Hasil untuk "Systems engineering"

Zeyu Zhang, Long Wan, Yong Yang et al.

Overcoming the strength-ductility trade-off dilemma is paramount for advanced materials engineering. Herein, we prepared 7075 aluminium alloys with superior strength and ductility via additive friction stir deposition (AFSD) and subsequent heat treatment. Compared with the commercial base material, the heat-treated 7075 aluminium alloy maintained a high ultimate tensile strength of 556 MPa, while the uniform elongation increased from 12.2% to 26.7%, exhibiting the highest strength-ductility synergy reported among commercial Al-Zn-Mg-Cu alloy systems. Grain boundary sliding was activated via the equiaxed grains to accommodate substantial plastic strain. This method provides a promising and cost-effective pathway for developing strength-ductility on Al-Zn-Mg-Cu alloys.

Milkias Berhanu Tuka, Seid Endris Ali

The increasing demand for electric power, coupled with rapid urbanization, necessitates a reliable and high-quality electricity supply to meet consumer expectations. However, existing passive distribution systems are inadequate to address the escalating power requirements, resulting in challenges such as increased power losses and suboptimal voltage profiles. In the base case scenario, the total active and reactive power losses were substantial, and many buses exhibited voltage magnitudes that fell outside acceptable limits. This study investigates the optimal placement and sizing of distributed generation (DG) resources to improve the performance of distribution feeders. A multi-objective optimization framework, utilizing a Genetic Algorithm (GA), was developed to minimize power losses and enhance voltage profiles. Load flow analysis was conducted using the Backward/Forward Sweep (BFS) method, allowing for precise evaluation of the distribution feeder under various DG configurations. Consequently, the study successfully enhanced the system through optimal DG allocation. Additionally, a comparative analysis was conducted to assess the performance of the proposed GA algorithm against other optimization techniques. The results indicate that, in nearly all cases, the GA method outperforms PSO by reducing system power losses and improving the voltage profile more effectively.

Ivana Valentina Lemmuela, Mewati Ayub, Oscar Karnalim

Background: Communication is important for everyone, including individuals with hearing and speech impairments. For this demographic, sign language is widely used as the primary medium of communication with others who share similar conditions or with hearing individuals who understand sign language. However, communication difficulties arise when individuals with these impairments attempt to interact with those who do not understand sign language. Objective: This research aims to develop models capable of recognizing sign language movements in Bahasa and converting the detected gesture into corresponding words, with a focus on vocabularies related to religious activities. Specifically, the research examined dynamic sign language in Bahasa, which comprised gestures requiring motion for proper demonstration. Methods: In accordance with the research objective, sign language recognition model was developed using MediaPipe-assisted extraction process. Recognition of dynamic sign language in Bahasa was achieved through the application of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) methods. Results: Sign language recognition model developed using bidirectional LSTM showed the best result with a testing accuracy of 100%. However, the best result for the CNN alone was 86.67 %. The integration of CNN and LSTM was observed to improve performance than CNN alone, with the best CNN-LSTM model achieving an accuracy of 95.24%. Conclusion: The bidirectional LSTM model outperformed the unidirectional LSTM by capturing richer temporal information, with a specific consideration of both past and future time steps. Based on the observations made, CNN alone could not match the effectiveness of the Bidirectional LSTM, but a combination of CNN with LSTM produced better results. It is also important to state that normalized landmark data was found to significantly improve accuracy. Accuracy within this context was also influenced by shot type variability and specific landmark coordinates. Furthermore, the dataset containing straight-shot videos with x and y coordinates provided more accurate results, dissimilar to those comprised of videos with shot variation, which typically require x, y, and z coordinates for optimal accuracy. Keywords: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), MediaPipe, Sign Language

C. E. Ngene, Prabhat Thakur, Ghanshyam Singh

ABSTRACT This paper proposes a controlled signal technique for visible light non‐orthogonal multiple access (VL‐NOMA) communication in an interference‐controlled environment with intelligent reflecting surfaces (IRS) for beyond 5G (B5G) and 6G communication networks. The light‐emitting diode (LED) is used for carrier signal generation to transmit signals to the two users (photodiodes, PDs) due to its advantages, such as its programmable nature and flexibility. The potential challenge is how the signals could be controlled with an IRS approach, which prompted this research. We have used IRS, which is a cutting‐edge enabling technology that modifies the signal's reflection by utilizing numerous inexpensive passive reflecting elements to improve the signal's performance. Furthermore, deep reinforcement learning (DRL) is deployed to control the reflected signals, simulate, make decisions, and link LED‐IRS‐PDs, redirecting the signals. The entire system is successfully synchronized, and then the bit error rate (BER), line of sight (LOS), and non‐line of sight (NLOS) performances are investigated. Furthermore, we place a blocker at the center of the model as a NLOS to check how the transmitted signals will perform. We observed that the propagated signal improved the BER as per LOS, hence, the NLOS blocker reduced the signal's performance. Furthermore, we optimized the signals to investigate BER, LOS, and NLOS signal performance. We observed that LOS signals performed better than NLOS signals.

Anshu Kumari, Manish Tiwari, Rahul Mor et al.

Abstract Gender and sustainability are crucial in agriculture, which remains a significant source of global employment. However, urbanization, industrialization, and technological advancements have reshaped the sector, impacting labor dynamics and gender roles. Traditional agricultural labor faces challenges due to low wages, physically demanding tasks, and unfavorable working conditions. Addressing gender disparities and promoting inclusive work environments is essential for achieving sustainability. According to the ILO (International Labour Office) decent work encompasses productivity and equal employment opportunities for both genders. This study aims to review the literature on gender, sustainability and agricultural development using a bibliometric analysis of Scopus-indexed articles. The findings identify five main research domains: gender dynamics and roles, agriculture and climate change, sustainability and development, human and labor dynamics, and environmental and technological aspects. Additionally, four key scientific communities led the research: Gender studies, agricultural economics, environmental management, and rural sociology. Emerging research trends focus on gender roles in sustainable farming, environmental innovation, and labor governance in agriculture. Spain, the United Kingdom, United States, and Canada lead in knowledge production, contributing significantly to these research domains. This review highlights the importance of interdisciplinary approaches to address the complex issues of gender and sustainability in agriculture. It also specifies a target for expectations research, highlighting that the ILO’s definition of appropriate employment can guide efforts to improve gender equity and labor conditions, ultimately supporting sustainable development in the agricultural sector.

Longjian Piao, Laurens de Vries, Mathijs de Weerdt et al.

Future energy markets for low voltage AC and DC distribution systems will facilitate prosumer participation in the market. To comply with market regulations and grid constraints, a tailored market design reflecting (DC) operational requirements is needed. Our previous work identified a locational energy market design. However, its real-life implementation faces challenges due to uncertainties in system operation, prosumer preferences, and bidding strategies. This article tests the market design under uncertain scenarios. To this end, we develop an agent-based model that simulates typical electric vehicle user preferences and bidding strategies, influenced by varying degrees of range anxiety. The market design is tested in challenging scenarios with a high share of solar panels and electric vehicles, modelled using the high-resolution Pecan Street database. Simulations indicate that the proposed market design maintains both economic efficiency and system reliability under real-life uncertainties. This in turn indicates the practical feasibility of locational energy markets in helping to integrate renewable generation sources and bidirectional power flows.

Dino Petrosa, Pamela Haverkamp, Jana Gerta Backes et al.

Abstract The construction sector significantly contributes to environmental issues and often relies on Life Cycle Assessment (LCA) for the quantification and optimization of its environmental impacts. One of the most time- and labour-intensive tasks in LCA is matching real elements (e.g., construction elements and materials) to suitable environmental datasets to get an idea of the element’s sustainability performance (emissions). In this regard, this study presents an open-access software tool that leverages artificial intelligence (AI) to support the matching process between construction elements in Building Information Modelling (BIM) with corresponding environmental datasets in a semi-automatic manner. Developed in Python and using the GPT-4o mini model from OpenAI for its matching mechanism, the tool demonstrates how AI-driven digital innovation can improve efficiency, reduce manual effort, and enhance early-stage environmental assessment in construction planning, while integrating sustainability data into BIM workflows. Through a series of use cases, the software’s ability to address key challenges in the integration of BIM and LCA tools is demonstrated, showcasing a high degree of automation and interoperability. Moreover, the accessible design of the tool allows use without extensive technical knowledge. The conducted validation tests confirmed the tool’s potential for accurate LCA matching, highlighting opportunities for AI to enhance sustainability workflows while offering BIM experts a better understanding of the challenges in sustainability assessment.

Dongdong Song, Lijun Geng, Zhendong Liu et al.

The on-load tap changer (OLTC), widely used as a voltage regulation device in power systems, requires regular assessment and maintenance to ensure reliable operation and avoid adverse impacts on the power system. These assessments encompass key parameters such as transition waveform, transition time, three-phase synchronization, and transition resistor, along with the operational status of the mechanical structure. However, the maintenance process, typically conducted offline, can diminish equipment efficiency. Moreover, the accuracy of some parameter measurements needs improvement. To bolster equipment reliability and refine detection methods for critical parameters, this study explores online detection techniques for key switching parameters of the OLTC body. This paper proposes a method to identify these key parameters in the switching circuit, using coordinate transformation as the core algorithm. We used a specific vacuum OLTC device for our research, conducted theoretical analyses, developed a simulation model to validate the proposed method for identifying OLTC switching parameters, and further built a test platform to verify the algorithm’s effectiveness. The results show a close alignment between simulation and actual measurement outcomes. Each switching process interval conforms to the manufacturer’s design specifications for the equipment, with the transition resistor parameter calculation accuracy ranging from approximately 95.39% to 100%. Similarly, the tap winding voltage calculation accuracy is between approximately 91.52% and 100%, satisfying engineering requirements and enterprise standard [1]. This method provides a basis for optimizing the measurement of working parameters in OLTC equipment and aims to offer ideas for the next step of prototype development.

Judith Owokuhaisa, Eleanor Turyakira, Frank Ssedyabane et al.

Abstract Background Cervical cancer continues to threaten women’s health, especially in low-resource settings. Regular follow-up after screening and treatment is an effective strategy for monitoring treatment outcomes. Consequently, understanding the factors contributing to patient non-attendance of scheduled follow-up visits is vital to providing high-quality care, reducing morbidity and mortality, and unnecessary healthcare costs in low-resource settings. Methods A descriptive qualitative study was done among healthcare providers and patients who attended the cervical cancer screening clinic at Mbarara Regional Referral Hospital in southwestern Uganda. In-depth interviews were conducted using a semi-structured interview guide. Interviews were audio-recorded, transcribed verbatim, and thematically analysed in line with the social-ecological model to identify barriers and facilitators. Results We conducted 23 in-depth interviews with 5 healthcare providers and 18 patients. Health system barriers included long waiting time at the facility, long turnaround time for laboratory results, congestion and lack of privacy affecting counselling, and healthcare provider training gaps. The most important interpersonal barrier among married women was lacking support from male partners. Individual-level barriers were lack of money for transport, fear of painful procedures, emotional distress, and illiteracy. Inadequate and inaccurate information was a cross-cutting barrier across the individual, interpersonal, and community levels of the socio-ecological model. The facilitators were social support, positive self-perception, and patient counselling. Conclusions Our study revealed barriers to retention in care after cervical cancer screening, including lack of partner support, financial and educational constraints, and inadequate information. It also found facilitators that included social support, positive self-perception, and effective counselling.

FENG Jianghua, TAN Bo, DOU Zechun et al.

With the continuous advancement of China's "dual carbon" goals and the ongoing optimization of the energy mix, the electrification of transportation equipment, as a low-carbon and environmentally-friendly approach, has become an important development trend in the transportation industry. This paper presents the exploration in high-power-density electrification technologies for transportation equipment, focusing on those in the chain-type key technology routes encompassing devices, components, equipment, systems and architectures. Taking products supplied by CRRC Zhuzhou Institute Co., Ltd. as an case study, detailed investigations were made into five key technologies for high-power-density design: high-frequency converters, customized devices, silicon-based equipment, structural integration, and diversified networking, and three high-power-density generic technologies: thermal management, electromagnetic compatibility, and reliability, highlighting their key roles in improving the performance, efficiency, and reliability of transportation equipment. The study summarizes the current research status concerning the development of transportation equipment towards higher power, lighter weight, and smaller size. For future development in high-power-density electrification technologies, this paper suggests a focus on continuous innovation and development in four areas: the innovation chain, intelligent systems, new power semiconductor device technologies, and safety. The research outcomes provide strong support for the green transformation and sustainable development of the transportation industry.

Subrata Chakraborty, Biswajeet Pradhan

Machine learning (ML) models have experienced remarkable growth in their application for multimodal data analysis over the past decade [...]

Nader Mohamed, Jameela Al-Jaroodi, Imad Jawhar et al.

Healthcare systems are complex systems that need effective and efficient operations, optimizations, management, and control to offer reliable, high-quality, and cost-effective healthcare services. There are different approaches to improve the management of healthcare systems including utilizing the healthcare systems engineering principles. Healthcare systems engineering views a healthcare organization as a system and applies the engineering analysis and design principles to improve different aspects of healthcare services provided in that system. While this approach can provide many advantages for healthcare organizations, there are also many challenges hindering the ability of healthcare systems engineers from effectively accomplishing their mission. The initiation of the digital twin technology formed several potential methods for various industrial sectors to enhance their operations. Accordingly, they can help improve productivity, cost-effectiveness, reliability, quality, and flexibility. This paper studies how digital twins can be utilized for improving healthcare systems engineering processes and outcomes to enhance different aspects of healthcare systems. The paper discusses some of the challenges of healthcare systems engineering and how these challenges can be relaxed by utilizing digital twins. The paper also develops a conceptual framework to utilize digital twins for improving healthcare systems engineering processes and outcomes and discusses the prospects of such utilization on achieving the goals of healthcare systems engineering. In addition, the paper provides some discussions on the impact of this utilization and the future research and development projections of the employment of digital twins for healthcare systems engineering.

Saad Kelam, Mohamed Chennafa, Mohamed Belkheiri et al.

The main objective of this paper is to solve state observation and external disturbances estimation for a class of second-order nonlinear systems. The proposed method relies mainly on the high gain observer as an estimator that tries to estimate the state vector and at the same time identifies the system’s unknown combined structured and unstructured uncertainties. The efficiency of the proposed method is demonstrated by estimating the flux and the speed of the induction motor by simulation.

Georgios Petmezas, Grigorios-Aris Cheimariotis, Leandros Stefanopoulos et al.

Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements the focal loss (FL) function to deal with training data imbalance. Features initially extracted from short-time Fourier transform (STFT) spectrograms via a convolutional neural network (CNN) are given as input to a long short-term memory (LSTM) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. The model was trained and tested on the ICBHI 2017 Respiratory Sound Database and achieved state-of-the-art results using three different data splitting strategies—namely, sensitivity 47.37%, specificity 82.46%, score 64.92% and accuracy 73.69% for the official 60/40 split, sensitivity 52.78%, specificity 84.26%, score 68.52% and accuracy 76.39% using interpatient 10-fold cross validation, and sensitivity 60.29% and accuracy 74.57% using leave-one-out cross validation.

Martin Spiller, Corinna Müller, Zara Mulholland et al.

Reducing the carbon emissions from hotels on non-interconnected islands (NII) is essential in the context of a low carbon future for the Mediterranean region. Maritime tourism is the major source of income for Greece and many other countries in the region, as well as hot-temperate and tropical regions worldwide. Like many NIIs, Rhodes attracts a high influx of tourists every summer, doubling the island’s energy demand and, given the high proportion of fossil fuels in the Rhodian energy supply, increasing carbon emissions. Using the theoretical framework ‘FINE’, this paper presents the optimisation of a medium-sized hotel’s energy system with the aim of reducing both cost and carbon emissions. By introducing a Photovoltaic (PV) net metering system, it was found that the carbon emissions associated with an NII hotel’s energy system could be reduced by 31% at an optimised cost. It is suggested that large-scale deployment of PV or alternative renewable energy sources (RES) in NII hotels could significantly reduce carbon emissions associated with the accommodation sector in Greece and help mitigate climate change.

Michael D. Watson, Bryan Mesmer, Phillip Farrington

Zhou Li, Yazhou Li, Ruopei Zhan et al.

Meshed dc grid technology is an attractive solution to improve the operational reliability of a voltage source converter-based multi-terminal HVDC transmission (VSC-MTDC) system; in addition, the VSC-MTDC becomes widely accepted for the integration of large-scale renewable energy and power supply to passive ac grids. Hence, the performance requirements of the dc voltage control and the power control of the VSC-MTDC system become higher, and this is the key issue of coordinated control strategies for the VSC-MTDC system. Considering that the mainstream coordinated control under the above new situations is facing challenges in terms of dc voltage dynamic control performance or dynamic power deviation, this paper proposes an ac grids characteristic-oriented multi-point voltage coordinated control strategy for the VSC-MTDC system to provide satisfactory dynamic dc voltage control performance with minimized dynamic and steady state power deviations of the converter stations. The theoretical analysis and the simulations results based on PSCAD/EMTDC are to verify the effectiveness of the proposed control strategy.

Stanisław KRAWIEC, Bogusław ŁAZARZ, Sylwester MARKUSIK et al.

The programing documents of the European Union determine the direction of transport systems development, including large cities and agglomerations. The context of these actions which aim to transform into ecologically clean and sustainable transport system is a significant reduction of greenhouse gas emissions. Assuming that public transport will significantly reduce the use of combustion-powered buses, studies on urban logistic enabling the use of electric buses for public transport are needed. The article presents the variants and scenarios for electric buses implementation in urban public transport, as well as the decision algorithm to support electric bus implementation based on technological, organisational, economic and ecological variables.

Jankowska-Polańska B, Dudek K, Szymanska-Chabowska A et al.

Beata Jankowska-Polańska,1 Krzysztof Dudek,2 Anna Szymanska-Chabowska,3 Izabella Uchmanowicz1 1Department of Clinical Nursing, Faculty of Health Science, Wroclaw Medical University, 2Department of Logistic and Transport Systems, Faculty of Mechanical Engineering, Wroclaw University of Technology, 3Department of Internal Medicine, Occupational Diseases, Hypertension and Clinical Oncology, Wroclaw Medical University, Wroclaw, Poland Background: Hypertension affects about 80% of people older than 80 years; however, diagnosis and treatment are difficult because about 55% of them do not adhere to treatment recommendations due to low socioeconomic status, comorbidities, age, physical limitations, and frailty syndrome.Aims: The purposes of this study were to evaluate the influence of frailty on medication adherence among elderly hypertensive patients and to assess whether other factors influence adherence in this group of patients.Methods and results: The study included 296 patients (mean age 68.8±8.0) divided into frail (n=198) and non-frail (n=98) groups. The Polish versions of the Tilburg Frailty Indicator (TFI) for frailty assessment and 8-item Morisky Medication Adherence Scale for adherence assessment were used. The frail patients had lower medication adherence in comparison to the non-frail subjects (6.60±1.89 vs 7.11±1.42; P=0.028). Spearman’s rank correlation coefficients showed that significant determinants with negative influence on the level of adherence were physical (rho =-0.117), psychological (rho =-0.183), and social domain (rho =-0.163) of TFI as well as the total score of the questionnaire (rho =-0.183). However, multiple regression analysis revealed that only knowledge about complications of untreated hypertension (β=0.395) and satisfaction with the home environment (β=0.897) were found to be independent stimulants of adherence level.Conclusion: Frailty is highly prevalent among elderly hypertensive patients. Higher level of frailty among elderly patients can be considered as a determinant of lower adherence. However, social support and knowledge about complications of untreated hypertension are the most important independent determinants of adherence to pharmacological treatment. Keywords: frailty syndrome, ageing, hypertension, medication adherence, geriatric syndrome