Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of soft water attack on Portland and natural zeolite blended cements

The durability of concrete infrastructure is related to the properties of the applied concrete and the effects of the aggressive external environment on it. When concrete is directly exposed to soft water, the leaching of calcium ions from hardened cement or cement-based pastes occurs, causing reduction in strength and further deterioration of the concrete structure. This paper presents the experimental results of soft water attack effects on phase composition and compressive strength of blended cement pastes, when cement is replaced with 0%, 10%, 20% and 30% of natural zeolite. In order to simulate soft water attack in laboratory conditions, paste specimens were exposed to leaching in deionised water up to 180 days. The evaluation of the changes in phase composition (ettringite, portlandite, calcium silicate hydrate gel) due to calcium ion leaching was made based on X-ray diffraction analysis, Fourier transform infrared spectroscopy and paste compressive strength tests. The presence of portlandite and ettringite after 180 days of exposure to deionised water indicates that leaching did not influence the stability of the hydration products in blended cement pastes. Moreover, blended cement paste with 10% of natural zeolite had a higher compressive strength than the reference one.
Associations between countertransference reactions towards patients with borderline personality disorder and therapist experience levels and mentalization ability

Abstract Objective This exploratory study locates countertransference as a pan-theoretical concept, comprising of thoughts, feelings, and behaviors expressed or experienced by therapists toward their patients. It aims to understand the patterns of countertransference experienced in working with borderline personality disorder. Associations between countertransference reactions and therapist-related variables of experience and mentalization ability are also examined. Method Psychotherapists (n = 117) completed the Therapist Response Questionnaire to assess patterns of countertransference experienced with a representative patient diagnosed with borderline personality disorder. They also completed a measure of mentalization ability that examined self-related mentalization, other-related mentalization, and motivation to mentalize. Results The profile of responses across eight countertransference dimensions is discussed, with the most strongly endorsed reactions being positive/satisfying, parental/protective, and helpless/inadequate. More experienced therapists reported less negative countertransference reactions in select dimensions. Therapists’ self-reported ability to reflect on and understand their own mental states was negatively correlated with a range of difficult countertransference experiences. There were few associations between their ability to make sense of others’ mental states, the motivation to mentalize, and the strength of their countertransference reactions. Conclusion The implications for countertransference management as well as therapist training and development are highlighted.
Improve Management of Mental Well-being and Empathy in Chinese Medical Students: A Randomized Controlled Study

Background: The Chinese medical students suffer from high prevalent mental health-related issues and low empathy. Effective strategies to improve the situations are lacking. This study aims to investigate the efficacy of the well-designed interventional courses to enhance the mental health and empathy of the senior Chinese medical students. Methods: A total of 146 3rd and 4th year medical students were randomized to intervention group (n=74) and control group (n=72). A pilot study including 5 pre-clinical students and 5 interns was first carried out to determine the themes and contents of the interventional courses. The designed courses were delivered in the intervention group once a month for three times, while the control group had no specific intervention. Five self-assessment questionnaires including the General Self-Efficacy (GSE), Medical Outcomes Study Short Form 8 (SF-8), Patient Health Questionnaire-9 (PHQ-9), Maslach Burnout Inventory (MBI), and Jefferson Scale of Empathy-Health Care Provider Student version (JSE-HPS) that separately evaluated the level of self-efficacy (SE), quality of life (QoL), depression, burnout, and empathy were completed by the students before and one month after the courses. Qualitative data were collected via e-mails two year after the intervention.Results: Compared to the control group, the interventional group showed significantly higher scores of the empathy (111.0 [IQR 102.0, 118.0] vs 106.0 [IQR 93.0, 111.5]; P = .01) and QoL (32.0 [IQR 28.0, 35.0] vs 29.5 [IQR 26.0, 34.0]; P = .04). The rate of depression was significantly decreased in the interventional group compared to that in the control group (13.5% vs 29.2%; Chi-square test, P = .02). However, no significant differences of self-efficacy (25.6 ± 4.8 vs 24.3 ± 6.3; P = .16) and burnout (27.0% vs 34.7%; Chi-square test, P = .31) were observed between the two groups.Conclusions: The well-designed interventional courses had a positive impact on the mental well-being and empathy in senior Chinese medical students, which may help provide novel information for incorporation into the medical school curriculum.
A Random Forests Approach to Predicting Clean Energy Stock Prices

Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices of clean energy companies. While the existing literature on forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price direction is more successful than predicting actual stock prices. This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds. Some well-known technical indicators are used as features. Decision tree bagging and random forests predictions of stock price direction are more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Tree bagging and random forests are easy to understand and estimate and are useful methods for forecasting the stock price direction of clean energy stocks.
Scenario Analyses of Exhaust Emissions Reduction through the Introduction of Electric Vehicles into the City

In recent years, policymakers of urban agglomerations in various regions of the world have been striving to reduce environmental pollution from harmful exhaust and noise emissions. Restrictions on conventional vehicles entering the inner city are being introduced and the introduction of low-emission measures, including electric ones, is being promoted. This paper presents a method for scenario analysis applied to study the reduction of exhaust emissions by introducing electric vehicles in a selected city. The original scenario analyses relating to real problems faced by contemporary metropolitan areas are based on the VISUM tool (PTV Headquarters for Europe: PTV Planung Transport Verkehr AG, 76131 Karlsruhe, Germany). For the case study, the transport model of the city of Bielsko-Biala (Poland) was used to conduct experiments with different forms of participation of electric vehicles on the one hand and traffic restrictions for high emission vehicles on the other hand. Scenario analyses were conducted for various constraint options including inbound, outbound, and through traffic. Travel time for specific transport relations and the volume of harmful emissions were used as criteria for evaluating scenarios of limited accessibility to city zones for selected types of vehicles. The comparative analyses carried out showed that the introduction of electric vehicles in the inner city resulted in a significant reduction in the emission of harmful exhaust compounds and, consequently, in an increase in the area of clean air in the city. The case study and its results provide some valuable insights and may guide decision-makers in their actions to introduce both driving ban restrictions for high-emission vehicles and incentives for the use of electric vehicles for city residents.
The impact of COVID-19 on pharmacy education: Perspectives of Nigerian undergraduate pharmacy students

Context: Globally, every school and college of pharmacy is challenged to respond to the impact of COVID-19 on pharmacy education and must find ways to continue to communicate with colleagues and educate their students. Aims: To describe the impact of COVID-19 on undergraduate pharmacy students’ education and learning in Nigeria. Methods: This was a cross-sectional study with an online questionnaire. A validated questionnaire was used to collect data from the students. Associations between learning and respondents’ characteristics were determined with either chi-square test or fisher’s exact test at p<0.05. Results: Most of the students (91.0%, n=343/377) were not taught during COVID-19 school closures in Nigeria. The school closures had a significant effect on the academic lives of most students (99.2%, p<0.05). Extension of graduation year was the highest (24.2%) academic impact. High cost of internet data (28.6%) was the most common challenge with online learning among the students. A marked proportion (70.9%, (p<0.05) of the students preferred a combination of online and face-to-face learning methods after the pandemic. Conclusions: COVID-19 has impacted pharmacy education in Nigeria due to the discontinuity of learning, a lack of effective online learning, and an extension of students’ expected year of graduation. A combination of online and face-to-face learning methods was perceived to be better after the pandemic. This finding highlights the need for Nigerian pharmacy educators to tap into the opportunities presented by the pandemic to improve pharmacy education and ultimately pharmacy practice in the country.
Uso de una aplicación móvil para aumentar la motivación del alumnado en formulación y nomenclatura química

El aprendizaje de la formulacion y nomenclatura quimica a nivel universitario resulta cada vez menos estimulante para los estudiantes de primer curso, que ademas poseen marcadas deficiencias tanto en el nivel inicial de conocimientos como en las destrezas asociadas a dicha materia. Esto hecho trae como consecuencia problemas de motivacion y de interes por la asignatura y, mas concretamente, porlas clases de seminarios. Con el fin de mejorar la participacion en clase de seminarios de formulacion, y de establecer las bases de un nuevo sistema de evaluacion, se ha propuesto una iniciativa que incluye el uso de una aplicacion movil gratuita (Socrative®) para la realizacion de cuestionarios iniciales y finales a la practica docente. Este trabajo detalla la nueva metodologia seguida asi como los resultados obtenidos. Los resultados ponen de manifiesto como el uso de la aplicacion movil mejora las calificacionesen las cuestiones de formulacion y nomenclatura, la asistencia a clase, asi como incrementa el grado de satisfaccion de los alumnos por este tipo de docencia.
How to Choose the Correct Branding? Application of Fuzzy VIKOR on Consumers Purchasing the Luxury Brand

This paper applies the fuzzy VIKOR method to the luxury brand decision-making behavior. The method adds the largest group utility with the smallest regret of alternatives on the basis of fuzzy theory to solve the multi-criteria decision-making problem. The research process and results show that the opinions of decision-making experts can be the epitome of consumers' buying behavior, brand managers can also make more accurate marketing strategies, and they will be more constructive in the use of future marketing communication budgets.
Optimization of the Post-Process Heat Treatment of Inconel 718 Superalloy Fabricated by Laser Powder Bed Fusion Process

In the present study, multi-objective optimization is employed to develop the optimum heat treatments that can achieve both high-mechanical performance and non-distinctive crystallographic texture of 3D printed Inconel 718 (IN718) fabricated by laser powder bed fusion (LPBF). Heat treatments including homogenization at different soaking times (2, 2.5, 3, 3.5 and 4 h) at 1080 °C, followed by a 1 h solution treatment at 980 °C and the standard aging have been employed. 2.5 h is found to be the homogenization treatment threshold after which there is a depletion of hardening precipitate constituents (Nb and Ti) from the γ-matrix. However, a significant number of columnar grains with a high fraction (37.8%) of low-angle grain boundaries (LAGBs) have still been retained after the 2.5 h homogenization treatment. After a 4 h homogenization treatment, a fully recrystallized IN718 with a high fraction of annealing twins (87.1%) is obtained. 2.5 and 4 h homogenization treatments result in tensile properties exceeding those of the wrought IN718 at both RT and 650 °C. However, considering the texture requirements, it is found that the 4 h homogenization treatment offers the optimum treatment, which can be used to produce IN718 components offering a balanced combination of high mechanical properties and adequate microstructural isotropy.
Retroperitoneal Soft-Tissue Sarcoma: Retrospective Study from a Cancer Hospital in Pakistan

Objectives: To determine the clinicopathological aspects and factors determining the recurrence and 5-year survival of patients diagnosed with retroperitoneal soft-tissue sarcoma in a Pakistani cohort of patients. Methods: This retrospective descriptive study was conducted at the Department of Surgical Oncology, Shaukat Khanum Memorial Hospital & Research Centre (SKMCH&RC), Lahore, Pakistan. Patients who underwent surgery for retroperitoneal sarcoma from January 2011 to December 2015 were included and followed for 5 years (December 2020). A total of 54 patients was included in this study. Results: Fifty-seven percent (n=31) cases were male with a median age at the time of presentation 43 (30-60) years. Well-differentiated liposarcoma was the most common variant. Non-compartmental resection was done in 40 (74%) of the cases. The overall 5-year survival was 74% with median Disease-Free Survival (DFS) of 14 (6-48) months. Recurrence was seen in 28 cases (52%). Conclusions: In the Pakistani cohort of patients. retroperitoneal soft-tissue sarcoma was more commonly seen in males at the median age of 43 years. The most common presentations were abdominal pain and mass. Well-differentiated liposarcoma was the most common histological variant. The overall 5-year survival was 74% that was affected by histological variant and grade. Recurrence was affected by histological variant and grade, margin positivity, and use of neoadjuvant therapy.
Superpixel-Based Multiple Statistical Feature Extraction Method for Classification of Hyperspectral Images

To improve the classification accuracies of hyperspectral images (HSIs), especially when using a limited number of training samples, a novel superpixel-based multiple statistical feature extraction (SPMSFE) method is proposed in this article. For each dimension-reduced pixel obtained by maximum noise fraction (MNF), the most similar superpixel-based neighbors of different sizes are first identified based on the spatial structures of the HSIs to exploit contextual spatial information accurately. Then, multiple statistical features, including the mean, covariance descriptor, and the Gaussian feature, are extracted for the set of superpixel-based neighbors to fully explore the spatial geometry information, tight correlations between different spectral bands, and spatial–spectral variations from different perspectives, respectively. In addition, these three statistical features of the pixels share the same size and can be utilized for uniform classification without any dimensionality obstacles even though the sizes of the superpixel-based neighbors for different pixels may be different. Next, we construct multiple kernels to map these multiple statistical features in the Euclidean and Riemannian manifold spaces to a uniform Hilbert space and embed them into a multitask kernelized sparse representation classification (MTKSRC) model. The constructed MTKSRC model provides a natural method to effectively fuse the multiple statistical features for excellent classification performance and robustness, especially when using limited numbers of training samples. The experimental results for three widely used HSI data sets demonstrate that the classification accuracy of the proposed SPMSFE method outperforms several latest and state-of-the-art classification methods by a large margin.
Polytopic Event-Triggered Robust Model Predictive Control for Constrained Linear Systems

This paper studies the event-triggered robust model predictive control (MPC) problem for constrained linear systems subject to bounded disturbances. For control computations, explicit MPC reduces online optimizations to simple function evaluations, thus is suitable for resource constrained systems. We propose an event-triggering mechanism (ETM) for an explicit robust model predictive controller such that function evaluations are not needed at every time instant and the ETM itself requires comparably less online computations. In the proposed ETM, we design a contracting polytopic trigger set with respect to system constraints and the explicit robust control law. The resulting event-triggered control scheme ensures robust constraint satisfaction and robust stability for the closed-loop system while avoiding the Zeno-like behavior. Simulation results illustrate the validity of the event-triggered robust control scheme.
Intracranial Inflammatory Myofibroblastic Tumor: A Review of 49 cases

ABSTRACT Inflammatory Myofibroblastic Tumor (IMT) is a rare pathologic entity that was first described in 1973. This lesion is most commonly found in the lungs, but other organs’ involvement has also been reported. Intracranial location of Inflammatory Myofibroblastic Tumor is rare, and the first case was reported in 1980. An intriguing fact about the intracranial IMT is its resemblance with meningioma on clinical presentation and neuroimaging. We came across a case of intracranial Inflammatory Myofibroblastic Tumor (IIMT) in a 27-year-old male who presented with recurrent episodes of seizures and was diagnosed as meningioma on neuroimaging. The lesion did not subside with medical management and kept on progressing in size. The patient had to undergo surgery, and diagnosis of Inflammatory Myofibroblastic Tumor was ascertained on histopathology. This ‘surprise’ diagnosis prompted us to review the literature on all cases of IIMTs reported to date to better understand the entity and its implications. In this review article, we present our observations regarding various studied parameters, including patient profile, clinical presentation, site of involvement, focality of the lesion, special associations, and lines of management of the 49 published cases of IIMTs.
Numerical simulation of fuel dribbling and nozzle wall wetting

The present work describes a numerical methodology and its experimental validation of the flow development inside and outside of the orifices during a pilot injection, dwelt time and the subsequent start of injection cycle. The compressible Navier-Stokes equations are numerically solved in a six-hole injector imposing realistic conditions of the needle valve movement and considering in addition a time-dependent eccentric motion. The valve motion is simulated using the immersed boundary method; this allows for simulations to be performed at zero lift during the dwelt time between successive injections, where the needle remains closed. Moreover, the numerical model utilises a fully compressible two-phase (liquid, vapour) two-component (fuel, air) barotropic model. The air’s motion is simulated with an additional transport equation coupled with the VOF interface capturing method able to resolve the near-nozzle atomisation and the resulting impact of the injected liquid on the oleophilic nozzle wall surfaces. The eccentric needle motion is found to be responsible for the formation of strong swirling flows inside the orifices, which not only contributes to the breakup of the injected liquid jet into ligaments but also to their backwards motion towards the external wall surface of the injector. Model predictions suggest that such nozzle wall wetting phenomena are more pronounced during the closing period of the valve and the re-opening of the nozzle, due to the residual gases trapped inside the nozzle, and which contribute to the poor atomisation of the injected fluid upon re-opening of the needle valve in subsequent injection events.
Graphite-type activated carbon from coconut shell: a natural source for eco-friendly non-volatile storage devices

Carbon from biomass as an active material for supercapacitor electrodes has attracted much interest due to its environmental soundness, abundance, and porous nature. In this context, activated carbon prepared from coconut shells via a simple activation process (water or steam as activation agents) was used as an active material in electrodes for eco-friendly supercapacitors. X-ray diffraction (XRD), Raman spectroscopy, conductivity, scanning electron microscopy (SEM), N2 sorption and thermogravimetry coupled to mass spectrometry (TGA-MS) studies revealed that activated carbon produced by this approach exhibit a graphitic phase, a high surface area, and large pore volume. The energy storage properties of activated carbon electrodes correlate with the morphological and structural properties of the precursor material. In particular, electrodes made of activated carbon exhibiting the largest Brunauer–Emmett–Teller (BET) surface area, i.e. 1998 m2 g−1, showed specific capacitance of 132.3 F g−1 in aqueous electrolyte (1.5 M H2SO4), using expanded graphite sheets as current collector substrates. Remarkably, this sample in a configuration with ionic liquid (1-methyl-1-propy-pyrrolizinium bis(fluorosulfonyl)mide) (MPPyFSI) as electrolyte and a polyethylene separator displayed an outstanding storage capability and energy-power handling capability of 219.4 F g−1 with a specific energy of 92.1 W h kg−1 and power density of 2046.9 W kg−1 at 1 A g−1 and maintains ultra-high values at 30 A g−1 indicating the ability for a broad potential of energy and power related applications. To the best of our knowledge, these values are the highest ever reported for ionic liquid-based supercapacitors with activated carbon obtained from the biomass of coconut shells.
Estimation of the central-axis-reference percent depth dose in a water phantom using artificial intelligence

ABSTRACT The water phantom is used as a standard device for the calibration of measuring instruments used in radiation therapy. To carry out this calibration, it is essential to characterize the distribution of the percent depth dose (PDD) along the central reference axis, since this is where the instruments to be calibrated are located. The PDD depends on some magnitudes, such as the size of the field in the phantom, the depth of the central reference axis, the source-to-surface distance (SDD) and the radiation energy used [23]. A phantom is a fundamental element for the training of cancer specialists and medical physicists, and can be used to propose more effective procedures for the clinical radiation treatment of patients. We report on some models and simulation of the PDD data provided by the International Atomic Energy Agency (IAEA) and the British Journal of Radiology [1] by using artificial intelligence. PDD predictions by using artificial neural networks (ANN) and genetic programming (GP) are hereby given. It is shown how our approach has superior performance compared to the current state of the art.