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.

Simulation-based learning (SBL) is widely recognized as an effective educational approach that bridges theory and practice in nursing education. Despite its global adoption, limited research has examined the experiences of Filipino nursing students with SBL, particularly in resource-constrained settings. This study explored the perspectives of Bachelor of Science in Nursing students from a university in Metro Manila, Philippines, on the impact of SBL on their skills, emotional responses, and challenges encountered. A descriptive qualitative design was employed using purposive sampling of ten students who had participated in at least one SBL activity. Data were collected through semi-structured interviews and short written reflections and analyzed thematically following Braun and Clarke’s framework to capture nuanced experiences. Three major themes emerged from the analysis. First, students reported initial anxiety, nervousness, and stress during their early SBL experiences, which gradually transformed into confidence, adaptability, and resilience as they gained familiarity and competence. Second, SBL enhanced technical and cognitive skills such as clinical judgment, decision-making, teamwork, and patient-centered care, supporting students’ readiness for real-world practice. Third, students identified resource limitations, insufficient equipment, and time constraints as significant barriers to optimal learning, though these challenges also fostered creativity and perseverance. The findings demonstrate that SBL fosters technical competence, critical thinking, and professional growth but requires institutional support to address resource constraints and faculty development needs. This study underscores the importance of expanding SBL in Philippine nursing curricula to align with international best practices and to contribute to Sustainable Development Goals 3 (good health and well-being), 4 (quality education), and 5 (gender equality).

Complexity Leadership Theory (CLT) is a new and revolutionary concept in addressing healthcare crises worldwide. Its relevance and applications were tested during the COVID-19 pandemic. However, no definite and encompassing research was done to apply it to nursing leadership. Thus, this study examines CLT integration into nursing leadership to address the challenges posed by the pandemic. Through a systematic review of literature from PubMed, Scopus, and Web of Science, relevant studies were analyzed to determine how complexity leadership theory was defined, conceptualized, and operationalized within nursing leadership context. The findings reveal that traditional hierarchical leadership models are insufficient in a dynamic crisis environment like the pandemic. Instead, CLT’s framework which encompasses adaptive, administrative, and enabling leadership facilitates innovation, resilience, and effective interprofessional collaboration. Nurse leaders employing these strategies are better positioned to manage resources limitation, foster shared decision-making, and implement technological advancements in rapidly changing healthcare settings. Overall, this study underscores the potential of complexity leadership theory to transform nursing leadership practices by promoting continuous learning and empowerment, thereby enhancing crisis response and preparedness for future pandemics.

The pharmaceutical industry is undergoing a profound transformation driven by advancements in Information Technology (IT), with Enterprise Resource Planning (ERP) systems playing a pivotal role in reshaping operations. These systems offer integrated solutions that streamline key business processes, such as production, inventory management, supply chain optimization, regulatory compliance, and data integration, contributing significantly to operational efficiency and organizational agility. This paper explores the evolution and impact of ERP systems within the pharmaceutical sector, highlighting their contributions to overcoming the industry’s inherent challenges, including complex regulatory requirements, the need for accurate and real-time data, and the demand for supply chain resilience. The integration of cloud-based ERP solutions, the incorporation of emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), and enhanced data analytics capabilities have revolutionized pharmaceutical IT. These advancements not only reduce operational costs, improve forecasting accuracy, and enhance collaboration but also ensure compliance with stringent global regulations, such as Good Manufacturing Practices (GMP) and FDA guidelines. Moreover, ERP systems have been instrumental in managing the pharmaceutical supply chain, ensuring product traceability, and improving inventory control and order fulfillment processes. This manuscript examines how ERP systems enable pharmaceutical companies to maintain high standards of product quality, improve decision-making, and ensure the safety and efficacy of drugs through robust tracking and auditing mechanisms. A case study of a pharmaceutical company that implemented an ERP system demonstrates the tangible benefits, including increased operational efficiency, improved compliance rates, and enhanced customer satisfaction. However, despite the clear advantages, challenges such as customization complexities, data integration issues, and resistance to change remain. As the pharmaceutical industry continues to evolve, ERP systems will remain a cornerstone of digital transformation, facilitating smarter decision-making, better resource management, and enhanced collaboration across global operations. This paper also identifies future trends, including the potential of AI and blockchain technologies in further strengthening ERP systems and transforming the pharmaceutical landscape.

Recent advancements in the screening, diagnosis, and management of obstructive sleep apnea (OSA) have significantly improved patient outcomes. For screening, the use of home sleep apnea testing (HSAT) has become more prevalent, offering a convenient and cost-effective alternative to traditional in-lab polysomnography. HSAT devices have shown good specificity and sensitivity, particularly in patients with a high pre-test probability of OSA. In terms of diagnosis, advancements in wearable technology and mobile health applications have enabled continuous monitoring of sleep patterns and respiratory parameters. These tools provide valuable data that can be used to identify OSA more accurately and promptly. Additionally, machine learning algorithms are being integrated into diagnostic processes to enhance the accuracy of OSA detection by analyzing large datasets and identifying patterns indicative of the condition. Management of OSA has also seen significant progress. Continuous positive airway pressure (CPAP) therapy remains the gold standard, but new developments include auto-adjusting CPAP devices that optimize pressure settings based on real-time feedback. Mandibular advancement devices and hypoglossal nerve stimulation are emerging as effective alternatives for patients who are CPAP-intolerant. Furthermore, lifestyle interventions such as weight management, positional therapy, and exercise have been shown to complement medical treatments, leading to better overall outcomes. This review article highlights these advancements that collectively contribute to improved patient adherence, reduced symptoms, and enhanced quality of life for individuals with OSA.

Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that matter, classify a text based on its topic like whether it is about a particular subject like physics or chemistry as is useful in search engines. The model leverages a sequential architecture, transforms words into dense vectors using an Embedding layer, and captures intricate sequential patterns with two Long Short-Term Memory (LSTM) layers. This model aims to effectively classify sentiments in text data using a 50-dimensional embedding dimension and 20 % dropout layers. The use of rectified linear unit (ReLU) activations enhances non-linearity, while the SoftMax activation in the output layer aligns with the multi-class nature of sentiment analysis. Both training and test accuracy were well over 80%.

The synthesis and optimization of Active Pharmaceutical Ingredients (APIs) is fundamental to pharmaceutical drug development, directly influencing drug efficacy, safety, and cost-effectiveness. Over recent years, significant advancements in synthetic methodologies and manufacturing technologies have transformed API production. This manuscript provides an overview of the latest innovations in API synthesis, focusing on key techniques such as green chemistry, continuous flow chemistry, biocatalysis, and automation. Green chemistry principles, including solvent substitution and catalytic reactions, have enhanced sustainability by reducing waste and energy consumption. Continuous flow chemistry offers improved reaction control, scalability, and safety, while biocatalysis provides an eco-friendly alternative for synthesizing complex and chiral APIs. Additionally, the integration of automation and advanced process control using machine learning and real-time monitoring has optimized production efficiency and consistency. The manuscript also discusses the challenges associated with regulatory compliance and quality assurance, highlighting the role of advanced analytical techniques such as HPLC, NMR, and mass spectrometry in ensuring API purity. Looking ahead, personalized medicine and smart manufacturing technologies, including blockchain for traceability, are expected to drive further innovation in API production. This review concludes by emphasizing the need for continued advancements in sustainability, efficiency, and scalability to meet the evolving demands of the pharmaceutical industry, ultimately enabling the development of safer, more effective, and environmentally responsible medicines.

Digital therapeutics (DTx) play a transformative role in diabetes management by leveraging technology to provide personalized, data-driven medical interventions. These tools enhance self-management by offering continuous monitoring and real-time feedback on glucose levels, diet, and physical activity. This personalized approach helps patients adhere to treatment plans and make informed lifestyle changes, leading to improved clinical outcomes such as reduced HbA1c levels and better overall diabetes control. The importance of DTx lies in their ability to make diabetes care more accessible and convenient. Mobile apps and telemedicine platforms enable patients to receive support and guidance from anywhere, reducing the need for frequent in-person visits. Additionally, DTx often include behavioral support features like reminders, educational content, and motivational tools, which are crucial for maintaining healthy habits and managing stress. Currently, the dynamics of DTx in diabetes are rapidly evolving, with increasing integration of artificial intelligence and machine learning to further personalize and optimize care. As the adoption of these technologies grows, they hold the potential to significantly improve patient outcomes and revolutionize diabetes management on a global scale. This article will focus on the benefits of novel digital therapeutics for prevention and management of type II diabetes that are currently available in the market.

This study investigates the benefits of forgiveness among adolescents in Junior High Schools (JHS) in Bimbilla, Ghana, focusing on the influence of age on the effectiveness of forgiveness interventions. The study adopted a mixed-method experimental design, a purposive selection of eight JHSs within the Nanumba North Municipality, from which 60 adolescents were randomly chosen to participate. The study employed the Enright Forgiveness Inventory, Depression Mood Scale, and Anger Self-Report items to assess participants' emotional states before and after the intervention. The interventions were structured around the REACH model of forgiveness, which included sessions aimed at helping participants identify sources of hurt, understand the concept of forgiveness, and recognise the emotional costs of holding onto grievances. Qualitative data were analysed into themes using an interpretative lens. A two-way Analysis of Covariance (ANCOVA) was used to analyse the data. The findings revealed that exposure to forgiveness therapies significantly reshaped participants' negative emotions, leading to a marked decrease in feelings of anger and depression. Post-intervention assessments indicated that participants developed a more positive outlook towards their offenders, highlighting the transformative power of forgiveness in fostering emotional well-being. The study's results align with previous research, indicating that forgiveness interventions can effectively reduce negative emotional states and promote psychological resilience. The implications of these findings suggest that integrating forgiveness education into school curricula could be beneficial for enhancing the mental health of adolescents. By fostering an environment that encourages forgiveness, educators and mental health professionals can help mitigate the adverse effects of unresolved emotional conflicts, ultimately contributing to healthier interpersonal relationships and improved overall well-being among young individuals.

Through an in-depth discussion of the International Design Conference in Aspen from 1951 to 1970, this paper explores how environmental discourse underwent a shift in its connotations in the field of design during the conference. Of particular importance in this process of discursive transformation was the 1970 conference. This year's conference erupted into a conflict over the connotations of environmental discourse as environmental discourse outside of design impacted on and transformed the environmental discourse within design. This article examines the different concepts of the term 'environment', as presented by speakers and participants at the International Design Conferences in Aspen from 1951 to 1970, and especially focuses on the debates surrounding 'environment' at the 1970 conference. The article concludes by exploring the implications of this event and summarises the role of the 1970 International Design Conference in Aspen at this crucial turning point in environmental discourse. The aim is to explain and strengthen the significance of discourse a design conferences in the history of design, and to explore a new direction of design history research.

The purpose of this study was to examine challenges facing the Church in dealing with Moral Issues and the way forward in Ghana. Qualitatively, the study sought to examine the Church's challenges in coping with Moral Issues and the way forward in Ghana. The study adopted a case study research design. The population of the study comprised leaders of Calvary Baptist Church – Adabraka and Shiashe. These include the Vice President of the Ghana Baptist Convention and departmental heads at the Ghana Baptist Convention headquarters. Others included the Senior Pastor of Calvary Baptist Church – Adabraka with its satellite mission at Shiashe as well as a cross-section of pastors of these churches; the church administrator; the past and present directors of Baptist Relief and Development Agency (BREDA). The purposive sampling technique was specifically used to locate respondents for the study. The churches and participants were chosen because of their efforts in dealing with the causes of immorality confronting Ghanaian society. The main tool for data collection was a semi-structured interview guide. The data gathered was organised and analysed manually using emerging themes. The study revealed that the challenges which the Baptist Church encounters in its effort to deal with moral issues are the politicisation of statements made by the clergy, and inadequate trained personnel who are willing and ready to champion the agenda of the church in that respect. Financial difficulties were also mentioned. In this regard, specific reference was made to the effort made by the Ghana Baptist Convention to free the Trokosi girls. It was indicated that it takes a lot of financial resources to train and settle the freed girls. Regarding the way forward as far as these challenges were concerned, it was suggested that the church ought to speak more and do what it is mandated by Christ to do to bring about transformation. It is recommended that Churches should seriously intensify education on what constitutes human rights and freedom so that there would be a clear understanding of the concept that enables people to think through and adopt the good aspects to enhance their circumstances. Human rights defenders should exercise restraints when it comes to practices which are alien to Ghanaian values, laws and religious faith.