Individual Contributions Research Articles

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

<p>Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.<strong></strong></p>

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

<p>Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.<strong></strong></p>

Field study on endogenous perfluoroalkyl acid release and their spatiotemporal distribution processes induced by inland navigation

Dense populations and industries in regions with developed inland waterways have caused the significant discharge of perfluoroalkyl acids (PFAAs) into surrounding waterways. Despite being the dominant energy input in the waterways, the impact of ship navigation on endogenous PFAA release is unclear. In this study, a field experiment was carried out in the Wangyu River (Taihu Basin, China) to investigate the spatiotemporal distribution processes of PFAAs in the water column after passage of ships with different tonnages, speeds, and draughts. The results showed that the PFAA contents did not decrease continuously with time but increased with a lag after the passing ship triggered a transient massive dissolution of PFAAs into the overlying water. In addition, PFAA contents in suspended particulate matter (SPM) exhibited a fluctuating downward trends after their peak at the moment of ship passage. Vertically, the PFAA concentrations among the layers of overlying water were relatively homogeneous, whereas SPM exhibited substantial heterogeneity in its distribution and adsorption of PFAAs. Moreover, the differences in jet scouring velocity (u), disturbance duration (t), and draught (h) of ships resulted in large variability in PFAA contents in the water column. Variance partitioning analysis further quantified the effects of u, t, and h on total PFAAs in the water column, with individual contributions of 53 %, 12 %, and 6 %, respectively. Furthermore, the release of endogenous PFAAs induced by ship passage involved rapid and slow processes, the former determining the overall PFAA release and the latter affecting PFAA concentration recovery in the water column. The findings provide in-situ observational data on spatiotemporal variations of PFAAs in multiphase media following ship passage, enhancing our understanding of endogenous pollution in inland waterways.

Effect of pineapple leaf and ramie fiber length and weight percentage on the hybrid polymer composites dry sliding wear and air jet erosion studies

AbstractIn this work, pineapple leaf fiber (PALF) and ramie hybrid fiber‐reinforced epoxy composite samples were evaluated for their hardness strength and resistance to dry slide wear and air jet erosion. Primary reinforcement using the hybrid material was chosen from different lengths (10, 15, 20, and 25 mm) and weight percentages (10%, 20%, 30%, 40%, and 50%). The hardness strength of the hybrid composite varied from 74 to 97 Shore D hardness strength. A dry sliding wear test was conducted with the key parameters of load (20, 30, and 40 N), time (20, 10, 7 min), and speed (160, 320, and 479 rpm). By using the data analyzing software, Taguchi optimization method, the key contribution parameters were identified. In the air jet erosion experiment, impact angle (45°, 60°, and 90°), erodent velocity (28, 41, and 72 m/s), and discharge rate (1, 2.5, and 3.3 g/min) parameters were used. Fiber and matrix materials agglomeration effect and bonding behavior were identified by using the scanning electron microscope (SEM) images. The 20 mm length with 30% of the PALF and ramie hybrid fiber‐reinforced composite sample achieved high hardness strength (97 shore D) and created a high resistance toward wear and erosion studies.Highlights Natural plant pineapple leaf and ramie fibers are mixed with an epoxy matrix. Used Taguchi analysis to determine the means and signal‐to‐noise ratios Different weight percentages of the PALF and ramie hybrid composites Dry sliding wear and air jet erosion studies are conducted. Individual parameter contributions were identified.

Explaining heatwaves with machine learning

AbstractHeatwaves are known to arise from the interplay between large‐scale climate variability, synoptic weather patterns, and regional to local‐scale surface processes. Though recent research has made important progress for each individual contributing factor, ways to properly incorporate multiple or all of them in a unified analysis are still lacking. In this study, we consider a wide range of possible predictor variables from the ERA5 Reanalysis version 5 and ask how much information on heatwave occurrence in Europe can be learned from each of them. To simplify the problem, we first adapt the recently developed logistic principal component analysis to the task of compressing large binary heatwave fields to a small number of interpretable principal components. The relationships between heatwaves and various climate variables can then be learned by a neural network. Starting from the simple notion that the importance of a variable is given by its impact on the performance of our statistical model, we arrive naturally at the definition of Shapley values. Classic results of game theory show that this is the only fair way of distributing the overall success of a model among its inputs. We find a nonlinear model that explains 70% of reduced heatwave variability. The biggest individual contribution (27% of the 70%) comes from upper level geopotential; top‐level soil moisture is in second place (15%). Beyond this decomposition, Shapley interaction values enable us to quantify overlapping information and positive synergies between all pairs of predictors.

Formations of C6H from reactions C3 + C3H2 and C3H + C3H and of C8H from reactions C4 + C4H2 and C4H + C4H.

We interrogated C6H and C8H produced separately from the reactions C3 + C3H2/C3H + C3H/C3H2 + C3 → C6H + H and C4 + C4H2/C4H + C4H/C4H2 + C4 → C8H + H using product translational and photoionization spectroscopy. Individual contributions of the three reactions to the product C6H or C8H were evaluated with reactant concentrations. Translational-energy distributions, angular distributions, and photoionization efficiency curves of products C6H and C8H were unraveled. The product C6H (C8H) was recognized as the most stable linear isomer by comparing its photoionization efficiency curve with that of l-C6H (l-C8H), produced exclusively from the reaction C2 + C4H2 → l-C6H + H (C2 + C6H2 → l-C8H + H). The ionization threshold after deconvolution was determined to be 9.3 ± 0.1eV for l-C6H and 8.9 ± 0.1eV for l-C8H, which is in good agreement with theoretical values. Quantum-chemical calculations indicate that the reactions of C3 + C3H2 and C3H + C3H (C4 + C4H2 and C4H + C4H) incur no energy barriers that lie above the corresponding reactant and the most stable product l-C6H (l-C8H) with H on the lower-lying potential-energy surfaces. The theoretical calculation is in accord with the experimental observation. This work implies that the reactions of C3 + C3H2/C3H + C3H and C4 + C4H2/C4H + C4H need to be taken into account for the formation of interstellar C6H and C8H, respectively.

Molecularly Detailed View of Strong Coupling in Supramolecular Plexcitonic Nanohybrids.

Plexcitons constitute a peculiar example of light-matter hybrids (polaritons) originating from the (strong) coupling of plasmonic modes and molecular excitations. Here we propose a fully quantum approach to model plexcitonic systems and test it against existing experiments on peculiar hybrids formed by Au nanoparticles and a well-known porphyrin derivative, involving the Q branch of the organic dye absorption spectrum. Our model extends simpler descriptions of polaritonic systems to account for the multilevel structure of the dyes, spatially varying interactions with a given plasmon mode, and the simultaneous occurrence of plasmon-molecule and intermolecular interactions. By keeping a molecularly detailed view, we were able to gain insights into the local structure and individual contributions to the resulting plexcitons. Our model can be applied to rationalize and predict energy funneling toward specific molecular sites within a plexcitonic assembly, which is highly valuable for designing and controlling chemical transformations in the new polaritonic landscapes.

Study of the Effect of Green Investment Policy, Technological Innovation, and Sustainable Development on Industrial Competitiveness

This research investigates the intricate relationships among green investment policy, technological innovation, sustainable development, and industrial competitiveness within the context of Indonesia. Employing a quantitative research design, data were collected from a stratified random sample of 110 participants representing diverse industries. Descriptive statistics, correlation analysis, regression analysis, and structural equation modeling were utilized for analysis. The results revealed positive correlations and significant coefficients, indicating the individual and combined contributions of green investment policy, technological innovation, and sustainable development to industrial competitiveness. The findings underscore the importance of a holistic approach to sustainability for enhancing the competitiveness of Indonesian industries.

H-Induced Restructuring on Cu(111) Triggers CO Electroreduction in an Acidic Electrolyte.

In acidic conditions, the electroreduction of CO or CO2 (noted CO(2)RR) on metal surfaces is conventionally hindered by intense competition with the hydrogen evolution reaction (HER). In this study, we present first-principles calculations of a mechanism wherein the formation of H-induced Cu adatoms on Cu(111) serves as a pivotal trigger for CORR in acidic environments. Through an analysis of the grand canonical surface state population, we elucidate that these newly formed adatoms create an array of active sites essential for both CO adsorption and subsequent reduction. Our ensemble-based kinetic models unveil the role of adatoms, enhancing the HER while simultaneously initiating CORR. Notably, the cumulative activity of the HER and CORR is contingent upon the combination of various surface states, with their individual contributions varying based on the electrode potential and pH. The interplay between surface state dynamics and electrochemical activity sheds new light on the potential-dependent nature of the active site and reaction kinetics governing CORR on Cu(111) in acidic media.

Erythrocyte Vulnerability to Airborne Nanopollutants.

The toxicological impact of airborne polluting ultrafine particles (UFPs, also classified as nanoparticles with average sizes of less than 100 nm) is an emerging area of research pursuing a better understanding of the health hazards they pose to humans and other organisms. Hemolytic activity is a toxicity parameter that can be assessed quickly and easily to establish part of a nanoparticle's behavior once it reaches our circulatory system. However, it is exceedingly difficult to determine to what extent each of the nanoparticles present in the air is responsible for the detrimental effects exhibited. At the same time, current hemolytic assessment methodologies pose a series of limitations for the interpretation of results. An alternative is to synthesize nanoparticles that model selected typical types of UFPs in air pollution and evaluate their individual contributions to adverse health effects under a clinical assay of osmotic fragility. Here, we discuss evidence pointing out that the absence of hemolysis is not always a synonym for safety; exposure to model nanopollutants, even at low concentrations, is enough to increase erythrocyte susceptibility and dysfunction. A modified osmotic fragility assay in combination with a morphological inspection of the nanopollutant-erythrocyte interaction allows a richer interpretation of the exposure outcomes. Membrane-nanoparticle interplay has a leading role in the vulnerability observed. Therefore, future research in this line of work should pay special attention to the evaluation of the mechanisms that cause membrane damage.

DVF:Multi-agent Q-learning with difference value factorization

In cooperative multi-agent games, agents are required to learn effective cooperative behaviors within complex action spaces. An effective approach is to utilize the Individual-Global-Max (IGM) principle to decompose the global reward signal into individual contributions from each agent. However, existing methods confront two significant challenges: ensuring the accuracy of the value function decomposition process and guaranteeing monotonic improvement in joint policies. These challenges become exacerbated in scenarios involving non-monotonic matrices. To address these challenges, we introduce a novel and flexible value factorization method called Difference Value Factorization (DVF). The key idea of our method is to transform the IGM principle into a new form DVF-IGM, which addresses non-monotonic constraints by ensuring the consistency of the IGM process between the joint difference value and the complex non-linear sum of independent difference values. A centralized evaluator is employed to estimate global Q-values, which not only enhances expressiveness but also constructs difference values for updating individual value functions. We demonstrate that DVF-IGM is an equivalent transformation of IGM and that DVF has the monotonic improvement property. Empirically, our method has been shown to maintain and recover the optimal policy in non-monotonic matrix games and achieve state-of-the-art performance in cooperative tasks within the StarCraft Multi-Agent Challenge (SMAC).

Contribution of neoadjuvant chemotherapy and radiation therapy (intensity-modulated radiation therapy with concurrent chemotherapy versus stereotactic body radiation therapy alone) on tumor volume regression in patients with borderline resectable or locally advanced pancreatic ductal adenocarcinoma: A single institution experience.

707 Background: Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis with a minority of patients (pts) eligible for curative resection. Pts with borderline resectable or locally advanced (BR/LA) PDAC frequently receive neoadjuvant chemotherapy (NAC) +/- radiation therapy (RT) to improve resectability. Here, we report the individual contribution of NAC and RT (either intensity modulated RT + concurrent chemotherapy (IMRT+CC) or stereotactic body RT (SBRT) alone) on tumor regression by measuring tumor volumes. Methods: We performed a single institution retrospective review of PDAC pts who underwent resection between 2011-2021 and were part of a tumor biobank registry. Tumor measurements were performed at baseline, post-NAC, and post-RT by five independent, blinded abdominal radiologists. Analyses were performed using paired t-tests, two-proportion z-tests, log-rank tests, and Mann-Whitney U test. Results: We identified 49 BR/LA pts who received NAC followed by RT who had complete pre- and post-treatment imaging. Fifteen pts received IMRT+CC and 28 received SBRT. The effect of NAC on tumor volumes (-29.2% vs -26.3%, p=0.66; mean percent change) and maximum diameter (-9.2% vs -12.0%, p=0.58) was similar within the IMRT+CC and SBRT groups. Both IMRT+CC (-7.12 cm3, p=0.003) and SBRT (-2.70 cm3, p=0.001) resulted in significant tumor regressions. IMRT+CC achieved a significantly greater overall mean percent change in tumor volume (-53.4% vs -28.8%, p=0.024) and maximum diameter (-30.4% vs -16.2%, p=0.020) compared to SBRT. Pts receiving IMRT+CC or SBRT had similar tumor volumes (10.88 vs 11.74 cm3, p=0.87, respectively) and maximum diameter (3.45 vs 3.17 cm, p=0.48) prior to RT. IMRT+CC resulted in improved tumor volume response (>30% regression; 80.0% vs 46.4%; p=0.033) but not an improved response rate by RECIST criteria (46.7% vs 21.4%; p=0.085). Mean resected tumor volumes (by pathological review) for those receiving IMRT+CC (n=9) and SBRT (n=23) were 4.34 vs 13.1 cm3 (p=0.049). The R0 (86.7% vs 85.7%, p=0.93) and N0 (73.3% vs 53.6%, p=0.21) resection rates between the two groups were similar. Compared to SBRT, IMRT+CC resulted in an improved recurrence free survival (RFS; 2.36 vs 1.31 years; p=0.026), but did not meet statistical significance for an overall survival benefit (mOS; 2.85 vs 2.03 years, p=0.19). Conclusions: We found that IMRT+CC yielded superior radiographic volumetric regression and improved RFS but did not significantly impact mOS. Correspondingly, resected tumors from patients treated with IMRT+CC were smaller than those obtained from patients treated with SBRT. The role of IMRT+CC and SBRT in PDAC remains to be clearly defined.

Exploring socio-educational learning transfer between school and non-formal school football in six- and seven-year-old children

ABSTRACT Background Extracurricular activities are an inexorable training complement to the curricular learning promoted at school. Among the spectrum of possibilities, physical and sports activities stand out. School football is a privileged activity in the Spanish context due to its volume of participation and involvement. The educational-sports institutionalization of the child converges between the formal curricular planning of the school and the dynamic learning in extracurricular activities. Purpose This study aims to understand the main educational implications in the transfer of learning between the non-formal environment of school football and school, in order to seek optimization of monitoring strategies. Method An ethnographic case study design is presented, where 57 six-year-old and 44 seven-year-olds schoolchildren are accompanied during their first and second year of participation in football through participant observation. 207 observation sessions include families, relatives, rival teams, monitors and organizers and are carried out between matches, training sessions and school breaks, which are complemented by interviews with 21 of their teachers. Results The results categorize four outstanding learning transfer areas: quasi-schooling, socialization, Construction of a competitive identity and self-assessment. First, quasi-schooling, that represents the mobilization of curricular contents in school and in the family environment through football, combines formal teaching and non-formal learning environments. Balancing extracurricular activities, such as football, with academic responsibilities is a challenge. Football serves as a valuable educational tool, linking it to curriculum content, promoting healthy habits, and establishing connections with students. Second, football-mediated socialization revolves around the complex network of relationships within school football. It fosters social skills and cooperation among team members, while also highlighting social hierarchies based on skill levels. Those who enjoy football form close-knit groups, impacting classroom dynamics. Third, construction of a competitive identity involves the child's self-esteem and adaptation to team roles. Expectations, attributed profiles, emotional management, competitive morality, and self-esteem are key factors in shaping the child's identity within the competitive football environment. Finally, Evaluation in football is predominantly result-oriented, with judgments made by adults based on performance. Children internalize this perspective, creating their own self-assessment standards focused on quantifiable success and individual contributions to the team's outcome. Conclusion The study highlights the importance of coordination between school football and formal education. Communication among educational agents (families, educators and sports monitors) is crucial for optimizing children's learning. School football strengthens the educational relationship, promotes knowledge transfer, and enhances children's identity. Addressing conflicts, developing social skills, and fostering a healthy understanding of competition are recommended. Coordinating extracurricular activities is essential for meaningful and equitable education, bridging educational and social gaps.

Streptococcus pneumoniae secretion chaperones PrsA, SlrA, and HtrA are required for competence, antibiotic resistance, colonization, and invasive disease.

Streptococcus pneumoniae is a Gram-positive bacterium and a significant health threat with the populations most at risk being children, the elderly, and the immuno-compromised. To colonize and transition into an invasive infectious organism, S. pneumoniae secretes virulence factors that are translocated across the bacterial membrane and destined for surface exposure, attachment to the cell wall, or secretion into the host. The surface exposed protein chaperones PrsA, SlrA, and HtrA facilitate S. pneumoniae protein secretion; however, the distinct roles contributed by each of these secretion chaperones have not been well defined. Tandem Mass-Tagged Mass Spectrometry and virulence, adhesion, competence, and cell wall integrity assays were used to interrogate the individual and collective contributions of PrsA, SlrA, and HtrA to multiple aspects of S. pneumoniae physiology and virulence. PrsA, SlrA, and HtrA were found to play critical roles in S. pneumoniae host cell infection and competence, and the absence of each of these secretion chaperones significantly altered the S. pneumoniae secretome in distinct ways. PrsA and SlrA were additionally found to contribute to cell wall assembly and resistance to cell wall-active antimicrobials and were important for enabling S. pneumoniae host cell adhesion during colonization and invasive infection. These findings serve to further illustrate the pivotal contributions of PrsA, SlrA, and HtrA to S. pneumoniae protein secretion and virulence.

Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics

Technological advancements have facilitated the development of sophisticated vision systems, integrating optical sensors with artificial vision and machine learning techniques to create applications in different fields of robotics. One such field is Search and Rescue (SAR) robotics, which has historically played a significant role in assisting brigades following post-disaster events, particularly in exploration phases and, crucially, in victim identification. The importance of employing these systems in victim identification lies in their functionality under challenging conditions, enabling the capture of information across different light spectrum ranges (RGB, Thermal, Multispectral). This article proposes an innovative comparative analysis that scrutinizes the advantages and limitations of three sensor types in victim detection. It explores contemporary developments in the state-of-the-art and proposes new metrics addressing critical aspects, such as functionality in specific scenarios and the analysis of environmental disturbances. For the indoor and outdoor testing phase, a quadrupedal robot has been equipped with these cameras. The primary findings highlight the individual contributions of each sensor, particularly emphasizing the efficacy of the infrared spectrum for the thermal camera and the Near Infrared and Red Edge bands for the multispectral camera. Ultimately, following system evaluations, detection precisions exceeding 92% and 86%, respectively, were achieved.

Influence of Temperature, Light, and H2O2 Concentration on Microbial Spore Inactivation: In‐Situ Raman Spectroscopy Combined with Optical Trapping

To gain insight on chemical sterilization processes, the influence of temperature (up to 70 °C), intense green light, and hydrogen peroxide (H2O2) concentration (up to 30% in aqueous solution) on microbial spore inactivation is evaluated by in‐situ Raman spectroscopy with an optical trap. Bacillus atrophaeus is utilized as a model organism. Individual spores are isolated and their chemical makeup is monitored under dynamically changing conditions (temperature, light, and H2O2 concentration) to mimic industrially relevant process parameters for sterilization in the field of aseptic food processing. While isolated spores in water are highly stable, even at elevated temperatures of 70 °C, exposure to H2O2 leads to a loss of spore integrity characterized by the release of the key spore biomarker dipicolinic acid (DPA) in a concentration‐dependent manner, which indicates damage to the inner membrane of the spore. Intensive light or heat, both of which accelerate the decomposition of H2O2 into reactive oxygen species (ROS), drastically shorten the spore lifetime, suggesting the formation of ROS as a rate‐limiting step during sterilization. It is concluded that Raman spectroscopy can deliver mechanistic insight into the mode of action of H2O2‐based sterilization and reveal the individual contributions of different sterilization methods acting in tandem.

Mindscapes of Mystery: Comparative Approaches in the Works of Arthur Conan Doyle and Agatha Christie

Mystery fiction, also known as detective fiction or crime fiction, is a literary genre that revolves around solving a puzzle or mystery often related to the criminal events. It typically features a central mystery, such as murder, theft, or other mysterious circumstances, that serves as the driving force of the narrative. This research delves into the comparative approaches adopted by two celebrated mystery writers, Arthur Conan Doyle and Agatha Christie, in their literary works. With a specific focus on their iconic detectives, Sherlock Holmes and Miss Marple, respectively, the study thoroughly analyzes various aspects of their storytelling techniques. By closely examining their works, the research sheds light on the distinct writing styles, plot structures, characterizations, and thematic elements employed by each author. This exploration provides valuable insights into the individual contributions of Doyle and Christie to the mystery fiction genre. Furthermore, the study explores the influence of historical context on the development of their characters and intricately woven mysteries. It unravels how societal and cultural factors impacted their narratives, adding depth to the characters and their adventures. The findings of this comparative analysis not only offer a deeper appreciation of the enduring appeal of Holmes and Miss Marple as fictional detectives but also demonstrate the broader impact of Doyle and Christie on the landscape of detective literature.

Analisis Peran Persepsi Mahasiswa Mengenai Pendidikan Kewarganegaraan Dalam Memajukan Ekonomi

This research uses a case study method approach to analyze student perceptions about citizenship education and its impact on economic progress. Citizenship education is an important component in the development of individuals as responsible citizens, and has the potential to influence a country's economic sector. In this research, we conducted surveys and interviews with students from various backgrounds and majors at a university. The data obtained revealed diverse perceptions about citizenship education and how understanding of social and political issues influences their views about individual contributions to economic progress. The results of this research can provide valuable insight into the role of citizenship education in shaping students' thinking about economic progress and how such education can be improved to more effectively prepare students to become active and productive citizens in driving the country's economic growth. This research also identifies the potential for collaboration between educational institutions and the economic sector to advance a more effective and relevant citizenship education agenda.

Thermodynamically Optimized Machine-Learned Reaction Coordinates for Hydrophobic Ligand Dissociation.

Ligand unbinding is mediated by its free energy change, which has intertwined contributions from both energy and entropy. It is important, but not easy, to quantify their individual contributions to the free energy profile. We model hydrophobic ligand unbinding for two systems, a methane particle and a C60 fullerene, both unbinding from hydrophobic pockets in all-atom water. Using a modified deep learning framework, we learn a thermodynamically optimized reaction coordinate to describe the hydrophobic ligand dissociation for both systems. Interpretation of these reaction coordinates reveals the roles of entropic and enthalpic forces as the ligand and pocket sizes change. In both cases, we observe that the free-energy barrier to unbinding is dominated by entropy considerations. Furthermore, the process of methane unbinding is driven by methane solvation, while fullerene unbinding is driven first by pocket wetting and then fullerene wetting. For both solutes, the direct importance of the distance from the binding pocket to the learned reaction coordinate is present, but low. Our framework and subsequent feature important analysis thus give useful thermodynamic insight into hydrophobic ligand dissociation problems that are otherwise difficult to glean.

The Microbial Diversity on the Surface of Smear-Ripened Cheeses and Its Impact on Cheese Quality and Safety.

Smear-ripened cheeses are characterized by a viscous, red-orange surface smear on their rind. It is the complex surface microbiota on the cheese rind that is responsible for the characteristic appearance of this cheese type, but also for the wide range of flavors and textures of the many varieties of smear-ripened cheeses. The surface smear microbiota also represents an important line of defense against the colonization with undesirable microorganisms through various types of interaction, such as competitive exclusion or production of antimicrobial substances. Predominant members of the surface smear microbiota are salt-tolerant yeast and bacteria of the phyla Actinobacteria, Firmicutes, and Proteobacteria. In the past, classical culture-based approaches already shed light on the composition and succession of microorganisms and their individual contribution to the typicity of this cheese type. However, during the last decade, the introduction and application of novel molecular approaches with high-resolution power provided further in-depth analysis and, thus, a much more detailed view of the composition, structure, and diversity of the cheese smear microbiota. This led to abundant novel knowledge, such as the identification of so far unknown community members. Hence, this review is summarizing the current knowledge of the diversity of the surface smear microbiota and its contribution to the quality and safety of smear-ripened cheese. If the succession or composition of the surface-smear microbiota is disturbed, cheese smear defects might occur, which may promote food safety issues. Hence, the discussion of cheese smear defects in the context of an increased understanding of the intricate surface smear ecosystem in this review may not only help in troubleshooting and quality control but also paves the way for innovations that can lead to safer, more consistent, and higher-quality smear-ripened cheeses.