Sentiment analysis plays a crucial role in identifying and interpreting emotions within textual data such as customer feedback, social media posts, and reviews. This study presents a sentiment classification system categorizing text into neutral, negative, and positive sentiments, aiding organizations in understanding public opinion and enhancing decision-making. To ensure accuracy, the system preprocesses data using cleaning algorithms to remove noise and irrelevant elements.The proposed model employs the TextBlob library for sentiment classification, leveraging its built-in predictive capabilities, while the clean-text library optimizes preprocessing by eliminating punctuation, stopwords, and unnecessary spaces, and standardizing text to lowercase. Key metrics such as polarity and subjectivity assess model performance to ensure reliable outcomes.A Streamlit-based interface enables user-friendly interaction, allowing organizations to extract actionable insights from large datasets. This sentiment analysis tool facilitates improved customer satisfaction, product refinement, and data-driven decision-making.
Drying serves as both a preservation method and a processing step for certain products. It is utilized in both rural and industrial settings,particularly within the food processing industry Dryers that operate on fossil fuels consume significant amounts of energy.Given the rising costs of oil in recent years, which are likely to continue increasing, it is essential to develop drying systems that do not rely on traditional energy sources. Solar drying is one such method that harnesses solar radiation as a free and renewable energy source Solar dryers are relatively easy to construct using locally available materials and tools, and they can function through natural or forced convection The drying process requires energy and heat, which depend on various factors, including the relative humidity of the air, the drying temperature, the characteristics of the product (such as thickness, surface area, and air resistance), and the moisture content of the item being dried.Consequently, the performance of the dryer is directly influenced by the level of solar irradiation and the relative humidity in the area where it is used.
This review examines the dynamic intersection of digital dentistry and material science, exploring current trends and advancements shaping modern dental practice. The integration of digital technologies, including intraoral scanners, cone-beam computed tomography (CBCT), and computer-aided design/computer-aided manufacturing (CAD/CAM) systems, has revolutionized diagnostic and restorative workflows. Concurrently, material science innovations, particularly in ceramics, composites, and 3D printing resins, are expanding the possibilities for dental restorations and prostheses. This review analyzes the accuracy, efficiency, and clinical outcomes associated with these technologies and materials, emphasizing the impact on patient care. It further explores the integration of digital workflows, including virtual treatment planning and teledentistry, and discusses the role of emerging technologies like artificial intelligence (AI), VR in future dental applications. By synthesizing current research and clinical evidence, this review provides a comprehensive overview of the evolving landscape of digital dentistry and material science, highlighting the potential for enhanced precision, predictability, and patient satisfaction in dental treatments, while also addressing current limitations and future directions.
The study examined the role of motivation, self-efficacy, and career counseling in the expansion of engineering and metallurgical sciences, with a focus on colonial and post-colonial educational policies in developing nations such as Nigeria, Ghana, Kenya, South Africa, India, and Brazil. The study was conducted in these nations, with three research questions guiding the investigation. The population comprised 948 respondents, including 520 career counselors and 428 psychologists. Due to its manageable size, no sampling was conducted, aligning with Nworgu (2015), who advocated studying the entire group to avoid sampling errors. The Motivation, Self-Efficacy, and Career Counseling in Engineering and Metallurgical Sciences Questionnaire (MSECCEMSQ) was used for data collection. The instrument was validated by two experts in the Department of Educational Foundations and one expert in the Department of Counselling and Human Development Studies, all in the Faculty of Education, University of Nigeria, Nsukka. The reliability index was established at 0.84. Data were analyzed using mean and standard deviation for the research questions. The findings revealed that motivation and self-efficacy significantly influenced students’ interest in engineering and metallurgical sciences. Additionally, career counseling played a crucial role in guiding students toward these fields, particularly in the post-colonial era. The study contributed to knowledge by highlighting the impact of educational policies on student enrollment and retention in engineering and metallurgical sciences. It was recommended that policymakers should integrate structured career counseling programs into the curriculum to enhance student participation in these fields.
In the recent times, low salinity water flooding (LSWF) has been observed as a promising enhanced oil recovery (EOR) technique both in sandstone and carbonate reservoirs. It involves injection of low salinity water which alters the wettability and interfacial properties of the reservoir rock and crude oil, thus leading to improved oil recovery. This paper presents an overview of the current state of knowledge on the mechanisms of LSWF, gained from both experimental studies and field applications. The results obtained from laboratory analysis and field applications of LSWF have been critically reviewed showing a mixed response in sandstone and carbonate reservoirs. However, the efficacy of LSWF is dependent on various factors, like, reservoir properties, injection water composition and operating conditions. In this paper, a review of the various mechanisms like fines mobilization, wettability alteration, pH reversal, multicomponent ion exchange (MIE), mineral dissolution, micro-dispersion formation etc., affecting the improved oil recovery with LSWF in both sandstone and carbonate reservoirs have been highlighted. This review work can be helpful in proper designing of LSWF in view of the operating parameters and its successful implementation, through a better understanding of its mechanisms depending on different reservoir properties.
ML (machine learning) is revolutionizing healthcare by allowing data-driven advancements in diagnosis, planning treatment, predicting risk, and keeping an eye on patients. The review tries to look at how ML has changed over time, what it is used for, how it works, how to measure its performance, and where it might go in the future in healthcare. The study uses a qualitative literature review method to look at results from supervised learning, deep learning, and predictive modelling techniques from a number of peer-reviewed sources. Diagnostic decision support, predictive analytics, personalized medicine, medical imaging, and remote patient tracking are some of the most important uses that have been named. A review of these models shows that they are very accurate, precise, and useful in clinical settings, especially when using methods like LSTM and CNNs. To check for robustness and generalizability, performance measures like F1-score, AUC-ROC, and cross-validation were always used. But challenges with data quality, interpretability, ethics, and legal gaps still make it hard for many people to use. ML has a bright future in healthcare, especially when IoT, digital twins, big data, and NLP are all used together to help with personalized, preventative, and effective care. This review shows how important it is for everyone to work together to fix the challenges that are happening now and fully use ML's potential for transforming healthcare.
With the proliferation of connected devices and the sensitive data they handle, the need of smart home security is rising. Attacks against smart homes have the potential to cause widespread damage to both consumers and communities, as seen in past cases. Although significant progress has been made in identifying security solutions for the Internet of Things (IoT) and intelligent homes, there is still a lack of agreement over the most effective approaches. This article offers a potential security strategy for smart homes and explains the relevance of relevant technology. Installed in a smart hub, the safety solution is a network-based system that detects harmful activity within a home network. In order to demonstrate the notion, two attack detection methods, namely botnet detection and evil-twin attack detection, are shown. From what we can see, the security supervisor is able to spot the former but not the latter.
Electrochromic materials, capable of reversible color changes upon electrical stimulation, have garnered significant attention for applications in smart windows, displays, and energy storage devices. This comprehensive review delves into the underlying mechanisms of color switching in both inorganic and organic electrochromic systems. Inorganic materials, such as transition metal oxides (e.g., tungsten oxide and nickel oxide), exhibit electrochromism primarily through intercalation processes where ions like Li⁺ reversibly insert into the material's lattice, altering its optical properties. Recent advancements have introduced multicolored inorganic electrochromic materials, expanding their application potential. Organic electrochromic materials, including conjugated polymers like polyaniline (PANI) and polythiophene derivatives, undergo color changes via redox reactions that modulate their conjugation length and electronic structure. For instance, PANI transitions from a yellow reduced state to a green oxidized state upon voltage application. Additionally, innovations in organic systems have led to devices capable of modulating between primary colors, covering the entire visible spectrum. Hybrid materials, such as MXenes, have emerged as promising candidates by combining the advantageous properties of both inorganic and organic systems. Notably, Nb₁.₃₃C MXene-based devices demonstrate colorless-to-black switching with significant transmittance modulation across a broad wavelength range, attributed to reversible ion insertion mechanisms. This review synthesizes recent progress in understanding the color-switching mechanisms of diverse electrochromic materials, highlighting their structural and compositional influences on optical behavior. By elucidating these mechanisms, we aim to inform the design and development of next-generation electrochromic devices with enhanced performance and expanded color palettes.
The pricing of options is a key concept in finance, and the Black-Scholes model, which was introduced in 1973, is one of the most important contributions to quantitative finance. This model provides a solid base for the valuation purposes of European options under some assumptions like the log-normality of asset prices and the constancy of volatility of the underlying asset. This paper makes a comprehensive study of the area of option pricing, with a particular focus on the development of the Black-Scholes model and its associated modifications. This paper adopts a scientometric review to bring together the growing body of literature on option pricing models and their variations. This paper finds yearly dissemination of publications, top research outlets, co-occurrence network of keywords, cluster analysis, collaboration network of authors in option pricing, co-authorship Patterns in Option Pricing Research, network analysis of article citation, most cited research articles in OP, influential Countries in Option Prices Research, Subject Area distribution in Option Pricing Research, and key Trends in Option Pricing Research. To the best of our knowledge, no previous research has tried to analyse the papers published in the Black-Scholes model. This paper contributes by conducting a scientometric analysis of Black-Scholes option pricing models. The research identified many themes, and it provides future research direction in this area of study.
In this work, BaSr(Al₀.₅Nb₀.₅)O₃ (BSAN) perovskite nanoparticles were synthesized using the solid-state reaction method and evaluated for potential application in solar cells. XRD confirmed the formation of a single-phase cubic perovskite structure with crystallite sizes between 33 nm and 55 nm. FTIR analysis indicated characteristic metal–oxygen vibrations, confirming phase formation. SEM analysis revealed uniform, granular nanoparticles with sizes predominantly in the 30–70 nm range. BET surface area analysis exhibited a high specific surface area of 58.4 m²/g and mesoporosity. EDS confirmed elemental homogeneity without impurities. UV-Vis spectroscopy showed strong visible light absorption with an optical bandgap of 1.56 eV, suitable for efficient solar energy harvesting. The combined results highlight BSAN’s suitability as an absorber layer or electron transport layer in photovoltaic devices.
Idiopathic pulmonary fibrosis (IPF), a devastating progressive interstitial lung disease (ILD) with no known cause, is the most common and deadly of the idiopathic interstitial pneumonias. With a median survival of 3–5 years following diagnosis, IPF is characterized by a progressive decline in lung function and quality of life in most patients. Prognostic factors recognized classically that influence mortality include functional, clinical and radiological parameters. However, in recent years, there has also been progress in the knowledge of genetic factors and biomarkers that may be useful in the prognostic evaluation of these patients. On the other hand, the monitoring of the disease throughout its evolution is key to improving the prognosis of the patients, as it allows for taking therapeutic measures based on this evolution, even early remission for lung transplantation. This article reviews the main prognostic factors of the disease, as well as the most useful way to monitor the disease follow-up.
Anomaly detection in smart cities is crucial for identifying unusual patterns in real-time data streams generated by diverse urban systems, such as traffic flow, energy consumption, air quality, and public safety. This study proposes a multi-modal machine learning framework for context-aware anomaly detection, integrating Convolutional Neural Networks (CNNs) for spatial feature extraction, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and contextual data (e.g., weather, public events) to improve detection accuracy. The hybrid CNN-LSTM model captures both spatial and temporal dependencies. At the same time, the inclusion of contextual information enables the model to adapt to changing conditions, improving the detection of anomalies such as traffic accidents or pollution spikes. Experimental results demonstrate that the proposed framework outperforms traditional anomaly detection methods in terms of accuracy, precision, and recall. The hybrid model's superior performance highlights its potential for real-time applications in smart cities, including sustainable urban management, fraud detection, and public safety monitoring.
Phishing websites have proven to be a major security concern. Several cyberattacks risk the confidentiality, integrity, and availability of company and consumer data, and phishing is the beginning point for many of them. Many researchers have spent decades creating unique approaches to automatically detect phishing websites. While cutting-edge solutions can deliver better results, they need a lot of manual feature engineering and aren't good at identifying new phishing attacks. As a result, finding strategies that can automatically detect phishing websites and quickly manage zero-day phishing attempts is an open challenge in this field. The web page in the URL which hosts that contains a wealth of data that can be used to determine the web server's maliciousness. Machine Learning is an effective method for detecting phishing. It also eliminates the disadvantages of the previous method. We conducted a thorough review of the literature and suggested a new method for detecting phishing websites using features extraction and a machine learning algorithm. The goal of this research is to use the dataset collected to train ML models and deep neural nets to anticipate phishing websites.
In this study, Y₃₋ₓGdₓFe₅O₁₂ (x = 0.0 and x = 0.4) nanoparticles were synthesized and characterized to examine the influence of Gd substitution on their structural, morphological, and optical properties. X-ray diffraction (XRD) analysis confirmed the formation of a garnet structure with an increase in lattice parameter due to Gd incorporation. Fourier Transform Infrared (FTIR) spectroscopy identified Fe–O and Y–O/Gd–O stretching vibrations, indicating structural modifications. Scanning Electron Microscopy (SEM) revealed a quasi-spherical morphology with increased particle size and enhanced agglomeration in the Gd-doped sample. UV-Vis spectroscopy demonstrated strong absorption in the UV and visible regions, with a red shift in the absorption edge upon Gd substitution. The results suggest that Gd incorporation alters the microstructure and optical behaviour of YIG, making it a promising candidate for magneto-optical and optoelectronic applications.
Sr(Al₀.₅Nb₀.₅)O₃ perovskite was synthesized via the solid-state reaction method and characterized to evaluate its suitability for solar cell applications. X-ray diffraction (XRD) analysis confirmed a well-crystallized cubic perovskite phase with an average lattice constant of 4.3810 Å. Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDS) revealed a nanostructured morphology with an average particle size of 33.54 nm, confirming the material’s high purity. Fourier Transform Infrared Spectroscopy (FTIR) identified characteristic metal-oxygen vibrational modes, ensuring proper perovskite phase formation. UV-Vis spectroscopy and Tauc plot analysis determined a direct bandgap of 1.54 eV and an indirect bandgap of 1.44 eV, making it a promising candidate for single-junction and tandem solar cells. The lead-free composition, strong UV absorption, and thermal stability of Sr(Al₀.₅Nb₀.₅)O₃ make it a potential material for next-generation photovoltaic applications.
Corrosion detection is essential for maintaining infrastructure safety, reliability, and longevity, particularly in industries such as oil and gas, where harsh environmental conditions accelerate material degradation. Corrosion in this industry affects the structural integrity of pipelines and increases life cycle costs. Carbon steel, a commonly used material, is highly susceptible to corrosion due to extreme operational conditions like high pressure, temperature fluctuations, and exposure to corrosive elements such as CO₂, H₂S, and chlorides. The extensive network of pipelines and remote locations make real-time corrosion detection challenging, as traditional inspection methods often prove insufficient, particularly for internal monitoring. Deep gas wells add another layer of difficulty, requiring reliable wireless communication for data acquisition. However, challenges persist in effectively detecting corrosion, especially in large, complex systems such as pipelines and offshore rigs, where traditional methods may not be sufficient. Additionally, advanced monitoring techniques using artificial intelligence (AI), and machine learning (ML), based solutions offer promising advancements, but they introduce new challenges related to cybersecurity, data management, and the need for specialized personnel. This review paper explores the different types of corrosion, detection techniques, their respective limitations, and the potential solutions to address these issues to ensure the long-term sustainability of the oil and gas industry.
Electroactive biomaterials are emerging as a transformative approach to enhance osseointegration and bone regeneration by mimicking the intrinsic electrical microenvironment of native bone. This review synthesizes recent advancements in piezoelectric, conductive, and composite electroactive materials, highlighting their capacity to modulate cellular behavior, promote osteogenesis, and combat implant-associated infections. Studies demonstrate that optimized 3D topographies and nanoarrays on piezoelectric substrates, coupled with conductive polymer coatings and antimicrobial surface modifications, significantly improve bone-implant integration and regeneration. Furthermore, the development of self-powered systems and multifunctional coatings exemplifies the pursuit of autonomous, biomimetic implants. Future directions should focus on integrating smart sensors for real-time feedback, developing biodegradable and self-healing materials, elucidating cellular mechanotransduction mechanisms, and establishing robust clinical translation pathways. By harmonizing electrical stimulation, antibacterial properties, and advanced material design, electroactive biomaterials hold immense promise for revolutionizing orthopedic and dental therapies.
Universities across post-transition and semi-formal education systems face a persistent paradox: while knowledge production lies at the heart of their mission, the structures guiding staff advancement, knowledge use, and internal governance remain informal, politicized, and resistant to change. This paper presents a strategic reform model that uses blockchain not as a digital overhaul, but as a trust-enabling backbone to support cultural and institutional formalization. Grounded in prior field research and lived insight into Eastern European academic environments, the model integrates blockchain infrastructure with knowledge management formalization and performance accountability. It offers a layered blueprint to address deep-rooted challenges such as misaligned strategic plans, opaque staff progression, fragmented budgeting, and siloed information systems. Each layer maps onto essential university functions—rectorate governance, HR, finance, research, international relations, and IT—while allowing phased, modular implementation rooted in organizational realism. Rather than replacing the culture, the design respects it, while offering scalable tools for traceable decisions, transparent recognition, and shared institutional memory. By linking strategy, governance, and knowledge in practical and culturally aware ways, this model offers a path toward universities that are not only better managed—but more transparent, future-ready, and self-aware.
This review explores the critical role of family physicians in the early detection and management of chronic diseases. With the prevalence of chronic conditions escalating globally, family physicians stand at the frontline of healthcare, offering a unique advantage in managing these diseases due to their holistic and continuous care approach. The review emphasizes the importance of integrating family health history into patient assessments to enhance the early identification of at-risk individuals, thereby facilitating timely and effective interventions. Moreover, it examines the influence of socio-economic and environmental factors on disease progression and the effectiveness of primary care in mitigating the burden of chronic diseases. Through the lens of the Chronic Care Model and other primary care frameworks, this paper discusses strategies that aim to bolster the management capabilities of family physicians, offering insights into the complexities of handling multiple chronic conditions and highlighting the need for systems that prioritize proactive over reactive care.
Maternity rights are fundamental human rights that ensure the health, dignity, and employment security of women during and after pregnancy. These rights encompass maternity leave, healthcare access, protection against workplace discrimination, and the right to return to work post-childbirth. In India, various legislative frameworks, most notably the Maternity Benefit Act, 1961, and subsequent amendments, aim to safeguard these rights. However, the effective realization of these protections often hinges on judicial interpretation and enforcement. The judiciary has played a crucial role in expanding and clarifying the scope of maternity benefits, ensuring equitable treatment, and addressing gaps in implementation. Landmark judgments have underscored the constitutional commitment to gender equality and non-discrimination, reinforcing that maternity rights are not mere statutory entitlements but integral to the right to life and dignity under Article 21 of the Constitution. This paper examines the evolution of maternity rights in India, highlights the judiciary's proactive interventions, and analyzes the ongoing challenges in translating legal provisions into lived realities for working women.
