Sophisticated Programs Research Articles

Topology, dynamics, and control of a muscle-architected soft arm

Muscular hydrostats, such as octopus arms or elephant trunks, lack bones entirely, endowing them with exceptional dexterity and reconfigurability. Key to their unmatched ability to control nearly infinite degrees of freedom is the architecture into which muscle fibers are weaved. Their arrangement is, effectively, the instantiation of a sophisticated mechanical program that mediates, and likely facilitates, the control and realization of complex, dynamic morphological reconfigurations. Here, by combining medical imaging, biomechanical data, live behavioral experiments, and numerical simulations, an octopus-inspired arm made of [Formula: see text]200 continuous muscle groups is synthesized, exposing "mechanically intelligent" design and control principles broadly pertinent to dynamics and robotics. Such principles are mathematically understood in terms of storage, transport, and conversion of topological quantities, effected into complex 3D motions via simple muscle activation templates. These are in turn composed into higher-level control strategies that, compounded by the arm's compliance, are demonstrated across challenging manipulation tasks, revealing surprising simplicity and robustness.

Joint probabilistic modeling of extreme wind-wave conditions under typhoon impact and applications to extreme response analysis of floating offshore wind turbines

The joint probabilistic modeling of extreme wind and wave conditions under typhoon impact is a critical task for extreme response analysis of floating offshore wind turbines (FOWTs) deployed in typhoon-prone areas. However, wind and wave observations under typhoon impact are quite limited, which poses great challenges to the probabilistic modeling task. Although some sophisticated programs can be employed to simulate typhoon-induced wave fields, they suffer from unbearable computational burdens. To improve the accuracy and efficiency of joint probabilistic modeling of extreme wind and wave conditions under typhoon impact, an approach that synthesizes the typhoon hazard analysis method and copula-based conditional modeling method is proposed in the present study. The proposed joint distribution model is expressed as the product of the distribution of extreme wind speed and the conditional distribution of significant wave height. The distribution of extreme wind speed is estimated through the typhoon hazard analysis method, and the conditional distribution of significant wave height is modeled by the copula-based method. The effectiveness of the proposed method is verified by comparing with observation data. Applications of the proposed method to extreme response analysis of FOWTs are illustrated as well.

생성형 인공지능 활용 교육프로그램을 통한 예비교사들의 인공지능에 대한 인식 변화

Purpose: This study explored pre-service teachers' perception of AI changes through an educational program that utilize AI. Methods: An educational program was developed that provides opportunities for pre-service teachers to design classes using AI and implement them through teaching practicum and used to investigate the educational effectiveness of seventeen pre-service teachers at a university in Chungcheongbuk province. Fourteen questions corresponding to the two categories of ‘Accessibility of AI’ and ‘Similarity between AI and Human’ were administered in pre- and post-surveys. Changes were also investigated in the pre-service teachers' perception of artificial intelligence based on the output and interview data submitted by the pre-service teachers that supplemented the survey results. Results: As a result of the study, the educational program developed in this study showed educational effectiveness. Through the educational program, pre-service teachers showed changes in their perception that AI can be used by anyone, that AI is safe, that AI cannot replace human jobs, and that AI learns through human input. However, it was found that pre-service teachers felt a financial burden in having to subscribe to artificial intelligence, and there was a limit to changing the thinking that artificial intelligence can solve problems like humans. Conclusion: In the future, in order to improve the pre-service teachers' perception of AI and to actively implement classes using generative artificial intelligence in schools, schools should provide financial support when using AI programs, or let pre-service teachers recognize the operating principles of artificial intelligence and the differences between humans. There is a need to develop more sophisticated pre-service teacher education programs for content that shows limited effectiveness.

Bayesian estimation of the mean time between failures of subsystems with different causes using interval‐censored system maintenance data

AbstractEnsuring an acceptable level of reliability stands as a primary imperative for any mission‐focused operation since it serves as a critical determinant of success. Inadequate reliability can lead to severe repercussions, including substantial expenses for repairs and replacements, missed opportunities, service disruptions, and in the worst cases, safety violations and human casualties. Within national defense organizations such as the USAF, the precise assessment and maintenance of system reliability play a pivotal role in ensuring the success of mission‐critical operations. In this research, our primary objective is to model the reliability of repairable subsystems within the framework of competing and complementary risks. Subsequently, we construct the overall reliability of the entire repairable system, utilizing day‐to‐day group‐censored maintenance data from two identical aircraft systems. Assuming that the lifetimes of subsystems follow non‐identical exponential distributions, it is theoretically justified that the system reliability can be modeled by homogeneous Poisson processes even though the number of subsystems of any particular type is unknown and the temporal order of multiple subsystem failures within a given time interval is uncertain due to interval censoring. Using the proposed model, we formulate the likelihood function for the mean time between failures of subsystems with different causes, and subsequently establish an inferential procedure for the model parameters. Given a considerable number of parameters to estimate, we explore the efficacy of a Bayesian approach, treating the contractor‐supplied estimates as the hyperparameters of prior distributions. This approach mitigates potential model uncertainty as well as the practical limitation of a frequentist‐based approach. It also facilitates continuous updates of the estimates as new maintenance data become available. Finally, the entire inferential procedures were implemented in Microsoft Excel so that it is easy for any reliability practitioner to use without the need to learn sophisticated programming languages. Thus, this research supports an ongoing, real‐time assessment of the overall mission reliability and helps early detection of any subsystem whose reliability is below the threshold level.

Toward a Poetics of Maya Art and Writing

This article identifies large-scale chiastic and bracketing structures in contemporary, colonial and Classic Maya verbal art and literature. These structures are composed of the repetition of lines, verses and stanzas that frame sections of texts and sometimes images. Initially, the argument focuses on an ethnopoetic analysis that directs attention to such forms in modern and colonial narrative and presents an extended contemporary Yucatecan story to illustrate key forms. Second, it turns to similar structures in Classic Mayan narrative written in Maya hieroglyphs to examine the way rhetorical and linguistic tropes intertwined with corresponding features in visual compositions to craft highly sophisticated artistic programmes. By tracking how specific structures are deployed and in what contexts, this article defines an aesthetic that not only sheds light on verbal narratives, but also elucidates visual programmes and their interrelationship with text to reveal a fundamental principle in Maya world conceptualization. This literary and visual analysis develops a cross-medial Maya aesthetics comparable to other global poetic traditions.

Predictive abilities of constitutive models for clay under monotonic and cyclic loading: element tests and centrifuge experiments

A critical investigation of three constitutive models for clay by means of analyses of a sophisticated laboratory testing program and of centrifuge tests on monopiles in clay subjected to (cyclic) lateral loading is presented. Constitutive models of varying complexity, namely the basic Modified Cam Clay model, the hypoplastic model with Intergranular Strain (known as Clay hypoplasticity model) and the more recently proposed anisotropic visco-ISA model, are considered. From the simulations of the centrifuge tests with monotonic loading it is concluded that all three constitutive models give satisfactory results if a proper calibration of constitutive model parameters and proper initialisation of state variables is ensured. In the case of cyclic loading, the AVISA model is found to perform superior to the hypoplastic model with Intergranular Strain.

Transformational Leadership, Secondary Traumatic Stress, and Burnout of Child Welfare Workers: Multilevel Moderation Effects.

This study examines the moderating effects of distant leader's practice of transformational leadership on the relationship between secondary traumatic stress (STS) and burnout among child welfare workers. Caseworkers and supervisors in a Midwest U.S. state (N = 210) rated their regional director's use of transformational leadership skills using a survey. Given the nature of the clustered data, multilevel modeling was employed to examine the main effects of transformational leadership on worker burnout and its cross-level interaction effect on the association between worker STS and burnout. Multilevel modeling demonstrated that worker burnout was positively associated with STS and negatively associated with organizational-level transformational leadership. The cross-level interaction between transformational leadership and STS was significant. Specifically, the positive association between workers' STS and burnout decreased as transformational leadership increased. These findings suggest that organizational approaches such as transformational leadership can influence workforce results. Further research will guide child welfare policymakers to develop more sophisticated training programs in leadership skills and strategies.

Time-encoded electrical detection of trace RNA biomarker by integrating programmable molecular amplifier on chip

One of the serious challenges facing modern point-of-care (PoC) molecular diagnostic platforms relate to reliable detection of low concentration biomarkers such as nucleic acids or proteins in biological samples. Non-specific analyte-receptor interactions due to competitive binding in the presence of abundant molecules, inefficient mass transport and very low number of analyte molecules in sample volume, in general pose critical hurdles for successful implementation of such PoC platforms for clinical use. Focusing on these specific challenges, this work reports a unique PoC biosensor that combines the advantages of nanoscale biologically-sensitive field-effect transistor arrays (BioFET-arrays) realized in a wafer-scale top-down nanofabrication as high sensitivity electrical transducers with that of sophisticated molecular programs (MPs) customized for selective recognition of analyte miRNAs and amplification resulting in an overall augmentation of signal transduction strategy. The MPs realize a programmable universal molecular amplifier (PUMA) in fluidic matrix on chip and provide a biomarker-triggered exponential release of small nucleic acid sequences easily detected by receptor-modified BioFETs. A common miRNA biomarker LET7a was selected for successful demonstration of this novel biosensor, achieving limit of detection (LoD) down to 10 fM and wide dynamic ranges (10 pM–10 nM) in complex physiological solutions. As the determination of biomarker concentration is implemented by following the electrical signal related to analyte-triggered PUMA in time-domain instead of measuring the threshold shifts of BioFETs, and circumvents direct hybridization of biomarkers at transducer surface, this new strategy also allows for multiple usage (>3 times) of the biosensor platform suggesting exceptional cost-effectiveness for practical use.

Mitigating long-term financial risk for large customers via a hybrid procurement strategy considering power purchase agreements

In facing urgent climate issues, large electricity customers committed to the RE100 initiative, aiming to transition entirely to renewable energy sources (RES). However, they encounter significant challenges in managing the unpredictability of RES generation and the volatility of market prices. This study unveils a groundbreaking hybrid procurement model that integrates Power Purchase Agreements (PPAs) with Battery Energy Storage Systems (BESS) to mitigate these financial risks through a novel method. Employing a sophisticated Mixed Integer Linear Programming (MILP) model alongside an innovative deep learning forecast for long-term PPAs planning, we present a unique solution that significantly boosts financial returns and enhances risk mitigation for large electricity customers. Validated with real-world data across three distinct customer profiles, our model demonstrates a notable increase in expected Net Present Value (NPV) by up to 13.58% compared to traditional strategies and improved earnings stability under adverse market conditions. Our proposed study not only charts a path toward more effective long-term RES procurement strategies but also provides large electricity customers with a strategic framework to skillfully navigate the complexities of the electricity market in alignment with their sustainability commitments.

A bio-economic model for estimating economic values of important production traits in the black soldier fly (Hermetia illucens)

Abstract Black soldier fly (BSF) larvae are exceptionally efficient at converting otherwise unusable waste products, while having a minimal impact on the environment. To optimise the production of BSF larvae, selective breeding could be used. With selective breeding, the best individuals to breed the next generation are selected, for example, with a breeding goal. This study aimed to make the first step towards defining such a breeding goal, by estimating the economic values (EV) of traits that are important for the production of BSF larvae. EV were estimated for larval mass on day 15, feed intake, feed conversion ratio, growth rate, dry mass-, protein- and fat content, development time, eggs per fly, egg hatching rate, and larval mortality. A bio-economic model was used to estimate the profit of a BSF production system that produces 1000 kg of wet larvae at harvesting. The EV of a trait was estimated as the increase in profit when the trait value was increased by 10%, whilst the other traits remained unchanged. EV were expressed per 10% increase, and changes in revenues and costs were specified. The results showed traits with the highest EV were related to composition (dry mass-, and protein-content) and growth (larval mass, development time, growth rate). Traits related to reproduction and flies (development from pupa to fly, number of eggs) had the lowest EV. Due to the lack of information on genetic parameters, the EV are expressed per +10% of the mean value of a trait. In the future, when more information is available, the EV should be expressed per genetic standard deviation. In conclusion, this is the first study that estimated EV for an insect species. The results are a first step towards developing a more sophisticated breeding program for optimizing BSF production.

Economy of Effort or Sophisticated Programming? The Prevalence of Bidirectional Promoter Complexes in the Human Genome.

An abundance of antisense promoters in the vicinity of the transcriptional start site of coding genes suggests that they play an important role in gene regulation. The divergent transcription of housekeeping genes by a common central promoter region allows for coordinated regulation of genes in related pathways and is also linked to higher promoter activity. However, closely positioned transcription start sites can also result in competition between overlapping promoter elements and generate a binary switch element. Furthermore, the direct competition resulting from the presence of an antisense promoter immediately downstream of the transcription start site of the gene produces an element that can exist in only one of two stable transcriptional states: sense or antisense. In this review, we summarize analyses of the prevalence of antisense transcription in higher eukaryotes and viruses, with a focus on the antisense promoters competing with the promoters of coding genes. The structures of bidirectional promoters driving the simultaneous expression of housekeeping genes are compared with examples of human bidirectional elements that have been shown to act as switches. Since many bidirectional elements contain a noncoding RNA as the divergent transcript, we describe examples of functional noncoding antisense transcripts that affect the epigenetic landscape and alter the expression of their host gene. Finally, we discuss opportunities for additional research on competing sense/antisense promoters, uncovering their potential role in programming cell differentiation.

PyF2F: a robust and simplified fluorophore-to-fluorophore distance measurement tool for Protein interactions from Imaging Complexes after Translocation experiments.

Structural knowledge of protein assemblies in their physiological environment is paramount to understand cellular functions at the molecular level. Protein interactions from Imaging Complexes after Translocation (PICT) is a live-cell imaging technique for the structural characterization of macromolecular assemblies in living cells. PICT relies on the measurement of the separation between labelled molecules using fluorescence microscopy and cell engineering. Unfortunately, the required computational tools to extract molecular distances involve a variety of sophisticated software programs that challenge reproducibility and limit their implementation to highly specialized researchers. Here we introduce PyF2F, a Python-based software that provides a workflow for measuring molecular distances from PICT data, with minimal user programming expertise. We used a published dataset to validate PyF2F's performance.

A Discourse Analysis Approach to Establishing Prevalent Concepts in the Maritime Community

The paper analyses prevalent topics found in narratives of the maritime discourse community in the past seven years, from 2016 to 2022. For this purpose, we compiled a corpus of eighty speeches given by the respected Mr. Kitack Lim, secretary-general of the International Maritime Organisation (IMO). The texts are found on the official IMO website and were delivered on the occasion of significant maritime events. The analysis section combines quantitative and qualitative methods. The sophisticated language program Wordstat was used to determine frequencies, dominant phrases, collocations and prevalent topics. In addition, critical discourse analysis was used to interpret linguistic facts through the lenses of an actual maritime setting. The results reveal how dynamic changes in the maritime discourse community are reflected in narratives. We note how traditional issues in seafaring have been complemented with new perspectives impacted by globalisation, digitalisation, automatisation, issues of social and human rights which have been raised, women’s empowerment, questions of environmental protection and many more. We conclude that the established concepts cannot be demarcated: they are increasingly interdependent and have the seafarer in their focus. We also point to the possibility of future research in the domain of comparative analysis on a sample of larger corpora that could make use of research articles or academic narratives about the maritime discourse community.

Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python

Background: Material Requirements Planning (MRP) is critical in Supply Chain Management (SCM), facilitating effective inventory management and meeting production demands in the manufacturing sector. Despite the potential benefits of automating the MRP tasks to meet the demand for expedited and efficient management, the field appears to be lagging behind in harnessing the advancements offered by Artificial Intelligence (AI) and sophisticated programming languages. Consequently, this study aims to address this gap by exploring the applications of Python in simplifying the MRP processes. Methods: This article offers a twofold approach: firstly, it conducts research to uncover the potential applications of the Python code in streamlining the MRP operations, and the practical examples serve as evidence of Python’s efficacy in simplifying the MRP tasks; secondly, this article introduces a conceptual framework that showcases the Python ecosystem, highlighting libraries and structures that enable efficient data manipulation, analysis, and optimization techniques. Results: This study presents a versatile framework that integrates a variety of Python tools, including but not limited to Pandas, Matplotlib, and Plotly, to streamline and actualize an 8-step MRP process. Additionally, it offers preliminary insights into the integration of the Python-based MRP solution (MRP.py) with Enterprise Resource Planning (ERP) systems. Conclusions: While the article focuses on demonstrating the practicality of Python in MRP, future endeavors will entail empirically integrating MRP.py with the ERP systems in small- and medium-sized companies. This integration will establish real-time data synchronization between the Python and ERP systems, leading to accurate MRP calculations and enhanced decision-making processes.

Solvi: A visual constraint modeling tool

Discrete constraint problems surface often in everyday life. Teachers might group students with complex considerations and hospital administrators need to produce staff rosters. Constraint programming (CP) provides techniques to efficiently find solutions. However, there remains a key challenge: these techniques are still largely inaccessible because expressing constraint problems requires sophisticated programming and logic skills. In this work we contribute a language and tool that leverage knowledge of how non-experts conceptualize problems to facilitate the expression of constraint models. Additionally, we report the results of a study surveying the advantages and remaining challenges towards making CP accessible to the wider public.

Fuzzing-based grammar learning from a minimal set of seed inputs

To be effective, a fuzzer needs to generate inputs that are well formed, so that they are not outright rejected by the Software Under Test (SUT) and can thus detect meaningful bugs. Grammar based fuzzers solve this problem, but they obviously require a grammar of the input language accepted by the SUT. Many times such grammar is unknown. Therefore, different black- and white-box algorithms have been proposed for learning them from SUTs. Black-box algorithms rely only on membership queries, but need access to carefully crafted well formed inputs in order to obtain good results. White-box algorithms require access to the source code and generally produce grammars with higher precision and recall, but at the expense of working only for specific programming languages and libraries. We propose a new algorithm and show through extensive experimentation that it can learn grammars from recursive descendent parsers with consistently high levels of both, recall and precision. Notably, this result was obtained starting with a couple of arbitrary seed inputs and includes evaluations with sophisticated languages such as Java Script Object Notation (JSON). Different to other state of the art white-box approaches, our method does not require sophisticated program analysis techniques such as dynamic tainting or symbolic execution. In fact, the experiments confirm that our method performs extremely well with just a (standard) generic Abstract Syntax Tree (AST) of the parsing program as input. The core of our method uses fuzzing techniques combined with fundamental theoretical results on grammar learning. Compared to other white-box approaches, ours is not tied to specific programming languages and tools, and thus can be easily ported. Regarding performance, we have shown that our algorithm works well in practice and that, under reasonable assumptions, its worst-case complexity is polynomial (with low exponents) w.r.t. time and space requirements.

Delineating spatial cell-cell interactions in the solid tumour microenvironment through the lens of highly multiplexed imaging.

The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment.​.

Detection of 3D Object in Point Cloud: Cloud Semantic Segmentation in Lane Marking

Managing a city efficiently and effectively is more important than ever as growing population and economic strain put a strain on infrastructure like transportation and public services like keeping urban green areas clean and maintained. For effective administration, knowledge of the urban setting is essential. Both portable and stationary laser scanners generate 3D point clouds that accurately depict the environment. These data points may be used to infer the state of the roads, buildings, trees, and other important elements involved in this decision-making process. Perhaps they would support "smart" or "smarter" cities in general. Unfortunately, the point clouds do not immediately supply this sort of data. It must be eliminated. This extraction is done either by human specialists or by sophisticated computer programmes that can identify objects. Because the point clouds might represent such large locations, relying on specialists to identify the things may be an unproductive use of time (streets or even whole cities). Automatic or nearly automatic discovery and recognition of essential objects is now possible with the help of object identification software. In this research, In this paper, we describe a unique approach to semantic segmentation of point clouds, based on the usage of contextual point representations to take use of both local and global features within the point cloud. We improve the accuracy of the point's representation by performing a single innovative gated fusion on the point and its neighbours, which incorporates the knowledge from both sets of data and enhances the representation of the point. Following this, we offer a new graph point net module that further develops the improved representation by composing and updating each point's representation inside the local point cloud structure using the graph attention block in real time. Finally, we make advantage of the global structure of the point cloud by using spatial- and channel-wise attention techniques to construct the ensuing semantic label for each point.

Interfacing AlphaGo: Embodied play, object agency, and algorithmic drama.

For decades, playing Go at a professional level has counted among those things that, in Dreyfus's words, 'computers still can't do'. This changed dramatically in early March 2016, at the Four Seasons Hotel in Seoul, South Korea, when AlphaGo, the most sophisticated Go program at the time, beat Lee Sedol, an internationally top-ranked Go professional, by four games to one. A documentary movie has captured and crafted the unfolding drama and, since AlphaGo's momentous win, the drama has been retold in myriad variations. Yet the exhibition match as a technology demonstration-in short, the 'AlphaGo show'-has not received much scrutiny in STS, notwithstanding or precisely because of all the media frenzy, game commentary, and 'AI' expertise in its wake. This article therefore revisits the second game's 'move 37', its surprise delivery by AlphaGo on stage, and the subsequent line of commentary by the attending experts, initiated by the news-receipt token 'ooh'. Drawing upon a reflexive video analysis, the article explicates the Go move's scenic intelligibility-its embodied delivery as part of the technology demonstration-as the contingent result of intricate 'human/machine interfacing'. For mainstream media to report on AlphaGo's 'superhuman intelligence', it both relied upon and effaced such interfacing work. In turn, the article describes and discusses how 'object agency' and 'algorithmic drama' both trade on skillfully embodied play as a pivotal interfacing practice, informing the exhibition match from within its livestream broadcast.

Buffer Times Between Scheduled Events in Resource Assignment Problem: A Conflict-Robust Perspective

Problem definition: In many resource scheduling problems for services with scheduled starting and completion times (e.g., airport gate assignment), a common approach is to maintain appropriate buffer between successive services assigned to a common resource. With a large buffer, the chances of a “crossing” (i.e., a flight arriving later than the succeeding one at the gate) will be significantly reduced. This approach is often preferred over more sophisticated stochastic mixed-integer programming methods that track the arrival of all the flights to infer the number of “conflicts” (i.e., a flight arriving at a time when the assigned gate becomes unavailable). We provide a theoretical explanation, from the perspective of robust optimization for the good performance of the buffering approach in minimizing not only the number of crossings but also the number of conflicts in the operations. Methodology/results: We show that the buffering method inherently minimizes the worst-case number of “conflicts” under both robust and distributionally robust optimization models using down-monotone uncertainty sets. Interestingly, under down-monotone properties, the worst-case number of crossings is identical to the worst-case number of conflicts. Using this equivalence, we demonstrate how feature information from flight and historical delay information can be used to enhance the effectiveness of the buffering method. Managerial implications: The paper provides the first theoretical justification on the use of buffering method to control for the number of conflicts in resource assignment problem. Funding: This work was supported by the 2019 Academic Research Fund Tier 3 of the Ministry of Education-Singapore [Grant MOE-2019-T3-1-010] and the Research Grants Council of Hong Kong SAR, China [Grant PolyU 152240/17E]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0572 .