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Sodium alginate extracted from Sargassum oligocystum brown seaweed alga is an important blue economy development process of the Bay of Bengal. In the present study, sodium alginate was extracted from the brown seaweed alga collected from the Cox’s Bazar coastal area of the Bay of Bengal. The extracted product was characterized with the Ultraviolet-visible (UV–visible) spectrophotometer, Fluorescence spectrophotometer, Fourier transform-infrared radiation (FT-IR) spectrophotometer, X-ray diffractometer (XRD), and Scanning electron microscope (SEM). The product yield was optimized with different pH levels and the maximum yield was obtained at 32.5 % at pH 10.96. The moisture, and ash content of the extracted product was measured and found at 13.25 % and 4.35 %, respectively. The viscosity of the extracted product was obtained at 60.1 mPs. The UV–visible spectrophotometry showed different absorbance peaks found at 234 nm, 240 nm, 250 nm, and 264 nm which assigned the extracted product of sodium alginate, and the maximum absorption peak lies at 276.7 nm which assigned flavonoids, and their derivatives of alginates. The fluorescence spectrophotometry demonstrated a strong emission peak at 460 nm with higher intensity (200), indicating the presence of sodium alginate. The FTIR showed the characteristic peaks that demonstrated different functional groups of sodium alginate. The XRD pattern demonstrated 0.24 crystalline indexes indicating the semi-crystalline product in nature. SEM showed clear fibrils of sodium alginate morphology indicating the absence of impurity materials.

A facile chemical synthetic method was used to synthesize CuxO thin films on glass substrate via in-situ chemical reaction of CuI thin films in NaOH solution at ambient temperature. CuI thin films were produced by a low-cost plasma-assisted dissociation of the chemical vapor of CuI in acetonitrile (CH3CN) at a pressure of 60 Torr. The prepared CuxO thin films were annealed at various temperatures. The XRD studies unveiled the amorphous character of the CuxO thin films. The SEM images indicated that the surfaces of the films were smooth and smoothness depended on annealing temperature. The EDS studies revealed the presence of huge amounts of C and O with a little amount of Cu in the films which resulted due to the deposition of C produced through dissociation of CH3CN in plasma and O produced from chemical reaction in solution. The optical studies showed that transmittance of the CuxO thin films decreased from that of the CuI thin films and depended on the annealing temperatures. The bandgap of the CuxO thin films decreased with annealing temperature and was in the range of 2.30–2.98 eV. The CuxO thin films exhibited resistivity switching behavior in the first heating-cooling cycle and then the resistivity increased with temperature indicating the metallic behavior. These results are propitious for the application CuxO thin films in solar cells and memristor devices.

Recent decades have witnessed a remarkable pace of innovation and performance improvements in integrated circuits (ICs), which have become indispensable in an array of critical applications ranging from military infrastructure to personal healthcare. Meanwhile, recent developments have brought physical security to the forefront of concern, particularly considering the valuable assets handled and stored within ICs. Among the various invasive attack vectors, micro-probing attacks have risen as a particularly menacing threat. These attacks leverage advanced focused ion beam (FIB) systems to enable post-silicon secret eavesdropping and circuit modifications with minimal traceability. As an evolved variant of micro-probing attacks, reroute attacks possess the ability to actively disable built-in shielding measures, granting access to the security-sensitive signals concealed beneath. To address and counter these emerging challenges, we introduce a layout-level framework known as Detour-RS. This framework is designed to automatically assess potential vulnerabilities, offering a systematic approach to identifying and mitigating exploitable weaknesses. Specifically, we employed a combination of linear and nonlinear programming-based approaches to identify the layout-aware attack costs in reroute attempts given specific target assets. The experimental results indicate that shielded designs outperform non-shielded structures against reroute attacks. Furthermore, among the two-layer shield configurations, the orthogonal layout exhibits better performance compared to the parallel arrangement. Furthermore, we explore both independent and dependent scenarios, where the latter accounts for potential interference among circuit edit locations. Notably, our results demonstrate a substantial near 50% increase in attack cost when employing the more realistic dependent estimation approach. In addition, we also propose time and gas consumption metrics to evaluate the resource consumption of the attackers, which provides a perspective for evaluating reroute attack efforts. We have collected the results for different categories of target assets and also the average resource consumption for each via, required during FIB reroute attack.

This study intends to shed light on the environmental impacts of energy decisions in South Korea by analyzing the correlation between energy consumption patterns and environmental indicators such as carbon dioxide emissions. In 2021, global CO2 emissions increased by 6%—to the highest ever level of 36.3 billion tons—according to the International Energy Agency (IEA). This increase in CO2 emissions is a big problem for all countries around the world. The aim of this article is an analyze the impact of fossil fuels, renewable energy, and nuclear energy on South Korea’s environment based on the STIRPAT (stochastic impact by regression on population, affluence, and technology) model. Exploring the intricate nexus between economic and energy indicators and environmental outcomes, this study employs the STIRPAT model to analyze the influence of GDP, population dynamics, fossil fuels, renewable energy, and nuclear energy on South Korea’s environment. The yearly data from 1972 to 2021 are analyzed in this paper using an autoregressive distributed lag (ARDL) model. The reliability of this study is also examined by employing FMOLS (fully modified ordinary least squares) and CCR (canonical cointegrating regression) estimators. This study confirms the findings of previous research by showing that the rising South Korea GDP and population can lead to higher CO2 emissions and that a strategy switching to renewable energy can cut down on CO2 emissions in Korea, as it exhibits a coefficient of −0.085 *. The robustness results of FMOLS and CCR’s findings support baseline ARDL findings.

As the sustainability of the environment is a very much concerning issue for developed countries, the drive of the paper is to reveal the effects of nuclear, environment-friendly, and non-friendly energy, population, and GDP on CO2 emission for Italy, a developed country. Using the extended Stochastic Regression on Population, Affluence, and Technology (STIRPAT) framework, the yearly data from 1972 to 2021 are analyzed in this paper through an Autoregressive Distributed Lag (ARDL) framework. The reliability of the study is also examined by employing Fully Modified Ordinary Least Square (FMOLS), Dynamic Ordinary Least Square (DOLS), and Canonical Cointegration Regression (CCR) estimators and also the Granger causality method which is used to see the directional relationship among the indicators. The investigation confirms the findings of previous studies by showing that in the longer period, rising Italian GDP and non-green energy by 1% can lead to higher CO2 emissions by 8.08% and 1.505%, respectively, while rising alternative and nuclear energy by 1% can lead to falling in CO2 emission by 0.624%. Although population and green energy adversely influence the upsurge of CO2, they seem insignificant. Robustness tests confirm these longer-period impacts. This analysis may be helpful in planning and developing strategies for future financial funding in the energy sector in Italy, which is essential if the country is to achieve its goals of sustainable development.

Due to a rapidly growing population and economy, an increase in emissions from urban growth, industrial growth, and energy use hurt the environment’s health. This research examines how tourism, population, income, renewable energy, and electricity consumption affect carbon emissions in twenty-six low-income countries. There is no cross-sectional dependence (CSD) problem, so quantile regressions (QR) and generalized method of moments (GMM) are used. Results show that the environment is obtaining benefits because of tourism. CO2 emissions are rising because the per capita income, electricity consumption, and population are growing. CO2 emissions can be lowered by using more renewable energy and growing the economy faster. Environmental Kuznets Curve (EKC) is also valid in low-income countries. Thus, increasing income will not be harmful to the environment. Similarly, increasing tourism, renewable energy, and rising GDP per capita benefit low-income countries. The government can focus on sustainable tourism. Policymakers may convince more people to use renewable energy resources and grow the sustainable tourism industry. This study recommends that the government reduce greenhouse gas emissions, promote tourism that is good for the environment, take initiatives to limit population growth, and use renewable energy.

This study looks at the short- and long-term effects of fossil fuels, renewable energy, and nuclear energy on CO2 emissions in the South Asian Association for Regional Cooperation (SAARC) countries from 1982 to 2021. We assess the impacts of SAARC’s current and anticipated use of nuclear, fossil, and alternative energies by testing the environmental Kuznets curve (EKC) hypothesis. The study applied the second-generation unit root test, cointegration test, and the newly introduced AMG technique to handle the presence of cross-sectional dependence. The results indicate that EKC does not hold in SAARC, and a U-shaped quadratic link exists between GDP and environmental pollution. The findings also reveal that the environmental pollution in the SAARC is caused by fossil fuel, whereas using renewable (REN) and nuclear energy can cut long-term pollution. While renewable energy is critical to minimizing environmental deterioration in SAARC, empirical findings also show that more than rising national wealth is needed to meet environmental demands. According to the results of this study, SAARC countries should take the lead in achieving sustainable growth and the efficient use of clean energy.

The threat of global warming has increased due to industrialization, urbanization, population expansion, and changes in lifestyle among the Group of Seven(G7) Carbon dioxide emissions (CO2) directly affect how much electricity can be generated from various sources. This research aims to identify environmental hazards associated with various energy sources. Analyzing the impact of various energy sources on CO2 emissions from electricity and heat production using data from the G7. The data is analyzed using quantile regression (QR), generalized method of moments (GMM), random effects (RE), and fixed effects (FE). Our results indicate a substantial positive impact on CO2 emissions regardless of the technology used to generate coal and gas power. Coal-fired power plants have a larger impact on the environment than other sources of emissions. Also, all coal and gas coefficients are significant in FE, RE, GMM, and QR. Oil coefficients have a negative impact on environmental degradation and are significant for FE, RE, and D-GMM regressions. Hydroelectric and renewable energy production can reduce CO2 emissions in all regression models. Nuclear energy has a beneficial impact on the environment, but the coefficients are only significant for S-GMM and the last quantile. However, the most significant result of this study is the identification of a cause-and-effect relationship between CO2 emissions and energy production. Carbon dioxide (CO2) emissions can be lowered by shifting away from fossil fuels and toward renewable and hydroelectric sources. The research also suggests several renewable and alternative electricity production policies for sustainable energy.

An inset fed-microstrip patch antenna (MPA) with a partial ground structure is constructed and evaluated in this paper. This article covers how to evaluate the performance of the designed antenna by using a combination of simulation, measurement, creation of the RLC equivalent circuit model, and the implementation of machine learning approaches. The MPA’s measured frequency range is 7.9–14.6 GHz, while its simulated frequency range is 8.35–14.25 GHz in CST microwave studio (CST MWS) 2018. The measured and simulated bandwidths are 6.7 GHz and 5.9 GHz, respectively. The antenna substrate is composed of FR-4 Epoxy, which has a dielectric constant of 4.4 and a loss tangent of 0.02. The equivalent model of the proposed MPA is developed by using an advanced design system (ADS) to compare the resonance frequencies obtained by using CST. In addition, the measured return loss of the prototype is compared with the simulated return loss observed by using CST and ADS. At the end, 86 data samples are gathered through the simulation by using CST MWS, and seven machine learning (ML) approaches, such as convolutional neural network (CNN), linear regression (LR), random forest regression (RFR), decision tree regression (DTR), lasso regression, ridge regression, and extreme gradient boosting (XGB) regression, are applied to estimate the resonant frequency of the patch antenna. The performance of the seven ML models is evaluated based on mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and variance score. Among the seven ML models, the prediction result of DTR (MSE = 0.71%, MAE = 5.63%, RMSE = 8.42%, and var score = 99.68%) is superior to other ML models. In conclusion, the proposed antenna is a strong contender for operating at the entire X-band and lower portion of the Ku-band frequencies, as evidenced by the simulation results through CST and ADS, it measured and predicted results using machine learning approaches.

The Internet of Things (IoT) plays an indispensable role in present-day household electricity management. Nevertheless, practical development of cost-effective intelligent condition monitoring, protection, and control techniques for household distribution systems is still a challenging task. This paper is taking one step forward into a practical implementation of such techniques by developing an IoT Smart Household Distribution Board (ISHDB) to monitor and control various household smart appliances. The main function of the developed ISHDB is collecting and storing voltage, current, and power data and presenting them in a user-friendly way. The performance of the developed system is investigated under various residential electrical loads of different energy consumption profiles. In this regard, an Arduino-based working prototype is employed to gather the collected data into the ThingSpeak cloud through a Wi-Fi medium. Blynk mobile application is also implemented to facilitate real-time monitoring by individual consumers. Microprocessor technology is adopted to automate the process, and reduce hardware size and cost. Experimental results show that the developed system can be used effectively for real-time home energy management. It can also be used to detect any abnormal performance of the electrical appliances in real-time through monitoring their individual current and voltage waveforms. A comparison of the developed system and other existing techniques reveals the superiority of the proposed method in terms of the implementation cost and execution time.

In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly.

Charging a group of series-connected batteries of a PV-battery hybrid system exhibits an imbalance issue. Such imbalance has severe consequences on the battery activation function and the maintenance cost of the entire system. Therefore, this paper proposes an active battery balancing technique for a PV-battery integrated system to improve its performance and lifespan. Battery state of charge (SOC) estimation based on the backpropagation neural network (BPNN) technique is utilized to check the charge condition of the storage system. The developed battery management system (BMS) receives the SOC estimation of the individual batteries and issues control signal to the DC/DC Buck-boost converter to balance the charge status of the connected group of batteries. Simulation and experimental results using MATLAB-ATMega2560 interfacing system reveal the effectiveness of the proposed approach.

Condition monitoring and early fault diagnosis for wind turbines have become essential industry practice as they help improve wind farm reliability, overall performance and productivity. If not detected and rectified at early stages, some faults can be catastrophic with significant loss or revenue along with interruption to the business relying mainly on wind energy. The failure of Wind turbine results in system downtime and repairing or replacement expenses that significantly reduce the annual income. Such failures call for more systematized operation and maintenance schemes to ensure the reliability of wind energy conversion systems. Condition monitoring and fault diagnosis systems of wind turbine play an important role in reducing maintenance and operational costs and increase system reliability. This paper is aimed at providing the reader with the overall feature for wind turbine condition monitoring and fault diagnosis which includes various potential fault types and locations along with the signals to be analyzed with different signal processing methods.

The objective of this research is to analyze advertising as a means of mass communication which influences consumption patterns and affects social reality. The problem questions how advertising induces consumers in the midst of technological evolution. The methodology was developed through hypothetical-deductive method which is structured into three segments -- mass media communication, advertising, and globalization and consumption. It is concluded that the media contributes to the construction of social reality and that there are many possibilities of consumption as it sells satisfaction, educates the consumer and ensures that information is important. [ABSTRACT FROM AUTHOR]

Abstract With the rapid increase of renewable energy generation worldwide, real‐time information has become essential to manage such assets, especially for systems installed offshore and in remote areas. To date, there is no cost‐effective condition monitoring technique that can assess the state of renewable energy sources in real‐time and provide suitable asset management decisions to optimize the utilization of such valuable assets and avoid any full or partial blackout due to unexpected faults. Based on the Internet of Things scheme, this paper represents a new application for the Supervisory Control and Data Acquisition (SCADA) system to monitor a hybrid system comprising photovoltaic, wind, and battery energy storage systems. Electrical parameters such as voltage, current, and power are monitored in real‐time via the ThingSpeak website. Network operators can control components of the hybrid power system remotely by the proposed SCADA system. The SCADA system is interfaced with the Matlab/Simulink software tool through KEPServerEX client. For cost‐effective design, low‐cost electronic components and Arduino Integrated Development Environment ATMega2560 remote terminal unit are employed to develop a hardware prototype for experimental analysis. Simulation and experimental results attest to the feasibility of the proposed system. Compared with other existing techniques, the developed system features advantages in terms of reliability and cost‐effectiveness.