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Hong-Xia Duan, Xiu-Mei Wang, Yue Guo, Ling Wei, Rui-Xia Hao, Zhen-Shan Guo Department of Surgery, Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences), Taiyuan, Shanxi, People’s Republic of ChinaCorrespondence: Hong-Xia Duan, Department of Surgery, Shanxi Bethune Hospital (Shanxi Academy of Medical Sciences), No. 99 Longcheng Street, Taiyuan, Shanxi, People’s Republic of China, Tel +86-3518379228, Email Duan_hongxia@yeah.netObjective: To investigate the application of a multidisciplinary collaboration model to optimise the configuration management of orthopaedic external device sets in general hospitals.Methods: A pretest–post-test study design was used. Sixty patients who underwent unilateral total knee arthroplasty and 60 patients who underwent posterior lumbar interbody fusion between March and May 2022 were recruited as the control stage. Additionally, a total of 120 patients, 60 of each, who underwent the two procedures between September and November 2022, were recruited as the experimental stage. For the control stage, conventional external equipment management was used, and for the experimental stage, an external device management programme was implemented based on multidisciplinary collaboration with the control stage. Based on the PDSA cycle, the configuration management of orthopaedic external device sets was optimised, and the differences in collating and counting external devices, nurses’ overtime in the external device stage and orthopaedic surgeon satisfaction were compared between the two stages.Results: Compared with the control stage, the collation count took less time (8.65 ± 0.25 min vs 5.37 ± 0.13 min; 13.55 ± 1.10 min vs 7.85 ± 0.82 min), the number of overtime hours was shorter (175.80 ± 12.19 min vs 96.68 ± 13.66 min) and orthopaedic surgeon satisfaction was improved (4.58 ± 0.62 vs 4.10 ± 0.68; 4.33 ± 0.73 vs 3.87 ± 0.77; 4.20 ± 0.71 vs 3.82 ± 0.71; 4.12 ± 0.69 vs 3.87 ± 0.72; 4.05 ± 0.68 vs 3.79 ± 0.68) in the

The merging of human intelligence and artificial intelligence has long been a subject of interest in both science fiction and academia. In this paper, we introduce a novel concept in Human-AI interaction called Symbiotic Artificial Intelligence with Shared Sensory Experiences (SAISSE), which aims to establish a mutually beneficial relationship between AI systems and human users through shared sensory experiences. By integrating multiple sensory input channels and processing human experiences, SAISSE fosters a strong human-AI bond, enabling AI systems to learn from and adapt to individual users, providing personalized support, assistance, and enhancement. Furthermore, we discuss the incorporation of memory storage units for long-term growth and development of both the AI system and its human user. As we address user privacy and ethical guidelines for responsible AI-human symbiosis, we also explore potential biases and inequalities in AI-human symbiosis and propose strategies to mitigate these challenges. Our research aims to provide a comprehensive understanding of the SAISSE concept and its potential to effectively support and enhance individual human users through symbiotic AI systems. This position article aims at discussing poteintial AI-human interaction related topics within the scientific community, rather than providing experimental or theoretical results.

Dialogue-based language models mark a huge milestone in the field of artificial intelligence, by their impressive ability to interact with users, as well as a series of challenging tasks prompted by customized instructions. However, the prevalent large-scale dialogue-based language models like ChatGPT still have room for improvement, such as unstable responses to questions and the inability to think cooperatively like humans. Considering the ability of dialogue-based language models in conversation and their inherent randomness in thinking, we propose ChatLLM network that allows multiple dialogue-based language models to interact, provide feedback, and think together. We design the network of ChatLLMs based on ChatGPT. Specifically, individual instances of ChatGPT may possess distinct perspectives towards the same problem, and by consolidating these diverse viewpoints via a separate ChatGPT, the ChatLLM network system can conduct decision-making more objectively and comprehensively. In addition, a language-based feedback mechanism comparable to backpropagation is devised to update the ChatGPTs within the network. Experiments on two datasets demonstrate that our network attains significant improvements in problem-solving, leading to observable progress amongst each member.

Cooperative coordination at unsignalized road intersections, which aims to improve the driving safety and traffic throughput for connected and automated vehicles, has attracted increasing interests in recent years. However, most existing investigations either suffer from computational complexity or cannot harness the full potential of the road infrastructure. To this end, we first present a dedicated intersection coordination framework, where the involved vehicles hand over their control authorities and follow instructions from a centralized coordinator. Then a unified cooperative trajectory optimization problem will be formulated to maximize the traffic throughput while ensuring the driving safety and long-term stability of the coordination system. To address the key computational challenges in the real-world deployment, we reformulate this non-convex sequential decision problem into a model-free Markov Decision Process (MDP) and tackle it by devising a Twin Delayed Deep Deterministic Policy Gradient (TD3)-based strategy in the deep reinforcement learning (DRL) framework. Simulation and practical experiments show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve the traffic throughput in the realistic continuous traffic flow. The most remarkable advantage is that our strategy could reduce the time complexity of computation to milliseconds, and is shown scalable when the road lanes increase., Comment: in IEEE Transactions on Intelligent Transportation Systems

Bridge accessories are known to have an effect on stiffness of a bridge. In recent studies also presence of expansion joints between precast accessory elements and the joint positioning have been found to have an effect on the deck stiffness. Additionally stress concentrations are observed in bridge deck at the joint location. This thesis analyses load test data of two steel-concrete composite beams with different joint patterns in the concrete parts and conducts numerical analyses on both cases, in order to determine the effect of the joint location on strength and stiffness of the structure under negative bending moment. Furthermore, attempt is made to investigate formation of stress concentration at the joint locations observed in preceding research. In Case 1 the beam has a joint in the concrete part at the midpoint of the beam. In Case 2, two joints are placed in the concrete part, exactly halfway between midpoint and the support point on each side of the beam. The load type is negative three-point bending with concrete parts of the beams under tension. Both cases were loaded until measured force imposed on the beam started to decrease as displacement of the midpoint was increased. Numerical analyses were conducted on both cases using ANSYS finite element analysis software. Both cases were modeled with 3D components of exact specimen geometries using simplified shear connection. Load test results showed higher ultimate load capacity for Case 2. Case 2 was found to undergo plastic deformation at higher load compared to Case 1. In numerical analyses the models were loaded by imposing incremental displacement in 0.25 mm steps up to 10 mm. In Case 1 numerical model replicated load-displacement behavior with 4.9 % error and strain development in steel profile with 11.4 % error, while in Case 2 the error rate of numerical analysis was 5.8 % for load-displacement. However, up to 45 % differences were found in strain values compared to load test results in Case 2. Load, Siltojen varusteiden tiedetään vaikuttavan sillan jäykkyyteen, vaikka varusteita ei huomioida osana siltojen kantavia rakenteita. Elementtirakenteisten varusteiden saumoilla on viimeisimmissä tutkimuksissa havaittu vaikutuksia sillan jäykkyyteen. Lisäksi saumojen on havaittu aiheuttavan jännityskeskittymiä sillan kanteen. Tässä diplomityössä analysoitiin erilaisilla liikuntasaumasijoitteluilla toteutettujen liittopalkkien kuormituskokeiden tuloksia kahdesta koekappaleesta, sekä luotiin FEM-mallit molemmista kappaleista. Työn tarkoitus oli selvittää saumojen sijoittelun vaikutuksia palkin lujuuteen ja jäykkyyteen venytetyn yläpinnan taivutuksessa. Lisäksi työssä yritettiin tutkia aiemmassa tutkimuksessa havaittua jännityskeskittymien muodostumista. Kuormituskokeen koekappaleessa 1 sauma sijoitettiin palkin keskikohtaan ja koekappaleessa 2 kaksi saumaa keskikohdan molemmin puolin tuen ja palkin keskikohdan puoliväliin. Kuormitustapa oli käänteinen kolmipistetaivutus, jossa betonipoikkileikkaus oli vedettynä. Kummassakin tapauksessa kuormitusta jatkettiin kunnes taipuman lisäämiseen vaadittu voima alkoi pienentyä. Koekappaleiden muoto mallinnettiin kolmiulotteisesti ANSYS laskentaohjelmalla. Liittorakenteen leikkausliitos kuitenkin yksinkertaistettiin määrittelemällä osien välinen vuorovaikutus kitkakertoimella. Kuormituskokeessa havaittiin koekappaleen 1 murtolujuus suuremmaksi kuin kappaleen 2. Kappaleen 2 myötölujuus havaittiin myös suuremmaksi kuin kappaleen 1 FEM-analyyseissä kappaleet kuormitettiin määrittämällä keskipisteen siirtymäksi 10 mm 0,25 mm askelin. Kappaleen 1 mallin kuorman ja taipuman suhde noudatti kuormituskokeessa mitattua 4,9 %:n virhemarginaalilla ja teräsosan laskennalliset venymät vastasivat kuormituskokeessa mitattuja 11,4 %:n marginaalilla. Kappeleen 2 kohdalla virhe kuorma-taipumasuhteessa oli 5,8 %, sen sijaan teräsosan jännityksissä ero oli enimmillään 45 % laskentamallin ja kuormituskokeessa mitatun välillä. Kuormituskokeen tulosten peru

API recommendation in real-time is challenging for dynamic languages like Python. Many existing API recommendation techniques are highly effective, but they mainly support static languages. A few Python IDEs provide API recommendation functionalities based on type inference and training on a large corpus of Python libraries and third-party libraries. As such, they may fail to recommend or make poor recommendations when type information is missing or target APIs are project-specific. In this paper, we propose a novel approach, PyART, to recommend APIs for Python programs in real-time. It features a light-weight analysis to derives so-called optimistic data-flow, which is neither sound nor complete, but simulates the local data-flow information humans can derive. It extracts three kinds of features: data-flow, token similarity, and token co-occurrence, in the context of the program point where a recommendation is solicited. A predictive model is trained on these features using the Random Forest algorithm. Evaluation on 8 popular Python projects demonstrates that PyART can provide effective API recommendations. When historic commits can be leveraged, which is the target scenario of a state-of-the-art tool ARIREC, our average top-1 accuracy is over 50% and average top-10 accuracy over 70%, outperforming APIREC and Intellicode (i.e., the recommendation component in Visual Studio) by 28.48%-39.05% for top-1 accuracy and 24.41%-30.49% for top-10 accuracy. In other applications such as when historic comments are not available and cross-project recommendation, PyART also shows better overall performance. The time to make a recommendation is less than a second on average, satisfying the real-time requirement., Comment: 12 pages

The monoclinic-phase 1T′ MoTe2 crystal exhibits inversion symmetry as an anisotropic semi-metal, dictating its interesting quantum transport phenomenon and other novel physical properties. However, large-scale controllable growth of uniform MoTe2 crystals still remains a great challenge, hindering its further fundamental research and applications for novel devices. Herein, we report a modified growth method for synthesizing few-layer 1T′ MoTe2 crystals with large-scale uniformity with the assistance of molecular sieves. The theoretical simulations demonstrated that due to the temperature-dependent formation energies of different edges, the edge of (010) orientation shows a higher thermodynamic stability than that of (100) orientation, and results in the anisotropic growth behavior of 1T′ MoTe2 crystals while the temperature changes. The photoresponse of tri-layer 1T′ MoTe2-based devices shows a broad-spectrum response from 532 nm to 1550 nm. The photo-response time of 1T′ MoTe2 crystals demonstrates that it supposes to be the synergistic mechanism of photo-conductive and photo-radiation effects. Our findings not only provide a method for the controllable growth of anisotropic two-dimensional materials at a wafer scale, but also explore a broad-spectrum photodetector with the MoTe2-based device.

The target of rapamycin (TOR) plays a central role in eukaryotic cell growth control. With prevalent hyperactivation of the mammalian TOR (mTOR) pathway in human cancers, strategies to enhance TOR pathway inhibition are needed. We used a yeast-based screen to identify small-molecule enhancers of rapamycin (SMERs) and discovered an inhibitor (SMER3) of the Skp1-Cullin-F-box (SCF)(Met30) ubiquitin ligase, a member of the SCF E3-ligase family, which regulates diverse cellular processes including transcription, cell-cycle control and immune response. We show here that SMER3 inhibits SCF(Met30) in vivo and in vitro, but not the closely related SCF(Cdc4). Furthermore, we demonstrate that SMER3 diminishes binding of the F-box subunit Met30 to the SCF core complex in vivo and show evidence for SMER3 directly binding to Met30. Our results show that there is no fundamental barrier to obtaining specific inhibitors to modulate function of individual SCF complexes.

This paper examines the economic implications of demographic change in the Chinese context. We extend the growth equation by incorporating age structure dynamics and apply it to China's provincial-level data during the period 1989-2004. We find that changes in demographic structure, especially the contribution of fertility decline to lower youth dependency, have helped fuel China's economic growth since 1989. The effect of demographic change on income growth operates mainly through its impact on steady state income levels and the effect of age structure is more pronounced in provinces that are more open to market forces. We also find a significant feedback effect of economic growth on demographic behaviors through the mechanisms of birth rates, marriage age and life expectancy.

The present paper performs a development accounting analysis to investigate the sources of China's interprovincial income inequality over the period 1982-2005. We estimate a Cobb-Douglas aggregate production function with various specifications. Using the estimated parameters, we conduct a development accounting analysis as well as a variance decomposition. Our results suggest that differences in physical capital intensity and in total factor productivity are both important sources of cross-province income differences, each accounting for roughly half of the variation in income levels. Differences in human capital explain only a small amount of income differences across provinces. The results are robust to whether or not the assumption of constant returns to scale is imposed. The interaction between factor accumulation and total factor productivity is also discussed.