The high-quality development of the industrial software industry is of strategic significance to enhancing the core competitiveness of the manufacturing industry and promoting the high-quality development of China's industrial economy. By integrating the "capital- technology-environment-human" production factor theory and configuration perspective, this paper constructs a comprehensive analysis framework that drives the total factor productivity (TFP) of the industrial software industry. It uses 40 typical industrial software firms as case samples and uses fuzzy set Qualitative Comparative Analysis (fsQCA) was used to empirically explore the influencing factors and complex mechanisms that achieve high-quality development of the industrial software industry. It is found that: (1) a single industrial factor is hardly a necessary condition to drive industrial software industry; (2) there are four paths to achieve high TFP, which are summarized as "technology-human-environment" balanced driving type, "capital-human-environment" balanced driving type, "technology-capital" dual driving type, and "capital" single driving type. There are four driving mechanisms. There are also four not-high TFP configurations with asymmetric characteristics; (3) under certain conditions, the combination of capital factors, capital factors, environmental factors and human factors can drive TFP in a "all roads lead to Rome". In this process, the government's attention plays a more universal role. The study not only expands the application scenarios of fsQCA, but also provides decision guidelines for the practice of strategic emerging industrialization represented by industrial software industry.

Trends such as Industrial Internet of Things (IIoT) and Industry 4.0 have increased the need to use powerfull network technologies in industrial automation. The growing communication in industrial automation is harnessing the productivity and efficiency of manufacturing and process automation with minimum human intervention. Due to the ongoing evolution of industrial networks from Fieldbus technologies to Ethernet, the new opportunity has emerged to integrate the Software Defined Networking (SDN) technique. In this paper, we provide a brief overview of SDN in the domain of industrial automation. We propose a network architecture called Software Defined Industrial Automation Network (SDIAN), with the objective of improving network scalability and efficiency. To match the specific considerations and requirements of having a deterministic system in an industrial network, we propose two solutions for flow creation: Pro-active Flow Installation Scheme (PFIS) and Hybrid Flow-Installation Scheme (HFIS). We analytically quantify the proposed solutions in alleviating the overhead incurred from the flow setup cost. The analytical model is verified through monte carlo simulations. We also evaluate the SDIAN architecture and analyze the network performance of the modified topology using an emulator called Mininet. We further list and motivate SDIAN features and in particular report on an experimental food processing plant demonstration featuring Raspberry PIs (RPIs) instead of traditional Programmable Logic Controllers (PLCs). Our demonstration exemplifies the characteristics of SDIAN.

This study examines the environmental challenges faced by Malaysian industries in adhering to industrial effluent regulations with the research question, what are the challenges in complying with environmental legislations from industries? It investigates the primary factors contributing to non-compliance and the perspectives, knowledge, and needs of Malaysian industries regarding environmental compliance. This study recommends the best strategy for promoting self-compliance. Thirteen stakeholders from various industries with environmental management experience were interviewed. Over the past seven years, these industries have shown low com-pliance with the Industrial Effluent Regulation 2009, prompting an exploration of their adaptability to regulations. Challenges identified include cost, employee attitudes, technology, management support, public complaints, and jurisdictional issues. Responses varied from divergent to unified viewpoints, highlighting the complexity of compliance issues. Respondents suggested enhancing compliance through behavioral changes, including incentives, enforcement, awareness, guidance, industry ratings, management commitment, and public disclosure of emissions. Suggestions for improvement and innovative solutions emerged based on participants' experiences, emphasizing the importance of continuous improvement. The study emphasizes the government's crucial role in ensuring regulation adherence but underscores the need for analyzing barriers and exploring alternative approaches. Understanding industry perspectives and strategies is essential for progress. Compliance, however, requires an analysis of the current challenges and adopting adaptive measures. It is important to understand industries' perspectives and strategies to move forward.

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtualized Network Functions, adapting services to the complex and real-time network environment for enhanced network performance. Despite the benefits of NFV, virtual network functions (VNFs) migration and the energy consumption pose significant challenges due to the dynamic nature of the physical network, especially in the edge-cloud continuum network. Currently, people tackle this challenge by predicting the network traffic and then proactively migrating the VNFs using the predicted values. There are two challenges here. First, when SFCs belong to different network service providers, the historical network traffic data is held by the service providers, and they are reluctant to share the historical data due to privacy concerns. Without the historical network traffic data of each SFC, the resource provider that owns the underlying network cannot effectively predict the network traffic; in addition, without the feedback of the migration results of the underlying physical network, the network traffic prediction will be difficult to match the needs of the physical network. In this manuscript, we solve the problem of network traffic prediction in the NFV-based edge-cloud continuum network for multiple network service providers. We aim to improve migration performance and reduce energy consumption while ensuring resource and latency requirements. To protect the privacy of each network service provider, we apply an FL algorithm. After that, to improve the migration performance, we tailor the loss function. In the loss function, factors such as the number of migrations, the number of migration failures, and the energy consumption of the NFV-based edge-cloud continuum network are added. Additionally, to obtain the results of the SFC migration, we develop a mathematical model for the problem of multiple network service providers in the NFV-based network. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption while improving the SFC acceptance ratio compared to state-of-the-art schemes which only use the difference between predicted and actual flows to define the loss function.

Detecting cracks in structural health monitoring is crucial for ensuring infrastructure safety and longevity. Using drones to obtain crack images and automate processing can improve the efficiency of crack detection. To address the challenges posed by the limited computing resources of edge devices in practical applications, we propose CrackScopeNet, a lightweight segmentation network model that simultaneously considers local and global crack features, suitable for deployment on drone platforms with limited computational power and memory. CrackScopeNet features a multi-scale branch to improve sensitivity to cracks of varying sizes without substantial computational overhead and a stripe-wise context attention mechanism to enhance the capture of long-range contextual information, mitigating the interference from complex backgrounds. Experimental results on CrackSeg9k dataset demonstrate that CrackScopeNet leads to a significant improvement in prediction performance, with the highest mean intersection over union (mIoU) scores reaching 82.12%, while maintaining a lightweight architecture with only 1.05M parameters and 1.58G floating point operations (FLOPs). Besides, CrackScopeNet excels in inference speed on edge devices without GPU because of its low FLOPs. CrackScopeNet contributes to the development of efficient and effective crack segmentation networks, suitable for practical structural health monitoring applications using drone platforms.

Mineral nutrition is essential for plant growth and interaction of plants with biotic and abiotic stresses. Mango twig-tip dieback (MTTD) is a new type of mango decline, but its impact on the trees’ mineral nutrition is unknown. This study was conducted to determine the effect of MTTD infection on the nutrient status, balance, and resorption efficiency (RE) of mango. Leaf nutrient concentrations and deviation from the optimum percentage (DOP) indices of ‘Kensington Pride’ (KP) mango trees with low (LD) and high (HD) levels of MTTD infections were analyzed to compare the foliar nutrition status and nutrient balance between the LD and HD trees. Moreover, the nutrient resorption efficiency of MTTD-infected dried leaves (RE_D) was compared with the resorption efficiency of healthy (RE_S) leaves of KP mangoes. The concentrations of the total Ca, Mg, Cu, Fe, Mn, and Zn were lower in the HD trees than in the LD trees. But the total K content was higher in the HD trees, and its DOP index was sufficient, while the total K concentration was low and deficient level in LD trees. Moreover, DOP indices for total Ca, Mn, and Zn were less deficient in LD trees than in HD trees, and overall nutrient imbalances were exacerbated in HD trees. RE_D was significantly lower than RE_S for the total N, P, S, Cu, Fe, and Zn but significantly higher than RE_S for K. The study underscores the significant influence of MTTD on the mineral nutrition of KP mangoes, revealing distinct nutrient variations between trees with low and high MTTD infection levels. These findings have important implications for mango crop management, emphasizing the need for targeted nutrient interventions to address imbalances induced by MTTD and enhance the overall health and resistance of mango trees against MTTD infection.

EIMIs face the challenge of adapting to the Paris Agreement and the European Green Deal to achieve carbon neutrality. This change is being driven by consumer interest in low-carbon products, regulatory requirements from climate agreements and investors prioritizing sustainability. This pressure is pushing companies to go beyond short-term financial gains and integrate sustainability into their business. The growing awareness of the environmental impact of business models (BM) has fueled the development of Sustainable Business Models (SBMs). This study aims to link the discussion on the role of SBMs in the drivers of decarbonization, to improve the understanding of how the drivers can be integrated and enable new BMs for EIMIs. Based on a structured literature review, semi-structured interviews were conducted in the EIMIs. The study identifies BM-components that are influenced by these drivers and require sustainable business model innovation (SBMI). The results show that decarbonization drivers have a significant impact on all BM components. Experts emphasize the importance of collaborative approaches and cooperation throughout the value chain. This research elaborates on the importance of systematic analysis to understand how companies can effectively manage decarbonization drivers and suggests in-depth exploration within BM-research to uncover potential SBM design options.

Sharding shows great potential for extending the blockchain. The current challenge facing sharded blockchain technology lies in addressing the extended transaction confirmation times caused by isolated states between shards and unbalanced transaction allocation strategies. These factors contribute to an increase in cross-shard transactions and disproportionate shard workload, ultimately resulting in indefinite confirmation delays for cross-shard transactions. A critical priority for sharded blockchain systems is to conduct a comprehensive qualitative analysis to better understand and mitigate the prolonged transaction confirmation times. We introduce a Product-Form Queue Network (PFQN) model to address the transaction confirmation time problem in sharded blockchains and incorporate a new confirmation queue to more accurately simulate the actual transaction confirmation process in the blockchain. In addition, we provide a detailed quantitative analysis of the relationship between network load, consensus efficiency, and security in sharded blockchains, laying a theoretical foundation for achieving robustness and efficiency in sharded blockchains. This research not only contributes to addressing the scalability issues in sharded blockchains but also offers a new perspective for future research directions.

Mass customization, small batch sizes, high variability of product types and a changing product portfolio during the life cycle of an industrial plant are current trends in the industry. Due to an increasing decoupling of the development of software and hardware components in an industrial context, compatibility problems within industrial control systems arise more and more frequently. In this publication, a strategy concept for compatibility testing is derived and discussed by means of literature review and applied research. This 4-phased strategy concept identifies incompatibilities between software and hardware components in the industrial control environment and enables test engineers to detect problems at an early stage. By automating the compatibility test on an external I-PC, the test can be run both when new software is installed on the industrial controller and when the controller is restarted. Thus, changes to the components are constantly detected and incompatibilities are avoided. Furthermore, early incompatibility detection can ensure that a system remains permanently operational. Based on a discussion, additionally strategies are identified to consolidate the robustness and applicability of the presented concept.

The dynamic global automotive sector is characterized by an ever-evolving demand for high-quality vehicles and accessible prices. This research tackles the issue of scrap reduction in electric motor manufacturing for the automotive industry, specifically in a Chinese company, by employing the PDCA methodology. The paper delves into the company's background and current landscape, while pinpointing the problem: specific production lines segments experiencing hi-pot issues are the primary source of scrap. The overarching goal is to design and implement a strategy that, leveraging the PDCA approach, reduces scrap generated during the eight-months subassembly (armature) manufacturing process, from its current average of 2.92% to 0.7%, ultimately enabling the company to eliminate hi-pot defects.

The Covid-19 pandemic was a challenge for the whole world and it had major secondary effects on humans and environmental health. The viral infection induced, in many situations, secondary bacterial infections, especially enteric infections by destabilizing the balance of the gastrointestinal microbiota. The large-scale use of antibiotics and biocides both for curative and preventive purposes has determined an increase in bacterial resistance and at the same time the possibility of pathogenic microorganisms’ multiplication and their dissemination to natural environments. Wastewater is the main vector of faecal microorganisms that favour their dissemination into natural aquatic ecosystems. The present paper aimed to analyze the effect of Covid-19 pandemic on the microbiological quality of wastewater from sewage treatment plants in Romania and its impact on receiving rivers. In order to highlight different and important areas of Romania, 3 cities from the east, center and west were selected for a microbiological evaluation of their WWTPs influent and effluent between ante-Covid-19 and Covid-19 pandemic peak period, when the Covid-19 pandemic had a direct impact on WWTPs microbial composition. Our study showed a higher contamination with fecal bacteria linked to a higher Covid-19 incidence. The increased usage of pharmaceutical compounds, at their turn, increased the number of resistant bacteria reaching the environment via WWTP effluents.

Asphaltene-resin-paraffin deposition (ARPD) is a complicated and prevalent issue in oil and gas industry, impacting the efficiency, integrity of petroleum extraction, production, transportation and processing systems. Considering all witnessed ARPD problems in Azerbaijan oil fields, this paper proposed a chemical method and optimized the type and the concentration of chemical inhibitors. And then the effect of selected chemical reagent on inhibiting the ARPD amount and thus en-hancing oil recovery was detected by reservoir simulation during both waterflooding and CO2 flooding production. Three new chemical compounds (namely, Chemical-A, Chemical-B and Chemical-C) were examined in laboratory conditions and their impact on rheological properties of high-paraffin oilfield samples of Azerbaijan (X, Y and Z) were investigated. Experimental results show that Chemical-C with concentration of 600 g/t has the best efficiency for alleviating the problems. After adding Chemical-C to the crude oil, the freezing point of oil is decreased from 12 °C to (-4) °C, ARPD amount is declined from 0.185 to 0.016 g and oil effective viscosity is reduced from 16.2 mPa·s to 3.1 mPa·s. It is determined that for water and CO2 flooding, higher injection pressure resulted in reduced asphaltene precipitation. Adding selected ARPD inhibitor, the oil recovery for waterflooding can increase from 52% to 62%, while it can rise from 55% to 68% for CO2 flooding.

Currently, the textile industry is a poorly automated sector, due in part to problems in the handling of deformable leather and textile parts during production operations. In this work, several problems in the handling process of leather and textile parts are addressed, introducing methods to increase the automation of the process. A pneumatic actuator designed to pick up textile or leather parts avoiding their deformation during transport has been developed. This actuator maximizes the number of gripping points to improve handling, making it more stable and efficient. Additionally, a vision system has been implemented in the part picking task which, in conjunction with the CAD information of the part, sends the modified gripping position of the part to the robot. This allows customized handling of each textile or leather part. Finally, validation tests have been carried out on this development, both in simulations and in laboratory conditions, demonstrating its viability and direct applicability in the production line.

As Li-ion batteries use is spreading, incidents in large energy storage systems (stationary storage containers, …) or in large-scale cell and battery storages (warehouse, recyclers, …), often leading to fire, are occurring on regular basis. Water remains one of the most efficient fire extinguishing agents for tackling those battery incidents and large quantities are usually necessary. Since bat-teries contains various potentially harmful components (metals and their oxides or salts, sol-vents…) and that thermal runway induced battery incidents are accompanied with complex and potentially multistage fume emissions (containing both gas and particles) the potential impact of fire run-off water on the environment should be considered and assessed carefully. The tests presented in this paper focus on the analysis of the composition of run-off waters used to spray NMC Li-ion modules under thermal runaway. It highlights that waters used for fire-fighting are susceptible to contain many metals, including Ni, Mn, Co, Li and Al mixed with other carbonaceous species (soot, tarballs) and sometimes undecomposed solvents used in the elec-trolyte. Extra-polation of pollutant concentrations compared with PNEC values showed that, for large scale incidents, run-off water could be potentially hazardous to the environment.

The research presented in this article is based on a strategic approach to a rural area in the mountainous region of Romania and is underwritten by specialists in the economy of mountainous regions in order to elaborate relevant solutions to complex problems relating to sustainable development. In essence, the research underlines and promotes the particularities of performing research for the identification and valuation of endogenous resources, that is, creating relevant strategic options for the durable development of the mountainous region, centered on the renewal of economic activities related to the existent endogenous resources, by using methods, work techniques, and instruments of strategic analysis. These factors are subordinate to the process of harmonization to the requirements of community development and environmental protection according to the new strategy for the development of the European Union 2020 (European Green Pact). This pact also emphasizes the necessity of involving the different factor categories of local accountability, promotion, activation, and the development of specific economic activities of different local contexts of sustainability. The research methodology used involves a case study whose complexity necessitated the use of quality and quantity research methods in order to underline specific elements of the rural area in the Romanian mountainous region. Specifically, we used a strategic diagnosis of the Romanian mountainous region using the PESTEL and SWOT models. In order to have relevant strategic options available, the continuity of research was achieved through the use of SOR analysis, creating a problem tree and an objectives tree.

Productivity has been modest in construction due to many reasons, especially project-specific operations and fragmentation. The main aim of this research is to map the challenges towards industrialization in construction through a comparative study of the manufacturing industry. An inductive approach and a qualitative method are followed by applying semi-structured inter-views, and node-based thematic analysis. The key elements of industrialization in mature man-ufacturing companies are benchmarked. The findings indicate a specific order of steps for indus-trialization in construction. Data and information management with achieved systematization have been sources of productivity in manufacturing companies. However, the systematization starts from the offering, construction companies need to define their offering, that is the products, and define their respective data. Only accurate and good quality data on products enables the systematization of order delivery processes, which are the engine for a company’s success or failure. Continuous efficiency improvement is only possible with systematized processes and respective data related to the processes. The originality involves clarifying the industrialization of construction companies via products, respective data, and processes considering industrialized manufacturing companies.

(1) Background: Depressional wetlands are highly vulnerable to changes in temperature and rainfall, affecting littoral vegetation, thus HydroGeoMorphic (HGM) wetland types. This study aimed to assess the utility of Sentinel-2A vegetation indices in enhancing delineation and sub-classify depressional HGM types. (2) Methods: We sampled 84 pairs of four vegetation indices from 10x10m plots along 14 belt transects in eight depressional wetlands in Mpumalanga, South Africa. We evaluated vegetation index differences between and within wetlands, focusing on lit-toral gradients. (3) Results: Significant differences (Bonferroni p

Recently, methods to detect DoS and spoofing attacks that occur on in-vehicle networks using CAN Protocol are being studied through deep learning models such as CNN, RNN, and LSTM. These studies have produced significant results in the field of In-Vehicle Network attack detection using deep learning models. In addition, significant results are being achieved through research on applying time series-based deep learning models such as LSTM to detect DoS attacks and replay attacks occurring in in-drone networks by expanding them to drones using the UAVCAN protocol. In this paper, we conducted an experiment to detect in-drone network attacks through non-time series analysis using machine learning models and deep learning models, and through appropriate learning for each attack type, it can also be analyzed through non-time series analysis. The results showed that it was possible to detect attacks.

In the conservation of monumental heritage, the collection and utilization of information are of primary importance. The Heritage Building Information Modeling (HBIM) procedure harnesses the potential of three-dimensional models, offering significant advantages in accessing documentation, interoperability, multidimensionality of intervention design, cost evaluation and maintenance management. Attention here is focused on the Certosa di Pisa, a large historical complex built in the 14th century as a monastery of the Carthusian order, currently in a state of deterioration and in need of restoration and re-functionalization. The multifaceted nature of this monumental complex, with its intricate interplay of architectural elements spanning different historical periods and featuring diverse techniques, poses a significant challenge for structural safety assessment. This case study presents an opportunity to explore an HBIM approach to streamline the diagnostic process and facilitate the intervention design phase. The goal is achieved by utilizing an accurate 3D model enriched with data from multiple sources and automating certain operations for structural assessment. The usefulness of the HBIM methodology is highlighted as a valuable tool in the realm of Cultural Heritage structures for both technicians and scholars alike.

Robotic systems have been widely used in various industries, so the security of communication between robots and their components has become an issue that needs to be focused on. As a framework for developing robotic systems, the security of ROS2 can directly affect the security of the upper-level robotic systems. Therefore, it is a worthwhile research topic to detect and analyze the security of ROS2. In this paper, we adopt a formal approach to analyze the security of the communication mechanism of ROS2. First, we use a state transition system to model the potential vulnerabilities of ROS2 based on the ROS2 communication mechanism and the basic process of penetration testing. Secondly, we introduce the CIA model on the basis of the established vulnerability model and use LTL to define its security properties. Then, we design and implement a vulnerability detection tool for ROS2 applications based on the vulnerability model and security properties. Finally, we experimentally test some ROS2-based applications, and the results show that ROS2 has vulnerabilities without additional protection safeguards.

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal-injection-based topology identification algorithm is favored for its speed and efficiency. This study proposes a new signal-injection-based topology identification algorithm aimed at solving the issues of reduced completeness and correctness of identification caused by missing feature signal records (FSRs). By analyzing the correlations between FSRs of different positions, the algorithm employs vertical and horizontal completion methods to effectively address missing data, combined with inclusion detection algorithms, to identify the topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the algorithm not only significantly improves the completeness and correctness of topology identification for LVDNs but can also increase the number of devices in operation by evaluating the status of topological equipment, further enhancing the topology identification performance.

As modern systems are becoming more complex their control strategy cannot longer relay only on measurement information that usually comes from probes but also from mathematical models. Those systems models can lead to unbearable computation times due to their own complexity, turning the control process non-viable, which leads to the implementation of surrogate models that enable to achieve estimates within acceptable time to take decisions. A control trained with Deep Reinforcement Learning algorithm, using a Physics-Informed Neural Network to obtain the temperature map on the following time step, replaces the need of running Direct Numerical Simulations. On this work we considered an 1D heat conduction problem, which temperature distribution feeds a control system to activate a heat source aiming to obtain a constant, previously defined, temperature value. With this approach, control training becomes much faster without the need of performing numerical simulations or laboratory measurements, as well the control is taken based on Neural Network enabling its implementation on simple processors to edge computing.

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

For a long time, a company's Product Development Process (PDP) was seen as supporting the operations department, although PDP decisions and mistakes have a considerable impact on market performance. This is critical even in agriculture where bad habits and practices in the PDP can lead rural producers to great losses. Therefore, this research investigated the effect on the market performance of rural products (banana) in the southern region of Brazil, based on two analyses: (i) how sustainability practices support the PDP phases and (ii) how the phases of the Product Life Cycle Assessment (LCA) mediate sustainability practices and PDP phases. This study presents a quantitative analysis using Confirmatory Factor Analysis (CFA) and hierarchical ordinary least squares (OLS) regression of data obtained from a survey of 110 rural producers who directly participate in the banana production and planning process in southern Brazil. Our results shows that sustainability practices support the PDP, and we confirm that the product development and post-development phase has an effect on market performance. In addition, we identified that in the pre-development phase of the PDP, dealing with rural products (bananas), the maturity stage of the LCA mediates sustainability. In the PDP development phase, we concluded that rural families that develop economic and environmental practices with their products in the market growth phase may have reduced results. As for the post-development phase of the PDP, we conclude that when companies invest in environmental and social practices, there is a complete mediation of the effect, where these practices lose strength if the product is in the introduction and maturity phases in the market. As originality, our study contributed to demonstrate value on the product life cycle for the product development process in agriculture using sustainability practices through a systemic approach, filling the gap in the literature due to the lack of research on these areas seen in an integrated way.

Nowadays, digitalization present in all links of the product life cycle and within product design, packaging is a critical element, as it affects the customer’s purchase intention, and therefore, it is one of the most important spaces where to include techniques and methods based on Industry 4.0 that obtain results from the user and bring them closer to the process. The relationship between packaging and the expected quality and emotions of users is a topic that is addressed in the social axis of sustainability and involves an effort to strengthen the relationship with the user. This article studies the expected quality of nougat packaging in terms of shape and colour using ANOVA analysis. For this purpose, a survey was carried out among 122 participants in Spain and Mexico. The main conclusion is that a packaging with the combination of three shapes (rectangle, square and triangle) and colours (yellow, orange and red) enhances consumers’ emotions of admiration, satisfaction, pleasant surprise, attraction, fascination and joy.

The strong competition in the automotive industry has required manufacturers to implement lean production, both with methods and techniques specific to Industry 4.0. At the same time, universities must provide graduates with specific skills for applying these new production methods and techniques. In this context, a Lean Learning Factory was developed in the Pitesti University Center, which allows students to learn, experiment, and research, in an environment similar to that of enterprises, the new Lean Manufacturing methods and techniques, and Industry 4.0. The research presented in this study had as its objective identifying the minimum number of repetitions necessary to train operators who serve two differently organized workstations, one classic, the other including digital techniques, but on which the same assembly operation is performed. Several indicators were considered in the analysis, such as the number of errors, the number of stops, the effective duration of the work cycle, and the percentage ratio between the standard duration of cyclical activities and the effective duration of the work cycle. The evolution of these indicators was mathematically modelled by regression functions, using the least squares method. The obtained results also highlight the usefulness of applying the DOJO method as a Lean manufacturing-specific learning technique and the efficiency of implementing digital techniques in work organization.

Background: The study was conducted to investigate if the mRNA vaccine technology could be adapted for the ectothermic vertebrate Atlantic salmon (Salmo salar). The lipid nanoparticle (LNP) technology has been developed and optimized for mRNA vaccines in mammals, stabilizing mRNA and facilitating delivery into cells. However, its utility at the temperature and specific biological environment in ectotherms remains unclear. In addition, it is unknown if modified mRNA containing non-canonical nucleotides can correctly translate in salmonid cells. Methods: We used an mRNA transcript coding for enhanced green fluorescence protein, flanked by the untranslated regions of the hemagglutinin-esterase gene of infectious salmon anemia virus, and a 120-base-long poly(A) tail. The mRNA was generated by in vitro transcription where uridine residues were replaced with N1-methyl-pseudouridines, and then encapsulated in LNPs. Results: When transfected into the salmonid cell line CHH-1, the mRNA-LNP construct induced expression of EGFP. Furthermore, when mRNA-LNPs were injected intramuscularly into salmon, in vivo protein expression was demonstrated by immunohistochemistry. EGFP was observed in cells infiltrating the space between the muscle cells in a focal inflammatory response. Conclusion: The results indicate that the N1-methyl-pseudouridine modified mRNA encapsulated in LNPs can be used to express antigens of interest in salmonid fish.

A Q-switched Nd:YAG laser was focused on Pb-Ti alloy samples in several Laser Induced Breakdown experiments in order to measure the Stark parameters of several spectral lines (58) of single ionized Titanium including the 3504.89 Å, and the 3510.83 Ålines (where we aported new experimental and theoretical values).Diagnostics of the laser induced plasmas (electron density and electron temperature) was performed using Balmer’s H alpha line (6562.7 Å).Temperatures were obtained by the Boltzmann Plot technique with spectral lines of Pb I (after correction for its evident self-absorption).Subsequently, calculations by the Griem approach of the Stark broadening parameters for several spectral lines were performed using the Gaunt factors proposed by Van Regemorter and those proposed by Douglas H. Sampson. In the latter cases the values obtained were very close to the experimental values. It allows us to assume that the calculations made for the spectral lines of Ti II, without experimental information, are more accurate using the Gaunt factors proposed by Sampson.

Many anti-tumor effects of group-specific component-derived macrophage-activating factors (GcMAFs) have been reported; however, the specific mechanisms remain unclear. Controlling tumor-associated macrophages (TAMs) in the tumor microenvironment is essential for cancer treatment. This study assessed the role of GcMAF in macrophage activation, elucidated the mechanisms by which it exerts its anti-tumor effects, and determined its effects on TAMs in the tumor microenvironment. GcMAF-stimulated RAW264.7 macrophages and EMT6 breast tumor cells were co-cultured in a 0.4-µm pore cell culture insert, and gene and protein expression and cell viability were evaluated. DNA microarray analysis of GcMAF-stimulated RAW264.7 cells was conducted. The induction of M2 RAW264.7 cells by interleukin (IL)-4 and IL-13 was analyzed. GcMAF stimulation increased the tumor necrosis factor-α and inducible nitric oxide synthase mRNA and protein levels in RAW264.7 cells but decreased the viability of co-cultured EMT6 cells. These results were confirmed in the M2 RAW264M2.7 cells, suggesting that GcMAF exerts anti-tumor effects by inducing the differentiation of macrophages into the M1 type and reprogramming M2 macrophages to the M1 type. The anti-tumor activity of GcMAF via M2-to-M1 macrophage reprogramming could aid in developing novel cancer immunotherapies.

The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, leading to increased economic and social benefits. Currently, some simple AI and hybrid models has issues to deal with and struggle with multivariate dependencies, long-term dependencies, and non-linear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while Lasso regression serves as the meta-learner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in forecasting accuracy, with a substantial reduction in Mean Absolute Percentage Error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, suggesting potential operational efficiency benefits for the power industry.

The European Union Water Framework Directive (2000/60/EC; WFD) aims to achieve a good ecological and chemical status of all bodies of surface water by 2027. The development of an integrated guidance on surface water chemical monitoring (e.g. WFD Guidance Document No. 7/19) has been transferred into national German law (Ordinance for the Protection of Surface Waters, OGewV). For the majority of compounds, this act requires a monthly sampling frequency to assess the chemical water quality status of a surface water body. To evaluate the representativeness of the sampling strategy under the OGewV, high-frequency online monitoring data is investigated under different sampling scenarios and compared with current, monthly grab sampling strategy. About 23 million data points were analyzed for this study, three chemical parameters (dissolved oxygen, nitrate-nitrogen, chloride concentration) and discharge data were selected from four catchment of different sizes ranging from 51391 km² to 84 km² (Elbe, Vereinigte Mulde, Neiße and two stations at Lockwitzbach). In this paper we proposed short-term online-monitoring (STOM) as a sampling alternative. STOM considers the placement of online sensors over a limited duration and return interval. In general we: (I) compare the results of conventional grab sampling with STOM, (II) investigate the different performance of STOM and grab sampling using discharge data as proxy for analyzing event-mobilized pollutants and (III) investigate the related uncertainties and costs of both sampling methods. Results show, that STOM outperforms grab sampling for parameters where minimum/maximum concentrations are required by law as the probability to catch a single extreme value is higher with STOM. Furthermore, parameters showing a pronounced diurnal pattern, like dissolved oxygen, are also captured considerably better. The performance of STOM showed no substantial improvements for parameters with small concentration variability, as nitrogen-nitrate The analysis of discharge events as a proxy parameter for event-mobilized pollutants proved that the probability of capturing samples during events is significantly increased by STOM.

Volatile organic compounds (VOCs) released from plants have been correlated with disease-status. Analysis of VOCs using GC-MS is time-consuming, laboratory-based, and requires specialist training. Electronic nose devices (E-nose) provide a portable alternative. Three different E-nose devices were compared to assess how accurately they could detect Mango Twig Tip Dieback and Panama disease in banana. The devices were initially trained on known volatiles, then pure cultures of Pantoea sp., Staphylococcus sp., and Fusarium odoratissimum, and finally, on infected and healthy mango leaves and field-collected, infected banana pseudo-stems. The experiments were repeated three times with six replicates for each host-pathogen pair. The variation between healthy and infected host materials was evaluated by principal component and linear discriminant analysis, cross-validation and chemometric data analysis. GC-MS analysis was conducted contemporaneously and identified an 80% similarity between healthy and infected plant material. The portable C 320 was 100% successful in discriminating known volatiles but had a low capability in differentiating healthy and infected plant substrates. The advanced devices (PEN 3 / MSEM 160) successfully detected healthy and diseased samples with a high variance. The results suggest that E-nose devices are more sensitive and accurate in detecting changes of VOCs between healthy and infected plants compared to headspace GC-MS.

The focus of this work lies in the construction of monitoring system designed in the form of a patch using a long-lasting potentiometric electrolyte concentration sensor from metal electrodes, in a galvanic cell configuration Mg(s)|NaCl(aq)|Cu(s). Initially, a mathematical analysis was conducted on the expected sensor output through the Nernst equation. The experimental phase involved conducting controlled tests in the laboratory using various molar concentrations of sodium chloride (NaCl) in aqueous solution. Results showed that the electrolyte sensor allows for monitoring electrolyte levels in NaCl in the range of 0.02 to 1 M/L, with electrode lifespan exceeding 10 days. On the other hand, when patch is using human sweat during controlled exercise routines, it showed the sensor's capability for monitoring changes in the subjects' hydration levels in real time. The sensor output potential is greater than 1.7 volts when the sensor electrodes are in contact with human sweat, which is equivalent to measuring molar concentrations at normal hydration and moderately high hydration levels. Sensor results indicate that the system can function in a self-powered manner without external batteries. Additionally, the extended lifespan of its electrodes allows for continuous monitoring of imbalances in the human body, showcasing the system capability for long-term health assessment.

Chemical vapor deposition (CVD) is a vital process for depositing thin films of various materials with precise control over thickness, composition, and properties. Understanding the heat and mass transfer mechanisms during CVD is essential for optimizing process parameters and ensuring high-quality film deposition. This review provides an overview of recent advancements in the heat and mass transfer modeling for chemical vapor deposition processes. It explores innovative modeling techniques, recent research findings, emerging applications, and challenges in the field. Additionally, it discusses future directions and potential areas for further advancement in CVD modeling.

The early elongation events in transcription or translational control in the N-terminal of genes are assuming critical roles in their heterologous expression. The secretory expression of the alkaline pectin lyase APL in B. subtilis were studied to explore the effects of the N-terminal 5-7 amino acids sequences of different signal peptides on gene transcription and protein secretion in this study. The first 5 amino acid sequences of the N-terminal of the signal peptide LipA were identified to play an important role in promoting the expression of APL for the first time. Meanwhile, it also revealed that the signal peptide LipA could lead to higher secretory expression than other signal peptides not because of its sequence structure associated with secretion is stronger but because the N-terminal sequences showed greater advantages in gene transcription. At this basis, the recombinant strain structed in this work showed a new record for the highest extracellular yields (12 295 U/ml) of APL in B. subtilis which was 1.9 times higher than that expressed in the recombinant Escherichia coli strain we had reported before. The novel theories revealed in this study would assuming significance roles for improving expression of foreign proteins in or out of the cells.

Autonomous navigation in dynamic environments poses a significant challenge in the field of ro-botics with the primary goals of ensuring both smooth and safe movement. This study introduces control strategy based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance au-tonomous robot navigation in dynamic environments, emphasizing on collision-free path plan-ning. This strategy employs, a path planning technique to develop a trajectory that allows the ro-bot to navigate smoothly while avoiding collisions with both static and dynamic obstacles. The developed control system incorporates four ANFIS controllers: two are tasked with guiding the robot toward its end point, and the other two are activated for obstacle avoidance. The experi-mental setup conducted in CoppeliaSim involves a mobile robot equipped with ultrasonic sensors navigating in an environment with static and dynamic obstacles. Simulation experiments are conducted to demonstrate the model's capability in ensuring collision-free navigation, employing a path planning algorithm to ascertain the shortest route to the target destination. The simulation results highlight the superiority of the ANFIS-based approach over conventional methods, partic-ularly in terms of computational efficiency and navigational smoothness.

Structural deformations are one of the most significant factor that affects machine tool (MT) positioning accuracy. These induced errors are complex to be represented by a model, nevertheless they need to be evaluated and predicted in order to increase the machining performance. This paper presents a novel approach to calibrate a machine tool in real-time, analyzing the thermo-mechanical errors through Fibre Bragg Grating (FBG) sensors embedded in the MT frame. The proposed configuration consists of an adaptronic structure of passive materials, Carbon Fibre Reinforced Polymers (CFRP), equipped by FBG sensors that are able to measure in real-time the deformed conditions of the frame. By using a proper thermo-mechanical kinematic model, the displacement of the end effector may be predicted and corrected when it is subjected to external undesired factors. By starting from a set of FE simulations to develop a model able to describe the MT structure stresses, a prototype has been fabricated and tested. The scope was to compare the numerical model with the experimental tests using FBG sensors. The experimental campaign has been performed varying the structure temperature over time and measuring the tool tip point (TTP) positions. The obtained results showed a substantial matching between the real and the predicted position of TTP confirming the effectiveness of the proposed calibration system.

Simulated (sim) racing is an emerging esport that has garnered much interest in recent years and has been a relatively under-researched field in terms of expertise and performance. When examining expertise, visual attention has been of particular interest to researchers, with eye tracking technology commonly used to assess visual attention. In this study, we examined the visual attention allocation of high and low skilled sim racers during a time trial task using Tobii 3 glasses. Participants were allocated to either group according to their fastest lap times. Our results indicate that when eye tracking metrics were normalised to lap time, there was no difference in the relative length of attention allocation (fixation behaviour), although lower skilled racers did have significantly greater fixation counts and durations across laps overall. Interestingly, high and low skilled sim racers differed in where they allocated their attention during the task, with high skilled sim racers allocating significantly less attention to the track relative to other areas of the display. This would allow higher skilled racers to obtain relatively more information from heads-up display elements in-game, all whilst driving at faster speeds. This study provides evidence that high and low skilled sim racers show similar lap time-corrected fixation behaviour, but how they allocate attention while driving differs.

The normal growth and development of skeletal muscle is essential for the health of the body. The regulation of skeletal muscle by intestinal microorganisms and their metabolites has been continuously revealed. Acetate is the predominant short-chain fatty acid synthesized by gut microbiota through the fermentation of dietary fiber, however, the underlying molecular mechanisms governing the interaction between acetate and skeletal muscle during rapid growth stage remain to be further elucidated; Here, specific pathogen-free (SPF) mice, germ-free (GF) mice, and germ-free mice supplemented with sodium acetate (GS) were used to evaluate the effects of acetate on skeletal muscle growth and development of young mice with gut microbiota deficiency; We found the concentration of serum acetate, body mass gain, succinate dehydrogenase activity and expression of myogenesis maker gene of skeletal muscle in the GS group was higher than GF group following sodium acetate supplement. Furthermore, the transcriptome analysis revealed that acetate activated biological processes regulating skeletal muscle growth and development in GF group that are inhibited due to gut microbiota deficiency. In vitro experiment, we proved that acetate up-regulated Gm16062 to promote skeletal muscle cell differentiation; Overall, our findings proved that acetate promote skeletal muscle growth and development in young mice via increase Gm16062 expression.

The rapid growth of the sharing economy has propelled shared mobility to the forefront of attention. The continuous advancement of autonomous driving technology also brings new opportunities and challenges to the shared mobility market. This study comprehensively analyzes the market potential concerning autonomous vehicles (AVs) to provide shared mobility services, utilizing SWOT analysis, PESTLE analysis, and Porter’s Five Forces. The findings reveal that AVs can provide improved shared mobility services by increasing transportation safety, reducing emissions, reducing costs, enhancing traffic efficiency, and increasing customer satisfaction and the profitability of shared mobility services. However, challenges such as technological and policy uncertainties, safety concerns, high initial costs, inadequate public communication infrastructure, and the absence of standardized regulations can hinder the widespread adoption of AVs. The benefits are also restricted with a low market penetration rate of AVs. To promote AVs in the shared mobility market, this study also provides implications for AV stakeholders tailored to the evolving shared mobility market dynamics.

This research aimed to study and compare prevalence of work-related musculoskeletal (MSK) problem and perceived discomfort among port crane operators consisting of quay and rubber tyred gantry (RTG) cranes working in the Laem Chabang Deep-sea Port, Thailand. Direct interviews for a cross-sectional analysis was selected for this unique job with a small amount of sample size. A standardized modified version of the Nordic Musculoskeletal Questionnaire (NMQ) was lunched for 26 male participants, consisting of 11 quay and 15 RTG, where a priori power analysis using G*Power suggested the minimum as 24 for the hypothesis test. Socio-demographic characteristics showed no significant difference among them. The NMQ revealed lower back as the highest prevalent body part (76.9%) followed by neck, shoulders and upper back at the same percentage (23.1%) for all port crane group, with up to 96.2% reporting MSK problem over the past 12 months. And Borg scale rating, assessing the perceived discomfort, revealed lower back with the highest score (2.38) followed by upper back (0.65), shoulders (0.54) and neck (0.5). Remarkably, the comparation between the quey and RTG crane operators on the MSK prevalence (χ2 test) and perceived discomfort levels (Kruskal-Wallis test), regarding different 9 body parts, revealed a significant difference between the two on upper back with quay crane having more problem than the RTG at p < 0.05. while the rest of them were mainly in the same direction. MSK contributors such as: working awkward posture, mental stress, height, trolley distance and vibration are considerably the main causes of this. However, the larger in scale of exposure to those for the quey crane compared to the RTG might have led to the distinguishing MSK problem found in this study which require thoroughly deliberation for the particular solutions.

There is an increased attention on propositions on models, infrastructures and frameworks of IoT in both business reports and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies (e.g. Industrial Internet of Things, Cyber Physical Systems, Industry 4.0 etc). At present, the literature is missing a design process for integrating these constantly evolving systems and technologies in a clear and understandable step by step model. This paper contributes with a new reference architecture model for the integration of these systems and technologies. The reference architecture model is based on grouping of future and present techniques and presenting the design process through a new hierarchical framework and a new cascading model. With the application of the grounded theory, the hierarchical framework and the cascading model detail a new process for creating a taxonomy of categories and grouping of concepts into integration design. The new design process is tested and versified with an empirical review of Industry 4.0 frameworks and results with a new 5 levels reference architecture step by step model for the integration of these related systems and technologies (Industrial Internet of Things, Cyber Physical Systems, and Industry 4.0). We review 118 academic and industry papers published between 2010 and 2019. Then, we report the results of a qualitative empirical study that correlates academic literature with 14 world leading Industry 4.0 frameworks and initiatives. We therefore propose an architectural model that offers a better understanding of the systems integration between the Industrial Internet of Things and Industry 4.0.

The term "code smell" refers to an indication of a problem with the quality of source code. Numerous studies have been conducted to identify problematic features in source code. Initially, the focus was on utilizing metric-based and heuristic-based approaches. In recent years, however, there has been a shift towards using machine learning and deep learning (DL) techniques for smell detection. Nevertheless, the current algorithms are still considered to be in the early stages of development. Recognizing the challenges associated with identifying smells using DL methods, both academics and software developers have made efforts to address these obstacles. This work involves constructing and evaluating new DL models for code smell detection. Two models are built upon stacked autoencoders, employing a hybrid architecture that combines bidirectional long short-term memory and convolutional neural network components.

Mortality rates in sub-Saharan Africa remains unacceptably high, and disproportionately affecting economically disadvantaged communities. And healthcare delivery which is considered a major contributor to this is challenged by the limited resources and high service costs, prompting many patients to opt for outpatient department services. This study addresses the need for a data-driven approach to healthcare policy formulation by proposing a model for the implementation of a data warehousing system that integrates outpatients into the national healthcare framework. By leveraging data warehousing, healthcare delivery can be made more efficient through a robust repository that supports decision-making and policy formulation. The research focuses on the outpatient department in resource-scarce settings, proposing a novel multidimensional data model and practical framework implementation. The benefits include improved patient experiences, cost-effective service delivery, and enhanced patient management through electronic health records. Additionally, mobile apps are explored for patient navigation and reminders to enhance treatment adherence. The study’s results indicate that data warehousing can significantly improve healthcare delivery by consolidating critical data and facilitating better planning, resource allocation, and policy formulation. Ultimately, this approach offers valuable insights for optimizing healthcare systems in resource-constrained environments, enabling data-driven policies, and improving health outcomes across sub-Saharan Africa.

In this study, we analyzed the microbial community of traditional fermented foods in Jeju Island to identify the distribution of useful microorganisms and confirm their anti-inflammatory and anti-melanogenic effects to determine their potential use as cosmetic ingredients. Firstly, we examined the microbial communities of Omphalius rusticus Jeotgal (OR), Spratelloides gracilis Jeotgal (SG), Chromis notata Jeotgal (CN), Turbo cornutus Jeotgal (TC), Trichiurus lepturus intestine Jeotgal (TL), Branchiostegus japonicus Sweet Rice Punch (BJ), Salted Anchovy Sauce (SA), Jeju Soy Sauce (JSS), and Jeju Soybean Paste (JSP). We found that Latilactobacillus sakei (87.2%), Tetragenococcus halophilus (37.7%), T. halophilus (96.8%), Bacillus subtilis (23.4%), T. halophilus (71.3%), L. sakei (53.7%), Lentibacillus sp. (42.9%), Enterococcus durans (14.6%), and E. durans (32.8%) were the dominant species. Secondly, to study the nine Jeju fermented foods’ anti-inflammatory and anti-melanogenic effects, we employed RAW 264.7 and B16F10 cells, classic cell models for inflammation and melanogenesis studies. Ethyl acetate extracts of the nine Jeju fermented foods all inhibited nitric oxide (NO) and melanin production in a concentration-dependent manner. Thirdly, to test the applicability of the nine Jeju fermented foods to human skin, we used the MTT assay to assess their cytotoxic effects on human keratinocytes (HaCaT cells). Finally, the topical applicability of the nine Jeju fermented foods was tested through primary skin irritation, and it was found that they did not cause any adverse effects. Therefore, extracts from the nine Jeju fermented foods have potential applications as ingredients in anti-inflammatory and anti-melanogenic products and can be used in the cosmetic industry.

Very well into the dawn of the fourth industrial revolution (industry 4.0), we hardly distinguish between what is artificial and what is natural (e.g. man-made virus and natural virus). Thus, the level of discombobulation among people, companies or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector, makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption in the industrial sectors remains ill-defined in both academic and non-academic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technologies convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in Education industry (Education 4.0), Energy industry (Energy 4.0), Agriculture industry (Agriculture 4.0), Healthcare industry (Healthcare 4.0), and Logistics industry (Logistics 4.0) are described. The convergence of 12 disruptive technologies including 3D printing, Artificial intelligence, Augmented reality, Big Data, Blockchain, Cloud computing, Drones, Internet of things, Nanotechnology, Robotics, Simulation and Synthetic biology in agriculture, healthcare and logistics industries are illustrated.

Abstract: With the growing integration of the Internet of Things (IoT) in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of Wireless Sensor Networks (WSNs) in a commercial multi-story building, specifically addressing the difficulty of dealing with variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights that have the potential to signifi-cantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. By leveraging the capabilities of Contiki operating system, this study employs a specialized simulator to generate simulated data, allowing for a comparison between real-world and simulated scenarios. We provide an in-depth analysis to scrutinize the disparities and commonalities between the datasets obtained from real-world deployments and simulated en-vironments. We highlight the necessity of precise simulation to validate real-life sensor data, shedding light on the challenges and opportunities associated with implementing WSNs in in-telligent buildings. Results obtained show the significance of accurate simulation models for re-liable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. The findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental pa-rameters. This includes insights into temperature, humidity, and light intensity, offering oppor-tunities for enhanced comfort and efficiency in intelligent environments.

The Shimensi super-large tungsten polymetallic deposit is located in Late Jurassic- Early Cretaceous Porphyry-Skarn tungsten ore belt in the south Yangtze metallogenic belt. There are 3 types of mineralization: veinlet-disseminated type, thick quartze vein type and hydrothermal crypto-explosive breccia type. Based on geological studies, this paper presents new petrographic, microthermometric, laser Raman spectroscopic and hydrogen and oxygen isotope research of the fluid inclusions from the deposit. The results show that there are five different types of fluid inclusions: liquid-rich inclusions, vapor-rich inclusions, pure liquid inclusions, pure vapor inclusions, and fluid inclusions containing a solid crystal. The homogenization temperatures of the fluid inclusion range from 140℃-270℃, the salinities are 3%-5% NaCleq and the densities of ore-forming fluid range from 0.64 g/cm3 to 0.99 g/cm3. For the analyses of Laser Raman spectroscopic, the ore-forming fluids can be approximated by a Ca2+-Na+-SO42--Cl- fluid system with small amount of CO2, CH4 and N2. Otherwise, the datas of the pressure, pH and Eh show a fluid metallogenic environment of low pressure, weak acid and weak reduction. The values of the homogenization temperature in 3 types of orebodies suggest that the mineralization is characterized by the decrease of the temperature under the condition of the fluid immiscibility. The H-O isotope values are interpreted to indicate that the ore-forming fluids are mainly composed of magmatic water, and also the meteoric water was added with the process of the magma rising.

Facial expression has lots of applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on Action Unit (AU) weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. Sparse representation based approach is then proposed to detect the active AUs of testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for Jaffe and CK+ databases, respectively. Better cross-database performance has also been observed.

Breast cancer is the second most common cancer worldwide, primarily affecting women, while histopathological image analysis is one of the possibilities used to determine tumor malignancy. Regarding image analysis, the application of deep learning has become increasingly prevalent in recent years. However, a significant issue is the unbalanced nature of available datasets, with some classes having more images than others, which may impact the performance of the models due to poorer generalisability. A possible strategy to avoid this problem is downsampling the class with the most images to create a balanced dataset. Nevertheless, this approach is not recommended for small datasets as it can lead to poor model performance. Instead, techniques such as data augmentation are traditionally used to address this issue. These techniques apply simple transformations, such as translation or rotation, to the images to increase variability in the dataset. Recently, Generative Adversarial Networks (GANs) have emerged, which can generate images from a relatively small training set. This work aims to enhance the model’s performance in classifying histopathological images by applying data augmentation using GANs instead of traditional techniques.

Intermediate temperature solid oxide fuel cell (SOFC) operation provides numerous advantages such as high combined heat and power (CHP) efficiency, potentially long-term materials stability, and the use of low-cost materials. However, due to the sluggish kinetics of the oxygen reduction reaction at intermediate temperature, the cathode of SOFC requires an efficient and stable catalyst. Significant progress in the development of cathode materials has been made over recent years. In this article, multiple strategies for improving the performance of cathode materials have been extensively reviewed such as A and B site doping of perovskites, infiltration of catalytic active materials, the use of core-shell composites, etc. Emphasis has been given to intrinsic properties such as thermal expansion compatibility, chemical and thermal stability, and oxygen transport number. Furthermore, to avoid any insulating phase formation at the cathode/electrolyte interface, strategies for interfacial layer modifications have also been extensively reviewed and summarized. Based on major technical challenges, future research directions have been proposed for efficient and stable intermediate temperature solid oxide fuel cell (SOFC) operation.

Smartwatches are one of the most relevant fitness trends of the past two decades, and they collect increasing amounts of health and movement data. The accuracy of these data may be questionable and requires further investigation. Therefore, the aim of the present study is to validate smartwatches for use in triathlon training. Ten different smartwatches were tested for accuracy in measuring heart rate, distance (via global navigation satellite systems, GNSS), swim stroke rates and the number of swim laps in a 50 m Olympic-size pool. The optical heart rate measurement function of each smartwatch was compared to that of a chest strap. Thirty participants (15 females, 15 males) ran five 3-minute intervals on a motorised treadmill to evaluate the accuracy of the heart rate measurements. Moreover, for each smartwatch, running und cycling distance tracking was tested over six runs of 4,000 m on a 400 m tartan stadium track, six hilly outdoor runs over 3.4 km, and four repetitions of a 36.8 km road bike course, respectively. Three swimming protocols ranging from 200 m to 400 m were performed in triplicate in a 50 m Olympic-size pool, evaluating tracked distance and detected number of strokes. The mean absolute percentage errors (MAPEs) for the average heart rate measurements varied between 3.1% and 8.3%, with the coefficient of determination ranging from 0.22 to 0.79. MAPE results ranged from 0.8% to 12.1% for the 4,000 m run on the 400 m track, from 0.2% to 7.5% for the 3.4 km outdoor run, and from 0.0% to 4.2% for the 36.8 km bike ride. For the swimming tests, in contrast, the deviations from the true distance varied greatly, starting at a 0.0% MAPE for the 400 m freestyle and reaching 91.7% for the 200 m medley with style changes every 25 m. In summary, for some of the smartwatches, the measurement results deviated substantially from the true values. Measurements taken while road cycling over longer distances with only a few curves were in relative terms more accurate than for outdoor runs and even more accurate than for the 400 m track. In the swimming exercises, the accuracy of the measured distances was severely deteriorated by the medley changes among the majority of the smartwatches. Altogether, the results of this study should help in assessing the accuracy and thus suitability of smartwatches for general triathlon training.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via Stereolithography (SLA) of features at the micro-scale. Using a custom benchmark part, deviations in feature dimensions (micro-pores diameters and depths) were detected and measured by confocal microscopy. Samples characterization and experimental observations allowed the identification of inaccuracy sources, mainly due to the laser beam scanning strategy and to the incomplete removal of uncured liquid resin in post-processing (i.e., IPA washing). As a technology baseline, the measured dimensional errors on pores diameters were up to -46%. A compensation method of the nominal laser spot diameter (85m) was defined and implemented resulting in relevant improvements in dimensional accuracy of 9.8% and 11.3% on full-open pores diameter and depth, respectively. Further investigation was performed on a customized test part to adjust the calibration procedure. The measurements revealed that the estimate of the laser beam spot size is 968m. Measurements on micro-pores having different sizes revealed that a constant compensation parameter (i.e., C=85, 96, 120m) is not fully effective at the micro-scale, where average errors still remain at -24%, -18.8%, and -16% for compensations equals to 85, 96 and 120m, respectively. A further experimental campaign allowed the identification of an effective nonlinear compensation law where the compensation parameter depends on the micro-feature size C= f (D). Results show a sharp improvement in dimensional accuracy on micro-pore fabrication, with errors consistently below +8.2%. The proposed compensation method can be extended for the fabrication of any micro-features without restrictions on the specific technology implementation. Concerning the inaccuracy introduced by the laser path, experimentation with a customized part allowed the optimization of the part orientation to reduce the errors and their differences along the X and Y machine axes.

This paper details the comprehensive activities conducted in a laboratory setting to assess the Structural Health Monitoring (SHM) of prefabricated building envelopes. Aimed at enhancing the structural evaluation of buildings, the focus of this research lies in employing nondestructive sensors for SHM with the objective to advance in the understanding and assessment of facade conditions. The paper outlines the methodologies employed, starting with a modal analysis to define the structural vibration's modal frequency. Subsequently, tests were defined and executed to validate building envelope compliance in case of stress typical of high-rise building. Throughout these tests, continuous monitoring of the façade was conducted using both conventional (accelerometers, extensimeter washers) and non-conventional sensors (Fiber Optic Sensing) The gathered data underwent thorough processing and analysis to provide an overall perspective on the structure's behavior during the stress test. The investigation aimed to identify whether specific physical phenomena correlate with distinct signals and to evaluate if continuous façade monitoring over the building's lifespan aids in preventing potential structural damage. Moreover, two modal analyses were conducted to compare final data and frequencies with earlier data, enabling the detection of any structural alterations. The results from compliance testing and structural analysis yielded noteworthy insights contributing to the evolving field of structural health monitoring, offering valuable findings that advance our ability to proactively manage and maintain the integrity of building structures.

In the face of escalating climate concerns and the push for sustainable development, the global shift towards renewable and decentralized energy systems presents new challenges and opportunities. This study investigates integrating decentralized energy production, particularly photovoltaic (PV) systems, into national energy planning, aiming to optimize energy strategies that balance local production and consumption with national objectives. By analyzing the Swiss energy model, the research employs the EnergyScope and REHO models to assess the strategic implications of decentralized versus centralized energy systems. Results show that a decentralized approach can significantly reduce PV installation needs to 35 GW, about 23% of potential capacity, and decrease annual system costs by 10% to CHF 1230 per capita. This strategy emphasizes local consumption, minimizes grid reinforcement demands, and leverages economic advantages while addressing overproduction challenges through effective energy storage and grid management. Conclusions underline the strategic value of combining centralized and decentralized methods for resilient and sustainable energy planning. The study contributes to the discourse on energy policy and infrastructure planning, advocating for a hybrid model that accommodates both local conditions and broader energy objectives, urging further research into climate impacts and technology integration for a comprehensive energy future.

Manufacturing is a significant sector of the economy in many nations and is frequently regarded as an engine of economic expansion, particularly in developing nations. Despite its importance, the metal sector is seen as unsafe due to frequent and high accident rates as well as worker health issues. Therefore, the primary goal of this research is to create a safety model with the goal of development in occupational safety and health in order to reduce workplace injuries, diseases, and deaths in the Akaki Basic Metal Industry. The hypothesized model was developed and tested on a sample 215 respondents who worked for production businesses. The Statistical Package for the Social Sciences (SPSS) version 23.0 was used to enter and analyze the acquired data, and the Analysis Moment of Structure (AMOS) version 21 software was used to build the model. Through the use of structural equation modeling (SEM) and confirmatory factor analysis (CFA), research models were examined and confirmed. A good-fit structural model (PCLOSE=0.001, Goodness of Fit Index=0.971, Root Mean Square Error of Approximation=0.121, Comparative Fit Index=0.986 and TLI =0.906) indicated that Safety culture, safety police and safety climate constructs direct influence on firm productivity. The new structural model can be used to provide better understanding of the links between firm productivity indicators and contributing components, and make stronger recommendations for effective intervention in construction projects

The structure and physicochemical properties of polyvinyl alcohol (PVA) and PVA/glycerol films have been investigated by Fourier-transform infrared spectroscopy (FT-IR), X-ray Diffraction (XRD), thermogravimetry/differential thermal analysis (TG/DTA), and advanced scanning probe microscopy (SPM). In the pure PVA films, SPM allowed us to observe ribbon-shaped domains with a different frictional and elastic contrast, which apparently originated from a correlated growth or assembly of PVA crystalline nuclei located within individual PVA clusters. The incorporation of 1.0 wt.% glycerol led to modification in shape of those domains from ribbon-like in pure PVA to rounded in PVA/glycerol 1.0 wt.% films; changes in the relative intensities of the XRD peaks and a decrease of the amorphous halo in the XRD pattern were also detected, while the DTA peak corresponding to the melting point remained at almost the same temperature. For higher glycerol content, FT-IR revealed additional glycerol-characteristic peaks presumably related to the formation of glycerol aggregates, and XRD, FT-IR, and DTA all indicated a reduction in crystallinity. For more than 2.0 wt.% glycerol, the plasticization of the films complicated the acquisition of SPM images without tip-induced surface modification. Our study contributes to the understanding of crystallinity in PVA and how it is altered by a plasticizer such as glycerol.

Vertical-axis wind turbines (VAWTs) are receiving more and more attention as they involve simple design, cope better with turbulence and are insensitive to wind direction which has a huge impact on the cost since the yaw mechanism is not needed. However, VAWTs still suffer from low conversion efficiency. As a results tremendous efforts are exerted to improve its efficiency which mainly focus on two methods regardless of whether the study is a CFD simulation or a field test or lab test experiment. An active approach which involves the modification of the rotor itself such as the blade design, angle, trailing and leading edges, inner blades, chord thickness, contra-rotating rotor, etc. While the second approach involves passive techniques where the flow is directed to optimally face the downwind rotor by mounting a guiding vane such as a diffuser or other shapes at the upwind position of the rotor. Among all techniques undertaken, the CRWT rotor technique seems to be the most effective with an output even comparable to that of Horizontal Axis Wind Turbines (HAWT), while the Savonius rotor received more attention compared to other designs of VAWTs. Apart from technological issues, it was also suggested that geographical issues such as proper site siting of the wind turbine rotor at a particular location where a uniform flow could be guaranteed is of paramount importance to ensure an effective conversion capacity of wind turbines.

Direct Energy Deposition (DED) is a versatile and efficient method in metal additive manufacturing. However, humping, caused by abnormal dynamics in the melt pool (MP), poses a significant threat to the geometric integrity of manufactured products. Current state-of-the-art (SOTA) methods primarily detect humping by analyzing late-stage spatial abnormalities, such as MP detachment. This approach is fundamentally reactive, leading to a tendency to miss early humping spatiotemporal dynamics, like cyclic elongation of the MP. This study introduces a novel, proactive indicator named VIMPS (Variability of Instantaneous MP Solidification-Front Speed), a physics-based tool designed to quantify early abnormal fluctuations in MP solidification speed. The experiments demonstrate VIMPS correlation with humping-induced geometric inaccuracies. By capturing early spatiotemporal dynamics of the MP, VIMPS reduces detection latency by 30 seconds compared to existing SOTAs that focus solely on spatial abnormalities. This significant improvement transforms detection from reactive to proactive, providing the time needed for corrective actions to enhance the overall productivity and quality of the DED process.

Electrical contacts are important circuit components with diverse industrial applications, and their failure can lead to multiple unwanted effects. Hence, the behavior of electrical contacts is a widely studied topic in scientific literature based on various approaches, tools, and techniques. The present study proposes a numerical modeling and simulation approach based on the Holm contact theory to study the dependence between the electric potential and the temperature within an electrical contact. Structured in 5 sections, the research was conducted using COMSOL Multiphysics software and its solid-state mechanics, electric current, and heat transfer modules in order to highlight contact behavior from mechanical, electrical and thermal points of view: the von Mises stress, contact force, the electric field amplitude, the variation of the electrical potential along the current path, the temperature gradient, and dependence of temperature along the contact elements edges were obtained by simulation and graphically represented. The results are in line with the expectations and with past research that was studied in the literature review.

Taiwan's bed and breakfast (B&B) development has been more than 25 years ago, from the first generation of retirement to the B&B industry, serving tourists from all over the world accom-modation, providing breakfast and accommodation mode to the present, after the change of employees from the second generation, more and more attention is paid to the cultivation and cultivation of business ability. With the advent of the era of AI technology networking, the ca-pabilities required for B&B operation are more focused on all aspects of service, and the good marketing ability of B&B can promote a green chimney-free tourism economy, which is also a favorite industry of various governments and the public. This study explores the basic vocational competency indicators required for running a B&B, with (1)EQ emotion management skills, (2)Financial management ability, (3)(SK) technical skills, and (4)Marketing and promotion management capabilities. Through the study method, the K-S Z test was determined by the con-sensus conclusion of expert reliability and validity p value

The paper provides and comments on the results of studies of the effect of sandstone-based abrasives and quartz sand alone on the wear of martensitic surfaces of wear-resistant steels. The wear process was examined on a ring-on-ring test rig seeking to determine the mass decrement parameter which characterised wear. The tests were conducted for three sandstone varieties: Carboniferous, Permian, and Cretaceous, and they made it possible to determine that the most intense process of deterioration of wear-resistant steels took place in the presence of quartz sand grain, while less intense wear was observed in the case of sandstone-based abrasives. The mass decrement values established in the presence of the sandstones in question did not differ significantly between individual sandstone varieties. Based on a surface damage analysis, the basic damage mechanism was found to be micro-scratching, however, with regard to the sandstones examined, it was also determined that individual grains could be pressed into surface irregularities, as well as that films of soft hematite cement developed in the Permian sandstone and that inclusions of carbonaceous matter were formed in the Carboniferous sandstone. With reference to the wear process observations, a wear model was described for the surface of the steels examined in the presence of sandstone-based abrasives.

The per capita availability of freshwater resources on a global scale is gradually decreasing due to population growth, rapid urbanization, industrialization, and modern lifestyles. In this situation, wastewater management becomes a major concern. Conventional treatment processes used to treat wastewater discharges are not adequate for the advanced removal of pollutants, especially refractory organic pollutants, which are mainly organic compounds that are not easily biodegradable in the environment. The use of photocatalytic membrane reactors (PMRs) for tertiary wastewater treatment has attracted much interest in recent periods, and various configurations have been investigated. The performance of a PMR using an iron doped titanium photo catalyst and Polysulfone-based polymeric membrane for municipal wastewater treatment was evaluated under natural and simulated solar light. Obtained results proved that solar driven PMRs can remove organic loading (COD) with high efficiencies and may represent a suitable alternative for the tertiary treatment step of municipal wastewater.

This article summarizes the results of a research conducted to create an intelligent platform supporting small farmers growing vegetables. In Bulgaria, many farmers use relatively small areas for their production. Considering this situation, the platform is designed for regional use, trying to make maximum use of the specific climatic features of the monitored microdistrict. Consequently, special attention is paid to the possibilities of adapting the platform to the specific environment in which the observed plants grow. Thus, the platform is implemented as an agent-oriented system providing needed flexibility. The core of the platform is a personal assistant, which is responsible for tracking the vegetation cycle of the monitored crop and, upon detection of anomalies, preparing warnings to the farmer. The general architecture of the platform is also presented. In a distributed knowledge base, agriculture-specific knowledge are stored. Furthermore, a standardized interface for receiving data from the sensor networks located in the production areas is briefly described. Currently, the platform is being tested for monitoring four varieties of tomatoes grown on the territory of the national research center for potato breeding. Special attention is paid to the measurements carried out. Conclusions have been drawn that will help improve the platform.

The software industry has adopted component-based software development (CBSD) to rapidly build and deploy large and complex software systems with significant savings at minimal engineering effort, cost, and time. However, CBSD encounters issues on security trust, mainly with respect to dependability attributes. A system is considered dependable when it can produce the outputs for which it was designed with no adverse effect on its intended environment. Dependability consists of several attributes that imply availability, confidentiality, integrity, reliability, safety, and maintainability. Dependability attributes must be embedded in a CBSD model to develop dependable component software. Motivated by the importance of these attributes, this paper pursues two objectives: to design a model for developing a dependable system that mitigates the vulnerabilities of software components, and to evaluate the proposed model. The model proposed in this study is labelled as developing dependable component-based software (2DCBS). To develop this model, the CBSD architectural phases and processes must be framed and the six dependability attributes embedded according to the best practice method. The expert opinion approach was applied to evaluate 2DCBS framing. In addition, the 2DCBS model was applied to the development of an information communication technology (ICT) portal through an empirical study method. Vulnerability assessment tools (VATs) were employed to verify the dependability attributes of the developed ICT portal. Results show that the 2DCBS model can be adopted to develop web application systems and to mitigate the vulnerabilities of the developed systems. This study contributes to CBSD and facilitates the specification and evaluation of dependability attributes throughout model development. Furthermore, the reliability of the dependable model can increase confidence in the use of CBSD for industries.

The dynamic behaviour of a machine tool (MT) directly influences the machining performances. The adoption of lightweight structures may reduce the effects of undesired vibrations and increasing the workpiece quality. This paper aims to present and compare a set of hybrid materials that may be excellent candidate to fabricate the MT moving parts. The selected materials have high dynamic characteristics and capacity to damp mechanical vibrations. In this way, starting by the kinematic model of a milling machine that highlights the main critical factors, this study paper evaluates a number of prototypes made of Al Foam sandwiches (AFS), Al Corrugated sandwiches (ACS) and materials reinforced by carbon fibres (CFRP). A set of prototypes has been fabricated, represented the Z-axis ram of a commercial milling machine. The static and dynamical properties have been evaluated by using both FE simulations and experimental tests. The obtained results show that the proposed structures may be a valid alternative to the conventional materials of MT moving parts, increasing machining performances. In particular, the AFS prototype highlighted a damping coefficient that is 20 times greater than a conventional ram (e.g. steel). The CFRP structure is able to satisfy the machining requirements with a reduced weight of 48.5%, while the ACS prototype showed a good trade-off between stiffness and damping.

This paper proposes an innovative mechatronic piezo-actuated module to control vibrations in modern machine tools. Vibrations represent one of the main issues that compromise seriously the quality of the workpiece. The active vibration control (AVC) device is composed by a host part integrated with sensors and actuators synchronized by a regulator, able to make a self-assessment and adjust to the environmental alteration. This study presents the mechatronic model based on the kinematic and dynamic analysis of the AVC device. To ensure a real time performance, a H2-LQG controller has been developed and validated by simulations involving machine tool, PZT actuator and controller models. The Hardware-in-the-loop (HIL) architecture is adopted to control and attenuate the vibrations. A set of experimental tests has been performed to validate the AVC module on a commercial machine tool. The feasibility of the real time vibration damping is demonstrated and the simulation accuracy is evaluated.

Mannosylerythritol lipids (MELs), one of the most promising biosurfactants (BS), are glycolipids produced by yeasts or fungi, with great environmental performance and a high compatibility with the human body. MELs besides working as typical surfactants, can form diverse structures when in the critical aggregation concentration (CAC), reducing the surface tension of water and other solutions, and being stable over a wide range of conditions. Among others, MELs present anti-microbial, antitumor, antioxidant and anti-inflammatory activity, skin and hair repair capacity, which opens possibilities for their use in applications from cosmetics and pharmaceutics to bio-remediation and agriculture. However, their market share is still low when compared to other glycolipids, due to their less developed production process and higher production cost. This re-view gathers information on the potential applications of MELs mentioned on the literature since 1993. Furthermore, it is also explored the current strategies being developed to enhance the market presence of MELs, in parallel with the ones developed for rhamnolipids and sophorolipids.

The stinging nettle caterpillar, Darna pallivitta (Lepidoptera: Limacodidae) is a serious invasive pest of agricultural products and a health hazard on the Hawaiian Islands first discovered in 2001. Nursery workers and homeowners have been stung by the caterpillars while handling their plants, especially rhapis palms (Rhapis sp.). Throughout its invaded range, it causes widespread damage including the many cultivated and native palm species grown in Hawaii. Of medical importance are the stinging spines of the larva, which cause dermatitis on contact with the skin. Horticulture and nursery products impacted by the limacodid pest are estimated at $84.3 million (2018 value). An eradication attempt with pesticides and lure traps was made but proved unsuccessful and the moth population continued to spread to major Hawaiian Islands (Hawaii, Kauai, Maui, Oahu). The introduction of specific biological control agents from the native region was thought to be the long-term solution for this invasive pest. Initial exploration in Indonesia and Thailand resulted in the introduction of a pupal ectoparasitoid, Nesolynx sp. (Hymenoptera: Eulophidae: Tetrastichinae), that was not specific. The oriental wasp, Aroplectrus dimerus Lin (Hymenoptera: Eulophidae: Eulophinae), idiobiont gregarious ectoparasitoid of the stinging nettle caterpillar, was introduced from Taiwan in 2004 for host specificity studies and biocontrol in Hawaii. Host range testing showed the parasitoid attacked only limacodid species and it was approved for field release in 2010. The parasitoid identity, host specificity under containment facility conditions, reproductive performance, colonization on the major infested sites were assessed. A total of 13379 parasitoids were colonized on162 release sites on four Hawaiian Islands. Evaluations were conducted using field surveys of larvae, pupal counts, and male lure traps. Field parasitism was thoroughly investigated on Oahu Island averaging 18.9 ± 5.6 % of 3923 collected larvae during 2010 – 2023. The numbers of male moths caught/trap/month were significantly reduced on Oahu Island (P

A commercial phage biocontrol for reducing Listeria monocytogenes has been described as an effective tool for improving fresh produce safety. Critical challenges in the phage application must be overcome for the industrial application. The validation studies were performed in two processing lines of two industry collaborators in Spain and Denmark, selecting shredded iceberg lettuce as the ready-to-eat (RTE) product of higher process volume. The biocontrol treatment optimized in lab-scale trials for the application of PhageGuard ListexTM was confirmed in industrial settings by four tests, two in Spain and two in Denmark. Results showed that the method of application that included the device and the processing operation step were appropriate for the correct application. The proper dose of Phage Guard ListexTM was reached in shredded iceberg lettuce and the surface was adequately covered for the successful application of phages. There was no impact on the headspace gas composition (CO2 and O2 levels), nor on the color when untreated and treated samples were compared. The post-process treatment PhageGuard ListexTM did not cause any detrimental impact on the sensory quality, including flavor, texture, browning, spoilage and visual appearance of over the shelf-life as the phage solution was applied as a fine, mist solution.

This paper illustrates an experimental activity for the closed-loop position control of an actuator made using shape memory alloy (SMA) wire. A solution with the self-sensing effect was implemented to miniaturize the systems, i.e., without external sensors. A proportional control algorithm was initially used, demonstrating the idea's feasibility: the wire can behave simultaneously as an actuator and sensor. An experimental investigation was subsequently conducted for the optimization of the developed actuator. As for the material, a Flexinol wire, Ni-Ti alloy, with a diameter of 0.150 mm and a length of 200 mm, was used. Preliminarily, a characterization of the SMA wire at constant and variable loads was carried out: the characteristics detected are elongation vs. electric current and elongation vs. electrical resistance. The control system is PC-based with a data acquisition card (DAQ). A drive board has been designed and built to read the wire's electrical resistance and power it by pulse width modulation (PWM). A notable result is that the actuator works with good precision and in dynamic conditions, even when it is called to support a load up to 65% different from that for which the electrical resistance-length correlation has previously been experimentally obtained, on which the control is based. This opens up the possibility of using the actuator in a counteracting configuration with a spring, which makes hardware implementation and control management simple.

. Protein cysteine S-glycosylation is a relatively rare and less well characterized post-translational modification (PTM). Creating reliable model proteins that carry this modification is challenging. The lack of available model or natural S-glycosylated proteins significantly hampers the development of MS-based methodologies for detecting protein cysteine S-glycosylation in real-world proteomic studies. There is also limited MS-sequencing data describing easier to create synthetic S-glycopeptides. Here, we present the results of an in-depth manual analysis of automatically annotated CID/HCD spectra for model S-glucopeptides. The CID spectra show a long series of y/b-fragment ions with retained S-glucosylation, regardless of the dominant m/z signals corresponding to neutral loss of 1,2-anhydroglucose from the precursor ions. In addition, the spectra show signals manifesting glucosyl transfer from the cysteine position onto Lys, Arg side chains, and a peptide N-terminus. Other spectral evidence indicates that the N-glucosylated initial products of transfer are converted into N-furanosylated (i.e., glycated) structures due to Amadori rearrangement. We discuss the peculiar transfer of the glucose oxocarbenium ion (Glc+) to positively charged guanidinium residue (ArgH+) and propose a mechanism for the gas-phase Amadori rearrangement involving a hydride ion shift.

The raw material purchasing (RMP) problem is to determine the purchasing quantities of raw materials in given periods or cycles. Raw material purchasing optimization is crucial for large-scale steel plants because it is closely related to the supply of raw materials and cost savings. The raw material purchasing of large-scale steel enterprises is characterized by many varieties, large quantities, and high costs. The RMP objective is to minimize the total purchasing cost consisting of the price of raw materials, purchasing set-up costs, and inventory costs and meet product demand. We present a mixed integer linear programming (MILP) model and a column generation (CG) solution for the resulting optimization problem. The column generation algorithm is the generalization of the branch & bound algorithm with solving the linear programming (LP) relaxation of MILP using column generation (CG), and its advantage is to handle large-sized MILPs. Experimental results show the effectiveness and efficiency of the solution.

The surface characterization of indium tin oxide (ITO) films was processed by oxygen (O2) plasma gas using a magnetron sputtering method of varying O2-plasma gas from 20 sccm to 100 sccm for potentially fostering in electronic devices applications on dye-sensitize solar cells (DSSC) in this work. In order to gain an outstanding performance rate with high-quality thin films, the affect of correlation between the electrical, optical, and morphology properties was significantly investigated. Clearly, the content of investigation exhibited that the films properties was changed by variation of the gas composition. These changes have contemporary relevance to the sputtering gas during the deposition process. To conclude, the greatest electrical properties was displayed by O2 -plasma gas flow rate of 20 sccm, which showed the lowest resistivity. In addition to this, the final products were practically fabricated to active layers for dye-sensitize solar cells (DSSC) applications. The highest efficiency of DSSC device was indicated of approximately 0.35% which was located by 40 sccm of the O2-plasma gas. Consequently, the study could be a possibility way of preparing ITO thin films with improved special properties of substantial applications for solar cells devices in the near future.

Global food waste has a huge impact on the environment, as it is a source of greenhouse gas (GHG) emissions and wasted natural resources. Across the world, over 30% of food is lost or wasted each year. Aside from this, food industry, as well, is one of the biggest sources of agro-industrial waste and by-products, which can be valorized and used for different purposes. Such waste is a good source of bioactive organic compounds which can be extracted without al-tering their properties, where deep eutectic solvents (DESs) can serve as green solvents and excel-lent replacement for volatile organic solvents. Isolated compounds can be used in innovative food production, chemical production, cosmetics and other industries. DESs have attracted extraordi-nary attention due to their advantages such as environmental friendliness, availability and easy preparation, easy handling and utilization of non-toxic components for their formation. Due to those properties, they are a greener alternative to classic organic solvents for many processes, in-cluding extractions. In this paper, we review the utilization of deep eutectic solvents as potential green media for the extraction of organic compounds such as polyphenols, carbohydrates, pro-teins, alkaloids from by-products of the food industry and agro-industrial-waste.

Achieving the thermal conductivity required for efficient heat management in semiconductors and other devices requires the integration of thermally conductive ceramic fillers at concentrations of 60 vol% or higher. However, the increased filler content often negatively affects the mechanical properties of the composite matrix, limiting its practical applicability. To address this issue, in this paper, we present a new strategy to reduce the required ceramic filler content: the use of a thermally conductive ceramic composite filler with carbon nanotubes (CNT) grown on aluminum nitride (AlN). We have combined catalyst coating technology with vacuum filtration to ensure that the catalyst is uniformly applied to micrometer-sized AlN particles, followed by efficient and uniform synthesis of CNTs using a water-assisted process in a vertical furnace. By carefully controlling the number of vacuum filtration cycles and the growth time of the CNTs, we demonstrate precise control over the number and length of the CNT layers, thereby adjusting the properties of the composite to the intended specifications. When AlN/CNT hybrid fillers are incorporated into silicone rubber, while maintaining the mechanical properties of rubber, the thermal diffusivity achieved at reduced filler levels exceeds that of composites using AlN-only or simultaneous AlN and CNTs formulations. This demonstrates the critical role of CNTs on AlN surfaces. Our study represents a significant advancement in the design of thermally conductive materials with potential implications for a wide range of applications.

The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.

The construction industry is a key driver of environmental change due to its extensive ‎use of resources and high levels of emissions, thus placing a significant burden on global ‎efforts towards sustainable development targets. A large portion of the environmental ‎footprint of buildings results from the energy required to sustain indoor comfort levels. ‎Therefore, enhancing the energy efficiency of existing buildings becomes critical in ‎reducing their environmental impact. This study examines the influence of increasing ‎thermal performance with the aim of reducing heating ‎demand. Thus, numerical modelling tools and different energy performance assessment meth-odologies have been used. ‎Specifically, it evaluates two existing residential buildings in Romania and compares ‎heating demand predictions using PHPP and Mc001-2022 methodologies across various ‎climatic data sets and case studies. The results reveal significant variability in heating ‎demand predictions, emphasizing the importance of methodological choice and climatic ‎data. The ‎study highlights that both PHPP and Mc001-2022 methodologies yield varying results ‎depending on the specific conditions and characteristics of each building. This variation in results underscores ‎the necessity for a nuanced and context-specific approach to energy performance ‎assessment, in-corporating multiple energy efficiency measures tailored to the unique ‎features of each building.

Salmonella Typhimurium is a typical Gram-Negative bacterium associated with food-borne illness threatening public health. This pathogen is reported to have caused millions of infections and thousands of hospitalizations yearly in the United States alone. Salmonella Typhimurium has a sophisticated array of virulent factors, including its flagella, which contributes significantly to its pathogenicity. Cannabidiol (CBD), the non-psychoactive component of the cannabis plant, has attracted the interest of many researchers recently because of its antimicrobial properties. With the growing interest in exploring CBD’s antimicrobial properties, we, in this study, have delved into unravelling how prolonged exposure of Salmonella Typhimurium to CBD influences the expression of the flgA gene. We hypothesized that the exposure of Salmonella Typhimurium to CBD may result in flgA upregulation, causing the bacteria to withstand various stresses and potentially enhance its survival rates and pathogenicity. We have employed several assays, such as mRNA expression, motility assays, pH induction assays, nutrient starvation interventions, and biofilm analysis, to explore the influence of CBD on the pathophysiology of Salmonella Typhimurium. Our findings reveal that CBD-resistant strains exhibit enhanced resilience to extreme pH levels and motility and survive poorly under nutrient starvation, with more robust biofilm formation compared to the susceptible strains. The results contribute to the broader comprehension of CBD resistance mechanisms in bacteria. This work provides insight into the potential therapeutic interventions for controlling CBD-resistant bacterial infections. This study also underscores the essence of exploring novel alternative antimicrobial agents to tackle the ever-persistence of bacterial antibiotic resistance.

The rapid proliferation of Internet of Things (IoT) technology has ushered in a new era of connectivity, allowing billions of devices to produce extensive data. Significant challenges in software engineering need to be addressed for full utilization of Internet of Things (IoT) applications. This article examines the challenges in Internet of Things, software engineering and discusses current developments aimed at addressing these issues and seizing new opportunities. We examine the challenges associated with Internet of Things software development, including resource constraints, device diversity, security vulnerabilities, and data management concerns. New studies have indicated the use of advanced techniques like as containerization, model-driven development, AI and ML integration, and robust security frameworks. Moreover, the significance of system architecture is explained, including the device, network, and cloud layers, with an emphasis on the principles of reusability, scalability, and modularity. Results and conversations explore the complexities of security measures, emerging opportunities, and scaling solutions made possible by simulation tools, penetration testing, and cross-domain development platforms. The study expects IoT system evolution and emphasises the need for continual software and firmware changes, testing, and deployment.

In this paper, a new type of self-excited thermomechanical oscillator containing an oscillating shape memory alloy (SMA) filament with two symmetrically arranged spheres is investigated. The self-excitation of the oscillations is due to a heater of constant temperature, which causes periodic contractions of the filament when it approaches it. The contracted filament moves away from the heater a distance sufficient to cool it. Under the action of the weight of the spheres, the cooled filament re-approaches the heater, causing the above processes to repeat periodically. On the basis of experimental studies, approximating functions of the heater's heat field distribution are derived. A dynamic model of the oscillator has been created, in which the minor and major hysteresis in the SMA alloy and the distribution of the heat field around the heater have been taken into account. By numerical solutions of the differential equations, the laws of motion of the spheres are obtained. The displacements of the spheres in two perpendicular directions were measured using an experimental system. The obtained experimental results validate the proposed dynamic model and its assumptions with a high degree of confidence. Conclusions are drawn about the stochastic nature of the oscillations due to the hysteresis properties of the SMA and the temperature variation of the natural frequency of the oscillating system.

This study involves the development of a hybrid forecasting framework that integrates two different models in a framework to improve prediction capability. Although the concept of hybridization is not a new issue in forecasting, our approach presents a new structure that combines two standard simple forecasting models uniquely for superior performance. Hybridization is significant for complex data sets with multiple patterns. Such data sets do not respond well to simple models, and hybrid models based on the integration of various forecasting tools often lead to better forecasting performance. The proposed architecture includes serially connected ARIMA and ANN models. The original data set is first processed by ARIMA. The error (i.e., residuals) of the ARIMA is sent to the ANN for secondary processing. Between these two models, there is a classification mechanism where the raw output of the ARIMA is categorized into three groups before they are sent to the secondary model. The algorithm is tested on well-known forecasting cases from the literature. The proposed model performs better than existing methods in most cases.

Antioxidant, anti-inflammatory and chemo-preventive features have been reported for the carotenoid diatoxanthin. Diatoxanthin is only produced by a few groups of microalgae where it functions in photoprotection. Its large-scale production in microalgae is currently not feasible. In fact, rapid conversion into the inactive pigment diadinoxanthin is triggered when the cells are removed from the high-intensity light source, which will be the case during large-scale harvesting of microalgae biomass. Zeaxanthin epoxidase (ZEP) 2 and/or ZEP3 have been suggested to be responsible for the back-conversion of high-light accumulated diatoxanthin to diadinoxanthin in low light in diatoms. Using the CRISPR/Cas9 gene editing technology we knocked out the ZEP2 and ZEP3 genes in the already commercially used marine diatom Phaeodactylum tricornutum to investigate their role in the diadinoxanthin-diatoxanthin cycle, and to determine if one of the mutant strains could function as a diatoxanthin production line. Light shift experiments proved that ZEP3 encodes the enzyme converting diatoxanthin to diadinoxanthin in low light. Loss of ZEP3 caused the high-light accumulated diatoxanthin to be stable for several hours after the cultures had been returned to low light, suggesting that zep3 mutant strains could be suitable as commercial production lines of diatoxanthin.

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

Plato defined a quality as “some degree of perfection” [49]. Studies on quality have unveiled the internal and external quality factors [17] that significantly impact product quality [4]. Internal factors are invisible to the end customer and are dependent on overall work organisation by the development team [17]. This work organisation is called “the process” [11],[33]. In this article, we will discuss how to address the drone-dedicated software development process to support system verification and ensure product quality by supporting quality characteristics and therefore increase software development awareness and control. We will discuss the process's influence on overall work activities and relationships between those activities by addressing the software development process utilised in the drone-dedicated GNSS-denied navigation system development project. The result was observable after three weeks of starting the software development. It was focused on better team awareness, faster task completion, lower number of development mistakes, and better and faster change response than we observed in other projects that didn’t follow the predefined process.

Deep learning neural networks require an immense amount of computation, especially in the training phase of the network when networks with multiple layers of intermediate neurons need to be built. In this paper, we will focus on the PSO algorithm with the aim of significantly accelerating the DLNN training phase by taking advantage of the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimization method whose goal is to iteratively improve the solution to a (usually complex) problem by attempting to approximate a given objective. However, parallelizing an efficient PSO is not a straightforward process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In the present work, two parallelizations of the PSO algorithm have been implemented , both designed for a distributed execution environment. The synchronous parallel PSO implementation ensures consistency at the cost of potential idle time due to global synchronization, while the asynchronous parallel PSO approach improves execution time by reducing the need for global synchronization, making it more suitable for large datasets and distributed environments such as Apache Spark. Both variants of PSO have been implemented to distribute the computational load supported by this algorithm –due to the costly fitness evaluation and updating of particle positions– across the different Spark cluster executor nodes to effectively achieve coarse-grained parallelism, resulting in a significant performance increase over current sequential variants of PSO.

Without an established energy management plan, human lifestyle improvement is incomprehensible. Adequate energy assets are the key to human lifestyle development, however, energy assets are restricted and exorbitant too. In this paper, an energy control system for a green environment called PMC (Power Management and Control) is proposed. The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The blend of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is utilized to make a fusion methodology to improve occupant comfort index (OCI) and decrease energy utilization. The main theme of the proposed PMC technique is to improve OCI and decrease energy utilization. The proposed framework gives better OCI when compared with its counterpart Ant Bee Colony along with Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the proposed PMC methodology gives practically the same OCI, but consumes less energy. The proposed PMC methodology additionally accomplished the most extreme OCI (i-e 1) when compared with the existing model FA-GA (i-e 0.98). The proposed PMC model consumed less energy as compared to existing models ABCKB, GAP, PSO and AEO. The proposed model consumed more energy than SOHP but provided better OCI. The comparative outcomes show the viability of the proposed PMC framework in lessening energy utilization and improving the OCI. Unlike other existing mythologies except for the AEO framework, the proposed PMC technique is additionally confirmed through a simulated climate by controlling indoor climate using actuators, like Fan, light, AC and boiler.

In the fast-changing world of software development, the protection of software products has emerged as an imperative requirement. This paper presents a new way to increase the maturity of development teams to reach the highest level of software security. Based on these, the framework uses the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) to systematically evaluate and enhance the people maturity of software development projects. The essence of the fuzzy logic and AHP technique interaction is to handle the uncertainty and complexity of human factors and team dynamics in the evaluation framework. Using the decision-making model allows the project managers and stakeholders to determine the appropriate areas for improvement and develop the right strategies and actions to nurture a secure and mature development culture. The paper identifies 24 human success factors (HSFs) and human security vulnerabilities (HSVs) and 38 practices for addressing these HSFs and HSVs. Furthermore, we discuss the local and global ranks of each HSF and HSV practice and categorize the identified practices into 9 categories to determine the ranks and weight of each category. Based on collected data, FAHP prioritized these practices; the category “C4: Skill Development and Stakeholder Engagement” is ranked highest at rank-1 and possesses the most significant weight of 0.12435. Similarly, the highest global weight is 0.051506, and the global ranked (rank-1) HSF and HSV practice is “P15: Hands-On Practice and Stakeholder Communication”. Research evidence and case discussions show how the described framework assists in building secure software development (SSD) practices, which can be considered evidence of the application of team maturity to improve cybersecurity in organizations. Additional research directions include improving the framework, especially using highly developed learning techniques and applying the framework to other forms of development.

Logistics plays an important role in globalized companies and contributes to the development of foreign trade. A large number of external conditions, such as recession and inflation, affect logistics. Therefore, managers should find ways to improve operational performance, enabling them to increase efficiency while considering environmental sustainability due to the industry’s large scale of energy consumption. Based on data collected from the financial reports of top global logistics companies, this study uses a DEA model to calculate corporate efficiency by implementing a Grey forecasting approach to forecast future sustainability values. Consequently, the study addresses the problem of how to enhance operational performance while accounting for the impact of external conditions. This research can help logistics companies develop operation strategies in the future that will enhance their competitiveness vis-à-vis rivals in a time of global economic volatility.

Brand trust holds significant importance in the Mongolian cashmere industry. This study examines the primary predictors of customer brand trust among Mongolian cashmere users. Our research includes a survey with 800 participants and 20 individual interviews conducted among both Mongolian cashmere users and non-users. We utilized an online platform to reach participants, employing convenience sampling. The study investigates the relationship between various factors and brand trust within the context of Mongolian cashmere brands. Through a combination of in-dividual interviews and statistical analysis, we test six hypotheses. The findings indicate that higher brand quality, a strong reputation, greater transparency, effective issue resolution, commitment to environmental and social responsibility, and pre-purchase research on such commitments positively impact brand trust. Key insights gleaned from the interviews emphasize the importance of quality, reputation, transparency, customer service, and environmental responsibility in shaping consumer trust. These results offer valuable implications for cashmere brands aiming to bolster consumer trust and foster loyalty.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

Few studies have paid attention to the industrial lands along the Chinese Grand Canal from a cultural per-spective, but cities along the route like Hangzhou have relied on the Canal system to promote industrializa-tion over the past hundred years, and relocated, transformed, or demolished the factories to construct cul-tural space or products mostly after industrial recession at the beginning of 21st century. To deeply analyze this process is quite helpful to understand the typical cultural change and its gain and loss along the Canal especially after cultural and creative industries have been established as the pillar industries of China's na-tional economy. Therefore, along the Hangzhou section of the Canal, we dynamically monitored the land use of the selected 10 industrial lands, with the analysis of high-resolution satellite imageries and the in-dustrial past, the areas of different uses in 2022 were compared. Meanwhile, along with archival data, fieldwork, and experts’ consultation, we designed a mark sheet with factors and scores to comprehensively assess cultural changes over those lands, and its reasons and influence also have been argued, and the prob-lems and suggestions have been offered. This study can contribute to a better understanding of public poli-cy choices in China regarding cities along the Grand Canal after the industrial decline, and provide new perspectives for cultural research, heritage conservation, and urban planning of canals or waterfront spaces.

In order to foster a more sustainable and eco-friendly trajectory for the construction industry, while concurrently mitigating environmental pollution and energy inefficiency, it is imperative to cultivate an environmentally conscious building and urban environment. Under the background of carbon peak and carbon neutrality, green building evaluation system (GBRS) has become a research hotspot in the field of green building. This paper systematically summarizes the current research progress based on GBRS, focuses on the current weight and indicators setting, analyzes it with Cite Space software, and puts forward its future development direction. The results show that the weight setting method of green building evaluation system is determined by multidimensional and multi-method. From the perspective of environment, society and economy, the scope of indicators setting is expanding. The green building evaluation system will play a guiding role in green building design, and the evaluation process will continue to be optimized and developed in the direction of intelligence and automation.

The objective of this study was to analyze the mental workload using EEG and eye tracking data and classify it using machine learning algorithms. The machine learning model was developed based on the simultaneous recording of eye tracking and EEG measurements during the experimental process. The experiments involved 15 university students, consisting of 7 women and 8 men. Throughout the experiments, the researchers utilized the n-back memory task and the NASA-Task Load Index (TLX) subjective rating scale to assess various levels of mental workload. The findings revealed that as the task difficulty increased, there was an increase in the diameter of both the right and left pupils, the number of fixations, the number and duration of saccades, and the number and duration of blinks. Conversely, variables related to fixation duration decreased. The EEG results indicated that theta power in the prefrontal, frontal, and front central regions increased with task difficulty. Additionally, alpha power increased in the frontal regions but decreased in the temporal, parietal, and occipital regions as the task became more challenging. Furthermore, low beta power significantly decreased in almost all brain regions as the task difficulty increased. In terms of the four-class classification problem, the mental workload level can be predicted with an accuracy rate of 76.59% using 34 selected features. This study has made a significant contribution to the literature by presenting a four-class mental workload estimation model that utilizes different machine learning algorithms.

Predictive numerical models in the study of ground-borne vibrations generated by railway sys-tems have traditionally relied on the segmented approach of subsystems. In this approach, loads are individually applied, and the cumulative effect of rolling stock is obtained through superposi-tion. While this method serves to mitigate computational costs, it may not fully capture the com-plex interactions involved in ground-borne vibrations. Recent advancements in computational and software tools have enabled the development of more sophisticated vibrational pollution models. These advanced models encompass the entire dynamic system, from the rolling stock to the terrain, allowing for continuous simulations with a defined time step. Furthermore, the incorporation of computational contact tools between various ele-ments not only ensures temporal accuracy but also extends the analysis to the frequency domain. This paper explores the evolution of predictive numerical models in the context of ground-borne vibrations generated by trains on ballasted tracks. It highlights the shift from segmented models to continuous simulations, demonstrating the progressive advancement towards more comprehen-sive and accurate representations of railway-induced and generated vibrations in the track sur-roundings as an additional way of evaluate sustainability of infrastructures. In particular, this development of a numerical model from its inception aimed to expand upon ex-isting emission - prediction models by enriching the data with frequency-domain information. This approach has been driven by the aspiration to bridge the gap between numerical predictions and measured signals, striving for a higher level of accuracy in our obtained results. This model can predict the impact of a high-speed rail (HSR) vehicle passing on its own track. It possesses sufficient information in both the time and frequency domains to precisely characterize vibrations. This initiative marks a pivotal advancement, enabling us to more effectively capture the intricacies of ground-borne vibrations and their impact on the surrounding environment due to a deeper comprehension of the occurrences in the frequency domain, there's a heightened ca-pability to accurately characterize vibrations.

Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

Cloud data centers play a vital role in modern computing infrastructure, offering scalable resources for diverse applications. However, managing costs and resources efficiently in these centers has become a crucial concern due to the exponential growth of cloud computing. User applications exhibit complex behavior, leading to fluctuations in system performance and increased power usage. To tackle these obstacles, we propose the consolidation of virtual machines (VMs) to optimize resource utilization and reduce operational costs. This paper introduces a novel algorithm, leveraging the Modified Artificial Feeding Birds Algorithm (ModAFBA), to manage cost optimally in cloud data centers by reducing power usage while meeting service level agreements (SLAs). The ModAFBA algorithm, inspired by bird foraging behavior, dynamically adjusts solutions based on environmental changes. Comprehensive experimental evaluation demonstrates the superiority of the proposed algorithm in terms of power usage, migration count, and SLA compliance. Through this research, we aim to advance cost management and resource optimization in cloud data centers, providing a valuable tool for cloud service providers to enhance infrastructure efficiency and achieve cost savings.

The paper provides a discussion on the results of studies of the effect exerted by combined degradation factors typical of four types of wear: abrasion, impact-abrasion, tribocorrosion, and impact-abrasion-corrosion, conducted for chain wheels made of Ni-Cu alloyed Austempered Ductile Iron. The studies consisted in determining the content of retained austenite in the structure of the cast irons in question, establishing the measures of wear following wear testing, and identifying basic surface degradation mechanisms observed in the chain wheels tested following multifactorial wear processes. The chain wheels made of ADI were found to have sustained the greatest damage under the impact-abrasion-corrosion (three-factor) wear scenario, while the wear was least advanced in the abrasion (one-factor) wear case. Another observation derived from the studies is that the combined effect of dynamic forces, corrosion, and quartz sand-based abrasives causes increased surface degradation in the cast iron grades taken into consideration compared to processes characterised by a reduced number of degradation factors (i.e. one- or two-factor wear processes). Additional hardness tests and XRD analyses have revealed a distinctive effect attributable to combined degradation factors on the surface hardness increase value and implied that bench testing was followed by phase transition.

The transition to cleaner energy sources, particularly in hard-to-abate industrial sectors, often requires gradual integration of new technologies. Hydrogen, crucial for decarbonization, is explored as a fuel in blended combustions. Blending or replacing fuels impacts combustion stability and heat transfer rates due to differing densities. An extensive literature review examines blended combustion, focusing on hydrogen-methane mixtures. While industrial burners claim to accommodate up to 20% hydrogen, theoretical support is lacking. A novel thermodynamic analysis methodology is introduced, evaluating methane-hydrogen combustion using the Wobbe index. Findings highlight practical limitations beyond 25% hydrogen volume, necessitating a shift to "totally hydrogen" combustion. Blended combustion can be proposed as a medium-term strategy, acknowledging hydrogen's limited penetration. Higher percentages require burner and infrastructure redesign.