System Capacity Research Articles

Impact of AI in Humans Life

This research paper is designed to compile, synthesize, and analyze the effects of artificial intelligence on human life. AI technology has a long history of continuous and aggressive development and growth. This focuses on smart agents, which are sensors that analyze the world and take action to improve odds of success. Artificial intelligence is developing rapidly in today's society, with new technological developments being developed on a daily basis. The previous study defined the existing AI basics and multiple representative AI applications. The capacity for artificial intelligence (AI), computer programs, and systems to fulfill a person's logical and creative activities, such as solving problems independently, drawing conclusions, and making decisions. The majority of artificial intelligence programs are capable of allowing humans to learn over time. Recent work on AI technologies, that include machine learning, deep learning, and predictive analysis, aimed to enhance the ability to plan, understand, reason, analyze, and act. The basic goal of artificial intelligence is to create advanced, complicated systems that beat people in all areas, in order to improve all human activities and provide better solutions to issues than humans can. This suggestion tries to identify how human intelligence differs from artificial intelligence

Maximizing the IRS’s Gains in NLOS Communication through Optimal Positioning

Background: Recently, an IRS has been proposed as a low-cost and effective solution for performance improvement in wireless communication. However, the configuration and setup necessary to derive optimal results have not been identified yet. Objective: The paper seeks to build the knowledge by investigating the effects of IRS in the LOS and NLOS environment to verify the achievable gains from each and examine how different IRS positions impact the BER. Methods: An IRS model was simulated in MATLAB to investigate the channel capacity gain of an IRS in LOS and NLOS scenarios for the verification of performance gains. Further, the effects of the IRS positioning (with respect to transmitter and end-user) on the BER were investigated. Results: The channel capacity of an IRS system was found to be lower by 12Mbps compared to that of a direct link in a LOS scenario. This demonstrates that a passive IRS is only beneficial in the absence of a line of sight. In the case of a NLOS, for an SNR of 50, MIMO transmitter diversity and receiver diversity had channel capacities of 1.0 Mbps and 4.5 Mbps, respectively while the IRS system achieved 22 Mbps. As such, it is more beneficial to create an alternative path via the IRS than to increase the number of transmit or receive antennas. The positioning of the IRS had a significant impact on the BER. From the results, positioning the IRS mid-way between the AP and STA led to up to 57.69% improvements in BER compared to close to STA or close to AP arrangements. Conclusion: The results prove that an IRS is a cost-effective, low-power consumption solution for overcoming challenges associated with NLOS communication. The work also identified the ideal positioning of the IRS to be the midpoint between AP and STA, for the WLAN system. Further research will be needed to verify performance in other systems.

What kind of education research was funded by the government in China? A decade-long investigation (2010–2020)

PurposeThe key purpose of this study is to systematically examine the landscape of education research funded by the National Plan of Educational Research Funding (NPERF) in China. The study aims to: (1) identify the thematic focus areas that reflect the national education agenda, (2) analyze the general funding patterns of education research projects and (3) gain insights into the distinctive nature of the research agenda in China. The study employs a rigorous data-driven approach to offer valuable insights into the dynamic discourses within the field of education research in China, which has received relatively little attention despite its potential significance.Design/methodology/approachIn this study, we utilized word co-occurrence analysis and corpus-based frequency analysis to analyze the research projects funded by the National Plan of Educational Research Funding (NPERF) from 2011 to 2020.FindingsThe key characteristics of these projects highlight the focus on higher education research, addressing the interests of specific cohorts of students, teachers and disadvantaged populations. Furthermore, these projects demonstrate a remarkable responsiveness to the policy needs of the country and a robust inclination toward an international comparative framework.Research limitations/implicationsThe findings offer valuable insights into the landscape and features of funded education research in China, revealing a strong emphasis on addressing practical needs and enhancing the capacity of the education system in the country.Originality/valueThis paper presents a systematic examination of the topics covered in funded research under the National Plan of Educational Research Funding (NPERF) scheme from 2011 to 2020. It contributes to the advancement of understanding regarding knowledge traditions and practices in the Chinese context. Methodologically, this paper is the first in the literature to be prototyped with a word co-occurrence analysis approach to systematically investigate the funded education research in China. Additionally, it includes the development of a comprehensive corpus list to uncover the key characteristics of the funded projects. The analysis provides unique insights into the priorities and directions of education research supported by the Chinese government, which are of potential interest to international readers.

Data Integration to Support Child Care Business Sustainability: Iowa’s Child Care Connect

Working parents need access to childcare, and the unprecedented COVID-19 pandemic highlighted critical gaps in data system capacities to meet this need. This was particularly acute in the US where many child care providers closed their doors permanently because they could not sustain their business. The state of Iowa estimates losses over $930 million per year to businesses and individuals due to a lack of childcare. Childcare businesses are not sustainable if they cannot fill slots and be consistently paid for care – and families need to be able to find care when they need it to fill those slots. Iowa is addressing this gap by developing a real-time, integrated data solution that includes historically disparate data systems to better identify childcare supply and demand. Through intensive community engagement processes including leadership from state agencies, the development team created a system that includes multiple technology solutions for real-time data sharing; a secure cloud-based system for storage, integration, and analysis; and two public-facing applications that allow data to be aggregated and shared for strategic decision-making as well as used by families to identify available care when and where they need it. This presentation will discuss challenges in cross-sector data linkage with a focus on child care providers, families, and population demand. It will include an overview of the development process, governance structure, and analytic uses of the system. Highlights include identification of real-time vacancies by age group, geography, quality rating, and in relation to estimated potential demand using additional integrated data resources.

Bio-Rollup: a new privacy protection solution for biometrics based on two-layer scalability-focused blockchain.

The increased use of artificial intelligence generated content (AIGC) among vast user populations has heightened the risk of private data leaks. Effective auditing and regulation remain challenging, further compounding the risks associated with the leaks involving model parameters and user data. Blockchain technology, renowned for its decentralized consensus mechanism and tamper-resistant properties, is emerging as an ideal tool for documenting, auditing, and analyzing the behaviors of all stakeholders in machine learning as a service (MLaaS). This study centers on biometric recognition systems, addressing pressing privacy and security concerns through innovative endeavors. We conducted experiments to analyze six distinct deep neural networks, leveraging a dataset quality metric grounded in the query output space to quantify the value of the transfer datasets. This analysis revealed the impact of imbalanced datasets on training accuracy, thereby bolstering the system's capacity to detect model data thefts. Furthermore, we designed and implemented a novel Bio-Rollup scheme, seamlessly integrating technologies such as certificate authority, blockchain layer two scaling, and zero-knowledge proofs. This innovative scheme facilitates lightweight auditing through Merkle proofs, enhancing efficiency while minimizing blockchain storage requirements. Compared to the baseline approach, Bio-Rollup restores the integrity of the biometric system and simplifies deployment procedures. It effectively prevents unauthorized use through certificate authorization and zero-knowledge proofs, thus safeguarding user privacy and offering a passive defense against model stealing attacks.

Asynchronous Numerical Spiking Neural Membrane Systems with Local Synchronization.

Since the spiking neural P system (SN P system) was proposed in 2006, it has become a research hotspot in the field of membrane computing. The SN P system performs computations through the encoding, processing, and transmission of spiking information and can be regarded as a third-generation neural network. As a variant of the SN P system, the global asynchronous numerical spiking neural P system (ANSN P system) is adaptable to a broader range of application scenarios. However, in biological neuroscience, some neurons work synchronously within a community to perform specific functions in the brain. Inspired by this, our work investigates a global asynchronous spiking neural P system (ANSN P system) that incorporates certain local synchronous neuron sets. Within these local synchronous sets, neurons must execute their production functions simultaneously, thereby reducing dependence on thresholds and enhancing control uncertainty in ANSN P systems. By analyzing the ADD, SUB, and FIN modules in the generating mode, as well as the INPUT and ADD modules in the accepting mode, this paper demonstrates the novel system's computational capacity as both a generator and an acceptor. Additionally, this paper compares each module to those in other SN P systems, considering the maximum number of neurons and rules per neuron. The results show that this new ANSN P system is at least as effective as the existing SN P systems.

A Novel Battery Temperature-Locking Method Based on Self-Heating Implemented with an Original Driving Circuit While Electric Vehicle Driving: A Numerical Investigation

In extremely cold environments, when battery electric vehicles (BEVs) are navigating urban roads at low speeds, the limited heating capacity of the on-board heat pump system and positive temperature coefficient (PTC) device can lead to an inevitable decline in battery temperature, potentially falling below its permissible operating range. This situation can subsequently result in vehicle malfunctions and, in severe cases, traffic accidents. Henceforth, a novel battery self-heating method during driving is proposed to maintain battery temperature. This approach is ingeniously embedded within the heating mechanism within the motor driving system without any necessity to alter or modify the existing driving circuitry. In the meantime, the battery voltage can be regulated to prevent it from surpassing the limit, thereby ensuring the battery’s safety. This method introduces the dead zone into the space vector pulse width modulation (SVPWM) algorithm to form the newly proposed dSVPWM algorithm, which successfully changes the direction of the bus current in a PWM period and forms AC, and the amplitude of the battery alternating current (AC) can also be controlled by adjusting the heating intensity defined by the ratio of the dead zone and the compensation vector to the original zero vector. Through the Simulink model of the motor driving system, the temperature hysteresis locking strategy, grounded in the field-oriented control (FOC) method and employing the dSVPWM algorithm, has been confirmed to provide controllable and sufficiently stable motor speed regulation. During the low-speed phase of the China Light Vehicle Test Cycle (CLTC), the battery temperature fluctuation is meticulously maintained within a range of ±0.2 °C. The battery’s minimum temperature has been successfully locked at around −10 °C. In contrast, the battery temperature would decrease by a significant 1.44 °C per minute without the implementation of the temperature-locking strategy. The voltage of the battery pack is always regulated within the range of 255~378 V. It remains within the specified upper and lower thresholds. The battery voltage overrun can be effectively avoided.

Research on Operation Characteristics of Passive Containment Heat Removal System in Typical Accident Conditions

In order to verify the feasibility of the design of the third generation of nuclear power plant independently developed in China, this study investigated the operation characteristics of the passive containment heat removal system under five working conditions using the passive containment heat removal system general performance verification facility: the design conditions, low water level conditions, low pressure conditions in the containment, extreme accidents conditions and resistance increasing conditions are analyzed, and the thermal characteristics, power requirements and thermal parameters of the containment under different operating conditions are analyzed. The results show that the heat dissipation capacity of the passive containment heat removal system exceeds the design requirements under the design conditions. Under off-design low pressure conditions, the pressure in the system is significantly affected. The decrease in the cooling water tank’s water level not only did not adversely affect the operation of the system but instead led to a significant increase in the system’s heat dissipation power. In the case of extreme accidents, the system may initially stall, but a stable natural cycle can then be established. As the system resistance coefficient increases, the heat dissipation power of the passive containment heat removal system exhibits an approximate linear decreasing trend.

Joint Optimization-Based QoS and PAPR Reduction Technique for Energy-Efficient Massive MIMO System

Massive multiple input multiple output (MIMO) is an innovative wireless communication technology that significantly enhances the capacity and efficiency of data transmission in modern networks. Massive multiple input multiple output, despite benefits, faces difficulties in managing numerous antennas affecting signal quality, peak-to-average power ratio (PAPR), and energy efficiency due to its scale and complexity. However, overcoming these complexities is vital for unlocking massive multiple input multiple output’s potential in high-quality, energy-efficient wireless communication. A proposed joint optimization-based technique aims to address these issues in massive MIMO systems. In phase 1, joint optimization with quality of service maximizes capacity via power distribution, employing the hybrid spider wasp Fick’s law algorithm. In phase 2, zero-forcing with symbol-level linear precoding, especially the stacked convolutional sparse bidirectional long short-term memory autoencoder (SCS–BiLSTMAE) network, efficiently reduces peak-to-average power ratio, collaborating for reduced bit rate error and peak-to-average power ratio through adaptive mapping. Refining the model involves precise hyperparameter tuning using the enhanced influencer buddy optimization algorithm. The proposed framework not only demonstrates exceptional performance, but the metrics also underscore the model's effectiveness in addressing diverse challenges, establishing it as a robust solution for quality enhancement, peak-to-average power ratio reduction, and energy efficiency. Additionally, the proposed model attains 28.14% greater system capacity than existing methods.

Janus SiO2-surfactant dispersion designed for enhanced imbibition oil recovery in ultra-low permeability reservoirs

The use of nanofluids in formations with high temperatures and high salinity is currently encountering significant obstacles. Therefore, a Janus SiO2-surfactant dispersion consisting of Janus nanoparticles and surfactants was constructed in the current study. The Janus SiO2-surfactant dispersion is resistant to both high temperatures and high salinity. The Janus SiO2-surfactant dispersion, consisting of Janus SiO2 nanoparticles and the non-ionic surfactant Tween80, is abbreviated as JASN-T80 hereafter. The system can be dispersed and kept stable for one week at a salinity of 30,000 mg/L and a temperature of 85 ℃. The concentration of f nanoparticles and surfactant in the system was optimized at 0.02 wt% Janus SiO2 nanoparticles + 0.04 wt% Tween80 based on the particle size. An assessment was conducted to measure the system's capacity to decrease interfacial tension and alternate wettability. The interfacial tension between oil and water was reduced to 1.21 mN/m. The JASN-T80 system demonstrates a notable capacity to change wettability by decreasing the contact angle of water on an oil-wet rock surface from 100.6° to 70.5°. The combination of nanoparticle and surfactant leads to a synergistic effect, resulting in an imbibition recovery of 44.28 % for ultra-low permeability core. This work provides a novel concept for the advancement and utilization of nanofluid imbibition system for unconventional reservoir.

Enhancing the Flexural Capacity of Deteriorated Low-Strength Prestressed Concrete Beam Using Near-Surface Mounted Post-Tensioned Carbon Fiber-Reinforced Polymer Bar

This study aimed to address the critical issue of age deterioration in prestressed concrete (PSC) structures by investigating the strengthening of aged PSC structures using a near-surface mounted (NSM) post-tensioned carbon fiber-reinforced polymer (CFRP). A total of nine PSC beams, each with a length of 6.5 m, were fabricated for a four-point bending test. Various experimental parameters were taken into account, including the strengthening method, compressive strength of concrete in the PSC beam, and the prestressing force of the PSC beam. The results indicated that the NSM post-tensioned CFRP strengthening system proved more efficient when compared to the NSM non-post-tensioned CFRP strengthening system. The flexural capacity of the NSM post-tensioned CFRP strengthening system, under the deteriorated low-strength PSC beam, increased by up to 30.9% compared to the PSC reference beam. Additionally, the experimental results were compared to a finite-element analysis, and a parametric study was conducted to examine the material properties of the PSC beam. Consequently, the NSM post-tensioned CFRP strengthening system is expected to be an effective solution for addressing the issue of deteriorated low-strength PSC structures.

Capacity Optimization for RSMA-Based Multi-User System over Underwater Turbulence Channel

The underwater environment used for communication is harsh and complex, necessitating heightened standards for spectral efficiency and reliability in underwater wireless optical communication (UWOC) systems. The focus of this work is on the performance of multi-user UWOC systems operating in oblique channels of ocean turbulence downlink, where users are randomly distributed at a certain depth. A joint optimization scheme is proposed, which joints rate-splitting multiple access (RSMA) and power allocation so that the system’s ergodic sum capacity is optimized to improve the transmission bandwidth. Furthermore, the probability density function (PDF) and cumulative distribution function (CDF) models for the received signal-to-noise ratio (SNR) of a multi-user multiple-input multiple-output (MIMO) system operating in the turbulent underwater oblique channels are established, accounting for the avalanche photodiode (APD) shot noise and solar radiation noise. Theoretical derivations are presented to quantify the ergodic capacity and outage probability of the multi-user system utilizing the RSMA technology. Subsequently, a numerical analysis is conducted to investigate the influence of the power allocation coefficient, RSMA, and the joint optimization algorithm on the performance of a two-user MIMO system leveraging RSMA. The simulation results show that our optimization scheme effectively reduces the outage probability, thereby achieving the maximum system sum rate and validating the practical feasibility and efficacy of the proposed scheme.

Gelatin-carboxymethyl cellulose/iron-based metal-organic framework nanocomposite hydrogel as a promising biodegradable fertilizer release system: Synthesis, characterization, and fertilizer release studies

Application of fertilizers is a routine method in agriculture to increase the fertility of plants However, conventional fertilizers have raised serious health and environmental problems in recent years. Therefore, the development of biodegradable superabsorbent hydrogels based on natural polymers with the capability for fertilizer controlled release has attracted much interest. In the current research, a novel nanocomposite hydrogel based on gelatin and carboxymethyl cellulose polymers enriched with an iron based metal- organic framework (MIL-53 (Iron)) was prepared. The prepared nanocomposite hydrogel was loaded with NPK fertilizer to obtain a slow release fertilizer system. The structural properties of the nanocomposite hydrogel were investigated using FTIR, XRD, and SEM techniques. The swelling and fertilizer release behavior of the nanocomposite hydrogel were evaluated in conditions. Results showed that by adding iron-based metal organic framework to the hydrogel matrix, the water absorption capacity of the hydrogel system was increased to 345.8 (g/g). Fertilizer release studies revealed that the release of fertilizer from the nanocomposite matrix has a slow and continuous release pattern. Therefore, the synthesized nanocomposite has an appropriate strength and high potential to be used as a slow-release fertilizer system.

Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition

Cell-Free Massive Multiple-Input–Multiple-Output (CF-MIMO) systems have transformed the landscape of wireless communication, offering unparalleled enhancements in Spectral Efficiency and interference mitigation. Nevertheless, the large-scale deployment of CF-MIMO presents significant challenges in processing signals in a scalable manner. This study introduces an innovative methodology that leverages the capabilities of Dynamic Mode Decomposition (DMD) to tackle the complexities of Channel Estimation in CF-MIMO wireless systems. By extracting dynamic modes from a vast array of received signal snapshots, DMD reveals the evolving characteristics of the wireless channel across both time and space, thereby promising substantial improvements in the accuracy and adaptability of channel state information (CSI). The efficacy of the proposed methodology is demonstrated through comprehensive simulations, which emphasize its superior performance in highly mobile environments. For performance evaluation, the most common techniques have been employed, comparing the proposed algorithms with traditional methods such as MMSE (Minimum Mean Squared Error), MRC (Maximum Ration Combining), and ZF (Zero Forcing). The evaluation metrics used are standard in the field, namely the Cumulative Distribution Function (CDF) and the average UL/DL Spectral Efficiency. Furthermore, the study investigates the impact of DMD-enabled Channel Estimation on system performance, including beamforming strategies, spatial multiplexing within realistic time- and delay-correlated channels, and overall system capacity. This work underscores the transformative potential of incorporating DMD into massive MIMO wireless systems, advancing communication reliability and capacity in increasingly dynamic and dense wireless environments.

Carrying Capacity and Coupling Coordination of Water and Land Resources Systems in Arid and Semi-arid Areas: A Case Study of Yulin City, China

Carrying Capacity and Coupling Coordination of Water and Land Resources Systems in Arid and Semi-arid Areas: A Case Study of Yulin City, China

Numerical investigation of non-uniform arrays of directive antennas for radiation pattern analysis using spatial modulation schemes

This research presents a novel Non-Uniform Array (NUA) design for Spatial Modulation (SM) systems, aiming to improve Bit Error Rate (BER) performance. The NUA utilizes directional antennas with low-similarity radiation patterns, enabling the receiver to effectively distinguish between signals transmitted from different antenna elements. This enhanced signal discrimination capability directly contributes to improved system capacity and potential spectral efficiency gains for massive MIMO systems employing SM techniques. The proposed NUA was optimized for operation at 5.2 GHz, achieving a high degree of pattern distinguishability. Measured results confirm the effectiveness of the design, demonstrating good agreement with simulations. Moreover, an analysis based on the similarity of the radiation pattern was conducted

Identifying Dementia research priority for Qatar national dementia research plan: A Cross-sectional Survey.

The World Health Organization (WHO) published the Global Action Plan 2017-2025 seven years ago to address the dementia burden for those impacted, including persons living with dementia, their families, and health-care providers. There were seven action areas in the global action plan; the least achieved was action area seven (dementia research and innovation). The primary objective of the study was to assess the top 10 dementia research priorities among healthcare professionals, patients, caregivers, the public, and stakeholders to develop the Qatar National Dementia Research Plan. Convenience sampling was used in this cross-sectional survey. The study was conducted online with the involvement of HMC staff (physicians, nurses, and allied health staff) from all HMC facilities, patients and caregivers from Rumailah Hospital's Geriatric Department, and the public who attended the 2022 Advanced Dementia Research Conference. The survey was conducted during 22nd of October 2022 till April 31, 2024. Overall, 2000 participants provided their responses, which included health care professionals under HMC, including physicians, nurses, allied health staff, patients, caregivers, the public, and stakeholders in Qatar. Dementia Risk Reduction (79%) was the top priority for the survey participants. The remaining nine priorities were the impact of early treatment (76%), health system capacity (73%), implementation of best practices for care (73), access to information and services post-diagnosis (71), caregiver support (70%), emotional well-being (67%), care provider education (65%), end-of-life care (65%), and non-drug approaches to managing symptoms (65%). The survey results clearly indicated that most participants ranked Dementia Risk Reduction as their top priority, indicating the essential focus on dementia prevention. These findings, together with goals such as early treatment, healthcare system capacity, and caregiver support, highlight the importance of an integrated, multidisciplinary approach to dementia management.

An experimental study on beamforming architecture and full-duplex wireless across two operational outdoor massive MIMO networks

Full-duplex (FD) wireless communication refers to a communication system in which both ends of a wireless link transmit and receive data simultaneously in the same frequency band. One of the major challenges of FD communication is self-interference (SI), which refers to the interference caused by transmitting elements of a radio to its own receiving elements. Fully digital beamforming is a technique used to conduct beamforming and has been recently repurposed to also reduce SI. However, the cost of fully digital systems dramatically increases with the number of antennas, as each antenna requires an independent Tx-Rx RF chain. Hybrid beamforming systems use a much smaller number of RF chains to feed the same number of antennas, and hence can significantly reduce the deployment cost. In this paper, we aim to quantify the performance gap between these two radio architectures in terms of SI cancellation and system capacity in FD multi-user Multiple Input Multiple Output (MIMO) setups. We first obtained over-the-air channel measurement data on two outdoor massive MIMO deployments over the course of three months. We next study SoftNull and M-HBFD as two state-of-the-art transmit (Tx) beamforming based FD systems, and introduce two new joint transmit-receive (Tx-Rx) beamforming based FD systems named TR-FD2 and TR-HBFD for fully digital and hybrid radio architectures, respectively. We show that the hybrid beamforming systems can achieve 80%–99% of the fully digital systems capacity, depending on the number of users. Our results show that it is possible to get many benefits associated with fully digital massive MIMO systems with a hybrid beamforming architecture at a fraction of the cost.

Acoustic Metasurfaces for Efficient Modulation and Demodulation of OAM Multiplexed Signals in Acoustic Communication

Abstract Orbital angular momentum (OAM) multiplexing has been suggested as a potential solution to increase the data capacity of acoustic communication systems. A method of modulation and demodulation of OAM multiplexed signals using acoustic metasurfaces is proposed in this manuscript. The amplitude and phase of independent acoustic orbital angular momentum (AOAM) multiplexed signal provide orthogonal degrees of freedom. By designing appropriate acoustic metasurfaces and combining them with binary differential phase coding techniques, we demonstrated that efficient modulation and fast demodulation of AOAM multiplexed signals can be achieved. Orthogonal acoustic spiral modes prevent mode coupling and crosstalk, enhancing acoustic multiplexing. The resonance-based structure provides signal frequency selectivity, boosting efficiency and simplifying terminal equipment for practical applications, which leads to the creation of high-capacity acoustic communication systems.

Intelligent Transition Mechanisms in 5G DSA: A Logistic Regression Approach to Reuse Factor Adaptation

This research work use Dynamic Spectrum Allocation (DSA) embedded in 5G technology and logistic regression as Machine learning tool to alter the reuse factor of assigned telecommunication frequency to a more robust and reliable frequency. The switch from a frequency reuse of 3 (FR3) to a frequency reuse of 7 (FR7) was considered. Historical data generated from a Telecommunications Company in Nigeria was utilised. This data consist of User throughput and the number of user equipment to assess traffic load and congestion from user equipment (UE).The ideal was meant to determine the probability at which a switch or transition between reuse factors will occur. With a resolution threshold of 50 users which was set to commence the shift from reuse factor of 3 when the number of users have reached its threshold of 50 to reuse factor of 7 when the number of users exceeds 50 and above. The objective of this combination is to increase spectrum utilisation efficiency and network flexibility, by optimising spectrum resources in densely populated urban areas. This study explains the mechanisms and also demonstrate how logistic regression with DSA will enhance network capacity of wireless communication systems. The logistic regression model proved effective in making intelligent decisions regarding the transition from reuse factor 3 to reuse factor 7 as it was able to accommodate up to 79 users as against the threshold value of 50 users when running under the reuse factor of 3. With a high accuracy of 91.9% and balanced precision-recall values, it showcased its ability to optimize spectral efficiency.