Abstract: Time series generation (TSG) plays a fundamental role in data engineering and knowledge discovery, serving as a key enabler for data augmentation, representation learning, privacy preservation, and scenario simulation analysis in temporal data mining. By synthesizing realistic and controllable time series, TSG directly benefits downstream tasks such as forecasting, anomaly detection, and classification by data augmentation and improving model generalization. This survey presents a comprehensive and systematic review of TSG methodologies, spanning traditional non-deep learning approaches, such as rule-based, statistical, and simulation-based methods, and modern deep generative models, including variational autoencoders, generative adversarial networks, diffusion models, normalizing flows, and large language models. To organize the rapidly growing literature, we introduce a unified multi-level taxonomy to organize the technical landscape of TSG and clarify the relationships among diverse approaches. The taxonomy emphasizes underlying modeling principles rather than individual representative models. Specifically, it categorizes TSG methods according to modeling backbones, generation settings (conditional versus unconditional), sampling regularity, and modality characteristics. Building on this taxonomy, we systematically review advanced methods within each category and discuss how they address key challenges such as irregular sampling, long-sequence modeling, multivariate dependencies, and controllable generation, and how they are applied in real-world scenarios. Furthermore, we present a holistic evaluation framework encompassing five core dimensions, including fidelity, diversity, controllability, downstream performance, and privacy protection. Finally, we identify critical open challenges and outline promising research directions from data characteristics, application scenarios, and modeling paradigm perspectives. This survey aims to serve as a structured reference and roadmap for researchers and practitioners in this rapidly evolving field.

Abstract: NK cells are crucial for innate immunity and rapidly target abnormal cells through ligand-receptor signaling, without prior sensitization. Vitamin C is a bioenhancer of NK cells; however, its susceptibility to oxidation limits its efficacy in NK cell activation. This study evaluated the efficacy of Aptamin C, a stable conjugate of vitamin C and an aptamer, in enhancing NK cell activation. In in vivo study, 109 participants were ad-ministered either Aptamin C (36.057 mg/day) or placebo for 4 weeks. The results showed significant increases in NK cell cytotoxicity after 2 and 4 weeks in the Aptamin C group. Additionally, cytokine and granule levels associated with NK cell activity peaked in the serum 4 weeks after Aptamin C intake. In in vitro study, NK-92 cells treated with Aptamin C were compared to NK-92 cells treated with vitamin C. The results showed enhanced proliferation, survival, cytotoxicity, and cytotoxic granule production in NK-92 cells treated with Aptamin C compared to cells treated with vitamin C. These findings indicated that Aptamin C effectively promoted NK cell ac-tivation, suggesting its potential as an immunomodulatory supplement in NK cell therapy.

Abstract: This paper presents the results of research aimed at identifying deep-seated natural sealed reservoirs for the isolation of chemically active gases, including CO₂. A comprehensive analysis of geological, geophysical, and hydrogeological data was conducted to identify potential structures within the aquifers of Almaty and the Almaty region (Southern Kazakhstan) capable of capturing and storing carbon dioxide under geographically favorable and economically viable conditions. The study utilizes seismic survey results and drilling data from the eastern part of the Ili Basin, demonstrating the efficacy of seismic exploration in identifying stratigraphic horizons and their structural-tectonic settings. Based on an integrated analysis of available geo-physic information, the lithological and stratigraphic characteristics of the sedimentary cover in the Ili Basin are substantiated. Key caprock sequences and reservoir units for potential storage sites are identified, and recommendations for further geological exploration are provided. Five reflecting horizons were identified within the geological section of the troughs. It was established that the Miocene-Paleogene and Jurassic horizons contain sandstone reservoirs with a thickness exceeding 10 m and enhanced filtration properties. Clay complexes are prevalent in the Upper Jurassic deposits, which can serve as a caprock for these reservoir rocks. Furthermore, the Upper Cretaceous clay sequence may act as a fluid seal for the Neogene-Paleogene sandy horizons. Such conditions meet the requirements for sealed reservoirs for the isolation of chemically active gases, including CO₂. According to hydrogeological studies, seven aquifer complexes are distinguished: Permian, Triassic, Jurassic, Cretaceous, Paleogene, Neogene and Quaternary. The novelty and practical significance of this research lie in obtaining new information on the geological structure of deep horizons in poorly studied areas of the Ili Basin and establishing favorable geological factors for identifying potential sites suitable for carbon dioxide sequestration.

Abstract: Over the past decade, acoustically-actuated magnetoelectric (ME) antennas have been proposed as chip scale radiofrequency (RF) antennas compatible with post Complementary Metal Oxide Semiconductor (CMOS) fabrication processes. These devices have been reported to exhibit antenna gains far exceeding those of conventional electromagnetic (EM) antennas with comparable footprint. However, recent studies have challenged whether this enhanced gain originates from magnetoelastic coupling or from stray radiation sources, like the electric dipole moment in the piezoelectric film or currents in the probing pads. We resolve this controversy through a combined analytical, numerical, and experimental investigation. We model and quantify the radiated power and corresponding gain contributions from (I) magnetoelastic coupling; (II) the strain driven, time-varying electric dipole moment in the piezoelectric layer; and (III) the currents in the probing pads. Our results confirm that the radiation from magnetoelastic coupling exceeds that of the other sources by several orders of magnitude. In addition, we explain how to optimize the return loss and the radiated power of ME antennas when connected to a 50 Ω source, showing that the optimal operating point is the anti-resonance frequency. Based on this finding, we investigate the impact of the electromechanical coupling (k_t²) on gain and-10 dB fractional bandwidth. To corroborate our simulation results, we design, fabricate, and characterize the first two Aluminum Scandium Nitride (AlScN) magnetoelectric Bulk Acoustic Wave (BAW) antennas operating beyond 1.1 GHz. The two prototypes integrate different magnetostrictive materials (FeGaB and FeCoSiB) and exhibit measured realized gains of-31.8 dB and-29.7 dB, with-10 dB fractional bandwidths of 1.28% and 1.27% at 2.62 and 3.08 GHz, respectively. The achieved bandwidths are the highest reported for radiofrequency (RF) ME antennas, owing primarily to the enhanced piezoelectric coefficients of the AlScN. Benchmarking against control structures (unreleased FeGaB and FeCoSiB devices) confirms substantially degraded radiation performance in the absence of a strong magnetoelastic coupling. These results elucidate the working principle of ME antennas and provide RF designers with a rigorous framework for the design and modeling of acoustically actuated ME antennas for wireless communication and sensing.

Abstract: Background/Objectives: Prolonged smartphone use is associated with musculoskeletal and neurological hand symptoms; however, specialized tools for bilateral assessment remain limited. This study aimed to develop and evaluate the psychometric properties of the Smartphone-Related Hand Symptoms (S-HAND) scale. Methods: A total of 456 participants (mean age: 21.8 ± 6.65 years) were included. The sample was randomly divided into two independent subsamples for Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Internal consistency was assessed using Cronbach’s alpha and McDonald’s omega coefficients. Test–retest reliability was evaluated in 75 participants using Intraclass Correlation Coefficients (ICC). Measurement precision was determined using the standard error of measurement (SEM) and minimal detectable change (MDC95). Concurrent validity was examined using correlations with general hand pain intensity scores. Results: EFA and CFA supported a three-factor structure consisting of Pain Symptoms, Numbness Symptoms, and Functional Impact. The final CFA model demonstrated excellent fit (CFI = 0.991, TLI = 0.986, RMSEA = 0.049, SRMR = 0.033). Standardized factor loadings ranged from 0.685 to 0.932. Internal consistency was high (Cronbach’s α = 0.857–0.940; McDonald’s ω = 0.863–0.942), and test–retest reliability was excellent (ICC = 0.800–0.907). SEM and MDC95 for the total score were 4.67 and 12.95, respectively. Significant positive correlations with pain intensity scores supported concurrent validity (r = 0.61–0.73, p < 0.001). Conclusions: The S-HAND scale is a reliable and valid instrument for assessing smartphone-related hand symptoms in clinical and research settings.

Abstract: Inflammatory rheumatic diseases exhibit dynamic and heterogeneous inflammatory activity, yet clinical monitoring remains episodic and temporally sparse, limiting early intervention and delaying treatment adjustment. Advances in biosensing technologies, wearable monitoring, and computational modeling offer opportunities to transition toward continuous, data-driven disease assessment. In this review, we synthesize evidence across rheumatology, immunology, biosensing, and digital health to examine how multimodal measurement approaches can support clinically actionable decision-making. We introduce a structured framework—the “Measurement Stack”—that links three components: biological signal domains (systemic, synovial, imaging-derived, and physiological), sensing platforms with distinct temporal and specificity trade-offs, and computational inference layers including feature extraction, multimodal data integration, and predictive modeling. We emphasize that the clinical value of biomarkers depends not on association alone but on actionability, defined by temporal sensitivity, repeatability, robustness to heterogeneity and signal noise, and alignment with clinical decisions. Key methodological considerations include feature engineering for sparse and continuous data, handling missingness and signal drift, calibration-aware validation, temporal and external validation, and decision-curve analysis for clinical utility. A decision-centric mapping aligns measurement and modeling strategies with clinical tasks such as early flare detection, differentiation of flare from infection, therapy switching or tapering, and monitoring of treatment response. By integrating biosensing advances with clinically grounded evaluation standards, this review outlines pathways toward interpretable, deployment-ready monitoring systems enabling proactive and personalized management of inflammatory rheumatic disease.

Abstract: Aerodynamic drag is one of the two principal external sources of energy loss in on-road vehicles – the other being rolling resistance – and it critically affects the range of battery-electric and fuel cell-electric vehicles. To ensure accurate early-stage analysis such as vehicle range prediction and sizing of energy storage and powertrain components, it is essential to incorporate realistic representations of air resistance. Despite its importance, due to limited data availability air resistance is often simplified using zero crosswind and "nominal air conditions", which tend to underestimate the actual energy required to overcome aerodynamic drag. This approach also fails to capture the variability introduced by changing environmental conditions, leading to significant discrepancies in energy consumption and, consequently, vehicle range. As a result, evaluating system robustness and conducting meaningful trade-off analyses between different vehicles or vehicles configurations becomes challenging. This study demonstrates how publicly available meteorological data can be utilized to quantify long-term variations in aerodynamic drag. By analyzing multiple years of weather observations, we derive realistic distributions of aerodynamic energy losses – capturing not only mean values but also the full range of variability. These distributions enable probabilistic modeling of vehicle performance, thereby supporting robust system design and informed trade-off decisions across various levels of vehicle architecture. To demonstrate this, we compare two different tractor/semitrailer configurations.

Abstract: Public trust is essential for the effectiveness and long-term sustainability of open government data (OGD) initiatives, particularly in small island developing states (SIDS), where digital governance systems often operate under infrastructural and institutional constraints. Despite growing global research on OGD trust, limited research has examined how the quality dimensions of information systems' success models shape citizens’ trust in OGD platforms within Caribbean SIDS. This study investigates the effects of service quality, system quality, information quality, and data quality on public trust in OGD using an extended information systems success model (ISSM). Data were collected through an online survey of 904 respondents across Caribbean SIDS and analysed using partial least squares structural equation modelling (PLS-SEM). The findings indicate that all proposed relationships were statistically significant. Data quality emerged as the strongest predictor of public trust, followed by system quality. Service quality also significantly influenced system quality, information quality, and data quality. In addition, system quality, information quality, and data quality mediated the relationship between service quality and public trust in OGD. This study extends the ISSM framework by conceptualising data quality as a distinct construct within OGD environments. The findings provide practical insights for governments seeking to strengthen transparency, citizen engagement, and sustainable digital governance through higher-quality OGD systems and datasets. The results further highlight the role of open government platforms in improving public service delivery by providing citizens with complete, accurate, and accessible data, interactive feedback mechanisms, and effective data visualisation tools that support informed decision-making and public participation.

Abstract: The rapid advancement of generative large language models (LLMs) has sparked significant interest in their poten- tial to transform higher education, particularly in fostering student engagement. While these models offer novelopportunities for personalized learning and interactive experiences, their integration into academic settings remains underexplored, with varying implications for pedagogy, ethics, and institutional policy. This systematic literaturereview examines the role of generative LLMs in enhancing student engagement across multiple dimensions, includ- ing their impact on learning outcomes, academic writing, subject-specific applications, and ethical considerations.We synthesize existing research to identify key trends, challenges, and gaps in the current understanding of how these technologies are reshaping educational practices. A rigorous methodological approach was employed to select and analyze relevant studies, ensuring a comprehensive evaluation of the field. The findings reveal that generative LLMs can significantly influence student engagement by facilitating adaptive learning environments and supporting creative problem-solving; however, concerns about academic integrity, equitable access, and pedagogical alignment persist. The review also highlights emerging tools and systems designed to integrate LLMs into education, alongside institutional and student perspectives on adoption. Based on the synthesized evidence, we discuss future directions for research and policy, emphasizing the need for balanced frameworks that harness the benefits of generative AI while addressing its risks A. Chowdhury et al., 2025. This study contributes a structured overview of the current landscape, offering insights for educators, researchers, and policymakers navigating the evolving intersection of AI and higher education.

Abstract: Context: Accurate citrus crop mapping is essential for agricultural monitoring, production planning, and supply-chain management, particularly in Brazil, one of the world’s leading orange producers and the leading orange-juice exporter. Satellite image time series from Sentinel-2 provide rich spectral and temporal information for crop identification. However, citrus mapping remains challenging due to fragmented agricultural landscapes, cloud contamination, class imbalance, and spectral overlap with other vegetation classes. Problem: Conventional machine learning models often depend on handcrafted vegetation indices, while attention-based deep learning models may require larger datasets and can become unstable under geographically constrained conditions. Therefore, there is a need for a compact and robust deep learning architecture capable of extracting citrus phenological signatures directly from multispectral time-series data. Methods: This study evaluates a Spatio-Temporal Pixel-Set Encoder Convolutional Neural Network (PSE-CNN) for citrus crop classification in the immediate geographic regions of São João da Boa Vista and Mogi Guaçu, São Paulo, Brazil. MapBiomas Collection 10.1 data from 2019 to 2024 were used to derive reference polygons, and Sentinel-2 imagery was processed into cloud-masked, 15-day temporal composites using ten spectral bands. The proposed PSE-CNN was benchmarked against PSE-TAE, PSE-Transformer, Random Forest, and XGBoost using spatially grouped data partitioning and temporal test years. Results: The proposed PSE-CNN achieved the highest Unified F1-Score of 0.703 and the lowest coefficient of variation of 2.28%, indicating stronger inter-annual stability across test years and random seeds among the evaluated models. It also outperformed classical models that relied on handcrafted vegetation indices and demonstrated greater overall stability than attention-based deep learning alternatives. Conclusions: The results indicate that combining pixel-set encoding with temporal convolution provides a resource-aware and stable framework for retrospective citrus crop mapping from Sentinel-2 satellite image time series. These findings suggest that PSE-CNN can support scalable agricultural monitoring, contributing to sustainable crop inventory systems in regions where labeled data and computational infrastructure are limited.

Abstract: The sustainability-focused issues of the built environment require a change in architectural education not to form-based design methods but to adaptive, systems-based, and performance-oriented thinking. The paper explores a unified pedagogical model that incorporates biomimicry, parametric thinking, and modular design in improving sustainable design learning in architectural studios. The study adopts a qualitative case study method to investigate Architectural Design Studio 4 at the American University of Ras Al Khaimah (AURAK), where third-year architecture students undertake a discovery-based design process that takes three sequential stages. The students explored biological systems to first identify transferable principles, then implemented the principles in parametric modules with computational software like Dynamo and Revit and then reused these systems to create high-rise architecture. The results suggest that biomimicry combined with parametric workflows helps to achieve optimization but not maximization, which allows students to come up with flexible, efficient, and reusable design systems. The modular design approaches were essential in dealing with the architectural complexity, especially in the high-rise application and parametric tools enabled exploration of many variations and informed decisions based on the performance. The undisclosed final design goal promoted critical thinking, conceptualization, and problem-solving. The research provides the literature of architectural education with empirical evidence as it illustrates how an integrated process-based approach can improve the knowledge of sustainability, system logic, and adaptability in students. The study finds that integrating biomimicry and parametric design in modular and discovery-oriented studios is a sound pedagogical approach to equip future architects to deal with modern environmental and technological demands.

Abstract: Background: Reliable intraoperative tools for donor liver assessment are needed, particularly in the context of steatotic and extended-criteria grafts. While histology remains the reference standard, it is limited by sampling variability and logistical constraints. Preservation fluid may provide a complementary, whole-organ source of biochemical information. Methods: In this single-center prospective exploratory pilot study, liver tissue and preservation fluid were collected from 30 donation-after-brain-death grafts during the back-table procedure. Biochemical parameters, including arginase activity, β-hydroxybutyrate (βHB), acetoacetate, and total protein, were measured using standard assays. Associations with histological steatosis on wedge biopsy were assessed using nonparametric correlation analyses, and paired preservation-fluid samples were compared. Results: Most grafts demonstrated absent or mild steatosis; only two exhibited moderate steatosis, and none were severely steatotic. No preservation-fluid biomarker showed a statistically significant association with histological steatosis. Weak, non-significant positive correlations were observed for βHB and arginase activity (Spearman r ≈ 0.33–0.35). Protein concentration and arginase activity decreased between start and end samples, whereas ketone body levels remained relatively stable. Conclusions: Preservation-fluid biomarker measurement during routine graft preparation is feasible. Although no significant associations with histological steatosis were identified, the observed weak correlations suggest possible associations requiring validation in larger studies. Larger, adequately powered studies including a broader spectrum of steatosis and clinically relevant outcomes are required to determine potential clinical applicability.

Abstract: Background: Pregnancy-related transient osteoporosis of the hip (PR-TOH) is a rare and often underdiagnosed condition presenting with acute hip pain in late pregnancy or postpartum. Due to its nonspecific clinical presentation, it is frequently misinterpreted as common musculoskeletal or pelvic girdle pain, leading to delayed diagnosis and suboptimal management. Case Presentation: We report a rare case of bilateral PR-TOH in a 35-year-old primigravida diagnosed at 31+6 weeks of gestation. The patient presented with progressively worsening hip pain leading to severe mobility impairment. Initial investigations, including ultrasound and laboratory testing, were inconclusive. Definitive diagnosis was established by magnetic resonance imaging (MRI), demonstrating characteristic bone marrow oedema in both femoral heads. The patient was managed conservatively with analgesia, restricted weight bearing, and multidisciplinary care involving obstetrics, endocrinology, and orthopaedics. Pregnancy was successfully prolonged until 37+4 weeks, when caesarean section was performed due to clinical deterioration. Postpartum management included calcium and vitamin D supplementation and rehabilitation. Follow-up demonstrated significant improvement in bone mineral density on DEXA and complete clinical recovery at 12 months. Conclusions: PR-TOH should be considered in pregnant or postpartum women presenting with persistent hip pain and progressive functional limitation. Early use of MRI is essential for accurate diagnosis and differentiation from more common pregnancy-related conditions. Prompt recognition and multidisciplinary management are crucial to prevent complications and optimize maternal and obstetric outcomes.

Abstract: This study characterized standard biochars produced at 300, 400, and 500 °C alongside a locally made biochar (LBC, drum kiln method with newly devised method of Bababe) to assess fertilizer value and toxicity against IBI thresholds. Pyrolysis temperature strongly influenced properties: electrical conductivity and salt content increased with tempera-ture (BC300 and BC500 highest; LBC lowest). All standard biochars were highly alkaline (pH 10.26–10.57), while LBC was near-neutral (7.84). Maximum carbon content occurred at 300–400 °C (56.8–56.9 %). At 10 kg ha⁻¹, standard biochars supplied 308–331 kg ha⁻¹ K, with BC400 providing the highest Ca and Mg. LBC had the highest volatile micronu-trients (B, Cu, Fe, Mn), which decreased with rising temperature. It can be particularly well suited to fertilizer coating or blending systems, especially for salt-sensitive soils where application rates are kept low (< 10 t ha⁻¹), thereby limiting agronomic risks such as Mo contaminant loading. Nevertheless, molybdenum levels in all biochars were 5–8 times above IBI safe limits (5–75 mg kg⁻¹), posing toxicity risk at 10 t ha⁻¹ application. Cd was undetectable, reduced Pb by 90 % at 400–500 °C, and kept Ni and Pd within limits. SEM revealed BC400 had optimal honeycomb porosity and homogeneous mineral dis-tribution. BC400 is most suitable for agricultural fertilizer value, BC500 for carbon se-questration, BC300 for potassium supply, and LBC as a low-cost, low-salinity material. However, excessive molybdenum across all biochars relates feedstock composition as the paramount safety factor. The weakness and limitation of this studies lies in the resource constraints from use of one feedstock, absence of direct measurement of surface area and phosphorus, and absence of measurement of biochar stability.

Abstract: The number of human genetic variants cataloged in dbSNP has plateaued since 2021, with over ~1.1 billion variants housed. Since the human pangenome reference has enabled the precise identification of even structurally complex variants, capturing the entire spectrum of human genetic variants is almost achievable. However, the clinical impacts of most genetic variants still remain elusive. This is due to limitations in genome-wide association study (GWAS), the standard framework for variant interpretation, which relies solely on statistical assumptions. GWAS cannot interpret low‐frequency alleles and capture molecular interactions between variants, hindering its ability to explain complex traits and diseases. Recently, large language models (LLMs) enabled accurate inference of human genetic variants’ pathogenicity even without requiring a large sample size or prior annotations by modeling the biological principles encoded within the genome. For instance, Evolutionary Scale Modeling (ESM1b) successfully predicted missense variants in ClinVar, achieving an auROC of up to 0.905. In addition, Evo 2 classified non-coding pathogenic variants in ClinVar with an auROC of 0.987 for single nucleotide variants (SNVs) and 0.971 for non-SNVs. These results suggest that although yet limited to pathogenicity prediction, integrating multiomic and clinical data through LLM will enable the complete clinical interpretation of human genetic variants.

Abstract: Ammonia-oxidizing bacteria (AOB) are vital for the nitrogen cycle and wastewater treatment, yet their recalcitrance to isolation and cultivation hampers industrial application. To isolate an efficient strain and optimize its culture conditions for high-ammonia wastewater treatment, we collected water samples from a polluted river in Zhongshan City. After enrichment, a strain was isolated via gradient dilution and silica gel plating, identified by scanning electron microscopy and 16S rDNA sequencing as Nitrosomonas europaea W4 (99.93% similarity to the type strain). Single-factor medium optimization examined CaCO₃ and Fe²⁺/Fe³⁺, while temperature and initial ammonia nitrogen effects were tested, and landfill leachate was used for verification. CaCO₃ shortened the lag phase without affecting maximum specific growth rate; replacing Fe³⁺ with Fe²⁺ further reduced lag and enhanced the ammonia oxidation rate. Optimal growth occurred at 25–30 °C and an initial ammonia nitrogen concentration of ~2000 mg/L. In landfill leachate, the strain increased the ammonia degradation rate 6.3-fold. Overall, N. europaea W4 exhibits high ammonia oxidation efficiency, and the optimized medium and conditions improve its proliferation and metabolic stability, providing a basis for cultivation and application in treating high-strength ammonia nitrogen wastewater.

Abstract: Large language models (LLMs) increasingly require robust evaluation under realistic instruction-following conditions, particularly for fine-tuned task-specific adapters operating in multilingual environments. This study proposes a scenario-adaptive evaluation framework for assessing the reliability of fine-tuned text models across two application regimes: misinformation detection (disinfo) and knowledge-grounded factual biography generation (heroes). The framework integrates automated generation of balanced risk-oriented scenarios, bilingual evaluation in English and Ukrainian, the LLM-as-a-Judge paradigm, and multidimensional robustness analysis through the Alignment Robustness Index (ARI). Six LoRA-adapted models based on Qwen2.5-3B-Instruct, SmolLM2-1.7B-Instruct, and TinyLlama-1.1B-Chat-v1.0 were evaluated. The implemented pipeline generated 2052 scenarios and 6156 model responses, producing a final bilingual analytical subset of 4104 judged records. Experimental results show that task-specific adaptation produces task-dependent robustness profiles. In the disinfo case, Qwen2.5-3B achieved the strongest overall performance, combining the highest safety and classification accuracy. In contrast, the heroes case revealed a more compressed and multidimensional vulnerability space without a single dominant model. The results further demonstrate the importance of multilingual evaluation, as weaker adapters exhibited substantially larger cross-lingual safety gaps. Overall, the proposed framework provides a reproducible and practically applicable methodology for auditing fine-tuned language models under imperfect instructions.

Abstract: This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.

Abstract: International communication scholarship has undergone a paradigmatic reorientation since 2000, yet the field's conceptual repertoire has expanded more rapidly than it has been theoretically integrated. This systematic literature review interrogates that fragmentation by mapping the trajectory of the field across the period 2000–2026 and assessing the extent to which its proliferating frameworks—cultural imperialism, hybridization, network society, platform imperialism, data colonialism, computational propaganda, sharp power, and algorithmic governance—constitute cumulative theoretical advancement or analytically incommensurable parallel vocabularies. Following PRISMA 2020 procedures (Page et al., 2021) and a thematic synthesis design (Thomas & Harden, 2008), the review consolidates peer-reviewed scholarship across seven major communication databases into seven thematic clusters: cultural globalization and media flows; comparative journalism and cross-national media systems; de-Westernization and decolonial currents; phantomization, digital sovereignty, and media infrastructures; disinformation, computational propaganda, and information disorder; soft power, public diplomacy, and affective strategic communication; and the integration of generative artificial intelligence into transnational communication. Three theoretical findings emerge. First, the apparent succession of paradigms from broadcast-era to platform-era frameworks is better understood as conceptual layering, in which power-asymmetric models persist in modified form rather than being displaced by network-based alternatives. Second, the field's longstanding tension between structural and agentic accounts has been reconfigured—but not resolved—by the platform turn, with infrastructural analysis emerging as a potential synthesizing register (Parks & Starosielski, 2015; Plantin & Punathambekar, 2019). Third, the persistent disjuncture between the field's de-Westernization commitments and its bibliometric realities (Demeter, 2020) is theoretically consequential, indicating that epistemic asymmetries function not as residual artifacts but as constitutive features of contemporary international communication knowledge production. A seventh identified gap—the under-theorization of affective dimensions of international communication—extends the review's analytic horizon to include emergent comparative work on emotion, civilizational rhetoric, and cross-border public engagement (Çelik, 2025; Hameleers & Garnier Ortiz, 2024; Wahl-Jorgensen, 2019). The review proposes a future research agenda centered on epistemic pluralism, methodological diversification, infrastructural and material analysis, and sustained engagement with planetary-scale technological change.

Abstract: This research examines how climate change intensifies urban heat stress, particularly in public spaces where mechanical cooling is impractical. A climate-driven, systems-level numerical model is developed to evaluate the pre-installation feasibility of portable, solar-powered misting canopies. Hourly Typical Meteorological Year data (TMYx, 2009–2023) are analyzed for each city to estimate photovoltaic (PV) energy yield, electrical load, potential misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. Misting operation is governed by an adaptive, rule-based control strategy based on air temperature, relative humidity, and solar radiation. To enable systematic comparison, K-means clustering is applied to classify the cities into six archetypal summer climate zones. Results indicate that evaporative cooling feasibility is driven more by ambient humidity than by air temperature. Hot-dry interior cities achieve the longest average misting duration (502.65 hours) and highest water consumption (30,486 L per module), but exhibit the lowest PV-to-load autonomy ratio (1.53) due to high energy demand for pumping. In contrast, humid Black Sea cities show minimal misting duration (13.11 hours) and water use (478 L) yet achieve the highest autonomy (40.91) because of limited system operation. It is important to note that the autonomy ratio reflects a seasonal energy balance rather than continuous off-grid capability. Overall, the adaptive control approach effectively aligns water and energy use with climatic suitability across all clusters. The proposed framework offers a scalable and quantitative screening tool to inform the design and deployment of PV-powered outdoor cooling systems across diverse urban environments.