Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

Artificial Immune Systems (AIS) are bio-inspired computational frameworks that emulate the adaptive mechanisms of the human immune system, such as self/non-self discrimination, clonal selection, and immune memory. These systems have demonstrated significant potential in addressing complex challenges across optimization, anomaly detection, and adaptive system control. This paper provides a comprehensive exploration of AIS applications in domains such as cybersecurity, resource allocation, and autonomous systems, highlighting the growing importance of hybrid AIS models. Recent advancements, including integrations with machine learning, quantum computing, and bioinformatics, are discussed as solutions to scalability, high-dimensional data processing, and efficiency challenges. Core algorithms, such as the Negative Selection Algorithm (NSA) and Clonal Selection Algorithm (CSA), are examined, along with limitations in interpretability and compatibility with emerging AI paradigms. The paper concludes by proposing future research directions, emphasizing scalable hybrid frameworks, quantum-inspired approaches, and real-time adaptive systems, underscoring AIS's transformative potential across diverse computational fields.

With the rapid growth of online communication platforms, the identification and management of toxic comments have become crucial in maintaining a healthy online environment. Various machine learning approaches have been employed to tackle this problem, ranging from traditional models to more recent attention-based transformer networks. This paper aims to compare the performance of attention-based transformer networks with several traditional machine learning methods for toxic comments classification. We present an in-depth analysis and evaluation of these methods using a common benchmark dataset. The experimental results demonstrate the strengths and limitations of each approach, shedding light on the suitability and efficacy of attention-based transformers in this domain.

In Quantum Physics, the spin and angular momentum operators are magnitudes introduced by means of a vector transformation law. However, interpreting the eigenvalues of its Z "components" as projections on said axis leads to certain contradictions supposedly avoided by a mandatory (presented as a freely selected) Z's orientation. It is shown that an oriented physical space almost forces us to project the angular momentum's and spin's eigenvalues onto its orientation's 3-form, which sidesteps entering into inconsistencies. The final conclusion is that this "rare" magnitude called spin, downright naturally comes in and plays thanks to the orientation of our three-dimensional space.

The c-equivalence principle, commonly accepted as true by most physicists, is the unstated assumption that equals the kinematic speed of light. Should someone prove the principle false, it would render the composition of two Lorentz transformations meaningless. The second hypothesis of the Special Theory of Relativity in its strong form would also be invalidated. This paper examined some of the consequences for physics, should this principle be proven false and outline some experiments to determine light speed, which could falsify the principle and provide evidence for the ether.

Polychlorinated biphenyls (PCBs) are important class of persist organic pollutants that were used as a component of paints especially in printings, as plastificator of plastics and insulating materials in transformers and capacitors, heat transfer fluids, additives in hydraulic fluids in vacuum and turbine pumps. There is always a need to establish reliable procedures for predicting the bioconcentration potential of chemicals from the knowledge of their molecular structure, or from readily measurable properties of the substance. Hence, correlation and prediction of biococentration factors (BCFs) based on λ_max and vibration frequencies of various bonds viz υ(C-H) and υ(C=C) of biphenyl and its fifty-seven derivatives have been made. For the study, the molecular modeling and geometry optimization of the PCBs have been performed on workspace program of CAChe Pro 5.04 software of Fujitsu using DFT method. UV-visible spectra for each compound were created by electron transition between molecular orbitals as electromagnetic radiation in the visible and ultraviolet (UV-visible) region is absorbed by the molecule. The energies of excited electronic states were computed quantum mechanically. IR spectra of transitions for each compound were created by coordinated motions of the atoms as electromagnetic radiation in the infrared region is absorbed by the molecule. The force necessary to distort the molecule was computed quantum mechanically from its equilibrium geometry and thus frequency of vibrational transitions was predicted. Project Leader Program associated with CAChe has been used for multiple linear regression (MLR) analysis using above spectroscopic data as independent variables and BCFs of PCBs as dependent variables. The reliability of correlation and predicting ability of the MLR equations (models) are judged by R², R²_adj, se, q²_L10O and F values. This study reflected clearly that UV and IR spectroscopic data can be used to predict BCFs of a large number of related compounds within limited time without any difficulty.

Any history of science has its own geography as well. Geographers of science have tried to put science in its place. They study the socio-spatial settings in which scientific knowledge was generated, displayed and legitimated. For them, science is socially constructed in spatialities and temporalities. The main question should to be “how” spatialities are constructing scientific knowledge via its “causalities”. Geography of science is not just about special places, locations, and regions in which scientific knowledge is unequally produced/consumed and circulated or how the use of scientific knowledge can lead to the production and reproduction of unique places and spaces. Geography of science is also about a variety set of spatial causalities through which scientific knowledge can be formed and transformed. This also means that the innovative knowledge or ideas development takes place not only in the spatial contexts but because of the spatial causalities which rise from the myriad interlinkages and interdependencies among places. These imperatives of spatial significance operate across many spatial scales from the body to the global. Hence, in our increasingly glocalized world, we must seek knowledge in spatial encounters and betweenness of places, not merely within spaces and places.

An effective mentor is critical to the success of an online doctoral student. Researchers have found that online doctoral students prefer frequent interactions with their mentor, while faculty prefer mentees to be autonomous. Transformative online doctoral mentoring (ODM) requires the development of a strong collaborative working relationship between the mentee and mentor, who serves as the link between the student and academia, as well as their guide and working partner throughout the dissertation process. In this paper, I argue that the ultimate objective of ODM, the establishment of such a relation-ship between mentor and mentee, increases the likelihood of student success. I support this contention with a set of principles and practices grounded in relevant models and methods of human development, participative leadership, and collaborative change management that provide insights into the what, why, and how of transformative ODM.

This paper examines the transformation of Yi women’s social status in Daliangshan, Sichuan Province. It analyzes historical practices—including child marriage (wawaqin) and the tradition of high bridal gifts—along with the role of education, economic modernization, and cultural advocacy initiatives. The study situates these developments within the framework of the United Nations Sustainable Development Goals (SDGs), focusing on gender equality, poverty alleviation, and equitable development. Field interviews, observations, and community-based projects inform this analysis, which highlights both progress and persisting challenges for Yi women.