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SUSTAINABILITY REPORTS, CORPORATE GOVERNANCE, AND ORGANIZATIONAL UNITS
(2026) Claessen, Arni; Sigurjonsson, Throstur Olaf; Wendt, Stefan; Department of Business and Economics
This study examines how Icelandic companies disclose sustainability-related governance practices and evaluates the impact of the new Corporate Sustainability Reporting Directive (CSRD) and the European Sustainability Reporting Standards (ESRS) (European Commission, 2025) on future disclosure practices. Building on Claessen’s (2023) approach and using qualitative methodology, the research combines document analysis of sustainability reports from ten leading Icelandic firms with semi-structured interviews with company representatives. The study finds that current governance disclosures cover only 20–30 percent of ESRS minimum requirements. Key challenges include limited knowledge of ESRS among board members and consultants, weak communication structures, insufficient metrics and verification procedures, and resource constraints. The recent changes introduced by the 2025 Omnibus Act (European Council, 2025) further complicate compliance, intensifying the urgency for firms to adapt. The findings underscore the need for Icelandic companies to invest in training, enhance internal governance systems, and build robust reporting frameworks to meet regulatory demands. Without such efforts, companies risk legal and reputational repercussions. This research provides timely, country-specific insights into governance disclosure gaps and offers practical recommendations for aligning with the European Union’s (EU’s) evolving sustainability reporting landscape.
Erratum to : I Keep my Problems to Myself: Negative Social Network Orientation, Social Resources, and Health-Related Quality of Life in Cancer Survivors
(2017-12-01) Rini, Christine; Symes, Yael; Campo, Rebecca A.; Wu, Lisa M.; Austin, Jane; Department of Psychology
Interplay between Aharonov-Bohm and Altshuler-Aronov-Spivak oscillations in phase-pure GaAs/InAs core/shell nanowires of different lengths
(2026-10) Basarić, Farah; Wang, Kaiwen; Dumitru, Tudor Gabriel; Manolescu, Andrei; Alvarado Cesar, Francisco; Sanchez, Ana M.; Krause, Christoph; Grützmacher, Detlev; Pawlis, Alexander; Schäpers, Thomas; Department of Engineering
In GaAs/InAs core/shell nanowires, comprising a tubular conducting shell, interference phenomena observed under an axial field and originating from closed-loop states encircling the insulating core provide an ideal platform for superconducting quantum devices that utilize effects such as Aharonov-Bohm–type or Altshuler-Aronov-Spivak–type conductance oscillations. Both effects are different in nature with respect to phase rigidity because of interference of non-time-reversed or time-reversed paths, respectively. Since their occurrence is largely governed by averaging effects, which depend on sample dimensions and the transport regime, we present a systematic study of flux-periodic oscillations of phase-pure zinc-blende GaAs/InAs core/shell nanowires as a function of gate voltage for samples with different contact separation lengths. Our analysis shows that with increasing contact separation length, averaging effects result in gradual reduction of h/e-periodic Aharonov-Bohm–type oscillations, while relative to that the h/2e-periodic Altshuler-Aronov-Spivak oscillations and its h/4e-periodic higher harmonics are enhanced. The additional phase rigidity seen in the h/3e-periodic oscillations is attributed to phase rigidity propagating from the neighboring lower harmonics. Our tight-binding transport simulations on nanowires of different lengths which contain only a few scattering centers confirm the experimental observations regarding the different harmonics and their phase rigidity. Together, our experimental and simulation findings indicate quasiballistic transport with persistent Aharonov-Bohm–type and phase-rigid Altshuler-Aronov-Spivak–type oscillations despite few scattering centers.
Global, regional, and national prevalence of second-hand smoke and attributable disease burden in 204 countries and territories, 1990–2023 : a systematic analysis for the Global Burden of Disease Study 2023
(2026-10) E.GakidouEmmanuelaGBD 2023 Second-hand smoke Collaborators; Department of Psychology
Background Second-hand smoke (SHS) exposure remains a major source of morbidity and mortality among non-smokers. This study presents the first dedicated Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) publication to systematically quantify SHS prevalence and evaluate its effect as a global risk factor across a broader range of outcomes. Methods Using the GBD 2023 comparative risk assessment framework, we estimated SHS exposure prevalence and attributable disease burden across 204 countries and territories from 1990 to 2023, stratified by age and sex. Exposure estimates were derived by synthesising population-based surveys and household composition data through spatiotemporal Gaussian process regression. Relative risks for nine health outcomes, now including asthma, were estimated using the Burden of Proof methodology, and applied to calculate attributable deaths and disability-adjusted life-years (DALYs). Findings In 2023, an estimated 2·71 billion (95% UI 2·44–3·02) people worldwide were exposed to SHS, including 767 million (687–858) children aged 0–14 years. Age-standardised prevalence was highest in southeast Asia, east Asia, and Oceania (48·4% [44·0–53·4]), with female individuals in some countries experiencing nearly 1·8 times the exposure prevalence of male individuals. Despite a 22·2% (10·6–32·4) decline in global age-standardised prevalence since 1990, this progress has not been sufficient to reduce the absolute number of exposed individuals, which has remained stable globally and has risen sharply in sub-Saharan Africa (98·5% [62·0–144·4] increase) and North Africa and the Middle East (72·1% [47·8–101·8] increase). In 2023, SHS exposure accounted for 1·66 million (1·33–2·07) deaths and 44·8 million (35·8–54·3) DALYs globally. Ischaemic heart disease was the leading contributor to SHS-attributable DALYs (12·0 million [9·31–15·2]) overall, while lower respiratory infections predominated among children (5·16 million [3·48–7·12]). Interpretation We estimated that SHS remains a substantial driver of global health loss, particularly through cardiovascular and paediatric respiratory diseases. Persistent geographical disparities and growing absolute numbers of exposed individuals in several regions underscore the urgent need for accelerated implementation and enforcement of comprehensive tobacco control measures, particularly to protect women and children in both public and domestic environments. Funding Bloomberg Philanthropies and the Gates Foundation.
Automated deep learning-based arousal detection agrees with ten expert scorers in unseen data
(2026-10) Pitkänen, Henna; Huttunen, Riku; Tashakori, Masoumeh; Rissanen, Marika; Rusanen, Matias; Gretarsdottir, Heidur; Arnardottir, Erna Sif; Paunio, Tiina; Kulkas, Antti; Nikkonen, Sami; Leppänen, Timo; Korkalainen, Henri; Department of Engineering
Study Objectives: Automatic arousal scoring is essential for the cost-effective analysis of polysomnograms. Many previous automatic methods have been tested in homogeneous, single-center datasets, and all against one scorer, on event-by-event basis, or in 30-s segments. We developed a deep learning-based arousal scoring model and tested it at 1-s resolution in an independent dataset scored by ten expert scorers from seven different sleep centers. Materials and Methods: A fully convolutional neural network model was developed using 1847 openly available polysomnograms from the Multi-Ethnic Study of Atherosclerosis (MESA) and tested in 205 hold-out polysomnograms from the MESA cohort, and in an independent Sleep Revolution (SR) cohort of 50 polysomnograms, each scored by ten scorers. Model performance was evaluated on an event-by-event basis and in 1-s segments. In the SR dataset, the performance was tested against each scorer individually and against their majority agreement. Results: In the MESA test set, the model achieved an event-by-event F1-score of 0.77, a 1-s segment κ-value of 0.67, and an arousal index (ArI) intraclass correlation coefficient (ICC) of 0.83. In the multi-scorer SR dataset, the model reached a median event-by-event F1-score, a 1-s κ-value, and an ArI ICC of 0.61, 0.50, and 0.66, respectively, exceeding the corresponding median values of the scorers among themselves (0.57, 0.46, and 0.59, respectively). Conclusions: The model achieved higher median arousal scoring agreement with ten manual scorers than the scorers did among themselves in an independent dataset. This suggests that the model generalizes well and can reliably be used in different sleep recordings.
From trauma to tolerance : Psychological predictors of gambling harm among male UK Armed Forces Veterans
(2026-09) Ekenna, Adanma; Dekel, Dana; Engward, Hilary; Godier-McBard, Lauren; Kay, Chris; Kersey, Thomas; Fossey, Matt; Dymond, Simon; Department of Psychology
Introduction: Veterans face disproportionately high risks of gambling-related harm, influenced by trauma exposure, emotional dysregulation, and maladaptive coping. However, little is known about how these interconnected psychological traits contribute to gambling harm severity in Veterans. Methods: A cross-sectional survey of 184 UK Armed Forces Veterans assessed socio-demographic and military characteristics, gambling engagement, posttraumatic stress disorder (PTSD), adverse childhood experiences (ACEs), impulsivity, intolerance of uncertainty (IU), and psychological flexibility (PsyFlex). Associations with gambling harm severity, measured using the Problem Gambling Severity Index (PGSI), were examined through chi-squared tests, correlations, and stepwise logistic regression. Results: Of the sample, 25% scored ≥1 on the PGSI, with 3.3% meeting the threshold for problem gambling. Gambling harm severity was significantly associated with PTSD re-experiencing and avoidance, household dysfunction ACE, negative and positive urgency, inhibitory IU, and low PsyFlex. Regression analysis identified PTSD re-experiencing (OR 2.16), PTSD avoidance (OR 0.27), inhibitory IU (OR 7.96), and prospective IU (OR 0.12) as significant predictors. Discussion: The findings underscore the multi-faceted psychological vulnerabilities underlying gambling harm in Veterans, thus supporting the need for trauma-informed, personalized interventions. Targeted screening for IU, impulsivity, and PsyFlex could improve prevention and treatment efforts.
Correction to : Global scenarios for significant water use reduction in thermal power plants based on cooling water demand estimation using satellite imagery (Nature Energy, (2019), 4, 12, (1040-1048), 10.1038/s41560-019-0501-4)
(2025-03) Lohrmann, Alena; Farfan, Javier; Caldera, Upeksha; Lohrmann, Christoph; Breyer, Christian; Department of Computer Science
Correction to: Nature Energyhttps://doi.org/10.1038/s41560-019-0501-4, published online 25 November 2019.
Troubled waters : Estimating the role of the power sector in future water scarcity crises
(2023-11-01) Lohrmann, Alena; Farfan, Javier; Lohrmann, Christoph; Kölbel, Julian Fritz; Pettersson, Frank; Department of Computer Science
One of the effects of climate change is on freshwater availability. The widespread drought in the summer of 2022 impeded access to freshwater, putting into question the reliability of the current and future energy generation and evoking concerns of competition of different industries for water. In response to climate change, energy transition scenarios represent pathways to a more sustainable energy system, but often overlook the water footprint of the energy sector. Therefore, this study uses machine learning for the identification of thermal power plants’ cooling systems to estimate the water footprint of the current and future energy system using six energy transition scenarios. It is built on published data on thermal power plants announced globally, with a total capacity of 3277 GW, which are planned to be installed between 2020 and 2050. The results demonstrate that the water consumption of the global power sector may increase by up to 50% until 2050, compared to the 2020 level. The findings also emphasize that every new thermal power plant installed in the future will be associated with a higher average water demand per unit of generated electricity. Hence, the rising stress on water systems becomes another argument supporting the transition towards renewables.
NCOurd : modelling length distributions of NCO events and gene conversion tracts
(2023-08-01) Hardarson, Marteinn T.; Palsson, Gunnar; Halldorsson, Bjarni V.; Department of Engineering
Motivation: Meiotic recombination is the main driving force of human genetic diversity, along with mutations. Recombinations split into crossovers, separating large chromosomal regions originating from different homologous chromosomes, and non-crossovers (NCOs), where a small segment from one chromosome is embedded in a region originating from the homologous chromosome. NCOs are much less studied than mutations and crossovers as NCOs are short and can only be detected at markers heterozygous in the transmitting parent, leaving most of them undetectable. Results: The detectable NCOs, known as gene conversions, hide information about NCOs, including their number and length, waiting to be unveiled. We introduce NCOurd, software, and algorithm, based on an expectation-maximization algorithm, to estimate the number of NCOs and their length distribution from gene conversion data.
Unorthodox technique in sensing with the metamaterial-based resonator sensor at millimetre frequencies
(2023-08) Qureshi, Suhail Asghar; Abidin, Zuhairiah Zainal; Majid, Huda A.; Ashyap, Adel Y.I.; Esmail, Bashar A.F.
A metamaterial-based resonator is presented in this paper for liquid-sensing applications. The designed sensor operates at millimetre-wave (mm-w) frequencies, and it can characterise the samples that may possess identical characteristics. This paper relies on the extracted permittivity of the structure in the characterisation of the samples, mainly liquids. The sensor requires a very small amount of samples for sensing and it is used in distinguishing oil, ethanol, methanol, glycerol and water. A shift in the resonance frequency of about 200 MHz per unit increase in the epsilon value of samples was achieved. The oil sample showed the lowest value in the extracted permittivity value, while water showed the nearest to zero extracted permittivity. This relationship of variation in extracted permittivity parameter with the change in the sample’s epsilon value is found to be linear and reliable regardless of the change in thickness of the sample.
Identification of Parkinson's Disease Subtypes from Resting-State Electroencephalography
(2023-08) Yassine, Sahar; Gschwandtner, Ute; Auffret, Manon; Duprez, Joan; Verin, Marc; Fuhr, Peter; Hassan, Mahmoud
Background: Parkinson's disease (PD) patients present with a heterogeneous clinical phenotype, including motor, cognitive, sleep, and affective disruptions. However, this heterogeneity is often either ignored or assessed using only clinical assessments. Objectives: We aimed to identify different PD sub-phenotypes in a longitudinal follow-up analysis and their electrophysiological profile based on resting-state electroencephalography (RS-EEG) and to assess their clinical significance over the course of the disease. Methods: Using electrophysiological features obtained from RS-EEG recordings and data-driven methods (similarity network fusion and source-space spectral analysis), we have performed a clustering analysis to identify disease sub-phenotypes and we examined whether their different patterns of disruption are predictive of disease outcome. Results: We showed that PD patients (n = 44) can be sub-grouped into three phenotypes with distinct electrophysiological profiles. These clusters are characterized by different levels of disruptions in the somatomotor network (Δ and β band), the frontotemporal network (α2 band) and the default mode network (α1 band), which consistently correlate with clinical profiles and disease courses. These clusters are classified into either moderate (only-motor) or mild-to-severe (diffuse) disease. We showed that EEG features can predict cognitive evolution of PD patients from baseline, when the cognitive clinical scores were overlapped. Conclusions: The identification of novel PD subtypes based on electrical brain activity signatures may provide a more accurate prognosis in individual patients in clinical practice and help to stratify subgroups in clinical trials. Innovative profiling in PD can also support new therapeutic strategies that are brain-based and designed to modulate brain activity disruption.
Accuracy of a computer vision system for estimating biomechanical measures of body function in axial spondyloarthropathy patients and healthy subjects
(2023-08) Cronin, Neil J.; Mansoubi, Maedeh; Hannink, Erin; Waller, Benjamin; Dawes, Helen
Objective: Advances in computer vision make it possible to combine low-cost cameras with algorithms, enabling biomechanical measures of body function and rehabilitation programs to be performed anywhere. We evaluated a computer vision system's accuracy and concurrent validity for estimating clinically relevant biomechanical measures. Design: Cross-sectional study. Setting: Laboratory. Participants: Thirty-one healthy participants and 31 patients with axial spondyloarthropathy. Intervention: A series of clinical functional tests (including the gold standard Bath Ankylosing Spondylitis Metrology Index tests). Each test was performed twice: the first performance was recorded with a camera, and a computer vision algorithm was used to estimate variables. During the second performance, a clinician measured the same variables manually. Main measures: Joint angles and inter-limb distances. Clinician measures were compared with computer vision estimates. Results: For all tests, clinician and computer vision estimates were correlated (r2 values: 0.360–0.768). There were no significant mean differences between methods for shoulder flexion (left: 2 ± 14° (mean ± standard deviation), t = 0.99, p < 0.33; right: 3 ± 15°, t = 1.57, p < 0.12), side flexion (left: − 0.5 ± 3.1 cm, t = −1.34, p = 0.19; right: 0.5 ± 3.4 cm, t = 1.05, p = 0.30) and lumbar flexion (− 1.1 ± 8.2 cm, t = −1.05, p = 0.30). For all other movements, significant differences were observed, but could be corrected using a systematic offset. Conclusion: We present a computer vision approach that estimates distances and angles from clinical movements recorded with a phone or webcam. In the future, this approach could be used to monitor functional capacity and support physical therapy management remotely.
Dynamic rewiring of electrophysiological brain networks during learning
(2023-06-30) Ruggeri, Paolo; Miehlbradt, Jenifer; Kabbara, Aya; Hassan, Mahmoud
Human learning is an active and complex process. However, the brain mechanisms underlying human skill learning and the effect of learning on the communication between brain regions, at different frequency bands, are still largely unknown. Here, we tracked changes in large-scale electrophysiological networks over a 6-week training period during which participants practiced a series of motor sequences during 30 home training sessions. Our findings showed that brain networks become more flexible with learning in all the frequency bands from theta to gamma ranges. We found consistent increase of flexibility in the prefrontal and limbic areas in the theta and alpha band, and over somatomotor and visual areas in the alpha band. Specific to the beta rhythm, we revealed that higher flexibility of prefrontal regions during the early stage of learning strongly correlated with better performance measured during home training sessions. Our findings provide novel evidence that prolonged motor skill practice results in higher, frequency-specific, temporal variability in brain network structure.
Reproducibility Companion Paper : MeTILDA - Platform for Melodic Transcription in Language Documentation and Application
(Association for Computing Machinery, Inc, 2023-06-12) Lee, Mitchell; Lee, Chris; Penmetsa, Sanjay; Chen, Min; Miyashita, Mizuki; Fish, Naatosi; Wu, Bo; Khan, Omar
This companion paper supports the replication of the development and evaluation of "MeTILDA - Platform for Melodic Transcription in Language Documentation and Application"that we presented in the ICMR 2021. MeTILDA aims to help document and analyze pitch patterns of endangered languages including Blackfoot, whose prosodic system is characterized by pitch movements. It develops a new form of audio analysis (termed MeT scale which is a perceptual scale) and automates the process of creating visual aids (Pitch Art) to provide more effective visuals of perceived changes in pitch movement. In this paper, we explain the file structure of the source code and publish the details of our data as well as system operations. Moreover, we provide a link to the demo video for facilitating the use of our platform.
Energy Management and Network Traffic Avoidance Using GAODM and E-AODV Protocols in Mobile Ad-Hoc Network
(2023-06-01) Vatambeti, Ramesh; Pradhan, Nrusingha Charan; Sandhya, E.; Vinta, Surendra Reddy; Anbarasu, V.; Venkateswara Rao, K.
Because of the mobility of its nodes, MANET plays a significant role in mobile communication. As a result, network infrastructure is frequently changed, resulting in data loss and communication overheads. Despite this, the large packet size causes network congestion or traffic. The difficult task is efficient routing through a dynamic network. For node generation and energy management, the proposed approach in this paper employs GAODM (Geography-based Ad-hoc On Demand disjoint multipath) and E-AODM (Energy Ad-hoc On Demand Vector routing). The proposed GAODM routing protocol reduces congestion using Spider Monkey (SM) Optimization. The E-AODM protocol assesses the energy management solution based on parameters such as delay, energy consumption, routing overhead, and node energy. By choosing the best path through the network, the proposed protocol's effectiveness is increased. The proposed protocol reduces routing overload, delay, and congestion. The simulated results show that increasing the number of packets transmitted in the network using the proposed GAODM and E-AODM routing protocols over the existing protocols on NS 2 reduces node energy and, as a result, overload and delay.
Successful reproduction of a large EEG study across software packages
(2023-06) Kabbara, Aya; Forde, Nina; Maumet, Camille; Hassan, Mahmoud
As an active field of research and with the development of state-of-the-art algorithms to analyze EEG datasets, the parametrization of Electroencephalography (EEG) analysis workflows has become increasingly flexible and complex, with a great variety of methodological options and tools to be selected at each step. This high analytical flexibility can be problematic as it can yield to variability in research outcomes. Therefore, growing attention has been recently paid to understand the potential impact of different methodological decisions on the reproducibility of results. In this paper, we aim to examine how sensitive the results of EEG analyses are to variations in preprocessing with different software tools. We reanalyzed the shared EEG data (N = 500) from (Williams et al., 2021) using three of the most commonly used open-source Matlab-based EEG software tools: EEGLAB, Brainstorm and FieldTrip. After reproducing the same original preprocessing workflow in each software, the resulting event-related potentials (ERPs) were qualitatively and quantitatively compared in order to examine the degree of consistency/discrepancy between software packages. Our findings show a good degree of convergence in terms of the general profile of ERP waveforms, peak latencies and effect size estimates related to specific signal features. However, considerable variability was also observed in the magnitude of the absolute voltage observed with each software package as reflected by the similarity values and observed statistical differences at particular channels and time instants. In conclusion, we believe that this study provides valuable clues to better understand the impact of the software tool on the analysis of EEG results.
Analyzing the Relationship between Participation in Sports and Harmful Alcohol Drinking in Early Adolescence : Two-Year Prospective Analysis
(2023-06) Zenic, Natasa; Kvesic, Ivan; Corluka, Mate; Trivic, Tatjana; Drid, Patrik; Saavedra, Jose M.; Foretic, Nikola; Modric, Toni; Gilic, Barbara; Department of Sport Science
Alcohol drinking is an important health-related problem and one of the major risk factors for a wide array of non-communicable diseases, while there is a lack of studies investigating environment-specific associations between sports participation and alcohol drinking in adolescence. This study prospectively investigated the relationship between sports factors (i.e., participation in sports and competitive achievement), with the prevalence of harmful alcohol drinking (HD), and HD initiation in 14-to-16 years old adolescents from Bosnia and Herzegovina (n = 641, 337 females, 43% living in rural community). Participants were tested over 4-time points divided by approximately 6 months, from the beginning of high school to the end of the second grade. Variables included gender, factors related to sport participation, a community of residence (urban or rural), and outcome: alcohol consumption was assessed by the AUDIT questionnaire. Results evidenced that the prevalence of HD increased over the study period from 6 to 19%, with no significant differences between urban and rural youth. Logistic regression for HD as criterion evidenced adolescents who participated in sports and then quit as being at particular risk for drinking alcohol at the study baseline. Sports factors were not correlated with HD initiation in the period between 14 and 16 years of age. It seems that the problem of alcohol drinking should be preventively targeted in all youth, irrespective of living environment. Although sports participation was not evidenced as being a factor of influence on HD initiation, results highlight the necessity of developing targeted preventive campaigns against alcohol drinking for adolescents who quit sports.
Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction : A simulation study
(2023-05-01) Allouch, Sahar; Kabbara, Aya; Duprez, Joan; Khalil, Mohamad; Modolo, Julien; Hassan, Mahmoud
Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of connectivity patterns in this so-called "resting-state" has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different analytical choices induce significant discrepancies in results and drawn conclusions, thereby hindering the reproducibility of neuroimaging research. Hence, our objective in this study was to shed light on the effect of analytical variability on outcome consistency by evaluating the implications of parameters involved in the EEG source connectivity analysis on the accuracy of resting-state networks (RSNs) reconstruction. We simulated, using neural mass models, EEG data corresponding to two RSNs, namely the default mode network (DMN) and dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low-resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure, high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed significant variability in the performance of the tested inverse solutions and connectivity measures. Such methodological variability and absence of analysis standardization represent a critical issue for neuroimaging studies that should be prioritized. We believe that this work could be useful for the field of electrophysiology connectomics, by increasing awareness regarding the challenge of variability in methodological approaches and its implications on reported results.
Classification of a New-Born Infant's Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning
(2023-04) Inamanamelluri, Haritha Venkata Sai Lakshmi; Pulipati, Venkateswara Rao; Pradhan, Nrusingha Charan; Chintamaneni, Phanikanth; Manur, Manohar; Vatambeti, Ramesh
A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive.
Eating behaviours, menstrual history and the athletic career : a retrospective survey from adolescence to adulthood in female endurance athletes
(2023-03-02) Ravi, Suvi; Valtonen, Maarit; Ihalainen, Johanna K.; Holopainen, Elina; Kosola, Silja; Heinonen, Saara; Waller, Ben; Kujala, Urho M.; Parkkari, Jari
Aim To evaluate differences in menstrual and pubertal history and trends in eating behaviours among women with and without a competitive sports background. Additionally, we investigated if menstrual history and eating behaviours are associated with sports career-related factors. Methods This retrospective study was conducted on 100 women with a competitive endurance sports background and their age-matched, gender-matched and municipality-matched controls (n=98). Data were collected using a questionnaire using previously validated instruments. Generalised estimating equations were used to calculate associations of menstrual history and eating behaviours with outcome variables (career length, participation level, injury-related harms and career termination due to injury). Results Athletes reported higher rates of delayed puberty and menstrual dysfunction than controls. No differences between the groups were observed in the Eating Disorder Examination Questionnaire short form (EDE-QS) scores at any age. Previous disordered eating (DE) was associated with current DE in both groups. Among athletes, higher EDE-QS scores during the sports career were associated with a shorter career (B=-0.15, 95% CI -0.26 to -0.05). Secondary amenorrhoea was associated with lower participation level (OR 0.51, 95% CI 0.27 to 0.95), injury-related harms during the career (OR 4.00, 95% CI 1.88 to 8.48) and career termination due to injury (OR 1.89, 95% CI 1.02 to 3.51). Conclusion The findings indicate that DE behaviours and menstrual dysfunction, specifically secondary amenorrhoea, have a disadvantageous relationship with a sports career in women competing in endurance sports. DE during the sports career is associated with DE after the career.

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