Conventional Framework Research Articles

Evaluating the Efficacy of the SENO Decision Framework in Managing Metastatic Spinal Disease: A Comparative Study

BACKGROUND CONTEXTThe management of metastatic spinal disease presents a complex challenge, with no universally accepted guidelines currently available. A new treatment approach, termed the “SENO” (Systemic Conditions, Effectiveness of Systemic Treatment, Neurology, and Oncology) decision framework, was developed, incorporating a 12-point grading system for epidural spinal cord compression. PURPOSEThis study aims to assess the clinical efficacy of the SENO decision framework and compare its outcomes with those from traditional treatment frameworks. STUDY DESIGN/SETTINGThis is an observational cohort study that includes the propensity score matching analysis. PATIENT SAMPLEThis multicenter study included 840 patients with metastatic spinal disease, treated across two tertiary hospitals. OUTCOME MEASURESThe study evaluated therapeutic strategies, ambulatory status at hospital discharge, time to initiation of systemic cancer treatments, quality of life at three-, six-, and twelve-month post-discharge, and overall survival. METHODSOf all patients, 523 patients received treatment based on the SENO decision framework (the SENO group), while 307 patients were managed using conventional decision frameworks (the traditional group). Baseline characteristics of patients in both groups were collected and analyzed to ensure comparability. Quality of life was assessed using the Functional Assessment of Cancer Therapy-General Scale questionnaire. RESULTSPatients in the SENO group were less likely to undergo open decompression surgery (16.3% vs. 25.1%) and more likely to receive minimally invasive surgery (64.8% vs. 54.7%) compared to those in the traditional group (P=0.004). Furthermore, the SENO group demonstrated a higher rate of ambulation post-discharge (91.2% vs. 86.0%, P=0.026), a shorter interval before initiating systemic treatments for tumors (6.89 days vs. 10.14 days, P<0.001), and longer survival times (323 days vs. 298 days, P=0.008). Quality of life metrics, including physical, social, emotional, and functional well-being, were significantly better in the SENO group compared to the traditional group (All P<0.015). After the propensity score matching analysis, the baseline characteristics between the SENO and traditional treatment groups were perfectly matched. The SENO group still showed a lower percentage of patients undergoing open decompression surgery (P=0.038), a shorter time to receive systemic treatments (P<0.001), longer median survival times (P=0.016), and markedly better quality of life scores at three, six, and twelve months across various domains, with total scores reaching 92.08 compared to 85.51 in the traditional group at the final follow-up (P<0.001). CONCLUSIONSThe SENO decision framework has the potential to serve as an effective treatment strategy for patients with metastatic spinal disease. By facilitating the use of minimally invasive surgery, this framework leads to better overall functional outcomes, prolonged survival, and an improved quality of life. However, before widespread promotion of the SENO decision framework, it is essential to conduct randomized controlled trials to thoroughly validate its effectiveness.

Probabilistic Topology Optimization Framework for Geometrically Nonlinear Structures Considering Load Position Uncertainty and Imperfections

In this manuscript, a novel approach to topology optimization is proposed which integrates considerations of uncertain load positions, thereby enhancing the reliability-based design within the context of structural engineering. Extending the conventional framework to encompass imperfect geometrically nonlinear analyses, this research discovers the intricate interplay between nonlinearity and uncertainty, shedding light on their combined effects on probabilistic analysis. A key innovation lies in treating load position as a stochastic variable, augmenting the existing parameters, such as volume fraction, material properties, and geometric imperfections, to capture the full spectrum of variability inherent in real-world conditions. To address these uncertainties, normal distributions are adopted for all relevant parameters, leveraging their computational efficacy, simplicity, and ease of implementation, which are particularly crucial in the context of complex optimization algorithms and extensive analyses. The proposed methodology undergoes rigorous validation against benchmark problems, ensuring its efficacy and reliability. Through a series of structural examples, including U-shaped plates, 3D L-shaped beams, and steel I-beams, the implications of considering imperfect geometrically nonlinear analyses within the framework of reliability-based topology optimization are explored, with a specific focus on the probabilistic aspect of load position uncertainty. The findings highlight the significant influence of probabilistic design methodologies on topology optimization, with the defined constraints serving as crucial conditions that govern the optimal topologies and their corresponding stress distributions.

Introduction: Perceptions, Reflections, and Conceptualisations of War and Peace in Children’s Drawings

In this editorial, we introduce the focus of this thematic issue and its contributions. Addressing the themes of “war” and “peace” and their impacts on children requires contextualization within socio‐historical, socio‐cultural, socio‐psychological, and educational frameworks. Equally, it is essential to tackle the methodological challenges inherent in this field of empirical research. The rise of child‐centred and participatory approaches over recent decades—emphasizing children as active agents—has enriched the research landscape, offering a counterbalance to the quantitative and developmental psychological traditions. Yet, as the contributions demonstrate, there is no methodological “gold standard” for this field. Rather, the topic’s complexity calls for a diverse array of approaches and perspectives, including those that push beyond conventional academic frameworks and methods.

Electrochemical nucleation and growth kinetics: insights from single particle scanning electrochemical cell microscopy studies.

The kinetics of particle nucleation and growth are critical to a wide variety of electrochemical systems. While studies carried out at the single particle level are promising for improving our understanding of nucleation and growth processes, conventional analytical frameworks commonly employed in bulk studies may not be appropriate for single particle experiments. Here, we present scanning electrochemical cell microscopy (SECCM) studies of Ag nucleation and growth on carbon and indium tin oxide (ITO) electrodes. Statistical analyses of the data from these experiments reveal significant discrepancies with traditional, quasi-equilibrium kinetic models commonly employed in the analysis of particle nucleation in electrochemical systems. Time-dependent kinetic models are presented capable of appropriately analysing the data generated via SECCM to extract meaningful chemical quantities such as surface energies and kinetic rate constants. These results demonstrate a powerful new approach to the analysis of single particle nucleation and growth data which could be leveraged in differentiating behavior within spatially heterogeneous systems.

Three perspectives on entropy dynamics in a non-Hermitian two-state system

Abstract A comparative study of entropy dynamics as an indicator of physical behavior in an open two-state system with balanced gain and loss is presented. To begin with, we illustrate the phase portrait of this non-Hermitian model on the Bloch sphere, elucidating the changes in behavior as one moves across the phase transition boundary, as well as the emergent feature of unidirectional state evolution in the spontaneously broken P T -symmetry regime. This is followed by an examination of the purity and entropy dynamics. Here we distinguish the perspective taken in utilizing the conventional framework of Hermitian-adjoint states from an approach that is based on biorthogonal-adjoint states and a third case based on an isospectral mapping. In this it is demonstrated that their differences are rooted in the treatment of the environmental coupling mode. For unbroken P T symmetry of the system, a notable characteristic feature of the perspective taken is the presence or absence of purity oscillations, with an associated entropy revival. The description of the system is then continued from its P T -symmetric pseudo-Hermitian phase into the regime of spontaneously broken symmetry, in the latter two approaches through a non-analytic operator-based continuation, yielding a Lindblad master equation based on the P T charge operator C . This phase transition indicates a general connection between the pseudo-Hermitian closed-system and the Lindbladian open-system formalism through a spontaneous breakdown of the underlying physical reflection symmetry.

Mass transport modelling of two partially miscible, multicomponent fluids in nanoporous media

High-pressure fluid transport in nanoporous media such as shale formations requires further understanding because conventional continuum approaches become inadequate due to their ultralow permeability and complexity of transport mechanisms. We propose a species-based approach for modelling two partially miscible, multicomponent fluids in nanoporous media – one that does not rely on conventional bulk fluid transport frameworks but on species movement. We develop a numerical model for species transport of partially miscible, non-ideal fluid mixtures using the chemical potential gradient as the driving force. The model considers the binary friction concept to include the friction between fluid molecules as well as between fluid molecules and pore walls, and incorporates the key multicomponent transport mechanisms – Knudsen, viscous and molecular diffusion. Under single-phase conditions, the system under consideration is quantified by introducing multicomponent Sherwood number (Sh), Péclet number (Pe) and fluid–solid friction modulus (φ). Despite the complexity of fluid transport in nanopores, the steady-state single-phase transport results reveal the contribution of diffusion in nanopores, where all parameters collapse on a set of master curves for the multicomponent Sh with a dependence on multicomponent Pe and φ. Unsteady state, two-phase transport modelling of the codiffusion process shows that light and intermediate alkanes are produced much higher than heavy alkanes when the vapour phase appears. We demonstrate that the pressure gradient is also crucial in promoting CO2 and alkane mixing during counterdiffusion processes. These results stress the need for a paradigm shift from classical bulk flow modelling to species-based transport modelling in nanoporous media.

Colonialism and Local Elites: Lyautey's Colonial Experience in Morocco as a Case Study

This article presents a thorough analysis of the dynamics that developed between the Moroccan makhzen elites and the French Resident General Hubert Lyautey during the period of the French protectorate in Morocco (1912–1956). The article examines how Lyautey, by employing carefully crafted strategies, effectively utilized the Moroccan elite as a pivotal tool for strengthening colonial dominance, all the while preserving the traditional and social values inherent in Morocco. The makhzen elites represent a pivotal component of the traditional Moroccan framework, having wielded considerable administrative and religious authority before the advent of the protectorate, thus justifying their prominent role in this analysis. The article suggests that these elites stood at a crucial juncture, where they had to decide between opposing French dominance and collaborating with the colonial administration to preserve their status and privileges. Lyautey upheld the conventional social and political structure, leveraging this context to advance the execution of colonial policies. As a result, a considerable portion of them chose to engage in collaboration. The article highlighted the "dual cooperation policy" that Lyautey put into practice. He upheld various conventional administrative frameworks, such as the Sharia judiciary and the Emirate of the Faithful, thus presenting himself as a guardian of authentic Moroccan values. This approach ensured the relative stability of the country during that period by striking a delicate equilibrium between the enforcement of French authority and the maintenance of the traditional Moroccan regime's outward appearance. The article says that the makhzen elites weren't just tools of colonial power; instead, they showed that they could negotiate to protect their own interests in a colonial setting. In this light, it's clear that Lyautey’s relationship with these elites was more than just dependence; it was a complex one characterised by mutual interests that overlapped. This research reveals a deep understanding of the complexities of power within the context of colonialism, illustrating how France leveraged local traditional forces to bolster its colonial endeavours while refraining from dismantling or supplanting them. This methodology played a significant role in shaping the distinctive identity of the French colonisation in Morocco.

Science Outcome Design Parameters for Cluster-Randomized Trials Involving Teachers

Programs aiming to improve student science outcomes often involve teachers. When designing cluster-randomized trials (CRTs) to evaluate the causal effects of these programs, investigators need empirical design parameters as critical inputs in the study design phase. Using proper empirical design parameters to conduct power analysis, investigators can ensure designs have adequate statistical power to detect treatment effects and efficiently use resources. The current study uses two nationally representative samples to estimate empirical design parameters for science outcomes in settings of students (nested within teachers) nested within schools. We compile design parameters for intraclass correlation coefficients and proportions of variance explained by covariates. We illustrate how to use the results to design efficient and effective CRTs in both the conventional statistical power analysis framework and the optimal design framework that additionally considers sampling costs.

Various shapes of whiteness: A new look at racism and its institutional operation in Israeli academia

This article explores theoretical concepts that reveal the power structures within Israeli academia. Rather than employing the conventional framework that emphasizes practices of discrimination and underrepresentation to explain the ongoing preference for the dominant Western, white Jewish group and its cultural capital over Jewish and Arab Palestinian minorities, we propose a new theoretical perspective. By integrating whiteness theory, critical race theory, intersectionality and settler colonial paradigms, the article illustrates how this framework can effectively analyse academic epistemology and social dynamics. We then introduce the subsequent articles in this issue as examples of the successful application of these concepts, highlighting how they expose the concealed and sophisticated exercise of organizational power that aligns with the norms of the dominant culture, to the detriment of minority students and faculty.

Constructing Flexible Crystalline Porous Organic Salts via a Zwitterionic Strategy.

The unique ionic channels and highly polar pore structures have distinguished crystalline porous organic salts (CPOSs) from conventional porous frameworks in the past decade. Up to now, CPOSs were all constructed by a monoionic strategy, in which two types of building units individually bearing anionic or cationic groups were introduced, thus increasing complexity in the synthesis of CPOSs. In this study, by utilizing stereoisomeric compounds of TPE-NS-Z or TPE-NS-E bearing both anionic and cationic groups as a single building unit, the zwitterionic strategy was proven feasible in constructing CPOSs. Benefiting from the single building unit, the zwitterionic strategy simplified the preparation process and reduced the difficulty in studying the aggregation behavior of building units into CPOSs. And also, this novel strategy enabled precise control of the finally obtained CPOSs through fine-tuning of the initial building units. Surprisingly, the special parallel/vertical alternated stacking mode and unique ionic interaction networks in the crystal structure provided the flexible pore characteristic of CPOS-E, which further guaranteed the multitime controllable release of highly polar chemicals in different solvents.

Development and Empirical Validation of an Information Technology Acceptance Framework

This paper explores the constructs affecting IT acceptance in modern organisations using theoretical frameworks such as the TAM and the UTAUT models. Realising the drawbacks of such dichotomous models for capturing the dynamics of contemporary IT enablers and use processes, the present study plans to offer and test new antecedent and moderating constructs that may provide a more accurate picture of today’s IT deployment and utilisation. The type of research in the study is the quantitative research method, which is adopted in the study where the researcher adopted a structured questionnaire to be administered to 200 participants across the different sectors of the economy, such as health, education, and business. Hypotheses of primary concern are those related to the construct of IT acceptance, including perceived usefulness, ease of use, trust, system transparency, perceived risk, and ethical concerns. The analysis of the data gathered is done using Structural Equation Modeling (SEM) because of the possibility of analysing the associations between several variables. Behavioural construct analysis revealed a significant positive correlation with IT acceptance behaviours, with the value of R for the new construct being 0.935 and the R square value being 0.874, which means that newly derived constructs explain a fair percentage of IT acceptance behaviours. The findings, therefore, point out the lack of conventional frameworks in addressing the complex nature of IT use in various organisational and cultural environments. Full-spectrum system requirements in newly developing domains, such as AI, machine learning, and blockchain, require new fundamental concepts regarding user trust, system openness and ethical considerations. This work extends the IT acceptance literature by providing a better view of the determinants of IT uptake in modern organisations. In totality, the validated constructs have practical implications for organisations desirous of adopting new IT systems, particularly highlighting the role of trust and perceived risk and the need for transparency or the adoption of transparency to guarantee user satisfaction rates and consequent adoption of new IT systems. In conclusion, the study points to the need to develop new models that address today’s diverse IT contexts to inform the next generation of IT acceptance and adoption research.

Optimization of dual-stage controllers in renewable energy sources-based interconnected power systems through refinement of the African Vultures Optimization Algorithm

With an emphasis on a three-region system, this paper explores automaticgeneration control within a conventional framework. Area 2 uses thermal-thermal sources, Area 3 uses gas-thermal sources, and Area 1 uses thermal-biodiesel sources. IPD(1 + I) is a unique cascade controller that combines integral-proportional-derivative (IPD) components with one-plus-integral (1 + I) components. To find the best gains and settings for the IPD(1 + I) controller, a meta-heuristic method called the African Vulture Optimization Algorithm (AVOA) is applied. Reducing the integral squared error (ISE), as determined by the performance index (Pi), is the main goal. The performance of the IPD(1 + I) controller is evaluated by contrasting it with a range of secondary controllers. Hybrid Peak Area-ISE (hPA-ISE), a novel performance metric, is also assessed in addition to the conventional ISE. The incorporation of renewable energy sources, such solar thermal energy and solid oxide fuel cells, significantly improves the system’s performance. Interestingly, under normal conditions, the IPD(1 + I) controller’s parameter settings continue to work for both sinusoidal and random load situations, obviating the need for additional tuning.

RingGesture: A Ring-Based Mid-Air Gesture Typing System Powered by a Deep-Learning Word Prediction Framework.

Text entry is a critical capability for any modern computing experience, with lightweight augmented reality (AR) glasses being no exception. Designed for all-day wearability, a limitation of lightweight AR glass is the restriction to the inclusion of multiple cameras for extensive field of view in hand tracking. This constraint underscores the need for an additional input device. We propose a system to address this gap: a ring-based mid-air gesture typing technique, RingGesture, utilizing electrodes to mark the start and end of gesture trajectories and inertial measurement units (IMU) sensors for hand tracking. This method offers an intuitive experience similar to raycast-based mid-air gesture typing found in VR headsets, allowing for a seamless translation of hand movements into cursor navigation. To enhance both accuracy and input speed, we propose a novel deep-learning word prediction framework, Score Fusion, comprised of three key components: a) a word-gesture decoding model, b) a spatial spelling correction model, and c) a lightweight contextual language model. In contrast, this framework fuses the scores from the three models to predict the most likely words with higher precision. We conduct comparative and longitudinal studies to demonstrate two key findings: firstly, the overall effectiveness of RingGesture, which achieves an average text entry speed of 27.3 words per minute (WPM) and a peak performance of 47.9 WPM. Secondly, we highlight the superior performance of the Score Fusion framework, which offers a 28.2% improvement in uncorrected Character Error Rate over a conventional word prediction framework, Naive Correction, leading to a 55.2% improvement in text entry speed for RingGesture. Additionally, RingGesture received a System Usability Score of 83 signifying its excellent usability.

Advanced Ancient Steelmaking Across the Arctic European Landscape

Abstract This article presents results that seriously challenge conventional frameworks regarding the time, course, and cultural context of the introduction of iron and steel in Europe, in turn central metals in the narrative of civilization development. From focusing on 2,000-year-old finds from 42 different sites across the national borders of present-day northernmost Norway, Finland, and Sweden, our study reveals advanced iron and steel production contemporaneous with Roman steel production within the hitherto unthinkable cultural context of hunter-gatherers. By employing archaeometric analyses alongside a lens that considers the Arctic climate and landscape, long-standing inadequate interpretative frameworks are challenged and we manage to shed novel light on the metallurgical practices and skills of the ancient Arctic hunter-gatherers. By examining local conditions, the research specifically reveals adaptation strategies and previously underexplored social and organizational aspects of early ironmaking. The implications of these findings are profound, urging historians and archaeologists to adopt similar methods and perspectives to potentially uncover additional “unthinkable” locations/regions or contexts with advanced and early metalworking.

Efficiency analysis in bi-level on fuzzy input and output

To enhance the conventional framework of data envelope analysis (DEA), a novel hybrid bi-level model is proposed, integrating fuzzy logic with triangular fuzzy numbers to effectively address data uncertainty. This model innovatively departs from the traditional DEA’s ’black box’ approach by incorporating inter-organizational relationships and the internal dynamics of decision-making units (DMUs). Utilizing a modified Russell’s method, it provides a nuanced efficiency analysis in scenarios of ambiguous data. The study aims to enhance the accuracy and applicability of Data Envelopment Analysis in uncertain data environments. To achieve this, a novel hybrid bi-level model integrating fuzzy logic is presented. Validated through a case study involving 15 branches of a private Iranian bank, the model demonstrates improved accuracy in efficiency assessments and paves the way for future research in operational systems uncertainty management. The results indicated that, among the 15 branches of a private Iranian bank analyzed for the year 2022, branches 1, 10, and 11 demonstrated leader-level efficiency, while branch 3 exhibited follower-level efficiency, and branch 1 achieved overall efficiency. These branches attained an efficiency rating of E++, signifying a high level of efficiency within the model’s parameters.

Multi-Agent Reinforcement Learning for Smart Community Energy Management

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management.

Fractional holographic dark energy

Holographic dark energy theories present a fascinating interface to probe late-time cosmology, as guided by contemporary ideas about quantum gravity. In this work, we present a new holographic dark energy scenario designated “Fractional Holographic Dark Energy” (FHDE). This model extends the conventional framework of HDEs by incorporating specific features from fractional calculus and fractional Wheeler-De Witt equation recently applied, e.g., in cosmological settings. In this manner, we retrieve a novel form of HDE energy density. We then show how FHDE can provide a consistent picture of the evolution of the late-time universe even with the simple choice of the Hubble horizon as the IR (infrared) cutoff. We provide detailed descriptions of the cosmological evolution, showing how the fractional calculus ingredients can alleviate quite a few issues associated with the conventional HDE scenario. Concretely, we compute and plot diagrams using the Hubble horizon cutoff for HDE. The density parameters for DE and dark matter (DM), the deceleration parameter, and the DE EoS parameter indicate how the universe may evolve within our “FHDE” model, fitting within an appropriate scenario of late-time cosmology.

Fabrication of Scalable Covalent Organic Framework Membrane‐based Electrolytes for Solid‐State Lithium Metal Batteries

AbstractThe conventional covalent organic framework (COF)‐based electrolytes with tailored ionic conducting behaviors are typically fabricated in the powder morphology, requiring further compaction procedures to operate as solid electrolyte tablets, which hinders the large‐scale manufacturing of COF materials. In this study, we present a feasible electrospinning strategy to prepare scalable, self‐supporting COF membranes (COMs) that feature a rigid COF skeleton bonded with flexible, lithiophilic polyethylene glycol (PEG) chains, forming an ion conduction network for Li+ transport. The resulting PEG‐COM electrolytes exhibit enhanced dendrite inhibition and high ionic conductivity of 0.153 mS cm−1 at 30 °C. The improved Li+ conduction in PEG‐COM electrolytes stems from the loose ion pairing in the structure and the production of higher free Li+ content, as confirmed by solid‐state 7Li NMR experiments. These changes in the local microenvironment of Li+ facilitate its directional movement within the COM pores. Consequently, solid‐state symmetrical Li|Li, Li|LFP, and pouch cells demonstrate excellent electrochemical performance at 60 °C. This strategy offers a universal approach for constructing scalable COM‐based electrolytes, thereby broadening the practical applications of COFs in solid‐state lithium metal batteries.

Fabrication of Scalable Covalent Organic Framework Membrane-based Electrolytes for Solid-State Lithium Metal Batteries.

The conventional covalent organic framework (COF)-based electrolytes with tailored ionic conducting behaviors are typically fabricated in the powder morphology, requiring further compaction procedures to operate as solid electrolyte tablets, which hinders the large-scale manufacturing of COF materials. In this study, we present a feasible electrospinning strategy to prepare scalable, self-supporting COF membranes (COMs) that feature a rigid COF skeleton bonded with flexible, lithiophilic polyethylene glycol (PEG) chains, forming an ion conduction network for Li+ transport. The resulting PEG-COM electrolytes exhibit enhanced dendrite inhibition and high ionic conductivity of 0.153 mS cm-1 at 30 °C. The improved Li+ conduction in PEG-COM electrolytes stems from the loose ion pairing in the structure and the production of higher free Li+ content, as confirmed by solid-state 7Li NMR experiments. These changes in the local microenvironment of Li+ facilitate its directional movement within the COM pores. Consequently, solid-state symmetrical Li|Li, Li|LFP, and pouch cells demonstrate excellent electrochemical performance at 60 °C. This strategy offers a universal approach for constructing scalable COM-based electrolytes, thereby broadening the practical applications of COFs in solid-state lithium metal batteries.

Convergence and Innovation of Artificial Intelligence in Corporate Strategic Planning: Opportunities, Challenges and Future Research Directions

In the contemporary digital era, the swift evolution of Artificial Intelligence (AI) has not only revolutionized various sectors but has also become an indispensable asset in corporate strategic planning, decision-making processes, and operational efficiency. This paper delves into the multifaceted applications of AI across a myriad of industries, providing a comprehensive analysis of its integration into corporate strategy and its profound impact on business operations. This study adopts a comprehensive research methodology that merges qualitative case studies with quantitative data analysis to scrutinize the practical use of AI in strategic planning. It delves into the interdependent relationship between AI and conventional digital frameworks, such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems. The research aims to illuminate how the fusion of these technologies can substantially enhance corporate competencies and optimize operational workflows. The study's findings indicate that while AI has the potential to offer transformative solutions, its integration into strategic planning is not without challenges. Ethical considerations, legal ramifications, and societal implications must be carefully navigated. Additionally, the study reveals a notable void in existing literature concerning the enduring strategic ramifications of AI and its effects on corporate culture and talent management practices. This gap highlights the need for further exploration into how AI shapes the long-term strategic direction and internal dynamics within organizations.