Search Results (6,297)

Background/Objectives: Psychoanalytic theories of destructiveness highlight its deep, unconscious origins tied to primal emotional and motivational mechanisms. Traditional psychiatric models of suicidal risk assessment focus on classic risk factors, limiting diagnostic and intervention approaches. This study examines the neuropsychoanalytic foundations of destructive tendencies, integrating sublimation and evolutionary motivational systems, redefining their role in the destruction process. Methods: A total of 480 AI-generated illustrations were assessed for interpretative accuracy. The final set was used in an online projection task with 204 respondents. Analyses included factorial exploration of the structure of the tool, assessment of psychometric properties (Cronbach α, ROC, AUC), logistic regression and analysis of intergroup differences. Results: Factor analysis identified eight subscales. Six of the eight factors showed thematic resemblance to Panksepp’s emotional systems, although this interpretation remains theory-driven and requires empirical validation. The remaining two—pursuit of destruction and its sublimation—extend beyond natural evolutionary mechanisms. Destructiveness was best explained by depression and psychological pain (OR = 1.39, 95% CI [1.26–1.52]), aggression and impulsivity (OR = 1.68, 95% CI [1.36–2.06]), and anxiety and a sense of threat (OR = 1.55, 95% CI [1.27–1.90]). Key predictors of destruction sublimation were curiosity (OR = 3.15, 95% CI [2.43–4.09]), closeness and love (OR = 3.43, 95% CI [2.48–4.76]), and pleasure and fun (OR = 3.08, 95% CI [2.26–4.20]). Analyses showed higher levels of destructiveness in individuals receiving psychological or psychiatric support, those with prior diagnoses, and students compared to employed individuals. Conclusions: Results indicate high reliability (Cronbach’s α > 0.87) and discrimination among internal subscale-defined groups (ROC > 0.7), supporting the tool’s potential in assessing destructive and sublimation tendencies within a neuropsychoanalytic framework. Future studies will explore its external validity and clinical applications. Full article

This study tested whether classic and specific bioelectrical impedance vector analysis (BIVA) parameters could explain metabolic and mechanical performance in endurance-trained trail runners. Fifteen males ($\dot{\mathrm{V}}$O₂max 61.04 ± 6.91 mL·kg⁻¹·min⁻¹) completed a 60-min treadmill protocol at 70% $\dot{\mathrm{V}}$O₂max across randomized slopes (−7% to +7%), with continuous gas-exchange, heart-rate, and running-power recording; whole-body BIVA was obtained immediately pre- and post-exercise. Post-test, impedance and resistance increased (+2.73%, +2.84%), while reactance (Xc) and phase angle decreased (−2.36%, −4.91%); all were significant and mirrored by both classic and specific indices, consistent with acute fluid loss and altered cellular status. After Benjamini–Hochberg adjustment, baseline Xc/height correlated inversely with $\dot{\mathrm{V}}$CO₂peak and $\dot{\mathrm{V}}$CO₂mean, whereas exercise-induced changes in ΔXc/height and ΔXcspecific correlated positively with both metabolic variables and mean power. Stepwise regression retained ΔXc/h or ΔXcspecific as the only BIVA predictors for $\dot{\mathrm{V}}$CO₂peak, $\dot{\mathrm{V}}$CO₂mean, and mean power output, explaining ~31–36% and ~22–23% of the variance, respectively; classic and specific approaches performed similarly. No bioelectrical variable predicted $\dot{\mathrm{V}}$O₂max. These preliminary findings suggest that acute reactance shifts may provide a modest yet sensitive, non-invasive index of exercise-induced physiological responses, warranting confirmation in larger and more diverse cohorts. Full article

Symmetry is a fundamental principle in mathematics, physics, and biology, where it governs structure and invariance. Classical symmetry analysis focuses on exact group-theoretic descriptions, but rarely addresses how robust a symmetric configuration is to perturbations. In this work, we introduce a probabilistic framework for quantifying the stability of finite point-set symmetries under random deletions. Specifically, given a finite set of points with a prescribed nontrivial symmetry group, we define the probability $P_N$ that removing N points reduces the symmetry to the trivial group $C_1$. The complementary quantity $S_N=1-P_N$ serves as a measure of symmetry stability, providing a robustness profile of the configuration. We calculate $S_N$ explicitly for representative families of symmetric point sets, including linear arrays, polygons, polyhedra, directed necklace of points, and crystallographic unit cells. Our results demonstrate unexpected behaviors: the regular hexagon loses symmetry with a probability of 0.6 under the removal of three vertices, while cubes and tetrahedra exhibit the maximal robustness ($S_N=1$) for all admissible N. We further introduce a Shannon entropy of symmetry stability, which quantifies the overall uncertainty of symmetry breaking across all deletion sizes. This framework extends classical symmetry studies by incorporating randomness, linking group theory with probabilistic combinatorics, and suggesting applications ranging from crystallography to defect tolerance in physical systems. Full article

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning. Full article

Indoor air pollution is a major cause of noncommunicable diseases, and increasing people’s knowledge about the related risks is a key action for prevention. Many studies describe questionnaires for evaluating knowledge on indoor air quality that often involve selected population groups and take time to fill out. This study describes the validation of a questionnaire built “ad hoc” that aims to be easy to fill out, reliable, and valid. The validation process integrated two psychometric approaches: the Classical Test Theory (CTT), which uses the Kuder–Richardson 20 (KR-20) formula to measure the internal consistency and reliability of the questionnaire as a whole, and the Item Response Theory (IRT), which evaluates each statement (item)’s validity. The questionnaire, distributed using social media to a self-selected sample of people, reached a sample of 621 subjects. In terms of internal consistency, the questionnaire was found to be satisfactory, with a KR-20 value of 0.74 (CI 0.71–0.77). The IRT analysis showed that the statements included in the questionnaire can distinguish between high-performing and low-performing interviewees, since 100% of the items reached a value of the “discrimination parameter a_j” that was within or above the recommended range. In terms of difficulty, many statements (53.3%) showed a low level of difficulty, obtaining a low “difficulty parameter b_j” value, while another 20% of the items showed a high level of difficulty. Regarding the pseudo-guessing parameter, known as the c-parameter, the probability of answering correctly for a low-performing interviewee was observed in three items (1, 6, and 9), and the same statements fell outside the range for all three parameters evaluated in the IRT. The application of the IRT highlights the criticality of some questions that would not have emerged using the CTT approach alone. Although the questionnaire is acceptable overall, it will be appropriate to evaluate whether to revise or exclude the critical questions in order to improve the instrument’s performance. Full article

Artificial intelligence (AI) is an important tool for improving agricultural tasks. In particular, object detection methods based on convolutional neural networks (CNNs) enable the detection and classification of objects directly in the field. Combined with unmanned aerial vehicles (UAVs, drones), these methods allow efficient crop monitoring. The primary challenge is to develop models that are both accurate and feasible under real-world conditions. This study addresses this challenge by evaluating marigold flower detection using three groups of CNN detectors: canonical models, including YOLOv2, Faster R-CNN, and SSD with their original backbones; modified versions of these detectors using DarkNet-53; and modern architectures, including YOLOv11, YOLOv12, and the RT-DETR. The dataset consisted of 392 images from marigold fields, which were manually labeled and augmented to a total of 940 images. The results showed that YOLOv2 with DarkNet-53 achieved the best performance, with 98.8% mean average precision (mAP) and 97.9% F1-score (F1). SSD and Faster R-CNN also improved, reaching 63.1% and 52.8%, respectively. Modern models obtained strong results: YOLOv11 and YOLOv12 reached 96–97%, and RT-DETR 93.5%. The modification of YOLOv2 allowed this classical detector to compete directly with, and even surpass, recent models. Precision–recall (PR) curves, F1-scores, and complexity analysis confirmed the trade-offs between accuracy and efficiency. These findings demonstrate that while modern detectors are efficient baselines, classical models with updated backbones can still deliver state-of-the-art results for UAV-based crop monitoring. Full article

This paper presents the application of conic programming methods to the limit analysis of plane rigid-plastic problems in structural and geotechnical engineering. The approach is based on the formulation of yield criteria as second-order cone constraints and on the dual optimization problem, which directly provides collapse mechanisms and limit loads. Two benchmark examples are investigated. The first concerns a deep beam under uniform top pressure, analyzed with linear and quadratic finite elements. The results confirm the ability of the method to reproduce realistic collapse mechanisms and demonstrate the effect of mesh refinement and element type on convergence. The second example addresses the ultimate bearing capacity of a strip footing on cohesive-frictional soil. The numerical implementation was carried out in MATLAB using CVX with MOSEK as the solver, which ensures practical applicability and efficient computations. Different soil models are considered, including Mohr–Coulomb and two Drucker–Prager variants, and the results are compared with the classical Terzaghi solution. Additional elastoplastic FEM simulations carried out in a commercial program are also presented. The comparison highlights the differences between rigid-plastic optimization and incremental elastoplastic analyses, showing that both conservative and liberal estimates of bearing capacity can be obtained. The study shows that conic programming is an efficient and flexible framework for limit analysis of plane rigid-plastic problems, providing engineers with complementary tools for assessing ultimate loads, while also ensuring good computational efficiency. Full article

Background/Objectives: Hypertension is a global health challenge. Zhengan Xifeng Decoction (ZXD), a classical traditional Chinese medicine, has shown clinical efficacy against hypertension. This study aimed to identify the bioactive constituents of ZXD and elucidate its antihypertensive mechanisms by integrating plasma UPLC–MS (ultra-performance liquid chromatography–mass spectrometry) analysis, network pharmacology, and molecular dynamics (MD) simulations. Methods: ZXD constituents and plasma-absorbed compounds were characterized by UPLC–MS. Putative targets (TCMSP, SwissTargetPrediction) were cross-referenced with hypertension targets (GeneCards, OMIM) and analyzed in a STRING protein–protein interaction network (Cytoscape) to define hub targets, followed by GO/KEGG enrichment. Selected protein–ligand complexes underwent docking, Prime MM-GBSA calculation, and MD validation. Results: A total of 72 absorbed components were identified, including 14 prototype compounds and 58 metabolites. Network pharmacology identified ten key bioactive compounds (e.g., liquiritigenin, isoliquiritigenin, and caffeic acid), 149 hypertension-related targets, and ten core targets such as SRC, PIK3CA, PIK3CB, EGFR, and IGF1R. Functional enrichment implicated cardiovascular, metabolic, and stress-response pathways in the antihypertensive effects of ZXD. Molecular docking demonstrated strong interactions between key compounds, including liquiritigenin, caffeic acid, and isoliquiritigenin, and core targets, supported by the MM-GBSA binding free energy estimation. Subsequent MD simulations confirmed the docking poses and validated the stability of the protein–ligand complexes over time. Conclusions: These findings provide mechanistic insights into the multi-component, multi-target, and multi-pathway therapeutic effects of ZXD, offering a scientific basis for its clinical use and potential guidance for future drug development in hypertension management. Full article

Diabetes is a chronic disease with a rising global prevalence. Research focuses on understanding its metabolic implications and early signaling of disease onset and complications, particularly the interconnected effects on the kidneys and brain. The objective of this study was to evaluate the expression profile in the genes Mct1, Mct4, Cd147, Hif-1α and Vegf for different biological matrices in rats induced to diabetes in the determined periods of 7, 21, 30 and 40 days. Methods: Wistar rats (160–180g, n = 68), divided into sham and diabetic groups, were evaluated according to tissue samples from the brain and kidney, using classical biochemical analyses and assessing temporal intergroup differential gene expression by qPCR. Additionally, immunohistochemical analysis was performed on kidney samples to evaluate collagen deposition. In the renal tissues, we observed a decrease in the expression of Hif-1α (21 vs. 30 days) and Vegf (21 vs. 40 days), accompanied by an increase in collagen deposition. In the brain, alterations were observed in all evaluated genes when comparing the early group (7 days) to the later groups (30 and 40 days). We observed that the evaluated genes, as well as the collagen deposition analyzed by immunohistochemistry, are related to metabolic changes that, over time, contribute to the worsening of diabetes and the progression of secondary diseases directly and/or indirectly involving the studied tissues. Full article

The proposed study explores the consistency of the geometrical character of surfaces under scaling, rotation and translation. In addition to its mathematical significance, it also exhibits advantages over image processing and economic applications. In this paper, the authors used partition-based principal component analysis similar to two-dimensional Sub-Image Principal Component Analysis (SIMPCA), along with a suitably modified atypical wavelet transform in the classification of 2D images. The proposed framework is further extended to three-dimensional objects using machine learning classifiers. To strengthen fairness, we benchmarked against both Random Forest (RF) and Support Vector Machine (SVM) classifiers using nested cross-validation, showing consistent gains when TIFV is included. In addition, we carried out a robustness analysis by introducing Gaussian noise to the intensity channel, confirming that TIFV degrades much more gracefully compared to traditional descriptors. Experimental results demonstrate that the method achieves improved performance compared to traditional hand-crafted descriptors such as measured values and histogram of oriented gradients. In addition, it is found to be useful that this proposed algorithm is capable of establishing consistency locally, which is never possible without partition. However, a reasonable amount of computational complexity is reduced. We note that comparisons with deep learning baselines are beyond the scope of this study, and our contribution is positioned within the domain of interpretable, affine-invariant descriptors that enhance classical machine learning pipelines. Full article

This article aims to investigate the impact of sustainable assets on dynamic portfolio optimization under varying levels of investor risk aversion, particularly during turbulent market conditions. The analysis compares the performance of two portfolio types: (i) portfolios composed of non-sustainable assets such as fossil energy commodities and conventional equity indices, and (ii) mixed portfolios that combine non-sustainable and sustainable assets, including renewable energy, green bonds, and precious metals using advanced Deep Reinforcement Learning models (including TD3 and DDPG) based on risk and transaction cost- sensitive in portfolio optimization against the traditional Mean-Variance model. Results show that incorporating clean and sustainable assets significantly enhances portfolio returns and reduces volatility across all risk aversion profiles. Moreover, the Deep Reinforcing Learning optimization models outperform classical MV optimization, and the RTC-LSTM-TD3 optimization strategy outperforms all others. The RTC-LSTM-TD3 optimization achieves an annual return of 24.18% and a Sharpe ratio of 2.91 in mixed portfolios (sustainable and non-sustainable assets) under low risk aversion (λ = 0.005), compared to a return of only 8.73% and a Sharpe ratio of 0.67 in portfolios excluding sustainable assets. To the best of the authors’ knowledge, this is the first study that employs the DRL framework integrating risk sensitivity and transaction costs to evaluate the diversification benefits of sustainable assets. Findings offer important implications for portfolio managers to leverage the benefits of sustainable diversification, and for policymakers to encourage the integration of sustainable assets, while addressing fiduciary responsibilities. Full article

Modeling high-frequency volatility is an important topic of market microstructure, as it provides the empirical tools to measure and analyze the rapid price movements. Yet, volatility at a high frequency often exhibits abrupt shifts driven by news and trading activity, making accurate estimation challenging. This study develops a change-point duration (CPD) model to estimate spot volatility, in which price-change intensities remain constant between events but may shift at random change points. Using simulations and empirical analysis of Nasdaq limit order book data, we demonstrate that the CPD model achieves a favorable balance between responsiveness to sudden shocks and stability in volatility dynamics. Moreover, it outperforms benchmark approaches, including the classical autoregressive conditional duration model, nonparametric duration-based estimators, and candlestick-based measures. These findings highlight the CPD framework as an effective tool for volatility estimation in high-frequency trading environments. Full article

The educational philosophy of the Nuns’ Buddhist Academy at Pushou Monastery, Mount Wutai, is based on the principles of “Hua Yan as the foundation, precepts as the practice, and Pure Land as the destination.” This philosophy draws upon Buddhist scriptures, integrating descriptions of the Pure Land practice found in the Avatamsaka Sūtra and the Amitābha Sūtra. This approach translates the textual teachings of Buddhist classics into real-life practice, expressing the concept of “the non-obstruction of principle and phenomenon” in the tangible activities of practitioners. It also allows for the experiential understanding of the spiritual realms revealed in the scriptures during theoretical learning and practice. The philosophy of the Nuns’ Academy embodies the practical emphasis of Chinese Buddhism, guiding all aspects of learning and practice. This paper argues that the pure land practice is living. In order to understand pure land practice, there should be a comprehensive viewpoint. It is needed to explore this way of practice through the analysis of textual analysis, figuring its root in Buddhis sūtra, as well as a sociological method to investigate its manifestation at the present society. Moreover, the spiritual dimension should not be neglected for a full-scale study. In this sense, the pure land school is living at present. Full article

The appearance of a negative mass term in the classical, non-relativistic Klein–Gordon equation deduced from mechanical interactions describes a repulsive interaction. In the case of a traveling wave, this results in an increase in amplitude and a decrease in the wave propagation velocity. Since this leads to dissipation, it is a symmetry-breaking phenomenon. After the repulsive interaction is eliminated, the system evolves towards the original state. Given that the interactions within the system are conservative, it would be assumed that even the original state is restored. The analysis to be presented shows that a wave with a lower angular frequency than the original one is transformed back to a slightly larger amplitude. This description is a suitable model of the axion effect, during which an electromagnetic wave interacts with a repulsive field and becomes of a continuously lower frequency. Full article

The microbial community in a recirculating aquaculture system (RAS) is pivotal in fish health, contributing significantly to the productive performance during the growing-out phase. Classical and molecular methods using PCR for species-specific amplifications have traditionally been used for bacterial community surveillance. Unfortunately, these approaches mask the real bacterial diversity and abundance, population dynamics, and prevalence of pathogenic bacteria. In this study, we explored the use of Oxford Nanopore Technology to characterize the microbiota and functional metagenomics in a commercial freshwater RAS. Intestine samples from Atlantic salmon (Salmo salar (85 ± 5.7 g)) and water samples from the inlet/outlet water, settling tank, and biofilters were collected. The full-length 16S rRNA gene was sequenced to reconstruct the microbial community, and bioinformatic tools were applied to estimate the functional potential in the RAS and fish microbiota. The analysis showed that bacteria involved in denitrification processes were found in water samples, as well as metabolic pathways related to hydrogen sulfide metabolism. Observations suggested that fish classified as sick exhibited decreased microbial diversity compared with fish without clinical symptomatology (p < 0.05). Proteobacteria were predominant in ill fish, and pathogens of the genera Aeromonas, Aliivibrio, and Vibrio were detected in all intestinal samples. Notably, Aliivibrio wodanis was detected in fish showing abnormal clinical conditions. Healthy salmon showed higher contributions of pathways related to amino acid metabolism and short-chain fatty acid fermentation (p < 0.05), which may indicate more favorable fish conditions. These findings suggest the utility of nanopore sequencing methods in assessing the microbial community in RASs for salmon aquaculture. Full article