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Treatment-relevant predictors of Machiavellianism among substance users
No full textMachiavellianism is a set of personality traits characterized by a callous nature, a belief in engaging in manipulative tactics for personal gain, a cynical and distrusting view of others, and pragmatically moral stance. Behaviors and views of individuals with elevated Machiavellian traits can be seen to have a marked similarity with several behaviors and views of individuals with substance use issues, making it difficult to differentiate between them. Using regression analysis, this exploratory study sought to identify underlying predictors of Machiavellianism. Substance using undergraduate students completed a series of questionnaires related to social connectedness, coping styles, motivation for treatment, and treatment expectations. Significant predictors of Machiavellianism included having an avoidant coping style, endorsing controlled motivation for stopping to use substances, having low treatment outcome expectancies, and feeling socially unconnected to others. This study is an important initial step in discerning differences between substance using individuals scoring higher and lower on a measure of Machiavellianism. With replication and extension, this line of research may usefully inform treatment planning for substance users. Future directions and treatment implications are discussed
Visual-language transformer-based tomato leaf disease detection for portable greenhouse monitoring device
No full textTomato leaf diseases pose a significant threat to global food security, necessitating accurate and efficient detection methods. This paper introduces the Tomato Leaf Disease Visual Language Model (TLDVLM), a novel approach based on the BLIP-2 architecture enhanced with Low-Rank Adaptation (LoRA), for precise classification of 10 distinct tomato leaf diseases. Our methodology integrates a sophisticated image preprocessing pipeline, utilizing GroundingDINO for robust leaf detection and SAM-2 for pixel-level segmentation, ensuring that the model focuses solely on relevant plant tissue. The TLDVLM leverages the powerful multimodal understanding of BLIP-2, with LoRA applied to its Q-Former module, enabling parameter-efficient fine-tuning without compromising performance. Comparative experiments demonstrate that the TLDVLM significantly outperforms baseline models, including CLIP-LoRA and ConvNeXT-tiny, achieving an accuracy of 97.27%, a precision of 0.9587, a recall of 0.9789, and an F1-score of 0.9681. Beyond classification, the finetuned TLDVLM checkpoints are integrated into a practical application for new image inference. This application displays the raw and segmented images, the predicted disease, and offers functionalities to fetch comprehensive information on disease causes and remedies using external APIs (e.g., OpenAI), with an option to download a PDF summary for offline access on a portable device. This research highlights the potential of LoRA-adapted Vision-Language Models in developing highly accurate, efficient, and user-friendly agricultural diagnostic tools
The Lance: School Year 1958-1959
No full textSchool Year 1958-1959 Vol. 31: no. 12 (1959: Feb. 26) 4p.Vol. 31: no. 13 (1959: Mar. 12) 4p.Vol. 31: no. 14 (1959: Apr. 10) 8p. Page 8 is called The Shaf
Ligand-receptor dynamics in heterophily-aware graph neural networks for enhanced cell type prediction from single-cell RNA-seq data
No full textGraph Neural Networks (GNNs) have emerged as powerful tools for analyzing structured data, particularly in domains where relationships and interactions between entities are key. By leveraging the inherent graph structure in datasets, GNNs excel in capturing complex dependencies and patterns that traditional neural networks might miss. This advantage is especially pronounced in the field of computational biology, where the intricate connections between biological entities play a crucial role. In this context, Our work explores the application of GNNs to single-cell RNA sequencing (scRNA-seq) data, a domain characterized by complex and heterogeneous relationships. By extracting ligand-receptor (L-R) associations from LIANA and constructing Cell-Cell association networks with varying edge homophily ratios, based on L-R information, we enhance the biological relevance and accuracy of depicting cellular communication pathways. While standard GNN models like Graph Convolutional Networks (GCN), GraphSAGE, Graph Attention Networks (GAT), and MixHop often assume homophily (similar nodes are more likely to be connected), this assumption does not always hold in biological networks. To address this, we explore advanced graph neural network methods, such as (Formula presented.) Graph Convolutional Networks and Gated Bi-Kernel GNNs (GBK-GNN), that are specifically designed to handle heterophilic data. Our study spans across six diverse datasets, enabling a thorough comparison between heterophily-aware GNNs and traditional homophily-assuming models, including Multi-Layer Perceptrons, which disregards graph structure entirely. Our findings highlight the importance of considering data-specific characteristics in GNN applications, demonstrating that heterophily-focused methods can effectively decipher the complex patterns within scRNA-seq data. By integrating multi-omics data, including gene expression profiles and L-R interactions, we pave the way for more accurate and insightful analyses in computational biology, offering a more comprehensive understanding of cellular environments and interactions
Unveiling the Complex Path to Young Women’s Sexual Agency: Examining the Influence of Sexual Coercion and Oppressive Discourses on Sexual Agency
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Using Convoys of Social Relations to Understand Culture and Forgiveness from an Arab American Youth Perspective
No full textSlide notes included along with additional Power Point file.The study of forgiveness among Arab Americans is both unique and illustrative. First, the nature of social relations within Arab American culture, particularly the high value placed on family relations, draws attention to the ways that close and important relationships inform attitudes and behaviors around the act of forgiveness. Second, the growing sentiment of “otherness” that is both projected onto Arab Americans by mainstream U.S. society, as well as internalized by recent cohorts of Arab American youth, holds potential insights into relations between Arab Americans and other racial/ethnic groups. In this presentation, I focus on forgiveness as a potential window into understanding the increasing complexity of social relations and their pervasive influence on identity. Also noted is that context (i.e., situation) is important both with regard to expectations about social relations and the experience of circumstances, behaviors, or events that might warrant forgiveness
Comparative analysis of energy dispatch strategies in PV-integrated renewable energy systems
No full textThe growing global population and escalating energy demands have highlighted the urgent need for a transition to sustainable and renewable energy sources. This study investigates the design and optimization of hybrid energy systems (HES) for Pelee Island, Canada—a remote community facing unreliable single-phase grid supply and increasing seasonal demand. The proposed HES integrates photovoltaic (PV) systems with tracking technologies, a biogas gasifier, diesel generator, lithium-ion battery storage, and grid interaction, under 2 dispatch strategies: Load Following (LF) and Cycle Charging (CC). Among 8 configurations, the CC-based system with VCA tracking (776 kW PV, 73 batteries) performs best, achieving a Net Present Cost (NPC) of 0.083/kWh, and Renewable Fraction (RF) of 78.7%. It meets 1,537,217 kWh of a 1,537,271 kWh annual load, with only 54.3 kWh unmet. The LF-VCA system offers the highest RF at 86.3% and the lowest emissions at 21.6 t/year but at a higher NPC of 0.02 M and COE by 0.002/kWh, while producing 90,551 kWh/year of surplus energy. Grid imports peak in winter (>100 kW) and fall near zero in summer, while surplus exports exceed 200 kW during peak solar hours, enhancing system revenue through 0.15/kWh sales
The Lance: School Year 1980-1981
No full textSchool Year 1980-1981 (1980: Sept. ?) 16p. 8Orientation Supplement (Vol. 1: no. 1); no exact dateVol. 53: no. 1 (1980: Sept. 12) 16p.Vol. 53: no. 2 (1980: Sept. 19) 12p.Vol. 53: no. 4 (1980: Sept. 26) 20p. Really no. 3Vol. 53: no. 4 (1980: Oct. 3) 16p.Vol. 53: no. 5 (1980: Oct. 10) 20p.Vol. 53: no. 6 (1980: Oct. 17) 16p. Pages 2-16 mislabeled as vol. 8Vol. 53: no. 7 (1980: Oct. 24) 16p. Pages 2-16 mislabeled as vol. 8Vol. 53: no. 8 (1980: Oct. 31) 16p. Pages 2-16 mislabeled as vol. 8Vol. 53: no. 9 (1980: Nov. 7) 16p.Vol. 53: no. 10 (1980: Nov. 14) 16p.Vol. 53: no. 11 (1980: Nov. 21) 20p.Vol. 53: no. 12 (1980: Nov. 28) 16p.Vol. 53: no. 13 (1980: Dec. 5) 12p.Vol. 53: no. 14 (1980: Dec. 11) 12p.Vol. 53: no. 15 (1981: Jan. 23) 16p.Vol. 53: no. 16 (1981: Jan. 30) 16p.Vol. 53: no. 17 (1981: Feb. 6) 16p.Vol. 53: no. 18 (1981: Feb. 13) 16p.Vol. 53: no. 19 (1981: Feb. 20) 20p.Vol. 53: no. 20 (1981: Feb. 27) 12p.Vol. 53: no. 21 (1981: Mar. 6) 20p.Vol. 53: no. 22 (1981: Mar. 13) 16p.Vol. 53: no. 23 (1981: Mar. 20) 24p.Vol. 53: no. 24 (1981: Mar. 27) 16p.Vol. 53: no. 25 (1981: Apr. 3) 12p. (1981: Apr. 3) 16p. Supplement: The Irrational EnquirerVol. 53: no. 26 (1981: Apr. 10) 16p.Vol. 53: no. 25 (1981: Apr. 16) 12p. “Misnumbered� as no. 25 on cover; really no. 2