Lovely Professional University

Passive optical networks (PON) have transformed network access by offering cost-effective deployment, high-speed connectivity, scalability, and low power consumption. Integrating optical and wireless networks further enhances mobility and capacity while reducing operational costs. Radio over fiber (RoF) systems efficiently connect base stations to a central station via optical fibers but suffer from issues like high bit error rates (BER) and low Q-factor values. Wavelength division multiplexing (WDM) addresses these challenges by transmitting multiple signals over a single-mode fiber. This study simulates WDM-RoF performance under varying fiber lengths and channel spacings using OptiSystem, analyzing BER, Q-factor, and eye diagrams. To combat attenuation, semiconductor optical amplifier (SOA) and erbium-doped fiber amplifier (EDFA) are evaluated in a fixed ring topology. Results identify optimal configurations for improved signal efficiency and long-distance transmission, visualized using MATLAB.

Porphyra sp. (nori), a species of red seaweed, has garnered attention for its rich nutritional profile and diverse bioactive compounds. This review synthesizes current research on Porphyra nori, focusing on its composition, bioactive components, health benefits, and potential applications in functional foods and therapeutics. Key bioactives identified include polysaccharides, peptides, phenolics, and vitamins, each contributing to antioxidant, anti‐inflammatory, and anticancer properties and also modulating the immune responses, supporting cardiovascular health, and influencing metabolic pathways. Furthermore, it serves as a valuable source of vitamin B12 and plays a crucial function in the synthesis of DNA, the generation of red blood cells, and the cognitive development of the neurological system. It reduces dependence on animal‐derived sources for vitamin B12, whereas innovations in cultivation and processing methods significantly improve its absorption and market potential. Future research directions include elucidating molecular mechanisms, optimizing extraction methods, and exploring synergistic effects with other foods or pharmaceuticals. Porphyra nori emerges as a promising source of bioactive compounds, poised to contribute to personalized nutrition and preventive healthcare strategies.

In the evolving framework of the Intelligence of Social Things (IoST), which amalgamates social networks and IoT ecosystems, knowledge graphs are essential for facilitating networked systems to efficiently process and leverage intricate relational data. Knowledge graphs offer essential technical assistance for various artificial intelligence applications, such as e-commerce, intelligent navigation, healthcare, and social media. Nonetheless, current knowledge graphs frequently lack completeness, harboring a considerable quantity of implicit knowledge that remains to be revealed. Consequently, tackling the difficulty of finalising knowledge graphs has emerged as a pressing research priority. Most contemporary methods separately analyse entity neighbourhood information or connection routes, neglecting the significance of entity neighbourhood information in the investigation of relationship paths. A novel approach, RPEN-KGC (Relationship Path and Entity Neighbourhood Knowledge Graph Completion), is suggested to enable the fusion of relationship paths and entity neighbourhood information for knowledge graph completion. RPEN-KGC comprises a sampler and an inferencer. The sampler conducts random walks between entity pairs to furnish dependable inference methods for the inferencer. The sampler utilises a contrastive method grounded in entity neighbourhood similarity to steer random walks, hence enhancing sampling efficiency and augmenting inference strategies. The inferencer derives semantic characteristics of relationship paths and deduces a greater variety of relationship paths within the semantic domain. Experiments performed on the public NELL-995 and FB15K-237 datasets for the link prediction task indicate that RPEN-KGC significantly enhances most metrics relative to baseline approaches. These findings demonstrate that RPEN-KGC proficiently forecasts absent information in knowledge graphs.

Syngas are produced from wastewater through anaerobic reactions and thermochemical processes, using a catalyst that modifies the gas composition, reduces methane production, and achieves partial COD reduction. The current research is attempting to treat the tannery wastewater via an anaerobic process configured with 0.25, 0.3, and 0.35 volume units of granular activated carbon (GAC) with 10 nm size to minimize the concentration of chemical oxygen demand (COD) and improve the biological methane yield. During this anaerobic process, the up-flow anaerobic sludge blanket (UASB) reactor supports to enhance the bio-methane production and handle the high rate of organic load. Influences of GAC units and operating time (days) on COD and biological methane yield of an anaerobic system for tannery wastewater treatment are studied and measured in their value. The output results of COD and biological methane yield are compared, and it was spotted that the tannery water process with 0.3 volume units of GAC owns 87% of COD removal with biological methane yield of 66.3 mL/day (7.2 mL/g COD removed) and end of 30th day found 1939 mL.

Stringent emission regulations and the depletion of conventional fuel sources drive research on green fuels, additives, and the optimization of fuel injection and exhaust gas recirculation. This study analyzes the impact of butanol additives in diesel and cashew shell liquid biodiesel (CSLB) blends under optimal operating conditions. CSLB was produced with an 85.43% yield from waste cashew nut shell liquid under optimal conditions: a methanol/CSL molar ratio (MR) of 20:1, a process temperature (PT) of 70 °C, and a 4 wt% industrial waste-derived heterogeneous catalyst (IC), using the desirability function approach in the RSM-CCD model. The catalyst was characterized using XRD, FTIR, and BET analyses to confirm its catalytic activity. Engine performance improvements were achieved with specific modifications, including 4° CA timing retardation, 15% split injection, and a 20% exhaust gas recirculation rate when using CSLB blends. In common rail direct injection (CRDI) experimental investigations, diesel and CSLB blends were combined with butanol additives (2.5%, 5%, and 10%) and compared to the baseline test. Incorporating 10% butanol, with its higher latent heat, resulted in a lower combustion temperature, reducing NOx emissions by 47.09% in CSLB10. Additionally, the additive’s lower viscosity and higher oxygen content enhanced atomization, reducing CO (33%) and smoke (23.02%) emissions. However, a slight increase in CO2 (8.92%) and a decrease in HC emissions (27.14%) were observed in CSLB10. Improved combustion characteristics, reflected in higher peak pressure and heat release rate, resulted in a 4.75% increase in brake thermal efficiency and a 13.92% reduction in brake-specific energy consumption compared to ideal conditions. Overall, this study explores the impact of butanol additives on the performance and emissions of CRDI engines fuelled with CSLB blends derived from waste cashew nut shell liquids, providing insights for sustainable fuel optimization.

Balance functions have been regarded in the past as a method of investigating the late-stage hadronization found in the presence of a strongly-coupled medium. They are also used to constrain mechanisms of particle production in large and small collision systems. Charge balance functions for inclusive and identified particle pairs are reported as a function of charged particle multiplicity in proton–proton collisions simulated with the PYTHIA8 and the EPOS4 models. The charge balance functions of inclusive, pion, kaon, and proton pairs exhibit amplitudes and shapes that depend on particle species and differ significantly in the two models due to the different particle production mechanisms implemented in PYTHIA and EPOS. The shapes and amplitudes also evolve with multiplicity in both models. In addition, the evolution of the longitudinal rms width and that of balance functions integrals with multiplicity (and average transverse momentum) feature significant differences in the two models.

Hypoxia-inducible factor (HIF)-1 is a transcription factor that regulates the expression of target genes associated with oxygen homeostasis under hypoxic conditions, thereby contributing to tumor development and progression. Accumulating evidence has demonstrated that HIF-1α mediates different biological processes, including tumor angiogenesis, metastasis, metabolism, and immune evasion. Thus, overexpression of HIF-1α is strongly associated with poor prognosis in cancer patients. Natural compounds are important sources of anticancer drugs and studies have emphasized the decisive role of these mediators in modulating HIF-1α. Therefore, the pharmacological targeting of HIF-1α has emerged as a novel cancer therapeutic approach in recent years. The novelty of this review is that it summarizes natural products targeting HIF-1α in colorectal cancer that have not been presented earlier. We studied research publications related to the HIF-1α domain in cancer from 2010 to 2024. However, our main focus was to identify a better targeted approach for colorectal carcinoma management. Our review described HIF-1α role in tumor progression, summarizes the natural compounds employed as HIF-1α inhibitors, and discusses their potential in the development of natural compounds as HIF-1α inhibitors for colorectal cancer treatment.

Agricultural consumer electronics, such as drones, sensors, and robotics, play a pivotal role in addressing challenges like wheat lodging, which can significantly impact crop yield and quality. This study leverages consumer-grade UAVs to classify wheat lodging types-root lodging and stem lodging-using high-resolution RGB images captured at three altitudes (15, 45, and 91 meters). By employing automatic segmentation techniques, datasets were generated for each altitude, and a refined EfficientNetV2-C model was proposed for classification. The model incorporates a Coordinate Attention (CA) mechanism to enhance feature extraction and Class-Balanced Focal Loss (CB-Focal Loss) to address data imbalance, achieving an average accuracy of 93.58%. This research highlights the integration of advanced AI-based classification with low-carbon agricultural drones, underscoring their relevance to consumer electronics. Compared to four conventional machine learning and two deep learning models, EfficientNetV2-C demonstrated superior performance at all altitudes while maintaining minimal carbon emissions. The study also examines the influence of UAV flight altitude on classification efficacy, revealing that while machine learning models were unaffected, deep learning models showed reduced performance at higher altitudes due to feature loss. These findings emphasize the potential of UAVs as accessible, scalable, and sustainable tools for real-time agricultural monitoring in precision farming.

Sudden cardiac arrest among young people is a recent worldwide risk, and it is noticed that people with cardiac arrhythmia are more susceptible to various heart diseases. Manual classification can be error-prone, and certainly, there is a need for automation to classify ECG signals to predict cardiac arrhythmia accurately. The proposed self-attention artificial intelligence auto-encoder algorithm proved an effective cardiac arrhythmia classification strategy with a novel modified Kalman filter pre-processing. We achieved 24.00 SNRimp, 0.055 RMSE, 22.1 PRD% for -5db, 20.4 SNRimp, 0.0245 RMSE, 12 PRD% whereas 14.05 SNRimp, 0.010 RMSE, and 7.25 PRD%, which reduces the ECG signal noise during the pre-processing and improves the visibility of the QRS complex and R-R peaks of ECG waveform. The extracted features were used in network of neurons to execute the classification for MIT-BIH arrhythmia databases using the newly developed self-attention autoencoder (AE) algorithm. The results are compared with existing models, revealing that the proposed system outperforms the classification and prediction of cardiac arrhythmia with a precision of 99.91%, recall of 99.86%, and accuracy of 99.71%. It is confirmed that self-attention-AE training results are promising, and it benefits the diagnosis of ECGs for complex cardiac conditions to solve real-world heart problems.

In the current study, yttrium-doped manganese cobalt spinel ferrite (YxMn0.5Co0.5Fe2-xO4 (x = 0.00, 0.01, 0.02) electrode nanomaterials were used as supercapacitors. YxMn0.5Co0.5Fe2-xO4 was synthesized using the sol-gel auto-combustion technique. Various characterization techniques were employed, including X-ray diffraction, field emission scanning electron microscopy, vibrating sample magnetometry, cyclic voltammetry, galvanic charge-discharge (GCD), and electrochemical impedance analysis (EIS). The structural properties of YxMn0.5Co0.5Fe2-xO4 (x = 0.00, 0.01, 0.02) were examined using XRD, with all values of x yielding single-phase spinel ferrites within the Fd-3m space group. The FESEM average particle size was in the range 440–560 nm. Magnetic studies reveal that the synthesized ferrite is soft in nature, with saturation magnetization of 40.84 emu/g (x = 0.00), 63.56 emu/g (x = 0.01) and 64.39 emu/g (x = 0.02). Electrochemical studies of YxMn0.5Co0.5Fe2-xO4 (x = 0.00, 0.01, 0.02) electrodes were examined using CV, GCD, and EIS in a 3 M KOH electrolyte solution, revealing higher Csp in the range of 185.6–384.2Fg⁻¹ at 10mVs⁻¹. The fabricated Y0.02Mn0.5Co0.5Fe1.98O4 electrode is a promising contender for supercapacitor applications. Graphical Abstract

Brinjal shoot and fruit borer (Leucinodes orbonalis) is a major devastating brinjal pest that leads to substantial yield losses. Besides this, it also infests the other major vegetable crops such as tomato and okra. India has an excellent germplasm diversity of brinjal species that can be explored to manage this pest. This review article thoroughly discusses several eco-friendly and advanced approaches to managing the infestation of Leucinodes orbonalis (brinjal shoot and fruit borer). The collected information regarding pests has concluded that integrated pest management strategies effectively reduce yield losses in Brinjal. However, advanced approaches such as genetic engineering and utilization of molecular markers can also contribute to the screening and development of resistant cultivars, while this technique can also help to analyze the pesticidal resistance of plants. Graphical abstract

The total cost of assembly is a critical factor in robotic assembly line balancing, as it encompasses all the costs associated with the assembly line, including initial costs, setup, maintenance, and energy cost. This study introduces a different approach to the robotic assembly line balancing problem, with a dual focus on minimizing both cycle time and overall assembly costs. The effectiveness of the proposed approach is validated through three case study problems taken from the literature and results are compared to traditional assembly allocation methods. For case study 1, 89.4% (42 out of 47) of the solutions achieved a lower total cost, and 34% (16 out of 47) of the solutions utilized fewer workstations; and for case study 2, 96.4% (108 out of 112) of the solutions achieved a lower total cost, and 58.9% (66 out of 112) of the solutions utilized fewer workstations for the same cycle time. These results demonstrate a significant savings in cost and a notable improvement in workstation efficiency for a substantial portion of the solutions. This comprehensive approach allows an effective resource allocation, reduces inefficiencies, and enhances the overall cost-effectiveness and performance of the robotic assembly line. It also supports decision-makers in selecting more sustainable and economically viable assembly line solutions that optimize both productivity and energy efficiency.

With the rapid development of intelligent transportation systems presents significant opportunities for vehicular ad hoc networks (VANETs) present themselves; yet, these networks also encounter numerous security challenges. In order to maintain road safety and traffic efficiency, information is usually shared through communication between vehicle nodes or between vehicle nodes and roadside units (RSUs). Vehicle nodes, RSUs, and trusted authorities (TAs) constitute the majority of VANETs. An approach to hybrid trust management that is distributed, HTMS‐V, is presented to mitigate potential internal attackers and misleading messages in VANETs. This framework considers the attributes of VANETs and employs an enhanced subjective logic model to assess the trustworthiness of vehicle nodes through both direct and indirect trust metrics. Trust links among nodes are formed by information about interactions, and the trustworthiness of messages is determined by the degree of trust between nodes and the distance between them. The assessment outcomes are utilized to detect erroneous communications and malevolent nodes within the network. To assess the efficacy of the proposed approach, four distinct assault scenarios were devised for comparative experiments on the Veins vehicular network simulation platform. The experimental findings indicate that HTMS‐V proficiently withstands diverse attacks in VANETs, successfully detecting many false messages and malevolent nodes, even at a malicious node rate of 40%. The percentage of malicious nodes is 3%, 5%, 7%, and 9%, meaning that the overall rates of malicious nodes are 9%, 15%, 21%, and 27%. The malicious node anomaly detection accuracy of the HTMS‐V scheme was over 96%, the message judgment accuracy was over 95%, and the false positive rate was less than 4%.

Background Polycystic ovary syndrome (PCOS) is a common, lifelong condition affecting about 20% of women, characterised by symptoms such as infertility, obesity, acne and excess facial hair, which can negatively impact both physical and mental health. Purpose This study aimed to assess the effect of Surya Namaskar on reducing social physique anxiety (SPA) in women diagnosed with PCOS. Methods The study applied a pre- and post-test design with 60 women diagnosed with PCOS; there were 100 participants in the study. Out of these 100, only 70 participants had high levels of SPA. Out of these 70 participants, only 60 responded and agreed to participate in the study. The participants were selected from colleges in Dehradun, India, and were between the ages of 18 and 30 years. Split into intervention and control groups. The intervention group practiced Surya Namaskar for 12 weeks, while the control group did not participate in any structured physical activity. SPA and body dissatisfaction were measured using established scales at the beginning and end of the study. Statistical analyses, including paired and independent samples t -tests, were conducted to assess changes within and between the groups. Results The results indicated that Surya Namaskar significantly improved overall health and well-being, with the intervention group showing notable reductions in SPA and body dissatisfaction compared to the control group. Statistical analyses confirmed significant differences in psychological outcomes, supporting the effectiveness of Surya Namaskar in this context. Conclusion Surya Namaskar significantly alleviates SPA and body dissatisfaction, enhancing the psychological health of women with PCOS. Given its accessibility and low cost, Surya Namaskar shows potential as an effective complementary treatment to improve the quality of life for women with PCOS. Further research is needed to explore its long-term effects and the mechanisms behind these improvements.

Offering hitherto unheard-of capacity for real-time, label-free detection of disease indicators, Surface Plasmon Reso- nance (SPR) biosensing technology offers a revolutionary development in the field of medical diagnostics. The proposed work offers a thorough analysis of a new SPR biosensor design, carefully optimized via COMSOL Multiphysics simulations and rigorously verified using a large dataset including 1000 different samples. Developed sensor architecture uses carefully crafted gold thin film produced on a BK7 glass prism using advanced magnetron sputtering processes with a 47.3 nm optimal thickness. Under well- regulated experimental settings, the sensor exhibits extraordinary sensitivity qualities, with a figure of merit of 108.7 RIU − 1 and an optimal penetration depth of 357.2 nanometers. Maintaining a linear dynamic range spanning from 1.33 to 1.38 RIU, an excellent minimum detectable refractive index change of 5.72 × 10 − 1 RIU by means of thorough electromagnetic calculations and subsequently experimental validation is observed. With a correlation coefficient more than 0.9995, the transmittance properties of the sensor show clear and very consistent changes corresponding to analyte concentrations ranging from 0.1 ng/mL to 100 µg/mL. With an intra-assay coefficient of variation of 1.8% and an inter-assay coefficient of variation of 2.3%, statistical analysis of the 1000- sample dataset demonstrates extraordinary repeatability, therefore demonstrating the strong dependability of the suggested sensor design for clinical uses.

AZ31 is a widely used magnesium alloy recognized for its strength, ductility, and corrosion resistance. The AZ31/ZrO2 composite studied is produced through liquid stir casting, a process that can pose challenges such as poor wettability, the formation of an oxide layer, and the settling of nanoparticles. These issues can adversely affect the performance of the composite being produced. This research aims to achieve meticulous control of the AZ31 alloy composite melt within a potassium fluoride (KF) and inert argon (Ar) environment, with a uniform stir speed created in a semi-solid state. The study enhances the functional behaviour of the AZ31 alloy composite by incorporating zirconium dioxide (ZrO2) at 2 wt% and silicon nitride (Si3N4) at 1–5 wt% through the stir casting process associated with a gravity die-cast with a 1X105pa of applied vacuum and constant stir speed (400rpm) is followed. The effects of 1 wt% KF, Ar, and stir speed on the microstructural behaviour of the AZ31 alloy and its composites are analysed using a THERMOFISHER-TALOS F200X transmission electron microscope. Results show a homogeneous dispersion of particles without agglomeration/porosity. The grain size has been reduced dendritically, with an optimal grain size of 24 µm observed in the AZ31/2 wt% ZrO2/5 wt% Si3N4 hybrid nanocomposite. The impact of the Si3N4 content on density, porosity, and mechanical properties has been evaluated. The AZ31/2wt% ZrO2/5wt% Si3N4 hybrid nanocomposite demonstrates significant improvements in density, a reduction in porosity (less than 1%), and enhanced mechanical properties, including microhardness (94 HV), Charpy impact toughness (16 J/mm2), and yield/tensile strength (180 MPa/343 MPa). This composite is proposed for use in bicycle frame applications.

Autophagy is a crucial mechanism that maintains cellular homeostasis and has emerged as a pivotal factor in cancer progression and drug resistance. Despite autophagic regulations being a complex process, convincing evidence shows that PI3K-Akt-mTOR, LKB1-AMPK-mTOR, and p53 pathways are the primary upstream regulators of the autophagy process. Currently, there is an immense amount of evidence demonstrating that autophagy plays a crucial role in cancer. It is worth noting that autophagy increases cancer cells' resistance to chemotherapy and anticancerous drugs. According to studies, cancer cells employ autophagy to evade the cytotoxic impacts of several anticancer drugs, resulting in autophagy-mediated drug resistance. This resistance brings a significant challenge to cancer management, emphasising the need for improved therapeutic strategies to overcome this obstacle and enhance the efficacy of cancer treatments. Therefore, this review gathers current data and findings to understand the intricate mechanism between autophagymediated drug resistance and cancer progression. Moreover, this study highlights the intriguing role of natural compounds and nano-formulations in combating autophagy-mediated drug resistance in various carcinomas, presenting a promising avenue for the effective management of cancer treatment.

Hydrocolloids play a crucial role in enhancing the quality of fruit‐based products, aligning with global demands for healthier and more sustainable food options. This review highlights the latest advancements in the application of plant‐based, algae‐based, animal‐based, microorganism‐based, and chemically modified hydrocolloids in fruit purees, fruit leathers, fruit juices, and fruit fillings. This work highlights the importance of hydrocolloids in enhancing the textural stability, thermal properties, and nutritional retention of fruit‐based products, while maintaining their desirable sensory attributes. The central aim of this review is to evaluate comprehensively the techno‐functional properties of hydrocolloids, including thickening, gelling, encapsulating, thermal stabilizing, syneresis inhibiting, and colloidal stability. Additionally, the interactions between hydrocolloids and fruit ingredients, particularly sugars, are analyzed to provide insights into their bonding mechanisms and their influence on product quality. This review consolidates recent findings to provide guidance for researchers and industry professionals on utilizing hydrocolloids to improve the quality, stability, and consumer acceptability of fruit‐based products, offering benefits to both manufacturers and consumers.

Nanomaterials have revolutionized sensor technology by offering enhanced sensitivity, selectivity, and miniaturization capabilities. However, the commercialization of nanomaterial‐based sensors remains challenging due to the complexities involved in bridging laboratory innovations to market‐ready products. This review article explores the various marketing strategies that can facilitate the successful commercialization of nanomaterials for sensor applications. It emphasizes the importance of understanding market needs, regulatory landscapes, and the value proposition of nanomaterials over traditional materials. The study also highlights the role of strategic partnerships, intellectual property management, and customer education in overcoming market entry barriers. Through a comprehensive analysis of case studies and industry practices, this review provides a framework for companies and researchers to effectively transition from lab‐scale innovations to commercially viable sensor products. The findings suggest that a well‐rounded marketing strategy, combined with robust product development and stakeholder engagement, is crucial for capitalizing on the unique benefits of nanomaterials in sensor applications.