Miracle-dz/UniFAR-FTUs huggingface dataset:
Computationally efﬁcient parameter estimation for high-dimensional ocean biogeochemical models

Biogeochemical (BGC) models are widely used in ocean simulations for a range of applications but typically include parameters that are determined based on a combination of empiricism and convention. Here, we describe and demonstrate an optimization-based parameter estimation method for high-dimensional (in parameter space) BGC ocean models. Our computationally efﬁcient method combines the respective beneﬁts of global and local optimization techniques and enables simultaneous parameter estimation at multiple ocean locations using multiple state variables. We demonstrate the method for a 17-state-variable BGC model with 51 uncertain parameters, where a one-dimensional (in space) physical model is used to represent vertical mixing. We perform a twin-simulation experiment to test the accuracy of the method in recovering known parameters. We then use the method to simultaneously match multi-variable observational data collected at sites in the subtropical North Atlantic and Paciﬁc. We examine the effects of different objec-tive functions, sometimes referred to as cost functions, which quantify the disagreement between model and observational data. We further examine increasing levels of data sparsity and the choice of state variables used during the optimization. We end with a discussion of how the method can be applied to other BGC models, ocean locations, and mixing representations.
Review of Uncertainty Quantification of Measurement and Computational Modeling in EMC Part I: Measurement Uncertainty

In this two-parts paper, methods for the quantification of uncertainty in measurement and computational modeling are reviewed, with an emphasis on applications in electromagnetic compatibility (EMC). The current status of international standards relating to measurement uncertainty in EMC is provided (in part I), as well as a review of selected alternative methods and recent developments in the domain of computational modeling and empirical uncertainty quantification (in part II).
Sleep problems in children with autism spectrum disorder: clinical correlates and the impact of attention deficit hyperactivity disorder

Purpose High prevalence of sleep problems has been reported in children with autism spectrum disorder (ASD). However, there is limited literature about the types and clinical correlates of sleep problems. This study aims to compare sleep disturbances between children with ASD and healthy children and investigate the relationship between sleep difficulties and clinical symptoms of ASD. Materials and methods The sample consisted of 112 children in ASD patient group and 112 healthy controls, with an age range of 2–18 years. The Children’s Sleep Habits Questionnaire (CSHQ) was used for sleep problems; Turgay DSM-IV Disruptive Behavior Disorders Rating Scale parent form (T-DSM-IV-S) was used to assess hyperactivity/impulsivity and inattentiveness; Childhood Autism Rating Scale (CARS), Autism Behavior Checklist, and Aberrant Behavior Checklist were used to evaluate the severity of autistic symptoms and behav-ioral problems. Results Total score, bedtime resistance, and sleep anxiety subscores of CSHQ were significantly higher in children with ASD than the control group. Among ASD children, intellectual capacity was not found to be associated with CSHQ scores. Bedtime resistance and night waking sub-scores of CSHQ were found to be positively correlated with CARS total score. Inattentiveness subscore of Parent T-DSM-IV-S was significantly higher in children with moderate-to-severe sleep problems. Conclusion Sleep difficulties in ASD patients may occur independently of intellectual disability. Bedtime resistance and night waking appear to be linked with ASD symptoms. Inattentiveness in ASD children may be associated with moderate-to-severe sleep problems.
Analisis Raskin dan Ketahanan Pangan Rumah Tangga di Indonesia (Analisis Data Susenas 2011)

Studi ini menganalisis determinan ketahanan pangan rumah tangga menurut karakteristik rumah tangga di Indonesia dengan analisis deskriptif dan multinomial logit, serta menentukan karakteristik rumah tangga yang perlu intervensi Raskin di Indonesia tahun 2011. Hasil analisis deskriptif dan multinomial logit menemukan bahwa semakin tinggi pendidikan kepala rumah tangga akan meningkat pula ketahanan pangannya jika jumlah anggota rumah tangga kecil, pekerjaan kepala rumah tangga di non-pertanian, pendapatan per kapita besar, dan daerah tempat tinggal di perkotaan. Secara umum, Raskin relatif tepat sasaran. Raskin sebaiknya diprioritaskan pada rumah tangga yang dikepalai perempuan, berpendidikan dasar, dan bekerja di pertanian maupun non-pertanian.
Pinnipeds and Paleocoastal Middens: A Case Study From Point Bennett, San Miguel Island, California

ABSTRACT In this report, we discuss the results of a 2009 investigation of a Paleocoastal shell midden (CA-SMI-693) on western San Miguel Island, an 8,800-year-old site identified within the Point Bennett pinniped rookery. Analyses of faunal remains at this site suggest that the local biogeography was significantly different than today. We discuss the results of our test investigations, including zooarchaeological analysis and human exploitation of marine fauna on western San Miguel Island. We also summarize what is known about Paleocoastal sites in the Point Bennett area, contributing to a broader understanding of the impacts of coastal erosion and pinniped activities on archaeological resources in island and coastal environments around the world, as well as potential discontinuities in the distribution of ancient versus modern pinniped biogeography.
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

Deep neural networks are susceptible to various inference attacks as they remember information about their training data. We design white-box inference attacks to perform a comprehensive privacy analysis of deep learning models. We measure the privacy leakage through parameters of fully trained models as well as the parameter updates of models during training. We design inference algorithms for both centralized and federated learning, with respect to passive and active inference attackers, and assuming different adversary prior knowledge. We evaluate our novel white-box membership inference attacks against deep learning algorithms to trace their training data records. We show that a straightforward extension of the known black-box attacks to the white-box setting (through analyzing the outputs of activation functions) is ineffective. We therefore design new algorithms tailored to the white-box setting by exploiting the privacy vulnerabilities of the stochastic gradient descent algorithm, which is the algorithm used to train deep neural networks. We investigate the reasons why deep learning models may leak information about their training data. We then show that even well-generalized models are significantly susceptible to white-box membership inference attacks, by analyzing state-of-the-art pre-trained and publicly available models for the CIFAR dataset. We also show how adversarial participants, in the federated learning setting, can successfully run active membership inference attacks against other participants, even when the global model achieves high prediction accuracies.
How Successfully can Decision-Making Style Predict the Orientation toward Well- or Ill-Structured Decision-Making Problems

A decision-making style is the manner through which a manager acquires, processes and uses information in the decision-making process. It is a set of qualitative indicators which within the decision-making process manifest themselves in the form of a decision-making style and is a typical method of implementing a decision. Knowledge of decision-making styles and their active use in a management structure contributes directly to building the competitiveness of the business. Identifying decision-making styles and delegating the right decision tasks to the right employee who possesses the fundamental knowledge to address a particular type of decisionmaking problem (well or poorly structured) is the essence of competent decision-making. This research paper deals with the relationship between the decision-making style of a manager in terms of the type of decision-making problems. Binary logistic regression was used to predict the relationship between the type of decision problem (binary dependent variable) and the overall score in the decision-making style (cardinal independent variable) through which the probability of the occurrence of the dependent value was calculated. Based on these results, we assert that as the unit score in the intuitive decision-making style increases, the odds ratio in a group of ill-structured decision-making problems increases as well. The prediction for the growth of the Sensing and Thinking odds ratio has not been confirmed. Results of non-parametric testing using the Mann Whitney U test confirmed that the preference for the type of decision-making problem has a statistically significant effect on the score achieved in the Sensing and Intuiting decision-making style. The effect size for the Mann Whitney U test was then calculated. To analyze the dependence between the nominal data, the non-parametric Chi Square Test of Independence was used, and Cramer’s V was used to determine the strength of association between the variables.
Choice Preference of Middle-Aged and Elderly People on Integrated Medical Services and Elderly Care Model: A Cross-Sectional Study

Facing the increasingly severe aging situation, China has started to implement the “integrated medical services and elderly care (IMSEC)” policy, which covers a variety of IMSEC models. However, there is currently little research on middle-aged and elderly people’s choice preference for these IMSEC models and their associated factors. Through the face-to-face questionnaire method, the choice preference of middle-aged and elderly people aged 45 years and over in Zhejiang Province, China, to the IMSEC model is explored. Through the multinomial logistic regression model, the influencing factors of choice preference are analyzed. A total of 1034 people are included in 2022. Their choice preference for the 4 major types of IMSEC models are Home IMSEC model (48.07%), Community IMSEC model (23.79%), Institutional IMSEC model (21.76%), and Internet Plus IMSEC model (6.38%). “C1. Home elderly care and contracted with a family doctor” is the most chosen subtype, accounting for 34.53%. The rural elderly are more likely to choose “Home IMSEC model” (OR(95%CI) = 2.977(1.343-6.601)). Elderly people with relatively large life care needs are more likely to choose “Institutional IMSEC model” (OR(95%CI) = 1.114(1.042-1.190)). Moreover, age, education, and self-reported health status are also influencing factors of choice preference. The government should focus on promoting the development of the “Home IMSEC model” and increase the promotion of “Internet Plus IMSEC model. ” In addition, the life care service capacity and spiritual comfort capacity of IMSEC institutions, as well as the medical service capacity of the community, need to be enhanced.
Handbook biomass gasification

The Handbook on Biomass Gasification is meant to disseminate the results of the European Gasification Network (GasNet) to a wider audience, which started in 2001 with funding of DG TREN. The gasification network was clustered to the pyrolysis network, comprising the Thermonet project with 36 members of all EU countries including Switzerland. Each Network had its own work programme, but both ha also a common focus of addressing commercialisation issues and providing support for more rapid and more effective implementation of all the technologies in the market place. The Handbook describes specific topics discussed thoroughly within GasNet and additional chapters on more general aspects of biomass gasification including gasification of pyrolysis oil, market assessments, economics, legislative impacts, health and safety, tar standardisation and incentives for bio-energy through gasification. Authors and co-authors have been invited to contribute in various chapters.
Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection

Purpose To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations. Methods Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC). Results The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80–0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67–0.88). Conclusions The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model. Translational Relevance Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.
Lower percentages of natural killer cells in children with type 1 diabetes and their siblings

Introduction One of the most common children’s endocrine and autoimmune diseases in the world is type 1 diabetes mellitus (T1DM). The incidence of type 1 diabetes is constantly increasing, and according to current estimates, the number of children with T1DM in the world has exceeded 542,000. There are 3 main components emphasized in the pathogenesis: genetic and environmental factors, and the patient's immune system. Many publications have confirmed the role of natural killer cells (NK) in the pathogenesis of type 1 diabetes and other autoimmune diseases. Aim The aim of the study was to evaluate the population of NK cells and pancreatic β cell autoantibodies in a group of children with T1DM and their healthy siblings in comparison with children from families with no history of autoimmune diseases. Material and methods The research included 76 children with T1DM, 101 children from the sibling group, and 30 children from the control group. Peripheral blood was analysed on a FACSCalibur flow cytometer (Becton Dickinson) to evaluate the NK cell population. The results were presented as the percentage of NK cells among lymphocytes. Statistical analysis was performed using STATISTICA 10 PL software. Results The mean percentage of NK cells in children with T1D (10.59 ±5.37) and in the sibling group (11.93 ±5.62) was statistically reduced in comparison to the control group (14.89 ±7.78) in sequence (Student’s t-test: t = –3.24; df = 103; p = 0.002) (Student’s t-test: t = –2.30; df = 128; p = 0.02). There was no statistically significant difference in the percentage of NK cells between the group of children with T1DM and their siblings (Student’s t-test: t = –1.59; df = 173; p = 0.11). In the group of siblings, the younger the child, the lower the reported percentage of NK cells. This relationship was statistically significant (test for the Pearson correlation coefficient t = 3.41; p = 0.0009; r = 0.33). In the group of children with type 1 diabetes, a similar relationship was not found. The concentration of anti-IA2 and anti-Znt8 antibodies was statistically significantly higher in the sibling group compared to the control group (anti-IA2 p = 0.0000001; anti-ZnT8 p = 0.00001), and the concentration of anti-GAD antibodies was comparable in both groups. In the group of children with type 1 diabetes, a positive correlation was demonstrated between the reduced percentage of NK cells and the coexistence of anti-GAD and anti-ZnT8 antibodies (Mann-Whitney U test Z = –2.02; p = 0.04). There was no similar relationship in the group of siblings. Conclusions The reduced percentage of NK cells in children with T1DM and in their siblings compared to the control group suggests the role of NK cells in the pathogenesis of T1DM. Genetic predisposition and dysfunction of NK cells probably underlie the pathogenesis of T1DM.
A Social Cognitive Approach to Disaster Preparedness

Using the social cognitive perspective, the study sought to determine the individual and environmental factors that predict disaster preparedness. Specifically, the research determined the relationships between risk perception, disaster experience, community disaster preparedness, and disaster preparedness behaviors. Data were collected from 401 participants from areas affected by recent typhoons and heavy monsoon rains: Tacloban and Metro Manila. Risk perception, severity of disaster experience, and community disaster preparedness were found to significantly predict the participants’ disaster preparedness behaviors. Severity of previous disaster experience seems to be the strongest determinant of individual disaster preparedness. Implications to future research and development and improvement of disaster preparations programs are discussed.
Prevalence and factors influencing anemia in women of reproductive age visiting a tertiary care hospital (Jinnah Postgraduate Medical Center) in Karachi: A cross-sectional study

Introduction: Anemia is a significant public health concern, primarily affecting young children, pregnant and postpartum women, and menstruating adolescent girls and women. This study aimed to evaluate the prevalence of anemia and associated factors in women of reproductive age visiting a tertiary care hospital in Karachi, Pakistan. Objective: The primary objective was to determine the prevalence of anemia in women of reproductive age, while the secondary objective was to investigate potential causes of anemia within this demographic group. Design: A prospective cross-sectional approach was employed, adhering to Strengthening the Reporting of Observational Studies in Epidemiology guidelines. A questionnaire-based method was used to assess anemia, and data were collected from women aged 14 to 40 years. Method: The study was conducted at the Jinnah Postgraduate Medical Center from January to May 2023. The study was approved by the Institutional Review Board of Jinnah Sindh Medical University (Institutional Review Board reference number JSMU/IRB/2023/699). A sample of 397 women was included, and various demographic and lifestyle factors were assessed. Results: In this study of 397 participants, 71.5% were found to have anemia, primarily microcytic anemia (48.2%). Anemia prevalence was highest among the 14–18 years age group (80.7%) and those from lower socioeconomic backgrounds (73.6%). Factors such as frequent tea consumption, irregular mealtimes, and pica consumption were associated with higher anemia rates. Pregnant women and those with more children were at a heightened risk of anemia. Conclusion: The study reveals a notable prevalence of anemia among women of reproductive age with a surprising emphasis on younger individuals and lower socioeconomic groups. Dietary habits, lifestyle choices, and pregnancy status play significant roles in anemia development. Targeted interventions are essential, particularly for younger women, those from disadvantaged backgrounds, and pregnant individuals, to combat anemia effectively in this region.
Selecting the Optimal Subset of Antennas in Hyperthermia Treatment Planning

Hyperthermia treatment planning is a deeply patient-specific task which includes the optimal determination of the excitations of an array applicator. To enhance flexibility, various solutions exploiting different frequencies, antenna element, number, and applicator geometries have been proposed in the literature. Amongst them, increasing the frequency and the number of radiating elements have been effective for achieving more conformal heating. However, as each radiating element requires a power amplifier to control it, increasing the number of antennas considerably impacts the overall cost and complexity of the system. Accordingly, a procedure capable of selecting an optimal patient-specific subset of antennas from an oversized phased array applicator (with more antenna elements than available amplifiers) could help improve cost-effectiveness. In this study, we present an original approach, which allows improving performance by adaptively selecting the optimal subset of antennas to be activated for a given (redundant) applicator and a given patient. The proposed approach takes inspiration from the compressive sensing theory by embedding the sparsity promotion paradigm into a treatment planning procedure, which casts power deposition as a constrained convex optimization. Performances were demonstrated for the case of head and neck hyperthermia and benchmarked against the antenna selection procedure presently used in clinical practice.
The Right-Ear Advantage in Static and Dynamic Cocktail-Party Situations

When presenting two competing speech stimuli, one to each ear, a right-ear advantage (REA) can often be observed, reflected in better speech recognition compared to the left ear. Considering the left-hemispheric dominance for language, the REA has been explained by superior contralateral pathways (structural models) and language-induced shifts of attention to the right (attentional models). There is some evidence that the REA becomes more pronounced, as cognitive load increases. Hence, it is interesting to investigate the REA in static (constant target talker) and dynamic (target changing pseudo-randomly) cocktail-party situations, as the latter is associated with a higher cognitive load than the former. Furthermore, previous research suggests an increasing REA, when listening becomes more perceptually challenging. The present study examined the REA by using virtual acoustics to simulate static and dynamic cocktail-party situations, with three spatially separated talkers uttering concurrent matrix sentences. Sentences were presented at low sound pressure levels or processed with a noise vocoder to increase perceptual load. Sixteen young normal-hearing adults participated in the study. The REA was assessed by means of word recognition scores and a detailed error analysis. Word recognition revealed a greater REA for the dynamic than for the static situations, compatible with the view that an increase in cognitive load results in a heightened REA. Also, the REA depended on the type of perceptual load, as indicated by a higher REA associated with vocoded compared to low-level stimuli. The results of the error analysis support both structural and attentional models of the REA.