Search Results (3,957)

To increase productivity, more sophisticated cluster tools are developed. To achieve this, one of the ways is to increase the number of spaces in a process module (PM) and the number of fingers on a robot arm as well, leading to a cluster tool with multi-space PMs and a multi-finger-arm robot. This paper discusses the scheduling problem of cluster tools with four-space PMs and a four-finger-arm robot, a typical tool with multi-space PMs and a multi-finger-arm robot adopted in modern fabs. With two arms in such a tool, one is used as a clean one, while the other is used as a dirty one. In this way, wafer quality can be improved. However, scheduling such cluster tools to ensure the residency time constraints is very challenging, and there is no research report on this issue. This article conducts an in-depth analysis of the steady-state scheduling for this type of cluster tools to explore the effect of different scheduling strategies. Based on the properties, four robot task sequences are presented as scheduling strategies. With them, four linear programming models are developed to optimize the cycle time of the system and find feasible schedules. The performance of these strategies is dependent on the activity parameters. Experiments are carried out to test the effect of different parameters on the performance of different strategies. It shows that, given a group of parameters, one can apply all the strategies and choose the best result obtained by one of the strategies. Full article

Background: The Second Victim Phenomenon (SVP) significantly impacts the well-being of healthcare professionals and patient safety. While the SVP has been explored in various healthcare settings, there are limited data on its prevalence and associated factors among nurses in Austria. This study investigates the prevalence, symptomatology, and preferred support measures for SVP among Austrian nurses. Methods: A nationwide, cross-sectional, anonymous online survey was conducted September to December 2023 using the SeViD questionnaire (Second Victims in German-speaking Countries), which includes the Big Five Inventory-10 (BFI-10). Statistical analyses included binary logistic regression and multiple linear regression using the bias-corrected and accelerated (BCa) bootstrapping method based on 5000 bootstrap samples. Results: A total of 928 participants responded to the questionnaire with a response rate of 15.47%. The participants were on average 42.42 years old and were mainly women (79.63%). Among the respondents, 81.58% (744/912) identified as Second Victims (SVs). The primary cause of becoming an SV was aggressive behavior from patients or relatives. Females reported a higher symptom load than males, and higher agreeableness was linked to increased symptom severity. Notably, 92.47% of SVs who sought help preferred support from colleagues, and the most pronounced desire among participants was to process the event for better understanding. Conclusions: The prevalence of SVP among Austrian nurses is alarmingly high, with aggressive behavior identified as a significant trigger. The findings emphasize the necessity for tailored support strategies, including peer support and systematic organizational interventions to mitigate the impact of SVP on nurses and to improve overall patient care. Further research should focus on developing and implementing effective prevention and intervention programs for healthcare professionals in similar settings. Full article

The GPHF-Minilab™ is a portable toolkit for performing qualitative methods such as thin-layer chromatography (TLC) on common pharmaceuticals. It is particularly useful in resource-limited locations where it is more challenging to monitor for substandard and falsified (SF) medicines. However, the GPHF-Minilab™ TLC methods are only semi-quantitative at best and thus have issues monitoring product quality effectively. We have improved on the GPHF-Minilab™ TLC method for metronidazole, a common antibiotic, by making it fully quantitative. Sample solutions were spotted on TLC plates alongside three metronidazole standards at different concentrations. After development, plates were imaged in a lightbox with two different smartphone cameras. Images were processed through the open-source program ImageJ and resulting pixel data from the standard spots were used to create a calibration curve, enabling quantitation of the sample. The USP Metronidazole Tablet high-performance liquid chromatography (HPLC) assay was used as the reference method. We validated this TLC method using 250 and 500 mg metronidazole tablets from different manufacturers and assessed linearity, range, accuracy, precision, intermediate precision, specificity, and robustness. These improvements should enhance the GPHF-Minilab™ TLC methods for metronidazole product screening. Additionally, the procedure is extensible to other analytes, although further validation would be required for each Minilab method. Full article

An unsustainable rate of resource production and consumption is evident in urban environments. The absence of innovative approaches in conjunction with the exponential urbanization and expansion of the global population will inevitably result in substantial environmental consequences. There are two emerging alternatives: circular economy (CE) and urban metabolism (UM). The integration of these principles into novel methodology casts doubt on the linear model of contemporary economic and urban systems, which includes extraction, production, utilization, and disposal. In the development of a distinctive urban framework known as circular urban metabolism, the current study has illustrated the application of these principles. We design this study to motivate urban planners and decision-makers to investigate, develop, and supervise ecologically sustainable cities. Scholars from a variety of academic disciplines, intrigued by the intricacies of urban planning, design, and administration, can foster interdisciplinary collaboration in the circular urban metabolism (CUM) region. To address the research question, we implemented a bibliometric analysis, which involved the examination of 627 pertinent research papers, utilizing the R (R 3.6.0+) statistical programming language. The results emphasize the fundamental characteristics and significance of CUM in the management of refuse. In addition, the findings underscore the importance of creating a novel framework that incorporates the principles of urban political ecology, CUM, sustainability, and the novel dimension of waste metabolism. It is the goal of this framework to emphasize the significance of recycling in the informal sector as a waste management strategy in low- and medium-income countries (LMICs). Full article

Waste management involves the systematic collection, transportation, processing, and treatment of waste materials generated by human activities. It entails a variety of strategies and technologies to diminish environmental impacts, protect public health, and conserve resources. Consequently, providing an effective and comprehensive optimization approach plays a critical role in minimizing waste generation, maximizing recycling and reuse, and safely disposing of waste. This work develops a novel Possibilistic Multi-Objective Mixed-Integer Linear Programming (PMOMILP) model in order to formulate the problem and design a circular–sustainable–reliable waste management network, under uncertainty. The possibility of recycling and recovery are considered across incineration and disposal processes to address the main circular-economy principles. The objectives are to address sustainable development throughout minimizing the total cost, minimizing the environmental impact, and maximizing the reliability of the Waste Management System (WMS). The Lp-metric technique is then implemented into the model to tackle the multi-objectiveness. Several benchmarks are adapted from the literature in order to validate the efficacy of the proposed methodology, and are treated by CPLEX solver/GAMS software in less than 174.70 s, on average. Moreover, a set of sensitivity analyses is performed to appraise different scenarios and explore utilitarian managerial implications and decision aids. It is demonstrated that the configured WMS network is highly sensitive to the specific time period wherein the WMS does not fail. Full article

In typical business situations, sellers usually offer discount schemes to buyers to increase overall profitability. This study aims to design a supply chain network under uncertainty of demand by integrating an all-unit quantity discount policy. The objective is to maximize the profit of the entire supply chain. The proposed model is formulated as a mixed integer nonlinear programming model, which is subsequently linearized into a mixed integer linear programming model and hence able to obtain a global solution. Numerical examples in the manufacturing supply chain where customer demand follows normal distributions are used to assess the effect of quantity discount policies. Key findings demonstrate that the integration of quantity discount policies significantly reduces total supply chain costs and improves inventory management under demand uncertainty, and decision makers need to decide a balance level between service levels and profits. Full article

Introduction: Breast cancer has become one of the most serious and widespread public health concerns globally, affecting an increasing number of women—and, in rare cases, men—across the world. It is the most common cancer among women across all countries. In this study, we aimed to evaluate the influence of demographic factors, medical and reproductive history, diagnostic techniques, and hormone receptor status on the development and progression of breast cancer. Materials and Methods: A total of 687 female patients from Romania underwent standard breast examination techniques, including clinical breast examination, mammography, ultrasonography, and, ultimately, breast biopsy. Statistical analysis was performed using the R programming language and RStudio software. The study included a comparative analysis and a prediction analysis for malignancy and tumor size (cumulative histological dimension) through logistic and linear regression models. Results: The comparative analysis identified several variables associated with malignancy: older age (p < 0.001), non-vulnerability (p = 0.04), no daily physical activity (p = 0.002), no re-biopsy (p < 0.001), immunohistochemistry use (p < 0.001), use of larger gauge needles (p < 0.001), ultrasound-guided biopsy (p < 0.001), and vacuum biopsy (p < 0.001). The hormone receptor statuses—estrogen receptor (ER), progesterone receptor (PR), and androgen receptor (AR)—showed statistically significant differences in distribution across breast cancer B classifications. Logistic regression analysis identified ER, PR, and age as significant predictors of malignancy. Linear regression analysis revealed histopathological results, living environment, geographical region, vulnerability, prior breast examination, and the number of histological fragments as significant predictors of cumulative histological dimension. Conclusions: Our predictive models demonstrate the impact of demographic factors, medical history, diagnostic techniques, and hormone receptor status on breast cancer development and progression, accounting for a significant portion of the variance in malignancy and cumulative histological dimension. Full article

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML), built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future uncertain benefits that are linked by causation. This multi-period causal process, incorporating certainty and uncertainty, replaces the single-period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modeling and real-world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. With the growing interest in natural experiments in statistics and causal machine learning (CML) across many fields, such as healthcare, economics, and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision-making models that focus only on rationality, bounded to various degrees. To be most effective, machine learning must mirror human reasoning as closely as possible, an alignment established through CEML, which represents an evolution to truly “human AI”. This paper maps out how the non-linear optimization required for the CEML structural response functions can be accomplished through Sequential Least Squares Programming (SLSQP) and applied to data sets through the S-Learner CML meta-algorithm. Upon this foundation, the next phase of research is to apply CEML to appropriate data sets in various areas of practice where causality and accurate modeling of human behavior are vital, such as precision healthcare, economic policy, and marketing. Full article

Unmanned aerial vehicles (UAVs) are utilized for wireless communication support of Internet of Intelligent Vehicles (IoVs). Intelligent vehicles (IVs) need vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) wireless communication for real-time perception knowledge exchange and dynamic environment modeling for safe autonomous driving and mission accomplishment. UAVs autonomously navigate through dense urban areas to provide aerial line-of-sight (LoS) communication links for IoVs. Real-time UAV trajectory design is required for minimum energy consumption and maximum channel performance. However, this is multidisciplinary research including (1) dynamic-aware kinematic (kinodynamic) planning by considering UAVs’ motion and nonholonomic constraints; (2) channel modeling and channel performance improvement in future wireless networks (i.e., beyond 5G and 6G) that are limited to beamforming to LoS links with the aid of reconfigurable intelligent surfaces (RISs); and (3) real-time obstacle-free crash avoidance 3D trajectory optimization in dense urban areas by modeling obstacles and LoS paths in convex programming. Modeling and solving this multilateral problem in real-time are computationally prohibitive unless extensive computational and overhead processing costs are imposed. To pave the path for computationally efficient yet feasible real-time trajectory optimization, this paper presents UAV kinodynamic modeling. Then, it proposes a convex trajectory optimization problem with the developed linear kinodynamic models. The optimality and smoothness of the trajectory optimization problem are improved by utilizing model predictive control and quadratic state feedback control. Simulation results are provided to validate the methodology. Full article

This article presents methods of planning unmanned aerial vehicle (UAV) missions in which individual platforms work together during the reconnaissance of objects located within a terrain. The planning problem concerns determining the flight routes of a swarm, where each UAV has the ability to recognize an object using a specific type of sensor. The experiments described in this article were carried out for drone formation; one drone works as a swarm information hub and exchanges information with the ground control station (GCS). Numerical models for mission planning are presented, which take into account the important constraints, simplifying the description of the mission without too much risk of losing the platforms. Several types of objective functions were used to optimize swarm flight paths. The mission models are presented in the form of mixed integer linear programming problems (MILPs). The experiments were carried out on a terrain model built on the basis of graph and network theory. The method of building a network on which the route plan of a drone swarm is determined is precisely presented. Particular attention was paid to the description of ways to minimize the size of the network on which the swarm mission is planned. The presented methods for building a terrain model allow for solving the optimization problem using integer programming tasks. Full article

Low-carbon ammonia production is crucial for sustainable development. Brazil, a top ethanol producer, can boost competitiveness and cut emissions by integrating ammonia and ethanol production. However, optimal location and production strategy identification is challenging due to existing possibilities and uncertainties. For that, a new MILP superstructure with robust optimisation was developed and used to analyse low-carbon ammonia production integration in the ethanol industry in São Paulo state by ethanol and biomethane routes, in two different scenarios. As for the results, in scenario 1, biomethane and ethanol investments were USD 3.846 M and USD 314 M. In scenario 2, the investments were USD 316 M for biomethane and USD 259 M for ethanol. Despite the higher investment, the biomethane route results in lower hydrogen production cost (USD 1880/tonne) due to raw material prices; however, ethanol displays a higher hydrogen potential, consuming just 8% of total production against 54% of vinasse availability, which is used for biodigestion and biomethane production. In conclusion, the results suggest that the northern region of São Paulo has greater potential for ammonia facilities due to resource availability. These findings can inform and support more comprehensive studies and public incentive policies. Full article

This paper introduces an open-source tool capable of performing the Composite System Reliability evaluation developed in the high-level, dynamic Julia™ programming language. Employing Monte Carlo Simulation and parallel computing, the tool evaluates probabilistic adequacy indices for combined generation and transmission systems, focusing on both individual delivery points and the broader system. Proficiency in Optimal Power Flow problem formulations is demonstrated through two distinct methods: DC and linearized AC, enabling comprehensive resource and transmission adequacy analysis with high-performance solvers. Addressing replicability and the insufficiency of available software, the tool supports diverse analyses on a unified platform. The paper discusses the tool’s design and validation, particularly focusing on the two optimal power flow problem formulations. These insights significantly contribute to understanding transmission system performance and have implications for power system planning. Full article

In this study, a novel non-cooperative two-player game for minimizing static (Player 1) and dynamic (Player 2) compliances is introduced, implemented, and demonstrated using a multi-scale topology optimization framework for triply periodic minimal surface (TPMS)-based lattice structures. Player 1 determines the optimal macro-layout by minimizing the static compliance based on a micro-layout provided by Player 2. Conversely, player 2 identifies the optimal micro-layout (grading of the TPMS-based lattice structure) by minimizing the dynamic compliance given a macro-layout from Player 1. The multi-scale topology optimization formulations are derived using two density variables in each finite element. The first variable is the standard density, which dictates whether the finite element is void or contains the graded lattice structure and is governed by the rational approximation of material properties (RAMP) model. The second density variable represents the local relative density of the TPMS-based lattice structure, determining the effective orthotropic elastic properties of the finite element. The multi-scale game is implemented for three-dimensional problems, and solved using a Gauss–Seidel algorithm with sequential linear programming. It is numerically demonstrated for several benchmarks that the proposed multi-scale game generates equilibrium designs with strong performance for both static and harmonic load cases, effectively avoiding resonance at harmonic load frequencies. Validation is achieved through modal analyses of finite element models of the optimal designs. Full article

It is very important to clarify the optimization method of the rock-like material ratio for accurately characterizing mechanical properties similar to the original rock. In order to explore the optimal ratio of rock-like materials in gneissic granite, the water–paste ratio, iron powder content and coarse sand content were selected as the influencing factors of the ratio. An orthogonal test design and sensitivity analysis of variance were used to obtain the significant influencing factors of the ratio factors on seven macroscopic mechanical parameters, including compressive strength σ_c, tensile strength σ_t, shear strength τ_f, elastic modulus E, Poisson’s ratio ν, internal friction angle φ and cohesion c. A multivariate linear regression equation was constructed to obtain the quantitative relationship between the significant ratio factors and the macroscopic mechanical parameters. Finally, a rock-like material ratio optimization program based on genetic algorithm inversion was written. The results show that the water–paste ratio had extremely significant effects on σ_c, σ_t, τ_f, E, ν and c. The iron powder content had a highly significant effect on σ_c, σ_t, τ_f and c, and it had a significant effect on ν and φ. Coarse sand content had a significant effect on σ_c, E and c. The multiple linear regression model has good reliability after testing, which can provide theoretical support for predicting the macroscopic mechanical parameters of rock-like materials to a certain extent. After testing, the ratio optimization program works well. When the water–paste ratio is 0.5325, the iron powder content is 3.975% and the coarse sand content is 15.967%, it is the optimal ratio of rock-like materials. Full article

Background: Experiential learning (EL) experiences are an important component of a university education, positively impacting career-related attitudes, knowledge, and skills. Students also require EL opportunities to gain experiences required for admission to competitive graduate and professional programs. Students with disabilities face barriers accessing and benefiting from such opportunities. Purpose: This study examined the degree to which demographic factors, type and severity of disability, dispositional factors, and overall adjustment and well-being are predictive of the quality of EL experiences among university students with disabilities. Methodology/approach: A survey was distributed to undergraduate students with disabilities who have participated in EL courses. The results were analyzed using multiple linear regression. Findings/conclusions: Dispositional and adjustment and well-being variables, including the environmental mastery dimension of their psychological well-being rather than demographic factors, such as gender or type and severity of disability, are significant predictors of the quality of EL experiences among students with disabilities. Implications: For students with disabilities to have academically and professionally successful EL experiences, post-secondary institutions must continue to provide appropriate accommodations and educate instructors about the diverse and complex needs of this student group. This must include an understanding of the uniqueness of each individual student’s needs. Full article