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Blockchain-Driven Pharma Supply Chains towards Industry 6.0
No full textThe pharmaceutical supply chain is undergoing an unprecedented evolution in the wake of Industry 6.0, driven by the need for heightened transparency, security, and real-time intelligence. However, current systems suffer from legacy Enterprise Resource Planning (ERP) constraints, the risk of counterfeit products, temperature sensitivity, and scalability issues due to the surge in Internet of Things (IoT) data.
This research proposes a unified, blockchain-based framework that integrates legacy ERP systems, advanced AI driven forecasting, IoT-enabled traceability, and quantum-enhanced blockchain security to modernize pharmaceutical supply chains.
The study begins by addressing interoperability between ERP and blockchain using middleware and smart contracts, facilitating real-time, tamper-proof synchronization of critical supply chain data. To tackle demand uncertainty, a hybrid Gated Recurrent Unit with Genetic Algorithm (GRU-GA) model is developed for predictive synchronization, offering high-accuracy forecasting integrated with blockchain-triggered automation.
Auditability is further enhanced through an IoT-Blockchain framework that leverages Radio Frequency IDentification (RFID) sensors and secure IoT gateways equipped with edge-based storage and processing. This setup enables real-time tracking and localized decision-making, ensuring data integrity and responsiveness throughout cold chain logistics.
To resolve scalability and security bottlenecks, the research introduces a unique architecture leveraging Ethereum Layer-2 zk-Rollups and quantum randomness via Quantum True Random Number Generators (QTRNG) using Hadamard and Toffoli gates. This hybrid design improves throughput, resists quantum attacks, and ensures unbreachable data integrity.
The proposed solution is validated through simulations, smart contract deployments, and comparative analysis with existing systems. The result is a secure, scalable, and intelligent pharmaceutical supply chain ready for Industry 6.0. This work significantly contributes to digital transformation by combining blockchain, AI, IoT, and quantum technologies into a single, actionable blueprint for the future of pharmaceutical logistics
Two-Key Dependent Permutation (TKDP) and its Applications in Information Security
No full textTwo-Key Dependent Permutation (TKDP) algorithm for generating permutation sequences of fixed sizes, TKDP based Symmetric Block Cipher (TKDPSBC) and TKDP Audio encryption are being proposed in this thesis. TKDP algorithm is capable of generating different sequences for different key pairs. This makes it suitable for constructing dynamic S-boxes and P-boxes that have more degree of randomness and non-linearity to resist cryptanalytic attacks. Rigorous statistical tests validate the efficacy of the generated permutation sequences, affirming their suitability for cryptographic applications in conjunction with Fiestel network-based block ciphers. TKDPSBC encrypts a plaintext block into a ciphertext block of the same size. TKDP algorithm plays pivotal role in TKDPSBC and is instrumental in constructing dynamic, scalable and nonlinear S-boxes and P-boxes to enhance confusion and diffusion, effectively withstanding various forms of cryptanalytic attacks. This innovative cipher design boasts several noteworthy features which include reduction of weak keys, generation of distinct ciphertext blocks for repeated plaintext blocks, and support for processing plaintexts of variable key sizes. TKDP tables are integrated at various stages of encryption which improves security further. Tests conducted on plaintext blocks of various sizes ranging from 128 to 320 bits using keys of same sizes demonstrate that approximately half the bits in the ciphertext on an average undergo alteration. Extensive tests carried out to identify weak keys within the 216 key space reveal that TKDPSBC utilizes an increased number of usable keys compared to DES space. TKDPSBC along with other symmetric algorithms such as DES, 3DES, Blowfish and AES has undergone comprehensive testing to assess encryption time and speed. It is observed that TKDPSBC exhibits superior performance in terms of both encryption time and speed next to AES. For enhanced security, TKDPSBC can be deployed with key lengths ranging from a minimum of 128 bits to a maximum of 640 bits effectively mitigating cryptanalysis attacks. As an application of TKDP algorithm, TKDP Audio Encryption for encrypting short length uncompressed audio/speech signal has been proposed. The results show that audio encryption scrambles original speech signal by adding more chaos with the help of permutation sequences generated using TKDP algorithm. This produces an encrypted signal with high degree of speech unintelligibility making it secure against various attacks
Role of an SMC-like protein, RecN, in RecA-mediated homology search during double-strand break repair via homologous recombination
No full textThe most deleterious form of DNA damage is the formation of a double-strand break (DSB). If left unrepaired or repaired incorrectly, DSBs can result in genome rearrangements, mutations, or even cell death. Hence, cells across domains of life have resorted to homologous recombination (HR) for the faithful repair of DSBs. A critical step in homologous recombination is the search for the intact homologous sequence by the break ends, termed ‘homology search’. This process is not well understood in vivo, especially when the break site and intact homologous template are not positioned adjacently.
Spatial reorganization of chromosomes is maintained by structural maintenance of chromosome (SMC) proteins, some of which also play a role in DNA repair. This makes SMCs potential candidate proteins involved in homology search. Hence, we investigated the bacterial SMC protein, RecN, which is essential for DNA repair but its mechanism of action remains unexplored in vivo. To study ‘homology search’ involving RecN in living cells, we used Caulobacter crescentus as a model organism.
Caulobacter served as a promising model system due to a previously established protocol to study the repair of a single DSB. In this system, the spatial organization of the Caulobacter chromosome positions the break site and the intact homologous sequence ~1-2μm apart, allowing us to study long-distance homology search. We induced a single DSB using the I-SceI endonuclease and observed the dynamics of break ends coated with RecA nucleoprotein filaments during homology search and repair. To specifically study homology search and homology search followed by repair, we used non-replicating swarmer (1N) and pre-divisional (2N) cells, respectively.
Our first key observation was the directional translocation of RecA nucleoprotein filaments along the cell length. They performed multiple pole-to-pole traversals before finding the intact homologous sequence. These traversals were independent of the presence of the intact homologous chromosome harboring the template for repair. Most interestingly, these translocations were abolished in the absence of recN or its ATPase activity.
During traversals, RecA nucleoprotein filaments also show variations in morphology, especially length. We observed that this fluctuation in filament length in wild-type cells is significantly reduced in the absence of recN. Additionally, in the ATP hydrolysis mutant of RecN, the RecA filament lengths were noticeably shorter compared to those observed in the absence of recN or in wild-type cells, suggesting that ATPase activity might be essential for the spatial orientation of RecA nucleoprotein filaments during homology search.
Finally, we observed that the rate of translocation is contingent on the chromosomal content. In swarmer cells (1N), RecA nucleoprotein filaments complete pole-to-pole traversals in less than half the time compared to the traversal time observed in predivisional cells (2N). Taken together, we establish that SMC proteins are essential for the translocation of RecA nucleoprotein filaments to carry out homology search and repair during DSB. This work transitions into the next chapter of understanding homology search, where we will aim at unravelling the role of RecN in maintaining directionality during the translocation of RecA nucleoprotein filament
Agricultural Drought Prediction from Multispectral Image Processing Using Deep Learning Models
Get PDFAccurate and timely prediction of agricultural droughts is fundamental to the management of ecosystems, preservation of biodiversity, and environmental sustainability. It helps to reduce environmental exploitation, promotes eco-friendly irrigation techniques, and tackles climate change efficiently. Its social implications include food safety and financial stability through continuous rural employment. Drought prediction guarantees social progress and sustainability by ensuring zero hunger/poverty and effective water management. A multi-sensor image pre-processing framework that incorporates data from various satellite platforms like Landsat (30 metres), Sentinel-2 (10 metres, 20 metres, 30 metres), and MODIS (500 metres) is used to monitor and assess agricultural drought effectively.
Analysis of satellite imagery brings in many useful improvements. It enhances images using advanced correction techniques and a hybrid classification system that integrates Differential Evolution (DE) optimization with Optimized Learnable Parameter Artificial Neural Network (OLPANN) to produce precise land cover maps. As a complementary study of environmental monitoring, this study develops a framework for drought prediction using Holt Winter-Convolution 2D Long-Short Term Memory (HW-CONV2D-LSTM) models using 22 years of relevant satellite data and reveals useful patterns in temperature, rainfall, soil moisture, and plant growth.
The study’s first objective addresses issues in pre-processing satellite images by integrating radiometric and atmospheric corrections, spatial resolution, and advanced feature extraction techniques. Radiometric correction converts unprocessed satellite data in the form of Digital Numbers (DN) into reliable radiance values to ensure a precise depiction of surface reflectance attributes by considering solar illumination and the characteristics of sensors. The radiometric correction uses noise removal techniques, such as Gaussian, median, and anisotropic filters to eliminate sensor-specific artefacts and enhance the signal-to-noise ratio of multispectral imagery. The atmospheric correction balances the interactions between electromagnetic radiation and various atmosphere constituents to optimise the Aerosol Optical Depth (AOD) by incorporating Dark Object Subtraction (DOS) and considering the maximum pixel value of aerosol particles to achieve precise surface reflectance values during fluctuating atmospheric conditions. The second objective proposes to employ a novel classification system integrating DE optimization with OLPANN architecture. The DE optimization framework, configured with a population size of 42, maximum generations of 100, crossover rate of 0.7, and mutation factor of 0.5, optimizes the OLPANN\u27s hyperparameters, feature weights, and architecture. The OLPANN model examines the potentials of spectral, spatial, texture, and indices information from the multispectral data using Pearson correlation measures. The optimized deep features from the original data set increase the robustness of the artificial neural network model and provide a faster classification result; these optimized features are then trained by the ANN for further processing. Landsat 5 and 8, sentinel-2 data set is used to analyse three different types of features for evaluating seven different land cover classes. McNemar’s test is carried out to evaluate the changes, which endorses the OLPANN and Optimized Extreme Gradient Boosting (OXGB) to make it statistically significant. Friedman’s test demonstrates that the variance of Optimized Random Forest (ORF), Optimized Support Vector Classifier (OSVM), and Optimized Decision Tree (ODT) are significant at 0.01%. The numerical outcomes obtained establish that OLPANN has the potential to achieve the highest accuracy of 94.07%. The system\u27s performance is rigorously evaluated using comprehensive Kappa coefficient metrics. Based on the test results, the OLPANN classifier is recommended as the best candidate that produces ideal measures for the various characteristics considered for the study. This analysis empowers the government to identify urban extension, delineate any damages in natural land cover, ascertain legal boundaries for property assessment, and detect roads, bridges, and water and other land surface interfaces.
Agricultural droughts significantly affect rain-fed crops and thereby decrease employment possibilities and per capita income. Agriculture drought affects all nations significantly and cannot be avoided owing to the changing climate conditions. However, its impact on the environment could be reduced by predicting its occurrence in a timely and accurate manner. Presently, the detection of droughts largely relies on ground-based monitoring stations, however, satellites can scan vast land masses from above and provide accurate and reliable monitoring solutions. Multispectral imagery and spatio-temporal data from satellites offer quick responses, which enables the decision-makers to successfully manage agricultural resources and crop quality.
The third objective establishes a comprehensive time series analysis framework for drought prediction by implementing and comparing ARIMA, SARIMA, and Holt-Winters models based on 22 years (from 2000 to 2022) of multispectral data obtained from Thanjavur Station. The methodology incorporates sophisticated seasonal decomposition techniques, automated model selection protocols, and multi-scale temporal analysis evaluated with rigorous statistical metrics including Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC). The framework demonstrates superior performance by implementing advanced models, pattern recognition algorithms and adaptive parameter optimization techniques while maintaining robust validation protocols across diverse temporal scales. The proposed method employs the Holt Winter Conventional 2D-Long Short-Term Memory for meteorological and agricultural droughts\u27 prediction based on the precipitation index data sets from Climate Hazards Group Infrared Precipitation with Station, MODIS 11A1 temperature index, and MODIS 13Q1 vegetation index. The time series data for trends and seasonality are extracted from the satellite images based on Holt Winter alpha, beta, and gamma parameters. Finally, an efficient procedure for predicting drought is developed based on the Conv2D-LSTM for computing the spatio-temporal correlation among drought indices. The HW-CONV2D-LSTM presents an improved value of R2 equal to 0.97 and holds promise to act as an effective computer-assisted strategy for predicting drought and maintaining agricultural productivity, which is important to feed the ever-increasing human population
In Silico Investigations of Class II Trans Activator (CIITA): Its Plausible Implications in Neuroinflammation and Neurological Disorders
No full textInflammation plays a significant role in maintaining homeostasis. Neuroinflammation is a chronic inflammatory response in the central nervous system (CNS), leading to brain or spinal cord dysfunction. To promote mental health, it is crucial to understand the regulation of neuroinflammation. Class II transactivator (CIITA) is a master-regulator of the major histocompatibility complex II (MHCII).
Modulation of CIITA has been suggested as a promising intervention for inflammatory conditions, including neurological disorders. However, there is limited knowledge about the role of CIITA and its interactors in inflammation. This study aimed to understand the role of CIITA in neuroinflammation and neurological disorders using in-silico tools.
Here, we 1) constructed a phylogenetic tree for CIITA, 2) predicted its structures using computational tools, 3) analyzed its interactome with neuroinflammatory genes, 4) studied differential expression of CIITA and its interactors in the zebrafish brain following peripherally induced inflammation and, 5) performed in-silico screening to identify small molecules that could bind to the predicted structure of CIITA. Phylogenetic analysis revealed that the closest orthologs for human CIITA (hCIITA) are in monkeys, orangutans, mice, rats, frogs, and zebrafish.
The interactome analysis predicted that CIITA might primarily interact with the IL4/IL13 and hippo signaling pathways during neuroinflammation. LPS-induced inflammation in zebrafish suggested a role for CIITA in chronic inflammation in the brain.
Finally, based on our in-silico drug screening, we propose five molecules (ZINC5154833, F5254-0161, Arteannuin B, Creatinine, and Natural2) that could target CIITA. These results will help us understand the possible molecular pathways by which CIITA could regulate neuroinflammation and brain homeostasis
Fatigue Behaviour of Al 6063 alloy in Heat Treated, Deep Cryogenic Treated and Equal Channel Angular Pressing (ECAP) Processed Conditions
No full textThis research focuses on improving the Low Cycle Fatigue (LCF) life of Al6063 using three different processing techniques – Heat Treatment (HT), Deep Cryogenic Treatment (DCT), and Equal Channel Angular Pressing (ECAP). Al 6063, widely used in structural applications, has limited fatigue performance, necessitating the exploration of advanced processing techniques to enhance its LCF life.
In HT phase, Al 6063 T5 samples were subjected to solid solution HT followed by air and water quenching. The air-quenched (T6A) and water-quenched (T6W) samples were subjected to precipitation hardening. The Ultimate Tensile Strength (UTS) of the T6A sample increased to 188 MPa from 170 MPa of the T5 sample. The LCF life of T5 is 836 cycles, and T6A exhibited an enhanced LCF life of up to 1286 cycles due to the uniform distribution of Mg2Si precipitate and reduced residual stress.
The LCF life of the T6W sample was 708 cycles, which is lower than the T5 sample, indicating that water quenching led to coarser grains, compromising fatigue resistance In the DCT phase, samples are deep cryogenic treated between 2-24 hours with increments of 2 hrs, and its effect on the LCF life of Al 6063 was tested. UTS increased for the DCT samples up to 230 MPa, and elongation reduced to 17% for CT24, which refers to samples subjected to deep cryogenic treatment for 24 hours. LCF life increased up to 6 hr about 2216 cycles. Beyond 6 hr and for a longer duration of up to 24 hours, LCF life is gradually reduced due to prolonged exposure to DCT.
The influence of grain refinement achieved by ECAP at 90° and 120° angled dies and LCF life is studied. It was found that for the ECAP 90° sample, grain size reduced to 12 μm from 90 μm. UTS increased by about 312 MPa, and LCF life increased by 481% with 7489 cycles. For ECAP 120°, UTS is 265 MPa and 95 HV with an average grain size of 50 μm. It exhibited a higher LCF of 5860 cycles to failure than T6. ECAP 90° and 120° samples showed cyclic stabilization with a minimal plastic strain accumulation range of 0.001%.
In conclusion, the study demonstrates that ECAP 90° significantly enhances the mechanical properties with UTS of 312 MPa and 115 HV and LCF life of Al 6063. ECAP 90° provides enhanced Al 6063 LCF life with 7489 cycles and makes ECAP a capable Al 6063 processing technique for aerospace applications (hoop frame and stinger) where fatigue performance is critical
A Precoding, Companding, and Nonlinearity Reduction approach to optimize High-Speed ADO-OFDM for Visible Light Communication
No full textUsing light-emitting diodes (LEDs) for data transfer, visible light communication (VLC) presents a strong substitute for radio frequency communication. Yet, because of the non-linearity of LEDs, conventional Orthogonal Frequency-Division Multiplexing (OFDM) approaches in VLC are limited regarding spectrum efficiency, peak-to-average power ratio (PAPR), and system linearity. Multi-carrier asymmetrically Clipped Optical OFDM (MADO-OFDM) is introduced in this study. For VLC operations based on Optical Orthogonal Frequency Division Multiplexing (O-OFDM), a model-driven Deep Learning (DL) approach has been presented. Utilizing an Auto Encoder (AE) network technology reduced the non-linearity of the LEDs. A proposed improved ADO-OFDM protocol, called MADO-OFDM, adaptively modifies the number of subcarriers needed for ACO-OFDM and DCO-OFDM transmissions in response to downlinking multiple point-of-service requests. The Generalized Piecewise Linear Compander (GPLD) and Discrete Hartley Matrix Transform (DisHMT) precoder are merged with the proposed MADO-OFDM to enable high-speed data transmission with lower PAPR. Using its unlicensed spectrum, VLC overcomes the limitations associated with radio frequency (RF) communication. VLC is a response to the issue of insufficient bandwidth, driven by the quick development of LEDs
[1]. The use of visible band optical connections in free space is called VLC. VLC is starting to show promise as a future high-capacity interior wireless network. The modulation bandwidths of commercially sold LEDs range from 2 to 20 MHz
[2]. The modest LED modulation bandwidth (2–20 MHz) in VLC makes the adoption of multicarrier modulation (MCM) techniques necessary to provide high-speed data transport. These days, OFDM is frequently used in VLC as an MCM approach
[3]. Compared to single-subcarrier structures, OFDM has a higher optical efficiency and a stronger defense against inter-symbol interference (ISI). Unlike the RF-OFDM system, which employs a bipolar and misleading signal for intensity modulation (IM) and direct detection (DD), the VLC-OFDM method utilizes a unipolar and real signal
[4]. Since the immediate output power of LEDs modulates the time dimension signals transmitted by DD and IM VLC systems, those signals need to be real and non-negative. A without licensing bandwidth, high transmission power, an excellent signal-to-noise ratio, and inexpensive front ends can all be obtained with an LED-based VLC
[5]. With the help of these features, VLC can effectively supplement RF transmission of signals. The use of two types of multi-carrier modulation, Discrete Multi-Tone (DMT) and OFDM modulation, for VLC systems is currently receiving more attention
[6]. However, the impact of LED non-linearity is not taken into account in these studies. The resulting distortion causes the LED nonlinearity to become sensitive, which presents many challenges for the OFDM-based VLC technology
[7]. The biasing current ought to be carefully selected to limit the signal\u27s size to the largest linear range because an LED\u27s driving current has no direct effect on its output optical power. Use a time frame pre-distortion approach to compensate for the nonlinear deformation of OFDM symbols
[8]. It is crucial to possess prior understanding of the LED transfer function, nevertheless. The time-domain form OFDM symbols were divided into many portions in order to control different numbers of LEDs. OFDM is currently a well-established technique because of its many advantages, such as its large bandwidth and absence of electromagnetic interference
[9]. The exponential expansion in bandwidth needs has made research and technological advancement of utmost importance. Over multiple consecutive generations, optical communication has evolved [10]. While many LEDs will increase system complexity, several transmitters can help minimize nonlinearity. A DFE in non-linear feed-forward was used to reduce the LED\u27s non-linearity. Reducing the LED non-linearity of OFDM-based VLC devices requires an effective, albeit somewhat difficult, compensating strategy.
This research presents a comprehensive approach to address the limitations of traditional OFDM techniques in VLC systems. The introduction of MADO-OFDM offers a promising solution to improve spectral efficiency, reduce PAPR, and mitigate LED non-linearity issues. Utilizing a model-driven Deep Learning strategy as an AE network system to minimize LED non-linearity represents a significant contribution. This approach enhances system linearity and improves the overall performance of O-OFDM-based VLC processes. Furthermore, the proposed MADO-OFDM incorporates an adaptive adjustment of subcarrier numbers based on service requests in downlink multiple access scenarios. This adaptability ensures efficient utilization of resources and enables dynamic allocation of subcarriers, thereby enhancing system flexibility and scalability. Additionally, the integration of the DisHMT precoder and GPLD further enhances the performance of MADO-OFDM by reducing PAPR and enabling high-speed data transmission. Looking ahead, future research can explore advancements in DL techniques to further enhance LED non-linearity mitigation and spectral efficiency in VLC systems
An Integrated Approach to Enhance The Performance of Rainfall Forecasting by Leveraging Stacking Based Machine Learning and Deep Learning Techniques
No full textRainfall forecasting is critical for a variety of reasons, the most important of which is the substantial impact it has on many sectors of the community and the environment. It helps farmers with planting schedules, crop choices and irrigation techniques, all of which directly impact food production and agricultural yields. Rainfall forecasting is also vital in sectors such as hydroelectric power generation, since knowledge about water availability is essential for electricity generation. Accurate rainfall forecasts play very important roles in disaster planning and flood control. They enable authorities to take precautionary measures and, where necessary, plan for the evacuation of those who are at risk. Traditional approaches that have been employed earlier are often found to be biased and yield low accuracy.
Deep Learning (DL) and Machine Learning (ML) techniques are capable of handling complicated data patterns, thus increasing prediction accuracy. Traditional statistical approaches, such as Seasonal ARIMA (SARIMA) and Linear Regression (LR), often struggle to capture the irregular relationships in meteorological data. However, rainfall patterns often exhibit complex, non-linear behaviours that these models fail to represent accurately. Since ML and DL models are capable of learning non-linear correlations, they are better equipped to capture the complex relationships that exist between meteorological variables. Our work begins with the Variable Specific Hot Deck (VSHD) imputation technique, which provides a customized approach for addressing missing values in datasets. Missing data add a degree of uncertainty to data analysis, which can alter the characteristics of statistical estimators, reduce their power and lead to false conclusions.
Datasets considered in this work are from the Australian meteorological department and NASA’s POWER access portal. Brisbane, Sydney and Melbourne are the three Australian sites that have been taken into consideration for rainfall forecasting. Cuddalore and Karaikal, two regions in South India, are also included. The dataset is thoroughly examined using several machine learning classifiers. Among the classifiers used in this work are K-Nearest Neighbors (KNN), Decision Trees (DT), Support Vector Machines (SVM) and Random Forests (RF). Random Forests are especially notable for their higher accuracy after imputation, demonstrating how well the VSHD method maintains data integrity.
A meta-learner selection mechanism is incorporated into a stacking ensemble approach, which significantly improves accuracy and performance. XGBoost, a meta-learner selected for its robustness and efficiency, has led to notable improvements in accuracy. Despite these advances, additional refinement is still being pursued. Our research work makes use of Recursive Feature Elimination with Cross-Validation (RFECV) to extract eight pertinent features from a set of eighteen. The STEM-XG model is then fitted with these selected features. Prediction results are obtained by aggregating outcomes of base models. A comparative study shows that the combination yields high performance for all the locations. From the outcomes, it is inferred that the proposed STEM-XG enhances prediction performance.
We have also explored the integration of Particle Swarm Optimization with LSTM, Bi-LSTM and GRU. PSO-LSTM emerges as a powerful approach to tackle complex prediction tasks by leveraging the strengths of both techniques. By dynamically adjusting LSTM’s parameters using PSO, and through empirical evaluation, we have demonstrated that PSO-LSTM outperforms PSO-GRU and PSO-Bi-LSTM. Performance metrics such as RMSE and MAE for prediction, and Accuracy and F1-Score for classification, are considered in this evaluation
Enhancing Employment Readiness of Information Technology (IT) Professionals of India - A Talent Refactoring Approach
No full textThe Indian Information Technology (IT) sector is undergoing significant transformation due to the adoption of Industry 4.0 technologies such as Artificial Intelligence, Internet of Things, cloud computing, and cyber-physical systems. These changes have redefined workforce expectations, shifting employment readiness from mere technical competence to a holistic combination of adaptability, behavioral attributes, and continuous learning capabilities. In this context, the study proposes a Talent Refactoring Framework to address employability in a rapidly evolving VUCA–BANI environment by integrating organizational, career-oriented, and individual dimensions.
A review of existing literature reveals notable gaps in understanding employment readiness as an ecosystemic and psycho-social construct, particularly within the Indian IT sector. Prior studies predominantly focus on discrete skill sets, neglecting the combined roles of organizations, educational institutions, and individual traits. Moreover, limited research examines how career exploration mediates organizational support or how socio-demographic factors influence employability. This study addresses these gaps by empirically validating a comprehensive, stakeholder-integrated framework.
The research adopts a descriptive and correlational design, collecting data from IT professionals working in digital roles across Tier 1 and Tier 2 cities in India. Using purposive sampling, responses were gathered from 515 professionals employed in NASSCOM-listed organizations. The survey instrument was rigorously tested for reliability and validity. Analytical techniques included Exploratory and Confirmatory Factor Analysis, ANOVA, and Structural Equation Modeling, along with mediation and moderation analysis using Hayes’ PROCESS macro.
Findings identified three macro-level constructs—Organizational Support, Career Exploration, and Employment Readiness—supported by ten micro-level factors encompassing institutional, social, and personal attributes. The results demonstrate that Career Exploration fully mediates the relationship between Organizational Support and Employment Readiness. Differences were observed across demographics, with Tier 1 professionals exhibiting stronger institutional support and Tier 2 professionals displaying higher person-centric traits. Socio-economic background and parental education significantly influenced employability outcomes.
The study offers important practical implications for organizations, academic institutions, and policymakers. The validated framework can guide HR strategies in creating continuous learning ecosystems, structured career pathways, and targeted skill enrichment programs. Academic curricula can be realigned with industry demands through project-based learning, internships, and mentoring initiatives to enhance career exploration and resilience among learners.
In conclusion, the Talent Refactoring Framework presents a holistic and empirically grounded approach to strengthening employment readiness in the Indian IT sector. By recognizing employability as a psycho-social construct shaped by contextual and intrinsic factors, the study provides a roadmap for building a future-ready workforce. Addressing structural inequities and nurturing individual capabilities can enable India to sustain its global leadership in digital innovation
Fabrication of Protein-based Nanomicelle against Inflamed Synoviocytes in Rheumatoid Arthritis
No full textRheumatoid arthritis (RA) is a chronic autoimmune disease marked by persistent synovial inflammation, progressive cartilage and bone destruction, and systemic complications. 1% of the global population are affected by RA, when their immune system targets synovial tissues, leading to joint pain, swelling, and functional disability. Standard treatments, such as disease-modifying antirheumatic drugs (DMARDs) like methotrexate (MTX), are limited by systemic toxicity and inconsistent patient responses, highlighting the need for improved therapies. This research work explores protein-glycosaminoglycan nanomicelles engineered using Zein with Chondroitin or carboxymethylated Dextran Sulfate as targeted drug delivery vehicles for RA.
MTX was encapsulated within these nanomicelles to enhance joint-specific delivery and therapeutic efficacy. In vitro studies using a 2D RA model with inflammatory synovial cells demonstrated that the nanomicelles selectively reduced cell migration and invasion. In vivo evaluation using Complete Freund’s Adjuvant-induced RA rat model showed that MTXloaded nanomicelles effectively preserved bone and trabecular quality as confirmed by microCT.
Histopathology has confirmed the minimized synovial inflammation, bone erosion, cartilage component and less systemic toxicity. These findings support protein-GAG nanomicelles as a promising precision therapy for RA, offering targeted delivery, reduced side effects, and improved outcomes, with robust preclinical validation for future clinical translation