Miracle-dz/UniFAR-FTUs huggingface dataset:
Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.
Prevalence of self‐reported obesity among diverse Latino adult populations in New York City, 2013–2017

Latinos in the United States represent a heterogeneous population disproportionally impacted by obesity. Yet, the prevalence of obesity by specific Latino group is unclear. Using the New York City Community Health Survey (2013–2017), this study compared self‐reported obesity in the city's largest Latino adult populations (Puerto Ricans, Mexicans, Dominicans, Ecuadorians, and Colombians).
Impact of fatty acid coating on the CCN activity of sea salt particles

Abstract This study investigates the impact of fatty acids on the cloud condensation nuclei (CCN) activity of sea salt aerosol of initial size 30, 50, 70 or 90 nm. Two of the major fatty acids in the marine environment, palmitic acid (C16) and stearic acid (C18), were investigated along with their unsaturated analogues palmitoleic acid and oleic acid, respectively. Sea salt seed particles were generated by aeration through a diffuser placed inside a sea spray tank. Fatty acids were added to the particles via condensation of fatty acid vapours in a heated flask at different temperatures. The diameters and CCN activity of particles before and after condensation of fatty acids were monitored. Based on the change in mobility diameter, a coating thickness and an organic volume fraction were inferred. Addition of the unsaturated acids to the core sea salt particles did not result in hindered water uptake for any organic volume fractions studied (25–96%) and critical supersaturations generally followed the kappa addition rule assuming a kappa value of zero for the fatty acids and assuming a constant surface tension equal to that of water. For the saturated fatty acids, a deviation from the Zdanovskii, Stokes and Robinson assumption (kappa mixing rule) in the direction of hindered water uptake was observed for organic volume fractions corresponding to thick (25–29 nm) coatings of palmitic acid and even thinner coatings of stearic acid.
Non-Parametric Generalized Additive Models as a Tool for Evaluating Policy Interventions

The interrupted time series analysis is a quasi-experimental design used to evaluate the effectiveness of an intervention. Segmented linear regression models have been the most used models to carry out this analysis. However, they assume a linear trend that may not be appropriate in many situations. In this paper, we show how generalized additive models (GAMs), a non-parametric regression-based method, can be useful to accommodate nonlinear trends. An analysis with simulated data is carried out to assess the performance of both models. Data were simulated from linear and non-linear (quadratic and cubic) functions. The results of this analysis show how GAMs improve on segmented linear regression models when the trend is non-linear, but they also show a good performance when the trend is linear. A real-life application where the impact of the 2012 Spanish cost-sharing reforms on pharmaceutical prescription is also analyzed. Seasonality and an indicator variable for the stockpiling effect are included as explanatory variables. The segmented linear regression model shows good fit of the data. However, the GAM concludes that the hypothesis of linear trend is rejected. The estimated level shift is similar for both models but the cumulative absolute effect on the number of prescriptions is lower in GAM.
SMART AQUARIUM MENGGUNAKAN SENSOR LIGHT DEPENDENT RESISTOR BERBASIS INTERNET OF THINGS

Memelihara aquarium atau ikan hias di dalam rumah ternyata memiliki manfaat tersendiri, diantaranya mengurangi stress, mempercantik ruangan, dan menjadi hobi yang dapat menghasilkan uang.Dibalik kegemaran memeliharaikan hias, para pemelihara menemukan kesulitan ketika memiliki kesibukan atau sedang bepergian dengan waktu yang cukup lama, sehingga pemelihara tidak dapat memantau secara langsung dalam hal pemberian pakan ikan, lampu penerangan, suhu air, dan kejernihan air dalam aquarium karena ikan hias membutuhkan air yang jernih danperawatan yang khusus . Dengan bantuan teknologi robotic yang telah dianggap dapat meringankan dan menggantikan pekerjaan manusia maka dari itu pada penelitian ini menghasilkan sebuah alat bernama “Smart Aquarium menggunakan Light Dependent Resistor Berbasis Internet of Things”. Dengan menerapkan teknologi robotik dan juga menggunakan beberapa sensor berupa Sensor Ultrasonic, Sensor DSB18B20, Sensor Turbidity, Sensor Light Dependent Resistor. Dengan adanya Smart Aquarium ini diharapkan dapat mempermudah pengguna dalam menghemat waktu dan mempermudah untuk perawatan aquarium tersebut
Industry–University Collaborations in Emerging Economies: A Legitimacy Perspective

The need for universities to commercialize academic knowledge, in addition to their two traditional core missions of research and teaching, has made industry–university collaborations (IUCs) more relevant. Although research on IUCs has produced a significant body of knowledge explaining different factors which enable or inhibit the success of IUCs, the nature of IUCs in emerging economies remains poorly understood. The primary purpose of this article is to extend our cumulative understanding of IUCs and of how universities in emerging economies can successfully transition to become entrepreneurial universities. We drew upon insights from two streams of literature—legitimacy and IUCs—to develop an informed understanding of the phenomenon of IUCs and entrepreneurial university emergence in developing economies of Africa. We apply to IUCs and entrepreneurial university emergence the four typologies of the moral legitimacy perspective—personal, consequential, structural, and procedural. We propose how they can yield insights into the antecedents for successful IUCs in emerging African economies, as well as the processes that lead to the emergence of legitimate entrepreneurial universities. We highlight the paper's contributions to theory and practice and suggest that, just as research on IUCs benefits from an organizational legitimacy perspective, organizational legitimacy is also informed by research into IUCs.
EMDR : An Alternative Effective Tool for Reduction of Academic Stress?

The stress often experienced by students is academic stress. Academic stress is a situation where students cannot face academic demands and the exception of the academic demands received as a disorder. If not handled properly, it will disrupt the effectiveness of learning and absorption of information on students. This study aims to reveal the effect of EMDR (Eye Movement Desensitization and Reprocessing) in reducing student academic stress. The research method is an experimental study conducted with a repeated measure pretest and multiple posttest design. The subjects in this study were 15 students. The instrument used was an adaptation of the Academic Stress Inventory of students at universities and colleges of technology from Lin and Chen. Analyze the data using the one-way ANOVA for repeated measures. The results showed that EMDR is proven to be one of the effective tools to reduce academic stress on students.
The Role of Anions in Metal Chalcogenide Oxygen Evolution Catalysis: Electrodeposited Thin Films of Nickel Sulfide as “Pre-catalysts”

Oxygen evolution catalysts composed of a metal (Ni, Co, or Fe) and a pnictide or chalcogenide (P, S, or Se) counterion are a promising class of electrocatalysts for the oxygen evolution reaction (OER), an important reaction for the photoelectrochemical splitting of water. We synthesized a nickel-based oxygen evolution catalyst derived from pulse-electrodeposited nickel sulfide. This catalyst was found to produce current densities of 10 mA/cm2 at the relatively low overpotential of 320 mV in alkaline electrolyte (1 M KOH). Importantly, we found that the sulfur anion in the nickel sulfide is depleted in the active form of the electrocatalyst and that the NiS is converted into an amorphous nickel oxide in the potential range where water is oxidized to oxygen. The superior catalytic activity of this nickel sulfide is thus unrelated to the sulfur anions in the active catalyst but is instead related to the metal sulfide’s ability to act as a precursor to a highly active nickel oxide OER electrocatalyst. The nic.
The polymict carbonaceous breccia Aguas Zarcas: A potential analog to samples being returned by the OSIRIS‐REx and Hayabusa2 missions

On April 23, 2019, a meteorite fall occurred in Aguas Zarcas, Costa Rica. According to the Meteoritical Bulletin, Aguas Zarcas is a brecciated CM2 chondrite dominated by two lithologies. Our X‐ray computed tomography (XCT) results show many different lithologies. In this paper, we describe the petrographic and mineralogical investigation of five different lithologies of the Aguas Zarcas meteorite. The bulk oxygen isotope compositions of some lithologies were also measured. The Aguas Zarcas meteorite is a breccia at all scales. From two small fragments, we have noted five main lithologies, including (1) Met‐1: a metal‐rich lithology; (2) Met‐2: a second metal‐rich lithology which is distinct from Met‐1; (3) a brecciated CM lithology with clasts of different petrologic subtypes; (4) a C1/2 lithology; and (5) a C1 lithology. The Met‐1 lithology is a new and unique carbonaceous chondrite which bears similarities to CR and CM chondrite groups, but is distinct from both based on oxygen isotope data. Met‐2 also represents a new type of carbonaceous chondrite, but it is more similar to the CM chondrite group, albeit with a very high abundance of metal. We have noted some similarities between the Met‐1 and Met‐2 lithologies and will explore possible genetic relationships. We have also identified a brecciated CM lithology with two primary components: a chondrule‐poor lithology and a chondrule‐rich lithology showing different petrologic subtypes. The other two lithologies, C1 and C1/2, are very altered and possibly related to the CM chondrite group. In this article, we describe all the lithologies in detail and attempt a classification of each in order to understand the origin and the history of formation of the Aguas Zarcas parent body.
An intrusion detection system using optimized deep neural network architecture

Internet usage became increasingly ubiquitous. The concern regarding security and privacy has become essential for Internet users. As the usage of the Internet increases the number of cyber‐attacks also increases substantially. Intrusion detection is one of the challenging aspects of network security. Efficient intrusion detection is crucial for every organization to mitigate the vulnerability. This paper presents a novel intrusion detection system to detect malicious attacks targeted at a smart environment. The proposed Intrusion detection method uses a correlation tool and a random forest method to detect the predominant independent variables for improvising neural‐based attack classifier. To detect a malicious attack, a shallow neural network and an optimized neural‐based classifier are presented. The designed intrusion detection system has experimented on the KDDCUP99 dataset. The experimental results reveal that the performance of the proposed intrusion detection system is superior in terms of quantitative metrics. Thus, the proposed system can be deployed in the IoT and wireless networks to detect cyber‐attacks.
Recent progress in nanoformulations of cabazitaxel

The antitumor efficacy of various paclitaxel (PTX) and docetaxel (DTX) formulations in clinical applications is seriously affected by drug resistance. Cabazitaxel, a second-generation taxane, exhibits greater anticancer activity than PTX and DTX and has low affinity for the P-glycoprotein efflux pump because of its structure. Therefore, cabazitaxel has the potential to overcome taxane resistance. However, owing to the high systemic toxicity and hydrophobicity of cabazitaxel and the instability of its commercial preparation, Jevtana®, the clinical use of cabazitaxel is restricted to patients with metastatic castration-resistant prostate cancer who show progression after DTX-based chemotherapy. Nanomedicine is expected to overcome the limitations associated with cabazitaxel application and surmount taxane resistance. This review outlines the drug delivery systems of cabazitaxel published in recent years, summarizes the challenges faced in the development of cabazitaxel nanoformulations, and proposes strategies to overcome these challenges.
Prevalence of Protozoa and Indicator Bacteria in Wastewater Irrigation Sources in Kathmandu Valley, Nepal: Cases from Kirtipur, Bhaktapur, and Madhyapur Thimi Municipalities

Wastewater irrigation using sewage and water from polluted sources is widespread in Kathmandu Valley, Nepal. Data on the prevalence of pathogens in irrigation sources are important for assessing the health risks for both farmers and consumers. In this study, 13 and 15 water samples were collected during the rainy (September 2014) and dry (April 2015) seasons, respectively, from irrigation sources [river water (n = 17), sewage (n = 5), pond water (n = 3), canal water (n = 1), and effluent of a wastewater treatment plant (WWTP; n = 2)] in Kathmandu Valley. Based on fluorescence microscopy, all the analyzed samples (n = 19) tested positive for Giardia, whereas 68% tested positive for Cryptosporidium. In addition, indicator bacteria (Escherichia coli and total coliforms) were detected in all samples (n = 28), with significantly higher concentrations during the dry season than during the rainy season (t-test, p < 0.05). Indicator bacteria and protozoan concentrations were abundant in sewage and river water samples and comparatively less in the remaining samples. Positive correlations between indicator bacteria and protozoa concentrations suggested that E. coli and total coliforms could be considered as rough indicators of protozoa contamination of wastewater irrigation sources.
What drives risk perceptions? Revisiting public perceptions of food hazards associated with production and consumption

Abstract As food technology continues to advance, the potential for new food products to enter the food market grows, attracting considerable media interest. Whilst previous research has explored public perceptions of food-related hazards, much of this took place over 10 years ago. Continued technological developments have yielded new food products, for which there is no extant research on public perceptions. In light of this, there is a pressing need to update and extend research exploring public perceptions of food-related hazards. Using a psychometric approach, a nationally representative UK sample (n = 907) provided ratings of 11 old and new food hazards on a total of 12 risk characteristics (identified from previous research). Principal components analysis identified two main components: ‘dread’ and ‘knowledge’, which explained 80.8% of the variance in perceptions, consistent with past findings. Additives were perceived as the least dreaded and most known of the hazards considered, whereas ractopamine pork, atrazine corn and hormone beef were dreaded the most. 3D printed food and lab-grown meat were perceived as the least known. Our results highlight the importance of knowledge in shaping risk perceptions and have implications for risk management. An understanding of the factors which determine risk perceptions is vital for the development of effective risk management and risk communication strategies.
Evaluation of the Aerial Biomass of Three Sahelian Species in the Ferlo (North Senegal): Acacia tortilis (Forsk.) Hayn essp. Raddiana (Savi) Brenan, Acacia senegal (L.) Willd and Balanites aegyptiaca (L.) Del

In a context of climate change characterized by rising temperatures, increased greenhouse gases and frequent droughts, the Sahel is presented as one of the most vulnerable areas to the adverse effects of climate change. The Sahel is presented as one of the most vulnerable areas to the adverse effects of climate change. The objective of this study is to assess the above-ground biomass and carbon stock of three Sahelian species: Acacia raddiana tortilis (Forsk.) hayne ssp. raddiana (savi) Brenan, Acacia senegal (L.) Willd and Balanites aegyptiaca (L.). The study was carried out in northern Senegal commonly known as Ferlo. Biomasses of the populations of the three target species were first assessed by harvesting the entire epigenetic part of the species and then modelled by correlation using dendrometric parameters measured on each individual of the sample. Two models, mono-specific and multi-species, were used. The results obtained showed that the diameter at breast height (x) and the parameter best correlated to the epigeal biomass (y). The dry biomass of woody plants was 31.4 ± 15.2 kg/tree for B. aegyptiaca, 30.6 ± 13.2 kg/tree for A. senegal and 26.2 ± 11.1 kg/tree for A. raddiana; i.e. carbon equivalents of 14.75 - 14.38 - 12.31 kg/tree respectively. The amount of carbon contained in the above-ground woody biomass is estimated at 4.48 t/ha. The carbon equivalent, atmospheric CO2 is estimated at 16.44 tons of CO2/ha and based on the actual density of Ferlo (108.08 ± 49.79 ind/ha) the sequestered carbon of the area is estimated at 1777.008 tons of CO2. The comparison between the models developed in this study and the multispecific or mono-specific models from the literature showed substantial differences. This study contributes to a better understanding of the contribution of Sahelian woody species to carbon sequestration and the results could be used in the framework of adaptation to climate change.
A Hydrogen Concentration Monitoring System With Passive Tags

This article describes the design of a hydrogen concentration monitoring system based on ultrahigh-frequency radio-frequency identification (RFID) passive tags, which adopts ISO/IEC 18000–6 Type C standard. Tag devices have been designed by employing a microcontrol unit, making the proposed solution very suitable for low-cost hydrogen concentration measurement in wireless sensor networks. Furthermore, anti-collision capability has been realized by implementing a new true random number generator based on the hydrogen concentration sensing circuit and analog-to-digital converter, without employing any additional device. The correctness of all operations of the RFID system has been experimentally verified in a multiple-tag environment, as well as the hydrogen concentration monitoring capability. Particularly, measurement on an RFID tag prototype developed for the bandwidth 920–925 MHz has revealed that when the ambient temperature is 25 °C and humidity 80%, a maximum error of 9.8% is found within a hydrogen concentration range from 0.24% to 2.4%.
Design of Concurrent Dual-Band Band Pass Filter using Hybrid Metal insulator Metal for Nanoscale Applications

In this paper, investigation on Hybrid Metal Insulator Metal (HMIM) plasmonic waveguide (PWG) filters for dual-band applications. The filters are designed using circular ring resonator (CRR) and CRR with single as well as dual slits. The filters are connected using coupled line feed. The dual-band Band Pass Filter (BPF) characteristics can be realized by varying radius (r) and gap (g) between the feed and ring resonator. The proposed CRRs are operated at THz frequencies. The filters are designed and simulated using commercially available CST studio suite software under Perfect Matched Layer (PML) boundary conditions by keeping 5 nm x 5 nm mesh size. The transmission and reflection characteristics of the filters are explained by the resonance conditions, which agrees with theoretical calculations. The proposed work is best suitable for the plasmonic integrated optical circuits for nanoscale applications.
A Data-Driven Approach to Forecasting Heating and Cooling Energy Demand in an Office Building as an Alternative to Multi-Zone Dynamic Simulation

Nowadays, as more data is now available from an increasing number of installed sensors, load forecasting applied to buildings is being increasingly explored. The amount and quality of resulting information can provide inputs for smarter decisions when managing and operating office buildings. In this article, the authors use two data-driven methods (artificial neural networks and support vector machines) to predict the heating and cooling energy demand in an office building located in Lisbon, Portugal. In the present case-study, these methods prove to be an accurate and appealing alternative to the use of accurate but time-consuming multi-zone dynamic simulation tools, which strongly depend on several parameters to be inserted and user expertise to calibrate the model. Artificial neural networks and support vector machines were developed and parametrized using historical data and different sets of exogenous variables to encounter the best performance combinations for both the heating and cooling periods of a year. In the case of support vector regression, a variation introduced simulated annealing to guide the search for different combinations of hyperparameters. After a feature selection stage for each individual method, the results for the different methods were compared, based on error metrics and distributions. The outputs of the study include the most suitable methodology for each season, and also the features (historical load records, but also exogenous features such as outdoor temperature, relative humidity or occupancy profile) that led to the most accurate models. Results clearly show there is a potential for faster, yet accurate machine-learning based forecasting methods to replace well-established, very accurate but time-consuming multi-zone dynamic simulation tools to forecast building energy consumption.