bluuebunny/crossref_metadata_2025_split huggingface dataset:
An exosomes-related lncRNA prognostic model correlates with the immune microenvironment and therapy response in lung adenocarcinoma
Authors: Chu Daifang , Chen Liulin , Li Wangping , Zhang Haitao 
Published: May 2024

AbstractRecent research highlights the significance of exosomes and long noncoding RNAs (lncRNAs) in cancer progression and drug resistance, but their role in lung adenocarcinoma (LUAD) is not fully understood. We analyzed 121 exosome-related (ER) mRNAs from the ExoBCD database, along with mRNA and lncRNA expression profiles of TCGA-LUAD using “DESeq2”, “survival,” “ConsensusClusterPlus,” “GSVA,” “estimate,” “glmnet,” “clusterProfiler,” “rms,” and “pRRophetic” R packages. This comprehensive approach included univariate cox regression, unsupervised consensus clustering, GSEA, functional enrichment analysis, and prognostic model construction. Our study identified 134 differentially expressed ER-lncRNAs, with 19 linked to LUAD prognosis. These ER-lncRNAs delineated two patient subtypes, one with poorer outcomes. Additionally, 286 differentially expressed genes were related to these ER-lncRNAs, 261 of which also correlated with LUAD prognosis. We constructed an ER-lncRNA-related prognostic model and calculated an ER-lncRNA-related risk score (ERS), revealing that a higher ERS correlates with poor overall survival in both the Meta cohort and two validation cohorts. The ERS potentially serves as an independent prognostic factor, and the prognostic model demonstrates superior predictive power. Notably, significant differences in the immune landscape were observed between the high- and low-ERS groups. Drug sensitivity analysis indicated varying responses to common chemotherapy drugs based on ERS stratification, with the high-ERS group showing greater sensitivity, except to rapamycin and erlotinib. Experimental validation confirmed that thymidine kinase 1 enhances lung cancer invasion, metastasis, and cell cycle progression. Our study pioneers an ER-lncRNA-related prognostic model for LUAD, proposing that ERS-based risk stratification could inform personalized treatment strategies to improve patient outcomes.
Thyroxine administration at the end of the rutting season does not affect testosterone secretion or horn growth in the Iberian ibex (Capra pyrenaica)
Authors: Rodríguez-Luis Adriana , Toledano-Díaz Adolfo , Velázquez Rosario , López-Sebastián Antonio , Mogedas-Moreno María , Santiago-Moreno Julián 
Published: May 2024

AbstractThyroid hormones play a role in the control of seasonal reproduction in wild and domestic ruminants such as red deer and sheep. Horn growth shows an inverse relationship with reproductive activity, but the effect of thyroid function on the regulation of the seasonal pattern of horn growth is unknown. This work examines the seasonal pattern of plasma thyroxine concentration, and its effect on testosterone secretion and seasonal changes in horn growth, in male Iberian ibexes (Capra pyrenaica). Plasma testosterone, plasma thyroxine, and horn growth were measured over a year in four males kept under natural photoperiod conditions. In another four, thyroxine was administered coinciding with the period of physiological reduction in plasma testosterone (January–February). In the first four, seasonal changes in the plasma concentration of thyroxine were detected with the highest concentrations recorded in October, November, and December, coinciding with the greatest testosterone secretion. In the second four, thyroxine treatment affected neither plasma testosterone concentration nor horn growth. The plasma concentrations of testosterone and thyroxine appear to be closely related in the ibex, but the seasonal pattern of horn growth does not seem to be modulated by seasonal changes in thyroxine.
Farming with a mission: the case of nonprofit farms
Authors: Worosz Michelle R. , DuPuis E. Melanie 
Published: May 2024

AbstractOrganizations interested in food alterity, security, and justice are often governed as 501(c)(3) nonprofits. As such, they are required to fulfill missions beyond profit maximization. This study focuses on the role of nonprofits in the agrifood system. Looking at nonprofit farms as both farms and as nonprofits, we seek to understand whether nonprofit organizations, as an alternative mode of governance, creates the possibility of an alternative economic practice, set apart from the conventional food system. We constructed a national database of nonprofit farms and the characteristics of the counties in which they are located. Our findings indicate that nonprofit farms tend not to be in the places with the most need of the services provided, which we argue is due to the structure of nonprofit governance, namely that nonprofits, while not profit-maximizing, are dependent on external resources, particularly donations. While they do operate as an alternative economic practice, their nonprofit mode of governance renders them unable to repair the failures of the current food system. Nevertheless, these farms do contribute to their local communities, both in terms of meeting their mission and as members of a broader local food system infrastructure. This is true whether or not these farms specifically state that food system transformation is part of their mission.
Optimal multivariate mixture: a genetic algorithm approach
Authors: Sgarro Giacinto Angelo , Grilli Luca , Santoro Domenico 
Published: May 2024

AbstractThe Optimal Multivariate Mixture Problem (OMMP) consists of finding an optimal mixture which, starting from a set of elements (items) described by a set of variables (features), is as close as possible to an ideal solution. This problem has numerous applications spanning various fields, including food science, agriculture, chemistry, materials science, medicine, and pharmaceuticals. The OMMP is a class of optimization problems that can be addressed using traditional Operations Research (OR) approaches. However, it can also be effectively tackled using meta-heuristic techniques within Artificial Intelligence (AI). This paper aims to present an Artificial Intelligence perspective. It proposes a Genetic Algorithm (GA) for Optimal Multivariate Mixture (GA-OMM), a novel improved version of a GA whose modified genetic operators prove to improve the exploration efficiency. Here, the algorithm is described in its general framework, and a test case 8-items 5-features is conducted to evaluate efficiency by exploring various combinations of hyperparameters. Test cases are also set up for the previous version, as well as a linear programming (LP) approach. The data experiments indicate that the proposed GA is efficient, converges towards the global optimum, consistently outperforms its predecessor, and delivers highly competitive results. In particular, GA-OMM shows an average fitness of GA-OMMP/LP and standard deviation with an order of magnitude ranging between $$10^{-8}$$

10

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8

to $$10^{-4}$$

10

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4

. Moreover, it consistently outperforms its predecessor, which exhibits similar values around $$10^{-3}$$

10

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3
Mandated Parent Education: Applications, Impacts, and Future Directions
Authors: Shapiro Cheri J. , Hill-Chapman Crystal , Williams Stephanie 
Published: May 2024

AbstractMandated participation in parent education programs is a common practice across the United States for families who are undergoing divorce or who are involved in the child welfare or juvenile justice systems. Mandates to participate in parenting programs create substantial challenges for families, service providers, and service systems. Furthermore, the type and quality of the parenting services accessed vary widely, and their impacts need to be better understood. To address this need, an overview of the current state of the empirical literature on the impacts and outcomes of mandated parenting interventions for divorce and in child welfare and juvenile justice settings is provided, and suggestions to the field are offered to refine research related to mandated parenting programs. Given the challenges that mandated parenting programs pose, an alternative approach that views parenting through a public health lens is highlighted to build on the growing body of research on the impacts of population-wide applications of parenting support programs, and as a possible way to decrease the number of parents who are required to attend parenting programs. Opportunities to advance universal parenting support within a range of community settings, including primary care, early childhood education, and community mental health systems are offered. Gaps in knowledge regarding mechanisms of action of universal supports and impacts on the number of parents mandated to treatment are highlighted, and future directions for research in this area are suggested.
Acceptance and use of ChatGPT in the academic community
Authors: Strzelecki Artur , Cicha Karina , Rizun Mariia , Rutecka Paulina 
Published: May 2024

AbstractSince OpenAI released ChatGPT, the discussion on its usage in education has been conducted by students and teachers of every education level. Also, many studies have been performed on the tool’s possibilities and the threats related to its usage, such as incomplete or inaccurate information obtained or even plagiarism. Many universities worldwide have introduced specific regulations on ChatGPT usage in academic work. Furthermore, research on using ChatGPT by students and their attitudes towards it has appeared. However, a research gap exists in higher education teachers’ acceptance of AI solutions. The goal of this research was to explore the level of acceptance of the usage of ChatGPT by academics in Poland, as well as point out factors influencing their intention to use this tool. The study motivation was related to an ongoing academic discussion mainly focusing on the disadvantages of AI solutions used in scientific work and the willingness to fill the gap by showing teachers’ attitudes toward AI. The data was collected online by inviting academic teachers from Polish public universities to complete the prepared survey. The survey was prepared using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model extended with Personal Innovativeness. It revealed the acceptance level of ChatGPT usage in Polish universities by teachers and researchers and the antecedents influencing willingness to use this technology in academic work. The paper contributes to the theory of AI usage by structuring the studies regarding ChatGPT application for teaching and research, and provides practical recommendations on ChatGPT adoption in the work of academics.
The role of self-confidence in teamwork: experimental evidence
Authors: Bruhin Adrian , Petros Fidel , Santos-Pinto Luís 
Published: May 2024

AbstractTeamwork has become increasingly important in modern organizations and the labor market. Yet, little is known about the role of self-confidence in teamwork. In this paper, we present evidence from a laboratory experiment using a team effort task. Effort and ability are complements and there are synergies between teammates’ efforts. We exogenously manipulate subjects’ self-confidence about their ability using easy and hard general knowledge quizzes. We find that overconfidence leads to more effort, less free riding, and higher team revenue. This finding is primarily due to a direct effect of overconfidence on own effort provision, while there is no evidence that subjects strategically respond to the teammate’s overconfidence.
Combinatorial optimization methods for yarn dyeing planning
Authors: Duran Ege , Ozturk Cemalettin , Ornek M. Arslan 
Published: May 2024

AbstractManaging yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack, bin packing, and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However, several practical factors such as physical attributes of customer orders, dyeing machine eligibility conditions like flotte, color type, chemical recipe, and volume capacity of dye make this problem significantly unique. Furthermore, alongside its economic aspects, minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective, which adds another complexity for planners. Hence, in this paper, we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research, including a mixed-integer programming model for a novel industrial problem, providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly, and addressing the inefficiencies of manual scheduling in textile companies; reducing a time-consuming planning task from hours to minutes.
Supervisors’ emotion regulation in research supervision: navigating dilemmas in an accountability-based context
Authors: Han Jiying , Jin Lei , Yin Hongbiao 
Published: May 2024

AbstractGiven the complexity and high demands of research supervision and the intricate emotional experiences of supervisors, there is a need to explore how they regulate their emotions, particularly across various disciplinary backgrounds. The current study explored the emotion regulation strategies employed by research supervisors during the process of supervising graduate students. Based on data collected through semi-structured interviews, observations, and documentation from six research supervisors in different institutions in China, seven emotion regulation strategies employed by research supervisors were identified and further categorized into two groups, that is, antecedent-focused (prevention, intervention, reinterpretation, reconcentration, and detachment) and response-focused (suppression and expression) emotion regulation strategies. The findings shed light on the dilemmas faced by supervisors and the paradox aroused from the context-dependent and non-standardized nature of research supervision within an accountability-based managerial context. The implications for supervisors’ emotion regulation in authentic supervisory situations are discussed, and insights for universities’ policy-making are offered.
A methylene blue assay for the quantification of fucoidan – A specific application for seaweed extracts
Authors: Wilkinson Gina Lauren , Mapholi Zwonaka , Goosen Neill Jurgens 
Published: May 2024

AbstractFucoidan is a fucose-rich sulfated polysaccharide found mainly in brown seaweeds. Fucoidan has recently gained much attention due to its bioactive and medicinal properties, hence its high potential to be utilized in the pharmaceutical industry. Therefore, an accurate, reliable, and simple method for the specific determination of fucoidan would be a useful tool when looking to recover this valuable polysaccharide. This research focused on developing an assay utilizing the thiazine dye, methylene blue, for the quantification of fucoidan, with a specific application for fucoidan found in algal extracts. Fucoidan could be specifically determined at pH 1, to maximum concentrations of 4 g L-1 in solution without the interference of alginate and other seaweed extract contaminants. At pH 7 and 11, quantification of fucoidan led to interference from alginate and gallic acid. A conversion factor of 1.8 allowed the quantification of fucoidan from Ecklonia maxima extracts using a commercial standard from Fucus vesiculosis. An Ecklonia maxima extract was found to contain 5.47 ± 0.061 g L-1 of fucoidan. Measuring the fucoidan and L-fucose concentration in crude seaweed extract through the methylene blue assay and modified Dische and Shettles J Biol Chem 175:595–603, (1948) method, respectively, confirmed the accuracy and specificity of the method. The limit of quantification of the methylene blue assay at pH 1 was 0.62 g L-1 of fucoidan.
Linear Resources in Isabelle/HOL
Authors: Smola Filip , Fleuriot Jacques D. 
Published: May 2024

AbstractWe present a formal framework for process composition based on actions that are specified by their input and output resources. The correctness of these compositions is verified by translating them into deductions in intuitionistic linear logic. As part of the verification we derive simple conditions on the compositions which ensure well-formedness of the corresponding deduction when satisfied. We mechanise the whole framework, including a deep embedding of ILL, in the proof assistant Isabelle/HOL. Beyond the increased confidence in our proofs, this allows us to automatically generate executable code for our verified definitions. We demonstrate our approach by formalising part of the simulation game Factorio and modelling a manufacturing process in it. Our framework guarantees that this model is free of bottlenecks.
Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process
Authors: Surindra Mochamad Denny , Alfarisy Gusti Ahmad Fanshuri , Caesarendra Wahyu , Petra Mohamad Iskandar , Prasetyo Totok , Tjahjowidodo Tegoeh , Królczyk Grzegorz M. , Glowacz Adam , Gupta Munish Kumar 
Published: May 2024

AbstractAlthough the aspects that affect the performance and the deterioration of abrasive belt grinding are known, wear prediction of abrasive belts in the robotic arm grinding process is still challenging. Massive wear of coarse grains on the belt surface has a serious impact on the integrity of the tool and it reduces the surface quality of the finished products. Conventional wear status monitoring strategies that use special tools result in the cessation of the manufacturing production process which sometimes takes a long time and is highly dependent on human capabilities. The erratic wear behavior of abrasive belts demands machining processes in the manufacturing industry to be equipped with intelligent decision-making methods. In this study, to maintain a uniform tool movement, an abrasive belt grinding is installed at the end-effector of a robotic arm to grind the surface of a mild steel workpiece. Simultaneously, accelerometers and force sensors are integrated into the system to record its vibration and forces in real-time. The vibration signal responses from the workpiece and the tool reflect the wear level of the grinding belt to monitor the tool’s condition. Intelligent monitoring of abrasive belt grinding conditions using several machine learning algorithms that include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Decision Tree (DT) are investigated. The machine learning models with the optimized hyperparameters that produce the highest average test accuracy were found using the DT, Random Forest (RF), and XGBoost. Meanwhile, the lowest latency was obtained by DT and RF. A decision-tree-based classifier could be a promising model to tackle the problem of abrasive belt grinding prediction. The application of various algorithms will be a major focus of our research team in future research activities, investigating how we apply the selected methods in real-world industrial environments.
Deep Visual-guided and Deep Reinforcement Learning Algorithm Based for Multip-Peg-in-Hole Assembly Task of Power Distribution Live-line Operation Robot
Authors: Zheng Li , Ai Jiajun , Wang Yahao , Tang Xuming , Wu Shaolei , Cheng Sheng , Guo Rui , Dong Erbao 
Published: May 2024

AbstractThe inspection and maintenance of power distribution network are crucial for efficiently delivering electricity to consumers. Due to the high voltage of power distribution network lines, manual live-line operations are difficult, risky, and inefficient. This paper researches a Power Distribution Network Live-line Operation Robot (PDLOR) with autonomous tool assembly capabilities to replace humans in various high-risk electrical maintenance tasks. To address the challenges of tool assembly in dynamic and unstructured work environments for PDLOR, we propose a framework consisting of deep visual-guided coarse localization and prior knowledge and fuzzy logic driven deep deterministic policy gradient (PKFD-DPG) high-precision assembly algorithm. First, we propose a multiscale identification and localization network based on YOLOv5, which enables the peg-hole close quickly and reduces ineffective exploration. Second, we design a main-auxiliary combined reward system, where the main-line reward uses the hindsight experience replay mechanism, and the auxiliary reward is based on fuzzy logic inference mechanism, addressing ineffective exploration and sparse reward in the learning process. In addition, we validate the effectiveness and advantages of the proposed algorithm through simulations and physical experiments, and also compare its performance with other assembly algorithms. The experimental results show that, for single-tool assembly tasks, the success rate of PKFD-DPG is 15.2% higher than the DDPG with functionized reward functions and 51.7% higher than the PD force control method; for multip-tools assembly tasks, the success rate of PKFD-DPG method is 17% and 53.4% higher than the other methods.
A Minimalistic 3D Self-Organized UAV Flocking Approach for Desert Exploration
Authors: Amorim Thulio , Nascimento Tiago , Chaudhary Akash , Ferrante Eliseo , Saska Martin 
Published: May 2024

AbstractIn this work, we propose a minimalistic swarm flocking approach for multirotor unmanned aerial vehicles (UAVs). Our approach allows the swarm to achieve cohesively and aligned flocking (collective motion), in a random direction, without externally provided directional information exchange (alignment control). The method relies on minimalistic sensory requirements as it uses only the relative range and bearing of swarm agents in local proximity obtained through onboard sensors on the UAV. Thus, our method is able to stabilize and control the flock of a general shape above a steep terrain without any explicit communication between swarm members. To implement proximal control in a three-dimensional manner, the Lennard-Jones potential function is used to maintain cohesiveness and avoid collisions between robots. The performance of the proposed approach was tested in real-world conditions by experiments with a team of nine UAVs. Experiments also present the usage of our approach on UAVs that are independent of external positioning systems such as the Global Navigation Satellite System (GNSS). Relying only on a relative visual localization through the ultraviolet direction and ranging (UVDAR) system, previously proposed by our group, the experiments verify that our system can be applied in GNSS-denied environments. The degree achieved of alignment and cohesiveness was evaluated using the metrics of order and steady-state value.