bluuebunny/crossref_metadata_2025 huggingface dataset:
A survey on wind power forecasting with machine learning approaches
Authors: Yang Yang , Lou Hao , Wu Jinran , Zhang Shaotong , Gao Shangce
Published: May 2024

Wind power forecasting techniques have been well developed over the last half-century. There has been a large number of research literature as well as review analyses. Over the past 5 decades, considerable advancements have been achieved in wind power forecasting. A large body of research literature has been produced, including review articles that have addressed various aspects of the subject. However, these reviews have predominantly utilized horizontal comparisons and have not conducted a comprehensive analysis of the research that has been undertaken. This survey aims to provide a systematic and analytical review of the technical progress made in wind power forecasting. To accomplish this goal, we conducted a knowledge map analysis of the wind power forecasting literature published in the Web of Science database over the last 2 decades. We examined the collaboration network and development context, analyzed publication volume, citation frequency, journal of publication, author, and institutional influence, and studied co-occurring and bursting keywords to reveal changing research hotspots. These hotspots aim to indicate the progress and challenges of current forecasting technologies, which is of great significance for promoting the development of forecasting technology. Based on our findings, we analyzed commonly used traditional machine learning and advanced deep learning methods in this field, such as  classical neural networks, and recent Transformers, and discussed emerging technologies like large language models. We also provide quantitative analysis of the advantages, disadvantages, forecasting accuracy, and computational costs of these methods. Finally, some open research questions and trends related to this topic were discussed, which can help improve the understanding of various power forecasting methods. This survey paper provides valuable insights for wind power engineers.
Analysis and design framework for the development of indoor scene understanding assistive solutions for the person with visual impairment/blindness
Authors: Valipoor Moeen , de Antonio Angélica , Cabrera Julián
Published: May 2024

This paper discusses the challenges of the current state of computer vision-based indoor scene understanding assistive solutions for the person with visual impairment (P-VI)/blindness. It focuses on two main issues: the lack of user-centered approach in the development process and the lack of guidelines for the selection of appropriate technologies. First, it discusses the needs of users of an assistive solution through state-of-the-art analysis based on a previous systematic review of literature and commercial products and on semi-structured user interviews. Then it proposes an analysis and design framework to address these needs. Our paper presents a set of structured use cases that help to visualize and categorize the diverse real-world challenges faced by the P-VI/blindness in indoor settings, including scene description, object finding, color detection, obstacle avoidance and text reading across different contexts. Next, it details the functional and non-functional requirements to be fulfilled by indoor scene understanding assistive solutions and provides a reference architecture that helps to map the needs into solutions, identifying the components that are necessary to cover the different use cases and respond to the requirements. To further guide the development of the architecture components, the paper offers insights into various available technologies like depth cameras, object detection, segmentation algorithms and optical character recognition (OCR), to enable an informed selection of the most suitable technologies for the development of specific assistive solutions, based on aspects like effectiveness, price and computational cost. In conclusion, by systematically analyzing user needs and providing guidelines for technology selection, this research contributes to the development of more personalized and practical assistive solutions tailored to the unique challenges faced by the P-VI/blindness.
Context-dependent benefits of forest soil addition on Aleppo pine seedling performance under drought and grass competition
Authors: Herol Lior , Avidar Mor , Yirmiahu Shahar , Zach Yair Yehoshua , Klein Tamir , Shemesh Hagai , Livne-Luzon Stav
Published: May 2024

Seedling establishment under natural conditions is limited by numerous interacting factors. Here, we tested the combined effects of drought, herbaceous competition, and ectomycorrhizal inoculation on the performance of Aleppo pine seedlings grown in a net-house. The roots of all pine seedlings were strongly dominated by Geopora, a fungal genus known to colonize seedlings in dry habitats. Ectomycorrhizal fungi (EMF) inoculum significantly increased seedling height, biomass, and the number of side branches. However, under either competition or drought, the positive effect of EMF on seedling biomass and height was greatly reduced, while the effect on shoot branching was maintained. Further, under a combination of drought and competition, EMF had no influence on either plant growth or shape. The discrepancy in pine performance across treatments highlights the complexity of benefits provided to seedlings by EMF under ecologically relevant settings.
An “out of the box” approach for prevention of ketoacidosis in youth with poorly controlled type 1 diabetes: combined use of insulin pump and long-acting insulin
Authors: Barash Galia , Lerman Liat , Ben-Ari Tal , Abiri Shirly , Landau Zohar , Ben Ami Michal , Brener Avivit , Lebenthal Yael , Pinhas-Hamiel Orit , Mazor-Aronovitch Kineret , Haim Alon , Yeshayahu Yonatan , De Vries Liat , Rachmiel Marianna
Published: May 2024

Background

Poorly controlled adolescents living with type 1 diabetes (T1D) and pump failure of insulin delivery leading to diabetic ketoacidosis (DKA) are still challenging in the western world.

Aim

To investigate the effect of a combination modality of long-acting insulin for basal coverage and a pump for boluses, on the incidence of DKA and glycemic parameters in pediatric and young adults with poorly controlled T1D.

Methods

This multicenter, observational retrospective study included 55 patients (age range 3–25 years, 52.7% males) who were treated with the combination modality for a median of 18 months [(IQR)12,47], as part of their clinical care. Data were retrieved at initiation of the combined modality, after 6 months, and at last visit.

Results

Cohort’s median age at combination modality initiation was 14.5 years [IQR12.4,17.3], and its median HbA1c level was 9.2% [IQR 8.2,10.2]. The main reasons for combination modality initiation were: (a) concern about sustained hyperglycemia on current management in 41.8%, (b) previous DKA episodes in 30.8%, and (c) refusal to wear a pump continuously in 14.6%. The percent of patients experiencing DKA who used the modality till end decreased from 25.4 to 8.8%. The frequency of DKA events per patient month decreased after 6 months from 0.073 (min 0, max 0.5) to 0.020 (min 0, max 0.5), p = 0.01, and at end to 0.016 (min 0, max 0.25), p = 0.007.

Conclusions

The combination modality of once-daily long-acting insulin and pump for boluses is safe, feasible, and effective in preventing DKA among poorly controlled young people living with T1D, unable or un-willing to use advanced closed pumps.
Three-year follow-up of the grip concept: an open, prospective, observational registry study on biomechanically calculated abdominal wall repair for complex incisional hernias
Authors: Nessel R. , Löffler T. , Rinn J. , Kallinowski F.
Published: May 2024

Purpose

We studied the effectiveness of biomechanically calculated abdominal wall reconstructions for incisional hernias of varying complexity in an open, prospective observational registry trial.

Methods

From July 1st, 2017 to December 31st, 2020, four hospitals affiliated with the University of Heidelberg recruited 198 patients with complex incisional hernias. Hernias were repaired using biomechanically calculated reconstructions and materials classified on their gripping force towards cyclic load. This approach determines the required strength preoperatively based on the hernia size, using the Critical Resistance to Impacts related to Pressure. The surgeon is supported in reliably determining the Gained Resistance, which is based on the mesh-defect-area-ratio, as well as other mesh and suture factors, and the tissue stability. Tissue stability is defined as a maximum distension of 1.5 cm upon a Valsalva maneuver. In complex cases, a CT scan of the abdomen can be used to assess unstable tissue areas both at rest and during Valsalva’s maneuver.

Results

Larger and stronger gripping meshes were required for more complex cases to achieve a durable repair, especially for larger hernia sizes. To achieve durable repairs, the number of fixation points increased while the mesh-defect area ratio decreased. Performing these repairs required more operating room time. The complication rate remained low. Less than 1% of recurrences and low pain levels were observed after 3 years.

Conclusions

Biomechanical stability, defined as the resistance to cyclic load, is crucial in preventing postoperative complications, including recurrences and chronic pain.
The distance decay effect and spatial reach of spillovers
Authors: Elhorst J. Paul , Tziolas Ioanna , Tan Chang , Milionis Petros
Published: May 2024

This paper quantifies and graphically illustrates the distance decay effect and spatial reach of spillover effects derived from a spatial Durbin (SD) model with parameterized spatial weight matrices. Building on attributes of the concept of spatial autocorrelation developed by Arthur Getis, we adopt a distance-based negative exponential spatial weight matrix and parameterize it by a decay parameter that is different for each spatial lag in this model, both of the regressand and of all regressors. The quantification and illustration are applied to the spatially augmented neoclassical growth framework, which we estimate using data for 266 NUTS-2 regions in the EU over the period 2000–2018. We find distance decay parameters ranging from 0.233 to 2.224 and spatial reaches ranging from 700 to more than 1500 km for the different growth determinants in this model. These wide ranges highlight the restrictiveness of the conventional SD model based on one common spatial weight matrix for all spatial lags.
The use of indocyanine green for lateral lymph node dissection in rectal cancer—preliminary data from an emerging procedure: a systematic review of the literature
Authors: Kehagias D. , Lampropoulos C. , Bellou A. , Kehagias I.
Published: May 2024

Introduction

Lateral lymph node dissection (LLND) for rectal cancer is still not a widely established technique owing to the existing controversy between Eastern and Western countries and the lack of well-designed studies. The risk of complications and the paucity of long-term oncological results are significant drawbacks for further applying this technique. The use of indocyanine green (ICG) near-infrared (NIR) fluorescence for LLND appears as a promising technique for enhancing postoperative and oncological outcomes. This review aims to evaluate the emerging role of ICG during LLND and present the benefits of its application.

Materials and methods

Systematic electronic research was conducted in PubMed and Google Scholar using a combination of medical subject headings (MeSH). Studies presenting the use of ICG during LLND, especially in terms of harvested lymph nodes, were included and reviewed. Studies comparing LLND with ICG (LLND + ICG) or without ICG (LLND-alone) were further analyzed for the number of lymph nodes and postoperative outcomes.

Results

In total, 13 studies were found eligible and analyzed for different parameters. LLND + ICG is associated with significantly increased number of harvested lateral lymph nodes (p < 0.05), minor blood loss, decreased operative time, and probably decreased urinary retention postoperatively compared with LLND-alone.

Conclusions

The use of ICG fluorescence during LLND is a safe and feasible technique for balancing postoperative outcomes and the number of harvested lymph nodes. Well-designed studies with long-term results are required to elucidate the oncological benefits and establish this promising technique.
Cost-effectiveness analysis of procalcitonin and lung ultrasonography guided antibiotic prescriptions in primary care
Authors: Cisco Giulio , Meier Armando N. , Senn Nicolas , Mueller Yolanda , Kronenberg Andreas , Locatelli Isabella , Knüsli José , Lhopitallier Loïc , Boillat-Blanco Noemie , Marti Joachim
Published: May 2024

Antimicrobial resistance comes with high morbidity and mortality burden, and ultimately high impact on healthcare and social costs. Efficient strategies are needed to limit antibiotic overuse. This paper investigates the cost-effectiveness of testing patients with lower respiratory tract infection with procalcitonin, either at the point-of-care only or combined with lung ultrasonography. These diagnostic tools help detect the presence of bacterial pneumonia, guiding prescription decisions. The clinical responses of these strategies were studied in the primary care setting. Evidence is needed on their cost-effectiveness. We used data from a cluster-randomized bi-centric clinical trial conducted in Switzerland and estimated patient-level costs using data on resource use to which we applied Swiss tariffs. Combining the incremental costs of the two strategies and the reduction in the 28-days antibiotic prescription rate (APR) compared to usual care, we calculated Incremental Cost-Effectiveness Ratios (ICER). We also used the Cost-Effectiveness Acceptability Curve as an analytical decision-making tool. The robustness of the findings is ensured by Probabilistic Sensitivity Analysis and scenario analysis. In the base case scenario, the ICER compared to usual care is $2.3 per percentage point (pp) reduction in APR for the procalcitonin group, and $4.4 for procalcitonin-ultrasound combined. Furthermore, we found that for a willingness to pay per patient of more than $2 per pp reduction in the APR, procalcitonin is the strategy with the highest probability to be cost-effective. Our findings suggest that testing patients with respiratory symptoms with procalcitonin to guide antibiotic prescription in the primary care setting represents good value for money.
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

Recent 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

Thyroid 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

Organizations 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

The 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

-
8

to $$10^{-4}$$

10

-
4

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

10

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

Mandated 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

Since 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

Teamwork 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

Managing 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

Given 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

Fucoidan 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

We 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

Although 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

The 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

In 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.
OffRoadSynth Open Dataset for Semantic Segmentation using Synthetic-Data-Based Weight Initialization for Autonomous UGV in Off-Road Environments
Authors: Małek Konrad , Dybała Jacek , Kordecki Andrzej , Hondra Piotr , Kijania Katarzyna
Published: May 2024

This article concerns the issue of image semantic segmentation for the machine vision system of an autonomous Unmanned Ground Vehicle (UGV) moving in an off-road environment. Determining the meaning (semantics) of the areas visible in the recorded image provides a complete understanding of the scene surrounding the autonomous vehicle. It is crucial for the correct determination of a passable route. Nowadays, semantic segmentation is generally solved using convolutional neural networks (CNN), which can take an image as input and output the segmented image. However, proper training of the neural network requires the use of large amounts of data, which becomes problematic in the situation of low availability of large, dedicated image data sets that consider various off-road situations - driving on various types of roads, surrounded by diverse vegetation and in various weather and light conditions. This study introduces a synthetic image dataset called “OffRoadSynth” to address the training data scarcity for off-road scenarios. It has been shown that pre-training the neural network on this synthetic dataset improves image segmentation accuracy compared to other methods, such as random network weight initialization or using larger, generic datasets. Results suggest that using a smaller but domain-dedicated set of synthetic images to initialize network weights for training on the target real-world dataset may be an effective approach to improving semantic segmentation results of images, including those from off-road environments.
Spatial Heterogeneity and Subjective Wellbeing: Exploring the Role of Social Capital in Metropolitan Areas Using Multilevel Modelling
Authors: Lignier Phil , Jarvis Diane , Grainger Daniel , Chaiechi Taha
Published: May 2024

The role of social capital, the social networks that influence human wellbeing has been explored by empirical research in the US and Europe, however no study so far has undertaken a systematic investigation of the impact of the various dimensions of social capital in metropolitan areas. Addressing this gap in knowledge can have practical and policy-oriented implications by contributing to more informed decision-making processes in metro areas, better targeted interventions and ultimately an improved quality of life for residents. This study adopts a multi-level modelling approach to investigate life satisfaction and social capital heterogeneity within metropolitan areas in Australia. Our dataset was collected by the Household Income and Labour Dynamics in Australia survey and includes almost 4,000 individual respondents. Our results show that social trust, social engagement and connection, and a psychological sense of community measured at an individual level have a strong positive influence on individual life satisfaction. Conversely negative individual perceptions about neighbourhood criminality and shabbiness are associated with a lower level of life satisfaction. The application of a model using random slope coefficients for social capital variables suggests that most of the spatial heterogeneity between census districts is explained by between-individual (compositional) variations, rather than contextual differences. Only social connection and engagement appeared to have a distinctive contextual influence. These findings confirm the importance of social inclusion in enhancing wellbeing for everyone and may inform social policy on how to promote social networks in urban areas by all levels of government.
Publication Trends and Hot Spots of ChatGPT’s Application in the Medicine
Authors: Li Zhi-qiang , Wang Xue-feng , Liu Jian-ping
Published: May 2024

This study aimed to analyze the current landscape of ChatGPT application in the medical field, assessing the current collaboration patterns and research topic hotspots to understand the impact and trends. By conducting a search in the Web of Science, we collected literature related to the applications of ChatGPT in medicine, covering the period from January 1, 2000 up to January 16, 2024. Bibliometric analyses were performed using CiteSpace (V6.2., Drexel University, PA, USA) and Microsoft Excel (Microsoft Corp.,WA, USA) to map the collaboration among countries/regions, the distribution of institutions and authors, and clustering of keywords. A total of 574 eligible articles were included, with 97.74% published in 2023. These articles span various disciplines, particularly in Health Care Sciences Services, with extensive international collaboration involving 73 countries. In terms of countries/regions studied, USA, India, and China led in the number of publications. USA ot only published nearly half of the total number of papers but also exhibits a highest collaborative capability. Regarding the co-occurrence of institutions and scholars, the National University of Singapore and Harvard University held significant influence in the cooperation network, with the top three authors in terms of publications being Wiwanitkit V (10 articles), Seth I (9 articles), Klang E (7 articles), and Kleebayoon A (7 articles). Through keyword clustering, the study identified 9 research theme clusters, among which “digital health”was not only the largest in scale but also had the most citations. The study highlights ChatGPT’s cross-disciplinary nature and collaborative research in medicine, showcasing its growth potential, particularly in digital health and clinical decision support. Future exploration should examine the socio-economic and cultural impacts of this trend, along with ChatGPT’s specific technical uses in medical practice.
AI-Driven Synthetization Pipeline of Realistic 3D-CT Data for Industrial Defect Segmentation
Authors: Tenscher-Philipp Robin , Schanz Tim , Harlacher Fabian , Fautz Benedikt , Simon Martin
Published: May 2024

Training data is crucial for any artificial intelligence model. Previous research has shown that various methods can be used to enhance and improve AI training data. Taking a step beyond previous research, this paper presents a method that uses AI techniques to generate CT training data, especially realistic, artificial, industrial 3D voxel data. This includes that material as well as realistic internal defects, like pores, are artificially generated. To automate the processes, the creation of the data is implemented in a 3D Data Generation, called SPARC (Synthetized Process Artificial Realistic CT data). The SPARC is built as a pipeline consisting of several steps where different types of AI fulfill different tasks in the process of generating synthetic data. One AI generates geometrically realistic internal defects. Another AI is used to generate a realistic 3D voxel representation. This involves a conversion from STL to voxel data and generating the gray values accordingly. By combining the different AI methods, the SPARC pipeline can generate realistic 3D voxel data with internal defects, addressing the lack of data for various applications. The data generated by SPARC achieved a structural similarity of 98% compared to the real data. Realistic 3D voxel training data can thus be generated. For future AI applications, annotations of various features can be created to be used in both supervised and unsupervised training.
How Much Physiotherapy, Chiropractic, and Osteopathy Care Do Compensated Australian Workers with Low Back Pain Receive? A Retrospective Cohort Study
Authors: Di Donato Michael , Gray Shannon , Sheehan Luke R. , Buchbinder Rachelle , Iles Ross , Collie Alex
Published: May 2024

Objectives

To identify the prevalence and frequency of physiotherapy, chiropractic, and/or osteopathy care in Australians with workers’ compensation claims for low back pain (LBP).

Methods

We included workers with accepted workers’ compensation claims longer than 2 weeks from the Australian states of Victoria, Queensland, South Australia, and Western Australia. Workers were grouped by whether they attended physiotherapy, chiropractic, and/or osteopathy in the first 2 years of their claim. Descriptive statistics and logistic regression were used to describe differences between groups. Descriptive statistics and negative binomial regression were used to describe differences in the number of attendances in each group.

Results

Most workers had at least one physical therapy attendance during the period of their claim (n = 23,619, 82.0%). Worker state, socioeconomic status, and remoteness were the largest contributing factors to likelihood of physical therapy attendance. Most workers only attended physiotherapy (n = 21,035, 89.1%, median of 13 times). Far fewer only attended chiropractic (n = 528, 2.2%, median of 8 times) or only osteopathy (n = 296, 1.3%, median of 10 times), while 1,750 (7.5%) attended for care with more than one type of physical therapy (median of 31 times).

Conclusion

Most Australian workers with workers’ compensation time loss claims for LBP attend physiotherapy at least once during their claims. State of claim is the strongest predictor of which physical therapy profession they attend, possibly due to regional availability. Workers who see a physiotherapist have significantly more attendances. Future research should explore the relationship between these patterns of care and claimant outcomes, including work disability duration.