jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Objective: To analyze adverse event signals of growth hormone based on the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database and provide a reference for safe clinical drug use. Methods: The FAERS database was retrospectively queried to extract reports related to recombinant human growth hormone(rhGH) from 2014 to 2024. Various disproportionality analyses, such as the reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS), were used to identify and assess potential adverse events in patients receiving recombinant human growth hormone. Results: We detected 327 valid signals involving 22 SOCs. These included ADEs commonly associated with rhGH such as headache, dizziness, and arthralgia. Unexpected serious ADEs were also identified, including seizures, thrombosis, and gout. The median time to onset of ADEs associated with rhGH was 37.5 days (IQR 0–395 days), with the majority of cases occurring within the first month after administration (n = 3148). However, some ADEs may still occur after 1 year of rhGH treatment (n = 1740). Conclusion: Our investigation revealed several possible safety issues associated with rhGH in real-world clinical practice, which may provide necessary evidence for clinicians and pharmacist managers to be vigilant about the safety of rhGH.

Convectively coupled tropical waves (CCTWs) have been shown to significantly modulate rainfall in northern Australia during the Austral summer (December-February; DJF). However, the impact of these waves under varying summer conditions remains unclear. To investigate the influences of these waves in varying background conditions, we divide the period from 1981 to 2020 into years of dry and wet DJF based on the frequency of heavy rain in each season (mean ± 0.5SD). Stronger monsoons, more evaporation, and less frequent El Niño are observed in wetter DJF than in drier DJF. The results show that Madden-Julian Oscillation (MJO) and Equatorial Rossby waves (ER) make a higher contribution to the heavy rain days in drier DJF (i.e., 70% and 58% of the days), compared to the wetter DJF. A higher probability increase in heavy rain is observed during the drier DJF, with amplified (suppressed) impact when wet-wet (wet-dry) wave pair occurs concurrently.

This study evaluates the predictive performance of two advanced models—N-BEATS (Neural Basis Expansion Analysis for Time Series) and Deep Belief Networks (DBN)—in forecasting gold prices. Given the importance of gold as a financial asset, accurate price prediction is vital for investors and market analysts. Both models were tested on a dataset of historical gold prices and their performances were assessed using key metrics RMSE, MAE, MAPE, and R². The results indicated that N-BEATS outperformed DBN in three of the four metrics. Specifically, N-BEATS recorded an RMSE of 21.06, an MAE of 16.06, and a MAPE of 0.79%, while achieving an R² value of 0.99. In comparison, DBN achieved an RMSE of 21.61, an MAE of 16.14, a MAPE of 0.80%, and an identical R² of 0.99. Although both models demonstrated high accuracy in terms of R², N-BEATS exhibited superior performance in RMSE, MAE, and MAPE, suggesting a lower average magnitude of error in predictions. These findings highlight the efficacy of N-BEATS as a robust model for forecasting gold prices, offering better predictive accuracy and interpretability than DBN.

This study examines the feasibility of sustaining planetary habitation alongside space tourism through advancements in technology, environmental systems, and in-situ resource utilization (ISRU). Utilizing resources such as lunar regolith and Martian soil can reduce Earth’s dependency for constructing habitats and producing essentials like oxygen and water. Technologies like closed-loop life-support systems, exemplified by ESA’s MELiSSA project, offer innovative solutions for long-term sustainability. However, challenges such as radiation exposure, energy reliability, and extreme temperatures present obstacles to accommodating tourists in extraterrestrial settings. Augmented reality (AR)-based tools provide intuitive solutions for resource management and task guidance, enhancing experiences for both astronauts and tourists. Integrating sustainable technologies, intuitive interfaces, and scalable solutions positions space tourism as a pivotal factor in advancing sustainable planetary life and opening new frontiers for human exploration and commercial ventures. As commercial space operations expand, emerging threats like cyberattacks, and orbital terrorism pose significant risks. This study proposes a hybrid economic and security framework, advocating for international regulatory oversight, AI-driven cybersecurity measures, and radiation-shielded habitat designs to ensure both financial sustainability and operational safety. These findings contribute to the growing discourse on space tourism viability, regulatory challenges, and technological innovations necessary for sustained human habitation in extraterrestrial environments.

Revolutionary strategies for managing agricultural diseases have been made possible by recent developments in machine learning, especially in apple leaf disease prediction. According to recent research, convolutional neural networks with fewer connections between layers near their inputs and those near the output can be trained with far greater depth, accuracy, and efficiency. To make a more precise diagnosis of apple-leaf defects than existing architectures, this work proposed a method combining DenseNet-121 and optimising transfer learning strategy for multiclass classification. DenseNet-121 is used as a feature extractor as it strengthens feature propagation and reuse, leading to sustainable feature parameter reduction. The experiment is performed on 3 publicly accessible datasets with 3 classes, 6 classes and 9 classes of apple disease in leaf. The network architecture is fed with augmented data to avoid the problem of class imbalance. The proposed model has responded exceptionally well on all three datasets, claiming 99.9%, 99% and 96% accuracy. Comparative studies and experimental data demonstrate the competitive prediction accuracy of the suggested approach.

Detecting surface defects in steel manufacturing is crucial for product quality and production efficiency. However, real-time quality control faces challenges in automation and reliability. Surface flaws in steel strips vary in complexity, requiring robust defect detection algorithms with high generalization performance.For the purpose of addressing this issue, we visited JSW Steel Ltd, Vijayanagara, Ballari, Karnataka, India, and took pictures of any defects we found. Using deep convolutional neural networks (CNNs), we provide a new method for steel defect identification. Images of surface defects in steel were used to train and evaluate the deep CNN. This nine-layer CNN model solves the problem of finding flaws in steel strips as a whole. Implementation of data augmentation techniques to avoid overfitting. The performance of the proposed model was assessed using a dataset that included three classes of flaws and one class that was free of flaws. On the validation set, the model achieved an impressive accuracy of 93.27 percent. The experimental results demonstrate the deep CNN model’s higher performance for both intra and inter-class fault detection. So, the proposed deep CNN model gives a precise and real-time way to find surface flaws in steel strip production lines, leading to higher-quality steel strips overall.

jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Today, counseling and proper guidance in learning environments are among the most important factors that influence learners’ outcomes. The performance of students has been an area of interest in understanding how the students could be managed to improve performance and look forward to better employment opportunities. This paper aims to target the difficulty in developing stable classification models that cluster students according to learning styles, achievement, and other factors for recommendation and remediation. Support Vector Machine (SVM) is one of the commonly adopted supervised learning algorithms, but it has drawback of choosing of a kernel function and model parameters. To improve the classification performance of SVM for educational data, we use PSO with a specific kernel function. The PSO identifies the best features that need to be taken into model while the kernel enhances the model to handle specific relationship in educational data sets. This integration enhances the capacity of SVM to classify students and recognize those who might need special attention. The proposed model is then benchmarked with standard measures including accuracy, precision, recall, and F1 score with traditional SVM models using standard kernels. These results prove that this PSO-SVM with a customized kernel is superior to the traditional models and provides the best solution for the purpose of classification and intervention of students. It makes the process straightforward for the educational institutions to offer relevant recommendations that can enhance the achievement of the students.

Real-time Multivariate Statistical Process Monitoring (RT-MSPM) is essential to monitor health of bio-pharmaceutical processes and detect anomalies and faults early in the process. RT-MSPM methods are commonly used to monitor cell culture process operations in biologics drug substance manufacturing. Batch Evolution Models (BEMs) are among common RT-MSPM methods. As an alternative to BEMs, it is possible to develop multiple models to monitor different phases of a batch process. If certain statistical properties are satisfied, a multistage algorithm can be leveraged to detect steady state operation of a batch and process the corresponding time-series in a manner to leverage data from other product recipes to monitor a new product with no prior history. This is specifically useful in modern biopharmaceutical manufacturing facilities, which frequently switch from producing one medicine to another. In this article, a novel real-time deep learning framework to monitor the health of biopharmaceutical processes with no prior product-specific history is proposed. Autoencoders (AEs), in conjunction with a multistage real-time data processing algorithm, are leveraged to detect, prevent and identify the root causes of potential anomalies and faults in cell culture manufacturing processes to produce Monoclonal Antibodies (MABs) with no prior history. A novel algorithm for real-time root cause identification of anomalies is developed to generate real-time contribution charts for AEs. The performance of the new fault detection and isolation strategy is compared with conventional methods. Given the non-linear architecture of AEs in comparison to conventional linear methods, AEs consistently provide more robust and stronger evidence for anomalous patterns using a combination of information in residuals and latent space. The proposed framework is successfully tested within a scalable software product for real-time monitoring of manufacturing cell culture bioreactors.

This study introduces a novel method for monitoring microalgae growth and evaluating mass transfer efficiency in photobioreactors, specifically under non-limiting and controlled growth conditions. By leveraging data on elemental composition, gas transfer rates, and oxygen production rate, the method estimates biomass growth and assesses gas-liquid mass transfer coefficients. The approach uses indirect measurements to infer critical parameters, including biomass concentration, total inorganic carbon, and nitrogen levels. Results demonstrate accurate predictions of biomass growth and carbon dynamics, along with effective characterization of mass transfer coefficients. This method offers a robust tool for optimizing photobioreactor performance and enhancing process control.

The [2+1] cycloaddition of alkynes with fluoroalkyl carbenes is the most straightforward approach for synthesizing fluoroalkylated cyclopropenes. However, until now, this strategy has not been applicable to difluoromethyl carbene, as its precursor, difluoromethyl diazomethane, tendeds to undergo [3+2] cycloaddition with alkynes to form pyrazoles. This study presents the first example of cyclopropenation of alkynes with difluoromethyl carbene under copper catalysis, utilizing difluoroacetaldehyde triftosylhydrazone as the carbene precursor. A wide range of internal and terminal alkynes, featuring diverse functional groups, were successfully converted into the corresponding difluoromethyl cyclopropenes in good to high yields. Mechanistic insight, supported by DFT calculations, revealed that the bulky Tp Br3Cu(NCMe) catalyst was essential in facilitating the cyclopropenation of alkynes with difluoromethyl carbenes via a concerted pathway.

This article examines the narrative strategies and identity constructions of two far-right groups, the League of the South (United States) and the Grey Wolves (Turkey), through a cross-cultural lens. Using reflexive thematic analysis, we identify and compare five overarching themes, revealing both shared and unique dynamics shaped by each group’s socio-political context and proximity to power. The League of the South portrays out-group threats, including traditional far-right targets and mainstream Republicans, as existential, emphasizing victimhood and the struggle of an exclusive in-group to establish a neo-Confederate state. In contrast, the Grey Wolves frame their in-group identity within a broader Turkish-Islamic context, integrating national pride and moral superiority with confidence derived from their alignment with Turkey’s ruling coalition. Our findings highlight the role of power in shaping narratives: while the Grey Wolves capitalize on their proximity to power to project strength and stability, the League of the South’s marginal position fosters narratives of defiance and futility. Despite differences, both groups employ moralistic and historical framings to legitimize their aims. This study advances the literature on far-right movements by emphasizing the complex dynamics between identity, power, and context, offering insights into the broader dynamics of far-right populism in WEIRD and non-WEIRD settings.

Democracy has come under assault recently, and it appears it will remain so for the foreseeable future. However, few studies have examined what psychological factors might contribute to it. This study examines two factors that have received little attention (schadenfreude and freudenfreude) as predictors of political attitudes and behaviors, with a specific focus on support for democratic principles and institutions. Using an online MTurk survey of 236 respondents, I found that both are predictive of such support, although at times in unexpected ways. The results highlight the need for examination of such variables to better predicting voters’ attitudes and behaviors.

Antimicrobial peptides (AMPs) are small bioactive molecules produced by a wide range of organisms, including non-Saccharomyces yeasts. These peptides exhibit potent antimicrobial activity against bacteria, fungi, parasites, and some viruses, making them promising candidates as alternatives to conventional antibiotics. This review focuses on the current state of research on low molecular weight AMPs produced by non-Saccharomyces yeasts. We discuss the characteristics of these peptides, their mechanisms of action, and their potential applications in fields ranging from medicine and agriculture to food technology and cosmeceuticals. Despite the recognized potential of yeast-derived AMPs, further research is needed to fully characterize these molecules and explore their practical applications. Future perspectives include their integration into nanocarriers for enhanced stability and targeted delivery, as well as their role in reducing the emergence of microbial resistance.

I present a toy model of quantum communication which leverages supersymmetry to ensure protection against eavesdroppers. In this model, a file encoded onto a single qubit is poisoned in the presence of an adversary via a "superysymmetric killswitch" which causes the qubit to transition from a bosonic to a fermionic mode.

1. IntroductionArtificial Intelligence (AI) has reshaped various industries, and education is no exception. AI has increasingly become a significant factor in modern education. The potential of AI to personalize learning, provide instant feedback, and support diverse learning styles has transformed traditional educational practices. However, the integration of AI tools in the classroom is not without challenges. Its adoption is often met with hesitation, primarily due to misunderstandings about its limitations and potential misuse. [2,3]In this paper, we explore how AI tools can be effectively used in the classroom, focusing on both the potential benefits and limitations. This paper aims to discuss the use of AI tools in learning and teaching, focusing on a case study where initial resistance to AI tools was transformed into a productive learning experience, showcasing how AI can enhance learning outcomes when integrated thoughtfully through strategic guidance and effective utilization.

The idea behind this project is to make it as easy as possible for anyone with an interest in astronomy to join the hunt for potentially habitable exoplanets.

To accelerate the construction of composite deck slabs (CDSs), a novel detail (NCDS) is proposed, where regular perfobond ribs are replaced with notched ones so that the perforating rebars can be easily placed inside the holes. First, finite element models were established to reveal the static load transfer mechanism and compare it with regular CDSs. Then, the fatigue performance was evaluated based on the Smith-Watson-Topper model based on critical plane method. Finally, the effects of geometric parameters were investigated through a parametric study. Results show that NCDS shows no significant difference in static performance compared to regular CDSs. The fillet welds connecting the bottom plate and notched ribs are subjected to multiaxial stress, and the shear-bending region is the most critical position for fatigue crack initiation. The parametric study demonstrates that with proper design of rib geometries, NCDSs could reach a 36.4% higher fatigue life than regular CDSs.

Background: Equine osteoarthritis is a major cause of lameness in horses, directly reducing athletic performance and leading to significant economic losses in the equine industry. Studies have shown that platelet rich plasma (PRP) exhibited clinical benefits in equine patients with OA, but there is still lack of investigation on the effect of different types of PRP on OA. Objectives: This study aims to evaluate the effects of different concentrations and storage methods of PRP (fresh PRP and freeze-dried PRP) on alleviating inflammatory responses and regulating tissue metabolism in an equine osteoarthritis cartilage-synovial explant model. Study Design: An in vitro cartilage-synovial explant model was established to investigate osteoarthritis-related inflammatory responses and metabolic imbalances. Methods: Cartilage and synovium were co-cultured with 10 ng/mL interleukin-1β (IL-1β) for 48 hours to induce equine osteoarthritis. Fresh PRP and lyophilized PRP at concentrations of 25% and 50% were applied to the explants. Triamcinolone was applied as positive control. Inflammatory cytokines and metabolic pathways were analyzed, including nitric oxide levels and gene expressions of cyclooxygenase-2 (COX-2) and matrix metalloproteinase-13 (MMP-13). Results: The PRP treatment effectively suppressed inflammatory responses similar to triamcinolone, with fresh PRP demonstrating better inhibition of nitric oxide (NO) production. Metabolomic analysis revealed that both PRP storage forms could modulate glucose and purine metabolism, with fresh PRP exhibiting superior effects. The mean differences in inflammatory cytokines and metabolic pathways biomarkers were calculated with 95% confidence intervals. Main limitations: The study’s limitations include the model’s representativeness of natural complexity of osteoarthritis and the generalizability of findings to larger populations. Conclusions: The study demonstrated that both fresh and freeze-dried PRP possess significant anti-inflammatory effects and potential to regulate cartilage cell metabolism. Future research could further explore the mechanisms by which PRP influences metabolic pathways in equine osteoarthritis.

Colorectal cancer (CRC) development is closely associated with the accumulation of both genetic and epigenetic alterations. Many efforts have been made to investigate the role of epigenetic modifications in CRC metastasis. In this work, we present the first quantitative top-down proteomics study focusing on histone proteoforms between metastatic (SW620) and nonmetastatic (SW480) CRC cells to reveal potentially critical histone proteoforms in CRC metastasis. We isolated histone proteins from CRC cells, fractionated them by sodium dodecyl-sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE), and analyzed them by capillary zone electrophoresis (CZE)-tandem mass spectrometry (MS/MS). A total of 230 histone proteoforms were quantified in SW480 and SW620 cell lines, among which 34 proteoforms were significantly altered in abundance in the metastatic cells, indicating a significant transformation of histone proteoforms during metastasis. We observed a significant increase in abundance of all nine differentially expressed histone H4 proteoforms in metastatic SW620 cells compared to SW480 cells, while differentially expressed proteoforms of other histone proteins display diversified expression patterns. Additionally, two histone H2A proteoforms with a combination of N-terminal acetylation and phosphorylation were upregulated in the metastatic CRC cells. These differentially expressed histone proteoforms could be novel proteoform biomarkers of CRC metastasis.

Academia claims to sell knowledge, but knowledge is now free. If knowledge is free, universities cannot justify their existence by providing information alone. Therefore, they must sell something else: status.Universities no longer hold a monopoly on learning. The internet, AI, and open-access research have made high-quality education widely available at little or no cost. Yet, academia retains its power by maintaining a credential monopoly, reinforcing exclusivity, and controlling access to research funding and professional validation.This paper examines how academia transitioned from knowledge gatekeeper to status gatekeeper. It explores the historical role of universities, their resistance to disruptive change, and the structural incentives that prioritize prestige over accessibility. The conclusion is clear: higher education must evolve into an open, decentralized model or risk becoming obsolete.

As China’s urbanization grows by leaps and bounds, engineering slag has turn into one of the broadest forms of solid wastes. The purpose of this research was to utilize engineering slag as a new planting substrate component to improve the soil environment in mine sites and address the growing shortage of land resources. The substrate optimal formulation was filtrated utilizing an orthogonal experiment involving four factors: slow-release fertilizer (SRF), microbial inoculants (MI), water-retaining polymer (WRP), and soil-to-slag ratio (SS). The results presented that SRF significantly increased the nutrients availability of the slag substrate. The addition of MI induced changes in the physicochemical properties of the substrate, ultimately affecting plant germination. Furthermore, at a concentration of 0.8% WRP had a significant effect on the physical properties and soil dehydrogenase (S-DH). However, 1% WRP was most favorable for plant growth. Exogenous soil significantly improved SOC content and Soil alkaline phosphatase (S-ALP) activity when applied at a dosage of 500 g. There existed significant correlations between soil properties and plant indicators. Afterwards we comprehensively analyzed the effects of 20 parameters, and from an economic perspective, the optimal parameters were as follows: SRF content of 1 g kg -1, MI content of 90 mL kg -1, WRP concentration of 1.0%, and SS of 30:70. Additionally, amplicon sequencing showed a positive impact on soil microbial community diversity due to the treatment. The results of this research will establish a theoretical foundation on combining microbial inoculants with external soil spray seeding techniques in mine sites reclamation.

La sentenza della Corte Europea dei Diritti dell’Uomo (CEDU) del 30 gennaio 2025, nel caso Cannavacciuolo e Altri contro Italia, rappresenta una svolta giuridica epocale nella tutela ambientale e nella difesa dei diritti umani, stabilendo un precedente che ridefinisce il concetto stesso di responsabilità dello Stato nella protezione della salute pubblica e dell’ecosistema¹. Per la prima volta, la Corte ha riconosciuto che l’inquinamento ambientale sistemico e la mancata azione dello Stato costituiscono una diretta violazione dei diritti fondamentali della persona, condannando l’Italia per aver omesso di proteggere i propri cittadini dagli effetti devastanti della crisi della Terra dei Fuochi².La sentenza ha accertato la violazione dell’Articolo 2 della Convenzione Europea dei Diritti dell’Uomo, che tutela il diritto alla vita, e dell’Articolo 8, che garantisce il diritto al rispetto della vita privata e familiare, dimostrando come l’inquinamento diffuso e l’assenza di misure di contenimento abbiano compromesso irreparabilmente la qualità della vita della popolazione residente³. Questa pubblicazione nasce con l’obiettivo di offrire un’analisi giuridica, sociale e ambientale della decisione della CEDU, fornendo strumenti di comprensione più ampi e approfonditi rispetto alla semplice lettura della sentenza⁴.Attraverso un approccio interdisciplinare, il lavoro esamina in dettaglio le cause storiche e giuridiche della crisi ambientale della Terra dei Fuochi, mettendo in evidenza il ruolo della criminalità organizzata e la connivenza di settori economici e istituzionali, che hanno trasformato la gestione illecita dei rifiuti in un sistema di smaltimento alternativo illegale, con profitti multimiliardari⁵. L’analisi delle inchieste giudiziarie e delle testimonianze di collaboratori di giustizia dimostra come l’ecomafia abbia operato per decenni in assenza di un contrasto efficace da parte dello Stato, avvelenando terreni, acque e aria e causando un’emergenza sanitaria di proporzioni drammatiche⁶.Uno dei punti centrali della pubblicazione è l’impatto dell’inquinamento sulla salute pubblica, con un approfondimento sui dati epidemiologici che dimostrano l’anomala incidenza di tumori, leucemie infantili, malattie cardiovascolari e respiratorie nelle aree più contaminate⁷. Lo studio analizza le evidenze scientifiche raccolte da istituzioni nazionali e internazionali, come l’Istituto Superiore di Sanità (ISS), l’Organizzazione Mondiale della Sanità (OMS) e l’Agenzia Regionale per la Protezione Ambientale (ARPAC), mettendo in luce la correlazione tra esposizione prolungata a sostanze tossiche e incremento delle patologie letali⁸.Un ulteriore elemento di rilievo è l’analisi comparativa con altre sentenze internazionali, come il caso Urgenda vs. Paesi Bassi (2019), in cui la Corte Suprema olandese ha obbligato il governo a ridurre le emissioni di CO₂ per proteggere il diritto alla vita dei cittadini, e il caso Milieudefensie vs. Shell (2021), che ha sancito la responsabilità delle multinazionali per i danni ambientali causati dalle loro attività industriali⁹. Il raffronto con questi casi dimostra come il riconoscimento del diritto a un ambiente sano come diritto umano fondamentale stia assumendo una centralità crescente nella giurisprudenza internazionale¹⁰.Infine, la pubblicazione si concentra sulle prospettive future per la Terra dei Fuochi, delineando le misure imposte dalla Corte Europea e le azioni che lo Stato italiano sarà obbligato ad adottare per ottemperare alla sentenza, tra cui un piano di bonifica strutturato e trasparente, la creazione di un organismo indipendente per il monitoraggio ambientale e sanitario, l’implementazione di screening oncologici per la popolazione residente e l’inasprimento delle pene per i reati ambientali¹¹. L’ultima parte del lavoro è dedicata alle strategie per la riconversione economica e sociale del territorio, con un focus su energie rinnovabili, economia circolare e sviluppo sostenibile, per trasformare una terra devastata dall’inquinamento in un modello di riscatto e rinascita¹².Questa pubblicazione non si limita a una semplice analisi giuridica, ma vuole essere uno strumento di informazione e di consapevolezza civica, in grado di fornire chiavi di lettura utili per comprendere le implicazioni della sentenza della CEDU, le responsabilità dello Stato e le possibilità di intervento per una vera giustizia ambientale¹³. Il riconoscimento del disastro ambientale della Terra dei Fuochi come violazione dei diritti umani rappresenta una svolta che potrebbe avere conseguenze globali, aprendo la strada a nuove azioni legali e a un più incisivo impegno delle istituzioni nella tutela dell’ambiente e della salute pubblica¹⁴.L’esito della sentenza segna l’inizio di un nuovo capitolo nella battaglia per la giustizia ambientale, ma la sua reale efficacia dipenderà dalla capacità dello Stato di tradurre questa condanna in azioni concrete e dalla mobilitazione della società civile per garantire che la Terra dei Fuochi non sia più un simbolo di devastazione e impunità, ma un esempio di riscatto e rigenerazione¹⁵.

This study investigates the complexity, efficiency, and sectoral interdependencies of the S&P Global BMI indices during critical global events, including the COVID-19 pandemic and the Russia-Ukraine war. The analysis is conducted in three dimensions: 1) evaluating market efficiency using permutation entropy and Fisher information measure, 2) exploring sectoral alignments through clustering techniques (hierarchical and k-means clustering), and 3) assessing the influence of geopolitical risk using Multifractal Detrended Cross-Correlation Analysis (MFDCCA). Results highlight significant variations in informational efficiency across sectors, with Utilities and Consumer Staples exhibiting high efficiency, while Emerging Markets and Financials reflect lower efficiency levels. Temporal analysis reveals widespread efficiency declines during the pandemic, followed by mixed recovery patterns during the Ukraine conflict. Clustering analysis uncovers dynamic shifts in sectoral relationships, emphasizing the resilience of defensive sectors and the unique behavior of Developed BMI throughout crises. MFDCCA further demonstrates the multifractality in cross-correlations with geopolitical risk, with Consumer Staples and Energy showing stable persistence and Information Technology exhibiting sensitive complexity. These findings emphasize the adaptive nature of global markets in response to systemic and geopolitical shocks, offering insights for risk management and investment strategies.

The paper discusses the challenges marketers face in leveraging data-driven insights while adhering to ethical and legal standards. It highlights innovative approaches, including leveraging privacy-preserving technologies, employing first-party data strategies, and implementing contextual advertising to maintain relevance without compromising user privacy. Additionally, the research underscores the role of transparency, trust-building, and customer empowerment in fostering brand loyalty. Through case studies and emerging best practices, this work demonstrates how businesses can harmonize compliance with marketing creativity. It argues that by embracing privacy-first principles and fostering cross-functional collaboration, organizations can unlock new opportunities to connect with audiences, redefine value, and thrive in the everevolving digital landscape.