Miracle-dz/UniFAR-FTUs huggingface dataset:
Crystallinities of Nanocrystalline and Nanofibrillated Celluloses by FT- Raman Spectroscopy

Recently, a new method based on FT-Raman spectroscopy was proposed to determine cellulose I crystallinity. It was reported that in the Raman spectrum of cellulosic materials the peak intensity ratio of 380 and 1,096 cm bands can be used to determine cellulose crystallinity. In that study, Raman crystallinities of the calibration set Whatman CC31 samples were found to be greatly correlated with the WAXS data (Segal-WAXS-21°; coefficient of determination R = 0.98). Average standard error calculated from replicate Raman acquisitions indicated that the Raman crystallinity model was highly reliable. In the present investigation, the Raman and WAXS methods were applied to nanocrystalline and nanofibrillated celluloses. Calculated crystallinities by the two methods were found to be significantly different. INTRODUCTION Cellulose nanocrystals (CNCs) and cellulose nanofibers (CNFs) are important new materials in field of cellulose science and technology. While CNCs are elongated crystalline rod-like nanoparticles NFCs consist of alternating crystalline and amorphous domains. These materials have generated remarkable excitement in the scientific community. The materials have unique physicochemical properties and have been used as reinforcing components in nanocomposites. Additional benefits arise from the fact that they are derived from the biomass that is both renewable and sustainable. Among other things, crystallinity has an important effect on the physical, mechanical, and chemical properties of cellulose based materials. For example, with increasing crystallinity, tensile strength, dimensional stability, and density increase, while properties such as chemical reactivity and swelling decrease. Cellulose crystallinity is defined as the mass fraction of crystalline domains in cellulose materials.
Provoked vulvodynia: A peripheral neurological pain syndrome

Original article Anatomical outcomes of laparoscopic high uterosacral ligament suspension for primary apical prolapse BURAK SEZGİN, FATİH PİRİNÇÇİ, EREN AKBABA, AYSUN CAMUZCUOĞLU, EDA ADEVİYE ŞAHİN, AHMET AKIN SİVASLIOĞLU, HAKAN CAMUZCUOĞLU Bilateral Sacrospineous Colposupension in the treatment of female genital prolapse: Risk-Benefit Considerations and Six Months Follow-up FRANZISKA C. HEMPTENMACHER, STEFAN OLLIG, ALEXANDER WAGNER, ANNE DE VRIES, DIRK G. KIEBACK Impact of labour and delivery on pelvic floor: perineometry and clinical evaluation. Any protecting factor? MARIA GRAZIA MATARAZZO, SALVATORE DI PASQUA, GIUSEPPE SARPIETRO, GAETANO VALENTI, FRANCESCO SGALAMBRO, MARIA FIORE, ANTONIO CIANCI
Two Centuries of Hydroclimatic Variability Reconstructed From Tree‐Ring Records Over the Amazonian Andes of Peru

Almost half of the tributaries of the Amazon River originate in the tropical Andes and support large populations in mountain regions and downstream areas. However, it is difficult to assess hydroclimatic conditions or to evaluate future scenarios due to the scarcity of long, high‐quality instrumental records. Data from the Global Precipitation Climatology Project (GPCP) provide a complete record since 1979 and offer a good representation of rainfall over the tropical Andes. Longer records are needed to improve our understanding of rainfall variability and summer monsoon behavior at various scales. We developed the first annually resolved precipitation reconstruction for the tropical Andes in Peru, based on tree‐ring chronologies of Cedrela and Juglans species. The annual (November–October) reconstruction extends the short instrumental records back to 1817, explaining 68% of the total variance of precipitation over the 1979–2007 calibration period. The reconstruction reveals the well‐documented influence of El Niño‐Southern Oscillation (ENSO) on Amazon Rainfall at interannual scales (~19% of total variance) and significant multidecadal variability with alternating periods of about 40 years (~13% of rainfall variability) related to the Atlantic Multidecadal Oscillation (AMO). Both oscillatory modes can explain dry and humid periods observed within the reconstruction and are likely associated with the negative trends of rainfall in the short instrumental records and the increased drought recurrence in recent decades. Our results show that montane tropical tree rings can be used to reconstruct precipitation with exceptionally high fidelity, characterize the interannual to multidecadal variability, and identify remote forcings in the hydroclimate over the Andean Amazon Basin of Peru.
Do goats have a salivary constitutive response to tannins?

ABSTRACT The effect of tannin-rich fodder (TRF) consumption on the saliva response of Criollo goats without previous browsing experience was determined. Eighteen kids were allocated into three treatments (n = 6 each): control group (CG), short-term tannin stimulus (TS) and long-term tannin stimulus (TL). Three experimental periods were used: adaptation (two weeks) in which the three treatment groups were fed Pennisetum purpureum grass and supplemented with a balanced feed. In period 1 (five weeks), TS and TL treatments were fed TRF (Lysiloma latisiliquum) with 55.5 g/kg DM condensed tannins (CT), grass and balanced feed. In period 2 (three weeks), L. latisiliquum fodder was withdrawn from the TS group. Thus, both the TS and the CG kids were only offered grass and balanced feed, while TL kids continued to receive TRF. In each period, saliva samples were collected to measure the tannin–protein interaction, salivary protein and protein turbidity index (PTI). The salivary protein was similar in all treatments and periods (P > .10). The goats' saliva reacted similarly when mixed with either tannic acid or L. latisiliquum water-acetone extract. Although the PTI tended to increase in the TL group compared to CG and TS with time, such difference was not significant. Thus, TRF intake failed to further increase the salivary response or PTI. Therefore, the saliva of goats from Yucatan, without previous browsing experience do have a constitutive response as it can block tannins irrespective of TRF stimulus.
Metabolome profiling of normotensive and hypertensive rats during the early stages of pregnancy

Hypertensive disorders during pregnancy are a leading cause of maternal and fetal mortality worldwide. During a first pregnancy, some women may develop gestational hypertension, representing a major risk to the mother and child especially when associated with preeclampsia. This pathological condition arises from a defect in placentation that is the starting point of hypertension in pregnancy. The impact of hypertension and/or pregnancy on the metabolome of rats was assessed by 1H-NMR spectroscopy. This approach revealed specific metabolic patterns in urine, plasma and tissue samples associated with hypertension and/or pregnancy. In particular, alterations in the contents of fatty acids, amino acids and glycoproteins in plasma samples and tissue extracts were indicative of the development of a "metabolic syndrome”. Also, changes in urinary creatinine already reported by other authors as potential biomarkers of an alteration in glomerular filtration due to hypertension were confirmed in our study. Finally, changes in sarcosine were also noticed, which could be seen as indicative of the invasion of cytotrophoblast during the development of the placenta. These results support the use of 1H-NMR-based metabonomics as a promising tool to provide new information on metabolic changes associated with the physiopathology of hypertension and pregnancy in rats.
Comparison of spatial interpolation methods for estimation of weekly rainfall in West Bengal, India

Rainfall is one of the most eloquently researched contemporary meteorological phenomena affecting the agricultural practices dramatically, particularly along the humid, sub-tropics, where agriculture is predominantly rainfed. It is a key parameter of agricultural production in West Bengal due to lack irrigation facilities in most of the areas. Thus, it is very important to have detailed information of rainfall distribution pattern of West Bengal. In practice rainfall data is collected only at few discrete stations scattered all over the whole state. However, rainfall is a spatially continuous phenomenon rather than discrete. Thus it becomes essential to apply a robust spatial interpolation technique to transform the discrete values into a continuous spatial pattern. In the present study, three spatial interpolation techniques namely Kriging, Inverse Distance Weighted (IDW) and SPLINE, are used for a comparative analysis to identify the most efficient interpolation technique. Weekly average rainfall data available between 1901 and 1985 for 19 standard meteorological weeks (SMW), Week 22 to Week 40 are used for the analysis. The errors of the three interpolation techniques are analyzed and the best method is chosen based on the minimum mean absolute deviation (MAD) and the minimum mean squared deviation (MSD) criteria. The IDW method is found to be the best spatial interpolation technique.
Effect of organic additives in fluoacid-based Ti and Zr-treatments for galvanized steel on the stability of a polymer coated interface

The bonding properties of zirconiumand titanium-based conversion coatings were evaluated using model conversion solutions of H2ZrF6 and H2TiF6 with addition of various organic additives (PAA, PVA, PVP). Macroscopic testing techniques such as contact angle and pull-off adhesion measurements were performed on galvanized steel sheets. Complementary to this, molecular studies were performed on model zinc substrates using ATR-FTIR in the Kretschmann configuration. The macroscopic and molecular approaches showed a good correlation demonstrating ATR-FTIR in the Kretschmann configuration to be a valuable tool to gain fundamental insights in metal oxide-polymer interfacial phenomena. Zirconium-treated galvanized steel substrates were shown to have a higher bonding affinity for the polyester coil coat primer than titanium-treated galvanized steel substrates. The presence of organic additives did not further improve the bonding properties. Yet, organic additives initially improved the interfacial stability of titanium-treated substrates. However, on the long term, organic additives are shown to be detrimental for polyester coil coat adhesion. This adverse effect of organic additives on the long term was assigned to its selective dissolution during immersion and was most pronounced for titanium-treatments. The limited effect of organic additives in case of zirconium-treatments was attributed to the higher portion of chemical interfacial bonds, as well as its tendency for crosslinking reactions causing entanglement of polymeric compounds in the zirconium oxide structure.
Lightweight Security Mechanism over MQTT Protocol for IoT Devices

Security is one of the main concerns with regard to the Internet of Things (IoT) networks. Since most IoT devices are restricted in resource and power consumption, it is not easy to implement robust security mechanisms. There are different methods to secure network communications; however, they are not applicable to IoT devices. In addition, most authentication methods use certificates in which signing and verifying certificates need more computation and power. The main objective of this paper is to propose a lightweight authentication and encryption mechanism for IoT constrained devices. This mechanism uses ECDHE-PSK which is the Transport Layer Security (TLS) authentication algorithm over Message Queuing Telemetry Transport (MQTT) Protocol. This authentication algorithm provides a Perfect Forward Secrecy (PFS) feature that makes an improvement in security. It is the first time that this TLS authentication algorithm is implemented and evaluated over the MQTT protocol for IoT devices. To evaluate resource consumption of the proposed security mechanism, it was compared with the default security mechanism of the MQTT protocol and the ECDHE-ECDSA that is a certificate-based authentication algorithm. They were evaluated in terms of CPU utilization, execution time, bandwidth, and power consumption. The results show that the proposed security mechanism outperforms the ECDHE-ECDSA in all tests. Keywords—Internet of Things (IoT); MQTT; Pre-Shared Keys (PSK); elliptic curve cryptography; Diffie-Hellman Ephemeral (DHE); Digital Signature Algorithm (DSA); Perfect Forward Secrecy (PFS); authentication; power consumption; wireless sensors
NOTES ON NOMENCLATURE WITHIN PETRORHAGIA (CARYOPHYLLACEAE)

Type: Bangladesh, Sylhet, Roxburgh s.n. (Tushee, Tusbee). Iconotype: Roxburgh Icon. No. 2291 (CAL; K-cf. Sealy, Kew Bull. 11: 391, 1956). Hyalostemma roxburghiana Wall. , Num. List No. 6434, before 31 Oct. 1832, nom. illeg. ; Griffith, Ic. PI. As. 4: t. 653, et Notulae 4: 715, 1854. Type: Same as for U. dioica Roxb. Miliusa roxburghiana Hook. f. & Thors. , Fl. Ind. 1: 150, 1855; et Fl. Brit. Ind. 1: 87. 1872; Sinclair, Gard. Bull. Sing. 14: 46. 1955; Whitmore in Hara et al., Enum. Fl. PI. Nepal 2: 27. 1979 [alior' (Wall.) Hook. f. & Thoms. ], nom. illeg. Type: Same as for H. roxburghiana Wall. Miliusa wallichiana Hook. f. & Thorns. , Fl. Ind. 1: 149. 1855. Syntypes: "In sylvis Assam, Sylhet et Cachar et in the montibus Khasia . 4,000 ped. usque" (K).
Haematological and Molecular Characterization of Sickle Cell-β Thalassemia in Southern Belt of Khyber-Pakhtun-Khwa, Pakistan

Sickle cell-β thalassemia (HbS-β thalassemia) is an inherited erythrocyte disorder affecting multiple organs. It results from compound heterozygosity for sickle cell trait and β thalassemia trait. As individuals are living well into middle age due to advances in diagnosis and treatment, further disease-related complications are coming into light. The present study was undertaken to determine the genetic factors responsible for hematological and molecular variability of HbS-β thalassemia patients in Southern belt of Khyber-Pakhtun-Khwa (KPK), Pakistan. The study was designed to analyse both molecular and haematological characterization of HbS-β thalassemia patients using Hb electrophoresis and allele specific primers through polymerase chain reaction (PCR) to determine both α and β-thalassemia, and restriction enzymes for characterization of γ-globin gene arrangement. A total of thirty-two HbS-β thalassemia cases with variable clinical manifestations were investigated. Twenty-four patients showed milder clinical presentation against eight patients who had severe clinical manifestations. Six β thalassemia mutations were identified: IVS 1-5 (G>C), codon 8/9 (+G), codon 30 (G>C), Cap+1 (A>G), -88 (C>T) and Codon 41/42 (-TCTT). Codon 30 (G>C) and -88 (C>T) mutations were found only in Pashtoon ethnic group while Cap+1 (A>G) mutation was observed only in Balochi ethnic group. Interestingly, eleven HbS homozygous patients, all in Pashtoon ethnic group were observed for the first time in Pakistan. Both α thalassemia and XmnI polymorphism in homozygous condition (+/+) were found to be common among the milder cases. The βS chromosomes were linked to the typical Arab-Indian subcontinent haplotype. Saudi haplotype is mostly associated with HbS and haplotype III is found only with Pashtoon ethnic group. The phenotypic expression of HbS-β thalassemia is not uniformly mild and α thalassemia and XmnI polymorphism in homozygous condition (+/+) are additional genetic factors modulating the severity of the disease in the Pakistan.
Analyzing Individual Neurons in Pre-trained Language Models

While a lot of analysis has been carried to demonstrate linguistic knowledge captured by the representations learned within deep NLP models, very little attention has been paid towards individual neurons.We carry outa neuron-level analysis using core linguistic tasks of predicting morphology, syntax and semantics, on pre-trained language models, with questions like: i) do individual neurons in pre-trained models capture linguistic information? ii) which parts of the network learn more about certain linguistic phenomena? iii) how distributed or focused is the information? and iv) how do various architectures differ in learning these properties? We found small subsets of neurons to predict linguistic tasks, with lower level tasks (such as morphology) localized in fewer neurons, compared to higher level task of predicting syntax. Our study also reveals interesting cross architectural comparisons. For example, we found neurons in XLNet to be more localized and disjoint when predicting properties compared to BERT and others, where they are more distributed and coupled.
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective

Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks. Recent studies, however, show that such BERT-based models are vulnerable facing the threats of textual adversarial attacks. We aim to address this problem from an information-theoretic perspective, and propose InfoBERT, a novel learning framework for robust fine-tuning of pre-trained language models. InfoBERT contains two mutual-information-based regularizers for model training: (i) an Information Bottleneck regularizer, which suppresses noisy mutual information between the input and the feature representation; and (ii) a Robust Feature regularizer, which increases the mutual information between local robust features and global features. We provide a principled way to theoretically analyze and improve the robustness of representation learning for language models in both standard and adversarial training. Extensive experiments demonstrate that InfoBERT achieves state-of-the-art robust accuracy over several adversarial datasets on Natural Language Inference (NLI) and Question Answering (QA) tasks.
Classification theorem and properties of singular solutions to the Tolman–Oppenheimer–Volkoff equation

The Tolman–Oppenheimer–Volkoff (TOV) equation admits singular solutions in addition to regular ones. Here, we prove the following theorem. For any equation of state that (i) is obtained from an entropy function, (ii) has positive pressure and (iii) satisfies the dominant energy condition, the TOV equation can be integrated from a boundary inwards to the center. Hence, the thermodynamic consistency of the EoS precludes pathological solutions in which the integration terminates at finite radius (because of horizons, or divergences / zeroes of energy density). At the center, the mass function either vanishes (regular solutions) or it is negative (singular solutions). For singular solutions, the metric at the center is locally isomorphic to negative-mass Schwarzschild spacetime. This means that matter is stabilized because the singularity is strongly repulsive. We show that singular solutions are causally well behaved: they are bounded-acceleration complete, and they are conformal to a globally hyperbolic spacetime with boundary. Finally, we show how to modify unphysical equations of state in order to obtain non-pathological solutions, and we undertake a preliminary investigation of dynamical stability for singular solutions.
StoqMA vs. MA: the power of error reduction

StoqMA characterizes the computational hardness of stoquastic local Hamiltonians, which is a family of Hamiltonians that does not suffer from the sign problem. Although error reduction is commonplace for many complexity classes, such as BPP, BQP, MA, QMA, etc.,this property remains open for StoqMA since Bravyi, Bessen and Terhal defined this class in 2006. In this note, we show that error reduction forStoqMA will imply that StoqMA = MA.
Using Soft Actor-Critic for Low-Level UAV Control

Unmanned Aerial Vehicles (UAVs), or drones, have recently been used in several civil application domains from organ delivery to remote locations to wireless network coverage. These platforms, however, are naturally unstable systems for which many different control approaches have been proposed. Generally based on classic and modern control, these algorithms require knowledge of the robot's dynamics. However, recently, model-free reinforcement learning has been successfully used for controlling drones without any prior knowledge of the robot model. In this work, we present a framework to train the Soft Actor-Critic (SAC) algorithm to low-level control of a quadrotor in a go-to-target task. All experiments were conducted under simulation. With the experiments, we show that SAC can not only learn a robust policy, but it can also cope with unseen scenarios. Videos from the simulations are available in this https URL and the code in this https URL.
StyleDGPT: Stylized Response Generation with Pre-trained Language Models

Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for training. In this work, we explore the challenging task with pre-trained language models that have brought breakthrough to various natural language tasks. To this end, we introduce a KL loss and a style classifier to the fine-tuning step in order to steer response generation towards the target style in both a word-level and a sentence-level. Comprehensive empirical studies with two public datasets indicate that our model can significantly outperform state-of-the-art methods in terms of both style consistency and contextual coherence.
Wound and Episode Level Readmission Risk or Weeks to Readmit: Why do patients get readmitted? How long does it take for a patient to get readmitted?

The Affordable care Act of 2010 had introduced the readmission reduction program in 2012 to reduce avoidable readmissions to control rising healthcare costs. Wound care impacts 15% [16] of Medicare beneficiaries, making it one of the major contributors of medicare health care cost. Health plans have been exploring proactive health care services that can prevent wound recurrences and readmissions from controlling wound care costs. With the rising costs of the Wound care industry, it has become of paramount importance to reduce wound recurrences & patient readmissions. What factors are responsible for a Wound to recur, which ultimately leads to hospitalization or readmission? Is there a way to identify the patients at risk of readmission before the occurrence using data-driven analysis? Patient readmission risk management has become critical for patients suffering from chronic wounds such as diabetic ulcers, pressure ulcers, and vascular ulcers. Understanding the risk & the factors that cause patient readmission can help care providers and patients avoid wound recurrences. Our work focuses on identifying patients who are at high risk of readmission and determining the time period within which a patient might get readmitted. Frequent readmissions add financial stress to the patient & Health plan and deteriorate the patient’s quality of life. Having this information can allow a provider to set up preventive measures that can delay, if not prevent, patients’ readmission. On a combined wound & episode-level dataset of patient’s wound care information, our extended autoprognosis achieves a recall of 0.92 and a precision of 0.92 for predicting a patient’s readmission risk. For new patient class, precision and recall are as high as 0.91 and 0.98, respectively. We can also predict the amount of time (in weeks) it might take after a patient’s discharge event for a readmission event to occur through our model with a mean absolute error of 2.3 weeks.