Processing Requirements Research Articles

Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical–classical deep learning architectures within 6G environments.

A Study on the Licensing Process for the Floating Nuclear Power Plant in Indonesia: A Suggestion for the Revision of Government Regulation Regarding Nuclear Power Plant Licensing

Indonesia is currently revising some Government Regulations regarding the licensing process of the nuclear power plant. In the current revision process, the latest technology such as small modular reactors and floating nuclear power plants are also considered to be regulated specifically. In current regulations, regardless of the type and technology, the licensing for nuclear power plants is regulated generally. Considering its unique nature and character, the floating nuclear power plant should have more specific requirements for the licensing process. This paper is carried out to analyze the specific requirements that should be considered for the licensing process of the floating nuclear power plant in Indonesia. The results of this study are expected to be a suggestion for the Government Regulations revision.

On Efficient Training of Large-Scale Deep Learning Models

The field of deep learning has witnessed significant progress in recent times, particularly in areas such as computer vision (CV), natural language processing (NLP), and speech. The use of large-scale models trained on vast amounts of data holds immense promise for practical applications, enhancing industrial productivity and facilitating social development. However, it suffers extremely from the unstable training process and stringent requirements of computational resources. With the increasing demands on the adaption of computational capacity, though numerous studies have explored the efficient training field to a certain extent, a comprehensive summarization/guideline on those general acceleration techniques of training large-scale deep learning models is still much anticipated. In this survey, we present a detailed review of the general techniques for training acceleration. We consider the fundamental update formulation and split its basic components into five main perspectives: (1) “data-centric,” including dataset regularization, data sampling, and data-centric curriculum learning techniques, which can significantly reduce the computational complexity of the data samples; (2) “model-centric,” including acceleration of basic modules, compression training, model initialization, and model-centric curriculum learning techniques, which focus on accelerating the training via reducing the calculations on parameters and providing better initialization; (3) “optimization-centric,” including the selection of learning rate, the employment of large batch size, the designs of efficient objectives, and model average techniques, which pay attention to the training policy and improving the generality for the large-scale models; (4) “budgeted training,” including some distinctive acceleration methods on source-constrained situations, e.g., for limitation on the total iterations; and (5) “system-centric,” including some efficient distributed frameworks and open source libraries that provide adequate hardware support for the implementation of the above-mentioned acceleration algorithms. By presenting this comprehensive taxonomy, our survey presents a comprehensive review to understand the general mechanisms within each component and their joint interaction. Meanwhile, we further provide a detailed analysis and discussion of future works on the development of general acceleration techniques, which could inspire us to re-think and design novel efficient paradigms. Overall, we hope that this survey will serve as a valuable guideline for general efficient training.

Validation of the technological process for the production of riamilovir-based solution for aerodispersible administration

BACKGROUND: An important step in the production of riamilovir-based solution for inhalation administration is the validation of critical stages of the technological process. First, a detailed study of all production steps, from the preparation of the initial ingredients to the final packaging, is carried out. Control tests are then conducted, including measuring key process parameters and analyzing the physical and chemical properties of the solution. One of the important aspects of process validation is also the training of production personnel. Proper understanding and adherence to all process instructions and requirements ensures that the process is followed correctly. AIM: Carry out validation activities throughout the stages of the technological process for the production of a solution for inhalation administration based on the active substance “riamilovir”. MATERIALS AND METHODS: The study was carried out using the developed Pilot Industrial Regulations for the production of the medicina. The quality assessment of pharmaceutical substances, excipients and pilot samples was carried out in accordance with the requirements of the State Pharmacopoeia of the Russian Federation and GMP rules. RESULTS: It has been established that the technological scheme for the production of a solution based on the active substance “riamilovir” contains 6 main stages. The greatest risk is observed at the following stages: Auxiliary work 3. Obtaining water — risk of bacterial contamination. Тechnological process 4. Preparation and sterilizing filtration of the solution — risks of insufficient concentration and unsterility of the solution. Тechnological process 5. Dosing the solution into vials is a risk due to underfilling, as well as violation of sterility. CONCLUSION: Validation of the technological process of production of solution for inhalation administration based on the active substance “riamilovir” has been carried out. Critical stages in the technological production process have been identified. The optimal parameters of the technological process have been determined and documented, leading to a predictable result — the release of a high-quality and safe drug.

Shadow Testing in Autonomous Vehicles : A Novel Approach to Validating Full Self-Driving AI Systems

As autonomous vehicles progress towards widespread deployment, ensuring the reliability and safety of Full Self-Driving (FSD) AI systems remains a critical challenge. This article presents a comprehensive analysis of shadow testing, an innovative approach to validating new FSD AI models in real-world conditions without compromising user safety. We examine the methodology of deploying new AI models in "shadow mode," where they process live sensory data alongside the operational system but do not control the vehicle. This approach enables the collection of valuable performance metrics and the identification of edge cases while mitigating risks associated with direct deployment. Our study demonstrates how shadow testing facilitates a continuous feedback loop for iterative improvement, enhancing model reliability through data-driven refinement. We further explore the challenges of implementing shadow testing at scale, including data processing requirements and ethical considerations. Our findings suggest that shadow testing not only accelerates the development of robust FSD systems but also plays a crucial role in building public trust in autonomous vehicle technology. This article contributes to the growing body of knowledge on AI safety validation techniques and provides insights for standardizing testing protocols in the autonomous vehicle industry.

Electrochemical cutting with flexible electrode of controlled online deformation

Abstract Improvements in aero-engine performance have made the structures of the aero-engine components increasingly complex. To better adapt to the processing requirements of narrow twisted channels such as an integral shrouded blisk, this study proposes an innovative method of electrochemical cutting in which a flexible tube electrode is controlled by online deformation during processing. In this study, the processing principle of electrochemical cutting with a flexible electrode for controlled online deformation (FECC) was revealed for the first time. The online deformation process of flexible electrodes and the machining process of profiles were analysed in depth, and the corresponding theoretical models were established. Conventional electrochemical machining (ECM) is a multi-physical field-coupled process involving electric and flow fields. In FECC, classical mechanics are introduced into the tool cathode, which must be loaded at all times during the machining process. Therefore, in this study, before and after the deformation of the flexible electrode, a corresponding simulation study was conducted to understand the influence of the online deformation of the flexible electrode on the flow and electric fields. The feasibility of flexible electrodes for online deformation and the validity of the theoretical model were verified by deformation measurements and in situ observation experiments. Finally, the method was successfully applied to the machining of nickel-based high-temperature alloys, and different specifications of flexible electrodes were used to complete the machining of the corresponding complex profiles, thereby verifying the feasibility and versatility of the method. The method proposed in this study breaks the tradition of using a non-deformable cathode for ECM and adopts a flexible electrode that can be deformed during the machining process as the tool cathode, which improves machining flexibility and provides a valuable reference to promote the ECM of complex profiles.

Faculty experiences in publishing in Scopus-indexed journals: Challenges and strategies among higher education institutions in the Philippines

Research is a cornerstone of education, with publication output often used as a key metric for research performance. This study investigates the experiences of Philippine Higher Education Institution (HEI) faculty as they work to publish in Scopus-indexed journals. By exploring faculty members' challenges, strategies, and perspectives, this research aims to inform how HEIs can enhance support for faculty in contributing to global scholarship and advancing academic careers. Using Husserl’s descriptive phenomenology, 12 faculty members from HEIs in Eastern Visayas, Philippines, were purposively selected as participants, with theoretical saturation guiding the sample size. Data was gathered through in-depth interviews and analyzed using Colaizzi's method. The study reveals four primary themes regarding the publication journey: motivation for publishing, preparation and publication process, challenges faced, and support systems. Faculty perceptions of publishing centered around five themes: career advancement and promotion, increased visibility and credibility, personal and professional satisfaction, institutional expectations, and incentives. Key challenges included financial barriers, the rigorous peer review and revision process, submission and formatting requirements, and emotional strain. To address these, participants highlighted strategies involving financial and personal investment, mentorship and collegial support, institutional resources, continuous learning, strategic journal selection, and maintaining resilience. Findings indicate that publishing in Scopus-indexed journals requires a blend of motivation, planning, and resilience, as faculty strive to contribute to knowledge and further their careers. Yet, they face substantial barriers, especially high publication fees and stringent formatting standards. Institutional support—especially through funding, mentorship, and workshops—is vital to overcoming these obstacles. For greater faculty success, institutions should cultivate a supportive research culture that recognizes and addresses the logistical and emotional demands of academic publishing.

Comparative Analyses of Dynamic Characteristics of Gas Phase Flow Field Within Different Structural Cyclone Separators

The gas phase flow field inside a cyclone separator is crucial to the particle separation process. Previous studies have paid attention to the steady-state characteristics of the gas phase flow field, while research on its dynamic characteristics remains insufficient. Meanwhile, cyclone separators often adopt different structural forms according to the process requirements, the evolution laws of the dynamic characteristics flow field within them are still not well understood. Therefore, in this study, a hot-wire anemometer (HWA) was employed to measure the instantaneous tangential velocity of the gas phase flow fields within different structural cyclone separators (cylinder type, cylinder–cone (no hopper), and cylinder–cone (with hopper)). Comparative analyses and discussions were conducted regarding the dynamic characteristic distribution rules of the flow field in the time domain and the frequency domain. The results revealed that the dimensionless tangential velocity distributions of different types of cyclone separators all conformed to the Rankine vortex structure. The instantaneous tangential velocity fluctuated with low frequency and high amplitude, and the low-frequency velocity fluctuation exhibited a transfer behavior along the radial direction. Compared with the cylinder–cone-type cyclone separator, the tangential velocity in the cylinder-type cyclone separator fluctuated more greatly, and its quasi-periodic behavior was also more obvious. The time-averaged tangential velocity, the tangential velocity fluctuation intensity (Sd), and the dominant fluctuation frequency all had obvious attenuation along the axial direction in the cylinder-type cyclone separator, while the above-mentioned parameters had no attenuation along the axial direction in cylinder–cone-type cyclone separators. Additionally, the backflow from the hopper of the cylinder–cone-type cyclone separator (with hopper) led to an increase in the instantaneous tangential velocity fluctuation intensity of the local flow field near the dust outlet, as well as the occurrence of the “double dominant frequencies” phenomenon.

An efficient deep reinforcement learning based task scheduler in cloud-fog environment

Efficient task scheduling in cloud and fog computing environments remains a significant challenge due to the diverse nature and critical processing requirements of tasks originating from heterogeneous devices. Traditional scheduling methods often struggle with high latency and inadequate processing times, especially in applications demanding strict computational efficiency. To address these challenges, this paper proposes an advanced fog-cloud integration approach utilizing a deep reinforcement learning-based task scheduler, DRLMOTS (Deep Reinforcement Learning based Multi Objective Task Scheduler in Cloud Fog Environment). This novel scheduler intelligently evaluates task characteristics, such as length and processing capacity, to dynamically allocate computation to either fog nodes or cloud resources. The methodology leverages a Deep Q-Learning Network model and includes extensive simulations using both randomized workloads and real-world Google Jobs Workloads. Comparative analysis demonstrates that DRLMOTS significantly outperforms existing baseline algorithms such as CNN, LSTM, and GGCN, achieving a substantial reduction in makespan by up to 26.80%, 18.84, and 13.83% and decreasing energy consumption by up to 39.60%, 30.29%, and 27.11%. Additionally, the proposed scheduler enhances fault tolerance, showcasing improvements of up to 221.89%, 17.05%, and 11.05% over conventional methods. These results validate the efficiency and robustness of DRLMOTS in optimizing task scheduling in fog-cloud environments.

A novel method: YOLO-CE and 3D point cloud-based feature extraction for welding seams of tower bases

Abstract Robotic automated welding of non-standard steel structures presents significant challenges, particularly for electric power tower bases. This study introduces a novel approach that integrates the You Only Look Once—Compact Invert Block and Efficient Local Attention (YOLO-CE) model, an enhanced version of YOLOV8 for 2D image segmentation, with 3D point cloud technology. The YOLO-CE model is used to accurately extract point cloud data from the target area, which is then processed using the MSAC algorithm for efficient plane segmentation. Weld lines are identified through plane equations, allowing for initial weld point cloud extraction. To further refine accuracy, an optimized evaluation equation is developed that accounts for both the distance between the weld point cloud and the fitted plane, and the angle between their normal vectors. This enables precise classification of the weld point cloud. From this classification, key weld feature points are identified, and their exact positions are determined by calculating the distances between these points and their intersections with three planes. The reliability of the proposed method was validated using a robot for precise measurements, with a total error margin of less than 1.5084 mm, demonstrating high accuracy and stability. Post-operation inspections confirmed that the welds were filled and free from defects, meeting all process requirements. The YOLO-CE model achieved a mIoU of 96.38% and a precision of 99.8%, highlighting its effectiveness. This method provides an efficient and precise solution for the automated welding of non-standard steel structural components and has promising application potential.

Dependence of cyanobacterium growth and Mars-specific photobioreactor mass on total pressure, pN2 and pCO2

In situ resource utilization systems based on cyanobacteria could support the sustainability of crewed missions to Mars. However, their resource-efficiency will depend on the extent to which gases from the Martian atmosphere must be processed to support cyanobacterial growth. The main purpose of the present work is to help assess this extent. We therefore start with investigating the impact of changes in atmospheric conditions on the photoautotrophic, diazotrophic growth of the cyanobacterium Anabaena sp. PCC 7938. We show that lowering atmospheric pressure from 1 bar down to 80 hPa, without changing the partial pressures of metabolizable gases, does not reduce growth rates. We also provide equations, analogous to Monod’s, that describe the dependence of growth rates on the partial pressures of CO2 and N2. We then outline the relationships between atmospheric pressure and composition, the minimal mass of a photobioreactor’s outer walls (which is dependent on the inner-outer pressure difference), and growth rates. Relying on these relationships, we demonstrate that the structural mass of a photobioreactor can be decreased – without affecting cyanobacterial productivity – by reducing the inner gas pressure. We argue, however, that this reduction would be small next to the equivalent system mass of the cultivation system. A greater impact on resource-efficiency could come from the selection of atmospheric conditions which minimize gas processing requirements while adequately supporting cyanobacterial growth. The data and equations we provide can help identify these conditions.

Automatic detection of landslide impact areas using Google Earth Engine

This research introduces an effective framework for automatically detecting landslide impact areas using Google Earth Engine (GEE). The Asia–Pacific region frequently experiences earthquakes and heavy rainfall, leading to frequent landslides that cause loss of life and property. Focusing on landslide catalogues from Taiwan and Japan, the study proposes an automatic landslide detection process using a new method termed multi-bitemporal images (MBTIs), which involves the collection of accumulated changes over time. First, set the event date and collect all images before and after the event. Second, analyse the change pixels in bi-temporal images. Third, review all change pixels to determine the total amount of accumulated changes. This method includes all bi-temporal image sets in the analysis, unlike traditional methods that only use single pairs of bi-temporal images. Clouds are filtered using a pixel-based approach and machine learning techniques. The landslide areas are analysed statistically, and appropriate thresholds for automatic landslide detection are suggested. Using reproducibility, which indicates the percentage of bi-temporal images that detect vegetation loss in mountainous areas, the proposed method achieves a 99% reduction in false positives with a reproducibility requirement of 24.21%, while maintaining true positives at 66.89%. This study analyzed 28–720 bi-temporal image sets from various regions using Sentinel-2 data, revealing that subsequent landslides can be 7–293 times larger than co-seismic landslides. In comparison, subsequent landslides were found to be 3–12 times larger than rainfall-induced landslides. Additionally, the impact of earthquake event on subsequent landslides is 2.3–24.4 times greater than that of rainfall-induced event. By using GEE, the accumulation of hundreds of satellite images can be completed within 15 min, depending on the processing requirements.

Study on steam energy efficiency enhancement, cost management and CO2 emissions in the food industry

Malaysia’s energy intensity per unit GDP has been increasing since the 1990s, leading to the government’s efforts to promote energy efficiency via policies and initiatives such as Energy Performance Contracting (EPC). Additionally, the industrial sector in Malaysia consumed 28% of total final energy in 2018, placing it as the country’s second-largest final energy consumer [1]. This case study focuses on improving energy efficiency in the steam system of an existing plant in the food industry in Malaysia. This work was performed in collaboration with a local Energy Service Company (ESCO) to study the existing steam system, recognize its process requirements and constraints and consequently propose and evaluate retrofit opportunities. These opportunities were evaluated across 3 performance parameters (power output or energy savings, cost savings and CO2 emissions savings). Simulations were performed using the Steam System Modeler Tool (SSMT). The study found that Combined Heat and Power (CHP) was not feasible due to a combination of low steam pressure and flowrate available in the system. However, the study also found that improving condensate recovery coupled with condensate flashing from the high pressure to low pressure header offered the highest increments in overall performance.

Evaluating sustainability of CO2-mediated pyrolysis of lignocellulose

Despite the growing interest in biomass as a carbon–neutral resource, technical challenges have limited its comprehensive utilization. Pyrolysis has emerged as a promising method for reducing the carbon footprint by more effectively valorizing carbon in biomass. This study investigated the use of carbon dioxide (CO2) in the pyrolysis of pine cone (PC), a lignocellulosic biomass. Thermogravimetric analysis confirmed that lignin was the primary component of the PC. Characterization and quantification of the three pyrolytic products (syngas, biocrude, and biochar) revealed that CO2 enhanced CO production and the surface area of the biochar, thereby improving its CO2 adsorption capacity. Additional heat and a Ni catalyst further amplified CO2′s functionality. The sustainability of the proposed pyrolysis system was evaluated by calculating energy requirements of the pyrolysis processes and the net CO2 emissions. Catalytic pyrolysis under CO2 was the most effective, achieving a reduction of 3.34 g of CO2 per gram of PC.

Qualitätssicherung in der Arbeitsmedizin – ein neues Modell zur Selbstevaluierung bis zum Erwerb eines arbeitsmedizinischen Gütesiegels in Österreich

Quality assurance in occupational medicine – a new model for self-evaluation up to the acquisition of an occupational health seal of approval in Austria Based on the mandatory quality check for registered doctors in Austria, a questionnaire was developed in the “Quality assurance in occupational medicine” project, which is intended to serve as an instrument for a voluntary quality check for occupational medical care. The questionnaire defines the necessary structural and process requirements as well as standardized procedures for occupational health activities. In this way, occupational physicians and occupational health centers are provided with instructions for action, and the occupational health service becomes transparent for the “customers”. The tool is to be applied in a three-stage approach. The questionnaire will be available for download to all interested persons and is intended to contribute to a low-threshold improvement in quality by raising awareness. The second stage of the proof of quality is a self-evaluation officially confirmed by a certification authority. Finally, an audit can be used to obtain a seal of quality as the third level.

Emerging Frontiers in In Situ Forming Hydrogels for Enhanced Hemostasis and Accelerated Wound Healing.

With a surge in the number of accidents and chronic wounds worldwide, there is a growing need for advanced hemostatic and wound care solutions. In this regard, in situ forming hydrogels have emerged as a revolutionary biomaterial due to their inherent properties, which include biocompatibility, biodegradability, porosity, and extracellular matrix (ECM)-like mechanical strength, that render them ideal for biomedical applications. This review demonstrates the advancements of in situ forming hydrogels, tracing their evolution from injectable to more sophisticated forms, such as sprayable and 3-D printed hydrogels. These hydrogels are designed to modulate the pathophysiology of wounds, enhancing hemostasis and facilitating wound repair. The review presents different methodologies for in situ forming hydrogel synthesis, spanning a spectrum of physical and chemical cross-linking techniques. Furthermore, it showcases the adaptability of hydrogels to the dynamic requirements of wound healing processes. Through a detailed discussion, this article sheds light on the multifunctional capabilities of these hydrogels such as their antibacterial, anti-inflammatory, and antioxidant properties. This review aims to inform and inspire continued advancement in the field, ultimately contributing to the development of sophisticated wound care solutions that meet the complexity of clinical needs.

Effect of cobalt-doped iron oxide as an electrocatalyst for water splitting and photocatalytic hydrogen evolution

Developing cost-effective and efficient electrocatalysts for the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is a significant challenge for economically viable energy conversion devices, such as electrolyzers, rechargeable metal-air batteries, and regenerative fuel cells. Here, we have synthesized cobalt doped iron oxides (CoFe2O3) with different Co:Fe wt%. Among them, Co[1:3]Fe2O3 catalyst demonstrated outstanding performance and stability positioning it as a standard electrocatalyst for OER and HER, and as a photocatalyst for H2 production. The catalysts were characterized using various techniques, including field emission scanning electron microscopy (FESEM), X-ray diffraction spectroscopy (XRD), and energy dispersive X-ray spectroscopy (EDS) demonstrating successful application in water electrolysis. Specifically, the onset potentials for OER and HER were 1.63 V/RHE and −0.23 V/RHE, respectively. The overpotential (η) required to achieve 10 mA/cm2 for OER (in alkaline medium) and HER (in acidic medium) was 0.44 and −0.3 V/RHE, respectively. Additionally, the developed OER and HER catalyst exhibited high current and potential stability over a 13-hour period. This approach is considered a promising avenue for making water electrolysis for hydrogen (H2) energy viable by minimizing the energy requirements of the electrolytic process. Similarly, the performance of the fabricated catalyst was also examined for the photocatalytic generation of H2 gas in a photo-reactor. Where the highest generation rate of 97.77 mL. (g.min−1) was recorded for Co[1:3]Fe2O3 as a photocatalyst. As a result, the Co[1:3]Fe2O3 catalyst has the potential to function as both a photocatalyst and an electrocatalyst for water splitting.

Targeted isolation of extracellular vesicles from cell culture supernatant using immuno-affinity chromatography

Extracellular vesicles (EVs) have emerged as promising therapeutics with broad clinical applications as diagnostic biomarkers and therapeutic drug delivery systems. Yet, these biopharmaceuticals pose a challenge in terms of manufacturing due to their complexity and heterogeneity. Despite advancements in the field, current purification technologies lack scalability and/or selectivity. Affinity chromatography (AC) − coupling unmatched specificity and scalability − could be used to simplify purification processing and generate clinical-grade EVs with higher titers and purity.In the present work, we report the implementation of an immuno-AC resin to capture and purify EVs directly from clarified cellular feedstocks. Firstly, to guide and support marker selection, vesicle phenotype characterization was conducted using single particle interferometric reflectance image sensing (SP-IRIS) coupled with immunofluorescence. CD81 was the marker which shown to be more present and more likely to have the other markers (CD63 and CD9). Thus, anti-CD81 VHH ligand was generated and evaluated towards recombinant CD81 protein and CD81 bearing EV particles using surface plasmon resonance (SPR). Different chromatographic studies with Anti-CD81 ligand immobilized onto agarose beads resin were conducted to optimize the process parameters (residence time, dynamic binding capacity and impurity clearance). At residence time of 2 min, on average 40 % of pure triple tetraspanin-positive EV fraction was recovered. The enrichment in EV particles herein obtained, based on scale-up calculations, it would be possible to produce 1 × 1013 EVs from a 1L cell culture, while meeting impurity requirements in a single-step purification process (impurity removal over 2 log reduction value). A single-step purification process is possible, enabling the successful isolation of homogeneous EVs population, counting with a final HCP titer of 60 ng/mL and 9 ng/mL of dsDNA impurities. EV’s morphological integrity and internalization ability were also demonstrated, showcasing elution’s efficiency under mild conditions.Overall, this work contributes to the development of a novel, highly specific, AC technology using a camelid-derived affinity ligand which, bridging the scalability requirements demanded of large-scale production, could potentiate the advent of EV-based therapies.

Unifying Community Efforts on Registration and Data Rights

At the Smithsonian National Museum of Natural History (SI NMNH), the Assistant Registrar and Informatics Manager in the Department of Paleobiology (Paleo) have identified key linkages between registering collections (documentation for specimens and data through acquisitions, loans and disposals) and management of digitized collections, especially for usage right designation. Both roles require extensive collaboration on a shared framework for managing data related to the provenance of fossil specimens and rights associated with any derivative digital assets. This eliminated a disconnect in overlapping responsibilities: those typically siloed in museum registration practices (e.g., provenance data, intellectual property rights) and the data typically siloed in collections management and digitization workflows (e.g., data quality, digital asset management, and usage rights). Paleo shifted to a new practical approach and mindset focused on communal thinking. This propelled implementation of data standard practices and laid the building blocks for a shared community perspective on digitizing paleontological collections. Since 2020, Paleo established processing workflows for digital asset requests of the fossil collections (new digital asset creations vs. use of existing digital assets) and baseline understandings about rights of digital assets. These products led to a comprehensive effort to outline SI NMNH’s options to designate usage rights for digital assets (Table 1) and distribute basic understandings about Smithsonian Terms of Use and Smithsonian Open Access Policy (SI OA) across SI NMNH. Paleo explored this topic further by engaging in virtual meetings, conferences, presentations and informal discussions with the collections management staff and data managers across SI NMNH and externally with domestic and international groups such as the Paleo Data Working Group (PDWG), Society for the Preservation of Natural History Collections (SPNHC) Registrars Group, and Biodiversity Information Standards (TDWG), to develop data standards initiatives for loans, permits and sensitive data (Fig. 1). Through regular community engagement, Paleo forged a concise, yet evolving, perspective on digitizing collections, which aligned both registration and informatics data management needs within the department and museum (Fig. 2). This multi-professional perspective enables the promotion of shared community knowledge and understanding about data rights. The primary purpose is to balance consistent goals, current needs, and long-term outcomes with the flexibility to adapt our practices based on institution-specific policies, new research methods, and varying domestic and international regulations and laws. The notable outcomes from almost four years of work are highlighted and incorporated into Paleo's departmental policy and documentation requirements for processing and fulfilling digital asset creation or use requests workflows and distribution of this information to the public website for visitors and borrowers. However, there are still persisting challenges related to registering and managing these digital assets. There are immediate and future obstacles such as: limitations in effective tracking methods to record creation of digital assets, use of digital assets, and designation of what usage rights and when the decision was made; gaps in community practice, policy and regulations at the institutional level and globally; past practices in workflows and staff structures or lack thereof, which have left digital assets in limbo without means of accessing the data or documentation to identify the appropriate use options and rights. We see a need to continue asking questions. How are varying policies across institutions globally, impacting compliance concerns in registration for sharing sensitive information with other research institutions? How have or will new research techniques and opportunities impact the need for flexibility in practices and decisions? Are there alternative ways to utilize a Collections Management Systems (CMS) or use other platforms to track, store and manage the digital assets? How have others engaged in community discussions to address overlapping needs for data standards and registration of digitized collections? These identify significant inconsistencies across the community that need further discussion with subject-matter experts before effective and comprehensive decisions in data standards for managing digitized collections can be made. Ultimately, there are outstanding questions and challenges at all stages for registering, digitizing and managing digital assets of natural history collections. We continue to unveil nuances in thinking about this topic and find the need for structures of shared knowledge built and actively engaged in by a professionally diverse community. The utilization of subject-matter experts encourages collaboration and alternative perspectives to standardize operations and policy while expanding community efforts and awareness.

Templates-Built Structural Designs for Piezoelectrochemical Pressure Sensors.

Self-powered sensors, capable of detecting static and dynamic pressure without an external power source, are pivotal for advancements in human-computer interaction, health monitoring, and artificial intelligence. Current sensing technologies, however, often fall short of meeting the growing needs for precise and timely pressure monitoring. This article introduces a novel self-powered pressure sensor utilizing electrochemical reactions. The sensor's ion conduction path and internal resistance adjust in response to external stress across a broad range. Its three-dimensional structure, crafted by using a simple template on the electrolyte, enables the efficient and cost-effective detection of various mechanical stimuli. This device not only achieves an optimized power density of approximately 2.34 mW cm-2─surpassing most existing technologies─but also features excellent flexibility, quick response, and recovery times (0.15 and 0.19 s respectively); high durability (2000 cycles); and a broad sensing range (0.23-20 kPa). Moreover, it serves as an ionic touchpad, enhancing data collection and recognition, and integrates seamlessly with a mouthpiece for accurate, real-time monitoring of respiratory activities. This innovative sensor offers minimal cost and simple process requirements while providing multifunctional capabilities for energy harvesting and pressure sensing, marking a significant step forward in the design of next-generation sensors.