilled SBR (80%) and improves its tensile strength by 300%, balancing perfectly well both properties. The healing efficiency is determined at different damage levels. The damage at the microscopic scale is easier to recover and does not depend on the presence of fillers, while the recovery at the macroscopic level clearly relies on the addition of filler, with more demanding healing conditions.
Two‐grid weak Galerkin method for semilinear elliptic differential equations
Authors: Chen Luoping , Wu Fanyun , Zeng Guoyan
Published: June 2022

In this paper, we investigate a two‐grid weak Galerkin method for semilinear elliptic differential equations. The method mainly contains two steps. First, we solve the semilinear elliptic equation on the coarse mesh with mesh size
, then, we use the coarse mesh solution as an initial guess to linearize the semilinear equation on the fine mesh, that is, on the fine mesh (with mesh size
), we only need to solve a linearized system. Theoretical analysis shows that when the exact solution
has sufficient regularity and
, the two‐grid weak Galerkin method achieves the same convergence accuracy as weak Galerkin method. Several examples are given to verify the theoretical results.
A novel sequential‐phase quad‐feed circularly polarized microstrip antenna for INMARSAT and GPS applications
Authors: Fu Shiqiang , Liu Lu , Li Chanjuan , Wang Zhongbao
Published: June 2022

In this letter, a compact circularly polarized microstrip antenna has been developed with a new type of feed structure. The key feature of the antenna is the integrated design of the quad‐feed network coplanar with the driven patch, which makes the antenna simple in structure and has good circular polarization performance. A parasitic patch is used to expand the impedance bandwidth, while a round hole in the center of the driven patch is used to improve impedance matching. In addition, a cavity is utilized to reduce the transverse size of the antenna and improve the front‐to‐back ratio. The simulation and experimental results show that the VSWR of the antenna is lower than 1.5 and the average gain is higher than 8dBic in the L‐band for International Maritime Satellite applications. Due to the centrosymmetric feed configuration, the antenna can also be used as a global positioning system antenna, which requires high stability of the phase center.
Acid‐base disorders: A primer for clinicians
Authors: Tucker Anne M. , Johnson Tami N.
Published: June 2022

An understanding of acid‐base physiology is necessary for clinicians to recognize and correct problems that may negatively affect provision of nutrition support and drug therapy. An overview of acid‐base physiology, the different acid‐base disorders encountered in practice, a stepwise approach to evaluate arterial blood gases, and other key diagnostic tools helpful in formulating a safe and effective medical and nutrition plan are covered in this acid‐base primer. Case scenarios are also provided for the application of principles and the development of clinical skills.
ASAP‐MS and DART‐MS as ancillary tools for direct analysis of the lichen metabolome
Authors: Ollivier Simon , Jéhan Philippe , Lambert Fabian , Olivier‐Jimenez Damien , Boustie Joël , Lohézic‐Le Dévéhat Françoise , Le Yondre Nicolas
Published: June 2022

Introduction

Lichens contain unique metabolites that most often need to be characterized from a limited amount of material. While thin layer chromatography is still the preferred analysis method for most lichenologists, liquid chromatography gives a deeper insight in the lichen metabolome, but an extractive step is needed before any analysis. Therefore, ambient ionization mass spectrometry (MS) analysis of lichen samples using Atmospheric Solid Analysis Probe (ASAP) and Direct Acquisition in Real Time (DART) techniques is evaluated.

Objective

We looked for a faster method to screen the metabolome by disrupting the classical workflow of analysis.

Methods

Four lichens selected for their metabolic diversity were analyzed with MS; namely Evernia prunastri, Lichina pygmaea, Parmelia saxatilis, and Roccella fuciformis. ASAP and DART analyses were compared against the reference electrospray ionization with a bioinformatic process including Van Krevelen diagrams as well as the multivariate comparison of the ionization methods in positive and negative modes.

Results

Metabolite profiles obtained from DART and ASAP analyses of lichen samples are consistent with classical analyses of lichen extracts. Through an easy and rapid experiment and without any extraction solvent, a large and informative profile of lichen metabolites is obtained when using complementary ionization modes of these high resolution mass spectrometry methods.

Conclusion

ASAP‐MS and DART‐MS are two ancillary methods that provide a comprehensive evaluation of the lichen metabolome.
Quantitative profiling of comprehensive composition in compound herbal injections: An NMR approach applied on Shenmai injection
Authors: Li Wenzhu , Zhao Fang , Xie Xinyuan , Yang Jiayu , Pan Jianyang , Qu Haibin
Published: June 2022

Introduction

Compound herbal injections (CHIs) can be regarded as a significant innovation in the modernisation of herbal medicine. Therefore, improving the quality control level of CHIs has always been an active research topic in traditional herbal medicine.

Objectives

In this study, Shenmai injection was used as a representative sample for investigating the ability of proton nuclear magnetic resonance (1H NMR) in the quality evaluation of CHIs.

Methods

A quantitative 1H NMR method was developed to simultaneously determine the contents of total ginsenosides, polysorbate 80, and 20 primary metabolites in Shenmai injection. Multivariate statistical analysis was combined to compare differences between samples from different manufacturers.

Results

It was found that the combined measurement uncertainty of each component is less than 1.61%, which demonstrates the reliability of the method. Furthermore, the components determined by this method account for up to 92.64% of the total solids, which is an unprecedented success in the analysis of Shenmai injection. In the end, the method was applied to the quality comparison of Shenmai injection from six manufacturers. The results showed that the differences among the samples from the six manufacturers were reflected in multiple types of components.

Conclusion

This study fully demonstrates the superiority of the quantitative 1H NMR method in comprehensive composition profiling of CHIs, which is conducive to improving the quality control level of Shenmai injection. Further, the present study can be used as a reference study for the research on the quality and safety of CHIs.
Time‐related biases in perinatal pharmacoepidemiology: A systematic review of observational studies
Authors: Ukah Ugochinyere Vivian , Aibibula Wusiman , Platt Robert W. , Dayan Natalie , Reynier Pauline , Filion Kristian B.
Published: June 2022

Background

Time‐related biases, such as immortal time and time‐window bias, frequently occur in pharmacoepidemiologic research. However, the prevalence of these biases in perinatal pharmacoepidemiology is not well understood.

Objective

To describe the frequency of time‐related biases in observational studies of medications commonly used during pregnancy (antibiotic, antifungal, and antiemetic drugs) via systematic review.

Method

We searched Medline and EMBASE for observational studies published between January 2013 and September 2020 and examining the association between antibiotic, antifungal, or antiemetic drugs and adverse pregnancy outcomes, including spontaneous abortion, stillbirth, preterm delivery, small‐for‐gestational age, pre‐eclampsia, and gestational diabetes. The proportion of studies with time‐related biases was estimated overall and by type (immortal time bias, time‐window bias).

Results

Our systematic review included 20 studies (16 cohort studies, 3 nested case–control studies, and 1 case–control study), of which 12 examined antibiotic, 6 antiemetic, and 2 anti‐fungal drugs. Eleven studies (55%) had immortal time bias due to the misclassification of unexposed, event‐free person‐time between cohort entry and exposure initiation as exposed. No included study had time‐window bias. The direction of effect varied for both studies with and without time‐related bias, with many studies reporting very wide confidence intervals around the effect estimates, thus making the direction of effect less interpretable. However, studies with time‐related bias were more likely to show protective or null associations compared with studies without time‐related bias.

Conclusion

Time‐related biases occur frequently in observational studies of drug effects during pregnancy. The use of appropriate study design and analytical approaches is needed to prevent time‐related biases and ensure study validity.
Anxiety and psychological needs of high school students: COVID‐19 period
Authors: Erden Zehra Şeyda , Aliyev Ramin
Published: June 2022

The study aims to examine the relationship between anxiety and psychological needs of high school students during the COVID‐19 pandemic period. The fact that the COVID‐19 virus poses a threat to mental health as well as physical health, the effects of the pandemic period on the psychological needs and anxiety level of adolescents, especially in a critical developmental stage, and the scarcity of studies concerning adolescent mental health in this period, constituted the purpose of the current study. The quantitative (N = 502) and qualitative data (N = 29) of the study, in which triangulation design was used, were collected from high school students studying in the Southeast of Turkey. In the quantitative part of the research Multiple Linear Regression was implemented and content analysis was used in the qualitative part. The results indicate that love, belonging and power, which are subdimensions of basic psychological needs, are significant predictors of anxiety level. Additionally, it is seen that the need for freedom subdimension is the least predictive variable for the anxiety level. It is concluded that there are moderate negative and significant relationships between the general and subdimensions of basic psychological needs and the level of state anxiety. In this sense, it has been found that the level of anxiety tends to decrease as the level of the satisfaction of basic psychological needs increases. The results in the qualitative analysis part largely overlap with the results in the quantitative part. Suggestions based on the findings of the study are made to researchers.
rrQNet: Protein contact map quality estimation by deep evolutionary reconciliation
Authors: Roche Rahmatullah , Bhattacharya Sutanu , Shuvo Md. Hossain , Bhattacharya Debswapna
Published: June 2022

Protein contact maps have proven to be a valuable tool in the deep learning revolution of protein structure prediction, ushering in the recent breakthrough by AlphaFold2. However, self‐assessment of the quality of predicted structures are typically performed at the granularity of three‐dimensional coordinates as opposed to directly exploiting the rotation‐ and translation‐invariant two‐dimensional (2D) contact maps. Here, we present rrQNet, a deep learning method for self‐assessment in 2D by contact map quality estimation. Our approach is based on the intuition that for a contact map to be of high quality, the residue pairs predicted to be in contact should be mutually consistent with the evolutionary context of the protein. The deep neural network architecture of rrQNet implements this intuition by cascading two deep modules—one encoding the evolutionary context and the other performing evolutionary reconciliation. The penultimate stage of rrQNet estimates the quality scores at the interacting residue‐pair level, which are then aggregated for estimating the quality of a contact map. This design choice offers versatility at varied resolutions from individual residue pairs to full‐fledged contact maps. Trained on multiple complementary sources of contact predictors, rrQNet facilitates generalizability across various contact maps. By rigorously testing using publicly available datasets and comparing against several in‐house baseline approaches, we show that rrQNet accurately reproduces the true quality score of a predicted contact map and successfully distinguishes between accurate and inaccurate contact maps predicted by a wide variety of contact predictors. The open‐source rrQNet software package is freely available at https://github.com/Bhattacharya-Lab/rrQNet.
Put Your “Hand Emotes in the Air:” Twitch Concerts as Unsuccessful Large‐Scale Interaction Rituals
Authors: Vandenberg Femke
Published: June 2022

Recent years have seen mediated concerts grow in popularity. As physical events, concerts accommodate both small‐ and large‐scale interaction. However, the extent to which these interactions establish emotional energy online is debated. Combining video observations and video elicitation interviews with participants of live music on the online platform Twitch, this paper explores this growing form of music consumption while also adding to the debate on the appropriateness of the virtual sphere in accommodating successfulinteraction rituals. It finds that the online sphere is more suited to small‐scale interaction. Large‐scale interaction rituals are unsuccessful, suffering from acontext collapse,social convergence, and norhythmic entrainment.
Water quality assessment using synchrotron‐based TXRF
Authors: Garg Vijay Kumar , Tanta Ankush , Lal Srivastav Arun , Tiwari M. K. , Sharma Ajay , Kanwar Varinder Singh
Published: June 2022

The pollutants released from pharmaceutical, steel, paper, and battery industries into water cause stress on the natural ecosystems, may mix with soil and water, enter into human food chain, and hence cause irreparable damage to the biotic system. Hence, the appropriate monitoring of water along with determination of heavy metals is very important for human beings. In present paper, total reflection X‐ray fluorescence (TXRF) spectrometry technique is employed to determine the level of different contaminants in the water samples gathered from the various sites of an identified industrial area. Experimentation is carried out at Raja Ramanna Centre for Advanced Technology (RRCAT), Indore‐India by using TXRF, which is one the advance techniques of element determination up to ppb levels. The elemental concentration of Cl, K, Ca, Fe, Cu, Zn, Ga, Br, Sr, As, Pb, and Ni is quantified and compared with the limits established by the WHO (World Health Organization) and BIS (Bureau of Indian Standard) guidelines regarding drinking water use. The levels of the Pb, Fe, As, Ni, Cr, Co, and Mn exceeded the values suggested by WHO and BIS at some locations/sites. To better understand the situation, water quality parameters such as Water Quality Index (WQI), Heavy metal Pollution Index (HPI), Contamination Index (CI), Metal Enrichment Index (MEI), and Heavy Metal Evaluation Index (HEI) have also been assessed for all the sites close to industrial hub. Among all sites except at A3 and A6, WQI is found to be much greater than WHO and BIS established limit. Level of arsenic in the water at A1 location was found 73 ppb. However, lead metal in water was found to be very high at all the six studied locations, and at A1 location, it is found extremely high 2613 ppb. Therefore, water at A1 and A2 sites is found to be unfit for drinking.

Practitioner Points

Total reflection X‐ray fluorescence (TXRF) spectrometry technique is employed to determine the level of different contaminants in the water samples

The elemental concentration of Cl, K, Ca, Fe, Cu, Zn, Ga, Br, Sr, As, Pb, and Ni is quantified and compared with the limits prescribed by the WHO

Water Quality Index (WQI), Heavy metal Pollution Index (HPI), Contamination Index (CI), Metal Enrichment Index (MEI), and Heavy Metal Enrichment Index (MEI) have also been assessed for all the sites

Water at some sites is found unfit for drinking purpose. Based on the observations, some remedial measures are suggested to reduce the level of water contaminants up to desired levels.
Resistance and tolerance of Mycobacterium tuberculosis to antimicrobial agents—How M. tuberculosis can escape antibiotics
Authors: Li Haoran , Yuan Jinfeng , Duan Shujuan , Pang Yu
Published: June 2022

Tuberculosis (TB) poses a serious threat to public health worldwide since it was discovered. Until now, TB has been one of the top 10 causes of death from a single infectious disease globally. The treatment of active TB cases majorly relies on various anti‐tuberculosis drugs. However, under the selection pressure by drugs, the continuous evolution of Mycobacterium tuberculosis (Mtb) facilitates the emergence of drug‐resistant strains, further resulting in the accumulation of tubercle bacilli with multiple drug resistance, especially deadly multidrug‐resistant TB and extensively drug‐resistant TB. Researches on the mechanism of drug action and drug resistance of Mtb provide a new scheme for clinical management of TB patients, and prevention of drug resistance. In this review, we summarized the molecular mechanisms of drug resistance of existing anti‐TB drugs to better understand the evolution of drug resistance of Mtb, which will provide more effective strategies against drug‐resistant TB, and accelerate the achievement of the EndTB Strategy by 2035.

This article is categorized under:

Infectious Diseases > Molecular and Cellular Physiology
Process Mining: A 360 Degree Overview
Authors: van der Aalst Wil M. P.
Published: June 2022

Process mining enables organizations to uncover their actual processes, provide insights, diagnose problems, and automatically trigger corrective actions. Process mining is an emerging scientific discipline positioned at the intersection between process science and data science. The combination of process modeling and analysis with the event data present in today’s information systems provides new means to tackle compliance and performance problems. This chapter provides an overview of the field of process mining introducing the different types of process mining (e.g., process discovery and conformance checking) and the basic ingredients, i.e., process models and event data. To prepare for later chapters, event logs are introduced in detail (including pointers to standards for event data such as XES and OCEL). Moreover, a brief overview of process mining applications and software is given.
Predictive Process Monitoring
Authors: Di Francescomarino Chiara , Ghidini Chiara
Published: June 2022

Predictive Process Monitoring [29] is a branch of process mining that aims at predicting the future of an ongoing (uncompleted) process execution. Typical examples of predictions of the future of an execution trace relate to the outcome of a process execution, to its completion time, or to the sequence of its future activities
Streaming Process Mining
Authors: Burattin Andrea
Published: June 2022

Streaming process mining refers to the set of techniques and tools which have the goal of processing a stream of data (as opposed to a finite event log). The goal of these techniques, similarly to their corresponding counterparts described in the previous chapters, is to extract relevant information concerning the running processes. This chapter presents an overview of the problems related to the processing of streams, as well as a categorization of the existing solutions. Details about control-flow discovery and conformance checking techniques are also presented together with a brief overview of the state of the art.
Responsible Process Mining
Authors: Mannhardt Felix
Published: June 2022

The prospect of data misuse negatively affecting our life has lead to the concept of responsible data science. It advocates for responsibility to be built, by design, into data management, data analysis, and algorithmic decision making techniques such that it is made difficult or even impossible to intentionally or unintentionally cause harm. Process mining techniques are no exception to this and may be misused and lead to harm. Decisions based on process mining may lead to unfair decisions causing harm to people by amplifying the biases encoded in the data by disregarding infrequently observed or minority cases. Insights obtained may lead to inaccurate conclusions due to failing to considering the quality of the input event data. Confidential or personal information on process stakeholders may be leaked as the precise work behavior of an employee can be revealed. Process mining models are usually white-box but may still be difficult to interpret correctly without expert knowledge hampering the transparency of the analysis. This chapter structures the topic of responsible process mining based on the FACT criteria: Fairness, Accuracy, Confidentiality, and Transparency. For each criteria challenges specific to process mining are provided and the current state of the art is briefly summarized.
Using Process Mining in Healthcare
Authors: Martin Niels , Wittig Nils , Munoz-Gama Jorge
Published: June 2022

This chapter introduces a specific application domain of process mining: healthcare. Healthcare is a very promising domain for process mining given the significant societal value that can be generated by supporting process improvement in a data-driven way. Within a healthcare organisation, a wide variety of processes is being executed, many of them being highly complex due to their loosely-structured and knowledge-intensive nature. Consequently, performing process mining in healthcare is challenging, but can generate significant societal impact. To provide more insights in process mining in healthcare, this chapter first provides an overview of healthcare processes and healthcare process data, as well as their particularities compared to other domains. Afterwards, an overview of common use cases in process mining in healthcare research is presented, as well as insights from a real-life case study. Subsequently, an overview of open challenges to ensure a widespread adoption of process mining in healthcare is provided. By tackling these challenges, process mining will become able to fully play its role to support evidence-based process improvement in healthcare and, hence, contribute to shaping the best possible care for patients in a way that is sustainable in the long run.
Foundations of Process Discovery
Authors: van der Aalst Wil M. P.
Published: June 2022

Process discovery is probably the most interesting, but also most challenging, process mining task. The goal is to take an event log containing example behaviors and create a process model that adequately describes the underlying process. This chapter introduces the baseline approach used in most commercial process mining tools. A simplified event log is used to create a so-called Directly-Follows Graph (DFG). This baseline is used to explain the challenges one faces when trying to discover a process model. After introducing DFG discovery, we focus on techniques that are able to discover models allowing for concurrency (e.g., Petri nets, process trees, and BPMN models). The chapter distinguishes two types of approaches able to discover such models: (1) bottom-up process discovery and (2) top-down process discovery. The Alpha algorithm is presented as an example of a bottom-up technique. The approach has many limitations, but nicely introduces the idea of discovering local constraints. The basic inductive mining algorithm is presented as an example of a top-down technique. This approach, combined with frequency-based filtering, works well on most event logs. These example algorithms are used to illustrate the foundations of process discovery.
Foundations of Process Enhancement
Authors: de Leoni Massimiliano
Published: June 2022

Process models are among the milestones for Business Process Management and Mining, and used to describe a business process or to prescribe how its instances should be carried out. It follows that they need to fulfill certain properties to be useful. If they aim to represent how the process is currently being executed, they need to be precise and recall the behavior observed in reality. If the goal is to ensure that the process is executed according to laws and regulations, its model should only allow the behavior that is valid from a domain viewpoint and provides some guarantee to ensure good performance level. Process enhancement is the type of Process Mining that aims at models that fulfill these properties, and the literature further splits it into two subfields: process extension and process improvement. Process extension aims to incorporate the process perspectives on data, decision, resources and time into the model: their inclusion in process models enable designers to fine-tune the model specifications, thus obtaining models with higher levels of precision. Process improvement passes through an “improved” process model. If the model contains portions of behavior that lead to unsatisfactory outcomes (high costs, low customer satisfactions, etc.) or that violate norms and regulations, one would like those portions to be disallowed by the model. In case some executions are observed in reality and are not allowed by the model, they should be incorporated into the model if they are observed to generally yield good performances. This chapter discusses these two types of process enhancement, and illustrates some basic and some advanced techniques to tackle it, highlighting the pros and cons, and the underlaying assumptions.
Systematic review exploring the relationship between sexual abuse and lower urinary tract symptoms
Authors: Selai Caroline , Elmalem Michael S. , Chartier-Kastler Emmanuel , Sassoon Natalia , Hewitt Sam , Rocha Maria Francisca , Klitsinari Larisa , Panicker Jalesh N.
Published: June 2022

Introduction and hypothesis

Patients presenting with lower urinary tract symptoms (LUTS) may report a history of sexual abuse (SA), and survivors of SA may report LUTS; however, the nature of the relationship is poorly understood. The aim of this review is to systematically evaluate studies that explore LUT dysfunction in survivors of SA.

Methods

A systematic literature search of six databases, Cochrane Database of Systematic Reviews, MEDLINE, EMBASE, CINAHL, AMED, and PsycINFO, was performed. The last search date was June 2021 (PROSPERO CRD42019122080). Studies reporting the prevalence and symptoms of LUTS in patients who have experienced SA were included. The literature was appraised according to the PRISMA statement. The quality of the studies was assessed.

Results

Out of 272 papers retrieved, 18 publications met the inclusion criteria: studies exploring LUTS in SA survivors (n=2), SA in patients attending clinics for their LUTs (n=8), and cross-sectional studies (n=8). SA prevalence ranged between 1.3% and 49.6%. A history of SA was associated with psychosocial stressors, depression, and anxiety. LUTS included urinary storage symptoms, voiding difficulties, voluntary holding of urine and urinary tract infections. Most studies were of moderate quality. Assessment of SA and LUTS lacked standardisation.

Conclusions

The review highlights the need for a holistic assessment of patients presenting with LUTS. Although most of the studies were rated as being of ‘moderate’ quality, the evidence suggests the need to provide a “safe space” in clinic for patients to share sensitive information about trauma. Any such disclosure should be followed up with further assessment.