Miracle-dz/UniFAR-FTUs huggingface dataset:
Biogeochemistry of microbial biofilms in Devils Hole, Nevada

Little is known about the role of microbial biofilms in nutrient cycling and ecosystem processes within desert springs. Furthermore, the difference between nutrient limitation of biofilms in desert springs and other ecosystems is unknown. Biofilms produce micro-scale physicochemical variation important to ecosystem function. We measured the variation in microscale physicochemical heterogeneity in biofilms of Devils Hole, Nevada. Microelectrodes were used to measure micro-scale chemical gradients of temperature, pH, O2, and H2S in addition to water column and pore water nutrient measurements in Spirogyra, cyanobacteria, and Beggiatoa biofilms over one year. Biofilm physicochemical gradients were used to calculate diffusion and metabolic rates. The rate of O2 and H2S diffusion ranged over two orders of magnitude. Biofilm production and respiration were influenced by biofilm type, light exposure, and sample month. Maximum O2 production occurred in spring and summer during direct light exposure. Oxygen production and consumption varied with light exposure and season. The H2S production and consumption varied with biofilm type. Higher concentrations of SO4 in Beggiatoa suggested that H2S production in Beggiatoa was quickly oxidized in the ecosystem. Spirogyra and cyanobacteria followed similar physicochemical trends; however, Spirogyra had more pronounced diurnal and seasonal variation. The differences between cyanobacteria and Spirogyra have implications on the ecosystem function of Devils Hole as well as other ecosystems with diverse biofilm communities. The heterogeneous physicochemistry of microbial biofilms and the differences in biofilm nutrient limitation suggests that a change in microbial biofilms or nutrient concentrations could alter ecosystem biogeochemical dynamics. Additionally, we assessed the nutrient limitation of two desert springs in comparison with a temperate stream. A nutrient diffusing substrata experiment was used to measure chlorophyll a, respiration, and biomass with phosphorus, nitrogen, and sulfide treatments. Autotrophic and heterotrophic biofilms responded differently to treatments and the temperate stream had higher chlorophyll a biofilm accrual but lower respiration relative to the desert springs.
Option pricing by using a mixed fractional Brownian motion with jumps

Option pricing is conventionally based on a Brownian motion (Bm). The Bm is a semimartingale process with stationary and independent increments. However, there are several stock returns that have a long memory or have high autocorrelation for long lags. A fractional Brownian motion (fBm) is one of the models that can solve this problem, but a model option with fBm is not arbitrage-free. A mixed fractional Brownian motion (mfBm) is a linear combination of a Bm and an independent fBm which can overcome the arbitrage problem. A jump process in time series is another problem found in stock price modeling. This paper deals with the problem of options pricing by using mfBm with jumps. Based on quasi-conditional expectation and Fourier transform method, we obtain a pricing formula for a stock option.
Supporting dynamic allocation of heterogeneous storage resources on HPC systems

Scaling up large‐scale scientific applications on supercomputing facilities is largely dependent on the ability to scale up efficiently data storage and retrieval. However, there is an ever‐widening gap between I/O and computing performance. To address this gap, an increasingly popular approach consists in introducing new intermediate storage tiers (node‐local storage, burst‐buffers, …$$ \dots $$ ) between the compute nodes and the traditional global shared parallel file‐system. Unfortunately, without advanced techniques to allocate and size these resources, they remain underutilized. In this article, we investigate how heterogeneous storage resources can be allocated on an high‐performance computing platform, just like compute resources. To this purpose, we introduce StorAlloc, a simulator used as a testbed for assessing storage‐aware job scheduling algorithms and evaluating various storage infrastructures. We illustrate its usefulness by showing through a large series of experiments how this tool can be used to size a burst‐buffer partition on a top‐tier supercomputer by using the job history of a production year.
Ultracompact MXene V2C-Improved Temperature Sensor by a Runway-Type Microfiber Knot Resonator

We demonstrate an all-fiber, compact-structure, high-sensing-efficiency temperature sensor using a resonator structure sensor device of a runway type and MXene V2C. The high-quality functional material MXene V2C, synthesized by a simple two-step method, has excellent photothermal conversion performance. As-prepared MXene V2C is integrated into the runway section of a runway-type microfiber knot resonator based on the coupling mechanism between the surface near the field of the fiber and materials. When the temperature variation range is ~25–70 °C, the corresponding transmission light intensity variation is linear, and the maximum normalized sensing efficiency is 2.21 dB/°C/mm. Our work demonstrates that the runway-type structure ensures the compactness of the sensor device and enhances the interaction distance between the material and the microfiber, which provides additional integration strategies for functional material-based sensor devices.
Development of a Preventive Health Screening Procedure Enabling Supportive Service Planning for Home-Dwelling Older Adults (PORI75): Protocol for an Action Research Study

Background In Finland, at least 1 in 4 residents will be >75 years of age in 2030. The national aging policy has emphasized the need to improve supportive services to enable older people to live in their own homes for as long as possible. Objective This study aimed to develop a preventive health screening procedure for home-dwelling older adults aged 75 years to enable the use of clinical patient data for purposes of strategic planning of supportive services in primary care. Methods The action research method was applied to develop the health screening procedure with selected validated health measures in cooperation with the local practicing interprofessional health care teams from 10 primary care centers in the Social Security Center of Pori, Western Finland (99,485 residents, n=11,938, 12% of them >75 years). The selection of evidence-based validated health measures was based on the national guide to screen factors increasing fall risk and the national functioning measures database. The cut-off points of the selected health measures and laboratory tests were determined in consecutive consensus meetings with the local primary care physicians, with decisions based on internationally validated measures, national current care guidelines, and local policies in clinical practice. Results The health screening procedure for 75-year-old residents comprised 30 measures divided into three categories: (1) validated self-assessments (9 measures), (2) nurse-conducted screenings (14 measures), and (3) laboratory tests (7 measures). The procedure development process comprised the following steps: (1) inventory and selection of the validated health measures and laboratory tests, (2) training of practical nurses to perform screenings for the segment of 75-year-old residents and to guide them to possible further medical actions, (3) creation of research data from clinical patient data for secondary use purposes, (4) secondary data analysis, and (5) consensus meeting after the pilot test of the health screening procedure for 75-year-old residents procedure in 2019 based on the experiences of health care professionals and collected research data. Conclusions The developed preventive health screening procedure for 75-year-old residents enables the use of clinical patient data for purposes of strategic planning of supportive services in primary care if the potential bias by a low participation rate is controlled. International Registered Report Identifier (IRRID) DERR1-10.2196/48753
Pilot contamination reduction in TDD-based massive MIMO systems

Channel estimation in time division duplexing (TDD)-based massive multiple-input multiple-output (MIMO) systems is heavily hampered by the pilot contamination, which constitutes a major bottleneck on the overall system performance. This study considers the pilot contamination problem in multi-cell TDD-based massive MIMO systems, and analytical expressions are presented on the normalised mean square error (NMSE) of the minimum mean square error channel estimation algorithm. Based on the obtained NMSE, this study proposes an optimal pilot assignment strategy to minimise the effect of pilot contamination. In order to further improve the system performance, a pilot design-based channel estimation scheme is proposed, where Chu sequences with perfect auto-correlation property are employed to design the optimal pilot sequences aiming at acquiring the accurate channel state information. Simulation results show that the proposed pilot assignment strategy outperforms the random pilot assignment method, and approaches to the performance of the exhaustive search method which requires high computational complexity. Moreover, the performance gain of the pilot design-based channel estimation scheme is verified in massive MIMO systems.
A Wideband Symmetrical Crossover Model With Analytic Analysis and Its Millimeter-Wave Application

A wideband symmetrical crossover model is proposed in this article. The analytic expressions between the $S$ -parameters and the structure parameters are built through even-odd method. One optimization method is presented to achieve a 44% of bandwidth at sub 6 GHz with the consideration of the physical structure constrain. With the analytic expressions, this model provides a concise and effective method to design crossovers from sub-6 GHz to millimeter-waveband. By utilizing the proposed model and optimization approach, a substrate integrated gap waveguide (SIGW)-based crossover at 30 GHz is designed and verified to provides 20% of the optimized bandwidth, which could be a good candidate component for the 5G Millimeter-wave beamforming network applications.
Explanatory Capacity of Postural Control and Physical Fitness in Cognitive Impairment and Support Needs among Individuals with Intellectual Disabilities—A Cross-Sectional Pilot Study

Postural control is a skill associated with most motor activities and is essential for the performance of activities of daily living. People with intellectual disabilities (ID) present postural control deficits that can be attributed to several causes. The aim of this study was to determine whether postural control and physical fitness could explain the cognitive impairment and support needs in this population. A cross-sectional pilot study was conducted with 18 people with ID. Data collection was based on assessments for postural control (Mini BESTest and Berg Balance Scale) and physical fitness (Senior Fitness Test). The data were analyzed using linear regression models. Anticipatory postural adjustments were associated with support needs, explaining up to 45% of these. Consecutive postural adjustments and upper limb strength were less significantly associated with support needs. However, none of the variables used explained cognitive impairment in ID. Knowledge of the relationships and behavior of the different measurement tools is essential for the development of appropriate interventions in this population.
Treatment of Upper Crossed Syndrome: A Narrative Systematic Review

Background and Objectives: Upper crossed syndrome (UCS) is a common musculoskeletal condition that is characterized by tightness and weakness of the muscles of the neck, shoulders, and upper back. The aim of this current study is to summarize and provide an overview of the treatment in patients with UCS. Materials and Methods: A MEDLINE (PubMed), Cochrane library, Embase, Scopus, and Web of Science database search was conducted for English-language articles about upper crossed syndrome that were published until 19 January 2023. To identify potentially relevant articles, the following key search phrases were combined: “upper crossed syndrome”, “upper cross syndrome”, “diagnosis”, and “treatment”. A total of 233 articles were identified. After reading the titles and abstracts and assessing their eligibility based on the full-text articles, 11 articles were finally included in this review. The risk of bias (RoB) was assessed using RoB-2 and ROBINS-I for the randomized controlled trials (RCTs) and the non-randomized clinical trial (non-RCT), respectively. Results: Among eleven studies that investigated the effect of treatment programs for UCS, five studies compared the therapeutic effect of exercise programs with controls, whereas six compared different rehabilitative treatment strategies, such as the muscle energy technique, soft-tissue mobilization, and stretching exercises. In addition, regarding the study design, ten studies were RCTs and only one study was a prospective observational study. Conclusions: Treatment programs including various types of exercises and techniques to correct an abnormal posture and restore neuromuscular imbalances are effective for decreasing pain and improving neck disabilities and postural deviations in patients with UCS.
Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM

Introduction: Lung cancer is one of the most frequent neoplasms worldwide with approximately 2.2 million new cases and 1.8 million deaths each year. The expression levels of programmed death ligand-1 (PDL1) demonstrate a complex association with lung cancer. Neuroblastoma is a high-risk malignant tumor and is mainly involved in childhood patients. Identification of new biomarkers for these two diseases can significantly promote their diagnosis and therapy. However, in vivo experiments to discover potential biomarkers are costly and laborious. Consequently, artificial intelligence technologies, especially machine learning methods, provide a powerful avenue to find new biomarkers for various diseases. Methods: We developed a machine learning-based method named LDAenDL to detect potential long noncoding RNA (lncRNA) biomarkers for lung cancer and neuroblastoma using an ensemble of a deep neural network and LightGBM. LDAenDL first computes the Gaussian kernel similarity and functional similarity of lncRNAs and the Gaussian kernel similarity and semantic similarity of diseases to obtain their similar networks. Next, LDAenDL combines a graph convolutional network, graph attention network, and convolutional neural network to learn the biological features of the lncRNAs and diseases based on their similarity networks. Third, these features are concatenated and fed to an ensemble model composed of a deep neural network and LightGBM to find new lncRNA–disease associations (LDAs). Finally, the proposed LDAenDL method is applied to identify possible lncRNA biomarkers associated with lung cancer and neuroblastoma. Results: The experimental results show that LDAenDL computed the best AUCs of 0.8701, 107 0.8953, and 0.9110 under cross-validation on lncRNAs, diseases, and lncRNA‐disease pairs on Dataset 1, respectively, and 0.9490, 0.9157, and 0.9708 on Dataset 2, respectively. Furthermore, AUPRs of 0.8903, 0.9061, and 0.9166 under three cross‐validations were obtained on Dataset 1, and 0.9582, 0.9122, and 0.9743 on Dataset 2. The results demonstrate that LDAenDL significantly outperformed the other four classical LDA prediction methods (i.e., SDLDA, LDNFSGB, IPCAF, and LDASR). Case studies demonstrate that CCDC26 and IFNG-AS1 may be new biomarkers of lung cancer, SNHG3 may associate with PDL1 for lung cancer, and HOTAIR and BDNF-AS may be potential biomarkers of neuroblastoma. Conclusion: We hope that the proposed LDAenDL method can help the development of targeted therapies for these two diseases.
Avoid Smoking: mHealth Evidence and Theory-Based Intervention to Promote Smoking Prevention Behaviors

Background: Mobile technologies and devices effectively maintain health and well-being is a promising opportunity for health policymakers and relevant stakeholders. Objectives: The current study aimed to evaluate a mhealth program to promote smoking prevention behaviors among university students in northern Iraq. The integrative model of factors influencing smoking behavior (IMFISB) was used as a theoretical framework. Methods: This quasi-experimental research study was conducted on 150 Kurdish students of Raparin University in northern Iraq. University students were randomly divided into intervention (n = 75) or control (n = 75) groups. The mhealth theory-based intervention program was evaluated using the mobile application (Avoid Smoking) in eight sessions. The data was collected using a questionnaire before and two months after the program’s implementation and analyzed in SPSS software version 16. Results: The effect size was estimated as "small" for belief, value, and family pressure. The effect size was estimated as “medium" for knowledge, attitude, peer pressure, self-image, and intention. The average response for smoking prevention behaviors was 15.17, which significantly increased to 16.88 after program implementation among the intervention group (P < 0.001), and the effect size was estimated to be "medium" for smoking prevention behaviors. Conclusions: Based on the results, smoking prevention interventions can be effective among students using mobile health evidence-based interventions.
Self-branding and content creation strategies on Instagram: A case study of foodie influencers

The purpose of this study is to better understand the processes and procedures adopted by micro-in ﬂ uencers to create ‘ instagrammable ’ content. It is based on 17 in-depth interviews with foodie micro-in ﬂ uencers based in London and Barcelona. Interview data was complemented with participant observation in restaurants or cafes. This paper makes three original contributions. Firstly, the study expands the understanding of the concept of ‘ instagrammability ’ by approaching it from the perspective of in ﬂ uencers creating content to satisfy and/or grow an audience. Secondly, it illustrates how two dominant factors drive in ﬂ uencers ’ content creation process: the self/audience focus content branding orientation. The ‘ audience-focus ’ content development process varied drastically, with some in ﬂ uencers being very conscious of responding to their audiences ’ needs whereas others maintained ﬁ rst and foremost a very strong ‘ self-focus ’ . However, even for the in ﬂ uencers who were the most responsive to their audiences ’ perceived wishes, a sense of ‘ self-focus ’ was maintained as an anchor point in all developed content, often linked to a passion for a certain type of food. Thirdly, this paper maps and describes the behind-the-scenes content creation process adopted by micro-in ﬂ uencers, including four stages (1) Content Planning, (2) Media Gathering , (3) Editing, and (4) Publishing , which was followed by an engagement phase. This study o ﬀ ers a timely contribution to better comprehend the content creation cycle adopted by micro-in ﬂ uencers by using foodie in ﬂ uencers as a case study.
A model for on-offshore sediment transport in the surfzone

Abstract : An energetics-based surfzone sediment transport model is evaluated for its ability to predict on-offshore sediment movements using nearshore sediment transport study current meter and beach volume measurements. The magnitude and distribution of pertinent wave velocity moments are also evaluated from the same data set. The latter were found to be linear functions of the significant wave height. Results of the study showed that the current meter data were of insufficient length to evaluate the sediment transport model's validity. A simplified version of the model coupled with estimated wave velocity moment regression equations was found to mimic observed beach volume variations as a function of wave height. (Author)
Metabolic syndrome in patients with obsessive-compulsive disorder

Objective Metabolic syndrome (MetS) is a collection of chemical and clinical risk factors. Patients with obsessive-compulsive disorder (OCD) might be at risk of MetS. This study aimed to investigate the prevalence and clinical correlates of MetS in an Iranian clinical sample of patients with OCD. Methods We included 107 patients with OCD in a cross-sectional study. Demographic and clinical characteristics including OC symptoms, duration of treatment, age of onset, medications history, and comorbidity with other psychiatric disorders were collected. Results The prevalence of MetS was 39.2%. Abdominal obesity was the most frequent component of MetS (68.2%), followed by low high-density lipoprotein cholesterol (50.5%). High serum triglycerides, high fasting serum glucose, high systolic blood pressure, and high diastolic blood pressure were observed in 47.7, 20.6, 18.7, and 9.3% of patients, respectively. Patients with MetS were older, married, had a low education level, had a high body mass index, and had no aggressive OC symptoms. MetS was not associated with psychiatric disorders comorbidities, age of onset, and duration of treatment. Conclusion The results of this study were in line with the results of other studies that reported the poor health status of patients with OCD. A large number of patients are affected or are at risk of developing MetS. These patients need medical care along with the usual OCD treatments.
Beyond culture shock: entering the complex world of Global South expatriates’ adaptation

This general review offers a literature-informed conceptual framework articulating the antecedents of issues facing Global South expatriates in relation to adaptation in western contexts. The analysis indicates that several complex issues impede Global South expatriates’ effective adjustment. These include culture distance, ethnic prejudice social isolation, children and family issues, and homesickness. Organisations need to create psychological safety for effective adjustment. This paper is one few to highlight the global south expatriates’ adaptation issues that are poorly addressed in the literature with reference to Global South expatriates. The expatriate literature is often biased towards Western expatriates, ignoring the situation of psychological insecurity caused by psycho-social factors that hamper Global South expatriates’ integration.
Predictive Modeling of Indoor Environmental Parameters for Assessing Comfort Conditions in a Kindergarten Setting

People tend to spend the majority of their time indoors. Indoor air properties can significantly affect humans’ comfort, health, and productivity. This study utilizes measurement data of indoor conditions in a kindergarten in Sofia, Bulgaria. Autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) recurrent neural network (RNN) models were developed to predict CO2 levels in the educational facility over the next hour based on 2.5 h of past data and allow for near real-time decision-making. The better-performing model, LSTM, is also used for temperature and relative humidity forecasting. Global comfort is then estimated based on threshold values for temperature, humidity, and CO2. The predicted R2 values ranged between 0.938 and 0.981 for the three parameters, while the prediction of global comfort conditions achieved a 91/100 accuracy.