User Demands Research Articles

Indetermsoft-Set-Based D* Extra Lite Framework for Resource Provisioning in Cloud Computing

Cloud computing is an immensely complex, huge-scale, and highly diverse computing platform that allows the deployment of highly resource-constrained scientific and personal applications. Resource provisioning in cloud computing is difficult because of the uncertainty associated with it in terms of dynamic elasticity, rapid performance change, large-scale virtualization, loosely coupled applications, the elastic escalation of user demands, etc. Hence, there is a need to develop an intelligent framework that allows effective resource provisioning under uncertainties. The Indetermsoft set is a promising mathematical model that is an extension of the traditional soft set that is designed to handle uncertain forms of data. The D* extra lite algorithm is a dynamic heuristic algorithm that makes use of the history of knowledge from past search experience to arrive at decisions. In this paper, the D* extra lite algorithm is enabled with the Indetermsoft set to perform proficient resource provisioning under uncertainty. The experimental results show that the performance of the proposed algorithm is found to be promising in performance metrics such as power consumption, resource utilization, total execution time, and learning rate. The expected value analysis also validated the experimental results obtained.

Assessing Performance of Cloud-based Heterogeneous Chatbot Systems.

Cloud-based heterogeneous chatbot systems represent a significant advancement in automating diverse tasks across various platforms, including customer service, healthcare, and education. These systems are designed with multi-modal capabilities, allowing them to interact via text, voice, and visual interfaces, and are built on a cloud infrastructure that ensures scalability and reliability. The cloud-based architecture allows dynamic resource allocation, enabling the system to handle high volumes of traffic without compromising performance. These chatbots also benefit from global availability, as cloud regions distributed across the world ensure low latency and high availability.The integration of advanced AI and NLP models, such as GPT and BERT, enhances the chatbot's response accuracy and contextual understanding, making interactions more precise and human-like. Moreover, the cloud infrastructure supports continuous learning through machine learning models that refine chatbot performance over time. With multitenant capabilities, these chatbots can cater to different users or organizations, providing tailored services while ensuring that performance are not affected. Additionally, their ability to integrate with external enterprise systems, like CRMs or ERPs, allows for real-time data exchange and task automation.In terms of security, cloud-based chatbots benefit from encryption protocols and compliance with industry regulations, ensuring safe handling of sensitive data, particularly in sectors like healthcare and finance. Despite the advantages, challenges such as network dependency and cost management need careful consideration, as performance can be affected by poor connectivity or high processing costs, especially when using complex AI models. Nonetheless, cloud-based heterogeneous chatbots excel in delivering scalable, reliable, and intelligent interactions, adapting to growing user demands and a wide range of applications.

Robots in Public Libraries: An Empirical Analysis of Current Applications and Future Opportunities

ABSTRACT The paper investigated the current application of robots in public libraries with specific consideration of its future opportunities. Three (3) objectives guided the conduct of the study. The study employed a qualitative research approach. The population for the study comprised librarians who have been certified by the Librarian Registration Council of Nigeria (LRCN) and worked at public library boards. Thematic analysis and content analysis techniques were adopted for this study. The study found that robots bring various advantages, including increased operational efficiency and streamlined access to information, as well as interactive educational opportunities and heightened user involvement. The findings indicate that embracing robotic technology enables public libraries to establish more accessible, dynamic, and user-focused environments, fostering an atmosphere of innovation and technological progress within the realm of information services. It could be concluded that the integration of robots in public libraries plays a vital role in modernizing and improving library services to meet the evolving demands of library users and communities. However, library stakeholders must address potential obstacles, including financial limitations, technological preparedness, and user acceptance. Prioritizing the training of staff, fostering community involvement, and establishing clear policy frameworks enables public libraries to effectively navigate the challenges related to incorporating robotics, ensuring a smooth transition toward a technologically advanced and user-focused library environment.

Fleet sizing and static rebalancing strategies for shared E-scooters: A case study in Indianapolis, USA

With the rapid development of shared e-scooters, it is essential to understand their usage patterns for formulating informed e-scooter fleet management policies. This study first analyzes the usage pattern of shared e-scooters in Indianapolis, USA, by mining big e-scooter trip data. The analysis reveals an oversupply of shared e-scooters relative to actual user demand. Thus, a minimum fleet sizing algorithm is proposed to determine the required minimum e-scooter fleet size with the objective of reducing total operation cost, while ensuring demand coverage. Furthermore, three heuristic algorithms are proposed to address the static e-scooter rebalancing problem, focusing on minimizing rebalancing distance cost and rebalancing time. These algorithms consider practical operational constraints, including the number of rebalancing vehicles, their capacity, and the frequency of visits to e-scooter stations by rebalancing vehicles. The proposed algorithms are applied to e-scooter rebalancing scenarios with comparisons between the minimum and actual fleet sizes. The case study results in Indianapolis, USA demonstrate that the rebalancing distance cost with the minimum fleet size is significantly lower than that with the actual fleet size. What’s more, the rebalancing time can be reduced by about 12.34% to 27.80% when using the minimum fleet size. The findings of this study offer valuable policy implications and managerial insights for shared e-scooter operators and policymakers in developing effective e-scooter management strategies.

The 2024 IPD-MHC database update: a comprehensive resource for major histocompatibility complex studies.

The IPD-MHC Database project (http://www.ebi.ac.uk/ipd/mhc/) serves as a comprehensive and expertly curated repository for major histocompatibility complex (MHC) sequences from non-human species, providing the necessary infrastructure and tools to study the function and evolution of this highly polymorphic genomic region. In its latest version, the IPD-MHC database has expanded both in content and in the tools for data visualization and comparison. The database now hosts over 18 000 MHC alleles from 125 species, organized into eleven taxonomic groups, all manually curated and named by the Comparative MHC Nomenclature Committee. A cetacean section has recently been included, offering researchers valuable data to study the immune system of whales, dolphins, and porpoises, as well establishing the official nomenclature platform for the Cetacea Leukocyte Antigens (CeLA). In response to user demand and reflecting broader trends in bioinformatics and immunogenetics, IPD-MHC now includes the predicted tertiary structure of over 8000 alleles and allows comparison and visualisation of allele variation within and between species at single residue resolution. These latest developments maintain the critically important link between official nomenclature of curated alleles and the ability to analyse this complex polymorphism using the most up to date methods within a single repository.

Predictive pretrained transformer (PPT) for real-time battery health diagnostics

Modeling and forecasting the evolution of battery systems involve complex interactions across physical, chemical, and electrochemical processes, influenced by diverse usage demands and dynamic operational patterns. In this study, we developed a predictive pre-trained Transformer (PPT) model equipped with 1,871,114 parameters that enhance identification of both short-term and long-term patterns in time-series data. This is achieved through the integration of convolutional layers and probabilistic sparse self-attention mechanisms, which collectively enhance prediction accuracy and efficiency in diagnosing battery health. Moreover, the customized hybrid-model fusion supports parallel computing and employs transfer learning, reducing computational costs while enhancing scalability and adaptability. Consequently, this allows for precise real-time health estimations across various battery cycles. We validated this method using a public dataset of 203 commercial lithium iron phosphate (LFP)/graphite batteries charged at rates ranging from 1C to 8C. By using only partial charge data—from an 80 % state of charge to the maximum charging voltage (3.6 V for LFP batteries, 4.2 V for ternary batteries)—and avoiding complex feature engineering, error metrics were achieved below 0.3 % for root mean square error (RMSE), weighted mean absolute percentage error (WMAPE), and mean absolute error (MAE), with an R2 of 98.9 %. The generalization capabilities were further demonstrated across 36 different testing protocols, encompassing 23,480 cycles throughout the entire life cycle, with a total inference time of 9.88 s during the testing phases. Further experiments on 30 nickel cobalt aluminum (NCA) batteries and 36 nickel cobalt manganese (NCM) batteries, across different battery types and operational scenarios, resulted in RMSE, WMAPE, and MAE all below 0.9 %, with R2 values of 94.1 % and 94.4 %, respectively. These findings highlight the potential of our customized deep transfer neural networks to enhance diagnostic accuracy, accelerate training, and improve generalization in real-time applications.

A bike-sharing demand prediction model based on Spatio-Temporal Graph Convolutional Networks

Shared bikes, as an eco-friendly transport mode, facilitate short commutes for urban dwellers and help alleviate traffic. However, the prevalent station-based strategy for bike placements often overlooks urban zones, cycling patterns, and more, resulting in underutilized bikes. To address this, we introduce the Spatio-Temporal Bike-sharing Demand Prediction (ST-BDP) model, leveraging multi-source data and Spatio-Temporal Graph Convolutional Networks (STGCN). This model predicts spatial user demand for bikes between stations by constructing a spatial demand graph, accounting for geographical influences. For precision, ST-BDP integrates an attention-based graph convolutional network for station demand graph’s temporal-spatial features, and a sequential convolutional network for multi-source data (e.g., weather, time). In real dataset, experimental results show that ST-BDP has excellent performance with mean absolute error (MAE) = 1.62, mean absolute percentage error (MAPE) = 15.82%, symmetric mean absolute percentage error (SMAPE) = 16.14%, and root mean square error (RMSE) = 2.36, outperforming the baseline techniques. This highlights its predictive accuracy and potential to guide future bike-sharing policies.

Assessing the capacity building programs pursued by university libraries in Meru County, Kenya

Changing user demands require libraries to develop unique capabilities for supporting the delivery of quality teaching, learning, and research. This shift further requires librarians to develop competencies in handling emergent technologies, dissemination of information, scholarly communication, and strategy formulation. Nevertheless, there has been an overreliance on conventional ways that have stifled innovation and creativity in most libraries, raising questions on continuous training programs for information professionals. The purpose of the study was to assess capacity-building programs pursued by university libraries in Meru County. The study used a qualitative approach and employed a case study research design. The Technology Organization Environment Model guided the study. The units of analysis were KeMU and MUST university libraries, and the unit of observation was 15 university library staff, specifically, 2 university library heads and 13 heads of library sections who were selected through the purposive sampling method. The study interviewed university librarians and conducted Focus Group Discussions [FGDs] with library heads of sections. The two FGDs comprised 5 and 7 participants in KeMU and MUST respectively. Validity of the instruments was established by conducting a pretest at Chuka University Library to appraise the dependability of interview and FGDs questions. One librarian and 2 library section heads participated. Data was analysed using content analysis and thematic categorization. The study identified several capacity-building initiatives undertaken by libraries which included leadership training, customer care training, information retrieval skills development and training on reference management systems. This led to a conclusion that library staff were prepared to deliver quality services through a combination of skill enhancement. Notably, the study highlighted opportunities for library personnel to engage in ongoing professional development. It urges library management to organize seminars and workshops periodically to help its staff to keep abreast with latest developments and innovations in the field of library science.

A Model-Driven Approach to Developing Scalable Educational Software for Adaptive Learning Environments

This research presents a model-driven approach to the development of scalable educational software tailored to adaptive learning environments. With the increasing demand for personalized education, adaptive learning systems play a crucial role in meeting diverse student needs by adjusting instructional content dynamically. This paper proposes a software engineering framework that integrates model-driven development (MDD) techniques with scalability principles, allowing for the efficient design and implementation of educational applications that can handle varying workloads and user demands. The framework emphasizes modular architecture, reusability, and flexibility to ensure that software can evolve with emerging educational requirements. Key components include the design of a learning content management system (LCMS) and the application of adaptive algorithms to personalize learning pathways. Additionally, this study explores the integration of cloud technologies to enhance the scalability and performance of educational platforms. A prototype system was developed and tested in a controlled environment, showing significant improvements in scalability, system performance, and student engagement compared to traditional static e-learning platforms. The results indicate that the model-driven approach not only improves software development efficiency but also offers a robust solution for creating adaptive educational systems that can scale to meet the growing needs of learners and institutions. This research contributes to the field of educational software development by providing a systematic methodology for building scalable and adaptive learning environments using advanced software engineering techniques.

Multi-Agent DRL for Air-to-Ground Communication Planning in UAV-Enabled IoT Networks.

In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions are unpredictable. To overcome these limitations, we propose a decoupling of uplink and downlink associations for ground-based users (GBUs), significantly improving network efficiency. We formulate a comprehensive optimization problem that integrates UAV trajectory design and user association, aiming to maximize the overall sum-rate efficiency of the network. Due to the problem's non-convexity, we reformulate it as a Partially Observable Markov Decision Process (POMDP), enabling UAVs to make real-time decisions based on local observations without requiring complete global information. Our framework employs multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which balances centralized training with distributed execution. This allows UAVs to efficiently learn optimal user associations and trajectory controls while dynamically adapting to local conditions. The proposed solution is particularly suited for critical applications such as disaster response and search and rescue missions, highlighting the practical significance of utilizing UAVs for rapid network deployment in emergencies. By addressing the limitations of existing centralized and distributed solutions, our hybrid model combines the benefits of centralized training with the adaptability of distributed inference, ensuring optimal UAV operations in real-time scenarios.

Ground-breaking approach to enabling fully solar renewable energy communities

Renewable Energy Communities are an important strategy of the EU, to promote and optimize the distributed deployment of renewable generation systems. At current incentives and costs, RECs that share the energy produced by PV/battery systems are expected to take the lion's share of distributed generation. In this paper, we highlight some important critical issues in electric system management that could result from the deployment of RECs if PV/battery system generation cannot meet the entire community demand. Thus, we show how a flexible PV system (PV/battery systems able to provide firm/dispatchable generation) can be cost-effectively dimensioned to supply 24/365 96%–97 % of the demand of a residential users' community. We demonstrate that flexible PV systems of 1/10 MWp with 1.2/12 MWh of storage capacity can supply communities of 142/1420 residential users at a least production cost of 0.12/0.096 €/kWh. We further provide two business models based on power purchase agreement to demonstrate that, at these production costs, fully solar RECs are currently techno-economically feasible and provide mutual benefits to both flexible PV producers and REC members. Suitable virtual corporate PPAs can reduce the electric bill of the REC members by 11%–5% without requiring any investments and increase the producer incomes by 18%–22 %.

MapGPT: an autonomous framework for mapping by integrating large language model and cartographic tools

ABSTRACT The mapping process generally involves intricate operations, such as symbol design, layout design, and text annotation, demanding a high level of professional expertise. The high requirement for map producers hinders the promotion and widespread adoption of mapping. Consequently, researchers are concentrating on techniques to automate and enhance the intelligence of the mapping process. For example, some studies attempt to train deep learning models for mapping, including methods like map style transfer. However, these approaches typically treat the entire map as a global input and generate a new map as output, lacking the flexibility to consider and control detailed elements within a map. Therefore, in this paper, we propose a large language model-based intelligent mapping framework, termed MapGPT, which can be used for mapping by considering the map as an integration of various map elements. Specifically, multiple professional mapping tools are designed in MapGPT, and each tool is designed to control a corresponding map element. With these tools, a large language model is used to first understand the demand of users based on mere natural language descriptions, and subsequently automatically invoke appropriate tools in sequence to generate a map. Furthermore, by utilizing a memory component to store interaction information, users can interact with MapGPT through conversation to adjust map elements such as color and position. In conclusion, MapGPT offers user-friendly mapping experience, showing potential to be a mapping assistant for professional map producers. A comprehensive demonstration of this framework is provided in a visual case study video, accessible at https://github.com/AGI-GIS/MapGPT.

Scholarly apps: enhancing research mobility and accessibility

Purpose This paper aims to explore how scholarly apps are changing the landscape of academic research by increasing mobility, accessibility and user engagement. It assesses the role of mobile-based applications in supporting research activities, enabling access to scholarly content and facilitating seamless communication between researchers and libraries. Design/methodology/approach A brief content analysis of prominent scholarly apps was performed to evaluate their prime features and usability. Findings Scholarly apps improve research mobility by offering on-the-go access to academic resources, databases, communication tools, personalized content recommendations and integrated automated citation tools. The integration of AI and machine learning into these apps holds potential for further personalizing and streamlining research processes. Research limitations/implications Although certain discussed mobile apps are well known and frequently used by users, their value in the field of mobile technology remains significant. Despite their vast popularity, these apps continue to play a significant part in the mobile app ecosystem due to their proven efficacy, widespread acceptance and continuous relevance in meeting user demands. Originality/value To the best of the authors’ knowledge, this is one of the first studies to comprehensively evaluate the scholarly apps and contributes to the literature on mobile-based library services.

Harnessing Multi-Armed Bandits for Smarter Digital Marketing Decisions

In the current digital age, software programs and applications provide convenience and added value in a number of ways. Multi-armed Bandit algorithms (MAB) are a prime example of this; In capital markets, they assist in the adaptive design of trading strategies that adjust to market shifts and investor behavior. In e-commerce, MAB helps optimize product recommendations by learning customer preferences in real-time. In SaaS, MAB can optimize user experience by personalizing services or pricing models based on user behavior, continuously adjusting to maximize engagement or revenue. In cloud engineering MAB are indispensable for optimizing resource allocation based on user demand. The objective of this paper is to demonstrate the adaptability of MAB algorithms through an examination of their applications, specifically focusing on the Digital Marketing domain and the insight they offer for optimal decision-making. The article highlights the usefulness of major MAB algorithms in digital advertising, e-commerce content suggestion, SaaS and strategic pricing by carefully examining Thompson Sampling, UCB, Restless Bandit, and Structured Bandit as key types of MAB algorithms. The study emphasizes how these algorithms adjust to fluctuating conditions, balance a trade-off between exploration and exploitation, and eventually improve marketing strategies. This article aims to improve knowledge of MAB algorithms and encourage more research in this promising area by providing a detailed analysis of their applications.

An on-demand collaborative edge caching strategy for edge–fog–cloud environment

In this paper, we tackle the critical challenges of content edge caching, such as limited storage capacity, content popularity prediction, dynamic user demand, and user privacy, issues that most existing studies only address partially. We present an innovative Genetic Algorithm-based On-demand Collaborative Edge Caching mechanism (GAOCEC), which introduces a multi-tiered caching architecture integrating cloud, fog, and edge computing. To enhance caching efficiency and minimize system cost, a novel on-demand caching quota mechanism is proposed that dynamically allocates cache resources to edge servers. To strengthen user privacy protection during content popularity prediction, a CNN-BiLSTM-based Federated Learning algorithm (CBFL) is presented that ensures high prediction accuracy without the need to upload local data to the cloud. We also refine the genetic algorithm for content placement by fine-tuning various parameter sets to identify the optimal balance between latency reduction and caching cost. Our experimental results validate the effectiveness of our approach, demonstrating increased cache hit rates, decreased content response times, and an overall improvement in system efficiency. This work provides a comprehensive, adaptive, and privacy-preserving solution for the edge–fog–cloud environment.

A Shared Economy Data Prediction Model Based on Deep Learning

In the dynamic and intricate shared economy, efficient resource management and forecasting are still crucial. In this research, a new prediction model is presented that aims to improve the operational efficiency of several shared economy services. Our method creatively combines Long Short-Term Memory (LSTM) networks with Genetic Algorithms (GA) to assess user demand and optimize resource deployment. We apply this methodology especially to e-scooter sharing services, but the underlying ideas and methods can be applied to other shared economy platforms as well, like peer-to-peer lending services, car sharing, and vacation rentals. The model starts with a GA to adjust the hyperparameters of the LSTM network, making the network better suited to handle specific characteristics of common economic data. Capturing the complex temporal and spatial patterns of user behavior and demand on these platforms requires this optimization. The LSTM element then predicts changes in service demand due to its capacity to analyze sequential records. Further to being useful for analyzing sequential data and predicting destiny wishes, this predictive functionality is important for a shared economy platform to correctly manage stock, allocate assets, and predict personal wishes. We use a large dataset to check our technique, demonstrating the predictive accuracy of the model and demand and its potential to aid strategic choice-making. Compared to traditional fashions, the consequences show a large development in forecast accuracy and resource allocation efficiency. Our methodology creates a robust basis for statistics-driven insights that decorate customer happiness and decorate the long-term increase of the shared financial system. This work highlights the blended ability of GA and LSTM inside the shared economy and paves the manner for future enhancements in using modern-day gadget mastering techniques to optimize and alter various shared services. In quick, effective useful resource control and forecasting in the shared economic system is tough, however our specific forecasting model combining GA-LSTM gives a manner ahead. Our technique, as it should be, predicts fluctuations in provider demand; it was first refined the usage of GA to regulate the LSTM hyperparameters. The consequences show how correct and powerful our version is and spotlight how it can enhance customer pleasure and operational performance. This research paves the manner for future trends within the software of gadgets, gaining knowledge of methods and supports the continuing enlargement and development of shared economic system offerings.

An universal energy-matching design and regulation method for combined cooling, heating, and power (CCHP) systems in different scenarios

In this study, the impact of the coupling matching mechanism between the systems and the users on the energy-saving characteristics of the systems was analyzed based on a universal representation and computation method to identify the energy saving potential of the systems. The physical model was detailed derived for the relationship between the load supply and user demands under different capacity design modes and operation strategies to construct a universal coupling matching diagram, and the impact of the coupling matching parameters (operational energy efficiency, load matching degree, etc) on system integration and operation in different energy use scenarios was quantitative analyzed to illustrate the inherent mechanism of energy saving characteristics. In addition, the coupling mechanism between the systems and the users from a global perspective was reconstructed, and a coupling method of capacity design factor and dual source regulation was proposed to realize the collaborative integration of centralized energy supply systems, combined cooling, heating, and power systems and load demands, as well as the active regulation of the internal cascade utilization of the systems, so as to improve the energy saving potential of the systems under all operating conditions. Finally, a case analysis and method verification were conducted using typical summer days at Kunming hotel as an example. The results show that the coupling matching relationship is the internal mechanism that affects the energy saving of the systems, and the average energy saving rate of the whole day can be increased by 7.45% through collaborative integration and active regulation.

End-to-End Resource Allocation Management Model in Next-Generation Network: Survey

Network communication has grown rapidly with massive demands of services. Moreover, resource allocation in networking is a fundamental and crucial issue that cannot ensure the network's stability and efficiency with the myriad requirements of different services. Various vertical businesses may seek varied network services, particularly in the Fifth-Generation networks and Beyond (5G+). The pros of Fifth-Generation communication networks are to outperform 4G in performance by having higher bandwidth, minimum latency, more capacity, and QoS (Quality of Service). Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two technologies that are combined in the next generation cellular network to provide improved network management. The primary idea behind resource allocation (RA) in the next generation network is the concept of network slicing where the network resources are virtually partitioning into many separate networks. Each separated network must satisfy the unique needs of the required service to achieve the required QoS. In this survey, we focus on resource management issues related to network slicing and tackling the biggest obstacles in this field while offering a thorough and up-to-date overview of this field. Thus, thorough analysis of the allocation of resources on the access side and core side of the network communication was sought. Also, demonstrates how revolutionary techniques that are used to support the management of sliced networks which are based on Machine Learning (ML) and Artificial Intelligence (AI). Importantly, use appropriate ML techniques such as deep learning for predicting the network condition and Reinforcement learning to learn optimal allocation policy without depending on prior knowledge and other techniques such as classification and clustering to aggregate the similar needs of users into separate slices. This could help to enhance resource utilization by allocating a sufficient amount of resource as needed based on ML algorithms and optimal utilization of resources and reducing operational costs by real-time adjustment of it based on user demands and network conditions

Advancements in Wireless Communication Technology: A Comprehensive Analysis of 4G to 7G Systems

This paper presents a comprehensive analysis of the advancements in wireless communication technology from 4G to 7G systems, focusing on critical aspects such as network architecture, spectrum allocation, data rates, security, and coverage. The evolution of wireless communication technology has been remarkable, with each generation introducing significant improvements to meet the growing demands of users and applications in an increasingly connected world. From the early adoption of 4G technology to the anticipated rise of 7G systems, sweeping changes have reshaped the landscape of wireless communications and changed the way we communicate, work and interact with our surroundings we are surrounded by communication. These changes signal a shift towards more efficient, high-speed and high-capacity systems designed to meet the needs of a variety of industries. Furthermore, the need to strengthen security measures and expand network services highlights the importance of ensuring reliability and security in networks in the face of competition and threats Continued research and innovation are essential and for further developments in this area and to realize and enable new work capabilities and services to benefit people, businesses and businesses This article examines the proposed mobile communication features from 4G to 7G in. Each generation brings unique spectrum allocation, usage and operational challenges. Each generation presents unique spectrum allocation, utilization, and efficiency challenges. Furthermore, this paper investigates network architecture aspects, data rates, and throughput, emphasizing latency and reliability improvements. Advancements in spectral efficiency, MIMO scheme usage, cell size, used frequency bands, and coverage enhancement strategies are considered. Additionally, the integration of energy efficiency with economic and social sustainability is analyzed. Security challenges, including privacy protection, new services, access control models, and security monitoring, are classified for each generation. One of the main objectives of this study is to identify the primary applications, impact areas, and overall impact of 4G to 7G technologies.

Modelling the Influence of Management Practices on Sustainable Market Performance in Serbian Enterprises

In the evolving global market, new business conditions necessitate that enterprises adapt and construct organizational structures grounded in new principles and the implementation of contemporary management methods. This is particularly crucial for enterprises in transitional economies, which need to be highly flexible and innovative to meet the increasing demands of users swiftly, employ modern management techniques, and gain a competitive edge. The modern business environment assumes that there are very few products, technologies, services, knowledge areas, or procedures unavailable to interested groups worldwide. This study examines the influence of modern management methods and techniques (MMMTs), human resource management (HRM), quality management (QM), and intellectual capital management (ICM) on the sustainable market performance (SMPC) of these enterprises. A structured survey was conducted among 146 managers from various Serbian industrial enterprises, and the data were analyzed using descriptive statistics, Pearson correlation analysis, linear regression, and multicollinearity tests. The results revealed significant positive correlations between MMMTs, HRM, QM, ICM, and SMPC, with quality management having the highest impact. These findings provide valuable insights for improving business competitiveness in Serbia’s industrial sector. The results also support the development of an integrated model for sustainable management practices in transitional economies.