Institute For Technology & Management

Purpose Gout, a type of inflammatory arthritis, arises from the accumulation of monosodium urate crystals in joints, leading to severe pain and inflammation. While conventional treatments, such as uric acid-lowering agents and anti-inflammatory drugs, are effective, they are often associated with adverse effects. This review aims to explore the potential of phytoconstituents as alternative therapeutic agents for gout, focusing on their mechanisms of action and strategies to enhance their clinical efficacy. Methods A comprehensive literature review was conducted to analyze the role of phytochemicals in gout management. Key compounds such as quercetin, curcumin, and resveratrol were examined for their effects on inflammatory pathways, oxidative stress, and uric acid regulation. Furthermore, advancements in drug delivery systems, including nanotechnology-based formulations and CRISPR-mediated pathway modulation, were explored to address the limitations of phytoconstituents. Results Phytoconstituents demonstrated significant anti-inflammatory, antioxidant, and xanthine oxidase inhibitory properties. These compounds modulated critical pathways such as NF-κB, NLRP3 inflammasome, and MAPK, reducing inflammation, oxidative stress, and uric acid levels. However, poor bioavailability and rapid metabolism remain key challenges, necessitating advanced formulation strategies to enhance their therapeutic potential. Conclusion Phytoconstituents offer a promising alternative for gout treatment by targeting multiple pathogenic mechanisms. Integrating nanotechnology and gene-editing approaches may improve their bioavailability and therapeutic efficacy. Further research is warranted to facilitate clinical translation and optimize their application in gout management. Graphical abstract

The pervasive nature of cyberbullying among adolescents necessitates a nuanced understanding of its underlying factors. This study examines the relationships between various predictors and cyberbullying behaviors, focusing on discriminant prejudice, exposure to harmful content, digital sex crimes, and cyberbullying education. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) on a sample of 1,999 cyberbullying perpetrators identified in the 2022 National Cyber Violence Survey conducted by the National Information Society Agency, this research provides comprehensive insights into these associations. Findings indicate that discriminant prejudice, exposure to harmful content, and digital sex crimes are significantly associated with cyberbullying. Additionally, cyberbullying education is positively related to online harassment perception but not significantly related to cyberbullying tendencies. Online harassment perception is negatively related to cyberbullying. Cyberbullying is positively associated with rationalization. Moreover, cyberbullying disclosure is associated with both increased remorse and rationalization. These results underscore the complex interplay between individual attitudes, online experiences, and educational interventions in shaping cyberbullying behaviors. The study contributes to the literature by emphasizing the role of targeted anti-prejudice programs and cyberbullying education in fostering a safer online environment for adolescents.

Helminth diseases from parasitic worms create major problems worldwide in human health and farming operations. Benzimidazole derivatives bind specifically to β‐tubulin and serve as essential components in worm treatment medications. Drug researchers need new approaches because current treatments are becoming less effective. This review delves into the pharmacological properties, mechanisms of action, and structure–activity relationships (SAR) of benzimidazole derivatives, with a focus on synthetic modifications such as lipophilic substitutions to enhance membrane permeability and carbamate moiety optimization for improved binding affinity. The review presents the latest drug resistance strategies, which include new delivery systems through nanotechnology and resistance‐overcoming agent combinations. Scientists found that the absorption and safety patterns of albendazole, mebendazole, and triclabendazole work well for parasite control across different treatment locations. The study investigates using modern screening methods and artificial intelligence to discover new treatments against worm infections. Our review discusses new developments to improve benzimidazole antiparasitic medicine for handling increasing parasite infection rates.

Aluminium alloys find diverse applications in building construction. Specifically, C-profiles being used as various structural elements in the building. Providing Edge stiffeners in the C-profile leads to increase the load carrying capacity. Limited research is available on compression behaviour c-profile with edge stiffeners. Hence, this article aims to study the behaviour of a Finite Element Analysis of aluminium alloy stiffened edge C profiles subjected to axial compression. Two different aluminium alloy materials, namely 6061-T6, and 6063-T5 were investigated. Finite element models were developed and results, including ultimate load, failure modes, and load vs. axial shortening curves, were verified against existing test data. A comprehensive parametric study was carried out based on the verified finite element models, involving variation in the orientation of the edge stiffener, column length, and section thickness. A total of 144 parametric results were compared with the design strengths calculated from Euro code 9. Keywords: Aluminium alloys; C-profile; Column; Compression; Euro code 9; Finite Element Analysis

Energy efficiency plays a vital role in the transition towards carbon neutrality. The rapid rise of the digital economy as an emerging growth engine raises important questions about its capacity to enhance energy efficiency. Understanding this relationship is essential for advancing sustainable development and minimizing carbon emissions. Using spatial Durbin models, mediation models, and panel threshold models, this paper empirically examines the nonlinear impact of digital economy development on energy utilization efficiency, taking Chinese cities as case study areas. This research shows that there is a nonlinear U-shaped relationship between the digital economy and energy efficiency, characterized by initial suppression followed by promotion, while the digital economy’s impact on energy efficiency improvement in surrounding cities shows an inverted U-shaped relationship, demonstrating initial promotion followed by suppression. Heterogeneity studies found that digital economy development in eastern Chinese cities has a significant positive promotional effect on energy efficiency, while cities in central and western regions show a suppressive effect. Mechanism tests indicated that the digital economy enhances energy efficiency by promoting industrial structure upgrading and improving green technological progress. However, its impact is significantly affected by external factors and exhibits threshold effects—only when urban development levels cross specific threshold values can the digital economy generate a positive promotional effect on energy efficiency.

The fourth industrial revolution has accelerated the development of artificial intelligence (AI). AI has permeated our lives by being installed as an assistant on smartphones. This study tries to pinpoint the critical factors influencing continued intention to use AI voice assistants. It provides a theoretical framework in which explanatory factors include attitude, interaction, novelty value, voice attractiveness, and discomfort. Data were gathered from 256 users of AI voice assistants. The partial least squares structural equation modeling (PLS-SEM) was used to empirically analyze the data. The findings reveal that attitude impacts continuance intention. Interaction significantly determines both continuance intention and attitude. Novelty value and voice attractiveness are the key factors in forming attitude. Discomfort was found to hurt continuance intention. The findings of this study might offer useful guidelines for future study and application of AI voice assistants.

Cancer prevention involves resisting cancer development at initial stages, retarding angiogenesis and initiating cancer cell apoptosis. Through the use of virtual screening, binding free energy calculations, and molecular dynamics simulations, we were able to identify compounds with potential anticancer activity."During the virtual screening process, compounds with promising drug-like properties were chosen using the Lipinski rule of five, and their binding affinities were evaluated by docking studies. In-silico activity of six different phytochemicals against established cancer specific proteins (NF-kB, p53, VEGF, BAX/BCl-2, TNF-alpha) were performed out of which p53, VEGF, BCl-2 has shown significant results. Sanguinarine has shown good docking score of -9.0 with VEGF and − 8.8 with Bcl-2 receptor and has been selected for molecular dynamics simulation. The results of Molecular Dynamics Simulations (MD) studies showed that RMSD and RMSF values of sanguinarine within an acceptable global minima (3–5.5 Å) for p53, VEGF, BAX/BCl-2. The computational models employed in this study produced important insights into the molecular mechanisms via which Sanguinarine prevents cancer by acting against p53, VEGF, and BCl-2 and by blocking the angiogenic, apoptotic, and proliferative pathways involved in the formation of cancer. The results suggest that the pharmacological activity of the selected phytomolecule (sanguinarine) is a promising avenue for cancer prevention. Graphical Abstract

Stoichiometric films of zinc sulfide (ZnS) were grown on quartz substrate using a wet chemical technique, both without and in the presence of copper (Cu) and manganese (Mn) dopants. The structural, morphological and luminescence properties of the as-deposited films were investigated using X-ray diffraction, atomic force microscopy, optical and luminescence spectroscopy. The sample compositions were analyzed using atomic absorption spectroscopy. It was found that changes in stoichiometry had a negligible effect on the crystalline phase and optical properties of the films, whereas variations in dopant concentration significantly altered their surface morphology and luminescence properties. The absorption edge of ZnS, determined using absorption spectroscopy was found to be blue-shifted from its bulk counterpart due to the confinement effect. The photoluminescence (PL) properties of the undoped and Mn:Cu co-doped ZnS samples have been studied in detail. The PL spectra of undoped samples consisted of a broad asymmetric peak which, upon deconvolution, was correlated with band edge transition and radiative recombination via intrinsic defect states. In contrast, doped samples showed intense Gaussian peaks positioned differently from the undoped samples, indicating the substitution of dopants at the zinc site in the ZnS lattice. The peak intensity also varied with changes in doping percentages in the samples. In this study, a high luminescence yield was achieved even at very low dopant concentrations.

Nickel oxide (NiO) nanoflowers decorated with reduced graphene oxide (RGO) were synthesised via the cost-effective hydrothermal method, followed by calcination to form composites. Various analytical techniques including FE-SEM, XRD, UV-visible, and Raman were employed to characterize the morphological, structural, and optical properties of the specimens, respectively. Electrochemical properties of NiO nano flower and RGO-decorated NiO nanoflowers (NRGO) materials, were evaluated through cyclic voltammetry, galvanostatic charge-discharge testing, and electrochemical impedance analysis. Findings indicate that the addition of RGO enhances the reversibility of NiO as an electrode material by providing a continuous framework and more active sites for redox reactions due to its unique configuration. The specific capacitance of the NRGO3 composites reached 396 Fg− 1 in a 6 M KOH electrolyte at a scan rate of 10 mV/s and has the lowest RCT value compared to others. All the samples have shown good stability with a percentage of retention of more than 80%, suggesting that, it is a good electrode material for energy storage applications.

In Wireless Sensor Networks, it is crucial to schedule packets efficiently while taking priorities into account. It helps congestion avoidance algorithms decide on rate adjustments and packet discarding and mitigates the performance hit from WSN’s resource constraints. The current PPI method works well for use cases where the threshold values are already known. FDMP (Fuzzy Based Dynamic Packet Priority Determination and Line Management) is presented in this article. FDMP is a congestion-aware packet scheduling algorithm that offers robust and universal mechanisms for dynamically determining packet priority and efficient line management. Therefore, it achieves minimum end-to-end delay for high priority packets while providing adequate fairness for low priority packets by scheduling packets within each line according to their execution time. From our simulations, we can deduce that the FDMP decreases end-to-end transmission delay and jitter while increasing packet delivery ratio, average residual energy, and throughput. Congestion in the network can be avoided through the optimal scheduling of packets to prevent packet retransmission and the removal of packets with expired deadlines from the transmission medium. When compared to the Dynamic Multilevel Priority (DMP) line scheduling approach, which is also a resource control algorithm, FDMP demonstrated superior performance.

Unlocking the power of ecosystem services reveals how their integration can drive meaningful economic sustainability and shape future development strategies. This study takes a multifaceted approach to explore the relationship between ecosystem services (ES) and economic sustainability, employing a rigorous quantitative research design. Data were gathered from 147 business leaders, 111 government officials, and 150 environmental and economic researchers using purposive sampling, with subsequent analysis conducted via SPSS software. The results revealed a significant positive correlation between the value of ES and economic sustainability indicators at both regional and national levels. Policy interventions that focus on enhancing ES valuation were found to lead to improved economic outcomes and long‐term sustainability. Furthermore, stakeholder collaboration emerged as a crucial factor in boosting the effectiveness of ecosystem management practices. Economic sectors that are heavily reliant on ES demonstrated greater resilience and adaptability to environmental changes compared to those with limited reliance. Moreover, the integration of ES valuation into economic decision‐making processes was shown to foster more informed and sustainable development strategies. The novelty of this research lies in its comprehensive approach, which integrates quantitative data analysis, policy evaluation, stakeholder perspectives, and sector‐specific assessments to provide a holistic understanding of the complex interplay between ecological conservation and economic prosperity. These findings underscore the critical role of ES in enhancing economic resilience and sustainability, emphasizing the importance of their strategic integration into policy frameworks and decision‐making processes for future development.

In the dynamic field of organizational behavior, comprehending the determinants of employee engagement, burnout, and job satisfaction is pivotal. This research investigates the influence of various workplace factors, such as recognition, fairness, leadership, and workload, on these key employee outcomes. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) for analysis, the study examines data from 25,285 employees. The results indicate that recognition significantly boosts employee engagement, while fairness and involvement also positively contribute, albeit to a lesser extent. Transformational leadership plays a dual role, enhancing engagement and reducing burnout. Notably, workload overload presents a nuanced impact, affecting both engagement and burnout. The study additionally reveals the detrimental effect of technological disruption anxiety on job satisfaction. A significant finding from the Multi-Group Analysis (MGA) is the varying impact of these factors between the private and public sectors, particularly in the context of transformational leadership’s effect on burnout and the differential influence of workload on burnout. These insights are critical for formulating effective organizational strategies and policies, highlighting the need for customized recognition initiatives, equitable management approaches, and well-balanced workload allocation.

Detection of abnormal heartbeats, or arrhythmias, is crucial for early diagnosis and management of cardiac diseases. Traditional methods, such as manual auscultation and basic signal processing techniques, often fall short in accuracy and depend heavily on the expertise of the practitioner. In this paper, we introduce a novel neural network-based multi-class model designed to enhance the detection of abnormal heartbeat audio signals. The model leverages convolutional neural networks (CNNs) to automatically extract intricate features from heartbeat audio signals, thus eliminating the need for manual feature engineering. The proposed system preprocesses the raw audio signals by employing noise reduction techniques, normalization, and segmentation into short, manageable frames. These frames are then processed by several convolutional layers, which learn hierarchical representations of the audio features. The extracted features are subsequently classified into multiple classes using fully connected layers, employing a softmax function to ensure proper probability distribution over the classes. The performance of the proposed model is rigorously evaluated using a comprehensive dataset containing various types of abnormal heartbeats. The dataset is split into training, validation, and test sets to ensure unbiased performance evaluation. The model is trained using cross-entropy loss and optimized with the Adam optimizer, incorporating early stopping and regularization techniques to prevent overfitting. Our experimental results demonstrate that the proposed model significantly outperforms traditional methods, achieving a classification accuracy of 95.2%, precision of 94.8%, recall of 95.1%, and an F1-score of 94.9%. The study highlights the potential of deep learning approaches, specifically CNNs, in capturing the nuanced patterns in heartbeat audio signals, making it a valuable tool for clinicians. By enabling early and accurate detection of arrhythmias, the model can aid in timely intervention, thereby improving patient outcomes. These results indicate a substantial improvement over baseline methods, which typically achieve lower performance metrics. The study highlights the potential of deep learning approaches, specifically CNNs, in capturing the nuanced patterns in heartbeat audio signals that are critical for accurate classification. The findings suggest that the proposed neural network-based multi-class model could be a valuable tool for clinicians in the early detection of arrhythmias, thereby aiding in timely intervention and treatment.

Naringin, a flavanone glycoside found abundantly in citrus fruits, is well-known for its various pharmacological properties, particularly its significant anticancer effects. Research, both in vitro and in vivo, has shown that naringin is effective against several types of cancer, including liver, breast, thyroid, prostate, colon, bladder, cervical, lung, ovarian, brain, melanoma, and leukemia. Its anticancer properties are mediated through multiple mechanisms, such as apoptosis induction, inhibition of cell proliferation, cell cycle arrest, and suppression of angiogenesis, metastasis, and invasion, all while exhibiting minimal toxicity and adverse effects. Naringin’s molecular mechanisms involve the modulation of essential signaling pathways, including PI3K/Akt/mTOR, FAK/MMPs, FAK/bads, FAKp-Try397, IKKs/IB/NF-κB, JNK, ERK, β-catenin, p21CIPI/WAFI, and p38-MAPK. Additionally, it targets several signaling proteins, such as Bax, TNF-α, Zeb1, Bcl-2, caspases, VEGF, COX-2, VCAM-1, and interleukins, contributing to its wide-ranging antitumor effects. The remarkable therapeutic potential of naringin, along with its favorable safety profile, highlights its promise as a candidate for cancer treatment. This comprehensive review examines the molecular mechanisms behind naringin’s chemopreventive and anticancer effects, including its pharmacokinetics and bioavailability. Furthermore, it discusses advancements in nanocarrier technologies designed to enhance these characteristics and explores the synergistic benefits of combining naringin with other anticancer agents, focusing on improved therapeutic efficacy and drug bioavailability.

Green innovation initiatives (GIIs) in enterprises showcase the potential to revolutionize operational efficiency by minimizing environmental impact. By adopting sustainable practices and eco‐friendly technologies, businesses can streamline processes, reduce waste, and optimize resource utilization. The global imperative to address environmental challenges has fueled a rush forward in green innovation (GI) efforts within organizations. As enterprises navigate the path to sustainability, evaluating and enhancing the efficiency of GII become imperative for long‐term viability in an increasingly eco‐conscious marketplace. Therefore, this study investigates the effectiveness of GII within enterprises, aiming to assess their efficiency in promoting environmental sustainability. This investigation employed a questionnaire survey to collect primary data from a sample of 202 individuals. The data that were gathered was examined using SPSS statistical software. The findings reveal that GII in enterprises exhibit a notable improvement in overall performance. The result reveals that the allocation of resources for GI significantly affects the effectiveness of GII in enterprises. The study's novelty lies in assessing the effectiveness of GII in various organizations, providing a nuanced understanding of their impact on resource use, cost‐effectiveness, and environmental performance. The findings indicate that the level of employee engagement significantly enhances the efficiency of GII in enterprises. Furthermore, the result clarifies that the economic conditions (ECs) significantly influence the level of investment in GI by enterprises.

The domain of medicinal chemistry has witnessed a notable surge of interest in triazoles owing to their unique structural characteristics and wide range of applications. The versatility of their synthesis methods has facilitated the creation of compound libraries with remarkable pharmacological activities, including antiviral, antiepileptic, anxiolytic, hypnotic/sedatives anticancer, antifungal, and antidepressant activities. Several techniques have been documented in the literature for generating 1,2,4‐triazole derivatives. The mechanisms governing the pharmacological effects of the formulations containing 1,2,4‐triazoles were elucidated and made clear. In addition to reviewing existing marketed formulations, this review explores emerging trends and innovations in this field. This review offers a comprehensive perspective on the importance of triazoles for influencing contemporary research, fostering innovation, and driving technological progress.

Objective This study investigates the influence of psychological factors—specifically affective and cognitive risk perceptions, social distancing attitudes, subjective norms, and cabin fever syndrome—on smartphone usage intensity during the COVID-19 pandemic, with a particular focus on university students. Methods Utilizing a cross-sectional survey design, data were collected from 314 university students from South Korea and Vietnam. Structural equation modeling was employed to analyze the relationships between the psychological constructs and their impact on smartphone usage. Results The analysis confirms that both affective and cognitive risk perceptions significantly influence attitudes towards social distancing. Furthermore, these social distancing attitudes are found to significantly affect cabin fever syndrome, suggesting that positive attitudes towards social distancing are closely associated with higher reports of cabin fever. Notably, cabin fever syndrome emerges as a significant predictor of increased smartphone usage, underscoring its role as a mediator between prolonged isolation and digital engagement. Additionally, subjective norms are also shown to significantly influence smartphone usage intensity, highlighting the impact of social expectations on digital behaviors during the pandemic. Conclusion The study highlights the complex interplay between psychological distress induced by social restrictions and increased reliance on digital technology for social connectivity. These insights suggest that mental health interventions and digital literacy programs tailored to university students’ needs can be effective in managing the negative impacts of prolonged social isolation.

This research analysis was conducted to explore the evolution of scholarly work on slow fashion, which prioritizes ethical production, sustainability, and transparency in the fashion industry. Utilizing bibliometric analysis with Biblioshiny and VOSviewer on a dataset of 343 documents, the study aimed to identify influential perspectives and significant research goals in this domain as bibliometric study aims to bridge the research gap by tracking the development of slow fashion and identifying both well-researched and emerging areas ripe for future investigation. The findings revealed a notable increase in the publication-to-citation ratio over recent decades, with ‘Sustainability Switzerland’ being the leading journal (90 publications) and Choi-T-M recognized as the most prolific author. Hong Kong Polytechnic University ranked first in institutional contributions, while collaboration was strongest among researchers from the USA, UK, and China. Sustainability emerged as a central theme, showing the highest link strength, and future trending topics included corporate social responsibility, consumer behavior, and ethical fashion. This analysis enhances the understanding of slow fashion’s integration with sustainable practices and provides a roadmap for emerging researchers in the field.

Water quality is a pivotal factor for maintaining human and ecological health. Traditional water quality assessments often depend on ground sampling and lab tests, which are costly, slow, and constrained by geographic limitations. The emergence of remote sensing technologies now allows for extensive and timely monitoring of water quality across vast regions. This study introduces an innovative approach that utilizes remote sensing data alongside a hybrid Generative Adversarial Network-Long Short-Term Memory (GAN-LSTM) model to transform the monitoring of water quality, focusing on pollution and sanitation management. We employed a comprehensive dataset from Kaggle, which includes 3276 data points and 10 essential water quality indicators, integrated with historical remote sensing data. The GAN model is designed to produce realistic synthetic datasets, which are then used by the LSTM model to predict water quality trends with high accuracy. The methodology achieved notable results, with an accuracy of 98%, precision of 97%, recall of 99%, and an F1-score of 98%. This approach leverages cutting-edge modeling techniques and extensive datasets to significantly improve the monitoring and management of water quality.