Miracle-dz/UniFAR-FTUs huggingface dataset:
Voice Quality

The first description of voice quality production in forty years, this book provides a new framework for its study: The Laryngeal Articulator Model. Informed by instrumental examinations of the laryngeal articulatory mechanism, it revises our understanding of articulatory postures to explain the actions, vibrations and resonances generated in the epilarynx and pharynx. It focuses on the long-term auditory-articulatory component of accent in the languages of the world, explaining how voice quality relates to segmental and syllabic sounds. Phonetic illustrations of phonation types and of laryngeal and oral vocal tract articulatory postures are provided. Extensive video and audio material is available on a companion website. The book presents computational simulations, the laryngeal and voice quality foundations of infant speech acquisition, speech/voice disorders and surgeries that entail compensatory laryngeal articulator adjustment, and an exploration of the role of voice quality in sound change and of the larynx in the evolution of speech.
Emission of trace gases and aerosols from biomass burning – an updated assessment

Abstract. Since the publication of the compilation of biomass burning emission factors by Andreae and Merlet (2001), a large number of studies have greatly expanded the amount of available data on emissions from various types of biomass burning. Using essentially the same methodology as Andreae and Merlet (2001), this paper presents an updated compilation of emission factors. The data from over 370 published studies were critically evaluated and integrated into a consistent format. Several new categories of biomass burning were added, and the number of species for which emission data are presented was increased from 93 to 121. Where field data are still insufficient, estimates based on appropriate extrapolation techniques are proposed. For key species, the updated emission factors are compared with previously published values. Based on these emission factors and published global activity estimates, I have derived estimates of pyrogenic emissions for important species released by the various types of biomass burning.
A dual-mode deep transfer learning (D2TL) system for breast cancer detection using contrast enhanced digital mammograms

Abstract Full-field digital mammography (FFDM) and magnetic resonance imaging (MRI) are gold-standard techniques for breast cancer detection. The newly contrast-enhanced digital mammography (CEDM) integrates the complementary strengths of FFDM and MRI, and is being incorporated into the practice of leading institutions. The current clinical practice using CEDM is sub-optimal because it is primarily based on clinicians' trained eyes. Automated diagnostic systems under the conventional machine learning paradigm suffer from drawbacks, such as the requirement for precise segmentation, extraction of shallow features that do not suffice for diagnostic images, and adoption of a sequential design without a global objective. We propose a deep learning (DL)-empowered diagnostic system using CEDM, the core of which is a novel dual-mode deep transfer learning (D2TL) model. The proposed system is innovative in several aspects, including (1) a dual-mode deep architecture design; (2) use of transfer learning to facilitate robust model estimation under small sample-size; (3) development of visualization techniques to help interpret the model results and facilitate inter- and intra-tumor malignancy quantification; and (4) minimization of human bias. We apply D2TL to classify benign vs. malignant tumors using the CEDM data collected from the Mayo Clinic in Arizona. D2TL outperforms competing models and approaches.
Nutrient Utilization Indices of NPK and Drought Management in Groundnut under Sandy Soil Conditions

ABSTRACT In arid zones, farmers are obligated to reduce water amounts used in irrigating their lands. Consequently, reduction in final yields is realized. Thus, dealing with such a case became a decisive act. We tried to investigate the acceptable degree of lowering irrigation water with sustaining the productivity of groundnut as a way for managing drought conditions. Therefore, in summer seasons of 2016 and 2017 at the Experimental Research and Production Station, National Research Centre, Egypt, field trials were conducted with growing groundnut plants under three irrigation levels, i.e., 50%, 75%, and 100% of crop evapotranspiration (ETc), denoted as I50, I75, and I100, respectively. N, P, and K contents in seed and shoot and their yields in addition to harvest and partitioning indices were estimated. In spite of irrigating groundnut plants by normal water amounts, I100, caused the maximum weight of seed biomass yield ha−1 and seed N and P contents, I75 was similar to I100 for producing shoot biomass yield ha−1 and seed K content. Moreover, N and P yields (for shoots and seeds) and k yield (for seeds) showed the maximum values with I100. Differences in all nutrient harvest indices (NHI, PHI, and KHI) between I100 and I75 were not significant. Furthermore, I75 statistically leveled with I100 in nutrient partitioning indices, viz., NPI, PPI, and KPI. In conclusion, groundnut straw residues produced by 25% less water supply than normal may share in managing drought stress by releasing nutrients and saving irrigation water in arid areas.
Online Orchestration of Cross-Edge Service Function Chaining for Cost-Efficient Edge Computing

Edge computing (EC) has quickly ascended to be the de-facto standard for hosting emerging low-latency applications, as exemplified by intelligent video surveillance, Internet of Vehicles, and augmented reality. For EC, service function chaining is envisioned as a promising approach to configure various services in an agile, flexible, and cost-efficient manner. When running on top of geographically dispersed edge clouds, fully unleashing the benefits of service function chaining is, however, by no means trivial. In this paper, we propose an online orchestration framework for cross-edge service function chaining, which aims to maximize the holistic cost efficiency, via jointly optimizing the resource provisioning and traffic routing on-the-fly. This long-term cost minimization problem is difficult since it is NP-hard and involves future uncertain information. To simultaneously address these dual challenges, we carefully combine an online optimization technique with an approximate optimization method in a joint optimization framework, through: 1) decomposing the long-term problem into a series of one-shot fractional problem with a regularization technique and 2) rounding the fractional solution to a near-optimal integral solution with a randomized dependent scheme that preserves the solution feasibility. The resulting online algorithm achieves an outstanding performance guarantee, as verified by both rigorous theoretical analysis and extensive trace-driven simulations.
A Low-Noise Chopper Amplifier Designed for Multi-Channel Neural Signal Acquisition

This paper proposed the design of a low-noise, low total harmonic distortion (THD) chopper amplifier for neural signal acquisition. A dc servo loop (DSL) based on active Gm-C integrator is proposed to reject the electrode-dc-offset (EDO). Architecture of a complementary input very low-transconductance (VLT) operational transconductance amplifier (OTA) was proposed and integrated in the active Gm-C integrator to improve the linearity as well as to reduce the noise, featuring a transconductance ranging from 45 pS to a few nS. The proposed amplifier was fabricated in a TSMC 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process, occupying an area of 0.2 mm <sup>2</sup>, featuring a power consumption of <inline-formula> <tex-math notation="LaTeX">$3.24~\mu \text{W}$ </tex-math></inline-formula>/channel under a 1.8-V supply voltage. The THD for a 5-mV<sub>pp</sub> input is lower than −61 dB. An input-referred thermal noise power spectral density (PSD) of 39 nV/<inline-formula> <tex-math notation="LaTeX">$\sqrt {\text {Hz}}$ </tex-math></inline-formula> is measured. The measured input-referred noise is <inline-formula> <tex-math notation="LaTeX">$0.65~\mu \text {V}_{\text {rms}}$ </tex-math></inline-formula> in the 0.3–200-Hz frequency band and <inline-formula> <tex-math notation="LaTeX">$2.14~\mu \text{V}_{\text {rms}}$ </tex-math></inline-formula> in the 200-Hz–5-kHz frequency band, respectively, leading to a noise-efficiency factor of 2.37 (0.3–200 Hz) and 1.56 (0.2 k–5 kHz). In addition, the high-pass corner frequency can be precisely configured and linearly adjusted with the external bias current from 0.35 to 54.5 Hz.
Remembrance agent: from theory to applications

In the information age, people have difficulty to process and organize large amount of incoming information from online social network sites due to our limited memory capacity. The problem arise when such information is needed as reference to make decisions. People may not be able to figure out proper clues to retrieve them or may even forget their existence at all. Remembrance agents (RAs) are a type of agents which proactively retrieve and present the user’s historical documents based on the his/her current context. Memory modeling is one of the fundamental components for RAs. Most of the existing RAs adopt memories with restricted semantic interpretation, which limit content relevance as the primary criterion for retrieval. Building user interest models based on the user documents stored in RA’s memory is very critical to inferring user preferences. Through user interest modeling, potentially interesting documents can be recommended to the user whenever needed. To date, little work has been done on integrating user interest model into RAs. A user’s documents are naturally distributed in multiple information sources. Each of the sources is of different characteristics and significance. This contradicts the assumptions made by most of the existing work that the user documents are collected from a single information source. To address these challenges, this thesis proposes a novel RA model, Episodic and Semantic memory based Remembrance Agent (ESRA), inspired by cognitive memory theories. The ESRA incorporates the functionalities of episodic memory (EM) and semantic memory (SM) of the human memory into the agent’s memory structure. EM stores documents pertaining to the events experienced by the user in chronological order. SM stores the concepts which are extracted from the documents in EM and their corresponding aggregated activation scores. By explicitly modeling EM and SM and their functional interactions, we can capture both the episodic information and categorical semantic information to improve the memory retrieval performance. Moreover, with an ontological user interest profile constructed from the aggregated categorical concepts in SM, the ESRA is able to provide accurate personalized recommendations for the user. In addition, we propose an approach for unifying a user’s historical documents from multiple information sources, and develop a source-aware retrieval algorithm subsequently to complement the user’s cognition ability with the proposed ESRA in various scenarios. To study how the proposed ESRA can help the user in real world applications, we
Attitude of Military and Paramilitary Officers towards the Role of Arabic Language in Addressing Security Issues in Nigeria

This study addresses security issues in Nigeria through Arabic Language Education. Arabic Language is one out of the international languages, it is a member of the Semitic family of language and perhaps the only one among them that has gallantly stood the test of the time. It gains wide range of currency and leaves an indelible mark on the course of world history, culture and civilization. The language has played a significant role in security challenges. It has the ability to solve problems between groups of people by ensuring atmosphere for understanding and peaceful co-existence. The study was a descriptive survey type. The population consisted of all Nigerian military and paramilitary officers using stratified random sampling technique, 50 officers were selected from each of the Nigerian Army, Air force, Police, and Civil Defence corps in the North-east totaling 200. A questionnaire titled “Arabic Language Education and National Security Questionnaire” was designed for the data collection. Test re-test method was employed for the reliability with 0.76 coefficient. One research question and one hypothesis guided the study. Percentage was used to answer the research question and ANOVA was used to test the hypothesis at 0.05significance level. The findings reveal that there was a significant difference in the understanding of Arabic Language among Nigerian security officers. Significant difference was found in addressing insecurity through Arabic than in other languages. It was recommended among others that, the government should recognize Arabic Language as a medium of communication/instruction and of the equal rank with English language in Nigeria. The Language should be incorporated into in-house training for the security officers. Keywords: Nigeria, Security, Arabic Language, Role
Kinematics of the distal tibiofibular syndesmosis - Radiostereometry in 11 normal ankles

In 11 healthy volunteers, the normal kinematics of the tibiofibular syndesmosis of the ankle during weight bearing and external rotation stress were compared to a nonweight-bearing neutral position by radiostereometry. We found very small rotations and displacements in this "normal" group, which indicated that the fibula is closely attached to the tibia, thereby preventing larger movements at the level of the ankle. We found no common kinematic pattern during weight bearing in the neutral position. Application of a 7.5 Nm external rotation moment on the foot caused external rotation of the fibula between 2 and 5 degrees, medial translation between 0 and 2.5 mm and posterior displacement between 1.0 and 3.1 mm. These data can be used as normal reference values for studies of patients with suspected syndesmotic injuries. 
Optimization of Time and Temperature Gelatin Extraction from Pink Perch (Nemipterus bathybius) Head using Response Surface Methodology (RSM)

Gelatin from fisheries byproducts was very potential to be used as an alternative gelatin substitute for bovine and porcine gelatin. In this study, optimization of pink perch head gelatin extraction was carried out based on temperature and extraction time. Factorial design and Response Surface Methodology (RSM) were used to optimize the gelatin extraction process of pink perch ( Nemipterus bathybius ) head. The purpose of this research was to determine the optimum condition of the pink perch head extraction process based on the temperature and time of extraction. The extraction temperature (50 °C - 90 °C), and the extraction time (3 - 7 hours) were the independent variables. The response variables of this study were the yield of gelatin (%), gel strength (g.bloom) and viscosity (cP). The optimum extraction conditions were obtained at the extraction temperature of 74.40 o C for 5.42 hours with a yield of 5.31 %, the gel strength of 311.01 g.bloom and the viscosity of 5 cP. Considering its similarity to the commercial gelatin, particularly on their chemical characteristics, it can be suggested that the head of pink perch is a potential alternative of gelatin source. Keywords: Extraction, Gelatin, Optimization, Pink Perch Head
Boosting Antimicrobial Activity of Imipenem in Combination with Silver Nanoparticles towards S. fonticola and Pantoea sp.

Silver nanoparticles have been considered as powerful antimicrobial agents recently, especially with the increasing incidence of diseases associated with biofilm and multi-drug resistant pathogens. The aim of this study was to synthesize silver nanoparticles by biological and chemical methods and combination with imipenem to eradicate biofilm-forming bacteria at phenotypic and genotypic levels. The biosynthesis of silver nanoparticles was done by using Enterobacter cloacae (cell-free suspension) while chemosynthesis was conducted using sodium borohydride. Biological and chemical silver nanoparticles were characterized by ultraviolet-visible spectrophotometry which showed absorbance peak at 400 and 390nm respectively. Fourier transformer infrared analysis revealed that carboxylic and polyphenolic groups were coated on surface of both silver nanoparticles. Scanning electron microscope and size analyser showed that the sizes of biologically and chemically silver nanoparticles were 63 nm and 25 nm, respectively. In addition, it showed the formation of cubical nanoparticles. The antimicrobial effect of synthesized silver nanoparticles were evaluated by agar well diffusion and macrodilution method to determine minimum inhibitory concentration value. The results showed that biological silver nanoparticles were more effective on biofilm forming bacteria (Serratia fonticola and Pantoea sp.) than chemical synthesized ones. In addition, the combination effect between silver nanoparticles and imipenem displayed synergistic effect. Gene expression of biofilm encoding genes (smaI and esaL) were evaluated by realtime quantitative polymerase chain reaction (RTqPCR) before and after treatment with silver nanoparticles in both types and imipenem and in combination between them. The results revealed that biological silver nanoparticles alone or in combination with antibiotics were more effective on biofilm gene expression by down regulation than other treatments.
Analysis of serum metabolomics among biopsy-proven diabetic nephropathy, type 2 diabetes mellitus and healthy controls

Type 2 diabetes mellitus (T2DM) has a rising prevalence and diabetic nephropathy (DN) is a major complication of T2DM. Metabolomics could provide novel insights into the pathogenesis, so we aimed to explore serum metabolomic profiles from DN to T2DM. Serum samples were collected from 14 biopsy-proven DNs, 14 age/gender-matched T2DMs without renal diseases (DM), 14 age/gender-matched healthy controls (CTRL) and household contacts of DM group (HH). Serum metabolomics was analyzed by untargeted liquid chromatography-tandem mass spectrometry (LC/MS) assays. There were a total of 1470 metabolites identified from all serum samples. 45 metabolites with significantly different intensity were found between DN and DM, e.g., biliverdin and taurine were reduced while l-arginine was increased in DN comparing to DM. DN could be distinguished from age/gender matched DM patients by l-arginine (AUC = 0.824) or taurine levels (AUC = 0.789). The metabolic pathways affected by metabolite distinctions between DN and DM also existed, among which taurine and hypotaurine metabolism exhibited the highest pathway impact. l-Methionine, deethylatrazine, l-tryptophan and fumaric acid were reduced in DM comparing with those of CTRL, but had no different intensity in DM and HH groups. The changes were demonstrated in the metabolomic profiles of biopsy-proven DN compared to DM. Biopsy-proven DN patients could be distinguished from age/gender matched DM by l-arginine or taurine levels in serum metabolomic profiles. Taurine and hypotaurine metabolism pathway had the highest impact in pathway set enrichment analysis, which potentially affected the pathogenesis of DN from T2DM.
Dipeptidyl Peptidase 4 Inhibition Increases Postprandial Norepinephrine via Substance P (NK1 Receptor) During RAAS Inhibition

Abstract Context Dipeptidyl peptidase 4 (DPP4) inhibitors may increase the risk of heart failure. Decreased degradation of vasoactive peptides like substance P [also degraded by angiotensin-converting enzyme (ACE)] and Y1 agonists peptide YY (PYY 1-36) and neuropeptide Y (NPY 1-36) could contribute. Objective This study tested the hypothesis that there is an interactive effect of DPP4 inhibition and ACE inhibition (vs antihypertensive control subjects) on vasoactive peptides after a mixed meal. Participants and Design Fifty-three patients with type 2 diabetes and hypertension were randomized to double-blind treatment with ramipril, valsartan, or amlodipine for 15 weeks in parallel groups. During the 5th, 10th, and 15th weeks, participants also received placebo + placebo, sitagliptin 100 mg/d + placebo, and sitagliptin + aprepitant 80 mg/d in random order. On the last day of each crossover treatment, participants underwent a mixed-meal study. Results Sitagliptin increased postprandial glucagon-like peptide-1 and decreased glucose in all antihypertensive groups. Sitagliptin increased NPY 1-36 and decreased Y2 agonists NPY 3-36 and PYY 3-36 in all groups. During ramipril or valsartan, but not amlodipine, sitagliptin increased postprandial norepinephrine; substance P receptor blockade with aprepitant prevented this effect. Despite increased norepinephrine, sitagliptin decreased postprandial blood pressure during ACE inhibition. Conclusion DPP4 inhibition increases postprandial concentrations of the Y1 agonist NPY 1-36. During treatment with an ACE inhibitor or angiotensin receptor blocker, DPP4 inhibition increased postprandial norepinephrine through a substance P receptor–dependent mechanism. Increased NPY 1-36 and norepinephrine could increase risk of heart failure but did not result in higher postprandial blood pressure.
The Use of Mental, Written and Calculator Strategies of Numerical Computation by Upper Primary Pupils within a

Abstract The study reported in this article examines the use of mental, written and calculator strategies of numerical computation by 56 upper primary (Year 6) pupils, forming a sample structured by number‐concept attainment, sex, attitude to calculator use, and curricular experience. Pupils were drawn from neighbouring schools with very different traditions in the teaching of number. The ‘calculator‐aware’ approach employed in one group of schools encouraged pupils, from an early age, to develop informal methods of calculation, and to use calculators to explore number and execute demanding computations. Such pupils were found to make greater use of mental computation, particularly of multiplication strategies based on distribution and compensation. These aspects of performance were found to be more strongly associated with curricular experience than with number‐concept attainment. Findings and observations from the study raise important questions about the model of numerate thinking and learning underpin.
Agronomical Screening of OGLE1040/TAM O-301 Oat Genetic Mapping Population

In recent years, mapping populations have provided improvements for oat genomic researches. A two-year study was conducted in East-Mediterranean conditions using Ogle1040/TAM O-301 pure-line mapping population including 136 individuals and parents. Stem diameter (SD), plant height (PH), panicle length (PL), vegetative period (VP), grain filling period (GFP), days to maturity (DM), grain number per panicle (GNP), grain weight per panicle (GWP), thousand kernel weight (TKW) and grain yield (GY) were investigated in 2014 and 2015 cropping seasons in Kahramanmaras. All the investigated traits were significantly different for years (p<0.01) and genotypes (p<0.05 and p<0.01) except SD and GNP. Genotype x year (G x Y) interaction was significant for PL, VP, GFP, DM and GY (p<0.01). In the first year, the average GY per row was 227.6 g, whilst it was 184.5 g in the second year. In terms of GY, the parents Ogle 1040 and TAM O-301 showed lower performance (154.5 and 111.5 g/row, respectively) compared to Ogle1040/TAM O-301 (OT) population average (206 g/row). OT129 genotype had the highest GY with 360 g/row. Principal component (PC) factor analysis yielded 10 PC explaining 100% of total variance in the data and the chi-square values of the PC1 to PC9 were found significant. According to PC biplot analysis, genotypes with high GY, TKW, GNP, GWP, PL and GFP were located throughout the right quadrants whereas the genotypes with high VP, DM and SD were located throughout the left quadrants. The relationships between PH × GY, GWP × GNP and GWP × TKW were positive and significant.