Miracle-dz/UniFAR-FTUs huggingface dataset:
SIGMA: Scala Internal Domain-Speciﬁc Languages for Model Manipulations

Model manipulation environments automate model operations such as model consistency checking and model transformation. A number of external model manipulation Domain-Speciﬁc Languages (DSL) have been proposed, in particular for the Eclipse Modeling Framework (EMF). While their higher levels of abstraction result in gains in expressiveness over general-purpose languages, their limitations in versatility, performance, and tool support together with the need to learn new languages may signiﬁcantly contribute to accidental complexities. In this paper, we present Sigma , a family of internal DSLs embedded in Scala for EMF model consistency checking, model-to-model and model-to-text transformations. It combines the beneﬁts of external model manipulation DSLs with general-purpose programming taking full advantage of Scala versatility, performance and tool support. The DSLs are compared to the state-of-the-art Epsilon languages in non-trivial model manipulation tasks that resulted in 20% to 70% reduction in code size and signiﬁcantly better performance.
EmoChannel-SA exploring emotional dependency towards classification task with self-attention mechanism

Exploiting hand-crafted lexicon knowledge to enhance emotional or sentimental features at word-level has become a widely adopted method in emotion-relevant classification studies. However, few attempts have been made to explore the emotion construction in the classification task, which provides insights to how a sentence’s emotion is constructed. The major challenge of exploring emotion construction is that the current studies assume the dataset labels as relatively independent emotions, which overlooks the connections among different emotions. This work aims to understand the coarse-grained emotion construction and their dependency by incorporating fine-grained emotions from domain knowledge. Incorporating domain knowledge and dimensional sentiment lexicons, our previous work proposes a novel method named EmoChannel to capture the intensity variation of a particular emotion in time series. We utilize the resultant knowledge of 151 available fine-grained emotions to comprise the representation of sentence-level emotion construction. Furthermore, this work explicitly employs a self-attention module to extract the dependency relationship within all emotions and propose EmoChannel-SA Network to enhance emotion classification performance. We conducted experiments to demonstrate that the proposed method produces competitive performances against the state-of-the-art baselines on both multi-class datasets and sentiment analysis datasets.
Inﬂuence of the stator current reconstruction method on direct torque control of induction motor drive in current sensor postfault operation

Modern induction motor (IM) drives with a higher degree of safety should be equipped with fault-tolerant control (FTC) solutions. Current sensor (CS) failures constitute a serious problem in systems using vector control strategies for IMs because these methods require state variable reconstruction, which is usually based on the IM mathematical model and stator current measurement. This article presents an analysis of the operation of the direct torque control (DTC) for IM drive with stator current reconstruction after CSs damage. These reconstructed currents are used for the stator ﬂux and electromagnetic torque estimation in the DTC with space-vector-modulation (SVM) drive. In this research complete damage to both stator CSs is assumed, and the stator current vector components in the postfault mode are reconstructed based on the DC link voltage of the voltage source inverter (VSI) and angular rotor speed measurements using the so-called virtual current sensor (VCS), based on the IM mathematical model. Numerous simulation and experimental tests results illustrate the behavior of the drive system in different operating conditions. The correctness of the stator current reconstruction is also analyzed taking into account motor parameter uncertainties, especially stator and rotor resistances, which usually are the main parameters that determine the proper operation of the stator ﬂux and torque estimation in the DTC control structure.
Eye Features Detection for Eye Gaze Tracking

Pupil and iris are among the most important eye features for gaze estimation. Success of any eye gaze tracking system is highly dependent on the precise detection of these features. This paper presented a new method for pupil and iris detection. The proposed method took into consideration the structural appearance and intensity distribution of eye region. First, pupil prospect point was detected within the dark pupil region and then horizontal projections and distance parameters were employed to detect pupil and the iris-sclera boundaries. Simple geometric properties were used to measure the pupil and iris centers. Unlike other methods, the proposed method was computationally quite efficient as the processing involved was linear in nature. The method was tested on Chinese Academy of Sciences - Institute of Automation eye database version 1.0 and accuracy of 98.6% and 96.7% for pupil and iris detection respectively had been achieved.
Food Security and Agriculture in Developing Countries: Measurement and hypotheses for impact evaluations

This paper reviews the challenges related to establishing credible causal links between particular interventions and aggregate food security. A first set of challenges result from the lack of a common measurement of food security, with a multitude of indicators and definitions being used in different studies, making comparisons and broader inferences particularly hard. We discuss various measures and the existing evidence on their validity. We also line out a possible approach to validating some of the multi-dimensional measures in a more comprehensive way. A second set of challenges comes from the need to have credible exogenous variation in order to establish a causal relationship between an intervention and resulting food security outcomes. We review the literature and conclude that the literature to date leaves many open questions regarding the type of interventions that might be most effective to increase food security. This is due in part to the multitude of approaches to measurement of food security, and in part due to methodological concerns that limit causal inference in many of the existing studies. Likely, the optimal policy will also be strongly context-specific, and understanding the sensitivity of impacts to contextual changes hence is equally important.
Economic Consequences and Positive Accounting Theory

This paper reviews economic consequences as a choice of attitudes toward accounting policies. Economic consequences are an anomaly to the theory of efficient securities markets. This theory fails to explain market behavior concerning the accounting policies made. To overcome this, a positive accounting theory is attempted to be built which can explain that accounting policy is a choice of attitude towards economic consequences.
Clinical Indicator Analysis for Predicting Pathogenic Pneumonia Infection in Newborns with Distributed Sensor Networks Data Analytics

Neonatal infections are prevalent as newborn children are prone to a range of infections because of their absence of immunity. Being susceptible to various diseases, the immune system of infants is not adequately developed to fight against protozoa bacteria, viruses and parasites. Pathogenic pneumonia is one of the most common infections identified within the neonatal group. It is a lung infection occurring in the neonates, which can start after a few hours of delivery or even after a week. The infection can even occur due to the normal flora found in the genital tract of the mother, and the respiratory distress caused by pathogenic pneumonia can even lead to the infant's death. The study examined the clinical indicator for assessing the occurrence of pathogenic pneumonia in infants. With the help of sensor networking in data analytics, the prediction of such a disorder has been assessed in-depth in the article.
Nitrogen driving force and pressure relationships at contrasting scales: Implications for catchment management

Abstract The major objective of this study was to compile and examine empirical relationships between the driving forces and pressures that affect nitrogen cycling. Special attention was paid to riverine loads, fluxes, and concentrations of nitrogen in relation to agricultural factors (e.g., land use and fertilisers) at various spatial scales (plot, field, catchment, river basin, nation, region, continent, and world). Results of the present analyses and assessments reported in the literature indicate that at global, continental and inter‐river basin scales, the level of riverine nitrogen loads (pressures) can be readily derived from simple empirical relationships with driving forces like population density and agricultural factors (e.g., proportion of arable land and fertiliser use). It seems that statistically derived relationships do not hold or are much weaker at the scales of single river basins and smaller catchments, whereas nitrogen losses appear to be correlated with fertiliser use at the smallest scales (plot and field). It is important to recognise this spatial nutrient paradox, particularly when such functional relationships are applied in the contexts of management and decision making. The implications for management are given further consideration in this paper.
MMTC Communications - Frontiers

The escalating environmental repercussions arising from the energy consumption and greenhouse gas emissions of information and communication technologies (ICT) have spurred a growing emphasis on researching and adopting Green communications and computing practices. The primary objective is to develop sustainable ICT-based services that meet users' needs and minimize their environmental impact. Despite the rapid progress in sustainable solutions, managing energy usage remains a pressing concern. Sustainable multimedia communications and services entail designing and implementing multimedia-based communication systems with a strong focus on reducing their environmental footprint while optimizing efficiency and user experience. Research efforts often centre around devising algorithms to enhance the utilization of renewable energy sources, decrease computational complexity, optimize resource allocation, minimize data storage, and enable automatic device switch-off when not in use. In addition to adhering to device-imposed energy and resource constraints, sustainable multimedia communications and services must also meet essential Quality of Service (QoS) and Quality of Experience (QoE) standards to meet user expectations. Thus, the Special Issue (SI) on Sustainable multimedia communications and services aims to tackle emerging concepts and challenges concerning energy efficiency and sustainability for multimedia-based applications and services. The SI endeavours to pave the way for a greener and more environmentally responsible ICT landscape by investigating these crucial research areas. The third paper introduces a new hybrid P2P-CDN framework that leverages Network Function Virtualization (NFV) and edge computing for live video streaming. The framework aims to reduce server energy consumption, support low latency, and enhance video quality by efficiently distributing tasks across a hybrid P2P-CDN network. The first paper addresses the challenge of deriving operational points for configuring Internet services, balancing QoE and energy consumption. It discusses the potential of creating a joint QoE-energy model for Internet services
communities associated with flavor formation during sour juice fermentation and the milk fan drying process

Milk fan is an acid-curd cheese with strong national characteristics (a traditional dairy product of the Bai nationality with a shape like a piece of paper) and a long history in Yunnan province, China. In our previous study, we characterized the microbial community diversity of milk fan, but the succession of microorganisms associated with flavor formation in milk fan is still unknown. Therefore, we examined the predominant microorganisms and their correlations with the formation of flavor in the fermentation of sour juice and drying of milk fan by gas chromatography mass spectrometry, high-throughput 16S rDNA sequencing, intergenic spacer sequencing and metatranscriptome analysis. We found that the relative abundances of Lactobacillus and Issatchenkia initially decreased and then increased with time during the fermentation of sour juice. However, the relative abundances of Acetobacter , Leuconostoc , Lactococcus, Geotrichum , and Dipodascus initially increased and then decreased. During the drying step, the relative abundances of Lactobacillus and Issatchenkia continuously increased and became the dominant microorganisms in the milk fan. The metatranscriptomes generated from the milk fan showed that “carbohydrate metabolism,” “translation,” and “signal transduction” were the main metabolic functions of the microbial communities. Rhodotorula and Yarrowia contained more differentially expressed genes than other genera, which indicated they may be associated with the production of the characteristic flavor. Furthermore, a Pearson correlation analysis showed that Lactococcus , Rhodotorula , Candida , Cutaneotrichosporon , and Yarrowia were significantly positively correlated with more aroma-active compounds, mainly ethyl acetate, 2-heptanone, isovaleraldehyde, butyric acid, nonanal, and hexanal. In conclusion, these findings contribute to a better understanding of the flavor production mechanism
Efficient Sparse Deep Neural Network Computation on GPU

This paper presents GPU optimizations using TVM in response to the MIT/IEEE/Amazon GraphChallenge.org Sparse Deep Neural Network (SpDNN) 2022 Challenge. Although various deep neural network models exist, SpDNNs have shown great improvements in the size and memory of neural networks. SpDNNs provide unique scalability difficulties in which optimizations and advancements can be made. Apache TVM is a machine learning compiler framework for CPUs and GPUs. It has been shown to have promising improvements for the performance, deployment, and optimizations of the networks. To evaluate its effectiveness for SpDNNs, this work builds SpDNNs with Apache TVM and compares with the GraphChallenge baseline code. When testing with the GraphChallenge dataset, TVM-based implementation can achieve promising optimization opportunities.
Factors Influencing Recurrence Rates and Surgical Outcomes in Lumbar Microdiscectomy: A Retrospective Study of 130 Patients in Turkey

Background Lumbar disc herniation surgery, specifically lumbar microdiscectomy, shows recurrence in 3–24% of patients, necessitating potential re-operations. This study focuses on the outcomes and recurrence causes in 130 cases of lumbar disc herniations. These cases were managed by a single surgeon at a single center, offering a unique perspective on a common issue in neurosurgery. Material/Methods The study involved 130 patients treated for lumbar disc herniations. Analysis considered various factors: age, sex, symptoms, surgical level, complications, pre- and postoperative pain levels, and quality of life indices. Results Of the 130 cases analyzed, all underwent initial surgery by the same surgeon or sought treatment from this surgeon after recurrence. Inclusion criteria were based on radiologic and clinical indications for re-operation. The demographic split was 76 males (56.4%) and 54 females (43.6%). A notable finding was a higher recurrence rate in males and patients under 45 years, although the sex difference was not statistically significant. The average age was 47.5 years. Significant changes were noted in the Oswestry Disability Index (ODI) levels during follow-ups (P<0.001). Conclusions This study underscores the effectiveness of lumbar microsurgery in treating recurrent lumbar disc herniations. Techniques such as medial facet utilization and laminectomy border expansion show promise. However, determining instability rates requires long-term patient follow-up. Our findings contribute valuable insights into surgical approaches and patient outcomes in recurrent lumbar disc herniation cases.
ANALYSIS OF TRANSPORT MODE PREFERENCES FOR WORK TRAVEL IN MEDAN

This study aimed at finding factors that affected transport mode preferences for work travel in Medan. Improvements in public transport services combined with various tariffs assumed in case study were to observe the behaviours of the choice and captive travellers. This study was done as an applied study for the third level of students at University of Sumatera Utara (USU) about organizing data statistically by emphasizing the procedure and process of obtaining data through questionnaires and home interview. The location of study area was at the National Residence of Simalingkar Medan. The factors which can influence transport mode preferences for work travel in Medan were gender, ages, income levels, kinds of job, policy systems of public transport, travel time and travel costs. Second, apparently the quality improvement of public transport services with higher tariffs would not be successful to choice travellers in Medan especially for residents in the study area.
The Impact of Genotype on Chemical Composition, Feeding Value and In Vitro Rumen Degradability of Fresh and Ensiled Forage of Native Maize (Zea mays L.) from Mexico

The objective of this study was to evaluate the impact of the genotype on the chemical composition, feeding value and in vitro rumen degradability of fresh and ensiled forage of four native maize varieties (Amarillo, Olotillo, Tampiqueño and Tuxpeño) from Tamaulipas, Mexico, and a commercial hybrid, as well as the stability and aerobic deterioration of the silage. In all genotypes, fresh forage consisted of whole plants of maize that were harvested when the grain reached a milky-mass state, and silage was fresh forage chopped and ensiled in plastic bags, where it fermented for 120 days. The hybrid presented the highest content (p < 0.05) of dry matter (DM), organic matter (OM), ether extract, non-fibrous carbohydrates (NFCs) and starch, as well as the lowest content (p < 0.05) of fibers (NDF and ADF), acid detergent lignin and water-soluble carbohydrates (WSCs). Furthermore, the hybrid and Amarillo genotypes obtained the lowest pH and ammoniacal nitrogen content (p < 0.05), intermediate values (p < 0.05) of lactic and butyric acid, and the lowest and highest acetic acid content (p < 0.05), respectively. Although OM did not differ (p > 0.05) between states of the forage, the fresh forage presented a higher (p < 0.05) content of DM, crude protein, NDF, ADF, WSCs, pH and butyric acid in all genotypes, while the rest of the parameters were higher (p < 0.05) in the silage. However, Amarillo obtained the highest feeding value (p < 0.05) in terms of DM intake, relative forage value, digestible energy, metabolizable energy and rumen degradability (DM, NDF and ADF), and between states of the forage, ensiled obtained the highest feeding value (p < 0.05). During the aerobic exposure, the Amarillo and hybrid silage showed greater (p < 0.05) stability (>38 h), and less (p < 0.05) deterioration, pH increase and loss of DM and OM, while Tuxpeño obtained less stability and greater deterioration. In conclusion, the genotype did influence the chemical composition of fresh and ensiled forage, which affected the feeding value and in vitro rumen degradability, and the Amarillo and hybrid genotypes presented the best values in the evaluated parameters.
Constraining Changes in Surface Dust Thickness on Mars Using Diurnal Surface Temperature Observations From EMIRS

Diurnal surface temperature data collected from the Emirates Mars Infrared Spectrometer have been used to characterize dust redistribution on Mars following a regional dust storm in January 2022. A comparative temporal analysis is conducted pre‐ and post‐dust storm to estimate net changes in surface dust thickness at Syrtis Major, Isidis Planitia, Tyrrhena Terra, Hesperian Planum, and Elysium Planitia. By leveraging data from a numerical thermal model, we relate differences in observed surface temperature to changes in the thickness of the surface dust layer. We describe the methodology for estimating net changes in surface dust thickness, including a description of the thermophysical model employed, provide a sensitivity analysis with respect to local time and surface properties, and discuss the implications of our findings. Surface temperature differences suggest both dust deposition and removal, with some regions experiencing a net removal as high as 340 μm, while other regions experienced net deposition reaching up to 120 μm. Visible‐wavelength imagery from the Emirates Exploration Imager, which is more sensitive to changes in surface dust distribution, is incorporated in our analysis to provide additional context. When the area of each region of interest is accounted for, we find that the dust storm had the potential to remove and/or deposit more than 1.51 × 109 and 1.20 × 109 kg, respectively, suggesting that dust reservoirs are capable of transporting vast quantities of dust over short timescales.
New Vaccine Introductions in WHO African Region between 2000 and 2022

Significant progress has been made in vaccine development worldwide. This study examined the WHO African Region’s vaccine introduction trends from 2000 to 2022, excluding COVID-19 vaccines. We extracted data on vaccine introductions from the WHO/UNICEF joint reporting form for 17 vaccines. We examined the frequency and percentages of vaccine introductions from 2000 to 2022, as well as between two specific time periods (2000–2010 and 2011–2022). We analysed Gavi eligible and ineligible countries separately and used a Chi-squared test to determine if vaccine introductions differed significantly. Three vaccines have been introduced in all 47 countries within the region: hepatitis B (HepB), Haemophilus influenzae type b (Hib), and inactivated polio vaccine (IPV). Between 2011 and 2022, HepB, Hib, IPV, the second dose of measles-containing vaccine (MCV2), and pneumococcal conjugate vaccine (PCV) were the five most frequently introduced vaccines. Hepatitis A vaccine has only been introduced in Mauritius, while Japanese encephalitis vaccine has not been introduced in any African country. Between 2000–2010 and 2011–2022, a statistically significant rise in the number of vaccine introductions was noted (p < 0.001) with a significant positive association between Gavi eligibility and vaccine introductions (p < 0.001). Significant progress has been made in the introduction of new vaccines between 2000 and 2022 in the WHO African Region, with notable introductions between 2011 and 2022. Commitments from countries, and establishing the infrastructure required for effective implementation, remain crucial.