Search Results (58)

In this study, we extract a 7-day binned $\gamma$-ray light curve from 2008 August to 2019 March in the energy range 0.1–300 GeV and identify four outburst periods with peak flux of >8.0$\times {10}^{-7}$ ph ${\mathrm{cm}}^{-2}{\mathrm{s}}^{-1}$. Four active states in the optical are also marked during this period. The fastest variability timescale suggests the emission region radius is R ∼ 2.4$\times {10}^{16}$ cm, and the observed emission region lies within <0.7 pc distance from the central engine. The majority of short-timescale flares exhibit a symmetric temporal profile, implying that the rise and decay timescales are dominated by disturbances caused by dense plasma blobs passing through the standing shock front in the jet region. To understand the properties of the source jets, we employ a standard one-zone leptonic scenario to model the broadband spectral energy distributions (SEDs) of flaring periods and determine that the $\gamma$-ray spectrum is better reproduced when the dissipation region of the jet is located within the molecular torus (MT). The $\gamma$-ray spectra from the outburst phases show an obvious spectral break with a break energy between 3.00 and 7.08 GeV, which may be attributed to an intrinsic break in the energy distribution of radiating particles. The studies of the survival time of a sheet before being destroyed by the turbulent motions of plasma (${\tau }_{cs}\sim 2.9\times {10}^4$ s), the shock acceleration time ($t_{acc}\sim$$4.3\times {10}^4$ s), and the minimum interaction height ($Z_{min}$ ≈ 2.57–4.55$\times {10}^{17}$ cm > $R_{BLR}$ ∼ 1.0$\times {10}^{17}$ cm) suggest that the $\gamma$-ray flaring event maybe caused by a magnetic reconnection mechanism, but we cannot completely rule out the shock-in-jet model. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

Convection associated with the leading mode of subseasonal variability of the tropical atmosphere, the Madden–Julian Oscillation (MJO), can excite Rossby wave trains that extend well into the extratropics and allow the MJO to modulate many components of the Earth system. To improve our understanding of teleconnections between the MJO and Antarctic sea ice, composite anomalies of daily change in sea ice concentration (ΔSIC) from 1989 to 2019 were binned by phase 0–20 days after an active MJO and compared to anomalies of surface air temperature, the meridional component of surface wind, and sea-level pressure. In May, ΔSIC anomalies were strongest in the Indian Ocean (IO) sector, 16 days after phase 8. There, a wavenumber-three pattern in sea-level pressure anomalies associated with the MJO resulted in anomalously poleward winds and warmer temperatures over the central and eastern IO that were collocated with anomalously negative ΔSIC. Furthermore, anomalously equatorward winds and colder temperatures in the western IO were collocated with anomalously positive ΔSIC. In July, ΔSIC anomalies were strongest in the Weddell Sea (WS) sector nine days after an active MJO in phase 2. There, a wavenumber-three pattern in sea-level pressure anomalies resulted in anomalously poleward winds and warmer temperatures over the western and central WS that were collocated with negative ΔSIC anomalies; anomalously equatorward winds and colder temperatures over the eastern WS were collocated with positive ΔSIC anomalies. In September, the largest ΔSIC anomalies were observed in the IO and WS sectors six days after an active MJO in phase 8. No meaningful modulation of sea ice anomalies was found after an active MJO in November or January. These results extend our understanding of teleconnections between the MJO and Antarctic sea ice on the subseasonal time scale. Full article

Background/Objectives: Metabolic syndrome (MetS) increases the risk of type 2 diabetes and cardiovascular disease. We aimed to identify the key metabolic predictors of MetS in a Central European cohort and to compare classical statistics with modern machine learning (ML) models. Methods: We analysed 956 adults from the Lower Silesia Healthy Donors cohort. Clinical, anthropometric, biochemical, and lifestyle variables were collected using standardised procedures. Group differences were tested with Mann–Whitney U tests and effect sizes. A multivariable logistic regression (outcome: binary MetS defined as ≥3 harmonised components, MetS_bin) estimated adjusted odds ratios. In parallel, ML models (logistic regression, Random Forest, XGBoost, LightGBM, CatBoost) were trained with stratified 5-fold cross-validation. Performance was evaluated by accuracy, F1-macro, and area under the receiver-operating characteristic curve (ROC AUC). Model interpretability used SHAP values. Results: Overweight/obese participants had higher fasting glucose (median 92.0 vs. 84.6 mg/dL), fasting insulin (9.9 vs. 6.6 µU/mL), and systolic blood pressure (134 vs. 121 mmHg) and lower HDL cholesterol (53 vs. 66 mg/dL) compared to normal-BMI individuals (all p < 0.001, r ≈ 0.39–0.41). Participants with a higher waist circumference also showed markedly increased HOMA-IR (2.16 vs. 1.34; p < 0.001). In multivariable logistic regression, waist circumference, BMI, triglycerides, HDL cholesterol, fasting glucose, and systolic blood pressure were independently associated with MetS, yielding a test ROC-AUC of 0.98 and PR-AUC of 0.88. Machine learning models further improved discrimination: Random Forest, XGBoost, LightGBM, and CatBoost all achieved very high performance (test ROC-AUC ≥ 0.99, PR-AUC ≥ 0.98), with CatBoost showing the best cross-validated PR-AUC (~0.99) and favourable calibration. SHAP analyses consistently highlighted fasting glucose, triglycerides, HDL cholesterol, waist circumference, and systolic blood pressure as the most influential predictors. Conclusions: Combining classical regression with modern gradient-boosting models substantially improves the identification of individuals at risk of MetS. CatBoost, XGBoost, and LightGBM delivered near-perfect discrimination in this Central European cohort while remaining explainable with SHAP. This framework supports clinically meaningful risk stratification—including a “subclinical” probability zone—and may inform targeted prevention strategies rather than purely reactive treatment. Full article

Sustainability in software engineering encompasses environmental, human, social, and economic dimensions, each essential for ensuring software’s positive and lasting impact. This paper presents an innovative Internet of Things (IoT)-based Smart Waste Management (SWM) system. The proposed system addresses key limitations in existing solutions, including lack of real-time responsiveness, inefficient routing, inadequate emergency detection, and limited user-centric design. While prior studies have investigated IoT applications in SWM, challenges remain in achieving dynamic, integrated, and scalable systems for sustainable urban development. The proposed solution introduces a holistic architecture that enables real-time monitoring of waste bin levels and fire incidents through Waste Bin Level Monitoring Units (BLMUs) equipped with ultrasonic and flame sensors. Data is transmitted via Wi-Fi to a centralized City Command and Control Center (4C), allowing for automated alerts and dynamic route optimization. A dual-platform software suite supports both administrative and operational workflows: a desktop web application and a role-based Android mobile app developed in Flutter, and integrated with Google Cloud Firestore, enabling centralized data management and efficient resource allocation. We validated the system through a working prototype, demonstrating notable contributions including enhanced emergency responsiveness, optimized waste collection routes, and improved stakeholder engagement. This research contributes to the advancement of sustainable urban infrastructure by offering a scalable, data-driven SWM framework grounded in software engineering principles and aligned with smart city objectives. This paper presents an innovative IoT-based Smart Waste Management (SWM) system that addresses key limitations in existing solutions, including insufficient real-time responsiveness, inefficient routing, inadequate emergency detection, and limited user-centric design. Full article

Traffic crashes remain a critical public safety issue and are among the leading causes of mortality worldwide. Understanding, analyzing, and forecasting crash trends are essential for implementing effective countermeasures and reducing injury severity. In response to the growing number of crashes and their associated economic and social costs, this study presents a geospatial analytical framework for prioritizing and classifying roadway segments based on crash trends. The framework focuses on a major freeway corridor in the United States, covering a four-year period across 20 counties. This methodology employs spatiotemporal analysis, which integrates both spatial (geographic) and temporal (time-based) dimensions to better understand how crash patterns evolve over time and space. A central component of the analysis is Space–Time Cube (STC) modeling, a three-dimensional GIS-based visualization, and an analytical approach that organizes data into spatial locations (x and y) across a sequence of temporal bins (z-axis) to reveal patterns that may not be evident in a two-dimensional analysis. Additionally, emerging pattern analysis, specifically Emerging Hotspot Analysis (EHA), is used to identify statistically significant trends in crash frequency over time. The results indicate a significant spatial clustering of crashes, with high-risk segments predominantly located in densely populated urban areas with high traffic volumes. Crash hotspots were classified into five distinct categories: persistent, intensifying, new, sporadic, and diminishing, enabling transportation agencies to tailor interventions based on temporal dynamics. The proposed geospatial framework enhances decision making for roadway safety improvements and can be adapted for use in other regional corridors to support infrastructure investment and advance public safety. Full article

Accurate characterization of surface solar irradiance at fine spatial, temporal, and spectral resolution is central to applications such as solar energy and environmental monitoring. On the one hand, modeling radiative transfer to achieve such accuracy requires detailed characterization of a wide range of factors, including the vertical profiles of gaseous and particulate absorbers and scatterers, wavelength-resolved surface reflectivity, and the three-dimensional morphology of clouds. On the other hand, satellite-based remote sensing products typically provide top-of-the-atmosphere irradiance at coarse spatial resolutions, where individual pixels can span several kilometers, failing to capture fine-scale intra-pixel variability. In this study, we introduce a machine learning framework that integrates large-scale remote sensing satellite data with hyperlocal, second-by-second ground-based measurements from an ensemble of low-cost spectral sensors to estimate the wavelength-resolved surface solar irradiance spectra at the hyperlocal level. The satellite data are obtained from the Harmonized Sentinel-2 MSI (MultiSpectral Instrument), Level-2A Surface Reflectance (SR) product, which offers high-resolution surface reflectance data. By leveraging machine learning, we model the relationship between satellite-derived surface reflectance and ground-based spectral measurements to predict high-resolution, wavelength-resolved irradiance, using target data obtained from an NIST-calibrated reference instrument. By utilizing a low-cost sensor ensemble that is easily deployable at scale, combined with downscaled satellite data, this approach enables accurate modeling of intra-pixel variability in surface-level solar irradiance with high temporal resolution. It also enhances the utility of the Harmonized Sentinel-2 MSI data for operational remote sensing. Our results demonstrate that the model is able to estimate surface solar irradiance with an R² ≈ 0.99 across all 421 spectral bins from 360 nm to 780 nm at 1 nm resolution, offering strong potential for applications in solar energy forecasting, urban climate research, and environmental monitoring. Full article

Gully erosion refers to the landform formed by soil and water loss through gully development, which is a critical manifestation of soil degradation. However, research on the spatio-temporal variations in erosion gullies at the county scale remains insufficient, particularly regarding changes in gully aggregation and their driving factors. This study utilized high-resolution remote sensing imagery, gully interpretation information, topographic data, meteorological records, vegetation coverage, soil texture, and land use datasets to analyze the spatio-temporal patterns and influencing factors of erosion gully evolution in Bin County, Heilongjiang Province of China, from 2012 to 2022. Kernel density evaluation (KDE) analysis was also employed to explore these dynamics. The results indicate that the gully number in Bin County has significantly increased over the past decade. Gully development involves not only headward erosion of gully heads but also lateral expansion of gully channels. Gully evolution is most pronounced in slope intervals. While gentle slopes and slope intervals host the highest density of gullies, the aspect does not significantly influence gully development. Vegetation coverage exhibits a clear threshold effect of 0.6 in inhibiting erosion gully formation. Additionally, cultivated areas contain the largest number of gullies and experience the most intense changes; gully aggregation in forested and grassland regions shows an upward trend; the central part of the black soil region has witnessed a marked decrease in gully aggregation; and meadow soil areas exhibit relatively stable spatio-temporal variations in gully distribution. These findings provide valuable data and decision-making support for soil erosion control and transformation efforts. Full article

Brassinosteroids (BRs) are essential phytohormones that orchestrate various stages of plant growth and development. The BR signaling cascade is mediated through a phosphorylation network involving sequential activation of the plasma membrane-localized receptor kinase Brassinosteroid-Insensitive 1 (BRI1), the cytoplasmic kinase Brassinosteroid-Insensitive 2 (BIN2), and the transcription factors BRI1-EMS suppressor 1 (BES1) and Brassinazole-Resistant 1 (BZR1). These transcription factors activate thousands of nuclear genes. Recent evidence highlights that ubiquitination has emerged as an equally pivotal mechanism that dynamically controls the BR signaling pathway by modulating the activity, subcellular localization, and protein stability of these core signaling components. In this review, we systematically analyze the central role of ubiquitination in determining the function, localization, and degradation of these proteins to fine-tune the outputs of BR signaling. We provide comparative perspectives on the functional conservation and divergence of ubiquitin-related regulatory components in the model plant Arabidopsis versus other plant species. Furthermore, we critically evaluate current knowledge gaps in the ubiquitin-mediated spatiotemporal control of BR signaling, offering insights into potential research directions to elucidate this sophisticated regulatory network. Full article

The sonoscape of a small town at the foot of the Northern Apennines Mountains in north–central Italy was studied using a regular grid of automatic recording devices, which collected ambient sounds during the spring of 2024. The study area is characterized by high landscape heterogeneity, a result of widespread suburban agricultural abandonment and urban development. Sonic data were analyzed using the Sonic Heterogeneity Index and nine derivative metrics. The sonic signatures from 26 stations exhibited distinct, spatially explicit patterns that were hypothesized to be related to a set of 11 landcover types and seven landscape metrics. The unique sound profile of each sample site was consistent with the emerging heterogeneity of landcover typical of many Mediterranean regions. Some sonic indices exhibited stronger correlations with landscape metrics than others. In particular, the Effective Number of Frequency Bins Ratio (ENFBr) and Sheldon’s Evenness (E) proved particularly effective at revealing the link between sonic processes and landscape patterns. The sonoscape and landscape displayed correlations significantly aligned with their variability, highlighting the ecological heterogeneity of the sonic and physical domains in the study area. This case study underscores the importance of selecting appropriate metrics to describe complex ecological processes, such as the relationships and cause-and-effect dynamics of environmental sounds among human altered landscapes. Full article

Background: Point-of-care (POC) diagnostics is an innovative approach to healthcare analysis that brings the diagnostic process closer to the patient’s immediate care setting. This study was conducted to assess POC testing devices’ use in diagnosing cancer and detecting the main challenges facing laboratory specialists. Method: A cross-sectional study was conducted on conveniently selected laboratory specialists working in the Prince Mohammed bin Abdulaziz Hospital in Riyadh for six weeks. Result: A total of 187 study participants (51% males and 49% females) were enrolled. Around one-half of them (96, 51%) were less than 30 years old, and 85% had 1–5 years of experience, with 61% (124) having no previous cancer diagnosis devices training. Most of this study’s cohort was using ABL 90 Radio meter/blood gases (45, 24%), followed by ABL 800 Radio meter/blood gases (39, 20.9%), as the main cancer diagnostic devices. Several challenges were faced by this study’s participants during their work with cancer diagnosis devices. The participants shared that some time was needed to use most of the devices, and learning how to use them was a significantly steep learning curve (2.99 ± 0.07 of participants). Most participants (113, 60.4%) carried out all the control testing, and their results were compared completely (100%) with the central laboratory. They took special precautions to keep the instruments safe (162, 86.6%). Conclusion: The correlation between type of devices used and the challenges faced during the use of POCT cancer diagnosis devices showed that there is a significant correlation between all challenges facing the participants and the type of devices (p = 0.001), except for the need for time to use these devices (p = 0.53). There are many challenges facing workers who operate point-of-care cancer diagnosis devices to a high degree. Full article

An inverse-square probability mass function (PMF) is at the Newcomb–Benford law (NBL)’s root and ultimately at the origin of positional notation and conformality. $Pr\left(Z\right)={\left(2Z\right)}^{-2}$, where $Z\in {\mathbb{Z}}^{+}$. Under its tail, we find information as harmonic likelihood $\mathcal{L}\left(\left[s,t\right)\right)=H_{t-1}-H_{s-1}$, where $H_n$ is the nth harmonic number. The global $\mathbb{Q}$-NBL is $Pr\left(b,q\right)={\displaystyle \raisebox{1ex}{$\mathcal{L}\left(\left[q,q+1\right)\right)$}\!\left/ \!\raisebox{-1ex}{$\mathcal{L}\left(\left[1,b\right)\right)$}\right.}={\left(q{H}_{b-1}\right)}^{-1}$, where b is the base and q is a quantum ($1\le q<b$). Under its tail, we find information as logarithmic likelihood $\ell \left(\left[i,j\right)\right)=ln{\displaystyle \raisebox{1ex}{$j$}\!\left/ \!\raisebox{-1ex}{$i$}\right.}$. The fiducial $\mathbb{R}$-NBL is $Pr\left(r,d\right)={\displaystyle \raisebox{1ex}{$\ell \left(\left[d,d+1\right)\right)$}\!\left/ \!\raisebox{-1ex}{$\ell \left(\left[1,r\right)\right)$}\right.}={log}_r\left(1+{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$d$}\right.}\right)$, where $r\ll b$ is the radix of a local complex system. The global Bayesian rule multiplies the correlation between two numbers, s and t, by a likelihood ratio that is the NBL probability of bucket $\left[s,t\right)$ relative to b’s support. To encode the odds of quantum j against i locally, we multiply the prior odds ${\displaystyle \raisebox{1ex}{$Pr\left(b,j\right)$}\!\left/ \!\raisebox{-1ex}{$Pr\left(b,i\right)$}\right.}$ by a likelihood ratio, which is the NBL probability of bin $\left[i,j\right)$ relative to r’s support; the local Bayesian coding rule is $\tilde{o}\left(j:i|r\right)={\displaystyle \raisebox{1ex}{$i$}\!\left/ \!\raisebox{-1ex}{$j$}\right.}{log}_r{\displaystyle \raisebox{1ex}{$j$}\!\left/ \!\raisebox{-1ex}{$i$}\right.}$. The Bayesian rule to recode local data is $\tilde{o}\left(j:i|r^{\prime }\right)=\tilde{o}\left(j:i|r\right){\displaystyle \raisebox{1ex}{$lnr$}\!\left/ \!\raisebox{-1ex}{$ln{r}^{\prime }$}\right.}$. Global and local Bayesian data are elements of the algebraic field of “gap ratios”, ${\displaystyle \frac{A-B}{C-D}}$. The cross-ratio, the central tool in conformal geometry, is a subclass of gap ratio. A one-dimensional coding source reflects the global Bayesian data of the harmonic external world, the annulus $\left\{x\in \mathbb{Q}|1\le \left|x\right|<b\right\}$, into the local Bayesian data of its logarithmic coding space, the ball $\left\{x\in \mathbb{Q}|\left|x\right|<1-{\displaystyle \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$b$}\right.}\right\}$. The source’s conformal encoding function is $y={log}_r\left(2x-1\right)$, where x is the observed Euclidean distance to an object’s position. The conformal decoding function is $x={\displaystyle \frac{1}{2}}\left(1+r^y\right)$. Both functions, unique under basic requirements, enable information- and granularity-invariant recursion to model the multiscale reality. Full article

The exponential growth of Internet of Things (IoT) devices has led to massive volumes of data, challenging traditional centralized processing paradigms. The cloud–edge continuum computing model has emerged as a promising solution to address this challenge, offering a distributed approach to data processing and management and improved performances in terms of the overhead and latency of the communication network. In this paper, we present a novel resource allocation optimization solution in cloud–edge continuum architectures designed to support multiple heterogeneous mobile clients that run a set of applications in a 5G-enabled environment. Our approach is structured across three layers, mist, edge, and cloud, and introduces a set of innovative resource allocation models that addresses the limitations of the traditional bin-packing optimization problem in IoT systems. The proposed solution integrates task offloading and resource allocation strategies designed to optimize energy consumption while ensuring compliance with Service Level Agreements (SLAs) by minimizing resource consumption. The evaluation of our proposed solution shows a longer period of active time for edge servers because of the lower energy consumption. These results indicate that the proposed solution is viable and a sustainability model that prioritizes energy efficiency in alignment with current climate concerns. Full article

Saffron (Crocus sativus L.) is one of the most expensive spices in the world due to its strong market demand combined with its labor-intensive production process, which needs a lot of labor and has significant costs. New cultivation methods and traceability systems are required to improve and valorize local Italian saffron production. In this study, we conducted a three-year trial in Umbria (Central Italy), looking for a soilless cultivation method based on wooden bins posted at a suitable height from the ground to ease the sowing of corms and harvesting of flowers. Moreover, the spice traceability could be based on investigating the genetic variability of Italian saffron populations using SNP markers. The proposed novel cultivation method showed significantly higher stigma and corm production than the traditional one. At the same time, the genetic analysis revealed a total of 55 thousand SNPs, 53 of which were specific to the Italian saffron populations suitable to start a food traceability and spice certification. Full article

Leveraging the photometric data of the Sloan Digital Sky Survey and the Galaxy Evolution Explorer (GALEX), we construct mean/median spectral energy distributions (SEDs) for unique bright quasars in redshift bins of 0.2 and up to $z\simeq 3$, after taking the GALEX non-detection into account. Further correcting for the absorption of the intergalactic medium, these mean/median quasar SEDs constitute a surprisingly redshift-independent mean/median composite SED from the rest-frame optical down to ≃$500 \mathring{\mathrm{A}}$ for quasars with bolometric luminosity brighter than ${10}^{45.5}\mathrm{erg}{\mathrm{s}}^{-1}$. Moreover, the mean/median composite quasar SED is plausibly also independent of black hole mass and Eddington ratio, and suggests similar properties of dust and gas in the quasar host galaxies since cosmic noon. Both the mean and median composite SEDs are nicely consistent with previous mean composite quasar spectra at wavelengths beyond ≃$1000 \mathring{\mathrm{A}}$, but at shorter wavelengths, are redder, indicating, on average, less ionizing radiation than previously expected. Through comparing the model-predicted to the observed composite quasar SEDs, we favor a simply truncated disk model, rather than a standard thin disk model, for the quasar central engine, though we request more sophisticated disk models. Future deep ultraviolet facilities, such as the China Space Station Telescope and the Ultraviolet Explorer, would prompt revolutions in many aspects, including the quasar central engine, production of the broad emission lines in quasars, and cosmic reionization. Full article