Multiple Technologies Research Articles

A digital manufactured microfluidic platform for flexible construction of 3D co-culture tumor model with spatiotemporal resolution

The specific spatiotemporal distribution of diverse components in tumor microenvironment plays a crucial role in the cancer progression.In vitrothree-dimensional (3D) tumor models with polydimethylsiloxane (PDMS) based microfluidic platform have been applied as useful tool to conduct studies from cancer biology to drug screening. However, PDMS has not been welcomed as a standardized commercial application for preclinical screening due to inherent limitations in scale-up production and molecule absorption. Here, we present a novel microfluidic platform to flexibly construct 3D co-culture models with spatiotemporal resolution by using multiple digital manufacturing technologies. The platform, which consist of reduplicative microfluidic chips, is made of biocompatible poly methyl methacrylate by fast laser cutting. Each replica includes a simple microfluidic chamber without internal structures which can be flexibly post-fabricated according to various research requirements. Digital light processing based 3D bioprinting was used to pattern fine hydrogel structures for post-fabrication on-chip. By multi-step bioprinting and automatic image alignment, we show that this approach provides sufficient design flexibility to construct 3D co-culture tumor model with spatiotemporal resolution to replicate microarchitecture of tumor microtissuein situ. And the tumor model has the potential to mimic tumor biology behaviors which can be used for mechanism study and drug test. Our microengineered tumor model may serve as an enabling tool to recapitulate pathophysiological complexity of tumor, and to systematically examine the contribution of the tumor microenvironment to the cancer progression. The proposed strategy can also be applied to help engineer diverse meaningfulin vitromodels for extensive biomedical applications, from physiology and disease study to therapy evaluation.

Effects of Substrate Temperature on Optical, Structural, and Surface Properties of Metal-Organic Vapor Phase Epitaxy-Grown MgZnO Films.

MgZnO possesses a tunable bandgap and can be prepared at relatively low temperatures, making it suitable for developing optoelectronic devices. MgxZn1-xO (x~0.1) films were grown on sapphire by metal-organic vapor phase epitaxy under different substrate-growth temperatures Ts of 350-650 °C and studied by multiple characterization technologies like X-ray diffraction (XRD), spectroscopic ellipsometry (SE), Raman scattering, extended X-ray absorption fine structure (EXAFS), and first-principle calculations. The effects of Ts on the optical, structural, and surface properties of the Mg0.1Zn0.9O films were studied penetratively. An XRD peak of nearly 35° was produced from Mg0.1Zn0.9O (0002) diffraction, while a weak peak of ~36.5° indicated MgO phase separation. SE measurements and analysis determined the energy bandgaps in the 3.29-3.91 eV range, obeying a monotonically decreasing law with increasing Ts. The theoretical bandgap of 3.347 eV, consistent with the SE-reported value, demonstrated the reliability of the SE measurement. Temperature-dependent UV-excitation Raman scattering revealed 1LO phonon splitting and temperature dependency. Zn-O and Zn-Zn atomic bonding lengths were deduced from EXAFS. It was revealed that the surface Mg amount increased with the increase in Ts. These comprehensive studies provide valuable references for Mg0.1Zn0.9O and other advanced materials.

Indoor Positioning Systems in Logistics: A Review

Background: Indoor Positioning Systems (IPS) have gained increasing relevance in logistics, offering solutions for safety enhancement, intralogistics management, and material flow control across various environments such as industrial facilities, offices, hospitals, and supermarkets. This study aims to evaluate IPS technologies’ performance and applicability to guide practitioners in selecting systems suited to specific contexts. Methods: The study systematically reviews key IPS technologies, positioning methods, data types, filtering methods, and hybrid technologies, alongside real-world examples of IPS applications in various testing environments. Results: Our findings reveal that radio-based technologies, such as Radio Frequency Identification (RFID), Ultra-wideband (UWB), Wi-Fi, and Bluetooth (BLE), are the most commonly used, with UWB offering the highest accuracy in industrial settings. Geometric methods, particularly multilateration, proved to be the most effective for positioning and are supported by advanced filtering techniques like the Extended Kalman Filter and machine learning models such as Convolutional Neural Networks. Overall, hybrid approaches that integrate multiple technologies demonstrated enhanced accuracy and reliability, effectively mitigating environmental interferences and signal attenuation. Conclusions: The study provides valuable insights for logistics practitioners, emphasizing the importance of selecting IPS technologies suited to specific operational contexts, where precision and reliability are critical to operational success.

Multi-omic markers of Intraductal Papillary Mucinous Neoplasms progression into pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal and common form of pancreatic cancer, it has no specific symptoms, and most of the patients are diagnosed when the disease is already at an advanced stage. Chemotherapy typically has only a modest effect, making surgery the most effective treatment option. However, only a small percentage of patients are amenable to surgery. One viable strategy to reduce PDAC death burden associated with the disease is to focus on precursor lesions and identify markers able to predict who will evolve into PDAC. While most PDACs are believed to be preceded by pancreatic intraepithelial neoplasms (PanINs), 5-10% arise from Intraductal papillary mucinous neoplasms (IPMNs), which are mass-forming cystic lesions that are very common in the general population. IPMNs offer an invaluable model of pancreatic carcinogenesis for researchers to analyse, as well as a target population for PDAC early detection by clinicians. The evolution of IPMN into cancer is a complex and multistep process, therefore the identification of individual markers will not be the solution. In recent years, multiple omics technologies have been instrumental to identify possible biomarkers of IPMN progression and carcinogenesis. The only foreseeable strategy will be to integrate multi-omics data, alongside clinical and morphological features, into a progression score or signature using either standard epidemiologic tools or artificial intelligence. The aim of this manuscript is to review the current knowledge on genetic biomarkers and to briefly mention also additional omics, such as metabolomics, the exposome, the miRNome and epigenomics of IPMNs.

Effects of pre-injection parameters on spray characteristics of high-pressure common rail diesel engines

A verified hydrodynamic spray model was used to investigate the effects of multiple injection strategies on fuel bundle development and atomization characteristics of diesel fuel under typical conditions of direct-injection, turbocharged, and high-speed automotive diesel engines. Emphasis is placed on the effect of injecting a small amount of pre-injection fuel prior to the main injection on the spray development process. In addition, the effect and degree of influence of pre/main-injection interval time and pre-injection fuel ratio on spray macro- and micro-parameters in the two-injection strategies. The results show that at the end of the injection process, as the pre/main-injection interval time increases, the spray gas phase penetration distance increases, the spray width and the spray volume of the high-temperature region decrease, and the fuel concentration in the vicinity of the nozzle is in a decreasing trend. As the pre-injection ratio increases, the spray gas phase penetration distance decreases, the spray width, the spray volume of the high-temperature region increases, the spray volume between the fuel equivalence ratio of 0.8–1.2 decreases, and the fuel concentration at the front end of the spray tends to increase. The effect of the proportion of pre-injection fuel on spray width, spray volume of the high-temperature region, and spray volume between the fuel equivalence ratio of 0.8–1.2 was 4.88, 4.56, and 11.5 times that of changing the pre/main-injection interval time, respectively. The research provides a basis for optimizing the injection strategy and applying multiple injection technology in high-pressure common rail diesel engines.

Intelligent Vehicle Accident Detection System

Road traffic accidents in Nigeria represent a significant public safety concern, with thousands of lives lost annually. The Intelligent Vehicle Accident Detection System (IVADS) addresses this issue by providing a system that detects accidents automatically and instantly notifies emergency responders with real-time location information. IVADS integrates multiple sensors, GPS, and GSM technologies to achieve accurate accident detection and rapid communication. This paper provides a comprehensive review of the system’s design, implementation, testing results, and potential implications for road safety improvements in Nigeria. This work builds upon existing research on accident detection, using innovative sensor integration and machine learning for precise detection.

A comprehensive review: State of art integrated technologies in IoHT applications

Healthcare delivery has advanced from in-person doctor visits to remote healthcare monitoring, disease prediction and surgical assistance systems. Digitization of healthcare data over the past decade has brought radical transformations to the healthcare domain. It has facilitated healthcare data to be more open and easily accessible. Only the use of cutting-edge technologies, such as Internet of Healthcare Things (IoHT) systems, has made this breakthrough possible. As patient’s data is sensitive, IoHT system affirms reliable and secured data communication and computation. There are different cutting-edge technologies integrated in IoHT systems. These technologies are being used in a wide range of healthcare applications that pertain to daily activities. In this study, we give a thorough analysis and comparison of four modern technologies, namely Blockchain, Cloud Computing, Encryption, and Artificial Intelligence, together with applications in the medical industry. The research intends to evaluate the potential synergistic benefits of integrating multiple technologies to enhance the outcomes in IoHT applications, proposing a framework that suggests affirmative usage of IoHT technologies to cover the gap in the traditional approach. The study also highlights historical and contemporary advancements of these technologies in Africa.

Adaptation of High-Altitude Plants to Harsh Environments: Application of Phenotypic-Variation-Related Methods and Multi-Omics Techniques.

High-altitude plants face extreme environments such as low temperature, low oxygen, low nutrient levels, and strong ultraviolet radiation, causing them to adopt complex adaptation mechanisms. Phenotypic variation is the core manifestation of ecological adaptation and evolution. Many plants have developed a series of adaptive strategies through long-term natural selection and evolution, enabling them to survive and reproduce under such harsh conditions. This article reviews the techniques and methods used in recent years to study the adaptive evolution of high-altitude plants, including transplantation techniques, genomics, transcriptomics, proteomics, and metabolomics techniques, and their applications in high-altitude plant adaptive evolution. Transplantation technology focuses on phenotypic variation, which refers to natural variations in morphological, physiological, and biochemical characteristics, exploring their key roles in nutrient utilization, photosynthesis optimization, and stress-resistance protection. Multiple omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have revealed genes, regulatory pathways, and metabolic networks associated with phenotypic variations at the genetic and molecular levels. At the same time, the limitations and deficiencies of current technologies used to study plant adaptation to high-altitude environments were discussed. In addition, we propose future improvements to existing technologies and advocate for the integration of different technologies at multiple levels to study the molecular mechanisms of plant adaptation to high-altitude environments, thus providing insights for future research in this field.

Research on Distributed Access Simulation of Large-Scale Multi-Source Terminals for 5G

Large-scale machine communication scenarios are faced with the characteristics of a large number of terminal devices, small communication packets and frequent transmissions. Traditional multiple access technology can’t fully apply to these characteristics, so it needs to be improved and designed. In order to further improve the effect of distributed access strategy formulation of large-scale multi-source terminal for 5G, this paper proposes a distributed access technology for large-scale multi-source terminals for 5G. For distributed access scenarios, antenna units in the distributed system are scattered in the system, and each antenna unit keeps communication with the surrounding terminal equipment, which can expand the number of terminal devices randomly accessed in the same time slot. Then, taking advantage of this inherent advantage and the advantage of time-shifted pilot, this paper proposes a distributed random access scheme based on time-shifted pilot. Finally, through analysis and simulation, this paper proves that this scheme can effectively reduce the pilot collision probability of each antenna element and the average access delay in the system, which provides a technical reference for the subsequent distributed access of large-scale multi-source terminals.

GoalBERT: A Lightweight Named-Entity Recognition Model Based on Multiple Fusion

Named-Entity Recognition (NER) as a core task in Natural Language Processing (NLP) aims to automatically identify and classify specific types of entities from unstructured text. In recent years, the introduction of Transformer architecture and its derivative BERT model has pushed the performance of NER to unprecedented heights. However, these models often have high requirements for computational power and memory resources, making it difficult to train and deploy them on small computing platforms. Although ALBERT as a lightweight model uses parameter sharing and matrix decomposition strategies to reduce memory consumption to some extent consumption, it does not effectively reduce the computational load of the model. Additionally, due to its internal sharing mechanism, the model’s understanding ability of text is reduced leading to poor performance in named-entity recognition tasks. To address these challenges, this manuscript proposes an efficient lightweight model called GoalBERT. The model adopts multiple fusion technologies by integrating a lightweight and efficient BiGRU that excels at handling context into part of the Transformer’s self-attention layers. This reduces the high computational demand caused by stacking multiple self-attention layers while enhancing the model’s ability to process context information. To solve the problem of gradient disappearance and explosion during training, residual connections are added between core layers for more stable training and steady performance improvement. Experimental results show that GoalBERT demonstrates recognition accuracy comparable to standard models with accuracy surpassing ALBERT by 10% in multi-entity type scenarios. Furthermore, compared to standard models, GoalBERT reduces memory requirements by 200% and improves training speed by nearly 230%. Experimental results indicate that GoalBERT is a high-quality lightweight model.

AI-Driven Predictive Maintenance in Data Infrastructure: A Multi-Modal Framework for Enhanced System Reliability

This article presents a comprehensive framework for implementing artificial intelligence-driven predictive maintenance in modern data infrastructure environments. While traditional maintenance approaches have relied on reactive or scheduled interventions, the proposed framework leverages multiple AI technologies, including machine learning, natural language processing, and reinforcement learning, to create a proactive maintenance ecosystem. The methodology integrates diverse data streams from infrastructure components, including sensor data, system logs, and historical maintenance records, to predict potential failures and optimize maintenance schedules. The approach combines time series analysis for trend identification, natural language processing for unstructured data analysis, and reinforcement learning for dynamic schedule optimization. Implementation across multiple case studies, including cloud service providers and manufacturing environments, demonstrates significant improvements in system reliability, reduction in unplanned downtime, and optimization of maintenance resource allocation. The results indicate that AI-driven predictive maintenance substantially outperforms traditional approaches in both accuracy and cost-effectiveness. This article contributes to the growing field of intelligent infrastructure management and provides practical guidelines for organizations seeking to enhance their data infrastructure reliability through advanced predictive maintenance strategies.

(Invited) Atomic-Scale Design of Advanced Transistors Via Ultrathin Negative Capacitance Gate Stacks

Negative capacitance [1,2] has emerged as a promising solution to overcome fundamental energy-efficiency limits in conventional electronics, in which internal ferroelectric order within the gate stack of a field-effect transistor can enable low-power operation. Thus far, claims of negative capacitance have been primarily demonstrated in perovskite-structure thick films or through misleading “S-curve” transient experiments in fluorite-structure thick films. However, integration into advanced semiconductor technology nodes will require stabilization at the ultrathin regime and evidence of capacitance enhancement (i.e. lower equivalent oxide thickness, EOT) to enable highly-scaled lower-power operation.Here, we present evidence of negative capacitance in atomic-scale HfO2-ZrO2 heterostructures down to two-nanometers and one-nanometer thickness [3,4], leveraging our recent work examining the atomic limits of fluorite-structure ferroelectricity on silicon [5,6]. ALD HfO2-ZrO2-based heterostructures on Si-SiO2 demonstrate ultralow EOT without having to scavenge the SiO2 interlayer, in stark contrast to conventional high-κ metal gate (HKMG) technology [7]. Accordingly, in comparison to industrial HKMG benchmarks, these SiO2-buffered antiferroelectric-ferroelectric gate stacks boast favorable performance and can provide multiple node technology boosts [8]. Due to seamless integration and transition from high-κ HfO2-based dielectrics to negative-κ HfO2-ZrO2-based ferroelectrics, these NC stacks have already been validated and integrated by prototyping foundries [8] and semiconductor foundries [9].In this talk, key differences between static versus transient negative capacitance will be highlighted to clarify misleading literature and explain their relative benefits for logic transistors [3,4] versus other applications [10]. Perspectives on further routes to EOT scaling via negative capacitance gate stacks for beyond-1-nm-node logic technology will also be discussed. R Landauer. “Can capacitance be negative.” Collect. Phenom. 2, 167–170 (1976).S Salahuddin & S Datta. “Can the subthreshold swing in a classical FET be lowered below 60 mV/decade?” in 2008 IEEE International Electron Devices Meeting (IEEE, 2008).S Cheema et al. “Ultrathin ferroic HfO2–ZrO2 superlattice gate stack for advanced transistors.” Nature 604, 65–71 (2022).S Cheema et al. “Physical and electric thickness limits of negative capacitance.” In preparation (2024).S Cheema et al. “Emergent ferroelectricity in subnanometer binary oxide films on silicon.” Science 376, 648–652 (2022).S Cheema et al.“Enhanced ferroelectricity in ultrathin films grown directly on silicon.” Nature 580, 478–482 (2020).T Ando “Ultimate Scaling of High-κ Gate Dielectrics: Higher-κ or Interfacial Layer Scavenging?” Materials 5, 478–500 (2012).N Shanker et al. “CMOS Demonstration of Negative Capacitance HfO2-ZrO2 Superlattice Gate Stack in a Self-Aligned, Replacement Gate Process.” in 2022 International Electron Devices Meeting (IEDM) 34.3.1-34.3.4 (IEEE, 2022).S Jo. et al. “Negative differential capacitance in ultrathin ferroelectric hafnia.” Nat. Electron. 6, 390–397 (2023).S Cheema et al.“Giant energy storage and power density negative capacitance superlattices.” Nature doi:10.1038/s41586-024-07365-5 (2024).

(Invited) Fuel Cells to Power Sustainable Mobility and Beyond

The transition to a climate-neutral society is both an urgent challenge and an opportunity to build a better future for all. By endorsing the Paris Agreement goal to limit global warming to 1.5°C Toyota has committed to achieve carbon neutrality by 2050 on a life cycle basis.Toyota’s use phase approach to carbon neutrality is to develop and offer multiple technologies to support diverse customer needs and market environments around the world. Toyota has in fact been steadily advancing an electrification strategy for more than twenty years, starting in 1997 with the first hybrid vehicle (Prius), later in 2015 with the fuel cell electric vehicle (Mirai) and more recently with plug-in hybrid electric vehicles and a large fleet of battery electric vehicles.To date Toyota sold over 20 million electrified vehicles worldwide, of which 4 million in Europe, cumulatively contributing to over 160 million tonnes of CO2 reduction. While constantly innovating in technology and making significant investments in leading-edge battery technologies, such as the solid state type, Toyota is a front runner in hydrogen fuel cells. The proton-exchange-membrane fuel cell technology that was introduced in the Mirai, the first mass-produced fuel cell electric vehicle, is also suitable for other applications. Through most recent technological developments we are now able to offer fuel cell modular systems for applications beyond automotive. To expand the potential of hydrogen from cars to other uses, we have repackaged our fuel cell technology into compact fuel cell modules. The main components (the fuel cell stack and components that handle air and hydrogen supply, cooling, and power control) are thus integrated in a compact package which can be easily adapted into a variety of products and applications.The new fuel cell module is based on the second generation proton-exchange-membrane fuel cell stack and has a wide voltage range (400 to 750 V). It can be connected to an electrical instrument thanks to its built-in fuel cell boost converter that simplifies the development and manufacture of fuel cell products. A modular approach improves convenience: the different models can be combined to match the output level and installation space available. Modularity also eliminates the need to create designs for individually installing and connecting fuel cell system-related components and decreases the number of locations the module must be connected to a device, which eases installation.Thanks to the characteristics of the very compact fuel cell system, which eliminates a vehicle’s humidifier by circulating the water generated during power generation inside the fuel cell stack, the new fuel cell module has achieved a world-class, top-level output density per unit volume. It is appropriate for longer distance, higher loads – and a key instrument to reach carbon neutrality within society as a whole.To build an Hydrogen economy Toyota is strongly promoting the establishment of Hydrogen ecosystems in Europe, where different sectors unite and bring their skills, technologies and applications together – such as truck, bus, taxi fleets and hydrogen infrastructure – to create viable business opportunities that can flourish and become the nucleus of larger-scale activities.Toyota has positioned hydrogen as a critical fuel to achieve carbon neutrality, and promotes the use of hydrogen not only through fuel cell electric vehicles, including passenger cars, pickups, commercial van, trucks, and buses, but also through the widespread use of fuel cell products, such as the development and test operation of stationary generators. To this end, Toyota is working with various industry partners in the areas of producing, transporting, storing, and using hydrogen and recently announced the development of water electrolysers, leveraging on the expertise and manufacturing capability of proton-exchange-membrane fuel cells.The electrolysis equipment, which utilizes the fuel stack from the Mirai is a newly developed equipment that takes as advantage the knowledge and expertise it has accumulated from a variety of usage environments around the world. The unit installed at DENSO Fukushima can produce approximately 8 kg hydrogen per hour, with 53 kWh/kg hydrogen energy required.This talk will review the efforts that Toyota is making in pursing different technologies in parallel and the challenges ahead, and refer to the impact of the more recently proposed European regulations.

Stopover regions, phenology, and spatiotemporal group dynamics of adult and juvenile common terns Sterna hirundo from inland lakes in North America

Understanding the behavior of migratory birds can help determine levels of connectivity and inform conservation actions for species of conservation concern. The common tern Sterna hirundo is a long‐distance migratory seabird that is considered a species of conservation concern in the North American Great Lakes region and that has experienced significant declines in breeding numbers across large lakes in Manitoba. To better understand the movement ecology of common terns, we used data from multiple tracking technologies (solar geolocation, GPS tracking, and Motus radio tracking) obtained from individuals (n = 83) across five breeding colonies on four inland lakes in North America. We identified key stopover regions used during southward migration and explored how demographics and social interactions influence connectivity. We identified three key stopover regions (Lake Erie, the southern Atlantic Coast, and Florida) and documented, for the first time, differences in post‐natal and post‐breeding migration for inland nesting terns. Juveniles arrived, on average, three weeks later than unrelated adults to their first major staging area. Although adult female arrival to and departure from Lake Erie was similar to adult males, female schedules became significantly earlier than males as southward migration progressed. Using a graph network to describe the spatiotemporal associations among adults from the same inland lake, individuals appeared to be highly connected, meeting up in different regions throughout the non‐breeding season, suggesting that social interactions may play an important role in maintaining spatial connectivity. Despite differences in migration schedules by sex and arrival to the first major staging area by age class, birds appeared to rely on the same key stopover regions during southward migration. The stopover regions identified in this study can help identify potential bottlenecks and guide future research aimed at assessing the impacts of climate change and human disturbance on common terns breeding in North America.

Elemental composition oriented biomass wastes sorting method towards efficient downstream energy recovery

Biomass wastes (BW) can be used to produce value-added products by multiple energy recovery technologies. However, the actual conversion results are unsatisfactory since heterogeneous feedstocks mixture with different characteristics are hard to optimally recovered in the same fine conversion process. This paper aimed to develop a new method to improve the energy conversion efficiencies, which focused on the rational matching of BW characteristics and demand for highly efficient energy recovery. Accordingly, this paper explored the correlation between BW representative characteristics and energy conversion efficiencies. The results showed that BW types with different characteristics had significant correlation with multiple utilization technologies (P value < 0.05). Existing BW sorting methods based on source/density showed limited promotion on downstream utilization since the large diversity of characteristics within groups. Furthermore, to assist efficient energy conversion of BW, a novel BW sorting mode, different from traditional manual and mechanical sorting methods, was established to achieve the matching between feedstocks and utilization technologies. The chemical properties, containing elemental composition and heating value, were selected as BW sorting criterion. According to new sorting categories, the fuel characteristics, reaction conditions and products performance in terms of its energy conversion process have more significant differences between groups than traditional sorting methods. The energy conversion efficiency can be improved by 10.71% than unsorted as least. The new BW sorting method towards efficient downstream energy recovery has feasibility in industrial applications. The future perspectives were also discussed. This work was helpful to improve the downstream energy utilization.

Identification and Characterization of Fully Human FOLR1-Targeting CAR T Cells for the Treatment of Ovarian Cancer.

CAR T cell therapy has been an effective treatment option for hematological malignancies. However, the therapeutic potential of CAR T cells can be reduced by several constraints, partly due to immunogenicity and toxicities. The lack of established workflows enabling thorough evaluation of new candidates, limits comprehensive CAR assessment. To improve the selection of lead CAR candidates, we established a stringent, multistep workflow based on specificity assessments, employing multiple assays and technologies. Moreover, we characterized a human FOLR1-directed CAR binding domain. Selection of binding domains was based on extensive specificity assessment by flow cytometry and imaging, to determine on-/off-target and off-tumor reactivity. CAR T cell functionality and specificity were assessed by high-throughput screening and advanced in vitro assays. Our validation strategy highlights that assays comprehensively characterizing CAR functionality and binding specificity complement each other. Thereby, critical specificity considerations can be addressed early in the development process to overcome current limitations for future CAR T cell therapies.

PLC based laser scanning system for conveyor belt surface monitoring

This paper presents the design, implementation, and testing of an advanced conveyor belt surface monitoring system, specifically engineered for harsh and complex industrial environments. The system integrates multiple cutting-edge technologies, including programmable logic controllers (PLC), laser scanning, industrial-grade cameras, and deep learning algorithms, particularly YOLOv7, to achieve real-time, high-precision monitoring of conveyor belt conditions. Key innovations include optimized detection location based on failure modes, advanced PLC integration for seamless automation, and intelligent dust-proof features to maintain accuracy in challenging conditions. Through strategic placement of detection devices and multi-mode control strategies (local, remote, and automatic), the system offers unparalleled adaptability and responsiveness. The system leverages robust data management for trend analysis and predictive maintenance, enhancing operational efficiency. The hardware architecture comprises PLC-based control systems, high-resolution industrial cameras, and laser emitters, while the software features a two-tier structure combining human-machine interaction (HMI) with real-time data processing capabilities. Experimental results show that the system is highly effective in detecting common belt defects such as foreign objects, tears, and shallow scratches, ensuring optimal operational efficiency and minimizing downtime. The system’s scalability, robust data management, and adaptability to low-light and dusty conditions make it ideal for deployment in large-scale industrial operations, where continuous monitoring and early fault detection are critical to maintaining productivity and safety.

Mapping the trajectories of digital technology innovation in the construction industry: an integrated SPNP and Louvain approach

PurposeDespite the continuous development and application of new digital technologies in the construction industry, there has been little research on digital technology trajectories in the construction industry. The study addresses the issue faced by the construction industry in exploring digital technology trajectories: how to comprehensively identify and analyse digital technology pathways across multiple technology fields in the construction industry.Design/methodology/approachFirstly, the digital technology patent identification and classification method based on text mining is used to identify digital technology patents and construct a digital technology innovation network. Second, the main path of the digital technology innovation network is identified with the help of SPNP. Then, the subpaths of the digital technology innovation network are identified with the help of the Louvain algorithm and SPNP. Finally, starting from the technology nodes where the main path and subpaths intersect, the technological similarity of the paths is analysed to explore the evolutionary characteristics of the technology trajectories. In light of this, the developed method is applied to the global construction industry patent dataset to analyse the trajectories of digital technologies.FindingsThe technological innovation path in the construction industry starts with construction materials and gradually expands to intelligence, automation and digital data processing technology. Equipment and devices with electronic digital data processing capabilities as well as improvements in green building technologies and user experience-enhancing technologies, may be the future of the construction industry. With the increasing demand for green buildings and intelligent buildings, the direction of digital technology innovation in the construction industry is gradually tilted towards these areas. In addition, influenced by geographic and economic factors, there is a spatial clustering effect of digital technology innovation in the construction industry.Research limitations/implicationsFuture research should analyse in depth the performance of different countries and regions in digital technology innovation and explore the root causes, motivations and influencing factors behind it, such as the policy environment, the level of the economy and the investment in research and development. Exploring the reasons affecting digital technology innovation can help formulate more targeted policies and promote cooperation and exchange of digital technology innovation in the global construction industry. Meanwhile, to solve the problems of overly broad IPC categorization and the difficulty of accurately describing cross-field innovations, combining IPC co-occurrence networks with patent citation networks is an effective strategy. This strategy can track technologically interrelated patents and provide more specific contents to know the advantages and challenges of the construction industry in the field of digital technology innovation.Practical implicationsThe study has practical implications for the construction industry. The identification of digital technology innovation trajectories provides valuable insights for industry firms and research institutes. It helps them understand the current and future directions of digital technology in construction, enabling them to stay at the forefront of technological advancements. The findings highlight the importance of focusing on areas such as solar energy utilisation, green energy, intelligence, automation and data applications. This knowledge can guide firms in developing new building materials, incorporating digital information technologies and enhancing user experiences. The study’s results can inform strategic decision-making, technology adoption and innovation management in the construction sector.Social implicationsThe social implications of this study are significant for various stakeholders. The identification of digital technology innovation trajectories in the construction industry highlights the potential benefits for society. The focus on green energy, intelligent buildings and enhanced user experiences aligns with the increasing demand for sustainability, energy efficiency and comfortable living environments. These technological advancements can contribute to reducing environmental impact, improving quality of life and promoting sustainable development. The findings can inform policymakers, urban planners and architects in shaping regulations, designing sustainable cities and creating buildings that prioritize energy efficiency and user well-being. Ultimately, the study’s social implications aim to foster a more sustainable and livable built environment.Originality/valueAn identification method integrated with SPNP and the Louvain algorithm is developed to map digital technology innovation trajectories in the construction industry. This study helps to reveal the trajectories of digital technology innovation, provides new perspectives, insight and ideas for research in related fields and has great potential for applications in practice to promote the innovation and development of the construction industry.

Exergy-economic based multi-objective optimization and carbon footprint analysis of solar thermal refrigeration systems

The increasing carbon footprint associated with conventional cooling methods underscores the urgent need for sustainable alternatives. This study investigates the economic and environmental advantages of various solar-thermal cooling systems, with a focus on optimizing their performance across different climate conditions. Employing a multi-objective approach, the research emphasizes exergy-economic indices to optimize selected cycles. The analysis covers multiple refrigeration technologies, including liquid absorption, solid adsorption, and solid desiccant cycles. Results indicate that the liquid absorption cycle performs optimally in hot, arid climates, reducing the payback period to approximately 8 years when optimized. In hot and humid regions, the solid desiccant cycle proves most effective due to its superior humidity control, yielding a payback period of 5.3 years. For cold and mountainous areas, the solid adsorption cycle is preferred, with a payback period of 13.5 years, while moderate and humid climates benefit from the solid desiccant cycle for both cooling and humidity regulation. The exergy-economic factors for the solar refrigeration systems across semi-arid, hot and arid, hot and humid, cold and mountainous, and moderate and humid climates are 0.758, 0.602, 0.698, 0.74, and 0.575, respectively.

AI-federated novel delay-aware link-scheduling for Industry 4.0 applications in IoT networks

PurposeWith the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.Design/methodology/approachWith an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.FindingsOur proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.Originality/valueThe concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.