Search Results (47)

The rapid proliferation of Internet of Things (IoT) devices in cloud-based e-learning platforms has posed significant security risks, particularly in protecting learner information, authentication of devices, and safe communication in the highly heterogeneous learning settings. Current cryptographic solutions are largely based on classical public-key infrastructure (PKI) protocols such as RSA and ECC, which will become vulnerable with the advent of large-scale quantum computers capable of executing Shor’s algorithm. In addition, traditional perimeter-based security models are inadequate for handling the dynamics, scattered, and resource-limited characteristics of IoT-enabled educational systems. As a solution to these problems, this paper introduces ZeroTrustEdu, a scalable zero-trust cryptographic solution that combines lightweight post-quantum key management with adaptive trust scoring of cloud-connected IoT e-learning infrastructure. The proposed framework makes three fundamental contributions namely: (1) a hierarchical zero-trust security model with no implicit trust, operating across device, edge, and cloud layers; (2) a lightweight key distribution protocol based on the Module-Lattice Key Encapsulation Mechanism (ML-KEM) compliant with NIST FIPS 203 standards and (3) an adaptive behavioral trust scoring engine that dynamically adjusts device and user trust levels based on real-time interaction analytics. The architecture is evaluated using extensive NS-3 network simulations with up to 100,000 concurrent IoT nodes with formal security analysis under Chosen Plaintext Attack (CPA) and Chosen Ciphertext Attack (CCA) threat models. Comparative evaluation against RSA-2048, ECC-P256, and AES-256 baselines demonstrates that, ZeroTrustEdu delivers a 62% ± 3% (95% CI, 10 independent runs) reduction in ML-KEM encapsulation latency (12.8 ms for key encapsulation/decapsulation, contributing to a complete device authentication latency of 47.3 ms including ML-DSA signature operations), 45% reduced communication overheads, and 38% reduction in energy consumption on ARM Cortex-M4 constrained devices compared to RSA-2048 and achieves provable post-quantum security reducible to the hardness of the Module Learning With Errors (MLWE) problem. These findings demonstrate that the proposed architecture provides a viable, scalable, and quantum-resilient security solution for next-generation IoT-enabled e-learning environments. The cryptographic security of ZeroTrustEdu is guaranteed at the primitive level through NIST-standardized ML-KEM (FIPS 203) and ML-DSA (FIPS 204), with IND-CCA2 and EUF-CMA security formally proven in the respective standards; full protocol-level formal verification using automated theorem provers (ProVerif, Tamarin) is identified as valuable future work to rule out protocol-composition vulnerabilities beyond primitive-level guarantees. Full article

Integrating sensors with wireless communication capabilities into smart wireless sensing devices allows us to form a wireless sensing network. This network works in conjunction with monitors to display and control parameters at different locations or in the environment. By deploying a wireless sensing network, the system can interact with the user by sending notifications when necessary, based on the environmental conditions and user activities detected by the wireless sensors, and make corresponding adjustments to or control the environment. The advancement and widespread adoption of the internet have enabled the development of this technology. Wireless sensors are widely used in product positioning and environmental monitoring management, making the management of complex products more accurate. The Monitor and Control System (MCS), which combines network cameras and wireless sensors with neural network technology and fuzzy control systems, improves the existing positioning method and enhances positioning accuracy. Product management, which comprises comprehensive digital services and is facing serious staff shortages, has turned to digital payment to reduce labor costs. This experiment was simulated using Network Simulator 2 (NS2). In the sensing system part, the application of a ZigBee network and its status were explored, and interference was analyzed. Information on network interference simulations and their impact on normal services was compiled for network management purposes. Using NS2 network simulation, this study utilizes ZigBee with different neuron nodes and different training times to find the best network model, compares various queuing mechanisms and functions as a network interference intrusion detection system, and explores its node defense capabilities in cases of interference. Node Density: Node density is typically determined by the number of nodes in the simulation area and the size of the scene. Low Density: Sparse node distribution, prone to network partitioning, is suitable for testing latency-tolerant networks (DTNs) or route discovery capabilities. High Density: It entails dense node distribution, severe signal interference, and packet collisions. It is suitable for testing MAC layer collision prevention mechanisms (such as CSMA/CA) and the scalability of outing protocols. Configuration Method: the “set Dest” tool is used in a Tcl script to generate a mobile scene file, defining the number of nodes, range (X, Y), and time to be more significant in product management. Full article

In this study, a polyvinyl alcohol/carboxymethyl chitosan (PVA/CCTS) hydrogel was synthesized via free radical polymerization and employed for the adsorption of Rhodamine B (RhB) from aqueous solution. The hydrogel was systematically characterized by FTIR, SEM, XPS, and BET analyses, confirming its interconnected porous network and functional group composition. Under optimized conditions (adsorbent dosage = 0.1 g, pH = 6, RhB concentration = 65 mg·L⁻¹, and T = 298.15 ± 2 K), the maximum adsorption capacity reached 15.88 mg·g⁻¹. Kinetic analysis showed that the pseudo-second-order model best described the adsorption behavior under optimal conditions, indicating that the uptake of RhB is governed by multiple interaction mechanisms rather than simple physisorption alone. The equilibrium data were best fitted by the Freundlich isotherm (R² = 0.976), indicating surface heterogeneity of the hydrogel. Thermodynamic evaluation revealed an endothermic (ΔH = 28.38 ± 4.40 kJ·mol⁻¹), with adsorption efficiency improving at elevated temperatures. The hydrogel retained appreciable adsorption capacity after three adsorption–desorption cycles (5.78 mg·g⁻¹ at the third cycle). Density functional theory (DFT) calculations identified -COOH and -NH₂ groups as the primary active sites, and molecular electrostatic potential analysis confirmed that electrostatic interactions between the negatively charged hydrogel surface and cationic RhB drive the initial adsorption. Molecular dynamics (MD) simulations over 100 ns further demonstrated that van der Waals forces constitute the dominant driving force, supplemented by electrostatic interactions and hydrogen bonding, with the hydrogel’s cross-linked network stabilizing adsorbed RhB molecules. The integrated experimental computational approach provides a comprehensive mechanistic understanding of RhB adsorption on PVA/CCTS hydrogel, offering guidance for the rational design of polysaccharide-based adsorbents for dye-contaminated wastewater treatment. Full article

The IEEE 802.15.6 standard defines wireless body area networks (WBANs) for communication in, on, and around the human body. However, commercially available hardware platforms that support direct experimental validation of IEEE 802.15.6-oriented WBAN studies remain limited. As a result, much WBAN research still relies on simulations or custom-built transceivers, leaving the practical validity of simulation results uncertain. In this study, we evaluated a configurable radio platform for GMSK-based narrowband WBAN PHY validation in the 420–450 MHz band by comparing theoretical calculations, ns-3 simulation results, and hardware measurements. Evaluations covered both on-body and off-body scenarios at transmit powers from −15 to −25 dBm. Our key findings are as follows: (1) lower transmit power consistently decreases the communication range in both simulated and hardware environments; (2) degradation trends in packet success rate are similar for both environments, supporting simulation credibility; and (3) in the off-body scenario, ns-3 simulations overestimate the communication range by approximately 10 m compared to hardware under identical conditions. The publicly available simulation framework facilitates reproducible WBAN research. Our results confirm that our ns-3 implementation can be used effectively to approximate key GMSK-based WBAN PHY behaviors in realistic conditions while identifying specific differences in range estimates. Full article

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters host cells when the spike receptor-binding domain (RBD) engages angiotensin-converting enzyme 2 (ACE2). Cannabinoid scaffolds have recently been reported to bind S1/RBD, block spike-mediated membrane fusion, and modulate host inflammatory pathways, making them attractive candidates for entry inhibition. Here, we applied an integrated computational pipeline to prioritize cannabis-derived compounds as interfacial blockers of the RBD–ACE2 complex across variants. Eleven phytocannabinoids were docked into the wild-type (WT) RBD–ACE2 interface, identifying three cavities, with ligands preferentially occupying pocket 1. Complexes were subjected to triplicate 200 ns all-atom molecular dynamics (MD) simulations for WT, Delta, and Omicron BA.1 RBD–ACE2. Binding energetics were quantified using molecular mechanics/generalized Born surface area (MM/GBSA) and solvated interaction energy (SIE), and per-residue contributions were analyzed together with solvent-accessible surface area (SASA) and residue interaction networks. Among all compounds, cannabidiol (CBD) and cannabinol (CBN) were the only ligands that remained stably bound in pocket 1 for all variants. CBN showed the most favorable ligand–complex binding in WT, whereas CBD preserved favorable binding in Omicron BA.1 despite reduced interface burial, indicating that van der Waals/electrostatic complementarity and solvation, rather than surface coverage alone, govern affinity. Both ligands weakened modeled RBD–ACE2 binding by perturbing hot-spot residues centered on Y505 or N501Y in RBD and E37, A387, and R393 in ACE2. Overall, our results highlight CBD and CBN as tractable, variant-spanning interface disruptors and illustrate how MD-based free-energy calculations can support computational drug discovery against evolving viral protein–protein interfaces. Full article

Alzheimer’s disease (AD) remains a significant global health challenge, highlighting the need for novel dual inhibitors targeting acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). This study investigated the trunk bark of Terminalia triptera Stapf. as a potential source of bioactive secondary metabolites for AD management. Bioassay-guided isolation led to the identification of two flavan-3-ol derivatives, epicatechin-(4β→8)-ent-catechin (1) and (−)-catechin (2), reported here for the first time from this species. In vitro assays demonstrated that the dimeric compound 1 exhibited stronger dual inhibitory activity against AChE and BChE, with IC₅₀ values of 4.41 × 10⁻⁴ and 4.75 × 10⁻⁴ mol/L, respectively, surpassing the reference compound berberine chloride. Molecular docking analysis revealed that compound 1 formed extensive interactions within both catalytic and peripheral anionic sites of the enzymes. Density Functional Theory (DFT) calculations indicated high kinetic stability, reflected by large HOMO–LUMO energy gaps (6.66–6.97 eV), while global reactivity descriptors suggested lower electrophilicity (ω = 2.19–2.34 eV), supporting a potentially favorable safety profile. Furthermore, 100 ns molecular dynamics simulations confirmed stable ligand–protein complexes stabilized by hydrogen-bond networks and deep binding within catalytic pockets. Overall, these findings highlight T. triptera and its dimeric proanthocyanidins as promising multi-target candidates for anti-Alzheimer drug development. Full article

Plant growth regulation is orchestrated by complex hormonal networks involving gibberellin and auxin signaling pathways. In this study, a comprehensive in silico approach was employed to comparatively evaluate the plant growth regulators (PGRs) forchlorfenuron (CPPU) and strigol (STG) against two key proteins from Arabidopsis thaliana: Gibberellin 3-beta-dioxygenase 2 (GA3Ox2), a rate-limiting enzyme in the biosynthesis of bioactive gibberellins, and the auxin signaling repressor IAA7. These targets were specifically selected because they represent critical regulatory nodes in two major hormonal pathways controlling plant growth: GA3Ox2 governs the final steps of gibberellin activation, while IAA7 modulates auxin-responsive gene expression through its interaction with Auxin Response Factors. Therefore, their combined analysis enables the evaluation of potential regulatory effects of PGRs on both gibberellin biosynthesis and auxin-mediated transcriptional control. Molecular docking analyses revealed that both ligands exhibited higher binding affinity toward GA3Ox2 than IAA7, with STG showing slightly more favorable binding energies (−7.91 kcal/mol for GA3Ox2 and −5.43 kcal/mol for IAA7) compared to CPPU (−7.18 and −4.79 kcal/mol, respectively). These results suggest a structural preference of both PGRs toward the gibberellin biosynthetic pathway. To further assess complex stability under near-physiological conditions, 100 ns molecular dynamics (MD) simulations were conducted using the CHARMM36m force field. Despite its slightly lower docking scores, CPPU demonstrated greater conformational stability, lower RMSD fluctuations, and more persistent hydrogen bonding patterns, particularly in complexes with IAA7. In contrast, STG induced more pronounced conformational rearrangements, although it promoted slightly more compact protein conformations in certain systems. Fourier-transform infrared (FTIR) spectroscopy supported the computational findings by confirming the presence of key functional groups responsible for hydrogen bonding and hydrophobic interactions. Collectively, the results indicate that although STG exhibits higher initial binding affinity, CPPU forms more dynamically stable complexes with both proteins. These findings suggest that CPPU may represent a more robust candidate for sustained modulation of auxin and gibberellin signaling pathways in plant growth regulation. Full article

Many existing healthcare facilities still rely on the legacy Wi-Fi 5 (IEEE 802.11ac) standard, which is based on Orthogonal Frequency-Division Multiplexing (OFDM). OFDM supports single-user-per-channel access, leading to increased contention, higher latency, jitter, and packet loss under dense device deployments commonly found in clinical environments. This study presents a quantitative performance evaluation of Wi-Fi 5 and Wi-Fi 6/7 by comparing the effectiveness of OFDM with Orthogonal Frequency-Division Multiple Access (OFDMA) and Target Wake Time (TWT) in a simulated dense IoMT environment. Simulations were conducted using Network Simulator 3 (NS-3), and relevant Quality of Service (QoS) metrics. The results demonstrated that OFDMA reduces average network delay by up to approximately 37%, improves throughput by approximately 20%, and reduces packet loss ratio by up to 85% compared to OFDM under high-density operations, while exhibiting marginally improved jitter performance (approximately 2%). In addition, the use of TWT achieved substantial reductions in device power consumption of up to approximately 90%, at the cost of reduced aggregate throughput of up to approximately 75% under high station densities. These results demonstrated that Wi-Fi 6/7 technologies can offer significant advantages in terms of QoS and energy efficiency over legacy Wi-Fi 5 for dense IoMT environments. Full article

Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder requiring safe, multitarget therapeutic strategies. Marine macroalgae represent an underexplored source of bioactives with pleiotropic metabolic effects. This study investigated the antidiabetic potential of an ultrasound-assisted ethanolic extract of Caulerpa racemosa (UAECr) and its key phytosterol, campesterol, through an integrative framework combining metabolomics, network pharmacology, molecular docking, molecular dynamics simulation, and in vitro validation. Untargeted ultra-high-performance liquid chromatography–high-resolution mass spectrometry (UHPLC–HRMS) metabolomics characterized UAECr constituents, followed by in silico bioactivity prediction, target-network analysis, molecular docking, and 100 ns molecular dynamics simulation of the peroxisome proliferator-activated receptor gamma (PPARγ)–campesterol complex. Functional validation was performed in differentiated 3T3-L1 adipocytes assessing glucose uptake, PPARγ expression, dipeptidyl peptidase 4 (DPP-4) inhibition, and cytotoxicity. Metabolomics identified campesterol as a prominent bioactive. Network pharmacology highlighted PPARγ as a central hub, supported by strong docking affinity of campesterol toward PPARγ (−11.4 kcal/mol) and DPP-4 (−8.3 kcal/mol). Molecular dynamics simulations demonstrated stable PPARγ–campesterol interactions, with preserved protein compactness and low residue fluctuation. In vitro, UAECr and campesterol significantly enhanced glucose uptake (up to 134% vs. control, p < 0.001), upregulated PPARγ expression (4-fold, p < 0.0001), and moderately inhibited DPP-4 activity (p < 0.01) without cytotoxicity. C. racemosa-derived extracts and campesterol exert antidiabetic effects primarily via stable PPARγ-mediated insulin sensitization with complementary DPP-4 modulation, supporting its potential as a marine-derived functional food candidate. Full article

Wee1-like protein kinase 2 (WEE2) is an oocyte-specific kinase that regulates meiotic arrest and fertilization. Its largely restricted expression in female germ cells and absence in somatic tissues make it a highly selective target for reproductive health interventions. Despite its central role in human fertility, no clinically approved WEE2 modulator is available. In this study, we employed an integrated in silico approach that combines structure-based virtual screening, molecular dynamics (MD) simulations, and MM-PBSA free-energy calculations to identify repurposed drug candidates with potential WEE2 inhibitory activity. Screening of ~3800 DrugBank compounds against the WEE2 catalytic domain yielded ten high-affinity hits, from which Midostaurin and Nilotinib emerged as the most mechanistically relevant based on kinase-targeting properties and pharmacological profiles. Docking analyses revealed strong binding affinities (−11.5 and −11.3 kcal/mol) and interaction fingerprints highly similar to the reference inhibitor MK1775, including key contacts with hinge-region residues Val220, Tyr291, and Cys292. All-atom MD simulations for 300 ns demonstrated that both compounds induce stable protein–ligand complexes with minimal conformational drift, decreased residual flexibility, preserved compactness, and stable intramolecular hydrogen-bond networks. Principal component and free-energy landscape analyses further indicate restricted conformational sampling of WEE2 upon ligand binding, supporting ligand-induced stabilization of the catalytic domain. MM-PBSA calculations confirmed favorable binding free energies for Midostaurin (−18.78 ± 2.23 kJ/mol) and Nilotinib (−17.47 ± 2.95 kJ/mol), exceeding that of MK1775. To increase the translational prioritization of candidate hits, we place our structure-based pipeline in the context of modern machine learning (ML) and deep learning (DL)-enabled virtual screening workflows. ML/DL rescoring and graph-based molecular property predictors can rapidly re-rank docking hits and estimate absorption, distribution, metabolism, excretion, and toxicity (ADMET) liabilities before in vitro evaluation. Full article

Sequential decision-making is critical for applications ranging from personalized recommendations to resource allocation. This study evaluates three decision policies—Greedy, Thompson Sampling (via Monte Carlo Dropout), and a zero-shot Large Language Model (LLM)-guided policy (Gemini-1.5-Pro)—within a contextual bandit framework. To address covariate shift and assess subpopulation performance, we utilize a Collective Conditional Diffusion Network (CCDN) where covariates are partitioned into $B=10$ homogeneous blocks. Evaluating these policies across a high-dimensional treatment space ($K=5$, resulting in $2^5=32$ actions), we tested performance in a simulated environment and three benchmark datasets: Boston Housing, Wine Quality, and Adult Income. Our results demonstrate that the Greedy strategy achieves the highest Model-Relative Optimal (MRO) coverage, reaching 1.00 in the Wine Quality and Adult Income datasets, though performance drops significantly to 0.05 in the Boston Housing environment. Thompson Sampling maintains competitive regret and, in the Boston Housing dataset, marginally outperforms Greedy in action selection precision. Conversely, the zero-shot LLM-guided policy consistently underperforms in numerical tabular settings, exhibiting the highest median regret and near-zero MRO coverage across most tasks. Furthermore, Wilcoxon tests reveal that differences in empirical outcomes between policies are often not statistically significant (ns), suggesting an optimization ceiling in zero-shot tabular settings. These findings indicate that while traditional model-driven policies are robust, LLM-guided approaches currently lack the numerical precision required for high-dimensional sequential decision-making without further calibration or hybrid integration. Full article

In ad hoc networks employing Autonomous Aerial Vehicles (AAVs), the importance of real-time applications and edge computing is steadily increasing. However, existing routing protocols still fail to meet the strict performance requirements under the unique conditions of AAV networks, where the network topology changes continuously, and nodes move at high speed. This paper presents SHARP-AODV (Stability Heuristic Adaptive Routing Protocol—AODV), an enhanced routing protocol specifically developed for AAV networks. SHARP-AODV introduces two key innovations: (1) an intelligent RREQ (Route Request) dissemination mechanism that combines neighbor density control with a multi-parameter probabilistic model, and (2) a multi-criteria path selection mechanism that jointly considers hop count, link quality, and resource state. Simulation results in NS-3 across four distinct mobility models and various numbers of AAV nodes show that SHARP-AODV significantly outperforms standard AODV, improving packet delivery ratio (PDR) by up to 23.9%, increasing throughput by up to 61%, while reducing end-to-end delay by up to 87.8% and jitter by up to 90.6%. The proposed protocol is especially suitable for AAV-enabled applications in Edge Computing and Metaverse ecosystems that require low-latency, highly reliable connectivity with adaptation to dynamic network conditions. Furthermore, SHARP-AODV satisfies 6G network requirements for connection reliability, ultra-low latency, and high device density, unlocking new opportunities for employing AAVs in smart cities, environmental monitoring, and distributed VR/AR systems. Full article

In this article, the 1,3-dipolar cycloaddition (1,3-DC) reactions between 4-acyl-1H-pyrrole-2,3-diones fused at the [e]-side with a heterocyclic moiety (FPDs) and diphenylnitrone are studied using Molecular Electron Density Theory (MEDT) at different computational levels. An analysis of the global reactivity descriptors has determined the role of the reagents. FPDs will act as electrophiles, while diphenylnitrone will be a nucleophile. It was found that the reactions proceed according to a one-step but asynchronous mechanism. Additionally, based on the Bonding Evolution Theory (BET) analysis of the model 1,3-DC reaction between FPDs 1b and diphenylnitrone 2, we can distinguish eight different phases. The formation of the first C1-O5 single bond takes place in phase VII through the disappearance of the V(C1) monosynaptic basin and the depopulation of the V″(O5) monosynaptic basin, while the formation of the second C2-C3 single bond begins at the last phase of the reaction through the connection of two V(C2) and V(C3) monosynaptic basins. Based on this, we can classify this reaction as a “one-step two-stage” process. Furthermore, molecular dynamics (MD) simulation analysis up to 100 ns demonstrated the stability of both the 2P3B–Ligand1 and 2P3B–Zidovudine complexes. An enhancer of shape compression was generated for ligand1, whereas Zidovudine generated a more packed and stable hydrogen bond network that would allow a better occupancy of the active site. Full article

This paper addresses the challenges of multicasting in Mobile Ad Hoc Networks (MANETs), where communication relies exclusively on direct interactions between mobile nodes without the support of fixed infrastructure. In such networks, efficient information dissemination is critical, particularly in scenarios where an event detected by one node must be reliably communicated to a designated subset of nodes. The highly dynamic nature of MANET, characterized by frequent topology changes and unpredictable connectivity, poses significant challenges to stable and efficient multicasting. To address these issues, we adopt a Publish/Subscribe (Pub/Sub) model that utilizes brokers as intermediaries for information dissemination. However, ensuring the robustness of broker-based multicasting in a highly mobile environment requires novel strategies to mitigate the effects of frequent disconnections and mobility-induced disruptions. To this end, we propose a framework based on three key principles: (1) leveraging the Disruption-Tolerant Networking Implementations of the Bundle Protocol 7 (DTN7) at the network layer to sustain message delivery even in the presence of intermittent connectivity and high node mobility; (2) dynamically generating broker replicas to ensure that broker functionality persists despite sudden node failures or disconnections; and (3) enabling brokers and their replicas to periodically broadcast advertisement packets to maintain communication paths and facilitate efficient data forwarding, drawing inspiration from Named Data Networking (NDN) techniques. To evaluate the effectiveness of our approach, we conduct extensive simulations using ns-3, examining its impact on message delivery reliability, latency, and overall network throughput. The results demonstrate that our method significantly reduces message delivery delays while improving delivery rates, particularly in high-mobility scenarios. Additionally, the integration of DTN7 at the bundle layer proves effective in mitigating performance degradation in environments where nodes frequently change their positions. Our findings highlight the potential of our approach in enhancing the resilience and efficiency of broker-assisted multicasting in MANET, making it a promising solution for real-world applications such as disaster response, military operations, and decentralized IoT networks. Full article

Direct wide-bandgap III-Ns and II-Os have recently gained considerable attention due to their unique electrical and chemical properties. These novel semiconductors are being explored to design short-wavelength light-emitting diodes, sensors/biosensors, photodetectors for integration into flexible transparent nanoelectronics/photonics to achieve high-power radio-frequency modules, and heat-resistant optical switches for communication networks. Knowledge of the elastic constants structural and mechanical properties has played crucial roles both in the basic understanding and assessing materials’ use in thermal management applications. In the absence of experimental structural, elastic constants, and mechanical traits, many theoretical simulations have yielded inconsistent results. This work aims to investigate the basic characteristics of tetrahedrally coordinated, partially ionic BeO, MgO, ZnO, and CdO, and partially covalent BN, AlN, GaN, and InN materials. By incorporating a bond-orbital and a valance force field model, we have reported comparative results of our systematic calculations for the bond length $\mathrm{d}$, bond polarity ${\mathsf{\alpha }}_{\mathrm{P}}$, covalency ${\mathsf{\alpha }}_{\mathrm{C}}$, bulk modulus $\mathrm{B}$, elastic stiffness $\mathrm{C}(={\displaystyle \frac{\left[{\mathrm{c}}_{11}-{\mathrm{c}}_{12}\right]}{2}})$, bond-stretching $\mathsf{\alpha }$ and bond-bending $\mathsf{\beta }$ force constants, Kleinmann’s internal displacement ζ, and Born’s transverse effective charge ${\mathrm{e}}_{\mathrm{T}}^{*}$. Correlations between $\mathrm{C}/\mathrm{B}$, $\mathsf{\beta }$/$\mathsf{\alpha }$, ${\displaystyle \frac{{\mathrm{c}}_{12}}{{\mathrm{c}}_{11}}},$ ζ, $\mathrm{a}\mathrm{n}\mathrm{d}$ ${\mathsf{\alpha }}_{\mathrm{C}}$revealed valuable trends of structural, elastic, and bonding characteristics. The study noticed AlN and GaN (MgO and ZnO) showing nearly comparable features, while BN (BeO) is much harder compared to InN (CdO) material, with drastically softer bonding. Calculations of microhardness $\mathrm{H}$, shear modulus $\mathrm{G},$ and Young’s modulus $\mathrm{Y}$ have predicted BN (BeO) satisfying a criterion of super hardness. III-Ns (II-Os) could be vital in electronics, aerospace, defense, nuclear reactors, and automotive industries, providing integrity and performance at high temperature in high-power applications, ranging from heat sinks to electronic substrates to insulators in high-power devices. Full article