Search Results (1,205)

Background/Objectives: Psoriasis is a chronic immune-mediated skin disease with an estimated global prevalence of 3%. Real-world studies have demonstrated that biologic therapies have transformed the management of moderate-to-severe psoriasis by providing optimal disease control and a favorable safety profile. However, a new challenge lies in identifying those most likely to achieve an early and sustained response, defined as ‘super-responders’ (SRs). This is particularly relevant given recent evidence suggesting that IL-23 inhibitors may have long-term disease-modifying effects by acting on tissue-resident memory T cells. Identifying positive and negative baseline predictors associated with achieving SR status in patients treated with anti-IL-23 agents. Methods: This retrospective observational study analyzed data from the electronic medical records of IRCCS Humanitas Research Hospital between June 2021 and June 2025. A total of 611 patients with moderate-to-severe psoriasis who were treated with risankizumab, guselkumab or tildrakizumab were included in the study. Clinical assessments were conducted at baseline and weeks 16, 28 and 52. SR status was defined as achieving a PASI score of ≤1 at week 16, with this score being maintained through weeks 28 and 52. Results: Of the 611 enrolled patients, 390 (63.8 %) achieved SR status. In multivariate logistic regression, disease duration ≤ 2 years was the strongest independent predictor (Odds Ratio [OR] 2.47, p = 0.025), followed by bio-naïve status (OR 1.53, p = 0.019). Obesity (OR 0.71, 95 % CI 0.45–1.13) and cardiometabolic comorbidities (OR 0.93, 95 % CI 0.63–1.38) were not significantly associated with response after adjustments. No serious adverse events or treatment discontinuations occurred during 52 weeks of follow-up. Conclusions: Shorter disease duration (≤2 years) and bio-naïve status were identified as independent predictors of SR status. Identifying these patients could inform the development of personalized treatment strategies, including dose optimization and extended dosing intervals. Full article

This study systematically investigates soil and stream sediment along the 165 km Ibar River to examine the origin and transfer of pollutants. The research focuses on the environmental impact of long-term mining and irregular waste management, as well as natural enrichment related to weathering processes. A comprehensive sampling campaign was conducted, collecting 70 samples from 14 locations. At each location, samples of river sediment, floodplain soil (0–5 cm and 20–30 cm depths), and river terrace soil (same depths) were collected. The contents of 21 elements (Ag, Al, As, B, Ba, Ca, Cd, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, P, Pb, Sr, V, and Zn) were determined using inductively coupled plasma atomic emission spectrometry (ICP-AES). Analysis of Variance (ANOVA) was performed to identify statistically significant differences in element contents between defined zones, sampled materials (river sediments, floodplain soils, and river terrace soils), and sampled soil horizons (topsoil, 0–5 cm, and subsoil, 20–30 cm). Multivariate analysis, including correlation coefficient, cluster analysis, and principal component analysis, revealed two distinct groups of elements with highly significant correlation coefficients (r > 0.7). The first group, comprising Ag, As, Cd, Cu, Mn, and Zn, indicates anthropogenic enrichment, likely resulting from mining and smelting activities in the middle flow of the Ibar River (The Mining and Metallurgical Complex Trepča). The second group, consisting of Cr, Mg, and Ni, suggests enrichment related to the weathering of elements from the ophiolite zone in the lower Ibar River. The study found high enrichment ratios of toxic elements like arsenic, cadmium, lead, and zinc, particularly in stream sediments and floodplains. Notably, arsenic contents exceeded European averages by up to 57 times in stream sediments, posing a significant environmental concern due to its high content. Full article

This paper provides a comprehensive investigation of nonlinear robust control methodologies, with a specific emphasis on the development of sliding mode controllers (SMCs) for quadcopter unmanned aerial vehicles (UAVs). Quadcopters are highly interconnected and underactuated and, thus, pose challenges in controlling them, especially in the presence of disturbances like wind. SMC is a widely employed approach that proves practical for managing the intricate nonlinear dynamics of UAVs with substantial coupling. The principal merit of SMC lies in its remarkable capability to reject external perturbations and uncertainties. This paper offers an extensive survey on robust control design techniques, specifically focusing on SMC design for quadcopter UAVs. This paper also delves into different SMC design approaches, such as classical SMC, super-twisting SMC (ST-SMC), terminal SMC(TSMC), adaptive SMC, backstepping SMC, event-triggered SMC, and neural network-based SMCs for quadcopters. This paper provides a detailed study of the different SMC designs to achieve various objectives for the UAV in the presence of uncertainties and disturbances. Simulations of the various SMCs are presented that demonstrate the comparative performance of the UAVs for different objectives. Finally, this article serves as an information foundation that covers various aspects of the SMC design for quadcopters. Full article

Food price volatility is a persistent challenge in Indonesia, where agriculture is central to food security and rural livelihoods. While price transmission has been studied, little is known about how volatility spreads sub-nationally in archipelagic economies with fragmented infrastructure. This study applies a Dynamic Conditional Correlation GARCH (DCC-GARCH) model to monthly rural producer price data from 2009 to 2022 for six commodities: rice, chicken, eggs, chili, cayenne, and shallots. Results show that Java functions as the core volatility transmitter, with long-run conditional correlations exceeding 0.92 in Sumatra, 0.91 in Kalimantan, and 0.90 in Papua, reflecting strong and persistent co-movements. Even in low-production regions such as Maluku, significant volatility linkages reveal structural dependence on Java. Volatility clustering is particularly intense for perishables like chili and shallots. The findings highlight the need for spatially differentiated stabilization policies, including upstream interventions in Java and cooperative-based storage systems in outer islands. This study is the first to apply a DCC-GARCH framework to rural producer price data in an archipelagic context, capturing volatility transmission across regions. Its novelty lies in linking these spillovers with regional market dependence, offering new empirical evidence and actionable insights for designing inclusive and geographically responsive food security strategies. Full article

This entry presents a comprehensive overview of the computational study of Old English that surveys the evolution from early digital corpora to recent artificial intelligence applications. Six interconnected domains are examined: textual resources (including the Helsinki Corpus, the Dictionary of Old English Corpus, and the York-Toronto-Helsinki Parsed Corpus), lexicographical resources (analysing approaches from Bosworth–Toller to the Dictionary of Old English), corpus lemmatisation (covering both prose and poetic texts), treebanks (particularly Universal Dependencies frameworks), and artificial intelligence applications. The paper shows that computational methodologies have transformed Old English studies because they facilitate large-scale analyses of morphology, syntax, and semantics previously impossible through traditional philological methods. Recent innovations are highlighted, including the development of lexical databases like Nerthusv5, dependency parsing methods, and the application of transformer models and NLP libraries to historical language processing. In spite of these remarkable advances, problems persist, including limited corpus size, orthographic inconsistency, and methodological difficulties in applying modern computational techniques to historical languages. The conclusion is reached that the future of computational Old English studies lies in the integration of AI capabilities with traditional philological expertise, an approach that enhances traditional scholarship and opens new avenues for understanding Anglo-Saxon language and culture. Full article

Bacteriophages (phages) play a pivotal role in shaping microbial communities and driving bacterial evolution. Among the diverse mechanisms governing phage–host interactions, the Arbitrium (ARM) communication system represents a recently discovered paradigm in phage decision-making between the lytic and lysogenic cycles. Initially identified in Bacillus-infecting phages, the ARM system employs a quorum-sensing-like peptide signaling mechanism to modulate infection dynamics and optimize population-level survival strategies. Recent studies have elucidated the structural and functional basis of ARM regulation, highlighting its potential applications in antimicrobial therapy, microbiome engineering, and synthetic biology. The significance of ARM systems lies in their ability to regulate bacterial population stability and influence the evolutionary trajectories of microbial ecosystems. Despite being a relatively recent discovery, ARM systems have garnered considerable attention due to their role in decoding phage population dynamics at the molecular level and their promising biotechnological applications. This review synthesizes current advancements in understanding ARM systems, including their molecular mechanisms, ecological implications, and translational potential. By integrating recent findings, we provide a comprehensive framework to guide future research on phage–host communication and its potential for innovative therapeutic strategies. Full article

The extraction of lithium from clay-type lithium ores has attracted significant attention, but the leaching behavior of associated elements, such as Rb, K, and Sr, remains less explored. This study quantitatively investigated the leaching behaviors and mechanisms of Li, Rb, K, Sr, and Mg in clay-type lithium ore through water leaching and roasting–water leaching processes. The results show that during direct water leaching, the leaching efficiency of K ranged between 10% and 13%, while Li and Sr exhibited lower extraction rates, requiring prolonged high-temperature leaching. Rb dissolution was minimal, and the leaching efficiency of Mg was significantly affected by temperature. In contrast, roasting–water leaching significantly enhanced the leaching efficiency, achieving extraction rates of 90.65% for Li, 92.91% for Rb, 75.85% for K, and 36.99% for Sr. However, Mg leaching was suppressed to below 1%. Roasting disrupted the original silicate and carbonate lattices, generating new phases that altered the ore’s microstructure into aggregated dense phases and needle-like porous phases upon water leaching, thereby facilitating the release of Li, Rb, K, and Sr. A research finding was that the new phase generated by magnesium inhibited its leaching, which indirectly enhanced subsequent Li, Rb, K, and Sr extraction and separation. These findings provide a quantitative foundation for optimizing multi-element co-extraction from clay-type lithium ores. Full article

This research analyzes the impact of crime news on users’ digital engagement on social networks. Specifically, this study reviews the influence of presentation format, crime details, and news or discursive values on people’s interaction with media content. To achieve the study’s objective, 1000 posts from the social network Instagram about crimes in the main media outlets of Colombia, Mexico, Paraguay, and Ecuador were reviewed. Content analysis was employed to code the variables, while negative binomial regression models were used to assess their impact on engagement, measured through likes and comments received on the posts. The findings show that shorter videos and image collections generate more engagement than other formats, while the type of crime did not show significant differences in interaction, suggesting a possible normalization of violence among the analyzed viewers. Among the news values, inseparability had a positive effect on engagement, while consonance and references to elite figures demonstrated negative effects. The original value of this research lies in empirically verifying how the characteristics of crime news influence engagement, providing relevant information for understanding the interaction between electronic media and the perception of criminality among Latin American viewers. Full article

This work mainly focuses on the energy and exergy analysis of a single-effect absorption cooling system operating with the couple H₂O-LiBr, under different climatic conditions in Senegal and France. A simulation model was developed, using the Engineering Equation Solver V10 (EES) software. Results indicate that the system can achieve a maximum COP of 0.76 and an exergy efficiency of 56%, which decreases as the generator temperature increases. Increasing the generator temperature from 87 to 95 °C significantly improves COP, but gains become marginal beyond 100 °C. The highest exergy destruction occurs in the generator, followed by the absorber, condenser, and evaporator. A temperature difference above 44 °C between the generator and the absorber is required to maintain H₂O-LiBr solution stability. Optimal temperatures for hot climates like Senegal are 90 °C (generator), 42 °C (absorber/condenser), and 7 °C (evaporator), while maximum exergy efficiency (56%) is reached at 81 °C, typical of moderate climates (France). Evaporator exergy efficiency increases from 16 to 52% with rising ambient temperature, while absorber and condenser efficiencies drop. Increasing the cooling water flow rate from 0.2 to 1.4 kg/s reduces exergy losses in the absorber and the condenser by up to 36%. The solution heat exchanger (SHE) optimal effectiveness of 0.75 reduces exergy consumption in the absorber and the generator. Full article

To address issues like haze, blurring, and color distortion in underwater images, this paper proposes a novel underwater image enhancement model called U-Vision Mamba, built on the Vision Mamba framework. The core innovation lies in a U-shaped network encoder for multi-scale feature extraction, combined with a novel multi-scale sparse attention fusion module to effectively aggregate these features. This fusion module leverages sparse attention to capture global context while preserving fine details. The decoder then refines these aggregated features to generate high-quality underwater images. Experimental results on the UIEB dataset demonstrate that U-Vision Mamba significantly reduces image blurring and corrects color distortion, achieving a PSNR of 25.65 dB and an SSIM of 0.972. Both comprehensive subjective evaluation and objective metrics confirm the model’s superior performance and robustness, making it a promising solution for improving the clarity and usability of underwater imagery in applications like marine exploration and environmental monitoring. Full article

Choosing the most sustainable and ecologically stable soil tillage techniques requires dependence on long-term field trials, which are essential for successful interventions and evidence-based decision-making. This research evaluated several factors, including soil biological activity (CO₂ emission), soil chemical properties (pH (KCl), soil organic matter (SOM)), plant growth physiological indicators (Leaf Area Index (LAI), Soil and Plant Analysis Development (SPAD)), crop yield, and grain quality (Zeleny index, protein %, oil %, and gluten % content), under six soil cultivation methods that represent varying degrees of soil disturbance in a long-term (23 years) tillage experiment. Conventional tillage (ploughing (P)) and conservational tillage techniques (loosening (L), deep cultivation (DC), shallow cultivation (SC), disking (D), and no-till (NT)) were examined for three years (2022, 2023, and 2024) in a winter barley–soybean–winter wheat cropping system. Results indicate that tillage intensity has a differential influence on soil biological parameters, with minor variations in SPAD values across treatments. The findings show significant variations in CO₂ emissions, LAI values, and grain quality in certain years, likely due to the influence of P and L tillage treatments. The novelty of this study lies in determining that, although the short-term effects of soil tillage on crop physiological parameters and grain yield may be minimal under fluctuating climatic conditions, long-term tillage practices significantly influence existing disparities, underscoring the necessity for site-specific and climate-resilient tillage strategies in sustainable crop production. Full article

Accurate genomic prediction of complex phenotypes is crucial for accelerating genetic progress in swine breeding. However, conventional methods like Genomic Best Linear Unbiased Prediction (GBLUP) face limitations in capturing complex non-additive effects that contribute significantly to phenotypic variation, restricting the potential accuracy of phenotype prediction. To address this challenge, we introduce a novel framework based on a self-supervised, pre-trained encoder-only Transformer model. Its core novelty lies in tokenizing SNP sequences into non-overlapping 6-mers (sequences of 6 SNPs), enabling the model to directly learn local haplotype patterns instead of treating SNPs as independent markers. The model first undergoes self-supervised pre-training on the unlabeled version of the same SNP dataset used for subsequent fine-tuning, learning intrinsic genomic representations through a masked 6-mer prediction task. Subsequently, the pre-trained model is fine-tuned on labeled data to predict phenotypic values for specific economic traits. Experimental validation demonstrates that our proposed model consistently outperforms baseline methods, including GBLUP and a Transformer of the same architecture trained from scratch (without pre-training), in prediction accuracy across key economic traits. This outperformance suggests the model’s capacity to capture non-linear genetic signals missed by linear models. This research contributes not only a new, more accurate methodology for genomic phenotype prediction but also validates the potential of self-supervised learning to decipher complex genomic patterns for direct application in breeding programs. Ultimately, this approach offers a powerful new tool to enhance the rate of genetic gain in swine production by enabling more precise selection based on predicted phenotypes. Full article

Sustainability has become a key focus in the hospitality industry, with travelers increasingly seeking accommodations with strong environmental commitments. As part of this trend, many hotels are adopting green marketing strategies to improve their brand image and appeal to eco-conscious consumers. However, the challenge lies in ensuring that these strategies are perceived as genuine rather than as “greenwashing,” which undermines their effectiveness and harms the brand’s credibility. This study examines the impact of green marketing strategies on the brand image of 5-star hotels in Lima, Peru. A survey of 206 hotel clients reveals that the implementation of green marketing positively influences the perceived benefits, corporate image, trust, and loyalty associated with these establishments. The results highlight that younger generations, particularly Millennials and Generation Z, are more likely to value sustainability initiatives, making them an important target for hotels seeking to enhance their brand image through eco-friendly practices. The findings suggest that effective communication of sustainable practices and transparency are essential to avoid greenwashing and build customer loyalty. This research contributes to the limited knowledge on green marketing in the Peruvian hotel sector and provides insights for both hotel managers and researchers on the importance of integrating genuine sustainability efforts into their marketing strategies. Full article

Bathymetry, the measurement of water depth and underwater terrain, is vital for scientific, commercial, and environmental applications. Traditional methods like shipborne echosounders are costly and inefficient in shallow waters due to limited spatial coverage and accessibility. Emerging technologies such as satellite imagery, drones, and spaceborne LiDAR offer cost-effective and efficient alternatives. This research explores integrating multi-sensor datasets to enhance bathymetric mapping in coastal and inland waters by leveraging each sensor’s strengths. The goal is to improve spatial coverage, resolution, and accuracy over traditional methods using data fusion and machine learning. Gülbahçe Bay in İzmir, Turkey, serves as the study area. Bathymetric modeling uses Sentinel-2, Göktürk-1, and aerial imagery with varying resolutions and sensor characteristics. Model calibration evaluates independent and integrated use of single-beam echosounder (SBE) and satellite-based LiDAR (ICESat-2) during training. After preprocessing, Random Forest and Extreme Gradient Boosting algorithms are applied for bathymetric inference. Results are assessed using accuracy metrics and IHO CATZOC standards, achieving A1 level for 0–10 m, A2/B for 0–15 m, and C level for 0–20 m depth intervals. Full article

Spectroscopic observations of Earth-like exoplanets and ultra-faint galaxies–top scientific priorities for the coming decades–involve measuring broadband signals at rates of only a few photons per square meter per hour. This imposes exceptional requirements on the detector performance, necessitating dark currents below 1 e⁻/pixel/kilo second, read noise under 1 e⁻/pixel/frame, and the ability to handle large-format arrays–capabilities that are not yet met by most existing infrared detectors. In addition, spaceborne LiDAR systems require photodetectors with exceptional sensitivity, compact size, low power consumption, and multi-channel capability to facilitate long-range range finding, topographic mapping, and active spectroscopy without increasing the instrument burden. MCT Avalanche photodiodes arrays offer high internal gain, pixelation, and photon-counting performance across SW to MW wavelengths needed for multi-beam and multi-wavelength measurements, marking them as a critical enabling technology for next-generation planetary and Earth science LiDAR missions. This work reports the latest progress in developing Hg_1−xCd_xTe linear-mode e-APDs at premier industrial research institutions, including relevant experimental data, simulations and major project planning. Related studies are summarized to demonstrate the practical and iterative approach for device fabrication, which have a transformative impact on the evolution of this discipline. Full article