Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a High Flow Rate Soft Pump Driven by Intersected Twisted Artificial Muscles Units

Small soft pumps are extensively explored in medical fields and soft robotics, but the insufficient flow rate constrains their applications. In this article, an intersected twisted artificial muscle unit (ITAMU) is proposed to achieve a large contraction ratio, which is adapted to drive the developed soft pump, and characteristics of high flow rate and no noise pollution are achieved. The configuration and principle of the ITAMU and the soft pump are depicted. Besides, the contraction ratio of ITAMU and the flow rate of the soft pump are calculated theoretically. Then, the prototypes are manufactured and tested. The results indicate that the maximum contraction ratio of ITAMU reaches 41.2%, and the output force is 3.27 N. Ultimately, the experiments on the flow rate and pressure of the soft pump are carried out. The maximum flow rate is 680 mL/min, and the pressure reaches 2.3 kPa. To sum up, the proposed soft pump exhibits a high flow rate, which shows its application prospect in the field like soft exploration robots.
Experiences of Nurses in Nursing Homes during the COVID-19 Pandemic in Germany: A Qualitative Study

(1) Background: The aim of this qualitative study was to examine pandemic-related changes in nursing work in nursing homes, the resulting work-related stresses and external as well as internal alleviating measures. (2) Methods: We conducted 10 interviews from March to June 2021 with nurses from eight facilities. Data were analysed according to qualitative content analysis. (3) Results: Nurses faced increased workloads due to regulations and guidelines paired with staffing shortages. Work became more difficult due to personal protective equipment (PPE), conflict with residents’ relatives and, in the case of outbreaks, excess death and suffering. Nurse-to-resident care work became more emotionally demanding, with residents more distressed due to the lockdown, while families and social workers were not allowed into the facility. Residents with dementia posed an additional challenge, as they did not remember hygiene and distancing rules. Internal and external measures were not sufficient to alleviate the situation. However, some measures, such as training programmes or existing palliative care concepts, were considered helpful. (4) Conclusions: Facing other possible upcoming pandemics, ways to improve facility administration to prepare for future pandemics are highly needed, such as regular training programmes to prepare for possible lockdown scenarios, PPE use or potential hygiene measures.
Real-Time Allocation of Multi-Mobile Resources in Integrated Distribution and Transportation Systems for Resilient Electrical Grid

This article proposes a novel real-time optimization-based critical load restoration strategy for the post-event stages. The key idea is to integrate multi-mobile and stationary resources into the emergency tools of the system operation as grid-support services. The role of the demand response (DR) program and distributed generation (DG) from the resiliency-boosting points of view is investigated. To prevent prolonged and extensive electric outages, mobile emergency generator (MEG), emergency vehicle energy storage (EVES), and electric vehicles (EVs) are aimed to be exploited by forming microgrids. Power system constraints are modelled according to the linearized DistFlow equations. The framework's main aim is to serve critical loads, weighted by their priority, with the coordinated operation of multiple resources. Mixed-integer quadratic programming-based model is solved by a state-of-the-art solver within 30-minute time slots while considering uncertainties in real-time traffic congestion. Different case studies are carried out on the IEEE-33 bus coupled distribution-transportation system and the results are discussed in detail on the load restoration basis.
A long‐term shift in the summer distribution of Hector’s dolphins is correlated with an increase in cruise ship tourism

1. Before the 2020 COVID-19 pandemic, cruise ship tourism had been one of the fastest growing segments of global tourism, presenting a range of potential impacts. At Akaroa Harbour, Aotearoa New Zealand, the number of annual cruise ship visits more than quadrupled following earthquake damage to Otautahi Christchurch's Lyttelton Port in 2011. Akaroa Harbour is an area of core use for endangered and endemic Hector's dolphins (Cephalorhynchus hectori). Dolphins here are exposed to some of the highest levels of cetacean tourism in Aotearoa New Zealand. 2. Relationships were examined between growth in cruise ship visits, as well as tours focused specifically on dolphins, and long-term trends in summer distribution of Hector's dolphins at Akaroa Harbour, from 2000 to 2020. Core use areas for Hector's dolphins within the harbour were quantified via kernel density estimation using data from 2,335 sightings from over 8,000 km of standardized survey effort. Data were allocated into four periods based on varying levels of tourism. 3. Dolphin habitat preference varied over time, with the greatest change occurring between 2005-2011 and 2012-2015. When comparing these periods, the spatial overlap of core habitat was less than 24%. Dolphin distribution shifted towards the outer harbour after 2011 and has remained relatively consistent since. 4. The observed shift in distribution coincided with the more than fourfold increase in annual cruise ship visits to Akaroa Harbour. Several pressures related to cruise ship tourism are likely to have influenced habitat preferences of dolphins. Further investigation into causal factors of the observed shift is warranted. 5. In the wake of the COVID-19 pandemic, the future of cruise ship and wildlife tourism is in flux. Our findings suggest that the future re-development of this industry should follow a precautionary approach, with the onus on industry to provide evidence of sustainability before proceeding.
Tadalafil-Loaded Self-Nanoemulsifying Chewable Tablets for Improved Bioavailability: Design, In Vitro, and In Vivo Testing

This research aimed to develop innovative self-nanoemulsifying chewable tablets (SNECT) to increase oral bioavailability of tadalafil (TDL), a nearly insoluble phosphodiesterase-5 inhibitor. Cinnamon essential oil, PEG 40 hydrogenated castor oil (Cremophor® RH 40), and polyethylene glycol 400 served as the oil, surfactant, and cosurfactant in the nanoemulsifying system, respectively. Primary liquid self-nanoemulsifying delivery systems (L-SNEDDS) were designed using phase diagrams and tested for dispersibility, droplet size, self-emulsifying capability, and thermodynamic stability. Adsorption on a carrier mix of silicon dioxide and microcrystalline cellulose was exploited to solidify the optimum L-SNEDDS formulation as self-nanoemulsifying granules (SNEG). Lack of crystalline TDL within the granules was verified by DSC and XRPD. SNEG were able to create a nanoemulsion instantaneously (165 nm), a little larger than the original nanoemulsion (159 nm). SNECT were fabricated by compressing SNEG with appropriate excipients. The obtained SNECT retained their quick dispersibility dissolving 84% of TDL within 30 min compared to only 18% dissolution from tablets of unprocessed TDL. A pharmacokinetic study in Sprague–Dawley rats showed a significant increase in Cmax (2.3-fold) and AUC0–24 h (5.33-fold) of SNECT relative to the unprocessed TDL-tablet (p < 0.05). The stability of TDL-SNECT was checked against dilutions with simulated GI fluids. In addition, accelerated stability tests were performed for three months at 40 ± 2 °C and 75% relative humidity. Results revealed the absence of obvious changes in size, PDI, or other tablet parameters before and after testing. In conclusion, current findings illustrated effectiveness of SNECT to enhance TDL dissolution and bioavailability in addition to facilitating dose administration.
Academic Performance of Grade V Pupils Using Science Strategic Intervention Material in Zambales, Philippines

Teachers in Science are challenged to use Strategic intervention materials to improve students’ academic performance. The objective of the study was to improve the academic performance of Grade V pupils through SIM (Strategic Intervention Material) in Science. The researcher used descriptive and quasi-experimental research methods to determine the effectiveness of SIM. Respondents were given sets of SIM to be utilized and answered. A pretest-post test design was used for comparison. The data collected were statistically treated through the mean, standard deviation, and T-test and Analysis of Variance. The results showed that the students were rated poor and “Did not meet the expectation” in the pre-test, while “Outstanding “in the post-test. There was a significant difference on the performance of the students based on the results of the pre-test and post-test. The teacher-respondents “Strongly Agreed” that the contents, interventional design and organizational presentation were acceptable. There was no significant difference in the assessment of acceptability as to contents, interventional design, organizational presentation, and assessment, respectively. SIM may be adopted as an “aid” for instructional materials in teaching to facilitate learning and improve performance of students. SIM made by the researcher can be used by teachers to re-teach the concepts and skills.
Avoiding Digital Divide in European Union through European Green Deal

The digital society is threatened by the existing divide that seems to be deepening between the population that has access to information technology and the population that for some reason does not. The phenomenon of digital divide has become a very important issue in the process of transformation of society and the authors relate it to European Green Deal, in order to identify the determinants and ways to reduce the negative effects that will be eliminated according to the proposed schedule. For this purpose, a narrative review of the literature was used as a research method, corroborated with a multidimensional analysis of the data regarding the Internet access in the households from the member countries of the European Union. As a result, eight determinants of the digital divide were identified, along with four examples of how they can be addressed. According to the authors, the Deal, which has committed all 27 member states to create new opportunities for innovation, investment, and jobs, can support the positive transformation of European society, even against a backdrop of enormous challenges and huge impacts that we can now imagine.
Multimodal Emotional Classification Based on Meaningful Learning

Emotion recognition has become one of the most researched subjects in the scientific community, especially in the human–computer interface field. Decades of scientific research have been conducted on unimodal emotion analysis, whereas recent contributions concentrate on multimodal emotion recognition. These efforts have achieved great success in terms of accuracy in diverse areas of Deep Learning applications. To achieve better performance for multimodal emotion recognition systems, we exploit Meaningful Neural Network Effectiveness to enable emotion prediction during a conversation. Using the text and the audio modalities, we proposed feature extraction methods based on Deep Learning. Then, the bimodal modality that is created following the fusion of the text and audio features is used. The feature vectors from these three modalities are assigned to feed a Meaningful Neural Network to separately learn each characteristic. Its architecture consists of a set of neurons for each component of the input vector before combining them all together in the last layer. Our model was evaluated on a multimodal and multiparty dataset for emotion recognition in conversation MELD. The proposed approach reached an accuracy of 86.69%, which significantly outperforms all current multimodal systems. To sum up, several evaluation techniques applied to our work demonstrate the robustness and superiority of our model over other state-of-the-art MELD models.
Radiation Damage Analysis of the Swept Charge Device for the C1XS Instrument

Since the dawn of the space age the ability to capture images from orbit and deep space missions has proved invaluable. Interference caused by the Earth’s atmosphere is bypassed, thus allowing for the detailed observation of distant and faint objects that would be hard to detect using ground based observatories. However, this method introduces a number of new problems, these include placing the spacecraft into a viable and suitable orbit, pointing stability, data retrieval, power consumption, and problems associated with the vacuum of space, micrometeoroids, orbital debris, and the thermal and radiation environment. The focus of this thesis is concerned with ensuring high energy resolution from the swept charge devices (SCDs), essentially a non-pixellated version of the charge coupled device (CCD), for use in the Chandrayaan-1 X-ray Spectrometer (C1XS). C1XS, launched onboard Chandrayaan-1, was designed to detect the X-ray fluorescence, caused by solar flares, from the lunar surface. To ensure the instrument was a success a radiation damage study was performed, making recommendations on device operating conditions, instrument design and the future development of the SCD. A full device characterisation and the assistance provided to the C1XS science teamare also discussed.
Seasonal ecophysiology of Fucus vesiculosus (Phaeophyceae) in the Northern Baltic Sea

ABSTRACT The brown macroalga Fucus vesiculosus is a foundation species in temperate rocky shores, subjected to seasonally fluctuating environmental conditions. To obtain a more complete picture of the seasonality of F. vesiculosus ecophysiology in the northern Baltic Sea, in situ photochemistry, elemental ratios and chlorophyll a and c content of the alga were investigated in field campaigns conducted in different months throughout the year during 2017. Carbon, nitrogen, carbon to nitrogen ratio and chlorophyll a and c content of the alga varied substantially throughout the year, with highest carbon content observed in summer, and highest nitrogen content in winter. C:N ratio in F. vesiculosus apical tissue ranged from 8.6 in February to 48.3 in July. Chlorophyll a and c content followed inversely the seasonal patterns of ambient irradiance. High chlorophyll a and c content in winter was associated with higher maximum photosynthetic efficiency of energy conversion (Fv/Fm), but not with efficiency of photosynthetic energy conversion under light limitation (α). Electron transport rate correlated strongly with seawater temperature, and the highest electron transport rates were observed in summer and correlated with highest internal carbon content of the alga. Redundancy analysis conducted on measured environmental variables against physiological responses identified day of year, temperature and macronutrients in seawater as the most important variables driving the observed seasonal patterns in F. vesiculosus ecophysiology. The results suggest elevated temperatures may increase Fucus growth and photosynthesis rates in the study area. Highlights Two distinct physiological states of Fucus vesiculosus were identified. The most important variables driving responses were season, temperature and macronutrients. Fucus ecophysiology shows substantial seasonality in the northern Baltic Sea.
Direct Evidence of a Slow‐Slip Transient Modulating the Spatiotemporal and Frequency‐Magnitude Earthquake Distribution: Insights From the Armutlu Peninsula, Northwestern Turkey

Earthquakes and slow‐slip events interact, however, detailed studies investigating their interplay are still limited. We generate the highest resolution microseismicity catalog to date for the northern Armutlu Peninsula in a ∼1‐year period to perform a detailed seismicity distribution analysis and correlate the results with a local, geodetically observed slow‐slip transient within the same period. Seismicity shows a transition of cluster‐type behavior from swarm‐like to burst‐like, accompanied by an increasing relative proportion of clustered (non‐Poissonian) relative to background (Poissonian) seismicity and gradually decreasing b‐value as the geodetically observed slow‐slip transient ends. The observed slow‐slip transient decay correlates with gradually increasing effective‐stress‐drop values. The observed correlation between the b‐value and geodetic transient highlights the influence of aseismic deformation on seismic deformation and the impact of slow‐slip transients on local seismic hazard.
Calculation of rf current drive in tokamaks

The toroidal plasma current is a key parameter for controlling MHD stability and fusion performances in tokamaks. Among the various methods for driving current, rf waves are a flexible and powerful tool. Therefore, their role in the design and optimization of advanced scenarios is considerable. The universal ray‐tracing code C3PO coupled with the fully implicit linearized 3‐D bounce‐averaged relativistic electron Fokker‐Planck solver LUKE is an illustration of the present day effort for performing fast and realistic calculations of the rf driven plasma current. The versatility of this tool is highlighted by simulations concerning the lower hybrid and electron cyclotron waves.
PENERAPAN METODE BOOTSTRAP RESIDUAL DALAM MENGATASI BIAS PADA PENDUGA PARAMETER ANALISIS REGRESI

Statistical analysis which aims to analyze a linear relationship between the independent variable and the dependent variable is known as regression analysis. To estimate parameters in a regression analysis method commonly used is the Ordinary Least Square (OLS). But the assumption is often violated in the OLS, the assumption of normality due to one outlier. As a result of the presence of outliers is parameter estimators produced by the OLS will be biased. Bootstrap Residual is a bootstrap method that is applied to the residual resampling process. The results showed that the residual bootstrap method is only able to overcome the bias on the number of outliers 5% with 99% confidence intervals. The resulting parameters estimators approach the residual bootstrap values ? OLS initial allegations were also able to show that the bootstrap is an accurate prediction tool.
Stakeholders’ perspectives on quality monitoring
mechanisms instituted by international university affiliates
in Omani Private Higher Education Institutions (PHEIs)

With increased impetus on internationalization of higher education, academic interest on this theme has witnessed a marked increment over the years. Parallel to the growing trend of internationalization of higher education, higher educational institutions have been emphatic upon maintaining rigorous quality standards in line with internationally-acclaimed standards. However, while internationalization of higher education has paced quite rapidly, emphasis upon upholding quality standards remains grim (Castle & Kelly, 2004). In this regard, my thesis purports to combine these twin concepts; viz. the issue of internationalization of higher education juxtaposed with the theme of quality in higher education, in the context of Omani higher education where privatization and internationalization of higher education have gained currency post-1995 with recent emphasis upon maintaining of high-quality standards at the same time. Conceding that international university partners are significant “ external ” stakeholders in the Omani Private Higher Educational Institutions (PHEIs), my research seeks to explore the effectiveness of the ongoing quality monitoring mechanisms in Omani PHEIs. Quality mechanisms in higher education have been trending globally for over a decade (Stensaker, 2003) which help in enhancing quality in HEIs besides setting international benchmarks for comparisons (Brennan & Shah, 2000). In the agreements attested by the international university partners and the local PHEIs, the former have instituted a number of quality monitoring mechanisms to be implemented in the PHEIs; my research pertains to ascertaining the gap between the formulation and implementation of these quality monitoring mechanisms from the perspective of different stakeholders.
Species Identification of Caterpillar Eggs by Machine Learning Using a Convolutional Neural Network and Massively Parallelized Microscope

Rapid, accurate insect identification is the first and most critical step of pest management and vital to agriculture for determining optimal management strategies. In many instances, classification is necessary within a short developmental window. Two examples, the tobacco budworm, Chloridea virescens, and bollworm, Helicoverpa zea, both have <5 days from oviposition until hatching. H. zea has evolved resistance to Bt-transgenic crops and requires farmers to decide about insecticide application during the ovipositional window. The eggs of these species are small, approximately 0.5 mm in diameter, and often require a trained biologist and microscope to resolve morphological differences between species. In this work, we designed, built, and validated a machine learning approach to insect egg identification with >99% accuracy using a convolutional neural architecture to classify the two species of caterpillars. A gigapixel scale parallelized microscope, referred to as the Multi-Camera Array Microscope (MCAM™), and automated image-processing pipeline allowed us to rapidly build a dataset of ~5500 images for training and testing the network. In the future, applications could be developed enabling farmers to photograph eggs on a leaf and receive an immediate species identification before the eggs hatch.
P-type conductivity in Sn-doped Sb2Se3

Antimony selenide (Sb2Se3) is a promising absorber material for thin-film photovoltaics. However, certain areas of fundamental understanding of this material remain incomplete and this presents a barrier to further efficiency gains. In particular, recent studies have highlighted the role of majority carrier type and extrinsic doping in drastically changing the performance of high efficiency devices (Hobson et al 2020 Chem. Mater. 32 2621–30). Herein, Sn-doped Sb2Se3 bulk crystals are shown to exhibit p-type conductivity using Hall effect and hot-probe measurements. The measured conductivities are higher than those achieved through native defects alone, but with a carrier density (up to 7.4 × 1014 cm−3) several orders of magnitude smaller than the quantity of Sn included in the source material. Additionally, a combination of ultraviolet, x-ray and hard x-ray photoemission spectroscopies are employed to obtain a non-destructive depth profile of the valence band maximum, confirming p-type conductivity and indicating a majority carrier type inversion layer at the surface. Finally, these results are supported by density functional theory calculations of the defect formation energies in Sn-doped Sb2Se3, showing a possible limit on the carrier concentration achievable with Sn as a dopant. This study sheds light on the effectiveness of Sn as a p-type dopant in Sb2Se3 and highlights avenues for further optimisation of doped Sb2Se3 for solar energy devices.
Study on a new chaotic system with analysis and circuit experiment

In this paper，we study a new chaotic system which is totally different from the Lorenz chaotic system，Chen chaotic system and Liu chaotic system. Firstly，the basic dynamical behaviors of new chaotic system are studied. The influence of system parameter on the chaotic system is discussed through Lyapunov exponent spectrum and bifurcation diagram. The results between simulation and experiment are in good agreement，thereby proving the veracity of analysis.