Primary Users Research Articles

Development of an Alokon Stock Management Information System Using a Lean Development Approach

This study explores the development of an Alokon Stock Management Information System using the Lean Development approach to enhance the efficiency and effectiveness of stock management for contraceptive devices and drugs (alokon) distributed by the Women's Empowerment, Child Protection, and Family Planning Service in Indonesia. Alokon plays a critical role in family planning and reproductive health services. The research aims to address challenges such as inaccurate stock data, distribution delays, and the inability to meet demand promptly, which can disrupt health services. The Lean Development approach, emphasizing principles such as eliminating waste, strengthening learning, and delivering results quickly, was employed to create a responsive and adaptive system tailored to user needs. The resulting system incorporates features for managing alokon data, monitoring stock levels, and generating reports to ensure adequate stock availability and timely distribution. Evaluation of the system's usability was conducted using the System Usability Scale (SUS), involving ten respondents from the primary user groups. The average SUS score was 75, indicating a good level of usability. Users reported the system as comfortable and easy to use, although feedback highlighted areas for further improvement.

Enhancing spectral efficiency of green metric cognitive radio network using an adaptive optimization and communication protocol

SummaryInformation technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy.

Enhancing Reconfigurable Intelligent Surface-Enabled Cognitive Radio Networks for Sixth Generation and Beyond: Performance Analysis and Parameter Optimization.

In this paper, we propose a novel system integrating reconfigurable intelligent surfaces (RISs) with cognitive radio (CR) technology, presenting a forward-looking solution aligned with the evolving standards of 6G and beyond networks. The proposed RIS-assisted CR networks operate with a base station (BS) transmitting signals to two users, the primary user (PU) and secondary user (SU), through direct and reflected signal paths, respectively. Our mathematical analysis focuses on deriving expressions for SU in the RIS-assisted CR system, validated through Monte Carlo simulations. The investigation covers diverse aspects, including the impact of the signal-to-noise ratio (SNR), power allocations, the number of reflected surfaces, and blocklength variations. The results provide nuanced insights into RIS-assisted CR system performance, highlighting its sensitivity to factors like the number of reflectors, fading severity, and correlation coefficient. Careful parameter selection, such as optimizing the configuration of reflectors, is shown to prevent a complete outage, showcasing the system's robustness. Additionally, the work suggests that the optimization of reflectors configuration can significantly enhance overall system performance, and RIS-assisted CR systems outperform reference schemes. This work contributes a thorough analysis of the proposed system, offering valuable insights for efficient performance evaluation and parameter optimization in RIS-assisted CR networks.

Spectrum and Energy Efficiency in CRNs Using Dynamic Channel Reservation and Komodo Mlipir Optimization

Cognitive Radio Networks (CRNs) block new arriving services and remove existing services to increase the spectrum efficiency. Particularly, Secondary Users (SUs) experience a decline in service deprivation in the arrival of Primary Users (PUs). To overcome this, a Spectrum and energy Efficiency in Cognitive Radio Networks using Dynamic Channel Reservation and Komodo Mlipir Optimization (SE-KMOA-CRN) is proposed. The proposed method splits reserved and non-reserved bands from the accessible spectrum. Numerous channels from the non-reserved band are dynamically assigned to the reserved band to support services that experience disruptions when functioning on non-reserved band. From the non-reserved band, a count of channels dynamically assigned to the reserved band to support services that experience disruptions when functioning on the non-reserved band. It enables priority-based channel assignment and termination while providing dynamic access to the obtainable spectrum. Here, Allocation of channels to Reserved Band CRN using Dynamic Channel Reservation Algorithm (DCRA). Komodo Mlipir Optimization Algorithm (KMOA) is employed for Service selection and Channel restoration. The proposed SE-KMOA-CRN method is modelled utilizing Continuous Time Markov Chain (CTMC) with mathematical equations for some quality of service (QoS) factors is obtained. The SE-KMOA-CRN approach attains higher Throughput, lower End-to-End Delay, higher Energy Efficiency, greater spectral efficiency and higher Packet Delivery Ratio compared with existing methods, like Multiple objective optimization for spectrum along energy efficacy trade-off in IRS-assisted CRNs using NOMA (SE-MOO-CRN), Energy efficacy in CRN utilizing cooperative spectrum sensing under hybrid spectrum handoff (SE-TOA-CRN) and Energy-effectual cross-layer spectrum sharing on CR green IoT (SE-CRG-CRN) respectively.

Physical-layer security for primary users in 5G underlay cognitive radio system via artificial-noise-aided by secondary users

The advent of the Internet of Things (IoT) has revolutionized connectivity by interconnecting a vast array of devices, underscoring the critical need for robust data security, particularly at the Physical Layer Security (PLS). Ensuring data confidentiality and integrity during wireless communications poses a primary challenge in IoT environments. Additionally, within the constrained frequency bands available, Cognitive Radio Networks (CRNs) has emerged as an urgent necessity to optimize spectrum utilization. This technology enables intelligent management of radio frequencies, enhancing network efficiency and adaptability to dynamic environmental changes. In this research, we focus on examining the PLS for the primary channel within the underlying CRNs. Our proposed model involves a primary source-destination pair and a secondary transmitter-receiver pair sharing the same frequency band simultaneously. In the presence of a common eavesdropper, the primary concern lies in securing the primary link communication. The secondary user (SU) acts as cooperative jamming, strategically allocating a portion of its transmission power to transmit artificial interference, thus confusing the eavesdropper and protecting the primary user's (PU) communication. The transmit power of the SU is regulated by the maximum interference power tolerated by the primary network's receiver. To evaluate the effectiveness of our proposed protocol, we develop closed-form mathematical expressions for intercept probability ( P int ) and outage probability (OP) along the primary communication link. Additionally, we derive mathematical expressions for OP along the secondary communications network. Furthermore, we investigate the impact of transmit power allocation on intercept and outage probabilities across various links. Through both simulation and theoretical analysis, our protocol aims to enhance protection and outage efficiency for the primary link while ensuring appropriate secondary OP, thereby contributing to advancements in IoT security and spectrum management.

Visualisations with semantic icons: Assessing engagement with distracting elements

As visualisations reach a broad range of audiences, designing visualisations that attract and engage becomes more critical. Prior work suggests that semantic icons entice and immerse the reader; however, little is known about their impact with informational tasks and when the viewer’s attention is divided because of a distracting element. To address this gap, we first explored a variety of semantic icons with various visualisation attributes. The findings of this exploration shaped the design of our primary comparative online user studies, where participants saw a target visualisation with a distracting visualisation on a web page and were asked to extract insights. Their engagement was measured through three dependent variables: (1) visual attention, (2) effort to write insights, and (3) self-reported engagement. In Study 1, we discovered that visualisations with semantic icons were consistently perceived to be more engaging than the plain version. However, we found no differences in visual attention and effort between the two versions. Thus, we ran Study 2 using visualisations with more salient semantic icons to achieve maximum contrast. The results were consistent with our first Study. Furthermore, we found that semantic icons elevated engagement with visualisations depicting less interesting and engaging topics from the participant’s perspective. We extended prior work by demonstrating the semantic value after performing an informational task (extracting insights) and reflecting on the visualisation, besides its value to the first impression. Our findings may be helpful to visualisation designers and storytellers keen on designing engaging visualisations with limited resources. We also contribute reflections on engagement measurements with visualisations and provide future directions.

An Approach to Potentially Increasing Adoption of an Artificial Intelligence-Enabled Electronic Medical Record Encounter in Canadian Primary Care: Protocol for a User-Centered Design.

Primary care physicians are at the forefront of the clinical process that can lead to diagnosis, referral, and treatment. With electronic medical records (EMRs) being introduced and, over time, gaining acceptance by primary care users, they have now become a standard part of care. EMRs have the potential to be further optimized with the introduction of artificial intelligence (AI). There has yet to be a widespread exploration of the use of AI in primary health care and how clinicians envision AI use to encourage further uptake. The primary objective of this research is to understand if the user-centered design approach, rooted in contextual design, can lead to an increased likelihood of adoption of an AI-enabled encounter module embedded in a primary care EMR. In this study, we use human factor models and the technology acceptance model to understand the results. To accomplish this, a partnership has been established with an industry partner, TELUS Health, to use their EMR, the collaborative health record. The overall intention is to understand how to improve the user experience by using user-centered design to inform how AI should be embedded in an EMR encounter. Given this intention, a user-centered approach will be used to accomplish it. The approach of user-centered design requires qualitative interviewing to gain a clear understanding of users' approaches, intentions, and other key insights to inform the design process. A total of 5 phases have been designed for this study. As of March 2024, a total of 14 primary care clinician participants have been recruited and interviewed. First-cycle coding of all qualitative data results is being conducted to inform redesign considerations. Some limitations need to be acknowledged related to the approach of this study. There is a lack of market maturity of AI-enabled EMR encounters in primary care, requiring research to take place through scenario-based interviews. However, this participant group will still help inform design considerations for this tool. This study is targeted for completion in the late fall of 2024. DERR1-10.2196/54365.

Valuing Outpatients' Perspective on Primary Health Care Services in Greece: A Cross-Sectional Survey on Satisfaction and Personal-Centered Care.

The aims of the study were to identify and analyze the determinants associated with outpatient satisfaction in Greek primary care. This is because there is a general consensus that primary care is the linchpin of effective person-centered care delivery. A cross-sectional survey was conducted with 1012 patients' exit interviews; sociodemographic variables were included in the questionnaire to obtain data on the satisfaction of primary care users with 20 public primary healthcare centers in Athens between June 2019 and April 2021. Statistical analysis was applied to 55 items and eight dimensions of patient satisfaction, namely, arrival and admission, waiting before the appointment, cleanliness of toilets, medical examination and behavior of physician, behavior of nursing staff, laboratories, departure, and contribution of the PHCs. Descriptive analyses and multiple linear regression were used to analyze the factors influencing patient satisfaction through coefficients (β) with 95% confidence intervals and associated tests of statistical significance. Τwo-thirds (74.21%) of this survey's participants ranged from 45 to 74 years of age. More than half of the participants were women (62.15%). The most common reasons for visits were pathological (26.48%), followed by cardiological conditions (9.78%), orthopedics (9.49%), gynecologic conditions (8.70%), and ophthalmologic problems (7.31%). In the center of satisfaction with primary care was the medical care and the behavior of the physician (β = 0.427; p < 0.01), followed by the time during appointment (β = 0.390; p < 0.01). Dimensions like "accessibility and availability, 2.19/5"; "waiting times, 2.89/5"; "infrastructure of facilities (2.04/5) and cleanliness of them, (2/5)"; "laboratories, 2.99/5" and "bureaucracy in the departure, 2.29/5" were crucial for the trust and satisfaction of patients. Overall satisfaction was rated at a moderate level (2.62 ± 0.18) while person-centered care was rated as weak (2.49 ± 0.28). Greece is recommended to increase the sensitivity of the use of the primary health care system by patients as a first contact, continuous, comprehensive, and effective patient- and family-focused care.

Ave Maria out of context: prosody, poetry or piety?

ABSTRACT Focusing on singability and audience reception, this is a case study on Duy ey sevgili sesimi! ‘Hear, o beloved, my voice’, a Turkish version of Schubert’s Ellens Gesang III (D. 839; Opus 52, No. 6). The study provides a data-driven discussion on the reception of this version by the primary text user, i.e. the performer, the secondary text users, i.e. the listeners, and a first-hand account of the intentions behind the choices of the target song lyricist. The data provide evidence for a hierarchical understanding of Johan Franzon’s three layers of singability: prosodic, poetic, and semantic-reflexive. However, the hierarchical ordering of the layers depends on the priorities of each user group. The performer has focused on the musical aspects of the song rather than the target text itself. Listeners with no background in music or translation have prioritized the semantic-reflexive layer. Listeners with a background in translation studies have paid attention to the poetic and semantic-reflexive layers. In contrast, those listeners with musical training or a special interest in music have regarded the lyrics as subordinate to the original composition, standing aloof from the translation of a classical song. Such diverging views point towards a more fine-grained understanding of singability.

Applying a User-Centered Design Framework to Develop a Remote Research Assessment Protocol for a Randomized Clinical Trial for Toddlers with Early Autism Characteristics.

COVID-19 required many research teams to shift from in-person to remote assessments, which posed both procedural and theoretical challenges. While research has explored the utility of remote assessments for autism diagnosis from the perspective of families and clinicians, less is known about their application in clinical trials. This paper describes the development of a remote research assessment protocol for a randomized clinical trial focusing on the implementation of reciprocal imitation teaching (RIT) with toddlers in Part C early intervention. This project spans two phases. For Phase 1, our team developed and documented a series of steps utilizing user-centered design (UCD) strategies (e.g., recruiting potential users, creating a prototype, engaging in iterative development) for the purpose of redesigning an assessment protocol for a remote environment. For Phase 2, we examined preliminary outcomes of the redesign process. Primary end users (assessors) rated post-redesign usability and acceptability, while acceptability was examined using attrition data from secondary end users (family participants). Preliminary fidelity of implementation was also examined. The iterative redesign process allowed the research team to refine aspects of the assessment that ultimately led to promising preliminary ratings of usability, acceptability, and feasibility, as well as high fidelity. Preliminary data suggest that the redesigned assessment appears to be an acceptable, feasible, and usable tool for autism clinical trial research and that assessors can use it with fidelity. Further research is needed to examine the reliability and validity of the assessment, as well as implementation characteristics on a larger scale.

Methodological Quality of Pulmonary Arterial Hypertension Treatment Evidence-Based Guidelines: A Systematic Review Using the AGREE II and AGREE REX Tools.

Pulmonary arterial hypertension (PAH) is a progressive disease with a poor prognosis, and its management should be grounded in well-developed clinical practice guidelines (CPG). Thus, we critically assess the methodological quality of the available CPG for pharmacological treatments for PAH. A systematic review (CRD42023387168) was performed in PubMed, Cochrane, Embase, and Tripdatabase (Jan-2023). Eligible records were appraised by four reviewers using the Appraisal of Guidelines, Research, and Evaluation Collaboration tool (AGREE II) and the complementary tool for assessing recommendations' quality and certainty, AGREE REX. Descriptive statistics were used to summarize the data. Overall, 31 guidelines, mainly authored by professional societies (90%), targeting only physicians as primary users (84%), were identified. Guidelines presented a moderate overall quality (scores of 63% and 51% in AGREE II and AGREE REX, respectively), with a few domains showing slight improvements over the years. AGREE II "Scope and Purpose" (94%) and "Presentation Clarity" (99%) domains obtained the highest scores. The items related to "Stakeholder involvement," "Editorial independence," and "Clinical applicability" (AGREE REX) were fairly reported. Conversely, CPG lacks rigor in development (32% score, AGREE II), scarcely discusses the role of stakeholders, and provides deficient data on the implementation of recommendations (scores of 35% and 46% in AGREE II and AGREE REX, respectively). No differences in the quality of guidelines published by different developers or countries were observed (p > 0.05). Methodological weaknesses are common among guidelines addressing PAH treatment, especially regarding scientific rigor, stakeholders' values and preferences, and facilitators and barriers to implementability. Particular attention should be given to developing future guidelines.

Maximizing energy-efficiency for RIS-UAV assisted mobile vehicles in cognitive networks

As an essential technology in the sixth generation of wireless communication, the reconfigurable intelligent surface (RIS) offers transformative solutions for the evolution of intelligent transportation. In the secondary network, RIS is equipped on an unmanned aerial vehicle (UAV) to establish a communication link between secondary base station (SBS) and secondary mobile vehicles (SMVs). At the same time, the communication within the secondary network must not interfere with the primary users (PUs) in the primary network. To achieve the optimal energy efficiency, we need to optimize the RIS passive beamforming, SMVs communication scheduling, SBS radiation power allocation and RIS-UAV trajectory. Since the original problem is difficult to solve, we use an alternating iteration framework to decompose the original problem into four subproblems and solve them with successive convex approximations (SCA). We have developed the CEEM scheme to compare it with benchmark schemes and demonstrate its superior performance, achieving up to a 43.48% improvement. In addition, RIS improves the communication quality by up to 57.53% in the simulation results, which have verified the correctness and effectiveness of the algorithm proposed in this paper.

Channel access mechanism for maximizing throughput with fairness in wireless sensor networks

The spectrum scarcity problem of wireless sensor networks (WSNs) is improved through amalgamation of cognitive radio networks (CRNs) into WSNs. However, spectrum allocation to secondary users (SUs) is challenging in cognitive radio wireless sensor networks (CR-WSNs) as channel is already crowded and at same time should not induce interference to primary users (PUs). In designing efficient spectrum access model for CR-WSNs recent work have adopted machine-learning game theory (GT) and statistical model. However, the major limitation of existing spectrum access model they fail to assure access fairness with maximal throughput with minimal collision. This work presents a maximizing channel access fairness model to handle the research challenges. To boost CR-WSN performance, the throughput maximization using channel access fairness (TMCAF) employs shared and non-shared channel access designs. Experiment outcome shows throughput is improved and collision in network is reduced in comparison with state-of-art channel access models.

ABC REAL ESTATE MANAGEMENT SYSTEM

Abstract— This thesis presents the development and implementation of the ABC Real Estate Management System, a web-based application built using PHP and MySQL. The system is designed to streamline property management for real estate agencies, property managers, and individual landlords. It offers a user-friendly interface that caters to two primary user groups: general users and administrators. Users can search for properties based on location and features, utilize a loan calculator for financial planning, and express interest in properties. Administrators have comprehensive control over the system, including managing user accounts, property listings, and potentially real estate agents. The system emphasizes three key aspects: improved user experience, enhanced efficiency, and increased security. User-friendliness ensures that both users and admins can interact with the system intuitively. Efficiency is achieved through quick property discovery and streamlined property management tasks. Security is reinforced by implementing role-based access control, ensuring that only authorized users can access and modify sensitive data. By leveraging PHP and MySQL, the ABC Real Estate Management System addresses common challenges in property management, fostering a more efficient, secure, and user-friendly experience for both property seekers and managers. This thesis provides a detailed analysis of the system’s design, development, and potential impact on the real estate industry. Keywords: Real estate management system (REMS), Efficiency in property management, Streamlining property operations.

ANALYSIS OF MOVEMENT DATA FROM GPS-MONITORED LIVESTOCK GUARDIAN DOGS (LGDS) AT TWO SHEEPFOLDS IN THE NORTH-WESTERN MARAMURES LAND, ROMANIA USING THE KERNEL DENSITY ESTIMATION METHOD. IMPLICATIONS FOR OUTDOOR TOURISM

The Carpathians and other mountainous regions around the world are renowned for their specific landscapes shaped by pastoralism, a millennia-old traditional and sustainable economic system. In Romania, this traditional occupation has an established place within the Romanian culture. In an environment where large predators are present, the livestock owners and shepherds have traditionally relied, and still do, on livestock guardian dogs (LGDs) to protect the flock against carnivores or theft, therefore, the dogs are perceived as an integral component of the traditional pastoral system. However, from late April until the end of September, many outdoor recreational activities like hiking, mountain trail running, or biking overlap with the pastoral calendar, creating a potential for conflict between two, very different categories of landscape users, with recurring incidents happening over the years. In this study, a winter GPS monitoring campaign was proposed, between November 2023 and January 2024 that used GPS professional collars to track the movements of two livestock guardian dogs stationed at two sheepfolds located at their winter bases in the hills at the foot of Ignis Mountains (part of the Romanian northern Carpathians) from northwestern Maramureș Land, Romania. The campaign generated point-based spatiotemporal data processed and analyzed in M. Excel and QGIS using Kernel density estimation as the main method to generate metrics and identify potential clusters of LGD activity in their usual environment. The results highlight high observational clusters near the winter folds but also lower observational clusters in areas situated hundreds of meters distance around the main compound, in certain locations. Although temporally limited, the results have the potential to help the understanding of the animal's preferred zone of habitation and substantiate future win-win cohabitation solutions that minimize conflictual encounters between the shepherds and their guardian dogs on one side, as primary land users, and outdoor recreationists as other landscape users.

Deep Learning-CT based spectrum sensing for cognitive radio for proficient data transmission in Wireless Sensor Networks

Spectrum sensing is a vital element of cognitive radio networks. It's the basis for unlicensed users to access underutilized bands without disturbing licensed or primary users (PUs). Deep learning has given a better way to analyze signals compared to energy detection and other conventional methods that were present earlier. This research introduces a CNN-TN-based spectrum approach that integrates both CNNs and transformer networks (TNs) to maximize efficient spectrum utilization. CNNs are good at finding features in signals, which helps them pick out important features from the IQ-based spectrum data. Transformer networks, on the other hand, are able to take into account long-range dependencies over time, which can help improve and fine-tune these features even more. This method achieves low sensing errors and a better probability of detection by capturing both local and global patterns in the spectrum data. Performance measures like Cohen's Kappa coefficient and F1 score show that this method's performance is better than earlier methods when it comes to performance. These findings also suggest that real-world wireless communications systems could potentially use the proposed methodology, thereby improving overall usage and indicating its potential for practical applications in the field.

Analyzing the performance of secondary users in satellite-terrestrial systems with cognitive radio assistance

&lt;p&gt;The paper investigates the outage probability (OP) of a cognitive radio-based satellite-ground transmission system. In this configuration, both direct and relay links are activated to facilitate transmission from the primary satellite source to terrestrial users. The primary metric under scrutiny is the outage probability for both the primary and secondary networks. Utilizing the Shadowed-Rician fading model, commonly applied to satellite channels, for the satellite segment, and Nakagami-m fading models for terrestrial channels, we assess the OP by analyzing the expressions for both primary and secondary users. Additionally, we explore the impact of key system parameters on the OP’s performance. Indeed, the signal-to-noise ratio (SNR) and target rate are the main factors affecting the outage behavior of users on the ground. We identify certain conditions necessary to achieve improved performance by controlling key system parameters. Furthermore, this paper provides guidelines for designing cognitive radio (CR) systems in satellite configurations to meet the quality requirements of received signals on the ground. The analysis results are validated through Monte Carlo simulations implemented using MATLAB.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

A Low Complexity Cooperative Spectrum Sensor for Cognitive –Radio Network Based on Approximate Computing

With the rapid growth of new wireless technologies, the issue of spectrum scarcity is becoming more prominent due to the limited availability of radio spectrum. Unutilized spectrum bands designated for primary users are known as spectrum holes or white spaces. Assigning these white spaces to secondary users can enhance spectrum utilization. Dynamic spectrum access is crucial for sharing licensed spectrum with secondary users, as opposed to static allocation policies which impede effective spectrum utilization. Spectrum sensing aims to detect licensed users in the spectrum to prevent interference from secondary users. Approximate computing has emerged as a strategy to enhance efficiency by leveraging the fault resilience of various applications. It introduces trade-offs in design optimizations, allowing for significant reductions in VLSI circuit area and energy consumption. A cooperative spectrum-sensing (CSS) algorithm based on approximate computing for cooperative cognitive-radio networks is proposed to reduce computational complexity for efficient hardware design while maintaining detection performance. The hardware-efficient architecture of the cooperative spectrum sensor (CSR) based on the CSS algorithm, combined with resource-sharing architectural optimization, outperforms existing implementations by occupying less area, achieving shorter sensing times, and displaying superior hardware efficiency. Keywords—Approximate Computing, Cooperative Spectrum Sensor(CSR), very-large scale integration(VLSI)

Impact of Mobile Booking Applications on Consumer Behavior in the Hospitality Industry

Purpose: The general objective of this study was to analyze the impact of mobile banking applications on consumer behaviour in the hospitality industry. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of mobile banking applications on consumer behaviour in the hospitality industry. Preliminary empirical review revealed that these applications significantly enhanced consumer convenience and engagement by streamlining the booking process and integrating features like real-time updates and secure payments. It found that younger generations were the primary users, while older generations adopted these technologies more cautiously. Social media and peer reviews were identified as critical factors influencing consumer trust and adoption. The study recommended further research into advanced technologies and security measures to enhance personalization and maintain consumer trust in mobile booking applications. Unique Contribution to Theory, Practice and Policy: The Technology Acceptance Model (TAM), Theory of Planned Behaviour (TPB) and Diffusion of Innovations (DOI) may be used to anchor future studies on mobile banking applications on consumer behaviour in the hospitality industry. The study recommended developing new theoretical models to better understand the multifaceted influences of mobile apps, enhancing user experience through continuous innovation, and ensuring robust security measures. It emphasized the importance of stringent regulatory frameworks to protect consumer data, targeted marketing strategies for different demographic groups, and strategic partnerships between hospitality businesses and technology developers. The study also called for ongoing research to track changes in consumer behavior and explore cross-cultural differences to develop globally relevant strategies. Keywords: Mobile booking applications, Consumer behavior, Hospitality industry, Social media influence, User experience, Personalized recommendations, Data security, Market segmentation, Demographic variations

A DRL-based resource allocation for IRS-enhanced semantic spectrum sharing networks

Semantic communication and spectrum sharing are pivotal technologies in addressing the perennial challenge of scarce spectrum resources for the sixth-generation (6G) communication networks. Notably, scant attention has been devoted to investigating semantic resource allocation within spectrum sharing semantic communication networks, thereby constraining the full exploitation of spectrum efficiency. To mitigate interference issues between primary users and secondary users while augmenting legitimate signal strength, the introduction of Intelligent Reflective Surfaces (IRS) emerges as a salient solution. In this study, we delve into the intricacies of resource allocation for IRS-enhanced semantic spectrum sharing networks. Our focal point is the maximization of semantic spectral efficiency (S-SE) for the secondary semantic network while upholding the minimum quality of service standards for the primary semantic network. This entails the joint optimization of parameters such as semantic symbol allocation, subchannel allocation, reflective coefficients of IRS elements, and beamforming adjustment of secondary base station. Recognizing computational intricacies and interdependence of variables in the non-convex optimization problem formulated, we present a judicious approach: a hybrid intelligent resource allocation approach leveraging dueling double-deep Q networks coupled with the twin-delayed deep deterministic policy. Simulation results unequivocally affirm the efficacy of our proposed resource allocation approach, showcasing its superior performance relative to baseline schemes. Our approach markedly enhances the S-SE of the secondary network, thereby establishing its prowess in advancing the frontiers of semantic spectrum sharing (S-SE).