Operational Space Research Articles

Analysis of symptoms and influencing factors of low back work-related musculoskeletal disorders among workers in a container manufacturing factory

Objective: To investigate the occurrence of low back work-related musculoskeletal disorders (WMSDs) among workers in a container manufacturing factory, and to explore the influencing factors. Methods: In June 2022, 952 workers from a container factory were selected as the research objects by cluster random sampling. Through questionnaire survey, the incidence of low back WMSDs symptoms among workers in the past one year was collected, and the influencing factors of low back WMSDs were analyzed by logistic regression. Results: The incidence rate of low back WMSDs was 46.7% (445/952). The factors with higher exposure at work were frequent slight bending (77.0%, 733/952), frequent overtime (74.1%, 705/952), and the need to turn around while working (62.3%, 593/952). Multivariate logistic regression analysis showed that age over 40 years old, smoking, drinking, often bending over slightly, sitting for a long time, maintaining a large bending posture for a long time, often working overtime, limited operating space, and there was always a need to complete conflicting things in the container manufacturing factory workers were the risks of increasing the low back WMSDs (OR=1.68, 1.96, 2.47, 1.49, 1.84, 2.11, 1.90, 1.82, 2.00, P<0.05). Standing at work, always friendly colleagues, and always supportive and helpful leaders were protective factors for low back WMSDs (OR=0.60, 0.32, 0.40, P<0.05) . Conclusion: The incidence of low back WMSDs symptoms in container manufacturing workers is high, and work-related factors such as frequent slight bending, long time holding large bending posture and limited operating space are the focus of ergonomic intervention in container manufacturing enterprises.

Reliable and Accurate Implicit Neural Representation of Multiple Swept Volumes with Application to Safe Human–Robot Interaction

In automated production using collaborative robots in a manufacturing cell, a crucial aspect is to avoid collisions to ensure the safety of workers and robots in human–robot interaction. One approach for detecting collisions is using the swept volume (SV) to identify safe protective space for operation. We learn an accurate and reliable signed distance function (SDF) network from raw point clouds of a pre-computed SV to represent a class of linear joint motion trajectories. The network requires only a set of parameters and constant execution time, thus reducing the computational time and memory of collision checking due to the complexity of explicit geometry during task execution. The distance of collision danger foresaw by the learned SDF is exploited to reduce the frequency of collision detection calls in the dynamic environment and reduce the computational cost further. We assess the relative merits of the implicit neural representation of multiple SVs in terms of F1-score, error distance from the surface of the truth geometry, and 3D visualization by comparing favorably with a binary voxel network for learning a single SV with similar inference time. All the predicted errors of the geometry lie within a distance of 4 voxels from the surface of the truth geometry, and most reconstruction errors are within 3 voxels. A simulation of pick-and-place task execution in the human–robot interaction scenarios by leveraging the learned SDF as an efficient continuous collision detector is performed. The improvement in execution time and collision detection number is validated in the simulation.

Side-channel security of practical quantum key distribution

We present general security proof for quantum key distribution (QKD) with imperfect states in whole space including both the operational space and the other space (side-channel space). We show that in a protocol where Alice (Bob) uses two states, the secure key rate for a real protocol using imperfect (mixed) states is the same as that of a perfect protocol if the real protocol can be mapped from the perfect protocol. The calculation of the secure key rate for imperfect sources is reduced to the lower bound of projection probability to vacuum for each state. This paves the operable way to side-channel-secure QKD under more realistic conditions, including imperfections of whole-space states, mixed states, and finite data size. Taking the sending-or-not-sending encoding as an example in calculation, we obtain good key rates and long secure distance for QKD that is both measurement-device-independent and secure with imperfect whole-space source states. Published by the American Physical Society 2024

Operational Security, Safe Quantum, Space Communications and Data Privacy Cyber Security

In recent years the internet has become an important part of daily life for people around the world. 18 On the other hand, as the effectiveness of the internet increases, cybercrimes also increase. Over the last 15 years, cybersecurity has emerged as a way to accelerate the pace of change in cyberspace. Cybersecurity refers to the procedures a country or organization can use to protect its assets and information in cyberspace. Twenty years ago the term "cybersecurity" was little known to the public [1]. Cybersecurity affects not only people but also organizations or governments. In recent years, everything has been going well and it has used many technologies such as cybernetics, cloud computing, smartphones, and Internet of Things technology. Cyber attacks raise concerns about privacy, security, and financial security. Cybersecurity is a set of technologies, processes, and practices designed to prevent attacks, damage, and unauthorized access to networks, computers, operations, and machines. The main purpose of this article is to provide detailed information about types of cybersecurity, why cybersecurity is important, cybersecurity frameworks, cybersecurity tools, and problem points of cybersecurity [2].

Safe system for sustainable development

This paper presents a novel governance concept for sustainable development, introducing the 'Safe System Approach' as a transformative model that shifts focus from individual behavioural change to systemic transformation. This approach challenges traditional governance models that emphasize individual responsibility in achieving sustainable development and decarbonization. Instead, it advocates for creating an enabling environment that inherently guides individuals and communities towards sustainable actions. The Safe System Approach is centred on delivering low-carbon services across essential sectors, including electricity, mobility, industry, buildings, human settlements, and agriculture, thereby embedding sustainability as a default choice in societal systems. Drawing parallels with successful models in road safety, the paper explores the potential of this approach in urban development and climate action. It emphasizes the need for a broad coalition and integrated approaches in managing shared resources, highlighting the significance of systemic adjustments over individual behavioral change. By proposing a structure where sustainability is facilitated by the system's design, the paper builds on key concepts from seminal works by scholars like Garrett Hardin, Mancur Olson, Elinor Ostrom, and Ahrend Lijphart. It discusses the challenges and opportunities in creating safe operating spaces for sustainable development, emphasizing the need for multi-actor, multilevel governance systems that can manage shared resources sustainably and are resilient to political volatility. The paper aims to offer a robust, efficient, and inclusive pathway to sustainable development, contributing to the global discourse on environmental and social resilience.

The modulus of a vector measure

It is known that if L is a Dedekind complete Riesz space and (Ω, Σ) is a measurable space, then the partially ordered linear space of all L-valued, finitely additive and order bounded vector measures m on Σ is also a Dedekind complete Riesz space (for the natural operations). In particular, the modulus |m| o of m exists in this space of measures and |m| o is given by a well known formula. Some 20 years ago L. Drewnowski and W. Wnuk asked the question (for L not Dedekind complete) if there is an m for which |m|o exists but, |m| o is not given by the usual formula? We show that such a measure m does indeed exist.

Inferring ventilation rates with quantified uncertainty in operational rooms using point measurements of carbon dioxide: Classrooms as a case study

We present a robust integral method to estimate the daily mean per-person ventilation rate Q¯pp based on carbon dioxide (CO2) concentration measurements in operational spaces, and limited other data. The method makes no assumptions regarding the ventilation provision throughout the day, nor requires the room to be in a steady state, nor the air within to be well-mixed. We demonstrate that several integral parameters remain reliably close to a value of unity, despite large variations in room conditions. Evaluating the likely distributions of integral parameters provides a method to quantify the uncertainty bounds and therefore assess the reliability of these ventilation estimates. Taking school classrooms as a case study, estimates of Q¯pp based on measured CO2 are shown to exhibit uncertainty bounds (of 95% confidence intervals) of approximately ±24% if no other data than the classroom timetable is available. Deploying four CO2 sensors within a classroom is expected to halve the uncertainty bounds to around ±12%. Moreover, the framework presented herein evidences that when the same classroom experiences similar usage on two different days, the relative per-person ventilation rate achieved during these two days can be simply determined by the ratio of their integral excess CO2 concentrations. These significant findings offer great scope to facilitate more reliable ventilation estimates, particularly from large-scale data sets of CO2 measured in operational spaces, to better inform assessments of indoor air quality.

Analysis of Displacement Influence of Span of Prestressed Fish-Belly Beam on Foundation Pit Plane

As a new technology of antagonistic internal support, prestressed fish-bellied steel support system has been widely used in various foundation pit projects because of its advantages such as reutilization, effective displacement control and large mechanical operation space. But at present, the plane layout depends on experience, and has not yet formed a set of complete and reasonable plane layout methods. In the plane layout, the corner brace is arranged according to the condition of the plane stress balance of the corner brace, then the brace is arranged based on the corner brace balance theory, and finally the optimal span of the fish-belly beam is selected. The displacement diagram of supporting structure is obtained by using Midas/Gen with the span of fish-belly beam as the variable, the influence of the span of fish-belly beam on the horizontal displacement of foundation pit is analyzed, the relationship formula between the span of fish-belly beam and the size of foundation pit is obtained, and the plane layout method of prestressed fish-belly beam system is proposed.

Avoiding fusion plasma tearing instability with deep reinforcement learning

For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance1–4. However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators5. Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D6, the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER.

Crack Width Recognition of Tunnel Tube Sheet Based on YOLOv8 Algorithm and 3D Imaging

Based on the tunnel crack width identification, there are operating time constraints, limited operating space, high equipment testing costs, and other issues. In this paper, a large subway tunnel is a research object, and the tunnel rail inspection car is an operating platform equipped with industrial cameras in order to meet the requirements of the tunnel tube sheet crack width recognition of more than 0.2 mm, with the measuring instrument to verify that the tunnel rail inspection car in the state of uniform motion camera imaging quality has the reliability through the addition of laser rangefinders, the accurate measurement of the object distance and the calculation of the imaging plane and the angle of the plane to be measured, to amend the three-dimensional cracks. The pixel resolution of the image is corrected, the images imaged by the industrial camera are preprocessed, the YOLOv8 algorithm is used for the intelligent extraction of crack morphology, and finally, the actual width is calculated from the spacing between two points of the crack. The crack detection width obtained by image processing using the YOLOv8 algorithm is basically the same as the value of crack width obtained by manual detection, and the error rate of crack width detection ranges from 0% to 11%, with the average error rate remaining below 4%. Compared with the crack detection error rate of the Support Vector Machine (SVM), the crack extraction model is reduced by 1%, so using the tunnel inspection vehicle as a platform equipped with an industrial camera, YOLOv8 is used to realize the recognition of the shape and width of the cracks on the surface of the tunnel tube sheet to meet the requirements of a higher degree of accuracy. The number of pixels and the detection error rate are inversely proportional to each other. The angle between the imaging plane and the plane under test is directly proportional to the detection error rate. The angle between the vertical axis where the lens midpoint is located and the line connecting the shooting target and the lens center point is αi and the angle θi between the measured plane and the imaging plane is reciprocal, i.e., αi + θi = 90°. Therefore, using the inspection vehicle as a mobile platform equipped with an industrial camera and based on the YOLOv8 algorithm, the crack recognition of the tunnel tube sheet has the feasibility and the prospect of wide application, which provides a reference method for the detection of cracks in the tunnel tube sheet.

Can crop production intensification through irrigation be sustainable? An ex-ante impact study of the south-central coastal zone of Bangladesh

In Bangladesh’s south-central coastal zone, there is considerable potential to intensify crop production by growing dry winter season ‘Boro’ rice, maize, wheat, pulses and oilseeds using irrigation from southward flowing and predominantly freshwater rivers. However, the impacts of surface water withdrawal for sustained irrigation and its safe operating space remain unclear. We used field measurements and simulation modeling to investigate the effects of irrigation water withdrawal for Boro rice–the most water-consumptive crop–on river water flow and salinity under different climate change and river flow scenarios. Under the baseline conditions, about 250,000 ha could potentially be irrigated with river water that has salinity levels below 2 dS/m. The impact on river water salinity would be minimal, and only between 0.71 to 1.12% of the cropland would shift from the 0–2 dS/m class to higher salinity levels. Similarly, for the moderate climate change scenario (RCP 4.5) that forecasts a sea level rise of 22 cm in 2050, there would be a minor change in water flow and salinity. Only under the extreme climate change scenario (RCP 8.5), resulting in a sea level rise of 43 cm by 2050 and low flow conditions that are exceeded in 90% of the cases, the 2 dS/m isohaline would move landward by 64 to 105 km in March and April for the Tentulia and Buriswar Rivers. This would expose an additional 36.6% of potentially irrigable cropland to salinity levels of 2 to 4 dS/m. However, Boro rice will already be well established by that time and can tolerate greater levels of salinity. We conclude that there is considerable scope to expand irrigated crop production without negatively exposing the cropland and rivers to detrimental salinization levels while preserving the ecosystem services of the rivers.

Weighted Hessian estimates in Orlicz spaces for nondivergence elliptic operators with certain potentials

We prove interior weighted Hessian estimates in Orlicz spaces for nondivergence type elliptic equations with a lower order term which involves a nonnegative potential satisfying a reverse Hölder type condition.

Higher-Order Process Matrix Tomography of a Passively-Stable Quantum Switch

The field of indefinite causal order (ICO) has seen a recent surge in interest. Much of this research has focused on the quantum switch, wherein multiple parties act in a superposition of different orders in a manner transcending the quantum circuit model. This results in a new resource for quantum protocols, and is exciting for its relation to issues in foundational physics. The quantum switch is also an example of a higher-order quantum operation, in that it transforms not only quantum states but also other quantum operations. To date, no quantum process without a definite causal order has been completely experimentally characterized. Indeed, past work on the quantum switch has confirmed its ICO by measuring causal witnesses or demonstrating resource advantages, but the complete process matrix has been described only theoretically. Here we report our performing higher-order quantum process tomography. However, doing so requires exponentially many measurements with a scaling worse than that of standard process tomography. We overcome this challenge by creating a new fiber-based quantum switch using active optical elements to deterministically generate and manipulate time-bin encoded qubits. Moreover, our new architecture for the quantum switch can be readily scaled to multiple parties. By reconstructing the process matrix, we estimate its fidelity and tailor different causal witnesses directly for our experiment. To achieve this, we measure a set of tomographically complete settings, which also spans the input operation space. Our tomography protocol allows the characterization and debugging of higher-order quantum operations with and without an ICO, while our experimental time-bin techniques could enable the creation of a new realm of higher-order quantum operations with an ICO. Published by the American Physical Society 2024

Bi-parameter and bilinear Calderón–Vaillancourt theorem with critical order

Abstract In this paper, we establish the sharp Calderón–Vaillancourt theorem on L p L^{p} spaces for bi-parameter and bilinear pseudo-differential operators with symbols of critical order by deriving a sufficient and necessary condition on its symbol. This sharpens the result of [G. Lu and L. Zhang, Bi-parameter and bilinear Calderón–Vaillancourt theorem with subcritical order, Forum Math. 28 2016, 6, 1087–1094] which was only proved for symbols of subcritical order.

AR-Assisted Guidance for Assembly and Maintenance of Avionics Equipment

The assembly and maintenance of products in the aviation industry constitute a crucial aspect of the product life cycle, with numerous tasks still reliant on manual operations. In order to solve the problem of narrow operation spaces and blind areas in the processes of manual assembly and maintenance, we proposed an augmented reality (AR) assistant guidance method specifically designed for such scenarios. By employing a multi-modality anti-occlusion tracking algorithm, pose data of assembly parts can be obtained, upon which AR guidance information is displayed. Additionally, we proposed an assembly step identification method to alleviate user interaction pressure. We developed an AR visualization assistant guidance system and designed and conducted a user evaluation experiment to measure the learnability, usability, and mental effort required. The results demonstrate that our method significantly enhances training efficiency by 128.77%, as well as improving assembly and maintenance efficiency by 29.53% and 27.27% compared with traditional methods. Moreover, it has significant advantages in learnability, usability, and mental effort, providing a feasible and effective resolution for addressing blind areas during assembly and maintenance within the aviation industry.

A novel and efficient murine model for investigating tendon-to-bone healing

BackgroundTendon-to-bone healing is a critical challenge in sports medicine, with its cellular and molecular mechanisms yet to be explored. An efficient murine model could significantly advance our understanding of this process. However, most existing murine animal models face limitations, including a propensity for bleeding, restricted operational space, and a steep learning curve. Thus, the need for a novel and efficient murine animal model to investigate the cellular and molecular mechanisms of tendon-to-bone healing is becoming increasingly evident.MethodsIn our study, forty-four 9-week-old male C57/BL6 mice underwent transection and reattachment of the Achilles tendon insertion to investigate tendon-to-bone healing. At 2 and 4 weeks postoperatively, mice were killed for histological, Micro-CT, biomechanical, and real-time polymerase chain reaction tests.ResultsHistological staining revealed that the original tissue structure was disrupted and replaced by a fibrovascular scar. Although glycosaminoglycan deposition was present in the cartilage area, the native structure had been destroyed. Biomechanical tests showed that the failure force constituted approximately 44.2% and 77.5% of that in intact tissues, and the ultimate tensile strength increased from 2 to 4 weeks postoperatively. Micro-CT imaging demonstrated a gradual healing process in the bone tunnel from 2 to 4 weeks postoperatively. The expression levels of ACAN, SOX9, Collagen I, and MMPs were detected, with all genes being overexpressed compared to the control group and maintaining high levels at 2 and 4 weeks postoperatively.ConclusionsOur results demonstrate that the healing process in our model is aligned with the natural healing process, suggesting the potential for creating a new, efficient, and reproducible mouse animal model to investigate the cellular and molecular mechanisms of tendon-to-bone healing.Graphical abstract

Physical and virtual water transfers in China and their implication for water planetary boundary.

China is an extremely water-scarce country with an uneven distribution of regional water resources. We define two absolute sustainability indicators, using the multi-regional input‒output (MRIO) model to outline the contribution of China's physical and virtual water transfers in mitigating the problem of regional water boundary-exceeding. Although the overall use of freshwater resources is within the safe operation space, 55% of province's water resource development transgresses the local water planetary boundary. Physical and virtual water transfers effectively mitigate the stress of water supply to the water planetary boundary in China's water-scarce regions. Among them, the role of virtual water transfers occupies the main part. The cost of using physical water in water-receiving regions and the situation of virtual water flowing from water-scarce regions to developed water-rich regions cannot be ignored, and a small number of provinces are responsible for most of the virtual water net imports and exports. The obtained results are helpful for the redistribution of water planetary boundary transgressing responsibilities among provinces and the formulation of absolute sustainable water resources management policies.

Rethinking the Healthcare Facilities: The Role of the Buffer Space.

A working group conducted a survey on the use of the principle of buffer space (BS), which in case of emergencies, could benefit healthcare settings. The aim of the preliminary investigation is to define new research lines in hospitals' functional design. The global experience of the COVID-19 pandemic highlighted challenges faced by hospitals when responding promptly to emergencies, including spatial reorganization and suspension of ordinary medical activities for ensuring adequate management of the emergency surge of patients. The group designed questionnaires to be administered to healthcare staff and healthcare designers aimed at understanding varied conceptions and features of BSs. Content across the two surveys overlapped significantly, allowing for direct comparisons of responses, while also including tailored questions in relation to the respective experience and skills of the two groups of respondents. 102 healthcare professionals and 56 designers took part to the survey. Analysis of the responses permitted for initial recommendations regarding BS typology including (a) proximity to the emergency department (ED), intensive care units (ICUs), and inpatient wards (IWs); (b) location within hospitals but separate from other medical areas; (c) need for independent access; (d) organizational and spatial features similar to ED, ICUs, and IWs; (e) existing as a fully flexible operational space; and (f) BS bed capacity to be approximately 12% of ED beds. Although the analysis is related to the Italian context, the expansion of this preliminary research to alternate healthcare facilities and geographic areas is necessary for reaching a wide consensus by different professionals on this field. It serves as a starting point for future investigations regarding the implementation of BS in hospital settings.

Weighted Strong-Type Estimates on Classical Lorentz Spaces

We present new estimates in the setting of weighted classical Lorentz spaces for important operators in Harmonic Analysis such as Calderón-Zygmund operators, sparse operators and the Bochner-Riesz operator among others.

Forecasting Ro-Ro Freight Transportation Demand at Samsun Port: A Hybrid Method Approach

ABSTRACT Türkiye's extensive coastline and geopolitics position necessitates the importance of Ro-Ro transportation with neighbouring countries. Türkiye's rapidly growing Ro-Ro transportation significantly contributes to imports and exports, which is of great importance to the national economy. Samsun Port is one of the most active ports in Türkiye's Ro-Ro transportation sector, operating in the Black Sea region. This study examined Ro-Ro transportation at Samsun Port, and future cargo forecasting was conducted. For this purpose, artificial neural networks and time series analysis methods were combined. Input variables used in the study included the number of Ro-Ro ships arriving at the port between 2009 and 2021, population figures, a specialized CPI indicator (fresh fruits and vegetables), and export values. The output variable was the amount of cargo carried by RoRo ships. According to the results obtained, it was observed that Samsun Port would have sufficient capacity for Ro-Ro transportation in the next 27 months in terms of wharf, port area, and operational space. Keywords: Samsun Port, Ro-Ro transportation, Port capacity, Forecasting