Miracle-dz/UniFAR-FTUs huggingface dataset:
Midfield Wireless Power Transfer for Deep-Tissue Biomedical Implants

Midfield wireless power transfer has proved its advantages in power delivery to miniature implantable medical devices located deeply in the human body; however, the delivered power level is still low. Besides, design proposals for a compact source structure concentrating the transmitter field on a miniature implant remain challenges. This letter presents a novel compact transmitting structure (TX) at the midfield band and shows the capability of focusing the magnetic field inside the human tissue. Source performance has been verified by implantable planar inverted-F antenna (PIFA) with a total size of 9 × 13 mm${^2}$ (RX) that positioned in the heart tissue layer at a 55 mm separated distance of TX and RX. In addition, the power transfer efficiency has been demonstrated by a measurement setup using pork muscle, which obtained a good match of the transmission coefficient between the simulation and experiment results. Consequently, for 1 W of output power from the midfield transmitter, the received energy could be achieved more than 5.6 mW via the implantable antenna at a subwavelength distance.
Comparative Evaluation of Quality of Obturation using Endoflas and Endoflas Powder with Aloevera Gel as Obturating Materials in Primary Mandibular Molars: A Double Blinded Randomized Controlled Trial

Paediatric endodontics is an evolving stream in paediatric dentistry and so is the materials used as obturating agents in primary teeth. A paradigm-shift change has been witnessed in the genre of obturating agents where more of natural plant products are preferred to be used as obturating materials in primary teeth due to its enriched biological and biocompatible properties.The aim of the present study is to comparatively evaluate the quality of obturation using Endoflas and Endoflas powder with Aloevera gel as an obturating material in primary mandibular molars.A double-blinded randomized clinical trial was conducted for recruiting participants in an age group of 4-9 years old requiring endodontic treatment (i.e; pulpectomy) in primary mandibular molars. Post-treatment, an immediate intra-oral periapical radiograph (IOPAR) was taken for assessment of the quality of obturation. Obturation quality was assessed using the modified Coll and Sadrians criteria. Highest percentage of optimal fillings were observed in endoflas group (15/75%) followed by endoflas powder with aloe vera gel group (10/50%) while increased percentage of overfilling was found in endoflas powder mixed with aloe vera gel (7/35%) followed by endoflas group (4/20%). In case of underfilling quality of obturation, more of underfilled cases were found in endoflas powder mixed with aloe vera gel (3/15%) while only one underfilled case was seen in endoflas group. The difference in the quality of obturation was found to be statistically significant (p= 0.019). Endoflas powder with aloe vera gel can be used as an alternative and suitable obturating agent in primary teeth due to its wide repertoire of medicinal properties which are highly beneficial in alleviating the persistent periapical infection in primary teeth.
Effect of Gold Nanocomposite on the Cytotoxicity of Human Cancer Cell Lines

Gold nanoparticles (AuNPs) have a biological property that have attracted significant attention in this decade, especially in the field of biomedical application with great therapeutic potential. Additionally, the chitosan molecule appears to be a suitable polymeric complex used in this field. The aim of the current study is to explores the cytotoxicity and the IC50 value of Au-doped chitosan-poly (vinyl alcohol) (Cs/PVA/Au) nanocomposite that was developed by gamma irradiation with promising anticancer activity. The anti-cancer activity of the prepared nanocomposites was demonstrated in human liver cancer cell line HEPG2 and MCF7 breast cancer cell lines. It has significant effects against both cancer cell line. Concluding that, the future nanomedicine will be impacted greatly by the collaboration of biomedical research and developing nanoparticle therapy in the right directions, which will improve the outcome of cancer patients. It is expected that the next decade will reveal real potential for metal nanoparticles to cross the regulatory barrier into clinical use as effective therapeutics.
How can neuroscience contribute to the science of intergenerational sustainability

Intergenerational sustainability is an existential problem for humans, and coping with this issue requires large-scale cooperation extended across generations. However, recent empirical evidence suggests that peopleâ€™s concern for future generations is typically low, which is rooted from humanâ€™s cognitive biases (e.g., temporal discounting and bounded empathy) and possibly exacerbated by modern social systems depreciating future generationsâ€™ rights and voices. To achieve sustainable society, we need to design and implement novel social institutions that leverage our concern for future generations. In this paper, we discuss how neuroscience can tackle this fundamental challenge in collaboration with other disciplines. We review psychological factors and neural substrates that may underlie decision-making regarding intergenerational sustainability. We also propose empirical approaches to study neural bases of intergenerationally-sustainable decision-making. Notably, neuroimaging research has potential to unveil â€œhiddenâ€ neurobiological processes that are difficult to identify by behavioral observations alone. In addition, neural data can be used to predict real-world outcomes, which complements behavioral and self-report measures that may not always reflect true motives behind decisions. Understanding the neurocognitive mechanisms would provide insights into effective institutions that promote concern for future generations. We prospect that future neuroscience research will accumulate evidence from both laboratory and field experiments, thereby contributing to policy making and the transformation toward sustainable society.
COMPUTER SIMULATION FOR SOLVING A SYSTEM OF MULTIDIMENSIONAL DIFFERENTIAL EQUATIONS OF PROBABILITY DENSITIES FOR RECOGNIZING SILVER NANOPARTICLES ON TEXTILE MATERIALS

Идентификация наночастиц на поверхности текстильных материалов представляет собой актуальную и достаточно сложную задачу. В статье предложен метод повышения точности и быстродействия распознавания наночастиц серебра на волокнистых материалах. При этом учитывалась поляризации лазерного излучения вдоль и поперек волокон по всему диапазону рамановского спектра с анализом последовательно и по порядку одновременно двух пиков по X – поперек и по Y – вдоль волокон. Представлены результаты составления и решения системы многомерных дифференциальных корреляционных уравнений плотностей вероятностей в векторно-матричной форме при распознавании наночастиц коллоидного серебра на текстильных материалах. При этом аналитическое решение получается автоматически. Приведено описание разработанной программы в математическом редакторе MathCad Edition 15, позволяющей получать графическую оценку пересечения эллипсов распределения пиков рамановских спектров, вычислять вероятность их пересечения. Предлагаемый авторами в работе метод позволяет при решении системы с использованием нелинейных квадратических и дифференциальных по XY уравнений плотностей вероятностей эллипсов распределения пиков рамановских спектрограмм текстильных материалов с наночастицами серебра и без них получить очень высокую точность p и pAg до 10 -17.
Multi-Turn Video Question Generation via Reinforced Multi-Choice Attention Network

Video question generation is a challenging task in visual information retrieval, which generates questions given a sequence of video frames. The existing methods mainly tackle the problem of single-turn video question generation, but single-turn conversation usually can’t meet the needs of video information acquisition. In this paper, we propose a new framework for single-turn VQG, which introduces attention mechanism to process inference of dialog history. And we introduce selection mechanism to choose from the candidate questions generated by each round of dialog history. In the framework, we leverage a recent video question answering model to predict the answer to the generated question and adopt the answer quality as rewards to fine-tune our model based on a reinforced learning mechanism. We also introduce a new task of multi-turn video question generation (M-VQG), which is generating multiple questions based on dialog history and video information to build conversation step by step. Our method achieves the state-of-the-art performance of the single-turn VQG task on two large-scale datasets, YouTube-Clips and TACoS-MultiLevel, and provides a baseline approach for M-VQG task.
Single-Point Array Response Control: An Efficient Beampattern Synthesis Approach Based on the Maximum Magnitude Response Principle

This article considers the beampattern synthesis problem, and a single-point array response control (SPARC) algorithm is presented to adjust the array response at an arbitrary direction. Based on the theoretical analysis, it is found that the adaptive array theory-based optimal weight vector is a linear combination of the signal steering vector and the interference steering vector multiplied by a complex factor, and that the complex factor determines the array response at the interference direction. Inspired by this observation, the SPARC algorithm is presented to achieve quiescent pattern control. In contrast to other array response control-based pattern synthesis techniques, the SPARC algorithm obtains a deterministic expression of the complex factor after maximizing the magnitude response at the main-beam direction, and the closed-form expression of the designed weight vector is devised. Because of different pattern constraints in focused and shaped beampatterns, two low-complexity pattern synthesis schemes are presented employing the SPARC algorithm in an iterative manner. Moreover, the parameter selection principle of the SPARC and its comparison with other pattern synthesis methods are discussed. Simulation results show the effectiveness of the SPARC algorithm and the efficiency of the beampattern synthesis schemes.
Spectral fingerprints or spectral tilt? Evidence for distinct oscillatory signatures of memory formation

Decreases in low frequency power (2-30 Hz) alongside high frequency power increases (>40 Hz) have been demonstrated to predict successful memory formation. Parsimoniously this change in the frequency spectrum can be explained by one factor, a change in the tilt of the power spectrum (from steep to flat) indicating engaged brain regions. A competing view is that the change in the power spectrum contains several distinct brain oscillatory fingerprints, each serving different computations. Here, we contrast these two theories in a parallel MEG-intracranial EEG study where healthy participants and epilepsy patients, respectively, studied either familiar verbal material, or unfamiliar faces. We investigated whether modulations in specific frequency bands can be dissociated in time, space and by experimental manipulation. Both, MEG and iEEG data, show that decreases in alpha/beta power specifically predicted the encoding of words, but not faces, whereas increases in gamma power and decreases in theta power predicted memory formation irrespective of material. Critically, these different oscillatory signatures of memory encoding were evident in different brain regions. Moreover, high frequency gamma power increases occurred significantly earlier compared to low frequency theta power decreases. These results speak against a “spectral tilt” and demonstrate that brain oscillations in different frequency bands serve different functions for memory encoding.
Trajectory Penetration Characterization for Efficient Vehicle Selection in HD Map Crowdsourcing

In this article, we investigate the worker (i.e., vehicle) selection problem in vehicle-based crowdsourcing (VBC), where vehicles in a specific area are recruited by the crowdsourcing platform to collect geographical information in real driving scenarios for autonomous driving. Given a limited recruitment budget, we formulate a cumulative platform utility maximization problem (CMP) to obtain the optimal worker set. The CMP is unsolvable directly as the platform has no prior information of workers at the initial stage (also known as “cold start”) and the cost of collecting all workers’ information is prohibitive. To solve the problem, we first conduct a comprehensive data analytics on two real-world vehicle traces and obtain two crucial observations: 1) trajectory of individual vehicle is highly uncertain that it is difficult to make accurate prediction and 2) the overall distribution of vehicular trajectory penetration (measured by collection quantity and coverage) has a diurnal pattern and varies with weekly periodicity. Inspired by the insights, we propose the performance transfer-based online worker selection (POSE) scheme, which works independently from trajectory prediction with two components, i.e., transfer learning-based performance estimation and online worker selection (OWS). Based on the diurnal pattern, the former component collects a short-period trajectory penetration data of vehicles for model fitting, which can output a specific numerical distribution. With the fitting model, we can identify and select vehicles with high trajectory penetration at the initial stage to cope with the “cold start” problem. Then, we map the worker selection problem into a multiarmed bandit problem and develop upper confidence bound-based approach to solve it. Extensive trace-driven simulations are carried out and the results demonstrate the efficiency of POSE in terms of cumulative platform utility.
The edible katydid Ruspolia differens is a selective feeder on the inflorescences and leaves of grass species

Ruspolia differens (Serville) (Orthoptera: Tettigoniidae, Conocephalinae) (its common names including ‘African edible bush‐cricket’, ‘edible grasshopper’, and ‘nsenene’) is an important source of food for humans in East Africa, but its ecology and biology are poorly understood. We explored the host plants of R. differens with a series of no‐choice and multiple‐choice laboratory experiments using 18 local common grass and sedge species in Uganda. In no‐choice experiments, the degree of acceptance differed significantly among the studied plant species, but in only three species were leaves rejected and in one species were inflorescences rejected. The pattern of acceptance among plant species was different in the local vs. swarming populations. Leaves were generally more accepted by the local population, whereas inflorescences were generally more accepted by the swarming population. Both leaves and inflorescences were more readily accepted by males than by females. According to the multiple‐choice experiments, R. differens preferred inflorescences over leaves. Our results demonstrate that R. differens is a facultatively oligophagous grass‐specialist, which has a clear preference for certain grass or sedge species (especially inflorescences), but it accepts many host plants if the preferred ones are not available. To preserve viable natural populations of R. differens in East Africa in the long term, our results draw special attention to the availability of grasslands where accepted and preferred host plants are available year‐round.
Increased Concentrations of C-Reactive Protein and IL-6 but not IL-18 Are Independently Associated With Incident Coronary Events in Middle-Aged Men and Women

Objectives— We performed a prospective case–cohort study in initially healthy, middle-aged men and women from the MONICA/KORA Augsburg studies conducted between 1984 and 2002 to assess the role of IL-18 in comparison with IL-6 and CRP in the prediction of incident coronary heart disease (CHD). Methods and Results— Concentrations of IL-18 were measured in 382 case subjects with incident CHD and 1980 noncases. Mean follow-up was 11 years. Baseline concentrations of IL-18 were slightly higher in cases than in noncases (172.4 [1.0] versus 161.3 [1.0] pg/mL, respectively; P=0.114), but were clearly elevated for C-reactive protein (CRP) and IL-6 in cases compared with noncases. In multivariable analyses, accounting for classical cardiovascular risk factors and inflammatory markers, no statistically significant association was seen between increased concentrations of IL-18 and incident CHD both in men (hazard ratio [HR] and 95% confidence intervals [CIs] comparing extreme tertiles, 1.20; 95% CI, 0.85 to 1.69), a.
Improvements in cardiac segmentation for cross-modality domain adaptation

In medical image computing, the problem of heterogeneous domain shift is quite common and severe, causing many deep convolutional networks to under-perform on various imaging modalities. Retraining the network is diﬃcult since annotating the new domain data is prohibitively expensive, speciﬁcally in medical areas that require expertise. While recent works show approaches to tackle this problem using unsupervised domain adaptation, segmentation modules in such methods can be improved vastly. Our implementation provides a segmentation improvement on the current state-of-the-art framework, Synergistic Image and Feature Adaptation(SIFA). We revisit atrous spatial pyramid pooling while using convolutional features as well as image features for multi-scale object segmentation. We have validated the eﬀectiveness of the improvement on the framework using the challenging application of cross-modality segmentation of cardiac structures. To demonstrate the robustness of the module, extensive experiments have been performed on Long-Axis(MMWHS) cross-modal cardiac segmentation tasks.
Crop productivity and potential

Chapter 3, â€œCrop Productivity and Potential,â€ documents and explains the causal factors for the rapid expansion in crop production in Ethiopia (official estimates show an increase in production of grain crops from 16.1 million metric tons to 30.6 million metric tons from 2007/2008 to 2017/2018). Using data on input use (such as land, improved seeds, fertilizer, and agricultural extension services), the authors estimate the combined effect of increased inputs. The residual, then, is an estimate of the gains in total factor productivity. Further rapid gains in yields seem less likely, though, given the high levels of input use already achieved. They illustrate some of the challenges in crop production, linked to the research and extension system, the limited access to irrigation, and the seed sector.
Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state-of-the-art review

During the last three decades, the water resources engineering field has received a tremendous increase in the development and use of meta-heuristic algorithms like evolutionary algorithms (EA) and swarm intelligence (SI) algorithms for solving various kinds of optimization problems. The efficient design and operation of water resource systems is a challenging task and requires solutions through optimization. Further, real-life water resource management problems may involve several complexities like nonconvex, nonlinear and discontinuous functions, discrete variables, a large number of equality and inequality constraints, and often associated with multi-modal solutions. The objective function is not known analytically, and the conventional methods may face difficulties in finding optimal solutions. The issues lead to the development of various types of heuristic and meta-heuristic algorithms, which proved to be flexible and potential tools for solving several complex water resources problems. This paper provides a review of state-of-the-art methods and their use in planning and management of hydrological and water resources systems. It includes a brief overview of EAs (genetic algorithms, differential evolution, evolutionary strategies, etc.) and SI algorithms (particle swarm optimization, ant colony optimization, etc.), and applications in the areas of water distribution networks, water supply, and wastewater systems, reservoir operation and irrigation systems, watershed management, parameter estimation of hydrological models, urban drainage and sewer networks, and groundwater systems monitoring network design and groundwater remediation. This paper also provides insights, challenges, and need for algorithmic improvements and opportunities for future applications in the water resources field, in the face of rising problem complexities and uncertainties.
Effect of Amyloglucosidase on Properties of Lager Beers Produced from Sorghum Malt and Waxy Grits

A trifactorial experiment with a level of confidence of P < 0.05 was performed to study worts and lagers produced with barley or sorghum malts, refined maize (MZ), or waxy sorghum (WXSOR) grits treated during mashing with or without amyloglucosidase (AMG). Worts were produced from the different malts, type of adjunct, and enzyme treatment combinations following a double-mashing procedure. The diastatic activity levels of the barley and sorghum malts were 119 and 20°Lintner, respectively. The barley malt produced 35% more 12°Plato (°P) wort with higher amounts of free amino nitrogen and total fermentable carbohydrates compared with counterparts produced from sorghum malt. The barley malt wort contained higher amounts of maltose but lower amounts of glucose compared with the sorghum malt wort. The WXSOR grits produced worts with similar yield and properties as did the worts produced from MZ grits. AMG addition led to a 3% increment on wort yield and increased glucose from 9.3 to 21.5 g/L of wort and total fermentable sugar equivalents from 59.2 to 72.6 g/L of wort. After 144 hr of fermentation, lagers from barley malt and treated with AMG contained 1.0 and 0.25% units higher ethanol than did the counterparts produced from sorghum malt and without AMG. For AMG treated beers, nonsignificant differences in ethanol content were observed between samples mashed with barley malt and beers produced from sorghum malt and MZ grits. Results demonstrated that AMG could be used to increase the initial amount of fermentable sugars especially in sorghum mashes.
Giant stem rosettes have strong facilitation effects on alpine plant communities in the tropical Andes

ABSTRACT Background: Giant rosettes constitute one of the most distinctive growth-forms in tropical alpine ecosystems. However, their interactions with other plant species remain unexplored in high Andean páramos. Aims: Quantify the effect of a dominant rosette (Coespeletia timotensis) on soil and microclimatic conditions and relate the impacts to plant community structure. Methods: We analysed topsoil temperatures, soil organic matter (SOM) and plant species cover in areas adjacent to C. timotensis and paired areas outside, at three sites (4250–4360 m a.s.l.) in La Culata National Park, Venezuela. Species richness, total vegetation cover and percent cover of each species near and away from the rosettes were compared. Results: Topsoil temperature amplitudes were lower and SOM greater near C. timotensis stems, compared to areas away from them. C. timotensis had a consistent positive effect on species richness, vegetation cover and the cover of many abundant species (including cushions and herbs). Conclusions: The facilitation effects of C. timotensis on abiotic conditions and community structure indicate they play a key role as nurse plants, pointing to giant rosettes as foundation species for the maintenance of plant diversity in the alpine tropics.