Miracle-dz/UniFAR-FTUs huggingface dataset:
‘Dare to be silent’: Re-conceptualising silence as a positive pedagogical approach in schools

In Western societies, school pedagogies tend to be biased in favour of talk and emphasise the links between talking, thinking and learning. Thus talk is often privileged over silence as the basis for learning activities in classrooms, sustained by theories of learning which afford priority to talk. Such cultural bias towards talk means that by contrast, silence can be perceived negatively and construed as a form of ‘non-participation’. Through a systematic literature review of journal articles relating to silence as a pedagogical approach published between 2000 and 2021, this article reappraises the role and value of silence in school education. Some of the apparent paradoxes of silence as a pedagogical approach, different types and uses of silence in the classroom, cultural dimensions of silence and the relationships between silence, power and critical pedagogy are examined. The pedagogical importance of silence as a participatory approach to learning emerges as a significant point for educators and the paper offers some suggestions for potential applications in classroom practice.
Lossless index coding for indexed colour images

Abstract This paper presents a simple and efficient index coding method to reduce the storage cost for indexed colour images. The method exploits the high degree of similarity among neighbouring indices by using a sorted palette. The indices that were compressed by the colour image quantisation are classified into three categories. Every category employs a different encoding rule to process the indices. To compress the indices of the second category, two optional approaches are provided. Experimental results show that the proposed method can significantly cut down the number of bit rates without incurring extra image degradation.
Geometrically constrained parasitic-aware synthesis of analog ICs

In order to speed up the design process of analog ICs, iterations between different design stages should be avoided as much as possible. More specifically, spins between electrical and physical synthesis should be reduced for this is a very time-consuming task: if circuit performance including layout-induced degradations proves unacceptable, a re-design cycle must be entered, and electrical, physical, or both synthesis processes, would have to be repeated. It is also worth noting that if geometric optimization (e.g., area minimization) is undertaken after electrical synthesis, it may add up as another source of unexpected degradation of the circuit performance due to the impact of the geometric variables (e.g., transistor folds) on the device and the routing parasitic values. This awkward scenario is caused by the complete separation of said electrical and physical synthesis, a design practice commonly followed so far. Parasitic-aware synthesis, consisting in including parasitic estimates to the circuit netlist directly during electrical synthesis, has been proposed as solution. While most of the reported contributions either tackle parasitic-aware synthesis without paying special attention to geometric optimization or approach both issues only partially, this paper addresses the problem in a unified way. In what has been called layout-aware electrical synthesis, a simulation-based optimization algorithm explores the design space with geometric variables constrained to meet certain user-defined goals, which provides reliable estimates of layout-induced parasitics at each iteration, and, thereby, accurate evaluation of the circuit ultimate performance. This technique, demonstrated here through several design examples, requires knowing layout details beforehand; to facilitate this, procedural layout generation is used as physical synthesis approach due to its rapidness and ability to capture analog layout know-how.
Memory-Aware Attentive Control for Community Question Answering With Knowledge-Based Dual Refinement

The question answering system in open domain enables a machine to automatically select and generate the answer for questions posed by humans in a natural language form on the website. Previous approaches seek effective ways of extracting the semantic features between question and answer, but the contextual information effects in semantic matching are still limited by short-term memory. As an alternative, we propose an internal knowledge-based end-to-end model, enhanced by an attentive memory network for both answer selection and answer generation tasks by considering the full advantages of the semantics and multifacts (i.e., timescales, topics, and context). In detail, we design a long-term memory to learn the top- $k$ fine-grained similarity representations, where two memory-aware mechanisms aggregate the series of semantic word-level and sentence-level similarities to support the coarse contextual information. Furthermore, we propose a novel memory refinement mechanism with the two-dimensional of writing heads that offer an efficient approach to multiview selection of the salient word pairs. In the training stage, we adopt the transformer-based transfer learning skill to effectively pretrain the model. Experimentally, we compare the state-of-the-art approaches on four public datasets, the experimental results show that the proposed model achieves competitive performance.
Bloch waves in periodic multi-layered acoustic waveguides

The band spectrum and associated Floquet–Bloch eigensolutions, arising in acoustic and electromagnetic waveguides, which have periodic structure along the guide while remaining of finite width, are found. Homogeneous Dirichlet or Neumann conditions along the guide walls, or an alternation of them, are taken. Importantly, in some cases, a total stop band at zero frequency is identified providing space for low-frequency localized modes; geometric defects in the structured waveguide also create these modes. Numerical and asymptotic techniques identify dispersion curves and trapped modes. Some cases demonstrate maxima and minima of the spectral edges within the Brillouin zone and also allow for ultraslow light or sound. Imaging applications using anomalous dispersion to generate subwavelength resolution are possible and are demonstrated.
Shooting and bouncing rays: calculating the RCS of an arbitrarily shaped cavity

A ray-shooting approach is presented for calculating the interior radar cross section (RCS) from a partially open cavity. In the problem considered, a dense grid of rays is launched into the cavity through the opening. The rays bounce from the cavity walls based on the laws of geometrical optics and eventually exit the cavity via the aperture. The ray-bouncing method is based on tracking a large number of rays launched into the cavity through the opening and determining the geometrical optics field associated with each ray by taking into consideration: (1) the geometrical divergence factor, (2) polarization, and (3) material loading of the cavity walls. A physical optics scheme is then applied to compute the backscattered field from the exit rays. This method is so simple in concept that there is virtually no restriction on the shape or material loading of the cavity. Numerical results obtained by this method are compared with those for the modal analysis for a circular cylinder terminated by a PEC plate. RCS results for an S-bend circular cylinder generated on the Cray X-MP supercomputer show significant RCS reduction. Some of the limitations and possible extensions of this technique are discussed. >
Exploiting Structural Symmetry in Unsymmetric Sparse Symbolic Factorization

This paper shows how to exploit structural symmetry in determining the nonzero structures of the lower and upper triangular factors L and U of an unsymmetric sparse matrix A. Two symmetric reductions of the graphs of L and U are introduced and used to formulate symbolic factorization algorithms. Experimental results demonstrate the effectiveness of these algorithms versus other schemes in the literature.
The role of occupational therapy in substance use

Substance use disorder is a current serious public health problem that negatively affects the client, the family and the community. Substance use has negative effects on the structure and function of the body, restrictions on daily personal activities and participation in life (O’Day, 2009). In addition, individuals with substance use disorder are socially isolated and exposed to stigmatisation. Therefore, it is essential to integrate them into society. Unfortunately, the rate of drug use has been steadily increasing, and intervention programmes remain inadequate. Many existing substance use programmes showed low success rates; approximately 80%–90% of individuals relapse within the first year of their recovery, and 60% were dissatisfied with the services provided and drop out of treatment (Shaffer et al., 2004; Wasmuth et al., 2015). Therefore, the risk of relapse into substance use and the difficulty quitting nature of the addiction requires a multidisciplinary approach (Cruz, 2019). In addition, substance use affects the individual negatively in many respects; in their occupational and social life, there seems to be a need for holistic approaches in addition to many medical approaches. It may be beneficial to consider occupational therapy approaches in recovery from substance use, as substance use negatively affects occupational participation (Stone, 2017). The aim of the present study was
REGIME-SWITCHING AUTOREGRESSIVE COEFFICIENTS AND THE ASYMPTOTICS FOR UNIT ROOT TESTS

Most of the asymptotic results for Markov regime-switching models with possible unit roots are based on specifications implying that the number of regime switches grows to infinity as the sample size increases. Conversely, in this note we derive some new asymptotic results for the case of Markov regime switches that are infrequent in the sense that their number is bounded in probability, even asymptotically. This is achieved by (inversely) relating the probability of regime switching to the sample size. The proposed asymptotic theory is applied to a well-known stochastic unit root model, where the dynamics of the observed variable switches between a unit root regime and a stationary regime.
Bioengineering of Soybean Oil and Its Impact on Agronomic Traits

Soybean is a major oil crop and is also a dominant source of nutritional protein. The 20% seed oil content (SOC) of soybean is much lower than that in most oil crops and the fatty acid composition of its native oil cannot meet the specifications for some applications in the food and industrial sectors. Considerable effort has been expended on soybean bioengineering to tailor fatty acid profiles and improve SOC. Although significant advancements have been made, such as the creation of high-oleic acid soybean oil and high-SOC soybean, those genetic modifications have some negative impacts on soybean production, for instance, impaired germination or low protein content. In this review, we focus on recent advances in the bioengineering of soybean oil and its effects on agronomic traits.
On the Propagation of the Electromagnetic Waves in an Inhomogeneous Atmosphere

We obtained the rigorous solutions of Maxwell's equations in an in homogeneous medium, which led to descriptions of the electromagnetic waves from a dipole source in the medium, where the inductive capacity is an arbitrary function of the distance from the center of the earth sphere. The first approximation of these descriptions give more abundant and complete results than those which have as yet been obtained by Fermat's principle of geometrical optics and lead to a satisfactory theory for the so-called superrefracted propagation in a layer called the duct. 1)2)3)
The current-modified nonlinear Schrödinger equation

By comparison with both experimental and numerical data, Dysthe's (1979) O(ε4) modified nonlinear Schrödinger; equation has been shown to model the evolution of a slowly varying wavetrain well (here ε is the wave steepness). In this work, we extend the equation to include a prescribed, large-scale, O(ε2) surface current which varies about a mean value. As an introduction, a heuristic derivation of the O(ε3) current-modified equation, used by Bakhanov et al. (1996), is given, before a more formal approach is used to derive the O(ε4) equation. Numerical solutions of the new equations are compared in one horizontal dimension with those from a fully nonlinear solver for velocity potential in the specific case of a sinusoidal surface current, such as may be due to an underlying internal wave. The comparisons are encouraging, especially for the O(ε4) equation.
A Neural Network Model Using Pain Score Patterns to Predict the Need for Outpatient Opioid Refills Following Ambulatory Surgery: Algorithm Development and Validation

Background Expansion of clinical guidance tools is crucial to identify patients at risk of requiring an opioid refill after outpatient surgery. Objective The objective of this study was to develop machine learning algorithms incorporating pain and opioid features to predict the need for outpatient opioid refills following ambulatory surgery. Methods Neural networks, regression, random forest, and a support vector machine were used to evaluate the data set. For each model, oversampling and undersampling techniques were implemented to balance the data set. Hyperparameter tuning based on k-fold cross-validation was performed, and feature importance was ranked based on a Shapley Additive Explanations (SHAP) explainer model. To assess performance, we calculated the average area under the receiver operating characteristics curve (AUC), F1-score, sensitivity, and specificity for each model. Results There were 1333 patients, of whom 144 (10.8%) refilled their opioid prescription within 2 weeks after outpatient surgery. The average AUC calculated from k-fold cross-validation was 0.71 for the neural network model. When the model was validated on the test set, the AUC was 0.75. The features with the highest impact on model output were performance of a regional nerve block, postanesthesia care unit maximum pain score, postanesthesia care unit median pain score, active smoking history, and total perioperative opioid consumption. Conclusions Applying machine learning algorithms allows providers to better predict outcomes that require specialized health care resources such as transitional pain clinics. This model can aid as a clinical decision support for early identification of at-risk patients who may benefit from transitional pain clinic care perioperatively in ambulatory surgery.
Carbon Pricing and its Monitoring System as a State Revenue

This study aims to examine the implementation of carbon pricing collection in Indonesia. This study also provides a review of the potentials for excise revenue, income tax (PPh), and value-added tax (VAT) from carbon emissions and an effort to build integrated supervision among stakeholders. This research uses descriptive qualitative method. The research data were obtained from a review of documents and regulations regarding carbon emissions. The research was conducted by comparing the best practices in the worldwide with the Indonesia government policy on carbon taxes. The results show a great potential for the country to apply carbon pricing collection through carbon taxes, carbon permits, or cap-and-trade (emission trading system). On the other hand, the collection of a tax on carbon or cap-and-trade schemes requires complex oversight. Therefore, a synergy among institutions is needed, such as the Directorate General of Taxes (DJP), the Financial Services Authority (OJK), and the Ministry of Environment and Forestry (KLHK). The digitization of the system and the establishment of a data warehouse are the main strategies to support the performance of the task force involving the three institutions in implementing joint audits. The recommendation from this research is that Indonesia should immediately implement the carbon pricing policy and form joint audits in monitoring its implementation. This research encourages further research on carbon pricing, carbon tax, PPh and VAT, as well as research on the economic impact of the policy at each level of carbon emission rates.
Abnormal coercivity dependence on the diameter of Co nanowires in anodic aluminium oxide templates

Co nanowire arrays with diameters of 20 and 50 nm are fabricated into anodic aluminium oxide templates by the ac electrodeposition technique. The coercivity of nanowire arrays with diameter of 50 nm is 1990 Oe, whereas that of nanowire arrays with diameter of 20 nm is only 925 Oe. This is quite different from earlier experimental results of magnetic metal nanowire arrays, which exhibit larger coercivity at a diameter of 20 nm. X-ray diffraction patterns suggest that the anomalous increase in coercivity is related to the appearance of the (0 0 2) and (1 0 1) reflections. Micromagnetism simulation reveals the mechanism of how the (0 0 2) texture of nanowires influences the magnetic properties of Co nanowire arrays.
Energy-Efficient Hierarchical Resource Allocation in Uplink–Downlink Decoupled NOMA HetNets

The dense deployment of small cell networks is a key feature of next-generation mobile networks aimed at providing the necessary capacity increase. It is noteworthy that small cell networks employ high-capacity backhaul links on millimeter-wave bands to develop multi-hop topologies in order to mitigate data transmission costs. The current static backhaul infrastructures cannot control severe fluctuating network traffic. To resolve this problem, this paper proposed a novel adaptive backhaul topology with the ability to adapt to different traffic patterns. Based on the graph theory, the adaptive system dynamically allows changes to the hybrid millimeter-wave backhaul architecture, and it also provides the possibility of effective channel allocation to each backhaul link to meet capacity and QoS demands. Also, regarding the importance of green networking in integrated-access-and-backhaul networks we proposed a dynamic optimization model which minimizes the overall energy consumption of UL/DL Decoupled NOMA heterogeneous networks in addition to providing the essential coverage and capacity. The proposed model optimizes user association/power utilization and presents an effective modular and scalable framework for analytical technology-oriented modeling of integrated multi-hop backhauls. The numerical results proved that the joint power optimization and hybrid backhaul architecture can increase the total network throughput by 18 percent compared to the current optimized static architectures. It can also reduce the energy consumption level by 30 percent, and enhance users’ quality satisfaction by 24.5 percent with respect to user distribution patterns.
In Vitro Anticancer Activity of Novel Ciprofloxacin Mannich Base in Lung Adenocarcinoma and High-Grade Serous Ovarian Cancer Cell Lines via Attenuating MAPK Signaling Pathway

Novel drugs are desperately needed in order to combat a significant challenge due to chemo-therapeutic resistance and bad prognosis. This research aimed to assess the anticancer activity of a newly synthesized ciprofloxacin Mannich base (CMB) on ovarian cancer (OVCAR-3) and lung cancer (A-549) cell lines and to investigate probable involved molecular mechanisms. The cytotoxic and pro-apoptotic impact of CMB on both cell lines was investigated using MTT assay, Annexin V assay, and cell cycle analysis, as well as caspase-3 activation. Western blotting was carried out to evaluate downstream targets of the MAPK pathway, while qRT PCR was used to evaluate the gene expression pattern of the p53/Bax/Bcl2 pathway. CMB treatment showed significantly reduced cell proliferation in both OVCAR-3 and A-549 cells with half maximum inhibitory concentration (IC50) = 11.60 and 16.22 µg/mL, respectively. CMB also induced apoptosis, S phase cell cycle arrest, and up-regulated expression of p53, p21, and Bax while down-regulated Bcl2 expression. CMB also halted cell proliferation by deactivating the MAPK pathway. In conclusion, CMB may be regarded as a potential antiproliferative agent for lung and ovarian cancers due to anti-proliferative and pro-apoptotic actions via inhibition of the MAPK pathway and p53/Bax/Bcl2.
Mechanochemical Synthesis of TiO2-CeO2 Mixed Oxides Utilized as a Screen-Printed Sensing Material for Oxygen Sensor

TiO2 and CeO2 are well known as oxygen sensing materials. Despite high sensitivity, the actual utilization of these materials in gas detection remains limited. Research conducted over the last two decades has revealed synergistic effects of TiO2-CeO2 mixed oxides that have the potential to improve some aspects of oxygen monitoring. However, there are no studies on the sensing properties of the TiO2-CeO2 obtained by mechanochemical treatment. We have tested the applicability of the mechanochemically treated TiO2-CeO2 for oxygen detection and presented the results in this study. The sensing layers are prepared as a porous structure by screen printing a thick film on a commercial substrate. The obtained structures were exposed to various O2 concentrations. The results of electrical measurements showed that TiO2-CeO2 films have a significantly lower resistance than pure oxide films. Mixtures of composition TiO2:CeO2 = 0.8:0.2, ground for 100 min, have the lowest electrical resistance among the tested materials. Mixtures of composition TiO2:CeO2 = 0.5:0.5 and ground for 100 min proved to be the most sensitive. The operating temperature can be as low as 320 °C, which places this sensor in the class of semiconductor sensors working at relatively lower temperatures.
Study the Effect of Relative Energy Deficiency on Physiological and Physical Variables in Professional Women Athletes: A Randomized Controlled Trial

Energy deficits are often observed in athletes, especially in female athletes, due to the high expenditure of sport and strict diets. Low energy availability can cause serious health problems and affect sport performance. The aim of this study was to evaluate the effects of different personalized dietary plans on physiological and physical factors related to energy deficit syndrome in female professional handball players. Twenty-one professional female handball players, aged 22 ± 4 years, 172.0 ± 5.4 cm and 68.4 ± 6.7 kg, divided into three groups (FD: free diet; MD: Mediterranean diet; and AD: high antioxidant diet), participated in this 12-week randomized controlled trial. Energy expenditure through indirect calorimetry, energy availability, 7 day dietary intake analysis, blood pressure, cholesterol, menstrual function, body composition by both anthropometry and bioelectrical impedance, and strength performance were assessed. All participants showed low energy availability (<30 kcal/lean mass per day); despite this, all had eumenorrhea. Significant improvements were found after the intervention in all components of body composition (p < 0.05). In the remaining variables, despite slight improvements, none were significant neither over time nor between the different groups. Low energy availability has been observed in all professional female handball players, which may lead to serious consequences. A longer period of intervention is required to assess the differences between diets and improvements in other parameters.