A comprehensive analysis of quadrature phase shift keying (QPSK) modulation in both single input single output (SISO) and multiple input multiple output (MIMO) systems is conducted using MATLAB. The investigation focuses on evaluating QPSK performance with metrics such as signal-to-noise ratio (SNR) and bit error rate (BER) across diverse channel conditions. Furthermore, the study extends to encompass the integration of QPSK with orthogonal frequency division multiplexing (OFDM), with a particular emphasis on assessing spectral efficiency and error rate implications. To validate the accuracy of the simulations, QPSK and QPSK-OFDM configurations are implemented on the WiComm-T hardware platform, enabling a direct comparison of real-world performance metrics against simulation results. By offering practical insights and recommendations for the deployment of robust communication systems, this research underscores the inherent advantages of integrating OFDM with QPSK across both SISO and MIMO configurations.
Substituting diesel technology with eco-technologies in public buses is one of the prominent efforts being made to achieve a sustainable transportation system goal. Among these eco-technologies, commonly used ones include electric vehicles, natural gas fuel, hydrogen fuel, and bio-diesel fuel technology. However, the performance comparison between these technologies in reducing environmental impact at each location where they are implemented remains unanswered by previous studies. Research to measure the effectiveness of each of the eco-technologies in reducing environmental issues has been conducted extensively, employing various methods and metrics. This study conducted a systematic review of 94 articles that met the predefined inclusion criteria to obtain performance comparisons among these technologies. As a result, a general trend has been observed that eco-technologies have successfully achieved their intended goals with various success rates, although electric bus technology has advantages over other technologies based on the articles. However, its effectiveness relies on specific aspects to optimize its environmental performance. Therefore, the suitability of implementation in a region will depend on many factors. This article contributes to determining the extent to which eco-technologies are implemented in buses worldwide, serving as a consideration for decision-makers, and identifying research gaps in this topic.
Perovskite relaxor-PT piezoelectrics are suitable materials for energy harvesting. Relaxor-PT crystals have electromechanical properties that can replace lead zirconate titanate (PZT). However, the growth of these relaxor-PT crystals is tedious and expensive. The important parameter for energy harvesting is the transduction rate (d×g) where d represents the piezoelectric coefficient and g represents the piezoelectric voltage constant. There is always a challenge to obtain a high (d×g) value. Lead magnesium niobate Pb (Mg1/3Nb2/3) O3-lead titanate PbTiO3 (PMN-PT) is a binary relaxor-PT ceramic and the morphotropic phase boundary (MPB) of (1-x) PMN-xPT ceramics occur around x=0.29-0.40 which is not exactly defined. So here we have synthesized 0.71PMN-0.29PT, 0.68PMN-0.32PT, and 0.65PMN-0.35PT compositions by two-step sintering solid-state method. The x-ray diffraction (XRD) patterns of the PMN-PT ceramics exhibit a characteristic perovskite structure and also show that PMN-PT ceramics fall within the region of MPB. Out of the three compositions, the 0.71PMN-0.29PT ceramics show a higher transduction rate (d×g). So, this piezoelectric ceramic has the potential to replace costly piezoelectric single crystals in the creation of affordable, high-frequency energy harvesting devices.
In recent years, sentiment analysis and online review analysis have gained popularity as critical components in the growth and development of educational courses. An innovative method has been created to increase the quality of learning experiences by rapidly collecting relevant data from course comments. This technique leverages bidirectional encoder representation from transformers (BERT) for word vector training. When combined with a learning mechanism, the recommended BERT accurately predicts the sentiment of online course reviews. Additionally, a dual-channel model based on Bi-directional long short-term memory (Bi-LSTM) is employed to improve sentiment data and semantics. Following data collection from the Coursera dataset, preprocessing approaches such as tokenization, stop words removal and sentence metric creation are applied to convert input data into word vectors and identify fundamental text units using text segmentation. The results demonstrate the proposed approach’s superiority over existing methods, offering an accuracy of 81.45%, recall of 94.9%, precision of 93.7%, and F-score of 93.7%.
Ischemic heart disease (IHD) is a leading cause of mortality worldwide, calling for advanced predictive models for timely intervention. Current literature reviews on machine learning (ML)-based IHD prediction frameworks often focus on predictive accuracy but lack depth in areas like dataset diversity, model interpretability, and privacy considerations. Existing IHD prediction frameworks face limitations, including reliance on small, homogenous datasets, limited critical analysis, and issues with model transparency, reducing their clinical utility. This review addresses these gaps through a systematic, comparative analysis of popular ML models, such as random forest (RF) and support vector machines (SVM), noting their strengths and limitations. Key contributions include a qualitative examination of prevalent tools, datasets, and evaluation metrics, identification of gaps in dataset diversity and interpretability; and recommendations for improving model transparency and data privacy. Major findings reveal a trend toward ensemble models for accuracy but highlight the need for explainable artificial intelligence (AI) to support clinical decisions. Future directions include using federated learning to enhance data privacy, integrating unstructured data for comprehensive prediction, and advancing explainable AI to build trust among healthcare providers. By addressing these areas, this review aims to guide future research toward developing robust, transparent ML frameworks that can be more effectively deployed in clinical settings.
Nowadays, the transition to open markets, the rapid growth of renewable energy sources like wind and solar, and the shift towards electrification in transportation and industry for decarbonization have increased the demand for advanced energy system models with detailed spatial and temporal data. This paper utilizes a comprehensive literature review and selects a representative set of open-source tools for evaluation. A comparative analysis of 17 open-source energy system modeling tools and their commercial alternatives was conducted. The paper analyzes many open-source aspects such as code commits, updates, programming languages, license details, citations, and energy system modeling features such as power flows (PFs), continuation PF, dynamic analysis, short-circuit analysis, contingency analysis, transportation model, optimal PF (OPF), multi-period OPF, unit commitment (UC), investment optimization, and graphic user interface. Based on the results, the paper suggests appropriate tools used for according power/energy system analysis objective: MATPOWER for power system analysis and Python for power system analysis (PyPSA) for energy system analysis.
When exposed to ultraviolet (UV) light, paper trash could release dissolved organic matter (DOM) as a disinfection byproduct (DBP) precursor in water. This study was undertaken to assess the regulated DBPs, trihalomethanes-4 (THM4) generation potential, and estimated cytotoxicity of paper trash soaked in water during UV radiation. The UV irradiation was carried out at 1, 6, 12, 24, and 48 hours. The released organic material was determined using ultraviolet-visible (UV-Vis) adsorption at wavelengths ranging from 200 to 700 nm. Chlorination was carried out for 24 hours to determine THM4 formation potentials. According to the research findings, the longer the radiation period, the more THM4 species are formed in plain paper samples. On the other hand, the production of trichloromethane (TCM) on paper samples containing writing decreased as the UV radiation period increased. THM4 levels reached 103.12 and 150.57 µg/L for plain and writing paper samples, respectively. UV absorbance at 272 nm (A272) shows a 92% association with THM4 production, making it suitable as an initial characterization approach. Paper trash pollution in water could form carcinogenic DBPs, THM4, if used as a water source in a water treatment plant using chlorine disinfection.
Fast charging technology has revolutionized the electric vehicle (EV) industry by addressing range anxiety and significantly reducing charging times. However, this convenience introduces challenges concerning battery longevity, as high charging currents and elevated temperatures accelerate battery degradation. This paper investigates the mechanisms through which fast charging impacts lithium-ion batteries, including thermal stress, lithium plating, and mechanical wear. It synthesizes findings from various studies, highlighting how fast charging can shorten battery lifespan by up to 20-30% compared to standard charging methods. Strategies to mitigate these effects, such as advanced materials, adaptive charging protocols, and efficient thermal management systems, are discussed. Furthermore, the paper emphasizes the importance of standards and policies to promote sustainable fast charging practices. By balancing charging speed with long-term battery health, the EV industry can achieve widespread adoption while ensuring sustainability. This work aims to provide a comprehensive understanding of the trade-offs associated with fast charging and offers actionable insights for improving EV battery durability.
Scheduling, monitoring, and decision-making are important factors in determining the general achievement of sustainable construction. Therefore, this study was conducted to determine the effectiveness of a dashboard as a measuring tool for construction project scheduling, monitoring, and decision-making. A survey with a Likert scale (5 scale) on each viewpoint, including planning, oversight, and independent direction, of 15 respondents from project executors and 7 respondents from supervisors was used as instrumentation. The results showed that the dashboard was evaluated with a value of 92.25 among executors and supervisors linked to product characteristics. Executors also used the scheduling dashboard with a value of 91.73, and the feature of employing the concept for supervision was appropriate as a measuring instrument, scoring 92.15. Furthermore, the final step was the aspect of using the dashboard for decision-making, which was tested and used with a value of 88.14. The use dashboard model is an effective tool for work progress scheduling, monitoring, and decision-making in construction projects.
In the rapid development of information technology, using enterprise data to support timely management decisions is crucial in helping businesses operate effectively and improve competitiveness. This study uses Microsoft power business intelligence (MPBI) to analyze data in retail systems, allowing managers to grasp the business situation in real time, track advanced sales, optimize inventory control, and analyze customer behavior and supply chain visibility. From the data generated by the business, the study uses the streaming extract transform load (ETL) model to support real-time data aggregation, then converts to the MPBI data visualization system to convert data into visual charts, helping businesses easily monitor, track, analyze, and make decisions to promote business activities. The study proposes a data structure to organize retail information storage. It proposes a system of calculation formulas and data synthesis, making integrate and convert tabular data into visual charts. Through analysis of real data from the LH83 retail system, the study shows the feasibility of implementing a data visualization system and the difficulties encountered when businesses want to deploy this model.
The drawbacks of fossil fuel-based energy sources, including high costs, pollution, scarcity, and environmental damage, highlight how urgent it is to switch to renewable energy sources. Multilevel inverters (MLIs) are currently required for the production of wind electricity. In this research, to get a reduced harmonic distortion, use 31-level inverter based on shifted carrier-pulse width modulation (SC-PWM) is developed for wind power generation using MATLAB/Simulink. It aids in minimizing the total harmonic distortion (THD) to 3.20, and the output voltage is enhanced by the suggested MLI. Wind energy extraction is optimized by combining with a proportional integral derivative (PID) control system. MATLAB/Simulink has been used to make sure the MLI structure and look into the implementation of wind energy conversion systems using a permanent magnet synchronous generator (PMSG). In order to show that the suggested inverter architecture improves power conversion efficiency and stability in renewable energy systems, the study also examines power efficiency, system dependability, and the viability of large-scale applications. Additionally, the study investigates grid integration, modulation strategies, and switching losses to guarantee increased sustainability, dependability, and efficiency in wind energy applications while lowering operating costs.
This work supported Indonesia's research focus area on information and communication technology (ICT) content improvement for information data on various forms of local wisdom. As one of the various forms of local wisdom, the Balinese script was supported by the ToLatin application that transliterates Balinese script into Latin text. It has been used to support Balinese script learning at the high school level in Buleleng Regency, Bali, Indonesia. To determine the acceptability of this application, which had not been studied before, a user acceptance evaluation was conducted using a combination of acceptance variables from the technology acceptance model (TAM) and success variables from DeLone & McLean. This study used a quantitative method with data collection through questionnaires from 385 respondents. The data analysis used the importance-performance analysis (IPA) method through suitability, gap, and quadrant analyses. The study results indicated that the acceptability of ToLatin could be more optimal. The suitability analysis revealed an average score of 87.91%, indicating the need for improvement in system quality, particularly the innovative indicator (SysQ3), based on the quadrant analysis. The gap analysis revealed an average score of -0.54 from 7 acceptance variables, indicating the need to improve system performance to meet user expectations.
This paper reviews literature on e-commerce waste management issues and challenges, focusing on potential improvements in Malaysia. It analyzes various sources, including Scopus, Web of Science (WoS), and Google Scholar (GS), using thematic and content analysis based on preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The review highlights the surge in packaging and electronic waste due to increased e-commerce activity. In response, Malaysia has introduced policies promoting sustainable practices, such as eco-friendly packaging, e-waste regulations, and circular economy (CE) principles. Growing consumer awareness has also driven demand for sustainable e-commerce options. However, the key challenge is to reduce waste generation rather than just managing it. Achieving this will require significant efforts to minimize excessive manufacturing and packaging. The review aims to provide insights for stakeholders to support effective waste management and foster sustainability in the e-commerce sector.
This study investigates the role of educational attainment in mobile banking (m-banking) adoption in Ghana, leveraging data from 598 respondents through a multi-group analysis. By integrating the technology acceptance model (TAM) and the theory of planned behavior (TPB) into a structural equation modelling framework, the research examines key factors such as subjective norms, perceived usefulness, ease of use, trust, and self-efficacy. Results reveal significant differences in adoption behaviors between lower- and higher-educated users. Subjective norms strongly influence higher-educated individuals, while perceived ease of use drives adoption among lower-educated users. Perceived usefulness positively affects higher-educated users but has a negative impact on lower-educated respondents. The findings highlight the moderating effect of education level on the adoption process, offering theoretical and practical insights into targeted strategies for enhancing financial inclusion in developing economies. These results underscore the importance of user segmentation in fostering broader acceptance and utilization of m-banking technologies.
The pitch period is a key component of many speech analysis research projects. In real-world applications, voice data is frequently gathered in noisy surround- ings, therefore algorithms must be able to manage background noise well in order to estimate pitch accurately. Despite advancements, many state-of–the-art algorithms struggle to deliver adequate results when faced with low signal-to- noise ratios (SNRs) in processing noisy speech signals. This research proposes an effective concept specifically designed for speech processing applications, particularly in noisy conditions. To achieve this goal, we introduce a fundamen- tal frequency extraction algorithm designed to tolerate non-stationary changes in the amplitude and frequency of the input signal. In order to improve the extrac- tion accuracy, we also use a cumulative power spectrum (CPS) based on discrete cosine transform (DCT) rather than conventional power spectrum. We enhance extraction accuracy of our method by utilizing shorter sub-frames of the input signal to mitigate the noise characteristics present in speech signals. According to the experimental results, our proposed technique demonstrates superior per- formance in noisy conditions compared to other existing state-of-the-art meth- ods without utilizing any kind of post-processing techniques.
Machine learning (ML) integration into various industries has revolutionized operations recently, enhancing efficiency and predictive capabilities. However, the rapid adoption of ML models also presents significant safety concerns that are highly demanded. To achieve this, scholarly articles from reputable databases such as Scopus and Web of Science (WoS) focus on studies published between 2022 and 2024, which were extensively searched. The study's flow is based on the PRISMA framework. The database found (n=40) that the final primary data was analyzed. The findings were divided into three themes: i) safety and risk management, ii) ML and artificial intelligence (AI) applications in safety, and iii) smart technology for safety. The conclusion highlights the need for continuous monitoring and updating of the safety protocols to keep in step with the growing ML landscape. This review contributes to the understanding of ML safety. It offers global lessons that can guide future research and policy-making efforts to ensure ML technologies' safe and ethical use.
Despite the growing demand for renewable energy, the utilization of tidal energy remains underdeveloped due to efficiency limitations in turbine design. Addressing this gap, this study investigates the performance of horizontal-axis tidal turbines (HATT) by comparing two foil designs, National Advisory Committee for Aeronautics (NACA) 2415 and OptA, to optimize energy extraction efficiency. The research employs computational fluid dynamics (CFD) simulations using OpenFOAM to evaluate the effects of foil modifications and non-linear twist distributions on turbine performance across varying tip speed ratios (TSR). The results indicate that the OptA foil significantly improves turbine performance, achieving a 41.4% increase in torque and a 40.2% increase in power coefficient (CP) at TSR 5, which was identified as the optimal operating condition. The OptA foil enhances velocity distribution, reduces flow separation, and improves vortex behavior, leading to greater efficiency and stability. These findings confirm that foil selection and blade design modifications play a critical role in HATT optimization.
Efficient online transactions now thrive through websites (e-commerce) and mobile apps (m-commerce). With the growth of m-commerce, marketers aim to boost profits by understanding impulsive buying behavior. This study investigates factors influencing online impulse buying (OIB) in m-commerce by analyzing key variables. Data were gathered via questionnaires from 449 Indonesian consumers who had made digital payments and impulsive purchases using smartphones. The framework includes sales promotion (SP), attractive advertising (EA), and mobile digital payment systems (MDPS) as situational factors; hedonic shopping motivation (HSM) as a motivational factor; and impulsiveness (I) and smartphone addiction (SA) as personal traits. Analysis used partial least squares structural equation modeling (PLS-SEM), with gender, income, and smartphone usage time as control variables. Results show that EA, MDPS, HSM, I, and SA significantly influence OIB, while SP does not. For consumer segmentation, t-distributed stochastic neighbor embedding (t-SNE) outperformed ISOmap and principal component analysis (PCA), achieving a silhouette score of 0.72. A paired t-test (p<0.01) confirmed t-SNE’s superior clustering accuracy. These findings reveal that t-SNE better captures consumer segmentation patterns, helping businesses refine marketing strategies and deepen their understanding of psychological drivers behind impulsive m-commerce purchases.
One of the main engineering challenges has been to design an economical soil retaining structure with high seismic resistance. From this perspective, reinforced soil walls have been developed with a focus on flexibility, in order to efficiently resist the effects of similar historical events in the event of a significant earthquake. The overall objective of this study was to compare the structural behavior of a geogrid-reinforced soil wall (Terramesh® system) under static and pseudo-static loads, and in a seismic environment simulated using the finite element method, in a shopping center in Trujillo, Peru. A case study was conducted using a mixed methodology, both applied and analytical-comparative in scope. Furthermore, the finite element methodology, material constitutive modeling, and dynamic time-history analysis of modal structures were chosen. It was determined that seismic loading can produce a 53.33% increase in deformations compared to the static state; Likewise, the overall safety factor under dynamic conditions tends to decrease by 27.85% compared to the static case. This study demonstrated the scope of geogrid reinforcement (Terramesh® system) through a practical case of a reinforced soil wall, using Plaxis 2D software to compare, estimate, and compare structural behavior in static, dynamic, and simulated environments.
The article provides a comprehensive analysis of modern trends and prospects for the use of solar batteries in various sectors of the economy and the agricultural sector. The purpose of this article is to analyze the possibility of energy saving for a private residential building in Gobustan using solar energy storage in a greenhouse extension and a heat pump to transfer heat to the heating system. The calculation showed that in the coldest month, December, the potential of solar thermal energy is 15-38% of the required heat demand, depending on the material used in the extension design. In March and April, excess heat is generated, which can be used for hot water supply needs. Thus, for an individual residential building, the use of solar heat accumulated in a greenhouse extension is relevant as an additional source of heat for the heating system. Surface density of solar radiation flux, W/m2: surface density of direct solar radiation flux: 1,680 (November), 1,530 (December), 1,870 (January), 2,730 (February), 3,270 (March), 3,180 (April); Surface density of diffuse solar radiation flux: 650 (November), 450 (December), 480 (January), 680 (February), 1180 (March), 1,830 (April).
