Miracle-dz/UniFAR-FTUs huggingface dataset:
Ice-ocean interactions in Milne Fiord

Widespread break up of ice shelves, glacier tongues, and the loss of ice-dammed epishelf lakes on the northern coast of Ellesmere Island has motivated a need to understand fjord dynamics, yet the oceanography of fjords here is not well studied. Here, we present ocean profiling and mooring data collected from 2011 to 2015 in Milne Fiord, the last ice shelf-epishelf lake-glacier tongue fjord in the Arctic. The data reveal that seasonal and interannual variations of fjord water properties and circulation are strongly impacted by the presence of the Milne Ice Shelf. The ice shelf forms a floating dam that traps surface runoff resulting in strong stratification and an elevated fjord heat content. Water exchange below the ice shelf is restricted to a narrow basal channel, prolonging the export of fjordmodified water, including subglacial runoff, by several months. In contrast, intermediate waters that penetrate to the Milne Glacier grounding line are freely exchanged and respond to offshore variations, with implications for submarine melting. Unexpectedly, the depth of the halocline between the epishelf lake and seawater, often used to infer the thickness of the ice shelf, varied by several meters each year. This variability resulted from rapid inflow of surface runoff during summer followed by slow drainage under the ice shelf over winter, which is well modelled as hydraulically-controlled flow through a channel. A mixing event also abruptly changed the depth of the halocline by 1.5 m in less than 24 hours, indicating caution must be used when inferring ice shelf mass balance from halocline depth. Submarine melt rates, estimated by two independent approaches, are strongly dependent on the vertical distribution of heat in the fjord. Spatial variation of ice thickness resulted in a heterogeneous distribution of melt. The highest estimated melt rate (4 m a 1) occurred where the glacier was in contact with warm Atlantic Water at the grounding line, and enhanced near-surface melting is driven by the elevated heat content of the upper water column. Estimated melt rates are limited by weak currents (⇠1 cm s 1) in the fjord imposed by the presence of the ice shelf.
Comparing effects of attractive interactions in crowded systems: nonspecific, hydrophobic, and hydrogen bond interactions

The equilibrium stability of a protein is determined by its amino acid sequence and the solution conditions, such as temperature, pH and presence of chemical denaturant. The stability of a single protein in two identical solutions can nonetheless differ if other macromolecules, termed cosolutes or crowders, are present in one of the solutions at concentrations high enough to occupy a substantial fraction of the solution volume. This effect, due to the presence of the crowders, decreases or increases the stability depending on the interactions between the protein and crowders. Hard-core steric repulsions, which are responsible for the reduction in free volume, are expected to entropically stabilize the protein while attractive interactions can be destabilizing. Here we use a coarse-grained protein model to assess the impact of different types of crowder-protein interactions on the stability of a 35-amino acid model sequence folding into a helical bundle. We find that, for the same interaction strength and concentration, spherical crowders with a hydrophobic character are more destabilizing than crowders interacting nonspecifically with the protein. However, the two types of interactions differ in the degree of association between crowders and protein. At an interaction strength for which the attractive interactions roughly counteracts the stabilizing hard-core repulsions, the nonspecific interactions lead to much stronger crowder-protein association than the hydrophobic interactions. Additionally, we study crowders in the form of polypeptide chains, which are capable of hydrogen bonding with the protein. These peptide crowders have a destabilizing effect even at relatively low crowder concentrations, especially if the sequence of the peptide crowders includes hydrophobic amino acids. Our findings emphasize the importance of the interplay between different types of attractive crowder-protein interactions and entropic effects in determining the net effect on protein stability.
A Generalized Linear Model and Machine Learning Approach for Predicting the Frequency and Severity of Cargo Insurance in Thailand’s Border Trade Context

The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machine learning techniques. Evaluations focus on mean absolute error (MAE) and root mean squared error (RMSE) metrics to comprehensively assess predictive performance. For frequency prediction, extreme gradient boosting (XGBoost) demonstrates the lowest MAE, indicating higher accuracy compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Despite XGBoost’s lower MAE, it shows higher RMSE values, suggesting a broader error spread and larger magnitudes compared to gradient boosting machines (GBMs) and a generalized linear model (Poisson). Conversely, the generalized linear model (Poisson) showcases the best RMSE values, indicating tighter clustering and smaller error magnitudes, despite a slightly higher MAE. For severity prediction, extreme gradient boosting (XGBoost) displays the lowest MAE, implying better accuracy. However, it exhibits a higher RMSE, indicating wider error dispersion compared to a generalized linear model (Gamma). In contrast, a generalized linear model (Gamma) demonstrates the lowest RMSE, portraying tighter clustering and smaller error magnitudes despite a higher MAE. In conclusion, extreme gradient boosting (XGBoost) stands out in mean absolute error (MAE) for both frequency and severity prediction, showcasing superior accuracy. However, a generalized linear model (Gamma) offers a balance between accuracy and error magnitude, and its performance outperforms extreme gradient boosting (XGBoost) and gradient boosting machines (GBMs) in terms of RMSE metrics, with a slightly higher MAE. These findings empower insurance companies to enhance risk assessment processes, set suitable premiums, manage reserves, and accurately forecast claim occurrences, contributing to competitive pricing for clients while ensuring profitability. For cross-border trade entities, such as trucking companies and cargo owners, these insights aid in improved risk management and potential cost savings by enabling more reasonable insurance premiums based on accurate predictive claims from insurance companies.
Reducing maternal deaths by skills-and-drills training in managing obstetric emergencies: A before-and-after observational study.

BACKGROUND The institutional maternal mortality ratio (iMMR) in South Africa (SA) is still unacceptably high. A key recommendation from the National Committee on Confidential Enquiries into Maternal Deaths has been to improve the availability and quality of care for women suffering obstetric emergencies. OBJECTIVES To determine whether there was a change in the number of maternal deaths and in the iMMR over time that could be attributed to the training of >80% of healthcare professionals by means of a specifically designed emergency obstetric care (EmOC) training programme. METHODS A before-and-after study was conducted in 12 healthcare districts in SA, with the remaining 40 districts serving as a comparison group. Twelve 'most-in-need' healthcare districts in SA were selected using a composite scoring system. Multiprofessional skills-and-drills workshops were held off-site using the Essential Steps in Managing Obstetric Emergencies and Emergency Obstetric Simulation Training programme. Eighty percent or more of healthcare professionals providing maternity care in each district were trained between October 2012 and March 2015. Institutional births and maternal deaths were assessed for the period January 2011 - December 2016 and a before-and-after-training comparison was made. The number of maternal deaths and the iMMR were used as outcome measures. RESULTS A total of 3 237 healthcare professionals were trained at 346 workshops. In all, 1 248 333 live births and 2 212 maternal deaths were identified and reviewed for cause of death as part of the SA confidential enquiries. During the same period there were 5 961 maternal deaths and 5 439 870 live births in the remaining 40 districts. Significant reductions of 29.3% in the number of maternal deaths (risk ratio (RR) 0.71, 95% confidence interval (CI) 0.66 - 0.77) and 17.5% in the number of maternal deaths from direct obstetric causes (RR 0.825, 95% CI 0.73 - 0.93) were recorded. When comparing the percentage change in iMMR for equivalent before-and-after periods, there was a greater reduction in all categories of causes of maternal death in the intervention districts than in the comparison districts. CONCLUSIONS Implementing a skills-and-drills EmOC training package was associated with a significant reduction in maternal deaths.
A 1.8–5.4-GHz GaN MMIC Distributed Efficient Power Amplifier With Reactance Compensation and Adaptive Biasing

This article presents an ultra-broadband distributed efficient power amplifier (DEPA) with reactance compensation and adaptive biasing. It is illustrated that the bandwidth of the DEPA can be improved apparently by applying a shunt short-circuited stub at the combining point. The principle of such method is that the back-off impedance variation with frequency can be compensated by the reactance of the shunt stub. Besides, to get over the high gain compression of the conventional DEPA, an adaptive biasing scheme is proposed for the auxiliary PAs. A DEPA is implemented in a 0.25- $\mu \text{m}$ GaN-HEMT process for validation. The shunt stub is realized by a LC tank equivalently for a compact size. The fabricated DEPA achieves a saturated output power of 41.5–43 dBm and an 8-dB back-off drain efficiency (DE) of 38%–46.8% from 1.8 to 5.4 GHz with a chip size of $3.2\times3.2$ mm2. The fractional bandwidth (FBW) is up to 100%. Applying a 100-MHz LTE signal with an 8.5-dB peak-to-average power ratio (PAPR), an average DE of 35%–43% is measured over the entire bandwidth, and adjacent channel power ratio (ACPR) is better than −47 dBc after applying digital predistortion. To the best of our knowledge, the proposed DEPA demonstrates the largest FBW among all reported integrated back-off efficient PAs without using digital techniques or reconfiguration.
Poverty–Food Insecurity Nexus in the Post-Construction Context of a Large Hydropower Dam in the Brazilian Amazon

Within the 2030 Sustainable Development Agenda, large hydropower dams are positioned as a sustainable energy source, notwithstanding their adverse impacts on societies and ecosystems. This study contributed to ongoing discussions about the persistence of critical social issues, even after the investments of large amounts of resources in areas impacted by the construction of large hydropower dams. Our study focused on food insecurity and evaluated this issue in the city of Altamira in the Brazilian Amazon, which has been profoundly socially and economically impacted by the construction, between 2011 and 2015, of Brazil’s second-largest dam, namely, Belo Monte. A survey in Altamira city featured a 500-household random sample. Structural equation modeling showed conditioning factors of 60% of the population experiencing varying food insecurity degrees. Poverty, female-led households, lower education, youth, and unemployment were strongly linked to higher food insecurity. Crowded, officially impacted, and resettled households also faced heightened food insecurity. Our findings underscore the food insecurity conditions in the region impacted by the Belo Monte dam, emphasizing the need to take into account this crucial issue while planning and implementing hydropower dams.
Late Holocene droughts in the Fertile Crescent recorded in a speleothem from northern Iraq

Droughts have had large impacts on past and present societies. High‐resolution paleoclimate data are essential to place recent droughts in a meaningful historical context and to predict regional future changes with greater accuracy. Such records, however, are very scarce in the Middle East in general, and the Fertile Crescent in particular. Here we present a 2400 year long speleothem‐based multiproxy record from Gejkar Cave in northern Iraq. Oxygen and carbon isotopes and magnesium are faithful recorders of effective moisture. The new Gejkar record not only shows that droughts in 1998–2000 and 2007–2010, which have been argued to be a contributing factor to Syrian civil war, were extreme compared to the current mean climate, but they were also superimposed on a long‐term aridification trend that already started around or before 950 C.E. (Common Era). This long‐term trend is not captured by tree ring records and climate models, emphasizing the importance of using various paleoclimate proxy data to evaluate and improve climate models and to correctly inform policy makers about future hydroclimatic changes in this drought‐prone region.
Integrated Simulation and Calibration Framework for Heating System Optimization

In a time where sustainability and CO2 efficiency are of ever-increasing importance, heating systems deserve special considerations. Despite well-functioning hardware, inefficiencies may arise when controller parameters are not well chosen. While monitoring systems could help to identify such issues, they lack improvement suggestions. One possible solution would be the use of digital twins; however, critical values such as the water consumption of the residents can often not be acquired for accurate models. To address this issue, coarse models can be employed to generate quantitative predictions, which can then be interpreted qualitatively to assess “better or worse” system behavior. In this paper, we present a simulation and calibration framework as well as a preprocessing module. These components can be run locally or deployed as containerized microservices and are easy to interface with existing data acquisition infrastructure. We evaluate the two main operating modes, namely automatic model calibration, using measured data, and the optimization of controller parameters. Our results show that using a coarse model of a real heating system and data augmentation through preprocessing, it is possible to achieve an acceptable fit of partially incomplete measured data, and that the calibrated model can subsequently be used to perform an optimization of the controller parameters in regard to the simulated boiler gas consumption.
High-Speed 645-nm VCSELs for Low-Scattering-Loss Gb/s Underwater Wireless Optical Communications

With the evolution of underwater wireless optical communications, semiconductor lasers, particularly blue and green diode lasers, have exhibited high performance as optical transmission sources. They have a wide modulation bandwidth and can offer data rates in the Gb/s range in pure water. However, blue and green wavelengths experience higher attenuation coefficients and more considerable scattering losses in the turbid harbor and turbulent water compared to red light. In this study, we investigated the capability of red vertical-cavity surface-emitting lasers (VCSELs) for data transmission in an underwater wireless optical communication (UWOC) system. The wavelength of our VCSEL is ~645 nm, and the output power is ~2 mW. A 2-Gb/s data rate was achieved underwater by direct-current-biased orthogonal frequency-division multiplexing (DC-OFDM) modulation. The bit error ratio (BER) was $3.5\times 10 ^{\mathbf {-3}}$ , which is below the hard-decision forward error correction (HD-FEC) limit of $3.8\times 10 ^{\mathbf {-3}}$ . The availability of 650-nm transmission window of plastic optical fibers (POFs) makes this VCSEL wavelength an attractive solution for POF communication and UWOC convergent systems. Moreover, the use of this wavelength will enable wavelength division multiplexing (WDM) in highly turbid water with other red wavelengths (e.g., 680 nm) to improve the data throughput in these harsh environments.
Efficacy of chemo-immunotherapy in metastatic BRAF-mutated lung cancer: a single-center retrospective data

Background The effectiveness of combining immune checkpoint inhibitors (ICIs) with chemotherapy in treating non-small cell lung cancers (NSCLCs) with BRAF mutations has not been sufficiently explored. Methods We compiled data from 306 NSCLC patients with identified BRAF mutations. We looked at efficacy by assessing the objective response rate (ORR) and disease control rate (DCR), as well as survival through measuring progression-free survival (PFS) and overall survival (OS). Results Out of the patient pool, 44 were treated with a regimen of immune-chemotherapy. Patients undergoing ICI in combination with chemotherapy had a median PFS of 4 months, and the median OS was recorded at 29 months. There was a notable increase in OS in patients receiving first-line treatment versus subsequent lines (29 vs 9.75 months, p=0.01); however, this was not the case with PFS (9 vs 4 months, p=0.46). The ORR for patients on ICIs was 36.3%. PFS and OS rates did not significantly differ between patients with the BRAF-V600E mutation and those with non-V600E mutations (p=0.75 and p=0.97, respectively). Additionally, we found a significant variation in PD-L1 expression between those who responded to treatment and those who didn’t (p=0.04). Conclusion Our findings indicate that chemo-immunotherapy as an initial treatment may lead to improved OS in patients with BRAF-mutated NSCLC when compared to its use in subsequent lines of therapy. Further studies are needed to validate these results and to delve deeper into how specific types of BRAF mutations and PD-L1 expression levels might predict a patient’s response to treatments in NSCLC.
Injection capability at the Raft River Geothermal Site

The Raft River Geothermal Resource Area in southern Idaho is the first location for an electric power plant utilizing a medium temperature (approx. = 145/sup 0/C) geothermal resource. For the projected 5 MWe pilot geothermal plant, a supply of 2500 gpm of the geothermal fluid is needed. The State of Idaho prefers that the spent brine be reinjected into zones deeper than the known agricultural aquifers. Wells RRGI-6 and 7 are to be used for injection. The objective of this study is to evaluate the injection capability of the formation. An analysis is presented of several injectivity tests performed by EG and G on RRGI-7 to characterize the injection capability of the formation. The available geological information about the area and preliminary results of a spinner survey have been included in the injectivity test analysis. A wellhead pressure limit of 500 psi has been imposed to prevent injection formation fracturing. The approach to analysis is to use a two dimensional radial numerical simulator with parameters determined by the test results and from geological data.
Roadside Levels of Ambient Air Pollutants: SO2, NO2, NO, CO and SPM in Dar es Salaam City

The quality of breathing level, urban ambient air quality was assessed for the city of Dar es Salaam, Tanzania, by measuring the levels of the atmospheric pollutants: sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), nitrous oxide (NO) and suspended particulate matter (SPM) at different localities within the city. The sites were selected to cover industrial, commercial, residential and tourist-beach areas. The concentrations of these gases were measured using standard methods. The results showed that several of the locations within the city had poorer air quality than that recommended for clean and safe working environment by the World Health Organization. Thus it can be concluded that many Dar es Salaam urban inhabitants, though unaware of it, were really working and living in hazardous atmospheric environment especially during the day and maybe in less hazardous environment during the night.
Density-Aware U-Net for Unstructured Environment Dust Segmentation

Vision-based segmentation methods rely heavily on image quality, and mining environments are full of dust, which greatly reduces visibility. Efficient and accurate segmentation of dusty regions in mining environments can improve the performance of unmanned vehicle environment perception. In this article, a dust segmentation method based on a novel density-aware nested U-structure convolutional neural network (DAUnet) is proposed. Compared with existing dust segmentation methods, our method has three advantages. First, we introduce the residual channel–spatial attention (RCSA) block. The block contains two attention layers and a residual structure, which can extract features more efficiently. Second, we introduce the difference expansion layer. This structure filters the predicted dust probabilities, eliminates pixels with lower probabilities, and then maps similar probability values to larger probability intervals, thus improving the network performance. Finally, for dust visualization, we use the predicted probabilities to reflect the dust density, which results in a smoother transition between the dust edges and the background. In addition, most of the current dust datasets are generated by simulation tools, and there is a lack of open-source real-world datasets. Therefore, we constructed the MineDust (MD) dataset based on a real open-pit mining environment. This dataset consists of dust-state images under different weather conditions and complex scenes. Experiments demonstrate that our algorithm can achieve 79.64% mIoU, which outperforms many existing segmentation methods.
Characterization and Preliminary Safety Evaluation of Akkermansia muciniphila PROBIO

In addition to providing certain health advantages to the host, a bacterial strain must possess a clearly defined safety profile to be regarded as a probiotic. In this study, we present a thorough and methodical assessment of the safety of a novel strain of bacteria, Akkermansia muciniphila PROBIO, which was isolated from human feces. Firstly, we examined the strain’s overall features, such as its gastrointestinal tolerance and its physiological and biochemical traits. Next, we verified its genotoxic properties through bacterial reverse mutation and in vitro mammalian cell micronucleus assays. The drug sensitivity of A. muciniphila PROBIO was subsequently examined through an analysis of its antibiotic resistance genes. Additionally, the toxicological impact was verified through acute and sub-chronic toxicity studies. A genome-based safety assessment was conducted to gain further insights into gene function, including potential virulence factors and pathogenic properties. Finally, we assessed whether moxifloxacin resistance in A. muciniphila PROBIO is transferred using in vitro conjugation experiments. A. muciniphila PROBIO exhibited superior gastrointestinal tolerance, with no observed hematological or histopathological abnormalities. Moreover, the outcomes pertaining to mutagenic, clastogenic, or toxic impacts were found to be negative, even at exceedingly high dosages. Moreover, no adverse effects associated with the test substance were observed during the examination of acute and sub-chronic toxicity. Consequently, it was plausible to estimate the no-observed-adverse-effect level (NOAEL) to be 6.4 × 1011 viable bacteria for an average individual weighing 70 kg. Additionally, only three potential drug resistance genes and one virulence factor gene were annotated. A. muciniphila PROBIO is naturally resistant to moxifloxacin, and resistance does not transfer. Collectively, the data presented herein substantiate the presumed safety of A. muciniphila PROBIO for its application in food.