bluuebunny/crossref_metadata_2025 huggingface dataset:
We Led the Way
Authors: Barrett Simone R.
Published: June 2024

This chapter examines the role of black women intellectuals within the context of black female student involvement in the Civil Rights Movement while highlighting Morgan State College, Baltimore, and the state of Maryland. Black Women Intellectuals such as Lucy Diggs Slowe, Lillie Carroll Jackson, Juanita Jackson Mitchell, Thelma Bando, and Gloria Richardson influenced and provided a strong and indelible foundation for Morgan State’s women students, and their supporters as they fought against racial oppression and inequality. These black women were “Intellectuals” who throughout their careers sought to elevate black women’s experiences by training and mentoring the next generation of black women “Intellectuals” who changed the racial and political landscape of the nation.
“She Did It for the Culture”
Authors: Rorie Lauren T.
Published: June 2024

Savage, Wheeler, and Fuller utilized visual art to make claims about racial uplift, social justice, and black humanity during the height of the Harlem Renaissance in the United States (U.S.) and abroad. They used their artistry to transform the public sphere and gave black society a voice, and were concerned with all things race and dubbed by W.E. B. Du Bois as “race women.” Several narratives on black women public intellectuals tend to focus on political activists, educators, or black churchwomen. Visual artists such as Savage, Waring, and Fuller responded to the racial climate of the early twentieth century through their art as illustrated in this chapter.
Seizing Opportunity
Authors: Roth Tanya L.
Published: June 2024

While it is not common to think of military service as a form of public intellectual discourse, it is a crucial way to understand the significance of black women’s military service. For men, military service has been both obligation and opportunity. The use of the draft in the twentieth century meant that many men experienced military service which was foisted on them. Black men had this same experience, but for them, military service offered an opportunity as well: a way to prove that they, too, were citizens deserving of the same rights and benefits as white servicemen. Yet because women have never been drafted, women’s military service has always been a choice. Women who made that choice became part of the process of reimagining black women’s citizenship. During World War II and the Cold War, military service became a significant avenue through which black women could engage in the intellectual discourse shaping what it meant to be a citizen with equal rights.
Ecosocialism and Degrowth
Authors: Foster John Bellamy , Spéth Arman
Published: June 2024

Originally published in the German journal Widerspruch, Arman Spéth interviews John Bellamy Foster about the growing interest in degrowth thought and the importance of incorporating democratic planning aimed at true equality into all levels of society. And what of the ecosocialist revolution? "Opportunities," Foster says, "are everywhere. Obstacles, largely a product of the present system, are also everywhere.… Nothing can or will remain the same. That is the very definition of a revolutionary situation."
The Actuality of Red Africa
Authors: Prashad Vijay , Erskog Mikaela Nhondo
Published: June 2024

What is "Red Africa"? Through an extended treatment of Kevin Ochieng Okoth's Red Africa: Reclaiming Revolutionary Black Politics (Verso, 2023), Vijay Prashad and Mikaela Nhondo Erskog illuminate the potential for a reinvigorated socialist politics in Africa. In turning away from Afropessimism and Decolonial Studies, the authors catalog the on-the-ground realities at play in pan-African and Marxist social movements today.
The Dream of a Thing
Authors: Gilbert Chris
Published: June 2024

In a vividly drawn account of El Maizal Commune, Chris Gilbert provides readers with a window into the inner workings of a community refounded with an eye toward building a new "alternative communal economy." The task, Gilbert finds, is not only one that is not only revolutionary, but liberating and creative, having the potential to collectively reimagine the social relations of a community.
Avaliação da maturidade tecnológica industrial brasileira no contexto da Indústria 4.0
Authors: Santos Junior Edson Velloso dos , Moura Delmo Alves de
Published: June 2024

O objetivo deste estudo foi avaliar o relacionamento entre as variáveis apuradas pelo questionário SENAI em sua plataforma de mensuração da maturidade industrial. O estudo foi dividido em duas fases. A primeira exploratória, a partir da adoção das técnicas de Análise das Correspondências Múltiplas ACM e, a segunda prospectiva, a partir do uso de modelos multinominais. A variável de interesse foi o grau de maturidade das empresas estimado pela plataforma SENAI. Na Análise das correspondências foi possível constatar o quanto essas empresas estão longe de alcançar essa meta. Nos mapas perceptuais foi possível observar que a variável de interesse separa bem as empresas em dois grupos, mas há uma assimetria considerável entre as demais. Como ferramenta de análise, foi possível estimar os ganhos com o uso incremental das variáveis do modelo na jornada para a Indústria 4.0. Em seguida, como ferramenta de gestão, foi possível estimar o hiato de inovação ao calcular a probabilidade de sucesso para as empresas proativas nessa jornada e para as demais. Por fim, e como um achado oportunista, seu uso como calculadora onde foi possível mensurar o risco associado as decisões tomadas pelos agentes interessados na jornada para I4.0.
Implantação da metodologia Lean Six Sigma em uma empresa de celulose e papel
Authors: Oliveira Gabriel Kum de , Santos Filho Vitor Hugo dos , Santos Pedro Vieira Souza
Published: June 2024

Nos últimos anos, as empresas têm reconhecido a importância de eliminar desperdícios em seus processos. Essa percepção levou ao desenvolvimento de metodologias voltadas para a redução de desperdícios na linha de produção. Duas dessas metodologias se destacam: o Lean Manufacturing e o Six Sigma. A integração dessas abordagens resultou no Lean Six Sigma, uma poderosa ferramenta para otimização de processos industriais. Este estudo tem como objetivo a implementação da metodologia Lean Six Sigma para reduzir a variabilidade na eficiência de uma caldeira de recuperação em uma empresa de fabricação de celulose e papel sediada no Paraná, Brasil. Para alcançar esse objetivo, foi adotada a estrutura DMAIC (Definir, Medir, Analisar, Melhorar e Controlar), complementada pela ferramenta SIPOC (Supplier, Input, Process, Output, Customer). Ao longo do estudo, foram calculados o nível sigma do processo, assim como os índices de Cp e Cpk. Além disso, foram aplicadas técnicas como o Diagrama de Ishikawa, FMEA (Análise de Modo e Efeito de Falha), matriz de priorização, aplicação versus impacto e o checklist de verificação. Os resultados demonstraram uma melhoria significativa no processo estudado: a eficiência da caldeira de recuperação evoluiu de um nível de 2 sigma para 3 sigma. Houve uma importante redução de 76% nos defeitos por milhão de oportunidades, enquanto o percentual de defeitos caiu de 30,9% para 6,68%. Esses resultados evidenciam a eficácia da aplicação da metodologia Lean Six Sigma na empresa.
Very Near Optimal Algorithm for the Traveling Salesman Problem
Authors: Copertari Luis F.
Published: June 2024

The Traveling Salesman Problem (TSP) is a P versus NP key problem that can be generalized to some instances of other problems. Using the commercial state-of-the-art branch-and-bound mixed integer linear programming solver (LINGO) solves the TSP problem in an amount of time that is proportional to a quadratic multiplied by an exponential of base two as a function of the number of cities or destinations. My algorithm solves the problem in a quadratic amount of time as a function of the number of cities or destinations. My algorithm basically solves the problem by sorting the cells in the cost or distance matrix from lower to higher values, while checking for cycles. My algorithm performs on average 43.67% better than LINGO for 100 cities or destinations. In the interconnected world of the present and future, fast and reasonably good solutions are better than reaching (if ever) the global optimal solution.
The affordances of artificial intelligence-based tools for supporting 21st-century skills:
Authors: Celik Ismail , Gedrimiene Egle , Siklander Signe , Muukkonen Hanni
Published: June 2024

Twenty-first-century skills should be integrated into higher education to prepare students for complex working-life challenges. Artificial intelligence (AI)-powered tools have the potential to optimise skill development among higher education students. Therefore, it is important to conceptualise relevant affordances of AI systems for 21st-century skills development in higher education. This study aimed to present an overview of journal articles published in the Web of Science database that specifically addressed the affordances of AI-based tools for 21st-century skills development. Four distinct categories of AI-based tools (intelligent tutoring systems, chatbots, AI-powered dashboards and automated grading systems) were identified as capable of promoting six main 21st-century skills (collaboration, communication, creativity, critical thinking, information and communication technology and problem-solving). The review revealed that the utilisation of AI-based tools might contribute to the simultaneous development of multiple 21st-century skills (e.g., collaboration and critical thinking). The results showed that adaptive feedback from AI plays a significant role as a facilitator in the development of 21st-century skills. Furthermore, the utilisation of diverse functional AI affordances (e.g., prediction and profiling) might contribute to the development of various skills. AI-based technologies appeared to target the 21st-century skills of problem-solving and its subskills the most.
 
Implications for practice or policy:

More functional affordances of AI (e.g., prediction and profiling) should be employed in AI-based tools. This could support higher education students’ 21st-century skills.
AI-based tools (e.g., chatbots and intelligent tutors) interact with end users through their data. AI systems have the potential to promote 21st-century skills by using students’ multimodal data.
AI technologies should be more integrated into the social sciences and humanities in the higher education context to support students’ 21st-century skills.
Preparation and application in the paper protection of carboxymethyl cellulose grafted with β-cyclodextrin
Authors: Li Yuxuan , Chen Xiaoshuang , Li Hua , Li Dandan
Published: June 2024

As a cultural carrier of historical inheritance, paper has high cultural, historical and scientific research value. But with the passage of time and the change of the environment, many paper are aging and damaged. In order to protect the paper, CMC-β-CD composite material for paper protection was synthesized by carboxymethyl cellulose as substrate, epichlorohydrin (ECH) as crosslinking agent, sodium hydroxide (NaOH) as catalyst and solvent, and water as medium, β-cyclodextrin as grafter. Through single factor experiment, the synthetic condition was obtained. The structure of the synthesized materials was characterized by infrared spectroscopy; CMC-β-CD was applied to paper reinforcement. The effects of solution concentration on the tensile strength, tearing degree, folding times, whiteness and gloss of paper were tested, and the suitable conditions for coating paper were found. The results show that the mass concentration of 2 % CMC-β-CD is coated on paper, its mechanical properties are greatly improved, the gloss is slightly increased, and the whiteness is basically unchanged, which is in line with the principle of “repairing the old as the old”.
Gold nanoparticles capped with α-cyclodextrin for dual colorimetric sensing of Fe3+ and Cr3+ in tap water
Authors: Istianah  , Charlena  , Sugiarti Sri , Andreani Agustina Sus
Published: June 2024

A rapid colorimetric assay for the dual detection of Fe3+ and Cr3+ using gold nanoparticles (AuNPs) capped with α-cyclodextrin was developed. The synthesized AuNPs produced a red wine color with a particle size of 8.60 ± 1.55 nm and were stable for 9 months. Detection of Fe3+ and Cr3+ changed the color of the AuNPs solution to bluish purple with a detection time of <1 and 10 min, respectively. Reaction of the AuNPs with Fe3+ and Cr3+ also increased the particle size of the AuNPs to 16.18 ± 2.28 nm and 17.33 ± 1.71 nm, respectively. Characterization of the AuNPs using Fourier-transform infrared spectroscopy and X-ray diffraction showed that the presence of Fe3+ would damage the structure of the AuNPs, whereas Cr3+ would not. This colorimetric sensor is sensitive toward Fe3+ and Cr3+ with limits of detection of 265.92 and 430.80 μg/L, respectively. The dual ion sensing ability of the AuNPs was also assessed by individually detecting Fe3+ and Cr3+ in tap water samples in South Tangerang, Banten, Indonesia.
Customized 3D printed porous titanium scaffolds with nanotubes loading antibacterial drugs for bone tissue engineering
Authors: Fan Shiqi , Tan Zhen , Peng Zhiyu , Li Shilei , Lei Haoyuan , Qin Yuxiang , Fan Hongyuan , Lin Yuanhua , Zhou Changchun
Published: June 2024

Due to the uncertainty of trauma or infection, customized bone substitutes are often required in clinic. Meanwhile, excessive use of antibiotics may lead to drug resistance. Therefore, the design of anti-infection bone tissue engineering scaffold is of very important. In this study, porous titanium alloy bone tissue engineering scaffolds were designed and fabricated by 3D printing. TiO2 nanotubes were further constructed on the scaffolds through electrochemical anodic oxidation, achieving the drug loading and anti-infection functions. The micron-level bionic pores were fabricated by the 3D printing process, and the secondary nanoscale-level nanotubes were achieved through the anodic oxidation process. Thereafter, the micro–nano structured porous bone tissue engineering scaffolds are presented. This structure features that the drug release rate can be regulated by loading the anti-infection drug minocycline and coating them with poly(lactic-co-glycolic acid) (PLGA) in the nanotubes. According to the results, the micro–nano composite porous scaffold showed uniform and controllable micro–nano pores, it may load anti-infection drugs and shown anti-infection ability. In addition, the PLGA coating may delay the drug release and maintain a sustained anti-infection function for the scaffold in a week. This study provides new ideas for designing antibacterial bone tissue engineering scaffold.
Fossil Fuels and European Power Markets
Authors: Morales Lucia , Hanly Jim
Published: June 2024

Fossil fuels were exposed to a major shock during the Global Financial Crisis of 2007/08. This paper examines the influence of Brent Crude Oil, Natural Gas and Coal on three major European power markets (APXUK, NordPool and Phelix) during this period of uncertainty. Both univariate and bivariate frameworks are employed to evaluate, short run, long run and asymmetric power market responses to fossil fuels behaviour. The results point to a stronger relationship between fossil fuel shocks and the APXUK market across all three generational fuels, while results for Phelix are significant only in the case of coal and crude oil. For the Nordic region there is no significant evidence of a causal effect between the power market and the broader energy markets. These findings highlight that the Nordic market appears to be insulated from fossil fuel shocks thanks to its strong focus on renewables, while APXUK and Phelix were significantly exposed to fossil fuels fluctuations.
Spot Prices and Forward Premia on the MISO Exchange
Authors: Jones Kevin
Published: June 2024

This paper explores information contained in the basis for wholesale electricity on the Midcontinent Independent System Operator (MISO) electricity exchange.  Utilizing Fama and French’s (1987) approach, the basis is found to have predictive power on changes in real-time (spot) prices but provides only limited evidence of a time-varying forward premium.  This result contradicts Huisman and Kilic’s (2012) theory that the basis in an electricity market which relies primarily on storable forms of power for electricity production (such as MISO) should contain information on both changes in spot prices and provide evidence of a time-varying forward premium.
Effectiveness of neurodynamic mobilization and MCGILL-type strengthening exercises in patients with piriformis syndrome: study protocol for a randomized controlled trial
Authors: Guevara Hernándeza David Marcelo , Ortiz Pérez Shirley Mireya , Baltodano Ardón Franklin , Tello Núñez Cintya Lisbeth , Otáñez Moreno Joselyn Germania
Published: June 2024

Purpose: Determine the effectiveness of adding a McGill-type strengthening exercise program to neurodynamic mobilization in patients with pyramidal syndrome (PS).

Material and methods: This study has a pre/post experimental design of a randomized single-blind clinical trial that will be carried out in the province of Chimborazo - Ecuador, with a duration of 8 months. The final sample will be formed by 58 patients with a diagnosis of SP who will be randomly assigned to a control group: neurodynamic mobilization and to an experimental group: neurodynamic mobilization plus McGill type strengthening exercises. Both groups will be evaluated before, immediately after and after two months of the intervention phase to determine changes in pain, disability, and range of motion.

Results: The comparison between the evaluations aims to reveal information on the effectiveness of combining neurodynamic mobilization plus McGill-type strengthening exercises, providing valuable information to contribute to the quality assurance of rehabilitation and physical activity behavior throughout rehabilitation patients.

Conclusions: Neurodynamic mobilization plus McGill-type strengthening exercises could reduce pain, improve range of motion and quality of life in patients diagnosed with SP.
V-Y adipose skin flap for the treatment of extensive perianal condylomatosis
Authors: U Parada , C Guarneri , L Ramirez , L Fernandez , J Pereyra , C Barberousse , L Cazaban
Published: June 2024

Introduction: Perianal condylomatosis is a common disease that can involve large areas of skin and anal mucosa, making treatment complex. From a surgical point of view, there are several therapeutic options ranging from primary closure to myocutaneous flap rotation. Aim: Describe the surgical technical details of the VY flap based on the report of 4 clinical cases with extensive perianal condylomatosis as part of the comprehensive treatment of the disease. Methods: Retrospective, descriptive study of patients operated on at Hospital de Clínicas (UDELAR) and CASMU mutual society. Analysis was performed on 4 patients who presented with extensive perianal condylomatosis of approximately 5-6 cm radius of the anal margin in the 5-year time period. Result: In all 4 cases, surgical treatment was performed by rotating the skin flap in VY of the disease; in 1 of the cases, complementary therapy with cryotherapy was also performed. We had no cases of stenosis or flap necrosis. Conclusion: Surgery is a fundamental arm in the treatment of perineal condylomatosis. Reconstruction using a VY adipose skin flap is a possible alternative. The use of several complementary therapeutic modalities is essential in the management of this pathology.
Experience in penetrating neck trauma in two university hospitals in Uruguay
Authors: Valentina Elicegui , Carolina Guarneri , Martina Tristant , Rocío Salada , Lucia Fernandez , Juan Follonier
Published: June 2024

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Efficacy of remote ischemic conditioning in acute ischemic stroke patients receiving thrombolysis and thrombectomy: a meta-analysis
Authors: C Hernandez Maria Kim , S Tee Joyce Angelie , A Juangco Dan Neftalie
Published: June 2024

Introduction: Remote ischemic conditioning is a non-invasive, easy-to-administer procedure providing brief, reversible episodes of ischemia conferring global protection to remote tissue or organs. In this study, we performed a meta-analysis on the available studies on patients receiving thrombolysis and thrombectomy. Methods: PubMed, Cochrane, Embase, and ClinicalTrials.gov were searched for articles from inception to 10 September 2023. Data were analyzed using Cochrane RevMan Web. Odds ratio (OR), mean difference (MD), and 95% confidence intervals (CI) were combined via fixed-effect analysis. Results: Seven randomized controlled trials were included with a total of 927 patients. Remote ischemic conditioning could reduce the recurrence of ischemic stroke at endpoints (OR 0.84, [0.30, 2.29]) and improve the clinical outcome (modified Rankin Scale 0-2) at 90 days (OR 0.96 [0.67, 1.36]) but the results are not significantly different from the control group. Conclusions: Remote ischemic conditioning shows promise in reducing ischemic stroke recurrence and improving patients’ prognosis at 90 days.
Post-traumatic cerebral venous thrombosis: report of a case and review of the literature
Authors: Gutierrez Jaime Luis Felipe , Rubio  Hernández Moisés Misael
Published: June 2024

Background: Cerebral venous thrombosis (CVT) is an uncommon cerebral vascular event with diverse clinical presentations. Traumatic brain injury (TBI) is an infrequent but potentially serious cause of CVT. Post-traumatic Cerebral Venous Sinus Thrombosis (PTCVST) is a rare manifestation with life-threatening implications. We present a case report to shed light on the underdiagnosed entity of PTCVST and its clinical management. Case Presentation: A 32-year-old male suffered head trauma. With no immediate neurological deficits. Subsequently, he developed persistent symptoms, including increasing headaches and dizziness worsened by physical activity or stress. Despite intensified treatment, symptoms persisted, prompting reevaluation. Discussion: PTCVST due to TBI is a rare condition often underdiagnosed. Headache is a common presenting symptom,. Focal neurological deficits are rare but notable. Our case aligns with the limited reports on PTCVST in medical literature. Diagnostic techniques such as venous tomography angiography and magnetic resonance angiography aid in identification, with the latter being sensitive to thrombus location. Anticoagulation, is the standard treatment, although factor Xa inhibitors like apixaban also demonstrate efficacy. Conclusion: PTCVST remains an underdiagnosed entity despite its potential severity. This case underscores the importance of considering PTCVST as a differential diagnosis in TBI.
Population dynamics of tsetse fly in Ido LGA of Oyo state in response to environmental factors and climate change
Authors: Akinseye Olanrewaju , Olaleye Olusola , Sunday Akhanolu Justus , Otunla Moses Temitayo , Edith Adeleye Kuburat , Mustapha Adelabu
Published: June 2024

Tsetse flies (Glossina spp.) are vectors of African trypanosomiasis, a disease of significant concern in sub-Saharan Africa. An understanding of the dynamics of tsetse fly populations is crucial for effective disease management. This study investigates the population dynamics of tsetse flies in the Ido Local Government Area (LGA) of Oyo State, Nigeria, and their response to environmental factors and climate change. Through field observations and data analysis, correlations between temperature, humidity, vegetation cover, and tsetse fly presence were identified. Results reveal positive associations between temperature and tsetse fly presence, highlighting the importance of warmer temperatures for fly activity. Additionally, strong positive correlations with vegetation cover suggest the significance of suitable habitats for fly survival. The role of humidity in shaping fly habitats warrants further investigation. These findings provide insights into the environmental determinants of tsetse fly ecology in the Ido LGA, with implications for disease transmission dynamics. Targeted interventions informed by this study can aid in controlling tsetse-borne diseases and protecting human and animal health in the region.
Treatment of sexual dysfunction, with platelet rich plasma in woman cancer survivors
Authors: Melo MD Isaac , Escobedo MSN Dolores , Aguilar MD Karla , Aguilar MD Wendy , Ballesteros MD Yazzmin , Durazo MD Carlos , Guerrero MD Omar
Published: June 2024

Objetives: Cancer treatments have managed to improve survival but leaving limitations on quality of life with consequences for sexuality. There is currently no adequate treatment for sexual dysfunction secondary to cancer treatment. Cancer centers offer multidisciplinary treatments with poor therapeutic response. It is necessary to find new and better ways to deal with this problem. In the last 20 years Platelet Rich Plasma (PRP) has been used in different medical areas with reparative and functional effects. Methods: Original, Quasi-experimental pilot survey. 21 volunteer patients were treated in four sessions of vulvar and vaginal PRP application. Results: Improvement in the Female Sexual Health Index (FSFI), Vaginal Health Index (VHI), and ability of introitus distention with statistical verification was archived, without improvement in vaginal length. Increase in FSFI from 12.8 to 30.1, VHI from 16 to 20. Conclusion: Platelet Rich Plasma is a magnificent choice in the treatment of Sexual Dysfunction Secondary to Cancer. Its effect on tissue function and repair is clear.
Neonatal Seizures
Authors: Stieren Emily S. , Rottkamp Catherine A. , Brooks-Kayal Amy R.
Published: June 2024

Neonatal seizures are common among patients with acute brain injury or critical illness and can be difficult to diagnose and treat. The most common etiology of neonatal seizures is hypoxic-ischemic encephalopathy, with other common causes including ischemic stroke and intracranial hemorrhage. Neonatal clinicians can use a standardized approach to patients with suspected or confirmed neonatal seizures that entails laboratory testing, neuromonitoring, and brain imaging. The primary goals of management of neonatal seizures are to identify the underlying cause, correct it if possible, and prevent further brain injury. This article reviews recent evidence-based guidelines for the treatment of neonatal seizures and discusses the long-term outcomes of patients with neonatal seizures.
Efficient and Exact Public Transport Routing via a Transfer Connection Database
Authors: Abuaisha Abdallah , Wallace Mark , Harabor Daniel , Shen Bojie
Published: June 2024

We explore the earliest arrival time problem in public transport journey planning. A journey typically consists of multiple scheduled public transport legs. The actual time required to transfer between these legs can substantially influence route planning. Therefore, we properly model transfers by incorporating their exact costs. We then introduce a novel oracle-based routing algorithm that constructs an efficient transfer database, considering the proposed transfer model. The database is leveraged online to quickly reconstruct the optimal journey in response to an earliest arrival time query. Our experimental results show that neglecting exact transfer costs often lead to either infeasible or suboptimal route plans. Furthermore, the findings highlight the efficiency of our algorithm in handling queries, demonstrated by response times within mere microseconds.
Introducing Delays in Multi Agent Path Finding
Authors: Kottinger Justin , Geft Tzvika , Almagor Shaull , Salzman Oren , Lahijanian Morteza
Published: June 2024

We consider a Multi-Agent Path Finding (MAPF) setting where agents have been assigned a plan, but during its execution some agents are delayed. Instead of replanning from scratch when such a delay occurs, we propose delay introduction, whereby we delay some additional agents so that the remainder of the plan can be executed safely. We show that finding the minimum number of additional delays is APX-hard. However, in practice we can find optimal delay-introductions using Conflict-Based Search for very large numbers of agents, and both planning time and the resulting length of the plan are comparable, and sometimes outperform the state-of-the-art heuristics for replanning.
A Deterministic Search Approach for Solving Stochastic Drone Search and Rescue Planning Without Communications
Authors: Mishlyakov Evgeny , Gruntov Mikhail , Shleyfman Alexander , Karpas Erez
Published: June 2024

In disaster relief efforts, delivering aid to areas with no communication poses a significant challenge. Unmanned aerial vehicles (UAVs) can be utilized to deliver aid kits to survivors in hard-to-reach areas; unfortunately, in some areas, lack of communication and infrastructure presents a key problem.
In this paper, we address a stochastic planning problem of planning for a set of UAVs that deliver aid kits to areas that lack communications, where we do not know in advance the locations where aid kits need to be delivered, but rather have probabilistic information about the locations of aid targets.
Our main insight is that, despite the stochastic nature of this problem, we can solve it through deterministic search by monitoring the expected reward for each partial solution. This insight enables the application of deterministic planning techniques, empirically demonstrating a notable improvement in efficiency and response speed. Our approach presents a promising solution to addressing the challenge of delivering aid in regions with limited radio infrastructure, as well as similar planning problems.
Optimised Variants of Polynomial Compilation for Conditional Effects in Classical Planning
Authors: Percassi Francesco , Scala Enrico , Gerevini Alfonso Emilio
Published: June 2024

Conditional effects are a key feature in classical planning, enabling the description of actions whose outcomes are state-dependent. It is well known that the polynomial removal of conditional effects necessarily increases the size of a valid plan by a polynomial factor while preserving exactly the plan size requires an exponential encoding of the problem.

The paper proposes and empirically evaluates optimisations for existing polynomial compilations. These optimisations aim to make the resulting compilations more suitable for planners while limiting the increase in plan size, which is inevitable if we want to keep the compilation polynomial. Specifically, the paper introduces a polynomial compilation technique that expands conditional effects when their number is below a certain threshold and sequentialises them otherwise. Additionally, the paper demonstrates that even straightforward optimisations can have a notable impact.
Unconstraining Multi-Robot Manipulation: Enabling Arbitrary Constraints in ECBS with Bounded Sub-Optimality
Authors: Shaoul Yorai , Veerapaneni Rishi , Likhachev Maxim , Li Jiaoyang
Published: June 2024

Multi-Robot-Arm Motion Planning (M-RAMP) is a challenging problem featuring complex single-agent planning and multi-agent coordination. Recent advancements in extending the popular Conflict-Based Search (CBS) algorithm have made large strides in solving Multi-Agent Path Finding (MAPF) problems. However, fundamental challenges remain in applying CBS to M-RAMP. A core challenge is the existing reliance of the CBS framework on conservative "complete" constraints. These constraints ensure solution guarantees but often result in slow pruning of the search space -- causing repeated expensive single-agent planning calls.
Therefore, even though it is possible to leverage domain knowledge and design incomplete M-RAMP-specific CBS constraints to more efficiently prune the search, using these constraints would render the algorithm itself incomplete. This forces practitioners to choose between efficiency and completeness.

In light of these challenges, we propose a novel algorithm, Generalized ECBS, aimed at removing the burden of choice between completeness and efficiency in MAPF algorithms. Our approach enables the use of arbitrary constraints in conflict-based algorithms while preserving completeness and bounding sub-optimality. This enables practitioners to capitalize on the benefits of arbitrary constraints and opens a new space for constraint design in MAPF that has not been explored. We provide a theoretical analysis of our algorithms, propose new "incomplete" constraints, and demonstrate their effectiveness through experiments in M-RAMP.
Tunable Suboptimal Heuristic Search
Authors: Wissow Stephen , Yu Fanhao , Ruml Wheeler
Published: June 2024

Finding optimal solutions to state-space search problems often takes too long, even when using A* with a heuristic function. Instead, practitioners often use a tunable approach, such as weighted A*, that allows them to adjust a trade-off between search time and solution cost until the search is sufficiently fast for the intended application. In this paper, we study algorithms for this problem setting, which we call `tunable suboptimal search'. We introduce a simple baseline, called Speed*, that uses distance-to-go information to speed up search. Experimental results on standard search benchmarks suggest that 1) bounded-suboptimal searches suffer overhead due to enforcing a suboptimality bound, 2) beam searches can perform well, but fare poorly in domains with dead-ends, and 3) Speed* provides robust overall performance.
Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning
Authors: Chen Dillon Z. , Thiébaux Sylvie
Published: June 2024

Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception. In this paper, we boost the performance of heuristic search for numeric planning with various powerful techniques orthogonal to improving heuristic informedness: numeric novelty heuristics, the Manhattan distance heuristic, and exploring the use of multi-queue search and portfolios for combining heuristics.
A Data Efficient Framework for Learning Local Heuristics
Authors: Veerapaneni Rishi , Park Jonathan , Saleem Muhammad Suhail , Likhachev Maxim
Published: June 2024

With the advent of machine learning, there have been several recent attempts to learn effective and generalizable heuristics. Local Heuristic A* (LoHA*) is one recent method that instead of learning the entire heuristic estimate, learns a "local" residual heuristic that estimates the cost to escape a region. LoHA*, like other supervised learning methods, collects a dataset of target values by querying an oracle on many planning problems (in this case, local planning problems). This data collection process can become slow as the size of the local region increases or if the domain requires expensive collision checks. Our main insight is that when an A* search solves a start-goal planning problem it inherently ends up solving multiple local planning problems. We exploit this observation to propose an efficient data collection framework that does
Finding a Small, Diverse Subset of the Pareto Solution Set in Bi-Objective Search (Extended Abstract)
Authors: Araneda Pablo , Hernández Ulloa Carlos , Rivera Nicolás , Baier Jorge A.
Published: June 2024

Bi-objective search requires computing a Pareto solution set which contains a set of paths. In real-world applications, Pareto solution sets may contain several tens or even hundreds of solutions. For a human user trying to commit to just one of these paths, navigating through a large solution set may become overwhelming, which motivates the problem of computing small, good-quality subsets of Pareto frontiers. This document presents two main contributions. First, we provide a simple formalization of good-quality subsets of a Pareto solution set. For this, we use measure of richness which has been employed in the study of Population Dynamics. Second, we propose Chebyshev BOA*, a variant of BOA*
to compute good-quality subset approximations.
Extreme Value Monte Carlo Tree Search (Extended Abstract)
Authors: Asai Masataro , Wissow Stephen
Published: June 2024

Monte-Carlo Tree Search (MCTS) combined with Multi-Armed Bandit (MAB) has had limited success in domain-independent classical planning until recently. Previous work (Wissow and Asai 2023) showed that UCB1, designed for bounded rewards, does not perform well when applied to the cost-to-go estimates of classical planning, which are unbounded in R, then improved the performance by using a Gaussian reward MAB instead. We further sharpen our understanding of ideal bandits for planning tasks by resolving three issues: First, Gaussian MABs under-specify the support of cost-to-go estimates as [−∞, ∞]. Second, Full-Bellman backup that backpropagates max/min of samples lacks theoretical justifications. Third, removing dead-ends lacks justifications in Monte-Carlo backup. We use Extreme Value Theory Type 2 to resolve them at once, propose two bandits (UCB1-Uniform/Power), and apply them to MCTS for classical planning. We formally prove their regret bounds and empirically demonstrate their performance in classical planning.
Finiding All Optimal Solutions in Multi-Agent Path Finding (Extended Abstract)
Authors: Bardugo Shahar , Atzmon Dor
Published: June 2024

The Multi-Agent Path Finding problem (MAPF) aims to find conflict-free paths for a group of agents leading each agent to its respective goal. In this paper, we study the requirement of finding all optimal solutions in MAPF. We discuss the representation of all optimal solutions, propose three algorithms for finding them, and compare the algorithms experimentally.
Traffic Flow Optimisation for Lifelong Multi-Agent Path Finding (Extended Abstract)
Authors: Chen Zhe , Harabor Daniel , Li Jiaoyang , Stuckey Peter J.
Published: June 2024

Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics that asks us to compute collision-free paths for a team of agents, all moving across a shared map. Existing scalable approaches struggle as the number of agents grows, as they typically plan free-flow optimal paths, which creates congestion. To tackle this issue, we propose a new approach for MAPF where agents are guided to their destination by following congestion-avoiding paths. Empirically, we report large improvements in overall throughput for lifelong MAPF while coordinating more than ten thousand agents.
Deployable Yet Effective Traffic Signal Optimisation via Automated Planning (Extended Abstract)
Authors: El Kouaiti Anas , Percassi Francesco , Saetti Alessandro , McCluskey Thomas Leo , Vallati Mauro
Published: June 2024

The use of planning techniques in traffic signal optimisation has proven effective in managing unexpected traffic conditions as well as typical traffic patterns. However, significant challenges concerning the deployability of generated signal plans remain, as planning systems need to consider constraints and features of the actual real-world infrastructure on which they will be implemented.

To address this challenge, we introduce a range of PDDL+ models embodying technological requirements as well as insights from domain experts. The proposed models have been extensively tested on historical data using a range of well-known search strategies and heuristics, as well as alternative encodings. Results demonstrate their competitiveness with the state of the art.
Evaluating Distributional Predictions of Search Time: Put Up or Shut Up Games (Extended Abstract)
Authors: Mariasin Sean , Coles Andrew , Karpas Erez , Ruml Wheeler , Shimony Solomon Eyal , Shperberg Shahaf
Published: June 2024

Metareasoning can be a helpful technique for controlling search in situations where computation time is an important resource, such as
real-time planning and search, algorithm portfolios, and concurrent planning and execution. Metareasoning often involves an estimate of the remaining search time of a running algorithm, and several ways to compute such estimates have been presented in the literature. In this paper, we argue that many applications actually require a full estimated probability distribution over the remaining time, rather than just a point estimate of expected search time. We study several methods for estimating such distributions, including some novel adaptations of existing schemes.
To properly evaluate the estimates, we introduce `put-up or shut-up games', which probe the distributional estimates without requiring infeasible computation.
Our experimental evaluation reveals that estimates that are more accurate in expected value do not necessarily deliver better distributions, yielding worse scores in the game.
Large-Scale Multi-Robot Coverage Path Planning via Local Search (Extended Abstract)
Authors: Tang Jingtao , Ma Hang
Published: June 2024

We study graph-based Multi-Robot Coverage Path Planning (MCPP) that aims to compute paths for multiple robots to cover all vertices of a given 2D grid terrain graph G. Existing graph-based MCPP algorithms rely on computing a tree cover on G and then employ the Spanning Tree Coverage (STC) paradigm to generate coverage paths on the decomposed graph D of G. In this paper, we take a different approach by exploring how to systematically search for good coverage paths directly on D. We introduce a new algorithmic framework, called LS-MCPP, which leverages a local search to operate directly on D. We propose ESTC, that extends STC to achieve complete coverage for MCPP on any decomposed graphs, even those resulting from incomplete terrain graphs. Furthermore, we demonstrate how to integrate ESTC with three novel types of neighborhood operators into our framework to effectively guide its search process. Remarkably, LS-MCPP scales efficiently to handle MCPP instances with 32 robots on terrain graphs with 11,892 vertices with just minutes of runtime, thereby showcasing its significant benefits for large-scale real-world coverage tasks.
Planning and Exection in Multi-Agent Path Finding: Models and Algorithms (Extended Abstract)
Authors: Zhang Yue , Chen Zhe , Harabor Daniel , Le Bodic Pierre , Stuckey Peter J.
Published: June 2024

In applications of Multi-Agent Path Finding (MAPF), it is often the sum of planning and execution times that needs to be minimised (i.e., the Goal Achievement Time). Yet current methods seldom optimise for this objective. Optimal algorithms reduce execution time, but may require exponential planning time. Non-optimal algorithms reduce planning time, but at the expense of increased path length. To address these limitations we introduce PIE (Planning and Improving while Executing), a new framework for concurrent planning and execution in MAPF. We first show how PIE for one-shot MAPF improves practical performance compared to sequential planning and execution.We then adapt PIE to Lifelong MAPF, a popular application setting where agents are continuously assigned new goals and where additional decisions are required to ensure feasibility. We examine a variety of different approaches to overcome these challenges and we conduct comparative experiments vs. recently proposed alternatives. Results show that PIE substantially outperforms existing methods for One-shot and Lifelong MAPF.
Arbitrarily Scalable Environment Generators via Neural Cellular Automata (Extended Abstract)
Authors: Zhang Yulun , Fontaine Matthew C. , Bhatt Varun , Nikolaidis Stefanos , Li Jiaoyang
Published: June 2024

We study the problem of generating arbitrarily large environments to improve the throughput of multi-robot systems. Prior work proposes Quality Diversity (QD) algorithms as an effective method for optimizing the environments of automated warehouses. However, these approaches optimize only relatively small environments, falling short when it comes to replicating real-world warehouse sizes. The challenge arises from the exponential increase in the search space as the environment size increases. Additionally, the previous methods have only been tested with up to 350 robots in simulations, while practical warehouses could host thousands of robots. In this paper, instead of optimizing environments, we propose to optimize Neural Cellular Automata (NCA) environment generators via QD algorithms. We train a collection of NCA generators with QD algorithms in small environments and then generate arbitrarily large environments from the generators at test time. We show that NCA environment generators maintain consistent, regularized patterns regardless of environment size, significantly enhancing the scalability of multi-robot systems in two different domains with up to 2,350 robots. Additionally, we demonstrate that our method scales a single-agent reinforcement learning policy to arbitrarily large environments with similar patterns. We include the source code at https://github.com/lunjohnzhang/warehouse_env_gen_nca_public.
Block Domain Knowledge-Driven Learning of Chain Graphs Structure
Authors: Yang Shujing , Cao Fuyuan
Published: June 2024

As the interdependence between arbitrary objects in the real world grows, it becomes gradually important to use chain graphs containing directed and undirected edges to learn the structure among objects. However, independence among some variables corresponds to multiple structures and the direction of edges among variables cannot be uniquely determined. This limitation restricts existing chain graphs structure learning algorithms to only learning their Markov equivalence class. To alleviate this limitation, we de ne the block domain knowledge and propose a block domain knowledge-driven learning chain graphs structure algorithm (KDLCG). The KDLCG algorithm learns the adjacencies and spouses of all variables, which are utilized to directly construct the skeleton and orient the edges of the complexes, thereby learning the Markov equivalence class of the chain graphs. Subsequently, the KDLCG algorithm then updates some edges with Meek rules, guided by block domain knowledge. Finally, the KDLCG algorithm directs some edges by estimating causal effects between two variables, driven by block domain knowledge. Meanwhile, we conduct theoretical analysis to prove the correctness of our algorithm and compare it with the LCD algorithm and MBLWF algorithm on synthetic and real-world datasets. The experimental results validate the effectiveness of our algorithm.
Expressing and Exploiting Subgoal Structure in Classical Planning Using Sketches
Authors: Drexler Dominik , Seipp Jendrik , Geffner Hector
Published: June 2024

Width-based planning methods deal with conjunctive goals by decomposing problems into subproblems of low width. Algorithms like SIW thus fail when the goal is not easily serializable in this way or when some of the subproblems have a high width. In this work, we address these limitations by using a simple but powerful language for expressing finer problem decompositions introduced recently by Bonet and Geffner, called policy sketches. A policy sketch R over a set of Boolean and numerical features is a set of sketch rules C → E that express how the values of these features are supposed to change. Like general policies, policy sketches are domain general, but unlike policies, the changes captured by sketch rules do not need to be achieved in a single step. We show that many planning domains that cannot be solved by SIW are provably solvable in low polynomial time with the SIWR algorithm, the version of SIW that employs user-provided policy sketches. Policy sketches are thus shown to be a powerful language for expressing domain-specific knowledge in a simple and compact way and a convenient alternative to languages such as HTNs or temporal logics. Furthermore, they make it easy to express general problem decompositions and prove key properties of them like their width and complexity.