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USER PRIVACY IN THE DIGITAL PLAYGROUND: AN IN-DEPTH INVESTIGATION OF FACEBOOK INSTANT GAMES
No full textAmid growing concerns over data privacy in web and mobile applications, this study aims to assess the privacy mechanisms in instant games on Facebook, a platform with approximately 3.03 billion monthly active users and a substantial repository of personal data. Instant Games have become increasingly popular due to their ease of access and social integration features. Investigating these games can provide insights into privacy mechanisms and practices, thereby informing the development of more fair, compliant, and user privacy-centric gaming experiences. Thus, this study proposes an integrated analytical framework that leverages a combination of descriptive, memory, and network analysis techniques to examine privacy mechanisms in Facebook Instant Games. It focuses on evaluating the permission model, default settings, configurations, and API usage, as well as their impact on user data access, transfer, and sharing. Our findings uncover discrepancies between privacy policies and actual user data notices. Through generalized settings and lack of explicit consent mechanism, our study reveals a system that often favors functionality over user privacy. Moreover, we highlight the reliance on powerful APIs that, while enhancing gameplay, pose additional privacy risks by granting broad data access to third-party services without direct user approval
Establishing a Framework of Best Management Practices for Sustainable Water Quality: Integration of Technology and Remote Sensing
No full textWater quality security is a complex management issue that is constantly challenged by factors of seasonal variability in climate change, urban population growth, and agricultural intensification. These factors warrant the need for innovative technological adaptations to management practices that integrate remote monitoring, predictive modeling, and sustainable resource utilization. This study establishes a framework for sustainable water quality practices through the integration of remote sensing technology for urban wastewater treatment and agricultural irrigation systems. This dual-site research study explores seasonal water quality dynamics and technological interventions to create a more proactive approach to water quality security.
In the first study, water quality metrics were collected continuously for a year from the Gonzales Wastewater Treatment Facility using Yellow System Instruments (YSI) EXO2 multiparameter sonde. The objective of this study was to characterize seasonal variation in water quality parameters that influence the performance of biological nitrogen removal (BNR) in subtropical climate zones. The collection of hourly data at points of influent and effluent wastewater presented significant correlations between water temperature, dissolved oxygen (DO), and the efficiency of nitrogen processing. The conclusion of this study resulted in the identification of critical factors of vulnerability for BNR and offers insight into a proactive water quality management of operations.
The second study assessed the impact of floating photovoltaic technology at the Louisiana State University Hammond Research Station (Hammond, LA) by sampling water quality (YSI EXO2) to aid algal biomass management in agricultural irrigation systems under the generation of panel shading. The floating photovoltaic array served a dual purpose of energy generation along with inhibiting solar radiation from entering the water column. By doing so, algal proliferation may be controlled in the irrigation detention pond while water quality parameters may be characterized to develop the framework for predictive modeling of algal biomass management.
This dual-site investigation offers insight for environmental management by establishing a comprehensive framework of climate influenced water quality management through the integration of technological solutions
Attosecond Clocking and Control of Strong Field Quantum Trajectories
No full textWe introduce a quantum trajectory selector method capable of resolving individual quantum trajectories responsible for strong-field phenomena in real time, revealing the dependence of the electron dynamics on the ionization time. Using an attosecond extreme ultraviolet pulse train, we select the moment of ionization and measure the rates of rescattered electron emission and double ionization driven by a phase locked near IR (1.77 or 2.4 μm) field. We show that there is an intensity-dependent shift in the ionization time associated with double ionization, and we clock this shift as it varies by 250 as. The quantum trajectory selector provides a new attosecond paradigm for expanding our understanding of recollision-driven physics
Variations in Soil Fungal Community Composition Along A Salinity Gradient in Yellow River Delta, China
No full textThe Yellow River Delta (YRD) of China is one of the most active land-sea interaction deltas in the world. However, due to human activities and climate change, it has undergone significant changes, including the degradation of natural wetlands and saltwater intrusion. As an integral part of soil microorganisms, fungi play a crucial role in maintaining and stabilizing the function of wetland ecosystems. To better understand the composition and diversity changes of fungal communities along a salinity gradient in the YRD of China and their relationship with environmental factors, fungal diversity, abundance, and composition in the sediments of four typical vegetation communities spanning from the riverbank to the seaside were investigated. The results showed that the electrical conductivity (EC) increased significantly from the riverbank to the coastal area (P \u3c 0.05), but the levels of total nitrogen (TN), total carbon (TC), total sulfur (TS), available phosphorous (AP), and ammonium (NH4+-N) increased in Phragmites australis community and then experienced a significant decrease in Tamarix chinensis community and Suaeda salsa community (P \u3c 0.05). The alpha diversity (Shannon and Simpson indices) of the soil fungal community exhibited a negative correlation with EC. There was a significant alteration in the structure of the fungal community, primarily influenced by EC and NO3--N. Ascomycota was found to be the most abundant phylum, and its relative abundance is positively correlated with pH and TS. The relative abundance of Sordariomycetes, the second-largest class of Ascomycota, reached 38.95%. Salinity was identified as the most important factor driving changes in soil fungal community composition. In summary, the fungal community changed significantly along the salinity gradient, and different environmental factors impacted various tiers of fungal populations differently. The findings of this study lay the groundwork for comprehending soil fungal communities and their primary influencing factors in newly formed wetlands
Modeling the Effects of Temperature and Resource Quality on the Outcome of Competition Between Aedes aegypti and Aedes albopictus and the Resulting Risk of Vector-Borne Disease
No full textThe community composition of vectors and hosts plays a critical role in determining risk of vector-borne disease transmission. Aedes aegypti and Aedes albopictus, two mosquito species that both transmit the viruses that cause dengue, chikungunya, and Zika, share habitat requirements and compete for resources at the larval stage. Ae. albopictus is generally considered a better competitor under many conditions, while Ae. aegypti is able to tolerate higher temperatures and is generally a more competent vector for many pathogens. We develop a stage-structured ordinary differential equation model that incorporates competition between the juvenile stages of two mosquito populations. We incorporate experimental constraints on competition coefficients for high and low quality food resources and explore differences in the potential outcomes of competition. We then incorporate temperature-dependent fecundity rates, juvenile development rates, and adult mortality rates for each species, and we explore competition outcomes as a function of temperature. We show that regions of coexistence and competitive exclusion depend on food quality and relative values of temperature-dependent life history parameters. Finally, we investigate the combined impacts of temperature and competition on the potential for dengue transmission, and we discuss our results in the context of present and future risk of mosquito-borne disease transmission
From the Mountains to the Beach: Water Purification Ecosystem Services and Recreational Beach Use in Puerto Rico
No full textRecreational beach use is important for coastal economies and is influenced by water clarity, a trait that may be maintained by water purification ecosystem services (ESs). However, few studies have addressed these linkages. In this study, we ask the following questions: (1) Do watershed-scale water purification ecosystem services influence coastal water quality? (2) Does coastal water quality help explain beach visitation rates? To address these questions, we focused on Puerto Rico (PR), where coastal tourism has economic and cultural importance. We estimated water purification ESs using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), coastal water quality using long-term monitoring data, and beach visitation rates using the InVEST Recreation model. We used Analysis of Variance (ANOVA) and regression analysis to evaluate these linkages accounting for influential anthropogenic factors (amenities, population density, and impervious surfaces). Water purification ESs strongly predicted coastal water quality, which, in turn, significantly explained beach water clarity. However, amenities and impervious surfaces best explained beach visitation. Our study suggests a disconnect between water quality and recreational beach use in PR, which should be explored further
Cell Death Helps to Stabilize Populations of Prochlorococcus
No full textProchlorococcus is one of the dominant phytoplankton species in tropical and subtropical pelagic marine ecosystems. To maintain its population equilibrium in oligotrophic waters, the dominant species Prochlorococcus requires high loss rates to offset its high abundance and growth rates. While theoretical frameworks recognize multiple loss processes for Prochlorococcus (viral lysis, environmental stress, programmed cell death), prevailing population studies attribute main mortality to grazing. This oversight stems from in situ data gaps of non-grazing mortality processes. In this study, we firstly formulated simple differential equations that took account of population losses due to both grazing and cell death. We then used ship-based flow cytometry to assess the abundance of both non-reproductive and live cells of two Prochlorococcus ecotypes in the South China Sea, and we calculated cell loss rates due to grazing and cell death. We found that: (a) vertical profiles of the abundance and cell death rate of Prochlorococcus were consistent, and they differed between high-light and low-light adapted ecotypes; (b) the high-light adapted ecotype dominated at depths shallower than 50 m; the low-light adapted ecotype dominated in the deep chlorophyll maximum layer (75 m); (c) during daylight hours, the cell death rate was significantly greater than the loss rate due to grazing, with nighttime reversal. The field results confirmed that natural cell mortality helped maintain population equilibrium
Interactive effects of CO2, temperature, and nitrate limitation on the growth and physiology of strain CCMP 1334 of the marine cyanobacterium Synechococcus (Cyanophyceae)
No full textThe marine cyanobacterium Synecococcus sp. (CCMP 1334) was grown in a continuous culture system on a 12:12 h light:dark cycle at all combinations of low and high pCO2 (400 and 1000 ppmv, respectively), nutrient availability (nitrate-limited and nutrient-replete conditions), and temperatures of 21, 24, 28, 32, and 35°C. The maximum nutrient-replete growth rate was ~1.15 day−1 at 32–35°C. Median nutrient-replete growth rates were higher at 1000 ppmv than at 400 ppmv pCO2 at all temperatures. Carbon:nitrogen ratios were independent of pCO2 at a fixed relative growth rate (i.e., growth rate ÷ nutrient-replete growth rate) but decreased with increasing temperature. Carbon:chlorophyll a ratios were decreased monotonically with increasing temperature and were higher under nitrate-limited than nutrient-replete conditions. Ratios of phycoerythrin to chlorophyll a were independent of growth conditions. Productivity indices were independent of temperature and nutrient limitation but were consistently higher at 1000 ppmv than 400 ppmv pCO2. Both growth rates and dark respiration rates were positively correlated with temperature, and the associated Q10 values were 2.2 and 2.3, respectively. A model of phytoplankton growth in which cellular carbon is allocated to structure, storage, or the light or dark reactions of photosynthesis accounted for the general patterns of cell composition and growth rate. This strain of Synechococcus appears well suited to changes in environmental conditions that are expected as the climate warms in response to anthropogenic emissions of CO2
Artificial Intelligence in Head and Neck Cancer: Towards Precision Medicine
No full textOver the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future directions to help overcome them and advance AI’s integration into HNC care as a step toward personalized medicine. Additionally, we highlight the importance of multidisciplinary and multispecialty collaboration in cultivating a robust body of research that will support the optimization of AI in clinical practice
APPLICATIONS OF MACHINE LEARNING TECHNIQUES TO UNDERSTAND HEAT RECOVERY IN GEOTHERMAL ENERGY SYSTEMS
No full textAbstract
This study presents a data-driven framework for modeling, interpreting, and forecasting heat recovery behavior in hydrothermal and fractured geothermal reservoirs. By combining scientific computing with unsupervised and supervised machine learning, this research offers a robust methodology for extracting dominant thermal patterns to optimize performance in various geothermal systems.
The approach begins with dimensionless modeling, identifying fifteen groups via inspectional analysis to represent key geophysical and operational dynamics of hydrothermal reservoirs. Self-Organizing Maps (SOM) cluster normalized production temperature profiles into four temporal regimes (early, early-intermediate, late-intermediate, and late). Non-negative Matrix Factorization with K-means (NMFK) extracts five latent temperature signatures tied to these regimes. Supervised models (XGBoost, Random Forest, and Deep Neural Networks) are trained on each regime, optimized with Optuna, and interpreted using SHAP analysis to identify the influence of key features such as temperature ratio, thermal Peclet number, fluid expansion, and reservoir geometry on the thermal regimes. These models achieve high accuracy (R² \u3e 0.95).
To scale the workflow, 250 geothermal simulations are developed from five base cases encompassing varied fracture configurations representing enhanced geothermal systems (EGS). Each model incorporates deterministic and stochastic fractures, dynamic well placements, and consistent thermal-fluid-rock properties. Simulation outputs including temperature and pressure data from fracture and matrix zones are processed with NMFK to reveal temporal thermal signatures. These patterns differentiate between fracture-dominated regimes, with early thermal breakthrough, and matrix-buffered regimes characterized by gradual heat conduction.
This integrated framework combining dimensionless physics, machine learning clustering, and predictive modeling offers a replicable toolset for geothermal reservoir characterization and supports scalable, interpretable solutions for sustainable energy development