Journal of Scientific and Technical Research

The objective of this study is to use electro mechanical impedance (EMI) based Structural Health Monitoring (SHM) to monitor the early age strength development of concrete with refuse derived fuel (RDF) ash used as a sustainable supplementary cementitious material. The global construction trend of a circular economy and low-carbon materials has already begun to deliver the double advantage of waste reduction and resource recovery by incorporating municipal solid waste by-products like RDF ash into concrete. In the current study, 30% of ordinary portland cement (OPC) was replaced with retrieved RDF ash and the hydration process was monitored by embedding piezo (PZT) sensor into samples. Conductance signatures (100–400 kHz) and the root mean square deviation (RMSD), mean absolute percentage deviation (MAPD) indices were evaluated in order to assess microstructural changes over a 90-day curing period. Results show progressions in conductance signatures that appear to have a significance as curing continues. During the early hydration stages (Days 1–10), rise of conductance magnitude was gradual, which we associate with the formation of calcium silicate hydrate (C-S-H) gel. Between Day 11 and Day 28 there was a concomitant increase of frequencies along with pronounced sharp peaks which signified secondary hydration reactions producing a more well-defined, denser stiffer concrete matrix. After the 28 days, signatures stabilized as pozzolanic activity plateaued. From the quantitative point of view, the increase in the RMSD index at early ages (10–28 days) goes from about 20–25% to approximately 55–75%, indicating a continuous swelling of compressive strength from 25 MPa to 40 MPa at 28-day age. The significative positive relation between the mechanical performance and RMSD strongly validates the non-destructive, real-time applicability of EMI-based SHM technique for high-fidelity monitoring of RDF ash concrete. These results promote the idea of coupling smart monitoring systems with sustainable infrastructure to maintain structural health while leveraging industrial by products.

Diffusion-weighted magnetic resonance imaging (DW-MRI) based white matter tractography has become an indispensable tool in neuroscientific research and therapeutic neuroimaging. DSI Studio is a comprehensive diffusion MRI image reconstruction, fiber tracking, quantitative analysis and connectome mapping software package. DSI Studio offers quantitative anisotropy (QA) based tracking and supports a number of reconstruction models, including diffusion spectrum imaging (DSI), generalized q-sampling imaging (GQI) and diffusion tensor imaging (DTI), and is thus quite versatile. DSI Studio is a sophisticated software application designed for processing, analysing, and visualizing diffusion MRI (dMRI) data using Diffusion Spectrum Imaging (DSI) techniques. Fiber tractography is useful in clinical practice for surgical planning by neurosurgeons for avoiding important white matter tracts and lowering surgical risks. It helps wi.th abnormality detection, diagnosis support, planning of treatments, and insights into neurological diseases.

The strength development of cement paste/mortar are of critical importance in structural health monitoring (SHM), as they directly influence early-age behaviour, long-term performance, and damage evolution in cement-based structures. This study explores the suitability of the electro-mechanical impedance (EMI) procedure in monitoring mortar strength development using agro-waste products such as rice husk ash (RHA), and sugarcane bagasse ash (SCBA) with embedded piezoelectric sensors (EPS). In order to measure the changes quantitatively, there are several statistical damage indices that are considered during the process of curing on the basis of EMI signatures. Moreover, the findings of the EMI-based sensing method are compared with others of other non-destructive and destructive testing techniques, such as rebound hammer and compressive strength (CS). The results show that the piezoelectric sensors installed in it can successfully record the strength behavior improvement during the curing process. Besides, the statistical indices as calculated based on the EMI signatures are correlated with the outcome of the destructive tests, to verify the accuracy and the efficiency of the EMI method regarding SHM in agro-waste-based cementitious materials.

A PMMA-based polymer electrolyte comprising potassium iodide (KI) and the ionic liquid 1-hexyl-3-methylimidazolium thiocyanate (HMIMI) is described in detail along with its electrochemical, structural, thermal, and photoelectrochemical characteristics. The ionic conductivity studies clearly show that adding HMIMSCN to the PMMA matrix enhances segmental motion and increases ionic mobility by supplying additional charge carriers. The polymer matrix becomes more amorphous upon the addition of KI and HMIMSCN, thereby facilitating ion transport and improving conductivity. X-ray diffraction (XRD) and differential scanning calorimetry (DSC) investigations verified these structural alterations. Additionally, the electrochemical device based on the PMMA-KI-HMIMSCN polymer electrolyte exhibited encouraging performance, suggesting its suitability for energy storage systems such as supercapacitors and EDLCs.

Multiple Myeloma is a type of blood cancer of plasma cells that can severely damage the bones and kidneys and can lead to death. For early-stage detection of this cancer, classification and segmentation of multiple myeloma cells via Computer-Aided Diagnosis (CADs) could be very helpful. We used Seg-PC 2021 challenge dataset consisting of microscopic images of stained cells, and their ground truth masks drawn for cancerous cells in the image by experts. Classification of cancerous and non-cancerous cells was performed using Convolutional Neural Networks along with Transfer Learning from several pre-trained Neural Networks as feature extractors, followed by a trainable dense classification layer. Our classification model achieved an accuracy of 97% on pre-trained MobileNet model training. For segmenting the cancerous cells into nucleus and cytoplasm, we used U-Net architecture, and achieved good performance with mean intersection over union (mIoU) of 86.03%.

The accelerating spread of antimicrobial resistance has intensified the search for novel small-molecule scaffolds that can deliver potent, broad-spectrum activity while retaining drug-like properties. Five-membered nitrogen/oxygen heterocycles—particularly triazoles (1,2,3- and 1,2,4-) and oxadiazoles (1,2,4- and 1,3,4-)—have emerged as privileged motifs in antimicrobial lead discovery. This short review summarizes the structural features, common synthetic approaches, structure–activity relationships (SAR), and representative antimicrobial profiles of triazole- and oxadiazole-based compounds including hybrid chemotypes that combine both rings to achieve synergistic effects. We highlight key substitution patterns, linker choices, and physicochemical trends associated with antibacterial and antifungal potency, and outline design principles to address resistance and optimize pharmacokinetics.

Abstract-Robotic systems are increasingly used in healthcare tasks such as medicine delivery, where both kinematic performance and efficiency are critical. This paper presents a task-based, manipulability-aware motion planning approach for a 7-DOF robotic arm. A finite state machine is used to manage task phases—rest, approach, delivery, and return—while cosine-based joint interpolation ensures smooth motion. Manipulability is evaluated using the Jacobian, and a gradient-based optimization term is introduced to enhance end-effector dexterity in real time. The approach leverages kinematic redundancy to maintain high manipulability throughout task execution. Simulation results in Webots demonstrate improved average and peak manipulability without compromising task accuracy. The robot achieves precise positioning, smooth motion, and reliable interaction, validating the effectiveness of the proposed method for healthcare applications.

Nanotechnology has emerged as a transformative discipline with significant implications for dental sciences, particularly orthodontics. The unique physicochemical properties of nanomaterials—arising from their nanoscale dimensions—have enabled the development of advanced orthodontic materials with improved mechanical strength, antibacterial activity, biocompatibility, and biological responsiveness. Conventional orthodontic appliances are associated with challenges such as plaque accumulation, enamel demineralization, frictional resistance, material degradation, and prolonged treatment duration. Nanotechnology offers innovative solutions to these limitations through nano-enhanced adhesives, brackets, archwires, elastomeric components, retainers, and biologically active materials. Additionally, emerging fields such as biological nanotechnology, green nanotechnology, and the integration of artificial intelligence with nanotechnology are shaping the future of orthodontic care. This narrative review summarizes the current applications of nanotechnology in orthodontics, discusses recent advancements, highlights biological and environmental considerations, and explores future perspectives and clinical challenges.

The present study evaluates the in vitro antimicrobial activity of solvent extracts of selected Indian spices commonly used in daily diets. Methanolic extracts of ginger (Zingiber officinale), turmeric (Curcuma longa), clove (Syzygium aromaticum), coriander (Coriandrum sativum), and black pepper (Piper nigrum) were tested against two pathogenic bacteria, Escherichia coli and Staphylococcus aureus. Antimicrobial efficacy was assessed using the agar well diffusion method, and zones of inhibition were recorded at different extract concentrations. Among the spices tested, ginger, turmeric, and clove exhibited strong antibacterial activity against both Gram-positive and Gram-negative bacteria, with methanolic extracts showing superior efficacy compared to other solvents. The findings suggest that selected spice extracts possess significant antimicrobial properties and may serve as potential natural alternatives to synthetic antimicrobial agents in food preservation and pharmaceutical applications.

The widespread adoption of electric vehicles (EVs) requires a power quality-compliant fast charging infrastructure. This paper presents a comparative performance analysis of a grid-integrated photovoltaic (PV) assisted DC fast EV charging station using maximum power point tracking (MPPT). The proposed system integrates a PV array, MPPT controller, grid interface, inverter, front-end converter, DC link regulation, and EV battery charging unit. The effectiveness of a three-level inverter and three-level front-end converter (3LI-3LFEC) is compared with a conventional two-level inverter and front-end converter (2LI-2LFEC). Performance evaluation was carried out using DC bus voltage regulation, EV charging power, PV power contribution, battery voltage, charging current, state-of-charge (SOC) variation, and harmonic analysis. Simulation results show both configurations maintain a stable DC bus voltage near 800 V. The three-level configuration EV charging power achieves 2.386% increase and charging voltage and current reach approximately 926.61V and 102.90 A, respectively. The SOC increases from 0.2000 to 0.201833, validating successful battery energy transfer during the simulation interval. In 3LI-3LFEC, total harmonic distortion (THD) in current is reduced by 4.753%, whereas voltage THD is negligible in both configurations. Overall, the 3LI-3LFEC-based solar-assisted EV charging system provides improved charging performance and enhanced power quality for high-power DC fast-charging applications.

A popular fresh acid-coagulated cheese in South Asia, paneer is a highly perishable dairy product that is prone to quick microbiological deterioration and physicochemical changes after production. The short shelf life of paneer under typical refrigeration conditions (2–4 days at 4–8°C) poses significant distribution and marketing challenges for the dairy industry. Packaging technology is the most practical and cost-effective technique to extend shelf life, preserve sensory attributes, and ensure the microbiological safety of paneer. This review covers both conventional approaches like vacuum packaging and polyethene pouches as well as state-of-the-art methods like modified atmosphere packaging (MAP), active packaging systems, nanocomposite and bio-nanocomposite films, edible coatings, and intelligent packaging technologies. The literature from 2020 to 2026, which highlights developments in compostable multilayer laminates, cellulose nanocrystal-based packaging, plant-derived antimicrobial coatings, graphene oxide-reinforced films, and blockchain-integrated cold-chain monitoring, is given special attention. Along with pertinent international and Indian regulatory frameworks, methods for assessing physicochemical, microbiological, and sensory quality are thoroughly investigated. Environmental sustainability, consumer acceptance, and emerging trends are all discussed. Future directions and research gaps are identified to guide the development of safe, sustainable, and commercially viable paneer packaging options.

Reliable fault detection, classification, protection, and location are essential for secure operation of modern HVAC and HVDC transmission networks. This task has become more complex due to long EHV corridors, DFIG-based wind farms, converter-interfaced renewable sources, LCC and MMC-HVDC links, series-compensated lines, and hybrid AC–DC systems. These networks may produce limited fault current, non-stationary transients, converter-controlled responses, frequency-dependent travelling-wave propagation, and reflection effects at converter or cable–overhead interfaces. Consequently, conventional overcurrent, impedance, distance, and differential protection schemes may lose sensitivity and accuracy under high-resistance faults, weak infeed, load encroachment, CT saturation, close-in faults, and converter-dominated conditions. This review analyses classical protection, travelling-wave methods, DWT, CWT, WTMM, WPT, S-transform, HHT, decaying DC methods, and intelligent classifiers including ANN, BPNN, SVM, decision tree, random forest, LSTM, RNN, and GCN. The study shows that travelling-wave and wavelet-based methods offer high-speed detection and accurate location, while intelligent methods improve classification when trained with diverse fault data. The review concludes that hybrid protection combining transient features, travelling-wave location, and machine-learning-assisted classification is a promising direction for practical transmission-line protection.

In this paper a comparative fault location approach is presented for a 400 km HVAC transmission line under different fault conditions. The proposed study evaluates three fault location techniques: direct signal based estimation, Discrete Wavelet Transform (DWT) processed estimation and Long Short Term Memory (LSTM) deep learning based estimation. Four major fault cases are considered, including single line to ground(LG), double line ground (LLG) line to line (LL), and three phase faults (LLL), applied at different locations along the transmission line. The direct signal method estimates the fault location directly from the measured waveform, whereas the DWT based method extracts high frequency transient components generated during fault inception. The LSTM based method further improves the estimation accuracy by learning nonlinear fault patterns from the measured and processed signal behavior. The simulation results show that the direct signal method provides acceptable fault location estimation but suffers from higher deviation due to unprocessed transients and signal disturbance. The DWT method significantly improves the location accuracy by enhancing the fault-generated travelling-wave information. However, the LSTM deep learning method achieves the best performance among all compared methods. The mean absolute error is reduced from 1.125 km for the direct signal method to 0.20850 km for the DWT method and further reduced to only 0.03227 km using the LSTM method. Similarly, the average accuracy improves from 98.69762% for the direct method to 99.87012% for DWT and reaches 99.97766% for the LSTM based method. The obtained results confirm that the LSTM model provides superior accuracy, lower error variation, and better robustness for different fault types and locations. Therefore, the proposed LSTM based fault location approach is suitable for accurate and reliable transmission-line protection studies and can be extended for real-time smart grid protection applications

The growing popularity of plant-based functional beverages has enhanced the creation of non-dairy fermented drinks like water kefir. The addition of fruits to fermented drinks may significantly impact their rheological and structural properties. This study was designed to determine the rheological characteristics of an apricot-based water kefir beverage (AWKB) compared to control water kefir made without fruit supplementation. The AWKB was prepared using dried apricots (20% w/v), brown sugar (8% w/v), and water kefir grains (8% w/v) and fermented at 32 ºC in 24 h. The values of rheological parameters (torque (%), shear stress (Pa), and apparent viscosity (mPa.s)) were recorded on a Brookfield digital viscometer at various shear rates (5.58-55.8 s-1). The flow behaviour parameters were estimated with the help of the power-law model. These findings demonstrated that AWKB exhibited higher torque and shear stress than the control (p < 0.0001), which is evidence of resistance to deformation. Apparent viscosity reduced with shear rate in AWKB and control sample, which supported non-Newtonian shear-thinning behaviour. AWKB showed higher viscosity (223.7-38.1 mPa.s) when compared to the control (2.01-0.68 mPa.s). The index of flow behaviour (n < 1) confirmed the presence of pseudoplastic behaviour in both the samples, whereas the coefficient of consistency was much greater in AWKB. Storage behaviour analysis for 21 days showed that viscosity gradually declined after 14th day in AWKB and control sample, but AWKB still retained comparatively higher rheological characteristics during the storage period. These results indicate that the addition of apricot increases the structure and rheological functionality of water kefir drinks. Keywords: Water-kefir, Fermentation, Viscosity, Apricot, Stability.

The graphene-supported copper selenide (CuSe/rGO) nanohybrid was effectively synthesized using a one-pot hydrothermal method, which was subsequently evaluated for its enhanced peroxidase-like (POD-like) catalytic activity. CuSe nanoparticles were homogeneously dispersed on the rGO nanosheets, resulting in enhanced interfacial interaction and improved electron transfer, as confirmed by structural and morphological analysis (FE-SEM and TEM). Compared to pristine CuSe, the CuSe/rGO nanohybrid exhibited superior catalytic efficiency in oxidizing color-changing substrates, such as o-phenylenediamine (OPD), with H2O2 as the oxidant. Mechanistic analysis revealed that the primary reactive species responsible for substrate oxidation were identified as hydroxyl radicals (•OH), whose formation is significantly enhanced by synergistic interactions and rapid electron transfer inside the nanohybrid. Overall, the CuSe/rGO nanohybrid exhibits remarkable properties as an economical and effective nanozyme for biomedical diagnostics, environmental monitoring, and biosensing applications.

Abstract For advanced low power electronics (in µW) and high frequency communication (100 Hz to 10 MHz), this paper emphasizes the significance of material selection and geometry optimization in TFT design thorough simulation based evaluation for two high dielectric oxides materials namely Titanium Oxide (TiO₂) and Tungsten Oxide (WO₃) for Thin Film Transistors (TFTs). A comparative analysis of the electrical behaviour for various geometries (W/L ratios) using MATLAB is done. The simulation framework extracts essential performance characteristics such as transconductance (gm), voltage gain, power consumption, latency, and Figure of Merit (FOM). Also thorough comparison of transfer, output, static, and dynamic characteristics is done to enhance the analysis. From the results we conclude that high performance communication circuits for RF/5G transceiver circuits and high speed digital switches Titanium Oxide (TiO₂) TFTs are ideal due to their improved gain (up to 17 dB), higher transconductance (70.8 µS) and faster switching (delay as low as 7.06 ns). On the other side for energy constrained applications at low frequency such as battery powered sensors, biosensors and wearable electronics Tungsten Oxide (WO₃) TFTs are ideal due to their very low power consumption (<1 µW) and larger latency (139 ns).

Facial expression based automatic emotion detection is important in several research fields, health, security and human computer interfaces. Recently, researchers have shown keen interest for the feature extraction using emerging techniques in order to achieve accurate results. The main steps in emotion detection include the image preprocessing, feature extraction followed by the classification of these features to detect emotions. Facial landmarks refer to key points on a human face and represent those regions in a face which help to distinguish between emotions. The paper starts by discussing the importance of expressions and the role they play in emotions thus concluding expressions can serve as an important input to detect emotions. This paper presents a comparative analysis of different artificial intelligence techniques initiated for automated facial expression recognition and explains the process followed including the preprocessing techniques, the use of the right neural network for that approach (e.g. CNN and ANN). Moreover the impact of each study including the benefits and flaws of AI techniques have been discussed which will help further in improving the recognition system. The paper will focus on the approach taken by different emotion detection algorithms with different datasets and highlighting the key features.

The advent of AI has changed perceptions about practices in many fields, including human resource management (HRM). The HRM is assisted globally through AI mechanisms, starting from talent acquisition for increasing efficiency in recruitment without compromising the merit of candidates to finding the most appropriate one for an organization. Key recruitment processes include candidate sourcing, screening, and selection, which are automated by AI tools. This can lead to greater scalability and perhaps lower human bias. On the other hand, some of the key challenges which HRM also faces with the integration of AI are the algorithmic bias, overlooking qualitatively strong candidates, and limitations regarding innate human attribute assessment. AI systems invariably fall short in accounting for the psychological traits, personality growth, emotional intelligence, and nuances of behavioral manifestations and socio-cultural background since their design lack the capacity to read the subtle human traits related to body language and responses. While state-of-the-art neuro-fuzzy logic and sophisticated algorithms have been developed, most of these systems are based on predefined parameters of each organization and require structured inputs regarding specific job roles. Although AI technology remains very user-friendly, there are certain doubts raised regarding candidate and organizational friendliness. This has resulted in AI technology needing to adapt and evolve according to human and organizational requirements. As long as these constraints are not resolved, human intelligence and AI technology will continue to work in tandem with each other. In the current literature on talent acquisition, there seems to be a lack of a comprehensive review of the role of AI in talent acquisition. The current research seeks to fulfill this knowledge gap by examining both the benefits and drawbacks of AI in talent acquisition.

Human language likely began between 100,000 and 200,000 years ago, around the time early Homo sapiens appeared in Africa. Recent genetic studies suggest humans had the capacity for language by about 135,000 years ago, and language may have become widely used socially around 100,000 years ago. Some researchers think simpler “proto-language” systems may have existed much earlier in species such as Homo erectus or Homo habilis, possibly over 1 million years ago, but fully developed human language is generally linked with modern humans. A perusal of literature suggests that language started developing with the development of vocal cords. Interestingly, developed vocal cord has been the exclusive privilege of Homo sapiens because no other species seems to have possessed this attribute. Different languages have evolved in different parts of the world. In the present paper, our focus is on the Significance of Classical English Literature in the Development of Lexicon and Semantics. Our study is based on some famous English literature prior to and from Victorian Eras. A careful analysis shows that words used today in the common parlance have had very different spelling and pronunciation. We have taken some such words from classical literature and cited them in the modern form highlighting the process of their evolution. English language is rampantly used globally as a source of communication across the disciplines of human activities. Besides English Literature, this is used for Science, Research and Development, Information technology, Computer and Medical Science and almost every conceivable discipline that humans need. The rich repertoire of language provides ample working flexibilities offering a corsage of words to select from for most impactful semantics. This in turn enriches the English Lexicon facilitating even adoption of words from other languages. The evolution of Lexicon and Semantics, using classical literature is highlighted. It is envisaged that this paper would be useful both for students of literature and science augmenting overall enrichment of the language providing additional sophistication to human communication.

Women’s reproductive health is of paramount importance, given their central roles in family and societal well-being. During the reproductive years, women are particularly vulnerable to physiological and psychosocial challenges arising from dynamic hormonal fluctuations, emotional stress, sexual practices, and complex interpersonal relationships. This vulnerability is further exacerbated by limited awareness of reproductive health and low socioeconomic status. Lifestyle factors, including dietary habits, sleep patterns, personal hygiene, and inadvertent exposure to pathogens, also significantly influence reproductive outcomes. The human vagina harbours a highly specialised and dynamic microbial ecosystem, predominantly composed of beneficial Lactobacillus species, including Lactobacillus crispatus, Lactobacillus gasseri, Lactobacillus iners, and Lactobacillus jensenii. These species play a crucial role in maintaining vaginal health by preserving an acidic pH, sustaining epithelial integrity, and regulating mucosal immune homeostasis. Disruption of this microbial balance leads to dysbiosis, which is associated with conditions such as bacterial vaginosis (BV), vulvovaginal candidiasis, and sexually transmitted infections (STIs). BV-associated microorganisms, including Gardnerella vaginalis, Prevotella bivia, bacterial vaginosis-associated bacteria (BVABs), and Sneathia species, along with STI-causing pathogens such as Neisseria gonorrhoeae, Chlamydia trachomatis, Treponema pallidum, Mycoplasma genitalium, human papillomavirus (HPV), herpes simplex virus (HSV), and human immunodeficiency virus (HIV), contribute significantly to reproductive morbidity. Additionally, opportunistic pathogens such as Escherichia coli, Enterococcus faecalis, and Group B Streptococcus further complicate vaginal health. This review comprehensively examines the vaginal microbiota in both healthy and diseased states, with particular emphasis on the mechanistic pathways that underlie its associations with epithelial barrier disruption, immune modulation, biofilm formation, microbial ascension, and chronic inflammation. These interconnected processes are implicated in adverse reproductive outcomes, including infertility, preterm labour, recurrent pregnancy loss, and cervical carcinogenesis. Furthermore, emerging multi-omics approaches such as metagenomics, metatranscriptomics, metabolomics, and organ-on-chip technologies hold significant promise for advancing diagnostic precision and therapeutic interventions. Finally, the limitations of conventional antimicrobial therapies are critically evaluated, and novel microbiota-targeted strategies, including strain-specific probiotics, vaginal microbiota transplantation, bacteriophage therapy, and partner-inclusive interventions, are highlighted. It is anticipated that this review will raise awareness and promote strategies to restore the functional balance and resilience of the vaginal microbiome, thereby improving women’s reproductive health and overall quality of life.

Chronic Obstructive Pulmonary Disease (COPD) is regarded as one of the major global health issues. It is a complex, heterogeneous condition that results from both genetic and environmental factors. The mechanisms involved in its pathophysiology are still not fully understood. The present study aims to investigate Angiotensin-Converting Enzyme Gene Polymorphism (Insertion/Deletion) in patients with Chronic Obstructive Pulmonary Disease and Hypertension Among Indian Population. The current study included 300 patients, divided into three groups: 100 with COPD, 100 with COPD and hypertension, and 100 with hypertension alone. Additionally, a control group of 100 healthy individuals was included. The ACE I/D gene was typed using Mutation-Specific Polymerase Chain Reaction (MSP). PCR products were analyzed by gel electrophoresis on 1% agarose gel, and the results were visualized using a UV Gel Doc. The results revealed that a notable correlation existed between the D allele and COPD (odds ratio [OR] = 4.987, p ≤ 0.001), for the patients with a combination of COPD and HTN (odds ratio [OR] = 2.691, p ≤ 0.001), and for hypertensive patients only (OR = 2.136, p-value ≤0.01). Nonetheless, non significant association recorded between the homozygous II, heterozygous ID, and homozygous DD genotypes and the incidence of COPD or HTN (p ≤ 0.05). Based on the findings, the study concludes that a significant correlation existed between the D allele in the ACE gene with an increased risk of developing HTN and COPD in patients. The study suggested that this allele may be associated with a higher likelihood of occurrence of both COPD and HTN.

Risperidone (RSP) is an antipsychotic drug widely prescribed for patients suffering from depression or other psychotic episodes. However, its misuse or overdose has been implicated in accidental and suicidal fatalities, underscoring the need for sensitive and reliable analytical methodologies for its determination in biological samples. In the present study, the ultrasonic-assisted dispersive liquid–liquid microextraction (UA-DLLME) technique, coupled with thin-layer chromatography (TLC) and a smartphone-based digital image colorimetry analytical method, was developed, optimized, validated, and applied for the determination of RSP in blood, urine, and pharmaceutical samples. Among the tested conditions, trichloroethylene (100 µL) and acetonitrile (800 µL) were found to be the most suitable extraction and dispersing solvents, respectively, at a pH of 10, with 1 minute of ultrasonication yielding the maximum extraction efficiency. An ethyl acetate:methanol:ammonia solution (85:10:5 v/v/v) was used as the mobile phase to achieve TLC separation. Calibration curves exhibited linearity in the concentration range of 0.25–10 mg/mL with limits of detection (LOD) of 0.18, 0.22, and 0.03 mg/mL and limits of quantification (LOQ) of 1.00, 0.08, and 0.46 mg/mL for blood, urine, and tablet samples, respectively. The accuracy and precision (intraday and interday) were also calculated according to SWGTOX guidelines. The recovery for the method was 165.03%, 81.88%, and 98.34% for blood, urine, and pharmaceutical tablet samples, respectively. In the next step, greenness of the method was identified using the complex Green Analytical Procedure Index (GAPI) and the Analytical Greenness (AGREE) calculators, and Environmental, Performance, and Practicality Index (EPPI), confirming its environmental sustainability. Developed an analytical method for UA-DLLME–TLC–smartphone colorimetry provides a rapid, cost-effective, environmentally sustainable strategy for the determination of risperidone in biological samples, with substantial applicability in forensic toxicology for the identification and quantification in suspected overdose or poisoning cases.

Artificial Intelligence (AI) has emerged as a transformative force reshaping work culture, organizational structures, and employment systems worldwide. The integration of AI-driven technologies into talent acquisition and human resource management has enhanced the efficiency, accuracy, and strategic capacity of recruitment, employee assessment, workforce analytics, and organizational decision-making. Technologies such as automated resume screening, predictive analytics, intelligent interviewing systems, and AI-based behavioural assessment tools have accelerated hiring processes, improved candidate-job compatibility, reduced operational costs, and minimized human error. Beyond recruitment, AI contributes significantly to human resource development through personalized learning, adaptive workforce planning, employee engagement, continuous skill enhancement, and data-driven performance evaluation. These intelligent systems enable organizations to identify talent gaps, predict workforce trends, and design customized professional development strategies suited to the evolving global economy. Despite these advancements, the increasing reliance on AI raises major concerns regarding job displacement, algorithmic bias, ethical accountability, workplace surveillance, data privacy, and widening inequalities between technologically skilled and unskilled labour. The evolving interaction between humans and intelligent systems demands continuous reskilling, digital literacy, interdisciplinary competence, and inclusive employment policies capable of addressing emerging socio-economic challenges. The future of work is likely to be defined not by the replacement of humans by machines, but by collaborative intelligence that integrates human creativity with machine efficiency. This paper critically examines the role of AI in talent acquisition, human resource development, and contemporary employment systems while exploring its opportunities, challenges, and implications for employers, employees, educational institutions, and policy-makers. It emphasizes the urgent need for ethical AI governance, human-centric organizational strategies, robust regulatory mechanisms, and adaptive labour frameworks to ensure equitable and sustainable employment in the digital era.