Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic Frontier Yield Function Analysis to Predict Returns to a New Crop: An Example of Camelina Sativa Yields Conditional on Local Factor Levels

The purpose of this study is to develop a model that calculates the probability distribution of camelina expected yields dependent on location-related variables such as precipitation, temperature, and solar radiation, as well as nitrogen rate and others. Camelina is an oilseed crop grown in cool climate with low input requirements including little water. The application to camelina addresses challenges in analysis of potential adoption of crops with limited field data. Our data include trials and crop yields in the United States from 2005 to 2012. They have been assembled from various published reports covering a range of locations, seasons, and production methods. We begin by fitting a least squares (LS) regression model to camelina yields. As a robustness check we also apply a stochastic frontier framework under Cobb-Douglas technology. Preliminary results indicate that the average maximum precipitation for the period of interest positively affected the mean camelina yields, whereas it has no impact on yield variability. An increase in average maximum precipitation will more likely decrease the technical inefficiency. Both higher nitrogen rates and higher average maximum growing degree days will more likely increase the average yields. A taller camelina plant positively affects the mean yields and the yield variability. In contrast, total solar radiation is negatively correlated with mean yields and variation. There is still much to be learned about the crop and its best management practices as production expands. The analysis of the interaction of managed input variables and environmental factors will help us assess varietal performance and provide location conditional predictions.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Jul 1, 2021 Revised Dec 22, 2021 Accepted Jan 2, 2022 Performance of word sense disambiguation (WSD) is one of the challenging tasks in the area of natural language processing (NLP). Generation of sense annotated corpus for multilingual word sense disambiguation is out of reach for most languages even if resources are available. In this paper we propose an unsupervised method using word and sense embedding or improving the performance of these systems using untagged. Corpora and create two bags namely ontological bag and wiki sense bag to generate the senses with highest similarity. Wiki sense bag provides external knowledge to the system required to boost the disambiguation accuracy. We explore Word2Vec model to generate the sense
An artificial intelligence model to identify snakes from across the world: Opportunities and challenges for global health and herpetology

Background Snakebite envenoming is a neglected tropical disease that kills an estimated 81,000 to 138,000 people and disables another 400,000 globally every year. The World Health Organization aims to halve this burden by 2030. To achieve this ambitious goal, we need to close the data gap in snake ecology and snakebite epidemiology and give healthcare providers up-to-date knowledge and access to better diagnostic tools. An essential first step is to improve the capacity to identify biting snakes taxonomically. The existence of AI-based identification tools for other animals offers an innovative opportunity to apply machine learning to snake identification and snakebite envenoming, a life-threatening situation. Methodology We developed an AI model based on Vision Transformer, a recent neural network architecture, and a comprehensive snake photo dataset of 386,006 training photos covering 198 venomous and 574 non-venomous snake species from 188 countries. We gathered photos from online biodiversity platforms (iNaturalist and HerpMapper) and a photo-sharing site (Flickr). Principal findings The model macro-averaged F1 score, which reflects the species-wise performance as averaging performance for each species, is 92.2%. The accuracy on a species and genus level is 96.0% and 99.0%, respectively. The average accuracy per country is 94.2%. The model accurately classifies selected venomous and non-venomous lookalike species from Southeast Asia and sub-Saharan Africa. Conclusions To our knowledge, this model’s taxonomic and geographic coverage and performance are unprecedented. This model could provide high-speed and low-cost snake identification to support snakebite victims and healthcare providers in low-resource settings, as well as zoologists, conservationists, and nature lovers from across the world.
Analysis and simplified modelling of brushless doubly-fed induction machine in synchronous mode of operation

Similar to conventional doubly-fed induction machines (DFIMs), brushless DFIM (BDFIM) obtains variable speed operation by means of a fractionally rated converter. Due to its brushless structure, BDFIM confers higher reliability and requires lower maintenance than the conventional DFIMs. Hence, it has potential future use in offshore wind turbines. This study elaborates the most attractive mode of BDFIM operation, i.e. the synchronous mode. The BDFIM air-gap flux is complex due to the presence of spatial harmonic components with different pole numbers. To facilitate analysis of the BDFIM performance, this study aims to simplify its steady state and dynamic models. To achieve this goal, the BDFIM electromagnetic torque is analysed to induction and synchronous components. Then, considering the share of each torque components, simplifications are proposed in the steady state and dynamic models of BDFIM. Thus, the BDFIM model becomes similar to conventional DFIMs. The equivalent parameters of the proposed simplified model of BDFIM and the model of conventional DFIMs are introduced. Supplementary experiments and simulations are carried out for a prototype D180 4/8 pole BDFIM.
Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population

Accelerometers are widely used to measure physical activity behaviour, including in children. The traditional method for processing acceleration data uses cut points to define physical activity intensity, relying on calibration studies that relate the magnitude of acceleration to energy expenditure. However, these relationships do not generalise across diverse populations and hence they must be parametrised for each subpopulation (e.g., age groups) which is costly and makes studies across diverse populations and over time difficult. A data-driven approach that allows physical activity intensity states to emerge from the data, without relying on parameters derived from external populations, offers a new perspective on this problem and potentially improved results. We applied an unsupervised machine learning approach, namely a hidden semi-Markov model, to segment and cluster the raw accelerometer data recorded (using a waist-worn ActiGraph GT3X+) from 279 children (9–38 months old) with a diverse range of developmental abilities (measured using the Paediatric Evaluation of Disability Inventory–Computer Adaptive Testing measure). We benchmarked this analysis with the cut points approach, calculated using thresholds from the literature which had been validated using the same device and for a population which most closely matched ours. Time spent active as measured by this unsupervised approach correlated more strongly with PEDI-CAT measures of the child’s mobility (R2: 0.51 vs 0.39), social-cognitive capacity (R2: 0.32 vs 0.20), responsibility (R2: 0.21 vs 0.13), daily activity (R2: 0.35 vs 0.24), and age (R2: 0.15 vs 0.1) than that measured using the cut points approach. Unsupervised machine learning offers the potential to provide a more sensitive, appropriate, and cost-effective approach to quantifying physical activity behaviour in diverse populations, compared to the current cut points approach. This, in turn, supports research that is more inclusive of diverse or rapidly changing populations.
Sensory properties of the sheep prosciutto

Sheep prosciutto is a traditional product of sheep meat in halves without shoulder, bones and internal round muscles prepared by procedure of salting, smoking and drying. This paper presents sensory properties of the sheep prosciutto. The examined sheep prosciutto was brownish with black note in appearance. Outer side of the sheep prosciutto is lighter than the inner side. Sheep prosciutto at the cross section of meaty parts is of uniform, evenly distributed, dark red color with black note (chocolate colour), and fatty tissue (tallow) is white with brownish-yellowish note of surface layers. Smell and aroma of sheep prosciutto are characteristic for sheep salted, smoked and dried meat. Besides smell and aroma of smoke, very specific smell of sheep meat is especially expressed. The taste of sheep prosciutto is moderately to prominently salty, where the parts of fatty tissue are slightly salty and the meat is sourish.
Alternative Cements For Durable Concrete In Offshore Environments

Many specifications for concrete coated offshore pipelines require that the normal weight or heavyweight concrete be manufactured with either ASTM C150 Type II (moderate sulphate resistance) or Type V (high sulphate resistance) Portland cement due to the presence of sulphate in seawater. An important property of cements that determine their resistance to sulphate attack is the tricalcium aluminate (C3A) content and therefore the amount of this mineral phase is limited to 8% and 5% in Type II and Type V cement, respectively. This paper presents a literature review of laboratory and long term field studies which indicate that sulphate attack is not the primary mechanism of deterioration caused by seawater. Furthermore, cement composition does not affect long term performance provided that the concrete has a low water/cement ratio and is impermeable. The available information therefore supports the use of either ASTM C150 Type I (with a maximum C3A content of 12%) or Type II Portland cement for the manufacture concrete pipeline coatings. A potential secondary benefit of using Portland cements with C3A levels greater than 5% will be their greater capacity to bind chlorides, reducing the apparent diffusion rate of chlorides in concrete. A reduction in the ingress of chlorides will extend the service life of offshore pipelines by delaying the onset of steel reinforcement corrosion.
Deep survival algorithm based on nuclear norm

This paper devotes to propose a nuclear-norm-based deep survival algorithm (NN-DeepSurv), to study the regression problem of survival data with right censoring. The nuclear norm method is used to impute missing covariates, and it's combined with DeepSurv algorithm to train the regression model. We compare our algorithm with other state-of-the-art methods: Cox proportional hazards regression model (Coxph), Cox proportional hazards model with lasso regression (Cox-lasso), random survival forests (RSF), DeepSurv, and Xgboost algorithm, on 2 simulated datasets and 6 clinical datasets, to show the superiority of the performance of our algorithm.
A Multilevel Fast Multipole Algorithm for Analyzing Radiation and Scattering From Wire Antennas in a Complex Environment

A hybrid VSIE-MLFMA formulation is applied to solve for scattering and radiation solutions from wire antennas, IBC surfaces, and inhomogeneous dielectric objects. Unlike the traditional method of moments, the VSIE-MLFMA formulation has a computational complexity of O( N log N) for both CPU time and memory requirement, where N is the number of unknowns. The surface integral equation part of the formulation is extended to a full curvilinear implementation with the invention of curvilinear junction basis. The curvilinear formulation enables efficient solution for radiation and scattering from highly curved PEC structures. In order to accurately model coaxial feeds for wire antennas, a variational formulation is applied to calculate the input impedances of horizontal and vertical frill sources. Numerical simulations agree well with experimental results after correctly modeling the SMA connector as a horizontal frill source and a lossy transmission line. Moreover, succinct spectral integrals are derived for efficient calculation of the fringe capacitances, which contribute to the main differences of delta-gap and magnetic frill sources in high-frequency simulation. In addition, physics-based preconditioners and basis reduction schemes are developed to reduce iteration counts for iterative solvers. The formulation is then applied for vehicle antenna and photolithography simulations. A fast field calculation based on MLFMA significantly reduces the time for computing near and far fields radiated from vehicle antennas and photomasks.
Identifying alternative mental health interventions: a systematic review of randomized controlled trials of chanting and breathwork

ABSTRACT This systematic review of randomized controlled trials investigated the effects of two religious/spiritual (R/S) practices (breathwork and chanting) on mental health outcomes (depression, anxiety, stress, and post-traumatic stress disorder (PTSD) symptoms). After registering with PROSPERO (CRD42020136645), a systematic search of four major databases (CINAHL, MEDLINE, PsycINFO, and ProQuest) was undertaken using predetermined eligibility criteria. Quality of included studies was assessed using the PEDro scale and Cochrane risk of bias tool. Although varying in quality, 15 included studies provide moderate to strong support for the effectiveness of chanting and breathwork to alleviate anxiety, depression, stress, and symptoms of PTSD.
Web Page Forensics：A Web Spider Based Approach

Cyber-crimes are growing rapidly,so it is important to obtain the digital evidence on the web page.Usually,people can examine the browser history on the client side and data files on the server side,but both of them have shortcomings in real criminal investigation.To overcome the weakness,this paper designs a web page forensic scheme to snapshot the pages from web servers with the help of web spider.Also,it designs several steps to improve the trustworthiness of these pages.All the pages will be dumped in local database which can be presented as reliable evidence on the court.
Comparison of maximal aerobic capacity during running and lifting activities : research article

Acknowledging the specificity of assessing maximal output, the purpose of this paper was to compare the physiological and perceptual responses during a traditional running activity and the common industrial task of lifting; both were taken to maximal effort. The responses of eight male participants were measured during both physical activities separated by at least five days: i) a progressive speed protocol (PSP) involved running on a treadmill at increasing speeds, starting at 10 km.h-1 and increasing by 1 km.h-1 every minute until exhaustion; ii) a progressive frequency protocol (PFP) required participants to lift a load of 20% body mass at increasing lifting frequencies, starting at one lift every 10s and reducing the time between lifts by 1s every minute until exhaustion. Physiological responses were measured using a portable on-line system, the K4b2 (Cosmed®). "Central" perceptual measures were obtained every minute using the rating of perceived exertion (RPE) scale. Results : Responses increased progressively with augmented exercise intensity in both protocols; however, oxygen consumption and peak oxygen values were both lower during the lifting protocol compared to the running protocol. In contrast to the oxygen uptake responses, peak RER values were significantly higher during lifting (mean of 1.32) than running (mean of 1.18), while maximal heart rate and perceptual responses revealed no significant difference between the two protocols. These findings caution against using the 'traditional' treadmill protocol to assess the aerobic capacity of manual labourers involved in lifting, as it is evident that the traditional lower body protocol will indicate a higher capacity than the maximum capacity of an upper body activity, which could lead to manual labourers being taxed beyond acceptable limits.
Effect of Propionic Acid on Diabetes-Induced Impairment of Unfolded Protein Response Signaling and Astrocyte/Microglia Crosstalk in Rat Ventromedial Nucleus of the Hypothalamus

Background The aim was to investigate the influence of propionic acid (PA) on the endoplasmic reticulum (ER), unfolded protein response (UPR) state, and astrocyte/microglia markers in rat ventromedial hypothalamus (VMH) after type 2 diabetes mellitus (T2DM). Methods Male Wistar rats were divided: (1) control, (2) T2DM, and groups that received the following (14 days, orally): (3) metformin (60 mg/kg), (4) PA (60 mg/kg), and (5) PA+metformin. Western blotting, RT-PCR, transmission electron microscopy, and immunohistochemical staining were performed. Results We found T2DM-associated enlargement of ER cisterns, while drug administration slightly improved VMH ultrastructural signs of damage. GRP78 level was 2.1-fold lower in T2DM vs. control. Metformin restored GRP78 to control, while PA increased it by 2.56-fold and metformin+PA—by 3.28-fold vs. T2DM. PERK was elevated by 3.61-fold in T2DM, after metformin—by 4.98-fold, PA—5.64-fold, and metformin+PA—3.01-fold vs. control. A 2.45-fold increase in ATF6 was observed in T2DM. Metformin decreased ATF6 content vs. T2DM. Interestingly, PA exerted a more pronounced lowering effect on ATF6, while combined treatment restored ATF6 to control. IRE1 increased in T2DM (2.4-fold), metformin (1.99-fold), and PA (1.45-fold) groups vs. control, while metformin+PA fully normalized its content. The Iba1 level was upregulated in T2DM (5.44-fold) and metformin groups (6.88-fold). Despite PA treatment leading to a further 8.9-fold Iba1 elevation, PA+metformin caused the Iba1 decline vs. metformin and PA treatment. GFAP level did not change in T2DM but rose in metformin and PA groups vs. control. PA+metformin administration diminished GFAP vs. PA. T2DM-induced changes were associated with dramatically decreased ZO-1 levels, while PA treatment increased it almost to control values. Conclusions T2DM-induced UPR imbalance, activation of microglia, and impairments in cell integrity may trigger VMH dysfunction. Drug administration slightly improved ultrastructural changes in VMH, normalized UPR, and caused an astrocyte activation. PA and metformin exerted beneficial effects for counteracting diabetes-induced ER stress in VMH.
Solving Emission Dispatch Problem with Multiple Fuel Option by Using Differential Evolution Immunized Ant Colony Optimization Technique

Emission dispatching is conducted to calculate the lowest amount of emission while generating satisfying output to the load demand. The utilities are restricted by emission regulation that limits the emission level to a certain amount. This paper proposes emission dispatch with multiple fuel option (EDMFO) to determine the optimal emission level. The EDMFO allows the operators to select different type of fuel according to the generation level and requirement. The emission dispatch problem is optimized by using Differential Evolution Immunized Ant Colony Optimization (DEIANT) technique. Validation process is conducted by comparing DEIANT with several optimization approaches including ACO and EP. The comparison took places in IEE 57-Bus RTS. Results indicate that DEIANT is superior in terms of calculating the lowest emission level, lower operating cost and the best selection of fuel according to the generation requirement.
Differentiating distinct and converging neural correlates of types of systemic environmental exposures

Systemic environmental disadvantage relates to a host of health and functional outcomes. Specific structural factors have seldom been linked to neural structure, however, clouding understanding of putative mechanisms. Examining relations during childhood/preadolescence, a dynamic period of neurodevelopment, could aid bridge this gap. A total of 10,213 youth were recruited from the Adolescent Brain and Cognitive Development study. Self‐report and objective measures (Census and Federal bureau of investigation metrics extracted using geocoding) of environmental exposures were used, including stimulation indexing lack of safety and high attentional demands, discrepancy indexing social exclusion/lack of belonging, and deprivation indexing lack of environmental enrichment. Environmental measures were related to cortical thickness, surface area, and subcortical volume regions, controlling for other environmental exposures and accounting for other brain regions. Self‐report (|β| = .04–.09) and objective (|β| = .02–.06) environmental domains related to area/thickness in overlapping (e.g., insula, caudal anterior cingulate), and unique regions (e.g., for discrepancy, rostral anterior and isthmus cingulate, implicated in socioemotional functions; for stimulation, precuneus, critical for cue reactivity and integration of environmental cues; and for deprivation, superior frontal, integral to executive functioning). For stimulation and discrepancy exposures, self‐report and objective measures showed similarities in correlate regions, while deprivation exposures evidenced distinct correlates for self‐report and objective measures. Results represent a necessary step toward broader work aimed at establishing mechanisms and correlates of structural disadvantage, highlighting the relevance of going beyond aggregate models by considering types of environmental factors, and the need to incorporate both subjective and objective measurements in these efforts.
State Capacity and American Technology: Evidence from the 19th Century

Robert Gordon's The Rise and Fall of American Economic Growth provides a compelling interpretation of how technical change and innovation has radically changed the living standards of the citizens of the US in the past 150 years. Lying behind these changes are the institutions which have allowed the country to harness its human potential. In this paper we conduct an empirical investigation of the impact of one key set of institutions, the capacity of the US state as proxied by the presence of post offices in a county, on innovation. We show that between 1804 and 1899, the time when the US became the world technological leader, there is a strong association between the presence and number of post offices in a county and patenting activity, and it appears that it is the opening of postal offices that leads to surges in patenting activity, not the other way around. Our evidence suggests that part of the yet untold story of US technological exceptionalism is the way in which the US created an immensely capable and effective state.