Miracle-dz/UniFAR-FTUs huggingface dataset:
Tracking Southern Ocean Sea Ice Extent With Winter Water: A New Method Based on the Oxygen Isotopic Signature of Foraminifera

Southern Ocean sea ice plays a central role in the oceanic meridional overturning circulation, transforming globally prevalent watermasses through surface buoyancy loss and gain. Buoyancy loss due to surface cooling and sea ice growth promotes the formation of bottom water that flows into the Atlantic, Indian, and Pacific basins, while buoyancy gain due to sea ice melt helps transform the returning deep flow into intermediate and mode waters. Because northward expansion of Southern Ocean sea ice during the Last Glacial Maximum (LGM; 19–23 kyr BP) may have enhanced deep ocean stratification and contributed to lower atmospheric CO2 levels, reconstructions of sea ice extent are critical to understanding the LGM climate state. Here, we present a new sea ice proxy based on the 18O/16O ratio of foraminifera (δ18Oc). In the seasonal sea ice zone, sea ice formation during austral winter creates a cold surface mixed layer that persists in the sub‐surface during spring and summer. The cold sub‐surface layer, known as winter water, sits above relatively warm deep water, creating an inverted temperature profile. The unique surface‐to‐deep temperature contrast is reflected in estimates of equilibrium δ18Oc, implying that paired analysis of planktonic and benthic foraminifera can be used to infer sea ice extent. To demonstrate the feasibility of the δ18Oc method, we present a compilation of N. pachyderma and Cibicidoides spp. results from the Atlantic sector that yields an estimate of winter sea ice extent consistent with modern observations.
Cephalometric norms for the north Indian population: A systematic review

The cephalometric norms for orthognathic surgery (COGS analysis) are the standard benchmark in cephalometrics for any orthognathic surgical diagnosis and treatment planning. Since the introduction of cephalometrics by Broadbent, numerous studies have been conducted to establish craniofacial norms of different ethnicities. Most of these studies, however, determined craniofacial norms for Caucasian people and may not be applicable to other ethnic groups due to differences in their facial appearance. Therefore, it is essential to study and compare the existing cephalometric parameters between Caucasians and Indians to validate their application during the treatment planning of the orthognathic surgery. This review focuses on studying the cephalometric norms for the North Indian (NI) population and establishing the COGS analysis for the population of North India. The objective is to determine the cephalometric parameters of Angle's dental and skeletal Class I faces for the NI population. The following databases were searched for the present study -PubMed, Ovid MEDLINE, and EMBASE. The initial inclusion criteria comprised studies written in English and quoting cephalometric norms in Indian population. The time period of publications was not determined. The quality features evaluated were sample description, variables analyzed, and how cephalometric standards were evaluated. Initially, 49 articles were retrieved. After removal of the duplicated records and assessing for the eligibility, four articles met the inclusion criteria. These four articles were included in the systematic review. The NI people are characterized by having small cranial base, short facial height, protrusive chin, and more inclination of mandibular incisors in comparison with Caucasians. Due to limited research on the Central Indian cephalometric norms, the Caucasian norms are still referred for the diagnosis and treatment planning of orthognathic surgery for NI population.
Parametric study of microlens array fabricated on polymer substrate through hot embossing with a micro-EDM mould and its optical characterisation

ABSTRACT The surface topography of embossed micro-patterns relies on hot embossing (HE) parameters and micro-patterns over the mould. Traditionally, moulds were made using photolithography (X-ray/UV/E-beam) followed by wet etching or reactive ion etching. This technique is costlier, time-consuming, and expensive. To resolve these issues, researchers are shifting to micromachining to make HE moulds. In this investigation, a mould fabricated by μ-electric discharge machining (μ-EDM) was employed in an in-house developed HE set-up. In this work, embossing temperature (Te), embossing pressure (Pe), embossing time (te), and deembossing temperature (Tde) were used as operational factors and their influence on micro (μ)-lens surface profile was evaluated. Te, Pe, and te increases the sag height (hsag) of μ-lens array. As hsag increases, radius-of-curvature (R) and focal length (f) of the embossed μ-lens array decrease, which enables the production of μ-lenses with different diameters by modifying HE parameters. The maximum percentile change in hsag is 292.49% with Te. The maximum percentile drop in calculated R and f values were observed in case of Tde, i.e. 70.89% followed by Pe (54.18%) and te (49.52%). Hemispherical shaped μ-lens array was obtained at Te of 140°C, Pe of 50 kg/cm2, te of 20 min, and Tde of 40°C.
Co-delivery of phagocytosis checkpoint and STING agonist by a Trojan horse nanocapsule for orthotopic glioma immunotherapy

Rationale: Cancer immunotherapy has demonstrated significant antitumor activity in a variety of tumors; however, extensive infiltration of immunosuppressive tumor-associated macrophages (TAMs) in the glioblastoma (GBM) tumor microenvironment (TME) and the existence of the blood-brain barrier (BBB) might lead to failure of the checkpoint blockade therapy. Methods: Herein, we have developed a smart “Trojan horse” BBB-permeable nanocapsule termed “NAcp@CD47” to deliver anti-CD47 antibodies and stimulator of interferon genes (STING) agonists into GBM tissues in a stealth-like manner to reshaped the immune microenvironment by switching the phenotype of microglia and macrophages. Results: Both in vitro and in vivo studies demonstrate that NAcp@CD47 could effectively penetrate the BBB, increase the polarization of M1-phenotype TAMs, help reduce tumor immunosuppression, and inhibit the orthotopic GBM growth by phagocytosis of macrophages and microglia. Conclusions: Our findings indicate that the well-designed NAcp@CD47 not only enhances the phagocytosis of cancer cells but also efficiently enhance antitumor immunogenicity and reverses immune suppression to convert uninflamed “cold” tumors into “hot” tumors.
Transformation toward sustainability in Finnish teacher education policy: Promises and shortcomings

While the state of the world is becoming ever more unsustainable, transformation and transformative learning have become increasingly relevant and raised attention in various sustainability education discourses. This is obvious in both policy and research. As teacher educators, we have studied how this sustainability and transformative education trend is visible in education policy. We have first read international policy and research on sustainability education and transformation. In a more thorough study, our focus has been on two recent and fundamental policy documents outlining the Finnish teacher education. Our results show that even if several UNESCO policies documents for years have called for a transformation toward sustainability through education, the Finnish teacher education policy has not yet fully acknowledged sustainability issues and teachers’ transformative agency in addressing them, but emphasize other aims. Therefore, it is mainly up to the individual teacher educators and the leaders of their faculties to decide on how to prepare student teachers not only to deal with changes in general, but to particularly bring about changes towards sustainability.
Application of Plackett–Burman Design for Spectrochemical Determination of the Last-Resort Antibiotic, Tigecycline, in Pure Form and in Pharmaceuticals: Investigation of Thermodynamics and Kinetics

Tigecycline (TIGC) reacts with 7,7,8,8-tetracyanoquinodimethane (TCNQ) to form a bright green charge transfer complex (CTC). The spectrum of the CTC showed multiple charge transfer bands with a major peak at 843 nm. The Plackett–Burman design (PBD) was used to investigate the process variables with the objective being set to obtaining the maximum absorbance and thus sensitivity. Four variables, three of which were numerical (temperature—Temp; reagent volume—RV; reaction time—RT) and one non-numerical (diluting solvent—DS), were studied. The maximum absorbance was achieved using a factorial blend of Temp: 25 °C, RV: 0.50 mL, RT: 60 min, and acetonitrile (ACN) as a DS. The molecular composition that was investigated using Job’s method showed a 1:1 CTC. The method’s validation was performed following the International Conference of Harmonization (ICH) guidelines. The linearity was achieved over a range of 0.5–10 µg mL−1 with the limits of detection (LOD) and quantification (LOQ) of 166 and 504 ng mL−1, respectively. The method was applicable to TIGC per se and in formulations without interferences from common additives. The application of the Benesi–Hildebrand equation revealed the formation of a stable complex with a standard Gibbs free energy change (∆G°) value of −26.42 to −27.95 kJ/mol. A study of the reaction kinetics revealed that the CTC formation could be best described using a pseudo-first-order reaction.
Using a conversational agent for thought recording as a cognitive therapy task: Feasibility, content, and feedback

E-mental health for depression is increasingly used in clinical practice, but patient adherence suffers as therapist involvement decreases. One reason may be the low responsiveness of existing programs: especially autonomous systems are lacking in their input interpretation and feedback-giving capabilities. Here, we explore (a) to what extent a more socially intelligent and, therefore, technologically advanced solution, namely a conversational agent, is a feasible means of collecting thought record data in dialog, (b) what people write about in their thought records, (c) whether providing content-based feedback increases motivation for thought recording, a core technique of cognitive therapy that helps patients gain an understanding of how their thoughts cause their feelings. Using the crowd-sourcing platform Prolific, 308 participants with subclinical depression symptoms were recruited and split into three conditions of varying feedback richness using the minimization method of randomization. They completed two thought recording sessions with the conversational agent: one practice session with scenarios and one open session using situations from their own lives. All participants were able to complete thought records with the agent such that the thoughts could be interpreted by the machine learning algorithm, rendering the completion of thought records with the agent feasible. Participants chose interpersonal situations nearly three times as often as achievement-related situations in the open chat session. The three most common underlying schemas were the Attachment, Competence, and Global Self-evaluation schemas. No support was found for a motivational effect of providing richer feedback. In addition to our findings, we publish the dataset of thought records for interested researchers and developers.
The effect of Tai chi on positive-activated affect in adults: a systematic review and multilevel meta-analysis of randomised controlled trials

ABSTRACT We extend findings from previous quantitative reviews of the effects of aerobic exercises on positive-activated affect (PAA) to evaluate the effect of Tai chi training on PAA in randomised controlled trials and to test whether effect varied according to participant features, Tai chi exposure, or characteristics of research design. English articles published before 31 December 2020 were searched according to PRISMA guidelines. Study quality was judged by using a revised Cochrane risk of bias (RoB 2) tool for RCTs. Hedges’ d effect sizes were calculated, and random effect model with multilevel meta-analysis were used to estimate the pooled effect. I2 tests were applied to assess the heterogeneity. Moderating effects were tested by multilevel meta-regression analysis according to moderators derived from participant characteristics, Tai chi exposure, and features of research design. Publication bias was assessed by examining the funnel plot according to Egger's method. From 577 articles, 32 RCTs articles were selected involving 2950 adults (18 years old and above), from which 61 effects were yielded. Tai chi training significantly increased PAA in adults by a heterogeneous standardised mean effect size of 0.41 (95% CI, 0.14 to 0.69, p < 0.01). Larger effects were found when (1) higher frequency (sessions/week) of Tai chi practice was implemented, and (2) when Tai chi versus no treatment, usual care or wait-list condition was compared. Tai chi has small-to-medium efficacy for nurturing PAA. Tai chi with higher frequency and against no treatment, usual care or wait-list condition, achieve the larger PAA-generating effects.
Search Engine Optimization (SEO): An Hour a Day

The third edition of the bestselling guide to do-it-yourself SEOGetting seen on the first page of search engine result pages is crucial for businesses and online marketers. Search engine optimization helps improve Web site rankings, and it is often complex and confusing. This task-based, hands-on guide covers the concepts and trends and then lays out a day-by-day strategy for developing, managing, and measuring a successful SEO plan. With tools you can download and case histories to illustrate key points, its the perfect solution for busy marketers, business owners, and others whose jobs include improving Web site traffic.A successful SEO plan is vital to any business with an online presenceThis book provides strategies for setting goals and gaining corporate support, developing and implementing a plan, and monitoring trends and resultsOffers hints, tips, and techniques for everyone from one-person shops to Fortune 500 companiesCompanion Web site includes downloadable tracking spreadsheets, keyword list templates, templates for checking rank and site indexes, and a calendar with daily SEO tasks that you can import into your own calendar systemFully updated and expanded, Search Engine Optimization: An Hour a Day, Third Edition will help you raise your visibility on the Web.
Application of RNN on GNSS Reflectometry Sea level monitoring

ABSTRACT Global Navigation Satellite System reflectometry (GNSS-R) can be used to monitor the sea level with the signal-to-noise ratio (SNR) using the spectral analysis approach where the additional frequencies in SNR lead to small errors. This study is the first attempt to apply the Recurrent Neural Networks (RNN) approach to reduce the effect of additional frequencies in SNR. The output from the RNN approach is processed by the spectral analysis to estimate the frequency. To analyse the number of effective results, all results are processed by the outlier removal method which removes the outliers only depending on the results under low elevation angles. The data set used in the experiment is sampled from the SC02 GPS station. The experiment is set up by using abnormal elevation angle ranges to observe the performance. The results from the RNN-based method, detrend SNR method and EMD-based method are compared to the reference sea level data. RNN models are trained differently in three experiments based on 7-day historical sea level data, 7-day tide prediction data and 1-month tide prediction data. The results show that the RNN increases the temporal resolutions for all experiments and provides sufficient accuracy as compared to the detrend SNR method and EMD-based method. Using tide prediction data instead of sea level data as the training data improves temporal resolutions and correlations. RNN can be applied as the data preparation and denoising method for SNR spectral analysis sea level monitoring. Compared to the EMD-based method, the RNN-based denoising method is more suitable for short SNR data records.
Operating Status Prediction Model at EV Charging Stations With Fusing Spatiotemporal Graph Convolutional Network

This article proposes the operating status prediction model at electric vehicle (EV) charging stations based on the spatiotemporal graph convolutional network (SGCN). The SGCN combines graph convolutional network (GCN) and the gated recurrent unit (GRU), alleviating the queuing time at charging stations due to the lack of information for EV users. First, an urban charging station-traffic flow model is established to portray the interrelationship between charging stations and traffic. Second, a multistep prediction model based on SGCN for operating status at charging stations is proposed to forecast the occupancy of charging stations over the next tens of minutes. The comparison case study with the forecast and actual data reveals that the mean forecast error is around 19.21% when estimating 18 min ahead. Incrementing errors are subtle even after adding random noise to the original data. Finally, the model is applied to charging guidance decisions. Our model can reduce the number of EV queuing by 60% during high charging demand. It also shortens the average charging waiting time by 4 min.
Revealing the hidden dynamics of the energy signature model

The energy signature (ES) model is a well-known method to measure the energy performance of a building. This method uses coarse data aggregation that omits valuable information about the dynamics of the thermal elements of a building. This work presents a method to reconstruct these hidden dynamics without increasing the measuring complexity. The proposed method uses a state space formulation of the ES, that is ﬁltered using hourly measurements of out-door temperature and heat consumption. It is shown that the reconstructed state variable works as a proxy of the indoor temperature, and reacts to the weather conditions. These results highlight the potential of this method for real-time heating performance evaluation, as well as anomaly detection.
A Survey on Map-Based Localization Techniques for Autonomous Vehicles

Autonomous vehicles integrate complex software stacks for realizing the necessary iterative perception, planning, and action operations. One of the foundational layers of such stacks is the perception one which is comprised of localization, detection, and recognition algorithms for understanding the location and the driving environment around the vehicle. More precisely, localization aims to identify the location of the vehicle on a global coordinate system and is considered one of the most critical parts in the stack since its accuracy and robustness affects the subsequent algorithms of the perception layer and also the following planning and action layers. Due to the rapid and significant interest in self-driving cars, several localization techniques have been proposed with different directions and approaches. Algorithms using prior maps are currently considered the most accurate ones and found almost in all current self-driving car prototypes. Thus, in this paper, we categorize, discuss and analyze the state-of-the-art map-based localization techniques in an attempt to examine their potentials and limitations. We first present techniques and approaches that aim to match prior maps with on-board observations from different sensor modalities. We then review methods that handle the localization problem as a probabilistic one and finally, we also go through the emerging domain of deep-learning localization algorithms and examine their potential in self-driving cars. For all three categories, we provide comparison tables and necessary insights for the optimal localization system design based on different requirements, specifications, and sensor configurations.
Parents of adult children with drug addiction dealing with shame and courtesy stigma

Abstract The study aims to provide insight into the lives of parents to adult children with drug addiction. We focus on how the parents’ social life and interactions were affected by feelings of guilt and shame, and how they dealt with the stigma that often accompanies drug addiction. 32 semi-structured interviews were conducted with parents (age 46–70) of adult children (age 18–47) with drug addiction in Sweden. Three themes emerged (1) Experiences of guilt, shame, and courtesy stigma, (2) Impact on social life and relationships and, (3) Strategies to reduce guilt and shame. Guilt, shame, and courtesy stigma complicated and restricted the parents’ social interactions. Many parents applied a selective openness, carried experiences of alienation and in some cases isolated themselves. At the same time, several parents described that they were open about their child’s addiction in certain contexts. For some parents, this meant a relief from guilt and shame and an opportunity for increased social interaction. Both informal and professional support for family members of individuals with drug addiction is vital to reduce experiences of guilt, shame, and courtesy stigma.
Accurate Channel Prediction Based on Transformer: Making Mobility Negligible

Accurate channel prediction is vital to address the channel aging issue in mobile communications with fast time-varying channels. Existing channel prediction schemes are generally based on the sequential signal processing, i.e., the channel in the next frame can only be sequentially predicted. Thus, the accuracy of channel prediction rapidly degrades with the evolution of frame due to the error propagation problem in the sequential operation. To overcome this challenging problem, we propose a transformer-based parallel channel prediction scheme to predict future channels in parallel. Specifically, we first formulate the channel prediction problem as a parallel channel mapping problem, which predicts the channels in next several frames in parallel. Then, inspired by the recently proposed parallel vector mapping model named transformer, a transformer-based parallel channel prediction scheme is proposed to solve this formulated problem. Relying on the attention mechanism in machine learning, the transformer-based scheme naturally enables parallel signal processing to avoid the error propagation problem. The transformer can also adaptively assign more weights and resources to the more relevant historical channels to facilitate accurate prediction for future channels. Moreover, we propose a pilot-to-precoder (P2P) prediction scheme that incorporates the transformer-based parallel channel prediction as well as pilot-based channel estimation and precoding. In this way, the dedicated channel estimation and precoding can be avoided to reduce the signal processing complexity. Finally, simulation results verify that the proposed schemes are able to achieve a negligible sum-rate performance loss for practical 5G systems in mobile scenarios.
Ckmt1 is Dispensable for Mitochondrial Bioenergetics Within White/Beige Adipose Tissue

Abstract Within brown adipose tissue (BAT), the brain isoform of creatine kinase (CKB) has been proposed to regulate the regeneration of ADP and phosphocreatine in a futile creatine cycle (FCC) that stimulates energy expenditure. However, the presence of FCC, and the specific creatine kinase isoforms regulating this theoretical model within white adipose tissue (WAT), remains to be fully elucidated. In the present study, creatine did not stimulate respiration in cultured adipocytes, isolated mitochondria or mouse permeabilized WAT. Additionally, while creatine kinase ubiquitous-type, mitochondrial (CKMT1) mRNA and protein were detected in human WAT, shRNA-mediated reductions in Ckmt1 did not decrease submaximal respiration in cultured adipocytes, and ablation of CKMT1 in mice did not alter energy expenditure, mitochondrial responses to pharmacological β3-adrenergic activation (CL 316, 243) or exacerbate the detrimental metabolic effects of consuming a high-fat diet. Taken together, these findings solidify CKMT1 as dispensable in the regulation of energy expenditure, and unlike in BAT, they do not support the presence of FCC within WAT.