Miracle-dz/UniFAR-FTUs huggingface dataset:
VELOCITY MEASUREMENTS IN NEAR‐SURFACE FORMATIONS

Procedures are described for measuring various seismic velocities at shallow depths in the earth, and examples of the resulting logs are presented. Velocities in Austin chalk and Eagle Ford shale show that these formations are not isotropic, and velocities in loose sand are seen to increase smoothly with depth except for an abrupt increase in compressional speed at the water table. Elastic constants for chalk and shale are computed. A discussion is given of the literature dealing with a packing of spheres as a model for loose sand, and an approximate theory is presented which includes tangential forces between spheres.
Determination of Wetting Front Suction in the Green-Ampt Equation1

Interest in the Green-Ampt equation to predict infiltration rates into uniform and layered soils has increased in recent years. The work reported here is a study of the theoretical justification of determining the suction (at the wetting front) parameter in the equation from the hydraulic conductivity versus capillary suction relationship for the soil. It is shown that the wetting front suction so determined and used in a form of the Green-Ampt equation which allows for the surface boundary condition initially being one of constant flux gives predictions of infiltration volumes comparable to those determined by numerical solution of the complex partial-differential equation of moisture flow.
Reconstructing three-dimensional surfaces of tissues from a single polarimetric image

By acquiring three-dimensional profiles of biological tissues, interventions can be performed with increased speed and accuracy, driving the development of next-generation image guided therapy. However, current three-dimensional reconstruction techniques relying on feature detection and matching struggle with tissues lacking distinct features, resulting in relatively sparse reconstruction results. In this paper, we propose a data-driven method for reconstructing three-dimensional surfaces from a single polarimetric image, utilizing physics-based priors. We constructed a calibrated imaging system consisting of a polarization camera and a 3D scanner to collect polarization information and ground truth 3D data. Using this system, we created a dataset with organ models, capturing polarization images, depth maps, and surface normal maps under different lighting conditions. To achieve our goal, we designed a deep neural network based on the Unet architecture. This network takes the polarization image and prior physical parameter maps (phase angle, degree of polarization, and unpolarized intensity) as inputs and is trained to output the surface normal map and relative depth map of the organ. Experimental results on the tissue phantom dataset demonstrate the effectiveness of our method in generating dense reconstruction results, even for the regions lacking distinct features. Furthermore, we validated the robustness of our method to changes in the light source direction, showcasing its ability to handle variations in lighting conditions. Overall, our proposed data-driven approach provides a promising solution for dense three-dimensional reconstruction from a single polarimetric image, leveraging physics-based priors and deep learning techniques.
Association between Social Support and Depressive Symptoms in Informal Caregivers of Adult and Older Dependents: A Systematic Review and Meta-Analysis

Social support is an important determinant of a carer’s mental health. In recent decades, despite many studies reporting on the relationship between social support and depressive symptoms in informal caregivers of adult and older dependents, there are no systematic reviews synthesizing the available evidence. The purpose of the present study was to perform a systematic review and meta-analysis on the relationship between social support and depressive symptoms in informal caregivers of adults and older dependent people. We searched PubMed, CINAHL (EBSCO), PsycINFO (ProQuest), and Scopus, up to 15 January 2023 for studies. We applied no date or language limits to our search. A random-effects model was used to pool effect estimates. The included studies were also independently assessed for quality. Publication bias was evaluated by funnel plots, Egger’s regression test, and the Trim and Fill method. Ninety-three studies were included in the review, reporting on a total of 15,431 informal caregivers. We found a moderate negative association between perceived social support and caregiver depressive symptoms (78 studies; r = −0.35, 95% CI = −0.39, −0.31; low heterogeneity and low risk of publications bias) and a small negative association between received social support and caregiver self-reported depressive symptoms (12 studies; r = −0.14, 95% CI = −0.20, −0.07; low heterogeneity and low risk of publications bias). Our results indicate that social support is a clinically relevant construct for carer well-being and an important protective factor for depressive symptoms in informal caregivers of adult and older dependents.
GPS velocity and strain fields in Sicily and southern Calabria, Italy: updated geodetic constraints on tectonic block interaction in the central Mediterranean

[1] We present an improved rendition of the geodetic velocity and strain fields in Sicily and southern Calabria obtained through the analysis of 18 years of GPS observations from continuous and survey station networks. The dense spatial coverage of geodetic data provides precise quantitative estimates of previously established first-order active kinematic features, including: i) a narrow east-west-elongated belt of contraction (∼1–1.5 mm/yr) extending offshore northern Sicily from Ustica to Stromboli across the Aeolian Islands; ii) a narrow east-west-trending contractional belt located along the northern rim of the Hyblean Plateau in southern Sicily, with shortening at up to 4.4 mm/yr; iii) right motion (∼3.6 mm/yr) on the Aeolian-Tindari-Letojanni fault (ATLF) system, a main shear zone extending from the Aeolian Islands to the Ionian coast of Sicily, with significant transpression and transtension partitioned between discrete sectors of the fault; iv) transtension (∼1 mm/yr) across the Sicily Channel between Sicily and North Africa. We use geodetic observations coupled to geological constraints to better elucidate the interplay of crustal blocks revealed in the investigated area. In particular, we focus on the ATLF, which forms the primary boundary between the Sicilian and Calabrian blocks. The ATLF juxtaposes north-south contraction between Sicily and the Tyrrhenian block with northwest-southeast extension in northeastern Sicily and Calabria. Contraction between Sicily and Tyrrhenian blocks probably arises from the main Europe-Nubia convergence, although Sicily has a component of lateral motion away from Nubia. We found that convergence is not restricted to the northern offshore, as commonly believed, but is widely accommodated between the frontal belt and the northern rim of the Hyblean foreland in southern Sicily. Geodetic data also indicate that active right shear on the ATLF occurs to the southeast of the mapped fault array in northern Sicily, suggesting the fault cuts through till the Ionian coast of the island. The small geodetic divergence between the Hyblean and Apulian blocks rimming on both sides the Calabria block and subjacent Ionian slab, coupled with marine geophysical evidences in the Ionian Sea lends credit to the proposed deep root of the ATLF and to a fragmentation of the Ionian domain.
TAPHONOMY OF A JUVENILE LAMBEOSAURINE BONEBED FROM THE TWO MEDICINE FORMATION (CAMPANIAN) OF MONTANA, UNITED STATES

Abstract The Sun River Bonebed is a monodominant assemblage of late juvenile lambeosaurine elements from the Upper Cretaceous Two Medicine Formation of north-central Montana, United States. Detailed excavation revealed an unusual paleobiologic and depositional signature. Although the bonebed occurs in a succession of beds representing anastomosing stream deposits in a seasonal paleoenvironment, the assemblage consists of a conglomerate of bone and calcareous clasts in a matrix of silty mud and free-floating sand grains. Internally, the bed exhibits normal grading of bone and calcareous clasts, poor sediment sorting, and preferred orientation of elongate elements, all characteristics common to debris flow deposits. The mud-rich matrix, poor sorting, and graded clasts of the bonebed suggest the assemblage was entrained and deposited by a cohesive debris flow, perhaps initiated through entrainment of fine overbank sediment by a seasonal flood. Nearly complete skeletal disarticulation and weathering of some bones indicate a brief period of postmortem exposure prior to debris flow entrainment. Fracture styles suggesting fresh breaks and frequent abrasion may reflect pre-flow trampling or chaotic flow transport. A significant number of elements also exhibit wet rot. The uniformity in taphonomic effects among elements suggests a mass mortality event, a rarity for debris-flow-hosted bonebeds, though the specific cause of death is uncertain. The age class dominance is interpreted to reflect original paleobiology, rather than abiotic postmortem selection, and establishes the Sun River Bonebed as the first bonebed of predominantly late juvenile material, with no adult material, in the Two Medicine Formation.
Detection of aerosol types over the East China Sea near Japan from four‐channel satellite data

Aerosol‐type detection from satellite remote sensing is difficult but an important challenge for improving the estimation of aerosol radiative forcing. In this paper, we have classified aerosols into four major aerosol types, that is, soil dust, carbonaceous, sulfate and sea salt aerosols using SeaWiFS four‐channel data. The retrieved results show that the East China Sea region was loaded with different aerosols in a highly mixed condition. We also succeeded in classifying the volcanic plume from Mt. Oyama located on Miyakejima Island as sulfate aerosols.
Remaining Useful Life Prediction via a Data-Driven Deep Learning Fusion Model-CALAP

As one of the key technologies in the field of Prognostic and Health Management (PHM), Remaining Useful Life (RUL) prediction technology plays an important role in equipment health maintenance and fault detection. For complex devices, the degradation process of the remaining useful life of the device is often difficult to be described with mathematical or physical models, so data-driven methods has become an important and feasible method in the field of RUL prediction. This paper proposes a data-driven deep learning fusion model based on CNN-ATTENTION-LSTM-ATTENTION-PARALLEL (CALAP) model, in which Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) are adopted to extract the spatial features and temporal features of the data in parallel, and both of them combine the corresponding attention mechanism allowing the network to focus on important factors. The CNN path fuses CBAM and the LSTM path fuses attention mechanism. We evaluate the proposed model on the C-MAPSS dataset released by NASA and compare it to the state-of-the-art. The RMSE of the proposed model is reduced by nearly 2–5%, and the score is reduced by nearly 8–10% under simple conditions. Experimental results prove that the model has relatively high prediction accuracy and good robustness.
An analysis of urgency in project management: the unified project urgency and economic speed analysis model

This paper aims to address the gap in the literature concerning the role of urgency in project management. Specifically, it seeks to develop a Unified Theoretical Model that incorporates key variables such as urgency, speed, duration, and cost. To achieve this aim, a detailed analysis of essential articles was conducted from the Scopus database to understand the relationships and trade-offs among the variables. The research method involves both literature synthesis and Theoretical Model Formulation, culminating in the Unified Project Urgency and Economic Speed Analysis Model. This model serves as a mathematical and theoretical framework tailored for managerial decision-making in High-Intensity Time-Sensitive Projects. It highlights the complex interactions between urgency, duration, speed, and costs, thereby providing a structure to guide managerial actions. The study contributes to enriching the theoretical foundations in the field of urgent project management. Finally, it opens avenues for future research by extending theoretical insights that are relevant for both academic researchers and industry professionals.
Seasonal Trends with the Comparison of Venous and Capillary Blood for Diagnosis of Malaria among Children in Karachi, Pakistan

Background: More than 250 million people are infected by malaria parasites annually while around one million children less than 5 years of age die every year due to malaria. We aimed to assess the seasonal trends and usefulness of capillary and venous blood for rapid diagnosis of malaria. Methods: This cross-sectional study of 18 months duration was conducted at the National Institute of Child Health (NICH), Karachi. All patients reporting fever as chief complaint were recruited as study subjects. A semi-structured questionnaire was used to collect demographic information, presenting complaints, awareness of caregivers regarding malaria, preventive measures and history of malaria fever. Three ml Venous (2–3ml) as well as peripheral blood (3–4 drops) samples of all patients were collected for microscopy and rapid diagnostic tests (RDTs). Results: Out of total 477 patients with fever Venous and Capillary Blood RDTs methods detected 33(6.9%) and 30(6.3%) as the malaria positive while Venous and Capillary Blood Microscopy detected 30(6.1%) and 32(6.7%) cases respectively. Plasmodium Vivax infection was the most prevalent (87.9%) and majority (39.39%) of the cases occurred in the quarter, July to September. Conclusion: July to September is the peak season for malaria and P. Vivax (87.9%) is the predominant strain in Karachi. Venous and capillary blood are equally useful for malaria diagnosis however, convenience and less invasiveness may justify the preference of capillary blood over venous blood for early diagnosis of malaria.
Dupuytren’s Disease Is Mediated by Insufficient TGF-β1 Release and Degradation

Dupuytren’s disease (DD) is a fibroproliferative disorder affecting the palmar fascia, causing functional restrictions of the hand and thereby limiting patients’ daily lives. The disturbed and excessive myofibroblastogenesis, causing DD, is mainly induced by transforming growth factor (TGF)-β1. But, the extent to which impaired TGF-β1 release or TGF-β signal degradation is involved in pathologically altered myofibroblastogenesis in DD has been barely examined. Therefore, the complex in which TGF-β1 is secreted in the extracellular matrix to elicit its biological activity, and proteins such as plasmin, integrins, and matrix metalloproteinases (MMPs), which are involved in the TGF-β1 activation, were herein analyzed in DD-fibroblasts (DD-FBs). Additionally, TGF-β signal degradation via caveolin-1 was examined with 5-fluoruracil (5-FU) in detail. Gene expression analysis was performed via Western blot, PCR, and immunofluorescence analyses. As a surrogate parameter for disturbed myofibroblastogenesis, 𝛼-smooth-muscle-actin (𝛼-SMA) expression was evaluated. It was demonstrated that latency-associated peptide (LAP)-TGF-β and latent TGF-β-binding protein (LTBP)-1 involved in TGF-β-complex building were significantly upregulated in DD. Plasmin a serinprotease responsible for the TGF-β release was significantly downregulated. The application of exogenous plasmin was able to inhibit disturbed myofibroblastogenesis, as measured via 𝛼-SMA expression. Furthermore, a reduced TGF-β1 degradation was also involved in the pathological phenotype of DD, because caveolin-1 expression was significantly downregulated, and if rescued, myofibroblastogenesis was also inhibited. Therefore, our study demonstrates that a deficient release and degradation of TGF-β1 are important players in the pathological phenotype of DD and should be addressed in future research studies to improve DD therapy or other related fibrotic conditions.
Interdecadal changes of surface temperature since the late nineteenth century

We present global fields of decadal annual surface temperature anomalies, referred to the period 1951–1980, for each decade from 1881–1890 to 1981–1990 and for 1984–1993. In addition, we show decadal calendar-seasonal anomaly fields for the warm decades 1936–1945 and 1981–1990. The fields are based on sea surface temperature (SST) and land surface air temperature data. The SSTs are corrected for the pre-World War II use of uninsulated sea temperature buckets and incorporate adjusted satellite-based SSTs from 1982 onward. Our results extend those published in the 1990 Intergovernmental Panel on Climate Change Scientific Assessment and its 1992 supplement. We assess the impact of various sources of error in the fields. Despite poor data coverage initially and around the two World Wars the generally cold end of the nineteenth century and start to the twentieth century are confirmed, together with the substantial warming between about 1920 and 1940. Slight cooling of the northern hemisphere took place between the 1950s and the mid-1970s, although slight warming continued south of the equator. Recent warmth has been most marked over the northern continents in winter and spring, but the 1980s were warm almost everywhere apart from Greenland, the northwestern Atlantic and the midlatitude North Pacific. Parts of the middle- to high-latitude southern ocean may also have been cool in the 1980s, but in this area the 1951–1980 climatology is unreliable. The impact of the satellite data is reduced because the record of blended satellite and in situ SST is still too short to yield a climatology from which to calculate representative anomalies reflecting climatic change in the southern ocean. However, we propose a method of using existing satellite data in a step toward this target. The maps are condensed into global and hemispheric decadal surface temperature anomalies. We show the sensitivity of these estimated anomalies to alternative methods of compositing the spatially incomplete fields. Running decadal zonal means and annual global and hemispheric time series are also shown. Finally, we discuss some salient features in terms of observed atmospheric circulation changes and of the results of climate model integrations with increasing atmospheric greenhouse gases.
Unleashing the potential: Enhancing technology adoption and innovation for micro, small and medium-sized enterprises (MSMEs)

Abstract This study conducts a comprehensive systematic review to gain insights into the challenges of technology adoption and innovation for Malaysian Micro, Small and Medium-sized Enterprises (MSMEs), which play a crucial role in the country’s economy. These MSMEs face obstacles, including challenges in adopting technology and a lack of technical efficiency. To address these issues, the study explores prominent technology adoption theories (e.g. TAM, DOI) and organizational theories (e.g. RBV, ST). By addressing these challenges, policymakers can enhance MSMEs’ competitiveness through targeted strategies, fostering innovation and effective technology adoption, thus contributing to their growth and resilience in the Malaysian economy.
TRANSLATION TECHNIQUES OF ARABIC PHRASES IN THE POETRY OF HAKADZĀ AKTUBŪ TĀRĪKH AL-NISĀ BY NIZAR QABBANI / TEKNIK TERJEMAHAN FRASA BAHASA ARAB DALAM PUISI HAKADZĀ AKTUBŪ TĀRĪKH AL-NISĀ KARYA NIZAR QABBANI

This study aims to evaluate the quality and the techniques of the Indonesian translation of Arabic phrases from Nizar Qabbani's poetry Hakadzā Aktubū Tārīkh al-Nisā. This research uses a descriptive analytic method, and the data sources are all Arabic phrases found in Nizar Qabbani's important poem, Hakadzā Aktubū Tārīkh al-Nisā. According to the study's findings, Hakadzā Aktubū Tārīkh al-Nisā poetry has eight different types of Arabic phrases: idhafy, bayani, athfi, maushuli, zharfy, mazjiy, adadi, and isnadi phrases. This translation makes use of the following eight methods: transference, naturalization, equivalent functional, descriptive equivalent, synonymy, loan translation, modulation, reduction, and expansion. The functional matching, reduction, and expansion procedures are a few that use source language. On the other hand, transfer, naturalization, descriptive matching, synonyms, borrowing, and modulation techniques, tend to favor target language. Based on translation techniques, six Arabic phrases tend to translate based on the source language and the other six tend to translate depending on the target language. It suggests that the translator is more likely to translate certain phrases based on its context.
Optimization of lidar data processing: a goal of the EARLINET-ASOS project

EARLINET-ASOS (European Aerosol Research Lidar Network - Advanced Sustainable Observation System) is a 5-year EC Project started in 2006. Based on the EARLINET infrastructure, it will provide appropriate tools to improve the quality and availability of the continuous observations. The EARLINET multi-year continental scale data set is an excellent instrument to assess the impact of aerosols on the European and global environment and to support future satellite missions. The project is addressed in optimizing instruments and algorithms existing within EARLINET-ASOS, exchanging expertise, with the main goal to build a database with high quality aerosol data. In particular, the optimization of the algorithms for the retrieval of the aerosol optical and microphysical properties is a crucial activity. The main objective is to provide all partners with the possibility to use a common processing chain for the evaluation of their data, from raw signals to final products. Raw signals may come from different types of systems, and final products are profiles of optical properties, like backscatter and extinction, and, if the instrument properties permit, of microphysical properties. This will have a strong impact on the scientific community because data with homogeneous well characterized quality will be made available in nearly real time.
Deep-Learning-Based Multi-Timestamp Multi-Location PM2.5 Prediction: Verification by Using a Mobile Monitoring System With an IoT Framework Deployed in the Urban Zone of a Metropolitan Area

The issue of air pollution in urban areas is gaining attention due to the rise of environmental and health concerns, especially for the particulate matter 2.5 (PM textsubscript 2.5), which poses the greatest health risk to humans. Accurate air quality prediction data allows government officials and the public to take preventive measures in advance. Recently, many air quality prediction studies have used machine learning techniques to identify patterns and rules in air quality data. However, these studies generally adopted under-represented background levels, and the prediction intervals were often in hours, which may not be suitable for residents who needed accurate air quality forecasts. Therefore, this study proposes a deep-learning-based multi-timestamp multi-location PM2.5 prediction system built on two acrlong RNN models: 1) long short-term memory (LSTM) and 2) gated recurrent unit (GRU). Airbox data for the Taipei metropolitan area serves as the main source of training data to develop a forecasting model that can predict changes of PM2.5 levels within the next 6–30 min in different locations. The prediction results are verified by comparing them with the PM2.5 measuring results from an Internet of Things (IoT)-based acrlong OVMS, which enables real-time data sensing and collection, and wireless transmission. The error and accuracy are $0.922 \mu \text{g}$ /m 3 and 100% for the LSTM-based prediction model, and $0.940 \mu \text{g}$ /m 3 and 95.7% for the GRU-based prediction model, respectively. These results can be sent out as warning messages to elderly and asthmatic patients, or serve as important information for route recommendations and policy formulation.