Extensive Studies Research Articles

“AI and Content in Marketing”- Correlations on Perception

The project course was an exploration on ‘AI and content in marketing’. Given the presence of AI in everyday lives today, the research was focused on understanding the usage of AI in the domain of marketing from both the perspectives, that of marketing professionals and the consumers who are the perceiver of the content produced. The research delved deeper using different processes like in-depth interviews and survey and an extensive study of existing literature to form a strong foundation and find ideas that can be used to further the scope of AI by finding different metrics to study its perception and making AI a part of the domain of marketing.

Phase 2b program with sonlicromanol in patients with mitochondrial disease due to m.3243A>G mutation.

Mitochondrial disease is a group of rare conditions, with no approved treatment to date, except for Leber hereditary optic neuropathy. Therapeutic options to alleviate the symptoms of mitochondrial disease are urgently needed. Sonlicromanol is a promising candidate, as it positively alters the key metabolic and inflammatory pathways associated with mitochondrial disease. Sonlicromanol is a reductive and oxidative distress modulator, selectively inhibiting microsomal prostaglandin E1 synthase activity. This Phase 2b program, aiming at evaluating sonlicromanol in adults with m.3243A>G mutation and primary mitochondrial disease, consisted of a randomized controlled (RCT) study (dose-selection) followed by a 52-week open-label extension study (EXT, long-term tolerability, safety, and efficacy of sonlicromanol). Patients were randomized (1:1:1) to receive 100- or 50-mg sonlicromanol, or placebo twice daily (bid) for 28 days with ≥2-week wash-out period between treatments. Patients who completed the RCT study entered the EXT study wherein they received 100-mg sonlicromanol bid. Overall, 27 patients were randomized (24 RCT patients completed all periods). 15 patients entered the EXT, and 12 patients were included in the EXT analysis set. All patients reported good tolerability and favourable safety, with pharmacokinetic results comparable to the earlier Phase 2a study. The RCT primary endpoint (change from placebo in the attentional domain of cognition score [IDN: visual identification, Cogstate]) did not reach statistical significance. Using a categorisation of the subject's period baseline a treatment effect over placebo was observed if their baseline was more affected (p=0.0338). Using this approach, there were signals of improvements over placebo in at least one dose in the Beck Depression Inventory (BDI, p=0.0143), Cognitive Failure Questionnaire (CFQ, p=0.0113), and the Depression subscale of the Hospital Anxiety and Depression Scale (p=0.0256). Statistically and/or clinically meaningful improvements were observed in the patient- and clinician-reported outcome measures at the end of the EXT study (Test of attentional performance [TAP] with alarm, p=0.0102; TAP without alarm, p=0.0047; BDI somatic, p=0.0261; BDI Total, p=0.0563; SF12 physical component score, p=0.0008). Seven of nine domains of RAND-Short form-36 like SF-36 pain improved (p=0.0105). Other promising results were observed in Neuro QoL-Fatigue-SF (p=0.0036), MiniBESTest (p=0.0009), McGill Pain Questionnaire (p=0.0105), EQ-5D-5L-VAS (p=0.0213) and EQ-5D-5L-index (p=0.0173). Most patients showed improvement in the 5× sit-to-stand test. Sonlicromanol was well-tolerated and demonstrated a favourable benefit/risk ratio for up to one year. Sonlicromanol was efficacious in patients when affected at baseline, as seen across a variety of clinically relevant domains. Long-term treatment showed more pronounced changes from baseline.

Reinforcement learning for online testing of autonomous driving systems: a replication and extension study

Reinforcement learning for online testing of autonomous driving systems: a replication and extension study

Vineyard maturity increases arbuscular mycorrhizal and decreases plant pathogen fungal relative abundance in bulk soil across a 1,000 km Chilean gradient

Societal Impact StatementWine production has a significant impact on the global economy. Despite this, the microbial composition of the terroir has not been studied sufficiently, particularly in the southern hemisphere. Here, we investigated how bulk soil fungal communities are affected by several abiotic and biotic factors across 1,000 km of Chilean vineyards. We found that geographical distance was the main driver of vineyard soil fungal communities. Irrespective of variety, older vineyards host a higher relative abundance of arbuscular mycorrhizal fungi (AMF) and a lower relative abundance of plant pathogenic fungi. This result could lead to significant recommendations on when to apply AMF‐based inoculants.Summary The microbial dimension of the terroir has been increasingly recognized as an essential factor determining vineyard productivity and quality. Despite this, few studies have analyzed the factors affecting soil fungal communities of vineyards in the southern hemisphere. Across a 1,000 km gradient encompassing 34 Chilean vineyards, we investigated the effects of grapevine variety, geographical distance, maturity, and soil chemical properties on the diversity and composition of soil fungi. We implemented standard soil chemical analyses and ITS‐based DNA metabarcoding of bulk soil. We found that the total soil fungal composition was significantly affected by geographical distance but not by grapevine variety and maturity. Soil chemical dissimilarity also significantly affected soil fungal composition. When analyzing specific fungal guilds, we found a contrasting successional pattern: arbuscular mycorrhizal fungal relative abundance was significantly higher at medium and old‐maturity vineyards compared with young vineyards, on which plant pathogenic fungi had a markedly lower abundance. Our results present several caveats: the molecular marker was not the most commonly used for arbuscular mycorrhizal fungi, bulk soil was sampled (instead of roots), and all abundances were relative. Still, this study constitutes one of the most extensive studies on soil fungi – both for vineyards and Chile – and might have practical implications, as knowing the drivers of fungal and mycorrhizal biodiversity can inform agricultural management decisions.

Gut Microbiome and Carotid Artery Intima-Media Thickness: A Narrative Review of the Current Scenario

Up to the last update, the gut microbiome (GM) had been associated with a different physiologic host process, including those affecting cardiovascular health. The carotid intima-media thickness (IMT) is an indicator of atherosclerosis and cardiovascular risk. The GM influence on atherosclerosis progression has garnered growing attention in recent years but the consensus in subclinical atherosclerosis remains elusive. The aim of this narrative review is to investigate the connection between the GM and carotid IMT, encompassing mechanisms like the microbiome impact on metabolite production, and systemic inflammation, and its effects on endothelial function. The literature analysis revealed that the GM appears to exert an influence on carotid IMT development, likely through mechanisms involving metabolites’ production, systemic inflammation, and endothelial function modulation. Additional research, however, is needed to finely elucidate the relationship between the GM and atherosclerosis. Specifically, more extensive studies are required to pinpoint individuals at the highest risk of developing atherosclerosis based on their GM composition. This will facilitate the enhancement and optimization of cardiovascular disease prevention strategies and enable the treatments’ customization for each patient. Further investigations are required to refine patient outcomes in the context of probiotics and other interventions aimed at improving microbiome composition and function.

SMART DENGUE CONTROL

This research presents a novel approach to combating dengue by integrating Biological Control methods with Artificial Intelligence (AI). The objective of the study is to reduce the Aedes aegypti mosquito population and decrease dengue transmission through sustainable strategies. A comprehensive research methodology was used, involving extensive literature review, case studies, and data analysis to identify the main factors driving the high incidence of dengue in Brazil. The research applied AI tools to analyze epidemiological, meteorological, and environmental data, allowing for early outbreak predictions and targeted interventions. AI-enhanced surveillance systems were also employed to monitor mosquito populations and identify breeding sites more accurately. These strategies were combined with biological control methods, such as using natural predators and genetically modified mosquitoes, to manage mosquito populations effectively. The study demonstrate that the integration of AI and biological control can significantly improve the efficiency, accuracy, and sustainability of dengue control measures. The study concludes that this data-driven approach, when adapted to local contexts, can reduce reliance on chemical pesticides and offer a more resilient framework for long-term dengue management.

Decompositions of Information Criteria in Joint Model Under Probit Link for Response

ABSTRACTThe item response and response time are naturally encountered in variety of computerized testing, which are jointly analysed to achieve efficient model fitting. Several novel Bayesian assessment criteria, which quantify the enhancement in the fit of one dataset given the other, are proposed by utilizing decompositions of the deviance information criterion (DIC) and the logarithm of the pseudo‐marginal likelihood (LPML) under a logistic link. However, the calculation of these criteria under a probit link, a commonly employed alternative, remains unexplored. Moreover, decomposing DIC and LPML under probit link presents greater challenges than in the logistic model due to the intricate additional dimension integral. This paper addresses this gap by demonstrating the application of Bayesian model assessment criteria with a normal ogive model in the context of item response data within a joint modelling framework. Additionally, we also illustrate how to implement Bayesian sampling via a powerful R package ‘nimble’. Extensive simulation studies are conducted to evaluate the empirical performance of those criteria. Further, a detailed analysis of the Programme for International Student Assessment (PISA) data is carried out to illustrate the methodology.

Enhancing Mathematical Interest through Visual Arts Integration: A Systematic Literature Review

This study explores the inclusion of the visual arts in mathematics instruction as a means of piquing students' interest in a topic that is frequently regarded as difficult. We evaluate numerous research that have been undertaken in this situation by completing an extensive literature study. Our findings show that the inclusion of visual arts in mathematics instruction has been helpful in piquing students' attention, boosting their motivation, and creating possibilities for them to investigate challenging mathematical ideas in a more imaginative way. These results provide a deeper understanding of the importance of visual arts in creating an engaging mathematics learning environment. The study also highlights challenges that need to be addressed and provides insights for further development in the application of visual arts in mathematics education. Thus, the integration of visual arts has significant potential to build interest in mathematics and enrich students' learning experiences.

The Refinement of a Common Correlated Effect Estimator in Panel Unit Root Testing: An Extensive Simulation Study

The Common Correlated Effect (CCE) estimator is widely used in panel data models to address cross-sectional dependence, particularly in nonstationary panels. However, existing estimators have limitations, especially in small-sample settings. This study refines the CCE estimator by introducing new proxy variables and testing them through a comprehensive set of simulations. The proposed method is simple yet effective, aiming to improve the handling of cross-sectional dependence. Simulation results show that the refined estimator eliminates cross-sectional dependence more effectively than the original CCE, with improved power properties under both weak- and strong-dependence scenarios. The refined estimator performs particularly well in small sample sizes. These findings offer a more robust framework for panel unit root testing, enhancing the reliability of CCE estimators and contributing to further developments in addressing cross-sectional dependence in panel data models.

Ceanothus: Taxonomic patterns in life history responses to fire.

Ceanothus (Rhamnaceae) is a large genus of shrubs that dominate California chaparral and are resilient to fires. Persistence is ensured by resprouting and/or seedling recruitment from dormant seed banks. Some species do both and others, the obligate seeders, are entirely dependent on seedling recruitment. The distribution of these two modes within the genus is poorly documented. We used all available publications that document species responses to fire and filled most gaps in the literature based on extensive field studies of more than 60 recent wildfires in California. The genus is divided into two subgenera, Ceanothus and Cerastes. Ceanothus is widely considered to comprise mostly resprouting species and Cerastes to consist of only obligate seeders. The subgenus Ceanothus includes resprouting species throughout their range from the eastern United States and Midwest to western United States. Within the California Floristic Province (CFP), a few species are unique in producing massive lignotubers that develop from repeated fires; however, within the CFP, the majority of species in this subgenus do not resprout and are obligate seeders. Two have disjunct subspecies that are facultative seeders or obligate seeders. Previously, speciation in this genus was contended to have occurred in the late Miocene within the CFP. The syndrome of obligate seeding is most strongly represented in this region, and we hypothesize that evolution of this syndrome was a response to increased predictability of fire driven by the Mediterranean climate and the long interval between fires.

Z-Scheme Enabled 1D/2D Nanocomposite of ZnO Nanorods and Functionalized g-C3 N4 Nanosheets for Sustainable Degradation of Terephthalic Acid.

The urgent need to mitigate water pollution and achieve Sustainable Development Goal 14 (SDG 14)-Life below water, necessitates developing efficient and eco-friendly wastewater treatment technologies. This research addresses this challenge by photocatalytic degradation of terephthalic acid, a precursor for PET bottles using environment-friendly and biocompatible photocatalysts. The 1D/2D nanocomposite comprising zinc oxide (ZnO) nanorods and functionalized graphitic carbon nitride (Zn-TG) nanosheets were synthesized and thoroughly characterized. The nanocomposite effectively mitigated the individual drawbacks of Zn-TG agglomeration and the wide band gap of ZnO as confirmed through zeta potential and Tauc's plot studies, respectively. The synthesized nanocomposite achieved ~100 % degradation within 60 minutes, exhibiting superior kinetics (~2.5 times) compared to pristine samples. The enhanced degradation efficiency was elucidated by efficient charge carrier transfer (~5 times faster) and separation (~2 times improved) as confirmed through electrochemical impedance spectroscopy and time-resolved photoluminescence studies. The proposed Z-scheme pathway provides mechanistic insights. This proposed mechanism is supported by extensive electron paramagnetic resonance (EPR) and scavenger studies. The liquid chromatography-mass spectrometry (LC-MS) analysis confirms the formation of less toxic byproducts for ensuring that the wastewater treatment process is efficient and environmentally friendly. This research helps in developing a highly effective and sustainable wastewater treatment technology.

Permutation Test for Image-on-Scalar Regression With an Application to Breast Cancer.

Image based screening is now routinely available for early detection of cancer and other diseases. Quantitative analysis for effects of risk factors on digital images is important to extract biological insights for modifiable factors in prevention studies and understand pathways for targets in preventive drugs. However, current approaches are restricted to summary measures within the image with the assumption that all relevant features needed to characterize an image can be identified and appropriately quantified. Motivated by data challenges in breast cancer, we propose a nonparametric statistical framework for risk factor screening that uses the whole mammogram image as outcome. The proposed permutation test allows assessment of whether a set of scalar risk factors is associated with the whole image in the presence of correlated residuals across the spatial domain. We provide extensive simulation studies and illustrate an application to the Joanne Knight Breast Health Cohort using the mammogram imaging data.

Two new species of Thomasomys (Cricetidae: Sigmodontinae) from the western Andes of Ecuador and an updated phylogenetic hypothesis for the genus

Abstract The Andean cloud forests of Ecuador are home to numerous unique mammals. Rodents of the tribe Thomasomyini are particularly abundant in many Andean localities, with Thomasomys – the largest genus in the subfamily Sigmodontinae (51 species) – especially species-rich and diverse. Despite recent advances on the systematics of the genus, where seven species have been described in the last five years, there is tantalizing evidence that its true diversity remains completely understood. Over the course of approximately ten years of fieldwork in Ecuador, a significant number of Thomasomys specimens were collected from various localities in both, the eastern and western Andean ranges. Through an extensive genetic study of these specimens, augmented with what is available in public databases, we argue that there exist at least 20 undescribed species in the genus, with no less that twelve potential new species in Ecuador alone. In this paper, we describe two of these species belonging to the group cinereus, one recently collected and the other previously referred to as Thomasomys sp. 1; further, we present an updated cyt b gene tree of the genus. The gene tree includes at least 56 valid and putative species and supports the monophyly of the genus, while at the same time suggest a paraphyletic “aureus” group. Our findings suggest that the genus likely exhibits additional hidden diversity in significant portions of Colombia, Peru, and Bolivia which calls for the need for a comprehensive reassessment of the entire genus. The recognition of these two new species brings the total number of known Thomasomys to 53 species, 19 of which occur in Ecuador, including 17 that are endemic to this country.

Learning Dynamic Selection and Pricing of Out-of-Home Deliveries

Home delivery failures, traffic congestion, and relatively large handling times have a negative impact on the profitability of last-mile logistics. A potential solution is the delivery to parcel lockers or parcel shops, denoted by out-of-home (OOH) delivery. In the academic literature, models for OOH delivery are so far limited to static settings, contrasting with the sequential nature of the problem. We model the sequential decision-making problem of which OOH location to offer against what incentive for each incoming customer, taking into account future customer arrivals and choices. We propose dynamic selection and pricing of OOH (DSPO), an algorithmic pipeline that uses a novel spatial-temporal state encoding as input to a convolutional neural network. We demonstrate the performance of our method by benchmarking it against two state-of-the-art approaches. Our extensive numerical study, guided by real-world data, reveals that DSPO can save 19.9 percentage points (%pt) in costs compared with a situation without OOH locations, 7%pt compared with a static selection and pricing policy, and 3.8%pt compared with a state-of-the-art demand management benchmark. We provide comprehensive insights into the complex interplay between OOH delivery dynamics and customer behavior influenced by pricing strategies. The implications of our findings suggest that practitioners adopt dynamic selection and pricing policies. History: This paper has been accepted for the Transportation Science special issue on TSL Conference 2023. Funding: This work was supported by TKI DINALOG.

Stoichiometric Lanthanide Compounds with Diglycolamides: A Synthetic Approach toward Understanding Rare-Earth Speciation in Solution.

A fundamental understanding of coordination chemistry across the lanthanide series is essential for explaining the chemical behavior of rare-earth metals in complex liquid-liquid extraction processes. Probing the exact bonding between the extractant and the metal is sometimes done through the synthesis of solid-state compounds that can serve as models for metal speciation in solution. In the case of diglycolamide (DGA), a commonly used neutral diamide extractant, extensive studies identify the stepwise formation of 1:1 [Ln(DGA)(H2O)6]3+, 1:2 [Ln(DGA)2(H2O)3]3+, and 1:3 [Ln(DGA)3]3+ complexes in solution. The crystallographic reports, however, exclusively show a 1:3 [Ln(DGA)3]3+ moiety in the solid state, while the structures of 1:1 and 1:2 complexes are yet to be isolated and comprehensively studied. In this work, we report the synthesis and characterization of three new families of stoichiometric N,N,N',N'-tetramethyldiglycolamide (TMDGA) compounds: [Ln(TMDGA)(H2O)5Cl]Cl2·2H2O, [Ln(TMDGA)2(H2O)3]Cl3·3H2O, and [Ln(TMDGA)3]Cl3·7H2O, where Ln = Nd, Eu, Gd, with Ln:TMDGA ratios of 1:1, 1:2, and 1:3, respectively. The compounds have been analyzed using vibrational spectroscopy (both Raman and FT-IR), as well as variable temperature fluorescence spectroscopy. A spectrophotometric titration of Eu(III) was performed to confirm the presence of the [Eu(TMDGA)n]3+ (n = 1, 2, and 3) species in solution and to compare the individual solid-state emission spectra to their respective analogues in solution.

Macrophytes Used as Multifaceted Benefits Including Feeding, Bioremediation, and Symbiosis in Freshwater Aquaculture—A Review

ABSTRACTThe utility of macrophytes in freshwater aquaculture is diverse. We review the benefits of macrophytes in aquaculture into broadly three useful perspectives: (i) Feeding, (ii) Bioremediation, and (iii) Symbiosis. Macrophytes are used as direct food by herbivores for their potential nutrient values with a maximal protein content of 35% (D/W basis) and as ingredients for fish feed preparation with optimal 50% protein inclusion to reduce feed cost in commercial aquaculture. Macrophytes are used for wastewater treatments to eliminate contaminants, including As, Cu, Cd, Cr, Hg, and Pb, to 99.9%, and to reduce BOD (Biological Oxygen Demand) to improve water quality. Intake of remedied wastewater into aquaculture enhances water productivity through phytoplankton growth. An estimate is that 1.0 L of remedied wastewater, particularly sewage effluent adds an average of 0.05 g N & P and 0.99‐L freshwater into aquaculture to yield an average of 0.309 g fish. Macrophytes are integrated with fish in commercial aqua‐farming. Plants and fish are farmed together in aquaponics. They are used as substrates for some commercial fish species to lay eggs in farming. They are raised with ornamental fish for benefits in aquariums. Nevertheless, extensive studies are required to harness more benefits from macrophytes.

SFMD‐X: A New Functional Data Classifier Based on Shrinkage Functional Mahalanobis Distance

ABSTRACTIn this article, we propose a novel classification approach for functional data based on a shrinkage estimate of functional Mahalanobis distance. We first introduce a new shrinkage functional Mahalanobis distance (SFMD), by using this new distance, we transform the functional observations into a set of vector‐valued pseudo‐samples. Furthermore, we adopt some good classification algorithms designed for multivariate data to this pseudo‐samples instead of the original functional data. The new approach has advantage of highly flexible and scalable, that is, it can easily combine with any classification algorithm, such as support vector machine, tree‐based methods, and neural networks. We demonstrate the performance of the proposed functional classifier through both extensive simulation studies and two real data applications.

Terraces in species tree inference from gene trees

A terrace in a phylogenetic tree space is a region where all trees contain the same set of subtrees, due to certain patterns of missing data among the taxa sampled, resulting in an identical optimality score for a given data set. This was first investigated in the context of phylogenetic tree estimation from sequence alignments using maximum likelihood (ML) and maximum parsimony (MP). It was later extended to the species tree inference problem from a collection of gene trees, where a set of equally optimal species trees was referred to as a “pseudo” species tree terrace which does not consider the topological proximity of the trees in terms of the induced subtrees resulting from certain patterns of missing data. In this study, we mathematically characterize species tree terraces and investigate the mathematical properties and conditions that lead multiple species trees to induce/display an identical set of locus-specific subtrees owing to missing data. We report that species tree terraces are agnostic to gene tree heterogeneity. Therefore, we introduce and characterize a special type of gene tree topology-aware terrace which we call “peak terrace”. Moreover, we empirically investigated various challenges and opportunities related to species tree terraces through extensive empirical studies using simulated and real biological data. We demonstrate the prevalence of species tree terraces and the resulting ambiguity created for tree search algorithms. Remarkably, our findings indicate that the identification of terraces could potentially lead to advances that enhance the accuracy of summary methods and provide reasonably accurate branch support.

Can neotectonic faults influence soil air radon levels in the Upper Rhine Graben? An exploratory machine learning assessment

Radon (Rn) is a naturally occurring radioactive gas that poses a significant lung cancer risk. Subsurface fault zones can act as pathways for fluid and gas migration, potentially amplifying Rn accumulation. This study investigates the impact of fault zones on Rn concentrations within a 25 km2 area in the Northern Upper Rhine Graben, Germany — a region with available detailed geophysical exploration data and active neotectonic faulting. We conducted 597 Rn soil air measurements along precisely located fault zones, integrating a comprehensive range of environmental parameters. Utilizing the advanced machine learning model eXtreme Gradient Boosting (XGBoost) in conjunction with SHapley Additive exPlanations (SHAP) values, we dissected the influence of soil types, environmental factors, and proximity to fault zones on soil air Rn concentrations at a 1-meter depth. Our results reveal that clay-rich soils and cumulative 30-day precipitation are the primary drivers of elevated Rn levels. Proximity to fault zones also significantly influences Rn concentrations, though its impact is less pronounced than the factors mentioned above. Additionally, environmental factors such as wind speed, air pressure, and temperature exhibited lesser effects on Rn levels. The negligible influence of measuring devices and operating personnel increases confidence in data integrity in extensive environmental studies. This study demonstrates the effectiveness of integrating XGBoost with SHAP values to identify and quantify key factors influencing Rn concentrations. By providing a robust framework for enhancing Rn prediction models through machine learning, our findings contribute to improved risk assessments and mitigation strategies, thereby advancing public health and environmental management.

Impact of Impedances and Solar Inverter Grid Controls in Electric Distribution Line with Grid Voltage and Frequency Instability

The penetration of solar energy into centralized electric grids has increased significantly during the last decade. Although the electricity from photovoltaics (PVs) can deliver clean and cost-effective energy, the intermittent nature of the sunlight can lead to challenges with electric grid stability. Smart inverter-based resources (IBRs) can be used to mitigate the impact of such high penetration of renewable energy, as well as to support grid reliability by improving the voltage and frequency stability with embedded control functions such as Volt-VAR, Volt–Watt, and Frequency–Watt. In this work, the results of an extensive experimental study of possible interactions between the unstable grid and two residential-scale inverters from different brands under different active and reactive power controls are presented. Two impedance circuits were installed between Power Hardware-in-the-loop (P-HIL) equipment to represent the impedance in an electric distribution line. Grid voltage and frequency were varied between extreme values outside of the normal range to test the response of the two inverters operating under different controls. The key findings highlighted that different inverters that have met the same requirements of IEEE 1547-2018 responded to grid instabilities differently. Therefore, commissioning tests to ensure inverter performance are crucial. In addition to the grid control, the residential PV installed capacity and physical distances between PV homes and the substation, which impacted the distribution wiring impedance which we characterized by the ratio of the reactive to real impedance (X/R), should be considered when assigning the grid-supporting control setpoints to smart inverters. A higher X/R of 3.5 allowed for more effective control to alleviate both voltage and frequency stability. The elimination of deadband in an aggressive Volt-VAR control also enhanced the ability to control voltage during extreme fluctuation. The analysis of sudden spikes in the grid responses to a large frequency drop showed that a shallow slope of 1.5 kW/Hz in the droop control resulted in a >65% lower sudden reactive power overshoot amplitude than a steeper slope of 2.8 kW/Hz.