Search Results (66)

Background/Objectives: Challenges in normothermic machine perfusion (NMP) remain, particularly concerning the duration for which individual organs can be safely preserved. We hypothesize that optimal preservation can be achieved by perfusing organs together in a multivisceral block. Therefore, our aim was to establish a platform for ex situ multivisceral organ perfusion. Methods: Multivisceral grafts containing the liver, kidneys, pancreas, spleen, and intestine were obtained from Yorkshire pigs. Three generation (gen) set-ups were tested during the iterative design process, and minor changes were made throughout. Gen 1 (n = 4) used a custom-designed single perfusion circuit. Gen 2 (n = 3) employed a dual perfusion circuit. Gen 3 (n = 4) featured a single perfusion circuit with an optimized basin and reservoir. Grafts underwent NMP using an autologous blood-based perfusate, while hemostatic parameters and function were assessed. Results: Comparing Gen 1 versus Gen 3, the mean aortic flow improved (1.018 vs. 2.089 L), resistance decreased (0.224 vs. 0.038), urine output increased (51.90 vs. 271.3 mL), oxygen consumption rose (43.56 vs. 49.52 mL O₂/min), perfusate lactate levels dropped (10.44 vs. 3.10 mmol/L), and the pH became more physiological (7.27 vs. 7.30). Cellular injury trended lower in Gen 3. Histological evaluation demonstrated minimal differences in Gens 2 and 3. Conclusions: We demonstrate the feasibility of abdominal multivisceral NMP for up to 8 h. Adequate arterial flow, stable perfusate pH, and high oxygen consumption in setup 3 indicated organ viability. Multivisceral perfusion may serve as a plat-form for long-term NMP. Full article

The rapid growth of e-commerce has intensified the need for efficient and sustainable last-mile delivery solutions in urban environments. This paper explores the integration of electric vehicles (EVs) and artificial intelligence (AI) into a combined framework to enhance the environmental, operational, and economic performance of urban logistics. Through a comprehensive literature review, we examine current trends, technological developments, and implementation challenges at the intersection of smart mobility, green logistics, and digital transformation. We propose an operational framework that leverages AI for route optimization, fleet coordination, and energy management in EV-based delivery networks. This framework is validated through a real-world case study conducted in Lisbon, Portugal, where a logistics provider implemented a city consolidation center model supported by AI-driven optimization tools. Using key performance indicators—including delivery time, energy consumption, fleet utilization, customer satisfaction, and CO₂ emissions—we measure the pre- and post-AI deployment impacts. The results demonstrate significant improvements across all metrics, including a 15–20% reduction in delivery time, a 10–25% gain in energy efficiency, and up to a 40% decrease in emissions. The findings confirm that the synergy between EVs and AI provides a robust and scalable model for achieving sustainable last-mile logistics, supporting broader urban mobility and climate objectives. Full article

Food consumption plays a fundamental role in human life, shaping not only daily nutrition but also economic and social behaviors. Household expenditures on food remain among the highest, and the time allocated to food purchasing and preparation is significant. Beyond biological necessity, food represents a cultural and social phenomenon, influencing consumer habits, market trends, and lifestyle choices. This study explores the key factors influencing purchasing decisions in the agro-food sector in the North-East Region of Romania, focusing on socio-cultural influences, product attributes, brand trust, tradition, and lifestyle. The research employs a quantitative approach, utilizing a structured questionnaire to collect data from 337 residents through a convenience sampling strategy. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4.0 to examine the interrelationships between subjective norms, product attributes, price, consumer trust, and purchasing decisions. The findings indicate that social norms, price, product attributes, brand trust, customer satisfaction, and lifestyle have a significant and positive impact on consumer purchasing decisions. By understanding the key drivers behind sustainable food choices, businesses can optimize product offerings and enhance consumer engagement, while policymakers can design initiatives that promote sustainable consumption at both regional and national levels. Full article

In South Korea, Generation Z’s influence on the growth of sports over-the-top (OTT) services is significant. For sustainable growth, OTT service providers need to focus on converting existing users into loyal customers. In this context, understanding Generation Z’s subjective perceptions of the constraints on the continued use of sports OTT services would be meaningful. Therefore, this study aims to identify and categorize the factors constraining Generation’s Z’s continued use of sports OTT services using the Q methodology. A total of 25 Q samples and 18 P samples were selected. The QUANL program was used to calculate the correlation coefficients for each respondent, and Q factor analysis was performed through principal component analysis and varimax rotation. As a result, three types were identified: ‘Demand for improvements in pricing plans and system quality’, ‘Dissatisfaction with subscription fees and demand for diverse content’, and ‘Demand for differentiated services’. The findings of this study suggest that, for Generation Z’s continued use of OTT services, it is essential to provide personalized experiences and benefits or satisfaction relative to subscription fees. On top of that, offering various pricing plans can provide flexibility in service choices and encourage continued use among Generation Z. As such, this study can provide insights into appreciating the consumption trends and factors influencing the continued use of OTT services by Generation Z and interpreting related research results in the future. Full article

In recent years, the global economy has experienced significant shifts, leading to a trend of consumption downgrading. Amid economic pressures and uncertainties, consumers are increasingly turning to cost-effective shopping methods. The COVID-19 lockdowns further accelerated the growth of e-commerce platforms, presenting both opportunities and challenges for sales. Electronic commerce has played a crucial role in enhancing the sales of agricultural products with regional characteristics in China, thereby opening new channels for farmers. This article utilizes tangerines, particularly popular in Zhejiang Province, as a case study to explore e-commerce reviews and assist merchants in delivering more satisfactory products. The analysis of tangerine reviews revealed that customers primarily focused on the taste, service, quality, and price. By applying the latent Dirichlet allocation (LDA) topic model, comments were categorized into four themes: ‘quality’, ‘service’, ‘price’, and ‘flavor’, with key terms identified for each theme. Through sentiment analysis using SnowNLP and bidirectional encoder representations from transformers (BERT), it was found that online shoppers generally expressed positive sentiment toward tangerines. However, there was also some negative feedback. These findings are of paramount importance for businesses aiming to meet consumer demands. The study acknowledges certain limitations including the reliability of data mining and the accuracy of Chinese corpus analysis. Future research could benefit from employing more precise language models to enhance the analysis, ultimately improving the consumer shopping experience and aiding businesses in service improvement. Full article

The rapid advancement of deep neural networks has significantly increased demands for computational complexity and data volume. This trend is especially evident with the emergence of large language models, which have rendered traditional architectures such as CPUs and GPGPUs insufficient in meeting performance and energy efficiency requirements. Spatial accelerators present a promising solution by optimizing on-chip compute, storage, and communication resources. In exploring spatial accelerator design spaces, analytical model-based simulators and cycle-accurate simulators are commonly employed, each offering distinct advantages: high computational speed and superior simulation accuracy, respectively. However, the limited accuracy of analytical models and the slow simulation speed of cycle-accurate simulators impede the achievement of globally optimal solutions during design space exploration. Therefore, effectively leveraging the strengths of both simulator types while mitigating their inherent trade-offs is a critical challenge in designing customized spatial accelerators. In this work, we introduce a novel co-exploration methodology that integrates both coarse-grained and fine-grained approaches to navigate design and mapping spaces effectively. We utilize the rapid simulation capabilities of analytical models to perform coarse-grained global exploration, quickly eliminating designs and mapping configurations with inferior performance. Building on the results of this initial exploration, we employ cycle-accurate simulators to conduct fine-grained local exploration within the identified promising regions of the design and mapping spaces. This dual-phase approach aims to identify optimal hardware designs and dataflow mapping strategies that enhance performance and energy efficiency. The experimental results demonstrate that, compared to state-of-the-art methods, our approach reduces the number of exploration points by up to 99%, while achieving a 17.9% reduction in latency, a 2.5% decrease in energy consumption, and a 30.3% improvement in throughput. Full article

The attitude tracking control for spacecraft with limited communication and actuator faults is investigated in this paper by employing event-trigger-based prescribed control. Traditional methods struggle to address arbitrary initial conditions and fault-induced saturation, which both lead to prescribed control singularities, limiting practical deployment. This paper proposes the adhesive-resilient prescribed control (ARPC), which dynamically adjusts the performance envelope by sensing fault and error trends through resilient correction and an adhesive mechanism, respectively. This approach significantly enhances conservatism and robustness, particularly under actuator faults that exceed the saturation level. Additionally, the challenge of balancing high performance with low communication burden under limited resources is addressed. To mitigate communication frequency and bit consumption without sacrificing performance, a z-ary compression event-triggered scheme (CES) is introduced. Compared to existing methods, this work provides substantial improvements in fault tolerance, communication efficiency, and performance adaptability. Numerical experiments demonstrate the superiority of our method in regulating tracking error within a custom envelope and appointed time, regardless of initial conditions, while minimizing communication usage. Full article

Copper anode slime (CAS) contains high concentrations of precious metals, particularly gold, which can reach up to 11 wt%. During copper anode electrorefining, 5–10 kg of CAS is generated per ton of copper cathode. Processing CAS is crucial for economic reasons, as gold significantly contributes to revenue for both miners and custom copper smelters. This paper provides a comprehensive review of industrial processes and technologies for CAS treatment, with a focus on gold recovery, covering studies from the early 1930s to the present. It documents traditional and recent trends and analyzes the advantages and disadvantages of existing methods. Key factors affecting revenue, such as gold production lead time, in-process inventory, and first-pass recovery rate, are discussed to mitigate losses in fluctuating gold markets. CAS processing routes are categorized into two main groups: traditional hybrid routes (involving hydrometallurgy, pyrometallurgy, and electrorefining/electrowinning) and rather recent purely hydrometallurgical routes. Traditional methods can take up to 45 days, with gold recovery occurring late in the process and losses arising in the anode, cathode, electrolyte, or slag. In contrast, purely hydrometallurgical routes have total processing times of 7–8 days, achieve early gold recovery, and can attain first-pass recovery rates as high as 99%. Additionally, the hydrometallurgical routes are more environmentally friendly, with lower pollution levels and reduced energy consumption compared to hybrid routes. These findings indicate that purely hydrometallurgical routes outperform traditional hybrid methods. This paper aims to serve as a guideline for industrial CAS processing, assisting custom copper smelters in navigating challenging market conditions marked by low treatment and refining charges, with an emphasis on enhancing gold recovery to promote sustainability. Full article

An advanced IoT-based Liquefied Petroleum Gas (LPG) cylinder monitoring and safety system is presented in this work. The proposed technique provides continuous monitoring of residential gas usage and detects any potential leakage. It utilizes an MQ135 gas sensor for gas leakage detection, a load cell to monitor the weight of the cylinder, and a DHT22 sensor for temperature sensing. The sensors are mounted on a customized trolley for domestic LPG cylinders. All the sensors are connected to a NodeMCU microcontroller, which exchanges sensor data with a cloud platform using HTTP GET and POST methods to transmit the data to a cloud-based MySQL database. Unlike other existing methods, the proposed approach does not necessitate any modifications to the existing setup, which includes the gas cylinder, regulating valve, and distribution pipe. Furthermore, a mobile application that emphasizes the needs of the user is developed to enable a wider range of functionalities using cloud data collected from the sensors. The software facilitates the real-time monitoring of gas levels, provides comprehensive usage records for daily, weekly, and monthly intervals, issues immediate alarms in the event of gas leakage and low gas levels, and detects any unauthorized movement of the LPG cylinder, such as theft. The proposed technique not only improves user safety but also streamlines gas cylinder management with predictive analytics based on gas consumption trends and projected days of usage. Moreover, the application includes functionality that automatically orders a new cylinder with the vendor when the gas level drops below a predetermined threshold, therefore ensuring continuous availability of gas supply. Full article

In recent years, the worldwide plant-based meat sector has undergone substantial and rapid expansion. The swift advancement of plant-based meat products in the Chinese market is ascribed to changes in customer dietary preferences. To accelerate the rapid expansion of China’s plant-based meat sector, it is essential to conduct research on consumer demand trends. Citespace was utilized in this study to conduct a bibliometric analysis of research pertaining to plant-based meat. A study model was then created to analyze the primary elements affecting the consumption behavior of plant-based meat products. This study employs Zhengzhou as a case study to construct a research model to examine consumers’ inclination to purchase plant-based meat products. The model is derived from survey data obtained from 570 consumers. The findings indicate that the characteristics of plant-based meat products significantly influence consumers’ purchase intentions and consumption behaviors by shaping their perceptual activity. Price rationality, technical security, and flavor richness are three principal factors influencing customer purchasing of plant-based meat products. The perceived value and trust of consumers can somewhat mediate the influence of plant-based meat consumption behavior. This study offers significant insights into purchasing intentions and consumer behavior in first-tier cities in China. The outcomes of this study can provide a beneficial framework for imitation meat producers to improve product development and stimulate customer interest in the plant-based meat market. Full article

In the construction sector, hospitals are the buildings with the highest energy consumption. Due to the high demand for energy, hospitals’ energy efficiency is becoming very important. This study aims to examine the trends and factors that determine energy consumption in Polish hospitals from 2010 to 2019, highlighting the impact of hospital size and medical activities on energy efficiency. The analysis was carried out using data from 3061 hospital reports obtained from the e-Health Center, a state budgetary unit established by the Minister of Health. To measure and compare the efficiency of energy usage in hospitals, we developed eight energy usage efficiency indexes based on hospital size and medical activity. The size of the hospitals was described by the number of beds, operation rooms, doctors, nurses, and fixed assets value. Hospital activity was measured by the number of person-days, patients, and operations. Statistical analysis was carried out using StatSoft Statistica software version 13.3. The results show that larger hospitals are more energy efficient across various measures of energy use than smaller hospitals. The findings revealed also several important relationships between energy usage and factors connected with size and hospital activity, such as the number of beds, patients and person-days, medical staff, operations, and fixed asset values, underscoring the necessity for customizing energy efficiency strategies. This research contributes empirical insights that can guide policymakers and hospital administrators in their endeavors to improve energy efficiency and promote sustainability within healthcare facilities. Full article

In an era marked by the expansion of the Internet economy and the intensification of environmental concerns, the convergence of digital finance and green finance has emerged as a significant global trend. China’s Alipay Ant Forest, an innovative green financial product, has successfully quantified carbon emission reductions resulting from users’ green consumption patterns, establishing the first carbon account-based green financial product and pioneering an innovative “green finance plus gamification” model. However, the academic literature has not fully explained the underlying mechanisms that drive consumer engagement with such green financial products. This study, motivated by the academic question of what factors influence consumers’ willingness to use green financial products, employs Ant Forest as a case study and develops a novel structural equation model based on self-determination theory, customer-perceived value, and the technology acceptance model. The model incorporates user type as a control variable and considers autonomy, gamification, and bonuses as key independent variables, with customer-perceived value serving as a mediating variable. Data collection involved 606 participants, enabling a comprehensive analysis of the factors influencing users’ willingness to engage with green financial products. The findings support the proposed hypothesis, identifying several significant predictors of users’ willingness to use green financial products, with the exception of age. This study advances the theoretical understanding of consumer behavior towards green financial products by integrating self-determination theory, customer-perceived value, and the technology acceptance model, while also offering practical insights for marketing strategies. It explores the interface between digital finance, environmental sustainability, and consumer behavior, highlighting opportunities for financial institutions to leverage Internet applications to promote green financial services and enhance their marketing approaches to influence consumer adoption. Full article

In the energy market, we observe a dynamic development of innovative business models that take into account various aspects related to the direction of zero-emission economic growth. Companies are intensifying their efforts in utilizing renewable energy sources, implementing significant photovoltaic projects, and advancing technologies related to wind and hydrodynamic energy. Within this trend, microgrids become a crucial element, enabling efficient management of local energy sources. Contemporary energy companies also focus on innovative digital technologies, harnessing the potential of the Internet of Things (IoT) and artificial intelligence (AI). These tools allow for precise monitoring and optimization of energy consumption, resulting in increased operational efficiency. The expansion of subscription-based energy services encompasses not only traditional energy deliveries but also new aspects, such as intelligent management of home energy installations or the provision of advisory services on energy conservation. This approach emphasizes the customer as a partner in sustainable energy usage. Hybrid energy models, integrating diverse energy sources, constitute a key element in the transformation of the sector. The combination of photovoltaic, wind, and traditional power plants allows for flexible adaptation to changing conditions and maintains stability in the energy supply. In the face of a changing energy landscape, companies consistently strive for sustainable practices, implementing strategies that not only reduce their carbon footprint but also contribute to improving efficiency, ecology, and the decentralization of the energy system. Adapting to these dynamic changes becomes not only a challenge but also an opportunity to create a more sustainable energy future. The objective of this research is to analyze key business models in the energy market and identify their impact on operational efficiency and market competitiveness. The main results indicate significant improvements in energy management and sustainability through the adoption of these models. Full article

With the trend of achieving both energy efficiency in buildings and occupants’ comfort, gamification strategies have started to be developed and applied as incentive mechanisms to increase social interaction and facilitate human energy behavior transformation. In this article, 306 published papers are reviewed, and 21 studies are identified to determine the challenges and potential for the development of gamification strategies to improve building energy efficiency. Specifically, this work reviews the implementation techniques of gamification and methods to assess the impact of gamification mechanisms on human energy behavior changes. This analysis demonstrates that, firstly, the choice of an optimal gamification implementation method should be inherently attuned to the distinct characteristics of the building type and its occupants. Secondly, it is imperative to strike a judicious balance between extrinsic and intrinsic motivations, in which customization of gamification design elements are based on users’ unique personality traits and preferences, to properly tailor gamification mechanisms. Thirdly, integrating a fusion of quantification of energy savings and qualitative interpretation of user behaviors to improve the energy efficiency in buildings is essential for a more holistic understanding of the impact of gamification on users’ energy-related behavior change. The findings indicate that gamification techniques can enable the effective reduction of energy consumption in buildings. Full article

Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly use these advancing technologies. This paper provides a holistic review of this important concept from the perspectives of process, materials science, industry, and initiatives. The goal of this research study is to collect and present the latest knowledge blocks related to the energy consumption of AM technologies from a number of recent technical resources. Overall, they are the collection of surveys, observations, experimentations, case studies, content analyses, and archival research studies. The study highlights the current trends and technologies associated with energy efficiency and their influence on the AM community. Full article