Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery

Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.
Using Acoustic Data Repositories to Study Vocal Responses to Playback in a Neotropical Songbird

Birds may alter song structure in response to territorial challenges to convey information about aggressive intent or fighting ability. Professional and amateur ornithologists upload daily many birdsong recordings into acoustic data repositories, usually scoring whether songs were recorded in response to a conspecific playback or produced spontaneously. We analyzed recordings from these repositories to evaluate if song traits of Rufous-browed Peppershrikes (Cyclarhis gujanensis) vary between playback-elicited songs and spontaneous songs. For each recording after playback, we chose one spatially closer spontaneous recording to avoid geographic bias. Birds recorded after playback produced slightly longer songs than birds that were singing spontaneously. This result was accounted for by increases in the amount of sound and silence within a song after the playback instead of changes in the mean number or duration of elements. Playback did not alter song frequency parameters (bandwidth, minimum, mean, and maximum frequencies) or song rate. These results indicate that song duration might mediate aggressive interactions in Rufous-browed Peppershrikes. Even considering limitations such as unknown playback stimulus identity and possible pseudoreplication, acoustic data repositories give a unique yet unexplored opportunity to gather insights into the evolution of song flexibility during aggressive encounters.
Equine-Assisted Therapeutic Intervention in Institutionalized Children: Case Studies

A significant number of institutionalized children have behavior problems. Socio-emotional skills are fundamental for their adaptation and success throughout life and are usually weakened in this population. Equine-assisted services (EAS) are a form of therapeutic mediation that facilitates and requires the practitioner’s participation, contributing to the promotion of various psychomotor and socio-emotional dimensions. This study was developed during 17 sessions of EAS with a psychomotor intervention, which took place individually and weekly and lasted approximately 45 min, with three institutionalized children. A quantitative and qualitative assessment was carried out before and after the intervention to study the effects of an EAS intervention on socio-emotional competencies in the three institutionalized children. There was an improvement in skills, with an impact on intrapersonal skills and marked improvement in self-regulation and self-control, in addition to an improvement in the intentionality of movement and adequacy of gesture to the context. This type of intervention underlies a renewed educational and therapeutic approach, contributing to mental health promotion in this population.
Pentacene: PTCDI-C13H27 molecular blends efficiently harvest light for solar cell applications

Solar cells exhibiting efficient photon harvesting are built from molecular blends of pentacene: PTCDI-C13H27. Absorption of the composition spans the whole visible spectrum with an onset at 730 nm. External quantum efficiency approaches unity at the peak absorption of pentacene. A 2.0% power conversion efficiency is obtained under 80 mW cm-2 AM 1.5 illumination, with 8.6 mA cm-2 short-circuit current. The comparison with bilayer devices suggests directions of improvement in conversion efficiency such as controlled growth of the blend layers.
Landlord: Coordinating Dynamic Software Environments to Reduce Container Sprawl

Containers provide customizable software environments that are independent from the system on which they are deployed. Online services for task execution must often generate containers on the fly to meet user-generated requests. However, as the number of users grows and container environments are changed and updated over time, there is an explosion in the number of containers that must be managed, despite the fact that there is significant overlap among many of the containers in use. We analyze a trace of container launches on the public Binder service and demonstrate the performance and resource usage issues associated with container sprawl. We present Landlord, an algorithm that coalesces related container environments, and show that it can improve container reuse and reduce the number of container builds required in the Binder trace by 40%. We perform a sensitivity analysis of Landlord using randomized synthetic workloads on a high-energy physics (HEP) software repository and demonstrate that Landlord shows benefits for container management across a wide range of usage patterns. Finally, we compare Landlord to offline clustering, and observe that the continuous churn in software necessitates an online approach.
Ethnicity in advertising and millennials: the role of social identity and social distinctiveness

Abstract Underpinned by the social identity theory and distinctiveness theory, the research examines the effects of mono- and multi-ethnicity advertising on brand evaluations among millennials. We conducted two experimental studies with Asian and Caucasian millennial consumers in Australia to test our hypotheses. The objective of Study 1 was to test the effectiveness of ethnic marketing among millennials using six different advertisement copies for a lipstick brand. In Study 2, we aimed to substantiate our predictions using another type of product (i.e. soap) and ascertain the impact of the product context on ethnicity marketing. Overall, the results of this study support that millennial consumers prefer multi-ethnic advertising to mono-ethnic advertising. The effect of body image is not significant in multi-ethnic advertisements. Social identity can influence the attitudes of socially distinctive consumers toward mono-ethnic advertisements. Finally, multi-ethnic advertising is an effective strategy for both publicly and privately consumed products, while mono-ethnic advertising is more applicable for privately consumed products.
Efficient Redirection of NK Cells by Genetic Modification with Chemokine Receptors CCR4 and CCR2B

Natural killer (NK) cells are a subset of lymphocytes that offer great potential for cancer immunotherapy due to their natural anti-tumor activity and the possibility to safely transplant cells from healthy donors to patients in a clinical setting. However, the efficacy of cell-based immunotherapies using both T and NK cells is often limited by a poor infiltration of immune cells into solid tumors. Importantly, regulatory immune cell subsets are frequently recruited to tumor sites. In this study, we overexpressed two chemokine receptors, CCR4 and CCR2B, that are naturally found on T regulatory cells and tumor-resident monocytes, respectively, on NK cells. Using the NK cell line NK-92 as well as primary NK cells from peripheral blood, we show that genetically engineered NK cells can be efficiently redirected using chemokine receptors from different immune cell lineages and migrate towards chemokines such as CCL22 or CCL2, without impairing the natural effector functions. This approach has the potential to enhance the therapeutic effect of immunotherapies in solid tumors by directing genetically engineered donor NK cells to tumor sites. As a future therapeutic option, the natural anti-tumor activity of NK cells at the tumor sites can be increased by co-expression of chemokine receptors with chimeric antigen receptors (CAR) or T cell receptors (TCR) on NK cells can be performed in the future.
Active Sulfate-Reducing Bacterial Community in the Camel Gut

The diversity and activity of sulfate-reducing bacteria (SRB) in the camel gut remains largely unexplored. An abundant SRB community has been previously revealed in the feces of Bactrian camels (Camelus bactrianus). This study aims to combine the 16S rRNA gene profiling, sulfate reduction rate (SRR) measurement with a radioactive tracer, and targeted cultivation to shed light on SRB activity in the camel gut. Fresh feces of 55 domestic Bactrian camels grazing freely on semi-arid mountain pastures in the Kosh-Agach district of the Russian Altai area were analyzed. Feces were sampled in early winter at an ambient temperature of −15 °C, which prevented possible contamination. SRR values measured with a radioactive tracer in feces were relatively high and ranged from 0.018 to 0.168 nmol S cm−3 day−1. The 16S rRNA gene profiles revealed the presence of Gram-negative Desulfovibrionaceae and spore-forming Desulfotomaculaceae. Targeted isolation allowed us to obtain four pure culture isolates belonging to Desulfovibrio and Desulforamulus. An active SRB community may affect the iron and copper availability in the camel intestine due to metal ions precipitation in the form of sparingly soluble sulfides. The copper-iron sulfide, chalcopyrite (CuFeS2), was detected by X-ray diffraction in 36 out of 55 analyzed camel feces. In semi-arid areas, gypsum, like other evaporite sulfates, can be used as a solid-phase electron acceptor for sulfate reduction in the camel gastrointestinal tract.
Learning incoherent light emission steering from metasurfaces using generative models

Spatiotemporal control over incoherent light sources is critically important for applications such as displays, remote sensing, clean energy, and illumination. Incoherent light emission made up of randomized wavefronts is incompatible with known beam steering techniques that rely on coherent electromagnetic wave interference. The emerging field of tunable dielectric metasurfaces consisting of sub-wavelength arrays of optical nanoresonators has recently enabled active re-direction of incoherent light (photoluminescence, PL) emission. This was achieved by illuminating (pumping) the metasurface with a pump laser reflecting off a programmable spatial light modulator (SLM) with sawtooth grating patterns as input. Achieving efficient beam steering requires the generation of optimal pump patterns programmed into the SLM to maximize the PL emitted towards a given direction. Given the innumerable possibilities and the lack of a theoretical physical framework to guide the exploration of pump patterns, we use an active learning algorithm running a closed loop optical experiment with a generative model to explore and optimize novel pump patterns. We achieve up to an order of magnitude enhancement in the steering efficiency by using pump patterns that are generated by a variational auto-encoder, with minimal number of experiments. The results presented in this paper highlight the unique ability of generative models and active learning to dramatically improve steering efficiency by finding novel optical pump patterns that are beyond human intuition. Our combination of advanced machine learning techniques driving closed loop nanophotonic experiments might pave the way to derive the underlying physics of emergent light-matter phenomena.
Differential evolution algorithms for constrained global optimization

In this thesis we propose four new methods for solving constrained global optimization problems. The first proposed algorithm is a differential evolution (DE) algorithm using penalty functions for constraint handling. The second algorithm is based on the first DE algorithm but also incorporates a filter set as a diversification mechanism. The third algorithm is also based on DE but includes an additional local refinement process in the form of the pattern search (PS) technique. The last algorithm incorporates both the filter set and PS into the DE algorithm for constrained global optimization. The superiority of feasible points (SFP) and the parameter free penalty (PFP) schemes are used as constraint handling mechanisms. The new algorithms were numerically tested using two sets of test problems and the results where compared with those of the genetic algorithm (GA). The comparison shows that the new algorithms outperformed GA. When the new methods are compared to each other, the last three methods performed better than the first method i.e. the DE algorithm. The new algorithms show promising results with potential for further research.
Assessing the role of ecological innovation and economic growth in enhancing educational performance: evidence of BRICS countries

Science symbolises education as an integral element in attaining environmental sustainability. Even after having elite-level human intelligence, this universe faces adverse effects of ecological deprivation. Therefore, this study specifically investigates the role of ecological innovation and economic growth on educational performance in BRICS nations. The present study selected the recent year ’ s data of BRICS nations to explore the repercussions of ecological innovation and the countries ’ GDP income, and chooses the potential statistical technique name CS-ARDL method. In addition, this study uses other testing tools such as the CD test, unit root analysis tests, Westerlund and Edgerton test, and Bai and Silvestre-I-Carrion cointegration test to explore the role of these variables on educational performance. The estimated outcomes of the unit root test and CD test signify the structural breaks and heterogeneity in the data, but later Pesaran and Yamagata (2008) test resolved the relative issues. Therefore, long-run and short-run analyses of the CS-ARDL method identified the positive and significant affluence of ecological innovation (GIN) and economic growth (GDP) on educational performance (EDU). Thus, some dynamic policies are introduced in the below section to nourish the educational system of BRICS countries with the help of ecological innovation and economic progression.
Coating Materials to Increase the Stability of Liposomes

Liposomes carry various compounds with applications in pharmaceutical, food, and cosmetic fields, and the administration route is especially parenteral, oral, or transdermal. Liposomes are used to preserve and release the internal components, thus maintaining the properties of the compounds, the stability and shelf life of the encapsulated products, and their functional benefits. The main problem in obtaining liposomes at the industrial level is their low stability due to fragile phospholipid membranes. To increase the stability of liposomes, phospholipid bilayers have been modified or different coating materials have been developed and studied, both for liposomes with applications in the pharmaceutical field and liposomes in the food field. In the cosmetic field, liposomes need no additional coating because the liposomal formulation is intended to have a fast penetration into the skin. The aim of this review is to provide current knowledge regarding physical and chemical factors that influence stability, coating materials for liposomes with applications in the pharmaceutical and food fields to increase the stability of liposomes containing various sensitive compounds, and absorption of the liposomes and commercial liposomal products obtained through various technologies available on the market.
Evolutionary algorithms with local search for combinatorial optimization

The goal of global optimization is to minimize (or maximize) an objective function over its entire domain. Heuristic methods such as evolutionary algorithms and simulated annealing are often employed. Alternatively, it is sometimes acceptable to find a local optimum, which is as good as all solutions in its neighborhood. Local search methods are comparatively well-understood, and local optima can often be found efficiently even for problems in which global optimization is difficult [66]. Global/local hybrid algorithms combine aspects of both global and local optimization to search more effectively than either global or local optimization by themselves. Evolutionary algorithms using local search have frequently been applied to problems in continuous optimization with great success. Not only does the addition of local search substantially improve the performance of the evolutionary algorithm, but the hybrid often outperforms other global optimization techniques such as simulated annealing. In this dissertation we explore the effectiveness of evolutionary algorithms with local search in the combinatorial domain. We show that the EA+LS hybrid is more effective for graph bisection than either the EA or LS alone, and that it is competitive with simulated annealing. A new variant of the EA+LS is presented which interleaves the global and local search operators. We find that while crossover is valuable to an EA without LS in this problem, the addition of LS obviates the need for crossover. We also show how appropriate mutation sizes in an EA+LS depend on the size of local basins. These insights point to the possibility of instance-specific heuristics, in which the search algorithm is tailored to specific features of the instances under consideration. We show that Darwinian evolution is as effective as Lamarckian, and is more robust under changes to the genetic operators. In addition, we explore the effectiveness of steady-state vs. generational EAs, different local search lengths, and various local/global ratios.
Development and validation of a stress response measure: the Daily Stress Response Scale (DSRS)

BACKGROUND To date, there is a lack of measures for capturing a broad spectrum of psychophysiological stress reactions that can be administered on a daily basis and in different contexts. A need for such a measure is especially salient in settings where stress processes can unfold momentarily and substantially fluctuate daily. Therefore, the main aim of the current study was to develop and validate the Daily Stress Response Scale (DSRS), an instrument capturing a broad spectrum of psychophysiological stress reactions that can be administered in real time and in different contexts. PARTICIPANTS AND PROCEDURE The study was conducted in the early stages of the COVID-19 pandemic in Europe. Participants were 7228 (81% female) Polish university students. The data were collected anonymously through self-completion questionnaires. The DSRS was subject to confirmatory factor analyses (CFA). RESULTS The DSRS is a 30-item, easy-to-use stress response measure with excellent psychometric properties. Based on CFA results, the scale consists of two subscales, psychological and physiological stress response, which form associations with related external criteria. CONCLUSIONS The DSRS is a reliable and valid measure of psychological and physiological stress reactions that can be used to assess the stress response to daily stressors, including those of an acute nature, such as a crisis, trauma, or surgery.
Effect of Fine Particulate Matter Exposure on Liver Enzymes: A Systematic Review and Meta-Analysis

Although previous studies have presented that fine particulate matter (PM2.5) regulates liver enzyme levels in the development of liver diseases, the evidence regarding the relationship between PM2.5 exposure and liver enzyme is not robust. We further aimed to conduct a systematic review and meta-analysis of observational studies to summarize the recent evidence on the effects of PM2.5 on liver enzyme in humans. In the meta-analysis, we retrieved online databases including PubMed and Web of Science database from 1982 up to 2022. A random-effects model was applied to evaluate the correlation between PM2.5 and liver enzyme level. A total of 10 studies fulfilled the inclusion criteria, including five prospective cohort studies, two cross-sectional studies, two longitudinal studies, and one time-series analysis. Each 10 μg/m3 increase in PM2.5 concentration was significantly correlated with a 4.45% increase in alanine aminotransferase (ALT) level (95% CI: 0.51–8.38%, p = 0.03), a 3.99% increase in aspartate transferase (AST) level (95% CI: 0.88–7.10%, p = 0.01), and a 2.91% increase in gamma-glutamyl transferase (GGT) level (95% CI: 1.18–4.64%, p < 0.001), but this significant association was not observed in alkaline phosphatase (ALP). Subgroup analysis revealed that PM2.5 has a significant correlation with ALT (5.07%, 95% CI: 0.81–9.33%), AST (4.11%, 95% CI: 0.74–7.48%), and GGT (2.74%, 95% CI: 1.09–4.38%) in Asia. Our meta-analysis showed that increments in PM2.5 exposure were significantly associated with a higher level of ALT, AST, and GGT. In addition, investigations into liver enzyme subtypes and specific chemical components of PM2.5 are important directions for future research.
Which Specific Exercise Models Are Most Effective on Global Cognition in Patients with Cognitive Impairment? A Network Meta-Analysis

(1) Introduction: Physical exercise interventions can impart significant cognitive benefits to older adults suffering from cognitive impairment (CI). However, the efficacy of these interventions can vary widely, depending on the type, intensity, duration and frequency of exercise. (2) Aim: To systematically review the efficacy of exercise therapy on global cognition in patients with CI using a network meta-analysis (NMA). (3) Methods: The PubMed, Embase, Sport Discus (EBSCO) and Cochrane Library databases were electronically searched to collect randomized controlled trials (RCTs) on exercise for patients with CI from inception to 7 August 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. The NMA was performed using the consistency model. (4) Results: A total of 29 RCTs comprising 2458 CI patients were included. The effects of different types of exercise on patients with CI were ranked as follows: multicomponent exercise (SMD = 0.84, 95% CI 0.31 to 1.36, p = 0.002), short duration (≤45 min) (SMD = 0.83, 95% CI 0.18 to 1.19, p = 0.001), vigorous intensity (SMD = 0.77, 95% CI 0.18 to 1.36, p = 0.011) and high frequency (5–7 times/week) (SMD = 1.28, 95% CI 0.41 to 2.14, p = 0.004). (5) Conclusion: These results suggested that multicomponent, short-duration, high-intensity, and high-frequency exercise may be the most effective type of exercise in improving global cognition in CI patients. However, more RCTs based on direct comparison of the effects of different exercise interventions are needed. (6) NMA registration identifier: CRD42022354978.