Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive model of phonological awareness focusing on errors and formation process through Shiritori

Language acquisition is supported by phonological awareness, which intentionally makes children aware of phonological units. By understanding the internal processes of children during language acquisition, this study aims to elucidate factors that can correct erroneous phonological generation. Therefore, we developed a cognitive model using innate and experiential factors of the memory retrieval in the cognitive architecture–ACT-R. Furthermore, we performed simulations using Shiritori, a Japanese word game, as an interaction task. The simulation included the observation of effects of the experiential factor of repeating a task and innate factors of different settings. It showed that repeating a single task causes incorrect convergence, and this convergence can be prevented by comprehensive activation of overall phonological knowledge during the interval of Shiritori tasks. Moreover, the simulation in specific innate settings exhibited commonalities with cases of developmental disorder by showing errors like consonant deletion. In the future, we will examine the correlation of the aforementioned findings with actual language development to realize the use of cognitive architecture in real world. GRAPHICAL ABSTRACT
Plastic on Plate-Plasticizers and Child Health

An estimated 400 million metric tonnes of plastics were globally produced in 2019 and with the advent of covid-19 pandemic, the production and distribution of plastics has dramatically escalated, leading to unintended massive environmental contamination [13]. This colossal indiscriminate production caters to the unregulated demand for plastic-made consumer products (including covid-19 masks, shields, gloves and protective medical gears), utilized on a daily basis, consequently leading to increased production and leaching of plasticizers, which are added to improve the quality of plastics and associated products. These plasticizing chemicals are also worryingly used in food processing and packaging industry [4] leading to increased migration into food items and human body through ingestion, inadvertently exposing the population to their endocrine and immune-disrupting properties. Moreover, recent evidence of increasing human, wildlife and marine contamination with micro-plastics, microbeads and nano-plastics [5-9], which are also deliberately added to several consumer and personalcare items such as toothpaste and cosmetics, and are detectable in table salt, tea bags, fish, shellfish and seaweed, industrially processed, packaged and canned food, have incrementally contributed to the gargantuan problem of plastic ingestion and chemical toxicity.
Progressive trends on the application of artificial neural networks in animal sciences – A review

In recent years, artificial neural networks have become the subject of intensive research in a number of scientific areas. The high performance and operational speed of neural models open up a wide spectrum of applications in various areas of life sciences. Objectives pursued by many scientists, who use neural modelling in their research, focus – among others – on intensifying real-time calculations. This study shows the possibility of using Multilayer-Perceptron (MLP) and Radial Basis Function (RBF) models of artificial neural networks for the future development of new methods for animal science. The process should be explained explicitly to make the MLP and RBF models more readily accepted by more researchers. This study describes and recommends certain models as well as uses forecasting methods, which are represented by the chosen neural network topologies, in particular MLP and RBF models for more successful operations in the field of animals sciences.
The specter of empty countrysides and wetlands—Impact of hunting take on birds in Indo‐Burma

Hunting for the wild meat trade, medicines and other human uses has decimated Indo‐Burma's vertebrate biota and has led to widespread defaunation. Yet, there is surprisingly little data on how hunting impacts wild bird assemblages in different landscapes here. Based on concurrent snapshot surveys of bird hunting, food markets and hunting attitudes across six Indo‐Burma countries, we found that hunting threatens species not only in forested landscapes but also wetlands and farmlands such as orchards and paddy fields—ecosystems overlooked by past studies, with at least 47 species associated with wetlands and agricultural lands identified from market surveys across the region. High rates of mortality are suffered when hunting tools such as nets are used to exclude perceived bird pests in both aquaculture and agricultural landscapes, with over 300 individual carcasses of at least 29 identifiable species detected in one aquaculture landscape sampled in Thailand. We warn that the potentially unsustainable trapping of species for consumption and trade in Indo‐Burma, coupled with high incidental mortalities, could decimate the populations of erstwhile common and/or legally unprotected species. There is an urgent need for stronger regulatory oversight on the hunting take of wild birds and the use of hunting tools such as nets. Alongside this, conservation practitioners need to better engage with rural communities to address unsustainable hunting practices, especially outside of protected areas.
Performance Assessment Of Different Singlephase Multi-Level Inverter

Use of multilevel inverter is becoming a trend in the recent years for high power applications. Various topologies and its assessment have been reported for recent literature. Power quality is an important phenomenon in the high power electric circuit and thus suitable topology must be selected for optimum result. Two multilevel inverter topology with the basic bridge configuration of single phase inverter have been analyzed on various parameters and the result are conclude based on the simulation model design from PSIM.
Real-time quantification of CD63 with anti-CD63 functionalized plasmonic fiber optic probe

Exosomes are important mediators of intercellular communications and carry parent cell-specific cargos of proteins, lipids, and genetic materials. Every cell releases exosomes as part of its cellular process. Different cell types, including normal cells and cancer cells, produce a heterogeneous mixture of exosomes in body fluids. Exosomes derived from cells with abnormal conditions can be exploited as a potential biomarker for the respective disease condition. While these exosomes carry various biomarkers, they share a common biomarker called CD63. Therefore, CD63 can be used as an analyte to track exosomes. Toward this endeavor, this work reports a fiber-optic sensor functionalized with anti-CD63 to measure the exosomes by quantifying exosomal CD63 in real-time. For this purpose, an anti-CD63 antibody functionalized surface plasmon resonance (SPR)-based fiber-optic sensor was developed that measured variations in localized SPR due to the changes in local refractive index in response to CD63 binding onto the fiber. Gold-thiol chemistry was used to covalently functionalize the fiber-optic probe with anti-CD63, wherein the antigen-binding sites of anti-CD63 were uniquely exposed to target CD63 molecules. In our preliminary experiment, the fiber-optic sensor exhibited a sensitivity of 2.83 % light reflection variations per Log(ng ml-1 ) concentration of CD63 per cm2 sensing area and a detection limit of 2.64 ng ml-1 . Protein concentrations in culture media were used to calibrate the sensor for precision measurements. Considering excellent dynamic detection range and sensitive thiol chemistry, the developed sensor could hold promises in future in-vivo probe development, significantly impacting real-time monitoring and therapeutic planning.
KARAKTERISTIK STRUKTUR KOMUNITAS MAKROZOOBENTOS DI PERAIRAN EKOSISTEM MANGROVE GILI LAWANG, LOMBOK TIMUR

The role of the mangrove ecosystem as an ecological link through high productivity compared to other ecosystems has had a major impact on the existence of the mangrove ecosystem distribution. This study aims to find out the type of organic ingredient content and abundance of macrozoobenthos and find out the relationship of organic ingredient content to the abundance of macrozoobenthos. The research was carried out in November 2021 using a purposive sampling technique method with a total of 5 stations. The samples taken were the sediments substrate and macrozoobenthos. The results shown that the organic content is dominated by saturated textures, muddy sand textures and saturated clay textures with an average organic content ranging from 1.24% - 1.90%. There were 15 types of macrozoobenthos found and dominated by the Gastropod class with an average abundance of macrozoobenthos ranging from 202 ind/m2 - 525 ind/m2. The types of macrozoobenthos found were Nassarius distortus, Anadara sp, Mesodesma sp, Mytillus sp, Donax sp, Abra soyoae, Cerithideopsilla djadjariensis, Telescopium telescopium, Cassidula nucleus, Cassidula angulifera, Litttoridina sp. , Potamopyrgus sp. , Melanoides sp. , Battilaria zonalis dan Lumbriculus sp. The effect of organic matter content on the abundance of macrozoobenthos by 10.7% and 89.3% was more influenced by physic-chemical factors in Gili Lawang waters.
Does the accuracy of the fingertip sphygmomanometer improve by measuring the outside temperature?

Several types of the cuffless blood pressure (BP) devices have been developed to calculate BP values mainly from the heart rate (HR). But the cuffless devices suffer from low accuracy compared to the BP values directly measured using sphygmomanometer a with inflatable cuff. Since cuffless BP estimated by optical technology is affected not only by HR but also by vascular resistance, vascular resistance can cause inaccuracies. Vascular resistance is determined by the stiffness of the blood vessels and the air temperature. To examine how air temperature affects the fingertip BP, we prototyped a Fingertip BP device with a non-contact thermometer for measuring outside temperature. This presentation clarifies the findings during the trial production.
Dye removal efficiency of fresh and dry alginate beads incorporating zinc oxide

Abstract Alginate beads incorporating zinc oxide (ZnO) nanoparticles were shown as an alternative to treat water contaminated with sedge dye by combining photocatalytic activity with adsorption. Dry and fresh alginate beads were prepared by changing ZnO contents in the range of 0.5, 1, and 2% (w/w) and then characterized for their morphology as well as dye removal. The dye removal efficiency was increased by increasing the active surface area of the alginate beads and ZnO photocatalytic activity. Under ultraviolet irradiation for 24 h, the dye removal efficiency reached 64% in the case of 2% ZnO loading. The use of alginate beads dried at 80 °C before ZnO loading increased the maximum value to 78%. With more than 20% removal during the first 2 h through the adsorption and diffusion, the dye removal of alginate beads incorporating 1 and 2% ZnO was modeled as a function of time according to the Weber-Morris intra-particle diffusion equation. The dye adsorption per mass of the adsorbent having 0 and 0.5% ZnO loading fitted the Elovich equation better.
Operationalising treatment success in aphasia rehabilitation

ABSTRACT Background Treatment success is the desired outcome in aphasia rehabilitation. However, to date, there is a lack of consensus on what defines a 'successful' result on a given aphasia outcome measurement instrument (OMI). Aim In this methodological paper, we present strategies for how to define and measure treatment success on a given OMI at the group-level, as well as for an individual person with aphasia. The latter is particularly important when research findings from group studies are clinically implemented for individuals in rehabilitation. Scope We start by presenting methods to calculate the average statistically significant change across several (group) studies (e.g., standardised mean difference, raw unstandardised mean difference) for a given OMI. Such metrics are useful to summarise the overall effect of the intervention of interest, particularly in meta-analyses. However, benchmarks based on group effects are not feasible for assessing an individual participant’s treatment success and thus for determining the proportion of patients who had a beneficial response to therapy (overall response rate of an intervention). We therefore recommend a distribution-based approach to determine benchmarks of a statistically significant treatment response at the individual level, i.e., the 'smallest detectable change' for a given OMI, which refers to the smallest change that can be detected by the OMI beyond measurement error. However, the statistical significance of an individual treatment effect does not necessarily correspond to its clinical impact. This requires an additional indicator. The benchmark to determine a clinically relevant improvement on a given OMI is the 'minimal important change'. The minimally important change is defined as the smallest OMI change score perceived as important by the relevant stakeholder group (i.e., people with aphasia, their relatives/caregivers, clinicians). It therefore requires relating the individual OMI change scores to 'anchors', i.e., meaningful external criteria, preferably based on patient-perceived therapy success. Currently, there is no consensus regarding the optimal 'anchors' and their respective definition of clinically important change in aphasia outcome research. Conclusions/Recommendations Operationalising individual treatment success based on both statistically significant and (patient-reported) clinically meaningful benchmarks is a key priority in aphasia rehabilitation. Availability of such measures will (a) facilitate estimates of therapy response rate in intervention studies and thus optimise therapeutic decisions and (b) provide stakeholder groups (e.g., the society, the stroke team, people with aphasia, family, clinicians, healthcare professionals) with objective, statistically reliable and meaningful feedback on individual treatment response in the clinical setting.
Calving Ease Risk Factors and Subsequent Survival, Fertility and Milk Production in Italian Holstein Cows

Simple Summary Several studies have analyzed the potential risk factors for assisted calvings and the consequences of calving ease on cow performances. The present study used national data to focus on Italian Holstein cow herds. In summary, herd, number of calvings/herd/year, cow parity, gestation length, twinning, calf sex, previous calving-to-conception interval, dry period and close-up length, and calving season were identified as risk factors associated with calving ease. Regarding lactation performance, cows with assisted calving had higher 30 days in milk (DIM) culling risk, lower 150 DIM pregnancy risk and lower milk yield, measured as 60-d cumulative and as 305-d predicted milk yield. These results may be beneficial to focus attention on control, management and specific factors associated with calving ease in Italian Holstein herds and also to support adequate training and education of the personnel. Abstract The objectives of this study were to investigate the main risk factors associated with calving ease (CE) in Italian Holstein cow herds, and to estimate the association between CE and subsequent survival, fertility and milk production. Data obtained from Holstein cows in 40 Italian herds were retrospectively investigated. Calvings were surveilled and classified into two categories of CE, unassisted calving or assisted calving, based on the need for intervention. The following factors were analyzed as possible risk factor affecting CE: herd, number of calvings/herd/year, age at first calving (AFC), cow parity, gestation length, twinning, calf sex, previous calving-to-conception interval, previous milk yield, dry period and close-up length, and season of calving. The association between CE and culling risk within the first 30 days-in-milk (DIM), cumulative 60-d milk yield and predicted 305-d milk yield, and pregnancy risk within 150 DIM were also investigated. Of the 47,672 calvings, 37,892 (79.5%) were unassisted, while 9780 (20.5%) required some type of assistance. Among the risk factors, only the AFC was not correlated with CE, while for all the other risk factors an association with CE was detected. Assisted calvings were associated with an increased culling risk at 30 DIM, decreased 60-d milk yield, decreased 305-d milk yield and reduced pregnancy risk at 150 DIM. In conclusion, dairy herd management should aim at correcting/reducing the risk factors in order to limit the incidence of assisted calving, and possibly improve the quality of calving assistance; controlling CE within the herd is crucial to reducing culling risk, and achieving higher lactation and reproductive performance.
How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?

The number of biomedical literature on new biomedical concepts is rapidly increasing, which necessitates a reliable biomedical named entity recognition (BioNER) model for identifying new and unseen entity mentions. However, it is questionable whether existing models can effectively handle them. In this work, we systematically analyze the three types of recognition abilities of BioNER models: memorization, synonym generalization, and concept generalization. We find that although current best models achieve state-of-the-art performance on benchmarks based on overall performance, they have limitations in identifying synonyms and new biomedical concepts, indicating they are overestimated in terms of their generalization abilities. We also investigate failure cases of models and identify several difficulties in recognizing unseen mentions in biomedical literature as follows: (1) models tend to exploit dataset biases, which hinders the models’ abilities to generalize, and (2) several biomedical names have novel morphological patterns with weak name regularity, and models fail to recognize them. We apply a statistics-based debiasing method to our problem as a simple remedy and show the improvement in generalization to unseen mentions. We hope that our analyses and findings would be able to facilitate further research into the generalization capabilities of NER models in a domain where their reliability is of utmost importance.
Distributed Model-Based Predictive Secondary Control for Hybrid AC/DC Microgrids

This article presents a novel scheme based on distributed model-based predictive control for the secondary level control of hybrid ac/dc microgrids (MGs). Prediction models based on droop control and power-transfer equations are proposed to characterize the generators in both the ac and dc sub-MGs, whereas power balance constraints are used to predict the behavior of interlinking converters. The operational constraints (such as powers and control action limits) are included in all the formulations. Experimental results validate the proposed scheme for the following cases: 1) load changes, working within operating constraints; 2) managing frequency regulation in the ac sub-MG, voltage regulation in the dc sub-MG, and global power consensus in the whole hybrid MG; and 3) maintaining the MG performance in the presence of communication malfunction while ensuring that plug-and-play capability is preserved.
Outcomes following severe septic shock in a cohort of Aboriginal and Torres Strait Islander people: a nested cohort study from the ADRENAL trial

Objective: To describe the pattern of acute illness and 6-month mortality and health-related quality-of-life outcomes for a cohort of Aboriginal and Torres Strait Islander patients presenting with septic shock. Design: Nested cohort study of Aboriginal and Torres Strait Islander participants recruited to a large randomised controlled trial of corticosteroid treatment in patients with septic shock. Setting: Royal Darwin Hospital, Northern Territory. Participants: All Aboriginal and Torres Strait Islander patients recruited to the Adjunctive Corticosteroid Treatment in Critically Ill Patients with Septic Shock (ADRENAL) trial at Royal Darwin Hospital were compared with a non-Indigenous cohort drawn from the same site, and a cohort matched for age, sex and severity of disease. Main outcome measures: Mortality at 90 days and 6 months, time to shock resolution, mechanical ventilation requirement, renal replacement therapy requirement, and five-domain, five-level EuroQol questionnaire (EQ-5D-5L) score at 6 months. Results: Aboriginal and Torres Strait Islander patients had significantly reduced risk of death at 90 days when compared with non-Indigenous patients recruited to ADRENAL at Royal Darwin Hospital (12/60 v 23/62; adjusted odds ratio, 0.40 [95% CI, 0.17 to 0.94]) which was robust to additional adjustment for baseline covariates (odds ratio, 0.35 [95% CI, 0.14 to 0.90]). When compared with the matched population drawn from the broader ADRENAL cohort, there was no significant difference in 90-day mortality (12/60 v 16/61; adjusted odds ratio, 1.43 [95% CI, 0.60 to 3.39]; P = 0.42). Only nine Aboriginal and Torres Strait Islander patients provided 6-month health-related quality-of-life data. Conclusions: Aboriginal and Torres Strait Islander patients had reduced risk of death at 90 days when compared with non- Indigenous patients recruited to the ADRENAL trial at Royal Darwin Hospital, which was robust to adjustment for covariates, but similar outcomes when compared with a cohort matched for age, sex and severity of disease.
Clustering and parallel indexing of big IoT data in the fog‐cloud computing level

In recent years, the large amount of heterogeneous data generated by the Internet of Things (IoT) sensors and devices made recording and research tasks much more difficult, and most of the state‐of‐the‐art methods have failed to deal with the new IoT requirements. This article proposes a new efficient method that simplifies data indexing and enhances the quality and velocity of the similarity query search in the IoT environment. In this method, the fog layer was divided into two levels. In the clustering fog level, the incremental density‐based spatial clustering of applications with noise (DBSCAN) algorithm was used to separate collected data into clusters in order to minimize data overlap during in parallel indexes construction. Parallelism was also used, in the indexing fog level to speed up the similarity‐based search process and speed up the similarity‐based search process. The data in each cluster were indexed using our proposed structure called B3CF‐tree (binary tree based on containers at the cloud‐clusters fog computing level). The objects in the leaf nodes of the B3CF‐trees are, finally, stored in the cloud. Using this approach for computing multiple datasets, the retrieve time of the similarity search is significantly reduced. The experimental results showed that the combination of DBSCAN clustering and parallel indexing make the B3CF‐trees outperform the latest real data indexing methods. For example, in terms of quality, the B3CF‐tree has the smallest number of nodes and leaf nodes. In addition, the use of parallelism during kNN search reduced, significantly, the retrieve time of the similarity query search.