Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Casein Coatings and Films for Perishable Foods: Structural Properties and Shelf-Life Extension

There is an urgent need to increase the food supplies to fulfil the demands of future generations as the population of the world is expected to grow beyond 10 billion by 2050. An essential component for ensuring global food security is to reduce food losses during the post-harvest stage. Active edible coatings and films are a promising sustainable preservation technology for shelf-life extension of food products by hindering decay kinetics of minimally processed fruits and vegetables (F&V), by restricting the mass transfer of moisture, aroma, or gases and carrying an active compound, such as an antioxidant or antimicrobial. Active protein-based coatings and films have the potential to extend the shelf-life of food products by decreasing their respiration rates, as they exhibit an excellent gas barrier and good mechanical properties as compared to other biopolymeric packaging. Among protein-based biopolymers, casein and its derivatives as packaging films have been extensively studied due to their low cost, complete biodegradability, and availability. Currently, there is no review study focusing on caseinate-based active coating and film, thus, this review aims to give insights on the composition, rheology, structure, and properties of caseinate-based formulations by critically discussing the results presented in the literature. A methodological approach was followed to obtain relevant literature to discuss the influence of additives on the shelf-life of F&V. Furthermore, changes in secondary structure of casein were observed after incorporation of bioactive compounds (i.e., phenolic acids). Likewise, there is a need to explore chemical interactions among bioactive compounds and biopolymer material by using in silico and laboratory trials as food additives have shown to influence the physicochemical properties of film and shelf-life of food products.
Burned Area Mapping over the Southern Cape Forestry Region, South Africa Using Sentinel Data within GEE Cloud Platform

Planted forests in South Africa have been affected by an increasing number of economically damaging fires over the past four decades. They constitute a major threat to the forestry industry and account for over 80% of the country’s commercial timber losses. Forest fires are more frequent and severe during the drier drought conditions that are typical in South Africa. For proper forest management, accurate detection and mapping of burned areas are required, yet the exercise is difficult to perform in the field because of time and expense. Now that ready-to-use satellite data are freely accessible in the cloud-based Google Earth Engine (GEE), in this study, we exploit the Sentinel-2-derived differenced normalized burned ratio (dNBR) to characterize burn severity areas, and also track carbon monoxide (CO) plumes using Sentinel-5 following a wildfire that broke over the southeastern coast of the Western Cape province in late October 2018. The results showed that 37.4% of the area was severely burned, and much of it occurred in forested land in the studied area. This was followed by 24.7% of the area that was burned at a moderate-high level. About 15.9% had moderate-low burned severity, whereas 21.9% was slightly burned. Random forests classifier was adopted to separate burned class from unburned and achieved an overall accuracy of over 97%. The most important variables in the classification included texture, NBR, and the NIR bands. The CO signal sharply increased during fire outbreaks and marked the intensity of black carbon over the affected area. Our study contributes to the understanding of forest fire in the dynamics over the Southern Cape forestry landscape. Furthermore, it also demonstrates the usefulness of Sentinel-5 for monitoring CO. Taken together, the Sentinel satellites and GEE offer an effective tool for mapping fires, even in data-poor countries.
COVID-19: the psychological impact and coping strategies among youth during lockdown in India: the role of education

Background: COVID-19 is a biological disaster and its impact on the psychological wellbeing among individuals in the society. It affected all age group in the society and the youth are more vulnerable in the pandemic. The aim of the study was to understand the stress, anxiety, depression level and coping strategies among youth during the early stage of lockdown period in India.Methods: A cross-sectional descriptive study and 302 youth across India were respond ended through snowball sampling method. An online survey through google form and the tools were socio-demographic details, DASS-21 and coping strategies questionnaire. The self-reported English version online questionnaire were analysed through SPSS.Results: The study found that 36.8% of youth had stress, 32% of them had anxiety whereas 48.7% had depressive symptoms and 30.4% send their time in social networking sites during the early stage of lockdown in India. There were significant comparison between depression and recreational activities (x2=6.92; p=0.03) and highly significant comparison between depression and spending times in hobbies (x2=8.32; p=0.01). Conclusions: The youth need psychosocial care and support during this pandemic and the youth need to take up physical-mental-spiritual self-care activities.
A Fractional Order Proportional-Integral-Derivative Controller for Series Continuous Stirred Tank Reactor System

: For series continuous stirred tank reactor system (CSTR), it is a complex problem to finetune the fractional order proportional-integral-derivative controller (FOPID). To solve the problem, this paper presents a parameter tuning method based on intelligent optimization genetic algorithm (GA) and integral time absolute error (ITAE). Firstly, the series CSTR system was mathematically modelled by vectorized modules, and an FOPID control system was established. Meanwhile, the intelligent optimization GA was introduced under the ITAE rule, and the empirical PID control parameters were taken as the initial values for iteration, aiming to enhance the effect of the search for optimal solution. To verify its superiority, the FOPID controller optimized by GA was compared with intelligent optimization GA and empirical PID controller through simulation. The results show that the optimized FOPID system achieved much better control effect and stronger anti-interference performance than the contrastive methods.
Innovative Approach to Preventive Maintenance of Production Equipment Based on a Modified TPM Methodology for Industry 4.0

Preventive maintenance (PM) in the production industry is one of the most essential measures to eliminate accidental machinery failures by replacing/repairing worn out machines or parts. The decision of when and where to perform preventive maintenance is non-trivial due to the complex and stochastic nature of the industry where PM is implemented. This article deals with the theoretical and practical implementation of preventive maintenance based on a unique modification of the total productive maintenance (TPM) methodology. The innovative approach of preventive maintenance management was implemented in the real production hall of ITT (Czech Republic) and has been verified. Within preventive maintenance, the new concept brings in an innovative method of managing the maintenance process as a whole, from abstract methodical conception to practical usage. The whole new approach has been verified and implemented on industrial equipment. A challenging task while implementing Industry 4.0 technologies is the issue of how to fully gather and analyse operational data from various items of equipment and users under various conditions, which would result in innovative services of equipment maintenance for clients. The solution to this problem is based on an innovative approach to preventive maintenance of complex equipment and could help many industrial companies to increase production and maintenance efficiency.
Formação Docente em Pensamento Computacional: Um Mapeamento Sistemático da Literatura

O Pensamento Computacional é uma estratégia para resolução de problemas através da utilização de habilidades cognitivas na área da Ciência da Computação. Neste contexto, o artigo apresenta um mapeamento sistemático da literatura visando a identificação de contextos metodológicos e ferramentas usadas para aprendizagem do Pensamento Computacional para a formação continuada dos professores no Brasil. Verificou-se que a ferramenta Scratch e a computação desplugada são as estratégias mais utilizadas por meio de oficinas e cursos. Além disso, buscou-se elencar as principais instituições e regiões que mais publicaram sobre o tema, como também buscou-se identificar as principais dificuldades e as conclusões reportadas pelos autores dos estudos mapeados.
Comprehensive preview decision-making method for direction and speed of intelligent vehicle based on rules and mechanisms

According to the existing driver model, the objective function is coupled with continuity factors and discontinuity factors, which makes it difficult to determine the weighting coefficients in multi-objective optimization, which will cause dangerous situations such as the vehicle rushing out of the road boundary; in response to this problem, this paper proposes a driver model adapted to the complex traffic environment, based on the mechanism modeling of continuity factors and rule modeling of discontinuity factors. In view of the difficulty of traditional optimization algorithms to find a balance between efficiency and accuracy, this paper proposes a grid optimization algorithm that takes into account both efficiency and accuracy. In order to reduce the amount of calculation in the preview decision-making process, this paper proposes a curve integral method based on the laws of vehicle kinematics to predict the position of the vehicle to judge whether a collision will occur. The driver model is established in the Simulink simulation environment, and the C-level prototype model in the vehicle dynamics simulation software CarSim is selected as the control object, the results show that the proposed the preview decision model effectively solves the problem of divergence in the optimization solution, and can also ensure safety and traffic rules in a complex traffic environment, improving the quality of the model.
North American East Coast Sea Level Exhibits High Power and Spatiotemporal Complexity on Decadal Timescales

Tide gauges provide a rich, long‐term, record of the amplitude and spatiotemporal structure of interannual to multidecadal coastal sea‐level variability, including that related to North American east coast sea level “hotspots. ” Here, using wavelet analyses, we find evidence for multidecadal epochs of enhanced decadal (10–15 year period) sea‐level variability at almost all long ( > 70 years) east coast tide gauge records. Within this frequency band, large‐scale spatial covariance is time‐dependent; notably, coastal sectors north and south of Cape Hatteras exhibit multidecadal epochs of coherence ( ∼ 1960–1990) and incoherence ( ∼ 1990‐present). Results suggest that previous interpretations of along coast covariance, and its underlying physical drivers, are clouded by time‐dependence and frequency‐dependence. Although further work is required to clarify the mechanisms driving sea‐level variability in this frequency band, we highlight potential associations with the North Atlantic sea surface temperature tripole and Atlantic Multidecadal Variability.
Exploring Biomolecular Interactions Through Single-Molecule Force Spectroscopy and Computational Simulation

Molecular interactions between cellular components such as proteins and nucleic acids govern the fundamental processes of living systems. Technological advancements in the past decade have allowed the characterization of these molecular interactions at the single-molecule level with high temporal and spatial resolution. Simultaneously, progress in computer simulation has enabled theoretical research at the atomistic level, assisting in the interpretation of experimental results. This thesis combines singlemolecule force spectroscopy and simulation to explore interand intra-molecular interactions. Specifically, we investigate the interaction between RecA and DNA to elucidate the underlying molecular mechanism of the DNA homologous recombination process. We also evaluate the stability of the von Willebrand Factor (vWF) A2 domain to determine the molecular origins of von Willebrand Diseases (vWD). This thesis also describes the development and application of a new single-molecule technique that combines the centrifuge force microscope (CFM) with DNA self-assembled mechanical switches to enable massively parallel repeating force measurements of molecular interactions.
MAX-DOAS Measurements of Nitrogen Dioxide and Aerosol

Multi-axis differential optical absorption spectroscopy (MAX-DOAS) was applied to retrieve tropospheric NO2 and aerosol vertical profiles from downtown Toronto, and King City, Ontario during select periods in 2006 – 2010. Linear regression of MAX-DOAS NO2 vertical column density (VCD) versus OMI (satellite) VCD yielded a good correlation (R = 0.88) and MAX-DOAS negative bias of 20%, which was within the reported uncertainty of the MAX-DOAS and OMI VCD. The average regional Toronto VCD (remotelysensed via MAX-DOAS and OMI) was half of the near-road VCD obtained in-situ (2.4 x 10 16 ± 1.2 x 10 16 molec/cm 2 ). MAX-DOAS measurements of O4 were coupled with radiative transfer modeling to obtain vertical aerosol extinction profiles and aerosol optical depth (AOD). A strong linear agreement was observed between PM2.5 concentration and aerosol extinction coefficient (R = 0.92), and MAX-DOAS versus sun photometer AOD (slope = 0.94; R= 0.90).
Activating the Role of Digital Volunteering in Early Detection of Unhealthy Behavior on University Campus in the Era of Covid 19 Crisis

BackgroundThe current study aims to identify unhealthy behaviors among university students, establish means of detecting them as well as identify obstacles to digital volunteering and also explore the relationship between volunteer preferred style of volunteering and the obstacles to volunteering. MethodsData for the study was gathered by administering unstructured, anonymous questionnaire to 207 female university students and staff. The survey design included questions about socio-demographic characteristics, views on different facets of volunteering, unhealthy behaviors and correlation between volunteering and unhealthy behaviors.ResultsThe result revealed unhealthy behavior detected by the respondents (51.7%). Twenty-eight (13.5%) of the 207 respondents reported to use social media in detecting women with offending behavior. The value of Pearson’s R is 0.245; thus is considered a weak or no correlation. There is hence no correlation between how respondents preferred volunteer work and the obstacle to volunteering. There is no much of difference in the obstacles to volunteering faced by respondents despite their preferred style of volunteering.Conclusion The findings reveal that digital volunteering is effectively gaining ground in detection and management of unhealthy behaviors among university students. Much more could be achieved through digital volunteering if more awareness is created and volunteering programs are designed to be more interesting and less time consuming to give more students the opportunity to participate.
Siloxane removal in the energy recovery of biogas: sequential adsorption/oxidation processes

The thesis deals with removal of organosilicon compounds in the energy recovery of biogas. Alternative adsorbents for the siloxane removal were studied, and the physical and chemical mechanisms involved in the siloxane adsorption were discovered, which improved the yield of the adsorption and regeneration technologies using both activated carbons and zeolites. Moreover, several technologies based on advanced oxidation processes have been applied in order to regenerate and reuse the exhausted adsorbents. The thesis studies the siloxane removal at lab-scale close to real scenario conditions, including the competitive adsorption with compounds usually found in biogas, in the presence of humidity and with the same gas matrix, which allowed to select the most efficient materials for this purpose. Therefore, the knowledge reported in the thesis dissertation is directly transferable to field application to upgrade the siloxane removal system Aquesta tesi estudia l'eliminacio de compostos volatis de silici (siloxans) per la recuperacio energetica del biogas. Es van estudiar diferents adsorbents per l'adsorcio de siloxans, determinants les propietats fisico-quimiques superficials que en condicionen l'eficiencia, permetent millorar el rendiment de l'adsorcio i permetent la regeneracio en carbons actius i zeolites esgotats. La tesi estudia l'eliminacio de siloxans a escala de laboratori en condicions properes a les reals del biogas, incloent l'adosrcio competitiva amb altres compostos organics volatics, en presencia d'humitat i amb diferents matrius gasoses, la qual cosa va permetre seleccionar els materials optims per aquest proposit.Per tant el coneixement derivat d'aquesta tesi es directament transferible per tal de millorar els sistemes d'eliminacio siloxanes
Isotopologperturbationsmethoden zur Untersuchung von Stoffwechselnetzwerken

Durch Einbauexperimente mit 13C- und 2H-markierten Vorstufen werden die Isotopologverhaltnisse im Stoffwechselnetzwerk von Modellorganismen gestort. Die nachfolgenden Relaxationsprozesse bilden den Metabolismus in den untersuchten Systemen unter weitgehend physiologischen Bedingungen qualitativ und quantitativ ab. Unter Benutzung des Konzepts der Retrobiosynthese konnte auf diese Weise ein unvollstandiger Citratzyklus in dem humanpathogenen Bakterium Listeria monocytogenes bewiesen werden, sowie eine neuartige Reaktion im Terpenstoffwechsel charakterisiert werden.
Can Binaural Beats Increase Your Focus? Exploring the Effects of Music in Participants’ Conscious and Brain Activity Responses

Music is a universal medium that can elicit strong emotion, and can significantly help us in gaining focus while doing specific tasks. However, it is unclear what types of music can help to improve focus while doing other activities. In this paper, we investigate the effects of six different music stimuli on participants’ verbal and physiological responses while identifying genuine and acted emotions from video clips. Initial analysis was conducted on the comments participants made on the different stimuli in order to identify emerging patterns. Then, participants’ verbal and EEG responses were collected, processed and analyzed to classify two types of emotion. Empirical analysis of the results show that binaural beats, which are believed to increase focus on tasks, can often cause discomfort and therefore hinder focus. On the other hand, music containing a sombre tone, or familiar popular music with high level valence can help improve focus. Identifying which music stimuli can improve focus can be highly beneficial in managing day-to-day tasks and activities. This study will also be useful in broadening the range of music stimuli used in affective computing studies.
Sulfate-Rich Metal–Organic Framework for High Efficiency and Selective Removal of Barium from Nuclear Wastewater

Capture of radioactive barium from nuclear wastewater is of great importance for environmental protection. Here, [Zr6(OH)10.8(SO4)3.6(BDC-NH2)3(H2O)7.4]·nH2O (Zr-BDC-NH2-SO4) was selected as an adsorbent for its high content of sulfate group, which is a strong barium-chelating group, and its binding sites are fully exposed. Zr-BDC-NH2-SO4 exhibits high adsorption capacity of 181.8 mg g–1, which is higher than those of most reported adsorbents, and showed excellent high selectivity even when the concentrations of background metal ions are 10 times of Ba2+. The breakthrough study showed good adsorption performance with fast kinetics and low outlet concentration. In addition, the great stability of Zr-BDC-NH2-SO4 under γ radiation has been confirmed, making it possible to be applied in real nuclear wastewater treatment. Moreover, the adsorption of Ba2+ is irreversible and therefore it could avoid secondary pollution. Overall, this work provides a stable, efficiency adsorbent for removing radioactive barium f.
Darstellung und Charakterisierung substituierter LiCoMnO₄-Spinelle zur Verwendung als Aktivmaterialien in Lithium-Ionen-Akkumulatoren

This thesis is focused on the synthesis, structural and morphological, as well as electrochemical characterization of the cubic high voltage spinel phase LiCoMnO4 and its derivatives. Therefore, a Pechini-based aproach was developed for all investigated materials, which yields a high phase purity and crystallinity. After synthesis, the materials were investigated in respect of their structural local and bulk properties (X-ray di raction, neutron di raction, nuclear magnetic resonance spectroscopy, Mößbauerspectroscopy), morphology (scanning electron microscope) and electrochemical performance. The structural changes due to the electrochemical lithium extraction/insertion were investigated via in situ synchrotron powder di raction, nuclear magnetic resonance spectroscopy, in situ Mößbauerspectroscopy). In order to make these investigations possible, it was necessary to nd an electrolyte, which is stable against the applied potentials in combination with the used active materials. Therefore, an organic mixture of uorinated carbonates and LiPF6 was used. The investigated materials can be divided into two classes. In the rst class, Mn4+ was substituted with Ti4+ to di erent degrees, so that a general molecular formula LiCoTixMn1-xO4 was ful lled. In the second class of materials, Co3+ was substituted with Fe3+ with the resulting molecular formula of LiCo1-yFeyMnO4. All these materials crystallize in the cubic spinel structure with the space group Fd3̄m, which means that there is no ordering of the transition metal ions, they are uniformly distributed on the octahedral 16d site.
A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas

Abstract In this paper, a deep convolutional neural network (CNN) is developed to classify the Unmanned Aerial Vehicle (UAV) derived multispectral imagery and normalized digital surface model (DSM) data in urban areas. For this purpose, a multi-input deep CNN (MIDCNN) architecture is designed using 11 parallel CNNs; 10 deep CNNs to extract the features from all possible triple combinations of spectral bands as well as one deep CNN dedicated to the normalized DSM data. The proposed method is compared with the traditional single-input (SI) and double-input (DI) deep CNN designations and random forest (RF) classifier, and evaluated using two independent test datasets. The results indicate that increasing the CNN layers parallelly augmented the classifier’s generalization and reduced overfitting risk. The overall accuracy and kappa value of the proposed method are 95% and 0.93, respectively, for the first test dataset, and 96% and 0.94, respectively, for the second test data set.