Multidisciplinary Digital Publishing Institute (Switzerland)
Multidisciplinary Digital Publishing InstituteNot a member yet
1861300 research outputs found
Sort by
A Systematic Review of Personality Disorders in Patients with Gambling Disorder
No full textBackground/Objectives: Gambling disorder (GD) is characterized by a high prevalence of co-occurring psychiatric disorders, including personality disorders (PDs), which may negatively influence clinical presentation, treatment outcomes, and relapse rates. The aim of this systematic review was to synthesize recent evidence regarding the association between GD and formally diagnosed PD and/or diagnostically anchored PD symptomatology, and to describe the main personality dimension most frequently reported in affected individuals. Methods: A systematic search was conducted in the PubMed and Dialnet databases for articles published between 30 November 2015 and 30 November 2025, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. PubMed was selected as the primary database because it is the most comprehensive source for peer-reviewed biomedical and psychiatric research, while Dialnet was included to complement PubMed by ensuring coverage of peer-reviewed psychiatric and psychological research published in other Romance-language journals, which are often underrepresented in international databases. The methodological quality and risk of bias of the included studies were evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for cross-sectional studies and the Newcastle–Ottawa Scale (NOS) for observational studies. Data extraction and synthesis were performed manually by two independent reviewers. Eight studies, predominantly cross-sectional in nature, assessing exclusively formally diagnosed personality disorders in adult individuals (≥18 years) diagnosed with GD were included. Results: Eight studies met the inclusion criteria, including a total of 4607 patients with GD. Across studies, personality pathology was highly prevalent among individuals with GD, with antisocial and borderline personality disorders most consistently reported. Elevated levels of impulsivity, emotional dysregulation, and narcissistic traits were frequently observed and were additionally associated with greater gambling severity, earlier onset, and poorer clinical outcomes. Antisocial personality symptoms were strongly linked to high-risk gambling subtypes, while obsessive–compulsive personality traits showed a more heterogeneous relationship with gambling severity. Conclusions: These results underscore the importance of personality assessment in individuals with GD and highlight the need for longitudinal studies using standardized diagnostic frameworks to inform tailored prevention and treatment strategies
Aggregation-Tuned Charge Transport and Threshold Voltage Modulation in Poly(3-hexylthiophene) Field-Effect Transistors
No full textIn this report, a thickness-driven, aggregation–structure–transport optimum in sonicated poly(3-hexylthiophene) (P3HT) FETs was investigated. Mobility peaks at ~10–20 nm, coincident with a minimum in the photoluminescence (PL) vibronic ratio I0-0/I0-1 (strong H-aggregate interchain coupling) and X-ray diffraction sharpening of the (100) lamellar peak with slightly reduced d-spacing, indicate tighter π–π stacking and larger crystalline coherence. Absorption analysis (Spano model) is consistent with this enhanced interchain order. The mobility maximum arises from an optimal balance: J-aggregate–like intrachain planarity supports along-chain transport, while H-aggregates provide interchain connectivity for efficient hopping. Below this thickness, insufficient interchain coupling limits transport; above it, over-aggregation and disorder introduce traps and weaken gate control. The sharp rise in threshold voltage beyond the critical thickness indicates more trap states or fixed charges forming within the film bulk. As a result, a larger gate bias is needed to deplete the channel (remove excess holes) and switch the device off. These results show that electrical gating can be tuned via solution processing (sonication) and film thickness—guiding the design of P3HT devices for photovoltaics and sensing
Cancer-Associated Fibroblast Heterogeneity Shapes Prognosis and Immune Landscapes in Head and Neck Squamous Cell Carcinoma
No full textBackground/Objectives: Head and neck squamous cell carcinoma (HNSCC) is a biologically heterogeneous malignancy with poor outcomes in advanced disease. Increasing evidence indicates that the tumor microenvironment, particularly cancer-associated fibroblasts (CAFs), plays an important role in tumor progression and immune regulation. However, the diversity of CAF subsets and their clinical relevance in HNSCC remain incompletely understood. This study aimed to characterize CAF heterogeneity and assess the prognostic significance of CAF subset-specific transcriptional programs. Methods: Single-cell RNA sequencing data from HNSCC tumors were analyzed to identify CAF subsets based on differentially expressed genes. CAF subset-specific gene signatures were used to construct prognostic risk models for overall survival (OS) and progression-free survival (PFS) in The Cancer Genome Atlas HNSCC cohort, with validation in an independent dataset. CAF-driven prognostic groups were defined, and their immune landscapes and biological pathways were evaluated. Bulk RNA sequencing of primary CAF cultures was performed for validation. Results: Six CAF subsets were identified, including myofibroblastic (myCAF), inflammatory (iCAF), antigen-presenting, and extracellular matrix-related CAFs. Risk scores derived from inflammatory CAF subsets consistently predicted shorter OS across independent cohorts, whereas PFS prediction showed greater cohort dependency. CAF-based stratification identified patient subgroups with distinct immune profiles and pathway enrichment patterns. These results were supported by validation analyses and by bulk RNA sequencing of primary CAFs, demonstrating preservation of myCAF- and iCAF-like transcriptional programs ex vivo. Conclusions: CAF heterogeneity has important prognostic and immunological implications in HNSCC. Inflammatory CAF-related transcriptional programs represent robust markers of patient survival and may complement tumor-intrinsic biomarkers
Mechanistic Exploration of N,N′-Disubstituted Diamines as Promising Chagas Disease Treatments
No full textIntroduction: Chagas disease, caused by the protozoan Trypanosoma cruzi, remains a major public health concern due to the limited effectiveness of current treatments, especially in the chronic stage. Objective: Here, we wanted to advance a library of 30 N,N′-disubstituted diamines as promising antichagasic agents and gain insight into the mechanism of action. Methods: The library was evaluated for activity against the T. cruzi amastigote stage and trypanocidal efficacy. In addition, selected compounds were tested as potential polyamine transport inhibitors, and a fluorescent analog was employed to investigate compound internalization. Results: Five compounds exhibited potent activity (pIC50 > 6.0), particularly those with short aliphatic linkers (3–6 carbon atoms), suggesting a structure–activity relationship favouring shorter chains. Mechanistic studies showed that compound 3c strongly inhibited polyamine transport, a vital pathway in T. cruzi, though this was not a universal mechanism among active hits, indicating the potential for multiple targets. A fluorescent analog confirmed intracellular uptake in amastigotes but lacked antiparasitic activity, likely due to disrupted pharmacophoric features. Importantly, none of the compounds demonstrated trypanocidal activity in long-term assays, and some showed cytotoxicity, particularly in the benzyloxy-substituted series. Conclusions: These findings position N,N′-disubstituted diamines as a viable scaffold for Chagas disease drug discovery. However, further optimization is required to enhance selectivity, achieve trypanocidal effects, and better understand the underlying mechanisms of action
A Prediction Framework of Apple Orchard Yield with Multispectral Remote Sensing and Ground Features
No full textAiming at the problem that the current traditional apple yield estimation methods rely on manual investigation and do not make full use of multi-source information, this paper proposes an apple orchard yield prediction framework combining multispectral remote sensing features and ground features. The framework is oriented to the demand of yield prediction at different scales. It can not only realize the prediction of apple yield at the district and county scales, but also modify the prediction results of small-scale orchards based on the acquisition of orchard features. The framework consists of three parts, namely, apple orchard planting area extraction, district and county large-scale yield prediction and small-scale orchard yield prediction correction. (1) During apple orchard planting area extraction, the samples of some apple planting areas in the study area were obtained through field investigation, and the orchard and non-orchard areas were classified and discriminated, providing a spatial basis for the collection of subsequent yield prediction-related data. (2) In the large-scale yield prediction of districts and counties, based on the obtained orchard-planting areas, the corresponding multispectral remote sensing features and environmental features were obtained using Google Earth engine platform. In order to avoid the noise interference caused by local pixel differences, the obtained data were median synthesized, and the feature set was constructed by combining the yield and other information. On this basis, the feature set was divided and sent to Apple Orchard Yield Prediction Network (APYieldNet) for training and testing, and the district and county large-scale yield prediction model was obtained. (3) During the part of small-scale orchard yield prediction correction, the optimal model for large-scale yield prediction at the district and county levels is utilized to forecast the yield of the entire planting area and the internal local sampling areas of the small-scale orchard. Within the local sampling areas, the number of fruits is identified through the YOLO-A model, and the actual yield is estimated based on the empirical single fruit weight as a ground feature, which is used to calculate the correction factor. Finally, the proportional correction method is employed to correct the error in the prediction results of the entire small-scale orchard area, thus obtaining a more accurate yield prediction for the small-scale orchard. The experiment showed that (1) the yield prediction model APYieldNet (MAE = 152.68 kg/mu, RMSE = 203.92 kg/mu) proposed in this paper achieved better results than other methods; (2) the proposed YOLO-A model achieves superior detection performance for apple fruits and flowers in complex orchard environments compared to existing methods; (3) in this paper, through the method of proportional correction, the prediction results of APYieldNet for small-scale orchard are closer to the real yield
Numerical Modelling of Loads Induced by Wind Power-Enhancing Parakites on Offshore Wind Turbines
No full textLighter-than-air parakites deployed at sea in the close proximity of wind turbines may offer the possibility of mitigating wake losses encountered in large offshore wind farms. Such devices, having an order of magnitude similar to wind turbine rotors, can divert the stronger winds available at high altitudes to the lower level within the atmospheric boundary layer to enhance the wind flow between turbines. Mooring the parakites directly to the offshore wind turbine support structures would avoid the need for additional offshore structures. This paper investigates a novel and simple approach for mooring a parakite to an offshore wind turbine. The proposed approach exploits the lift forces of the inflatable parakite to reduce the tower bending moment at the base of the turbine induced by the rotor thrust. An iterative numerical model coupling the parakite loads to a catenary cable piecewise model is developed in Python 3.12.7 to quantify the bending moment reduction and shear load variations at the wind turbine tower base induced by the different kite geometries, windspeeds, and mooring cable lengths. The numerical model revealed that the proposed approach for mooring parakites can substantially reduce the tower bending loads experienced during rotor operation without considerably increasing the shearing forces. It was estimated that the tower bending moment decreased by 7.7% at the rated wind speed, where the rotor thrust is at its maximum, while the corresponding shear force increased by 0.6%. At higher wind speeds, where the magnitude of the rotor thrust decreases, the percentage reduction in bending moment gradually increases to 51.7% at a wind speed of 24 m/s, with the corresponding shear force increasing by only around 4.6%. Furthermore, while upscaling the parakite augments the tower bending moment reduction, changes in cable length had little effect on bending moment reduction and shear increase
Discrete-Time Computed Torque Control with PSO-Based Tuning for Energy-Efficient Mobile Manipulator Trajectory Tracking
No full textMobile manipulator robots have an increasing number of applications in industry because they extend the workspace of a fixed base manipulator mounted on a mobile platform, making it important to further investigate their control and optimization. This paper presents an implementation proposal for a coupled base–arm dynamics computed torque controller (CTC) for trajectory tracking of a differential-drive mobile manipulator, which considers the dynamics of the fixed base manipulator and the mobile base in a coupled way and compares its performance with that of a Proportional Derivative (PD) controller. Both controllers are tuned using Particle Swarm Optimization (PSO) with a cost function that aims to simultaneously reduce the control energy and the end-effector tracking error for different types of trajectories, and they operate in discrete time, thus accounting for inherent process delays. Simulation and laboratory implementation results show the superior performance of the CTC in both cases: in simulation, the average end-effector positioning error is reduced by 51.55% and the average RMS power by 46.44%; in the laboratory experiments, the average end-effector positioning error is reduced by 43.29% and the average RMS power by 53.49%, even in the presence of possible model uncertainties and system disturbances
Energy Demand, Infrastructure Needs and Environmental Impacts of Cryptocurrency Mining and Artificial Intelligence: A Comparative Perspective
No full textThis perspective paper aims to set the stage for current development in the field of energy consumption and environmental impacts in two major digital industries: cryptocurrency mining and artificial intelligence (AI). To better understand current developments, this paper uses a comparative analytical framework of life-cycle assessment principles and high-resolution grid modeling to explore the energy impacts from academic and industry data. On the one hand, while both sectors convert energy into digital value, they operate according to completely different logics, in the sense that cryptocurrencies rely on specialized hardware (application-specific integrated circuits) and seek cheap energy, where they can function as “virtual batteries” for the network, quickly shutting down at peak times, with increasing hardware efficiency. On the other hand, AI is a much more rigid emerging energy consumer, in the sense that it needs high-quality, uninterrupted energy and advanced infrastructure for high-performance Graphics Processing Units (GPUs). The training and inference stages generate massive consumption, difficult to quantify, and AI data centers put great pressure on the electricity grid. In this sense, the transition from mining to AI is limited due to differences in infrastructure, with the only reusable advantage being access to electrical capacity. Regarding competition between the two industries, this dynamic can fragment the energy grid, as AI tends to monopolize quality energy, and how states will manage this imbalance will influence the energy and digital security of the next decade
Overexpression of the Pyrus sinkiangensis LEA4 Gene Enhances the Tolerance of Broussonetia papyrifera to the Low Temperature During Overwintering
No full textKorla fragrant pear (Pyrus sinkiangensis), valued for its unique flavor, suffers from freezing damage in its native Xinjiang. Previous studies indicated a strong correlation between low-temperature stress and the expression of LEA genes, particularly PsLEA4. This study cloned PsLEA4 from P. sinkiangensis and overexpressed it in paper mulberry (Broussonetia papyrifera). The encoded 368-amino-acid protein is localized to the endoplasmic reticulum. Under −4 °C stress, the proline and soluble protein contents in the overexpressing lines increased to 1.21-fold and 1.36-fold, respectively, compared to the wild type, while relative water content (RWC) reached 1.58-fold. And catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) activities increased by 9%, 16%, and 38%, respectively. During overwintering, the transgenic line exhibited soluble protein content and RWC at 1.78-fold and 1.49-fold compared to those of the wild type, respectively. Malondialdehyde (MDA) and relative electrolyte leakage (REL) levels were only 66% and 63% of the wild type, while CAT and POD activities reached 1.87-fold, and SOD activity peaked at 2.49-fold. These adaptations were associated with improved cold tolerance and with bud break occurring 7–10 days earlier than in WT the following year. These findings could help to understand the molecular mechanisms of P. sinkiangensis for overwintering and provide new genetic resources to breed varieties of pear that can resist cold temperatures
CLIP-RL: Closed-Loop Video Inpainting with Detection-Guided Reinforcement Learning
No full textExisting video inpainting methods typically combine optical flow propagation with Transformer architectures, achieving promising inpainting results. However, they lack adaptive inpainting strategy optimization in diverse scenarios, and struggle to capture high-level temporal semantics, causing temporal inconsistencies and quality degradation. To address these challenges, we make one of the first attempts to introduce reinforcement learning into the video inpainting domain, establishing a closed-loop framework named CLIP-RL that enables adaptive strategy optimization. Specifically, video inpainting is reformulated as an agent–environment interaction, where the inpainting module functions as the agent’s execution component, and a pre-trained inpainting detection module provides real-time quality feedback. Guided by a policy network and a composite reward function that incorporates a weighted temporal alignment loss, the agent dynamically selects actions to adjust the inpainting strategy and iteratively refines the inpainting results. Compared to ProPainter, CLIP-RL improves PSNR from 34.43 to 34.67 and SSIM from 0.974 to 0.986 on the YouTube-VOS dataset. Qualitative analysis demonstrates that CLIP-RL excels in detail preservation and artifact suppression, validating its superiority in video inpainting tasks