Analysis Of Systems Research Articles

A new framework for the analysis of media systems and media organisations

This article asks: How can we best conceptualise and analyse media systems and media organisations today? A discussion of approaches to media systems and organisation analysis shows that many of them seem to assume the correctness of Francis Fukuyama’s claim that liberal democracy has after the breakdown of the Soviet system been universalised. This article argues that the contemporary world situation shows that Fukuyama’s concept of the end of history is wrong and that we, therefore, need to rethink approaches to media systems and organisation analysis. This article introduces one such approach that is based on a concept of society that argues that all social systems and organisations have economic, political and cultural dimensions and that there is an antagonism between self-management and alienation. The article builds on and extends a typology of media organisations introduced by Colin Sparks. It introduces a systematic model of 12 different types of media organisation and six types of media systems. It stresses the importance of discerning between and analysing the interactions of capitalist media, public service media, civil society media, state media, authoritarian media and democratic media.

Statistical analysis in cellular systems for channel capacity improvement with dynamic pilots across different angles users

Accurate channel state information (CSI) is crucial for optimizing wireless communication systems. In scenarios with varying user-to-base station angles, the angle-dependent coherence time impacts conventional pilot strategies. Due to small angles, the coherence time of the user decreases dramatically because of doppler shift, which causes an increase in the number of pilots. We introduces an innovative sub-block design approach for systems with different user angles. This method harmonizes coherence time of high and low-angle users, while maintaining a constant pilot count. This not only improves spectral efficiency but also ensures accurate channel estimation. Through simulations, we demonstrate the effectiveness of our approach in enhancing both spectral efficiency upt to 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10 \\%$$\\end{document} and CSI precision. This breakthrough contributes to the advancement of channel estimation techniques in scenarios with angle-dependent coherence time, offering practical benefits to wireless communication systems.

SPICE-Compatible Circuit Models of Multiports Described by Scattering Parameters with Arbitrary Reference Impedances

New SPICE-compatible circuit models of a multiport are presented here that are suitable for the frequency-domain and time-domain analyses of hybrid systems containing linear distributed elements and possibly non-linear lumped elements. Distributed elements models are based on scattering parameters with potentially complex reference impedances, which are not necessarily equal for all ports. Both exact and approximated (lumped) models are proposed. The scattering parameters are directly taken as the model element values in the former case. In the latter case, the model element values are equal to the real and imaginary parts of the poles and residues of the rational approximation. The models comprise a multiport (with an admittance matrix numerically equal to the modeled scattering matrix or approximating it) equipped with a pair of coupled impedances at each port. A few examples validate the proposed approach and prove its efficiency in terms of matrix size and analysis time compared to some selected commercial counterparts.

Comparative Analysis of External Lightning Protection Systems Using Protective Angle and Rolling Sphere Method in Areas That Have High Lightning Intensity

Indonesia is a tropical country with a geographical condition where 71% of its area is water. This is what coueses Indonesia is considered a country with a high density of lightning strikes. This results in buildings in Indonesia being at a high risk of damage caused by lightning strikes. According to data from BMKG, the city of Malang has a high lightning intensity, with 108 thunderstorm days per year, posing a serious threat to the Electrical Engineering Building of the Faculty of Engineering at the University of Malang. Considering the building's age and expansion on the building's side, it is necessary to evaluate the lightning protection system to determine whether the existing system is still working effectively and efficiently. Improving the system's performance against lightning disturbances can be done by analyzing the previous external lightning protection system and then evaluating it using the angle of protection and rolling sphere methods. The analysis and evaluation of the external protection system are based on PUIPP, PUIL, SNI, and IEEE standards.

Stability analysis of linear systems with multiple time-varying delays via a region partitioning approach and reciprocally convex combination lemmas

The delays-dependent stability analysis of linear systems with multiple time-varying delays is addressed in this study. To estimate the integral term that results from the differentiation of Lyapunov–Krasovskii functional (LKF), an improved region partitioning approach and relaxed lemmas are proposed. Based on all the delayed state information, the maximum delay interval [−h,0] is separated into 2N non-overlapping subintervals. Secondly, two novel generalized reciprocally convex combination lemmas (GRCCLs) are proposed, with Bessel–Legendre-based inequality, to estimate the integral terms generated by the region partitioning approach to obtain less conservative stability criteria. Finally, the obtained stability criteria is applied to simple linear systems and load frequency control (LFC) scheme of the two-area power system for stability analysis, and the effectiveness of proposed method is verified.

Revolutionizing travel experiences: An in-depth analysis of intelligent booking systems and behavioral patterns

This study delves into the intricacies of booking behaviors, focusing on the preference for tropical coastal cities during colder seasons, given the burgeoning need for personalized and intelligent travel recommendations in the modern tourism landscape. Traditional booking systems, with their static algorithms, have often neglected the dynamic interplay of environmental factors and user preferences, necessitating an evolution towards more sophisticated, adaptive models. To address this, the research employed advanced machine learning and artificial intelligence techniques, amalgamating data from diverse sources like major tourism websites, hotel platforms, and social media. The methodology involved meticulous data preprocessing to ensure data quality and depth, followed by the implementation of deep learning models and time series analysis, which were instrumental in deciphering complex patterns and offering predictive insights. This comprehensive approach facilitated the integration of various environmental factors such as weather conditions and temporal events, enabling the system to craft hyper-personalized recommendations that resonate with user preferences and environmental nuances. The findings were illuminating, unveiling a significant inverse relationship between temperature and bookings and highlighting the influential role of temporal events in driving bookings. The advanced predictive models showcased commendable accuracy, underscoring the transformative potential of such intelligent systems in enhancing user experiences and engagement in the tourism sector. These insights pave the way for the development of innovative strategies that can significantly elevate user satisfaction and industry growth. The study concludes that the integration of machine learning and AI in booking systems can revolutionize the tourism industry by offering unprecedented levels of personalization and adaptability, catering to the diverse and evolving needs of modern travelers.

A static and dynamic analysis of nonionic-based binary surfactant systems for adsorption mitigation in a carbonate reservoir with high salinity

Surfactants are crucial in chemical enhanced oil recovery, but high adsorption onto rocks reduces their efficiency and economic viability. Sacrificial agents, such as alkalis, nanoparticles, and polymers have been investigated to mitigate this issue but they in turn present drawbacks such as poor oil interaction, compatibility issues, pore plugging, and increased costs. Binary surfactant systems, due to their favorable potential synergistic interactions, are proposed as solutions for reducing surfactant adsorption. Static adsorption experiments were conducted to assess the effect of the binary surfactants on adsorption efficiency, and the impact of salinity on the adsorption process equilibrium was also studied. Five core flooding experiments under reservoir temperature (41∘C), reservoir pressure (21 MPa), and high salinity (23.4 wt.%) were conducted to evaluate dynamic adsorption. The surfactant concentrations in supernatant and effluent, after the adsorption process were determined via interfacial tension tests using a spinning drop method. The results showed a significant reduction in surfactant adsorption on carbonate rocks, from 9.99 mg/g-rock to 0.68 mg/g-rock for anionic surfactants and from 5.38 mg/g-rock to 0.19 mg/g-rock for zwitterionic surfactants, with corresponding dynamic reductions of 61% and 89%. Our results were further evidenced by the little to no changes in the differential pH curves in the adsorption process of the binary surfactant systems. This study presents a cost-effective approach for significantly reducing surfactant adsorption on carbonate rocks and aids in designing surfactant formulations for adsorption reduction in high-temperature, high-salinity carbonate reservoirs.

Propagated (fragment) Pipek-Mezey Wannier functions in real-time time-dependent density functional theory.

Localization procedures are an important tool for analysis of complex systems in quantum chemistry, since canonical molecular orbitals are delocalized and can, therefore, be difficult to align with chemical intuition and obscure information at the local level of the system. This especially applies to calculations obeying periodic boundary conditions. The most commonly used approach to localization is Foster-Boys Wannier functions, which use a unitary transformation to jointly minimize the second moment of the orbitals. This procedure has proven to be robust and fast but has a side effect of often mixing σ- and π-type orbitals. σ/π-separation is achieved by the Pipek-Mezey Wannier function (PMWF) approach [Lehtola and Jónsson, J. Chem. Theory Comput. 10, 642 (2014) and Jónsson et al., J. Chem. Theory Comput. 13, 460 (2017)], which defines the spread functional in terms of partial charges instead. We have implemented a PMWF algorithm in the CP2K software package using the Cardoso-Souloumiac algorithm to enable their application to real-time time-dependent density functional theory. The method is demonstrated on stacked CO2 molecules, linear acetylenic carbon, boron and nitrogen co-doped graphene, and nitrogen-vacancy doped diamond. Finally, we discuss its computational scaling and recent efforts to improve it with fragment approaches.

Diverse Applications of Two-Dimensional Correlation Spectroscopy (2D-COS).

This second of the two-part series of a comprehensive survey review provides the diverse applications of two-dimensional correlation spectroscopy (2D-COS) covering different probes, perturbations, and systems in the last two years. Infrared spectroscopy has maintained its top popularity in 2D-COS over the past two years. Fluorescence spectroscopy is the second most frequently used analytical method, which has been heavily applied to the analysis of heavy metal binding, environmental, and solution systems. Various other analytical methods including laser-induced breakdown spectroscopy, dynamic mechanical analysis, differential scanning calorimetry, capillary electrophoresis, seismologic, and so on, have also been reported. In the last two years, concentration, composition, and pH are the main effects of perturbation used in the 2D-COS fields, as well as temperature. Environmental science is especially heavily studied using 2D-COS. This comprehensive survey review shows that 2D-COS undergoes continuous evolution and growth, marked by novel developments and successful applications across diverse scientific fields.

NONLINEAR DYNAMICS METHODS APPLICATIONS TO PRODUCT LIFECYCLE MANAGEMENT

The research being carried out is aimed at applying nonlinear dynamics methods in product life cycle management to improve communication between processes and achieve environmental sustainability. The purpose of the article is to create a nonlinear mathematical model for analysing product lifecycle management. Chaotic nonlinear dynamical processes of product lifecycle are deterministic but have the property that even small uncertainties in initial conditions lead to exponentially growing errors in the management that limit long-term forecasts. The emergence of chaotic behaviour in processes of product lifecycle was explored, focusing on fluctuations. The study employed methods such as systems analysis, correlation analysis, nonlinear dynamics, and the Surrogate model of chaotic Lorenz waterwheel. It was identified that sharp technological innovations as the primary drivers of short-term fluctuations impacting fixed efficiency and effectiveness of product life cycle process development. The resulting nonlinear dynamic model allowed for flexible operation, transitioning between equilibrium, periodic, and chaotic states based on coefficient values for asset growth rates and time constants reflecting product life cycle process dynamics. The research results are valuable for companies seeking to reduce their environmental impact and implement circular economy principles.

Martini-Based Coarse-Grained Soil Organic Matter Model Derived from Atomistic Simulations.

The significance of soil organic matter (SOM) in environmental contexts, particularly its role in pollutant adsorption, has prompted an increased utilization of molecular simulations to understand microscopic interactions. This study introduces a coarse-grained SOM model, parametrized within the framework of the versatile Martini 3 force field. Utilizing models generated by the Vienna Soil Organic Matter Modeler 2, which constructs humic substance systems from a fragment database, we employed Swarm-CG to parametrize the fragments and subsequently assembled them into macromolecules. Direct Boltzmann inversion (DBI) facilitated the determination of bonded parameters between fragments. The parametrization yielded favorable agreement in the radius of gyration and solvent-accessible surface area. Transfer free energies exhibited a strong correlation with hexadecane-water and chloroform-water values, albeit deviations were noted for octanol-water values. Comparing densities of modeled Leonardite humic acid systems at coarse-grained and atomistic levels revealed promising agreement, particularly at higher water concentrations. The DBI approach effectively reproduced average values of bonded interactions between fragments. Radial distribution functions between carboxylate groups and calcium ions showed partial agreement, however, reproducing certain peaks was challenging due to fixed bead sizes. Detailed analysis of atomistic systems revealed different configurations between the groups, explaining discrepancies. The present contribution provides a comprehensive insight into the properties, strengths, and weaknesses of the coarse-grained SOM model, serving as a foundation for future investigations encompassing pollutant interactions and varied SOM compositions.

Through the Lens of the ICJ: Bridging the Gap between Human Rights and Humanitarian Norms in Global Child Soldiering

Recently in June 2022, the United Nations Special Representative of the Secretary-General for Children and Armed Conflict expressed that the ongoing use and recruitment of children in armed conflict globally warrants "international concern". Notwithstanding the existence of proscriptive norms in terms of international humanitarian law (IHL) and international human rights law (IHRL), international legal violations are nevertheless committed by states and non-state actors. A systematic analysis of the respective normative systems identifies a lacuna between specific provisions thereof. Consequently, violations are committed in armed conflicts for the purposes of IHRL but not IHL, it is contended that this inconsistency in the law perpetuates ongoing child soldiering. It is further contended that this inconsistency establishes a genus of children who are legally unprotected as they fall between the cracks of international law. Based on recent progressive jurisprudence of the International Court of Justice, an approach which will bridge the lacuna between these norms is proposed. Contrary to the view of the International Committee of the Red Cross and with due consideration for the lex specialis principle, it is proposed that the IHL and IHRL instruments should apply in a complementary fashion, as opposed to the separate legal regimes under which they have developed.

Sustainable freshwater/energy supply through geothermal-centered layout tailored with humidification-dehumidification desalination unit; Optimized by regression machine learning techniques

The application of regression machine learning techniques is crucial for the analysis and optimization of energy systems based on geothermal energy to produce freshwater, power, and heating. This study applied regression machine learning techniques to investigate and optimize a geothermal tri-generation energy system that combines double-flash geothermal energy, humidification-dehumidification desalination, and transcritical carbon dioxide Rankine cycle. The goal was to produce freshwater, power, and heating. The algorithms performed remarkably well, with R-squared values surpassing 96 %. It is worth mentioning that for specific parameters like freshwater production, heating capacity, and efficiency, the R-squared values exceeded an impressive 99 %. The optimum conditions include maintaining a Rankine pressure ratio of 3, a mass flow rate of 30 kg/s, a geothermal temperature of 220 °C, and a turbine pressure ratio of 2.2. By following these ideal parameters, it is possible to achieve the highest level of system performance. This will lead to the production of 7.88 kg/s of freshwater, the generation of 1283 kW of power, a heating capacity of 30.8 kg/s, and an impressive system efficiency of 24.98 %. The findings reveal that the integration of regression machine learning algorithms in geothermal energy systems for freshwater, power, and heating production holds great promise for a sustainable and efficient future.

Africa, ChatGPT, and Generative AI Systems: Ethical Benefits, Concerns, and the Need for Governance

This paper examines the impact and implications of ChatGPT and other generative AI technologies within the African context while looking at the ethical benefits and concerns that are particularly pertinent to the continent. Through a robust analysis of ChatGPT and other generative AI systems using established approaches for analysing the ethics of emerging technologies, this paper provides unique ethical benefits and concerns for these systems in the African context. This analysis combined approaches such as anticipatory technology ethics (ATE), ethical impact assessment (EIA), and ethical issues of emerging ICT applications with AI (ETICA) with specific issues from the literature. The findings show that ChatGPT and other generative AI systems raise unique ethical concerns such as bias, intergenerational justice, exploitation of labour and cultural diversity in Africa but also have significant ethical benefits. These ethical concerns and benefits are considered crucial in shaping the design and deployment of ChatGPT and similar technologies responsibly. It further explores the potential applications of ChatGPT in critical domain areas such as education, agriculture, and healthcare, thereby demonstrating the transformative possibilities that these technologies can have on Africa. This paper underscores the critical role of AI governance as Africa increasingly adopts ChatGPT and similar AI systems. It argues that a comprehensive understanding of AI governance is essential not only for maximising the benefits of generative AI systems but also for facilitating a global dialogue. This dialogue aims to foster shared knowledge and insights between the Global North and the Global South, which is important for the development and creation of inclusive and equitable AI policies and practices that can be beneficial for all regions.

Scaled graphs for reset control system analysis

Scaled graphs allow for graphical analysis of nonlinear systems, but are generally difficult to compute. The aim of this paper is to develop a method for approximating the scaled graph of reset controllers. A key ingredient in our approach is the generalized Kalman-Yakubovich-Popov lemma to determine input specific input–output properties of a reset controller in the time domain. By combining the obtained time domain properties to cover the full input space, an over-approximation of the scaled graph is constructed. Using this approximation, we establish a feedback interconnection result and provide connections to classical input–output analysis frameworks. Several examples show the relevance of the results for the analysis and design of reset control systems.

Optimized Grid-Connected Hybrid Renewable Energy Power Generation: A Comprehensive Analysis of Photovoltaic, Wind, and Fuel Cell Systems

Optimized Grid-Connected Hybrid Renewable Energy Power Generation: A Comprehensive Analysis of Photovoltaic, Wind, and Fuel Cell Systems

Economic Analysis of Solid Oxide Fuel Cell Systems Utilizing Natural Gas as Fuel

Solid oxide fuel cell power generation systems are devices that utilize solid electrolytes to transfer ions for electrochemical energy conversion. A wide range of gases can be used as fuel gas, including hydrogen, natural gas, and carbon monoxide. Considering the high cost of pure hydrogen, hydrogen production from natural gas reforming has become a hot research area. In this study, the 4F-LCA method was employed to construct an evaluation framework, with a particular emphasis on the cost analysis of solid oxide fuel cell power generation systems, and uses a bottom-up approach to build a system economic analysis model to visualize the major costs involved in the system. An economic benefit analysis and sensitivity analysis were carried out for the 2 kW natural gas solid oxide fuel cell as a case by taking the financial net present value (NPV), internal rate of return (IRR) and payback period into account. In this study, the investment cost and payback period of a 2 kW solid oxide fuel cell system are obtained, which can provide a reference for the project construction and operation of solid oxide fuel cell systems.

Analysis of Multiantenna UAV-Aided NOMA Relay Systems for Short-Packet Communications between Multiuser Pairs

Analysis of Multiantenna UAV-Aided NOMA Relay Systems for Short-Packet Communications between Multiuser Pairs

High-Accuracy Analytical Model for Heterogeneous Cloud Systems with Limited Availability of Physical Machine Resources Based on Markov Chain

The article presents the results of a study on modeling cloud systems. In this research, the authors developed both analytical and simulation models. System analysis was conducted at the level of virtual machine support, corresponding to Infrastructure as a Service (IaaS). The models assumed that virtual machines of different sizes are offered as part of IaaS, reflecting the heterogeneous nature of modern systems. Additionally, it was assumed that due to limitations in access to physical server resources, only a portion of these resources could be used to create virtual machines. The model is based on Markov chain analysis for state-dependent systems. The system was divided into an external structure, represented by a collection of physical machines, and an internal structure, represented by a single physical machine. The authors developed a novel approach to determine the equivalent traffic, approximating the real traffic appearing at the input of a single physical machine under the assumptions of request distribution. As a result, it was possible to determine the actual request loss probability in the entire system. The results obtained from both models (simulation and analytical) were summarized in common graphs. The studies were related to the actual parameters of commercially offered physical and virtual machines. The conducted research confirmed the high accuracy of the analytical model and its independence from the number of different instances of virtual machines and the number of physical machines. Thus, the model can be used to dimension cloud systems.

Design and Outage Analysis for VLP-Assisted Indoor Laser Communication Systems

Design and Outage Analysis for VLP-Assisted Indoor Laser Communication Systems