Minimum Number Research Articles

IDEMPOTENT GENERATORS OF INCIDENCE ALGEBRAS

Abstract The minimum number of idempotent generators is calculated for an incidence algebra of a finite poset over a commutative ring. This quantity equals either $\lceil \log _2 n\rceil $ or $\lceil \log _2 n\rceil +1$ , where n is the cardinality of the poset. The two cases are separated in terms of the embedding of the Hasse diagram of the poset into the complement of the hypercube graph.

An optimal sparse sensing approach for scanning point selection and response reconstruction in full-field structural vibration testing

Non-contact vibration measurements, such as 3D scanning laser Doppler vibrometry (3D SLDV), are becoming more prevalent in testing next-generation lightweight aerospace structures. This approach reduces the impact of attached sensors and improves measurement reliability. Acquiring precise measurement data for the whole area is feasible, albeit it would demand a considerable amount of time and storage space for testing. The concept of compressed sensing has been recently approved as an effective way to exploit signal sparsity and achieve full response reconstruction with very few measurements. The objective of this work is to enhance the efficiency of non-contact vibration testing by utilizing the state-of-the-art compressive sensing approach. In contrast to conventional sensor placement methods that rely on effective independence, modal kinetic energy, or modal assurance criterion matrix as targets, this paper proposes a novel sensor placement methodology from the perspective of dynamic response reconstruction. The scanning points are chosen with a minimal number to reduce testing time and are well-placed such that full-field vibration responses of the test structure can be reconstructed accurately. This allows for the spatially-detailed vibration responses to be obtained efficiently and accurately with optimal sparse sensing placement and effective response reconstruction through ℓ1 algorithm. Two case studies will be presented in this work to demonstrate and validate the methodology. The first case study is focused on a simplified cantilever beam using the numerical data from the FE analysis to demonstrate the methodology. The second case study is focused on using 3D SLDV experimental testing data from a full-scale industrial fan blade. Based on the results, it is evident that the proposed approach can significantly decrease the scanning points required for a full-field dynamic response reconstruction during full-field vibration testing.

Too Many Meetings? Scheduling Rules for Team Coordination

Workers in knowledge-intensive industries often complain of having too many meetings, but organizations still give little thought to deciding when or how often to meet. We investigate the efficiency and robustness of various coordination scheduling rules. We consider workers who are engaged in a common activity (e.g., software programming) that can be divided into largely independent, parallel production tasks, but that necessitates periodic coordination. Coordination enables workers to address issues they have encountered in their independent work but takes time away from production. Using a stylized game-theoretic model, we show that small teams allow a more fluid, that is, worker-driven, approach to scheduling coordination, such as preemptive coordination (or production), under which any worker can impose coordination (or production). In larger teams this becomes inefficient. Several approaches can mitigate this effect. One option is to allocate the decision rights to produce or coordinate to the most productive worker. A more general version is to implement a voting-based scheme, where a minimum number of workers from a predetermined subset choose to coordinate. A third approach is to modify the preemptive coordination and production rules by adding time-based controls, to reserve some minimal amount of productive time or to enforce coordination after some point. Finally, a fixed-interval meeting schedule works well for very large teams. Our research helps formalize the tension between meeting (coordinating) and producing and indicates how to adapt team coordination scheduling rules to the degree of worker heterogeneity and team size. This paper was accepted by Jay Swaminathan, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03227 .

Sharp lower bounds for the number of maximum matchings in bipartite multigraphs

AbstractWe study the minimum number of maximum matchings in a bipartite multigraph with parts and under various conditions, refining the well‐known lower bound due to M. Hall. When , every vertex in has degree at least , and every vertex in has at least distinct neighbors, the minimum is when and is when . When every vertex has at least two neighbors and , the minimum is , where . We also determine the minimum number of maximum matchings in several other situations. We provide a variety of sharpness constructions.

Experimental and numerical study of mixed convection heat transfer in a vented cavity partially filled with a porous medium: Effects of reynolds and rayleigh numbers on Nusselt number and flow regimes

This article presents a comprehensive investigation of mixed convection flow and heat transfer within a vented, partially side-heated cubical cavity, incorporating a porous medium with low-conductivity square-shaped inclusions. The study encompasses an extensive range of Rayleigh numbers (106 < Ra < 6 × 106) and Reynolds numbers (200 < Re < 4000) while maintaining a fixed Prandtl number of Pr = 0.71. This investigation spans over three decades in Richardson numbers (Ri = Ra/(Re2 Pr)), aiming to discern the interplay between the Nusselt number, Nu, and the Richardson number. Our understanding of Nusselt number dependencies is enhanced by combining PIV measurements of heat transfer and velocity fields with temperature field data. The study uses both experimental and numerical PIV data, incorporating porous media representation to reveal heat transfer scaling with both Reynolds and Rayleigh numbers. Computational Fluid Dynamics (CFD) methods are used to explore the complex physical behavior under different flow conditions. Three distinct flow and heat transfer regimes have been identified, predicated upon the Richardson number. For Ri < 25, the flow structure and Nusselt number exhibit similarities with pure forced convection, where the Nusselt number scales as Nu ∼ Re0.6, independent of the Rayleigh number. Conversely, for Ri > 70, natural convection dominates the vicinity of the heating wall, rendering the Nusselt number less sensitive to Reynolds number variations and predominantly dictated by the Rayleigh number. Notably, the intermediate regime, ranging from 25 < Ri < 70, witnesses a competition between upward-directed natural convection flow at the heating wall and downward-directed forced flow, culminating in a minimum effective Nusselt number.

2 Number of spot samples required when using the GreenFeed System in grazing beef cows

Abstract Methane (CH4) production from beef cattle occurs through a natural digestive process called enteric fermentation. This has increasingly become an area of focus due to environmental and efficiency concerns. There are several techniques to quantify gas fluxes from beef cattle such as an open circuit gas quantification system (OCGQS). The GreenFeed (C-Lock, Inc.) is an OCGQS, which has been validated in previous literature, that is capable of quantifying CH4, carbon dioxide (CO2), and oxygen (O2) from grazing beef cattle. However, few studies have reported the minimum number of spot samples to quantify gas fluxes from an individual animal, particularly in a grazing environment. No recommendations have been made to evaluate the number of spot samples for the calculation of metabolic heat production, using the gas fluxes quantified by an OCGQS. A minimum of 100 spot samples each were collected from 17 grazing animals using an OCGQS. The mean gas fluxes were computed starting from the first 10 visits (forward) and increasing by increments of 10 until 100 visits was reached and also from visit 100 moving back in time in increments of 10 (reverse). Mean gas fluxes were used to calculate individual animal metabolic heat production for each interval with both the forward and reverse approach. Pearson and Spearman correlations were computed between the full 100 visits and each shortened visit interval. Mean forward and reverse gas fluxes and metabolic heat production were also computed starting at 30 visits and increasing by 2 until 40 visits and vice versa. The minimum number of spot samples was determined when correlations with the full 100 visits were greater than 0.95. The minimum number of spot samples needed for accurate quantification of CH4, CO2, O2, and metabolic heat production were 38, 40, 40, and 36, respectively. Simultaneous collection of gas fluxes for the calculation of metabolic heat production would require at least 40 spot samples given the recommendations for gas flux components. Animals had an average of 1.2 visits/day to the OCGQS in this study. Therefore, animals met the required number of spot samples for quantification of metabolic heat production, CH4, CO2, and O2 in 29.5 ± 8.7, 30.5 ± 9.1, 31.8 ± 9.2, and 31.8 ± 9.2 d, respectively. There was large variation in the number of days needed to achieve the recommended number of spot samples. For this reason, protocols for the OCGQS should be based on the total number of spot samples, rather than a test duration. These recommendations can be applied to grazing cattle that typically have more infrequent visits. More research is needed to establish if the minimum number of spot samples would be fewer if animals have more frequent visits, although this is difficult in grazing settings.

Sensor array optimization for the electronic nose via different deep learning methods

A sensor array is a key component of an electronic nose (E-nose). However, the practical applications of the E-nose are often inhibited by its size and energy consumption arising from the number of gas sensors. Achieving a high-performance E-nose with a minimum number of sensors is key and challenging for its practical applications. In this study, different machine learning models have been studied and compared to optimize the performance of the E-nose. The results show that the convolutional neural network (CNN) is the best-performing model, which has an accuracy of 0.986 for classification, and an R-square score of 0.979 for concentration prediction, outperforming the gated recurrent unit, long short-term memory, multi-layer perceptron neural network, and support vector machine. The performance of the E-nose experiences only a minor decrease when the number of sensors that participated in pattern recognition is reduced from eight to four, where the CNN model can yield an accuracy of 0.905 for classification and an R-square of 0.972 for concentration prediction. To further quantify the pros and cons of array optimization, a cost-effective metric is designed to reveal the suitable array size under different scenarios. This work can provide valuable guidance in the design of portable E-nose devices with a smaller size and optimal performance.

The Role of Economic and Social Factors Affecting the Efficiency of Small-Scale Sunflower Oil Production Companies in Tanzania

Most developing nations, including Tanzania, consider agro-processing a key to industrialization. Despite an attractive sunflower seed yield, Tanzania's sunflower oil processing and production have been staggering over the years. Sunflower oil production is inefficient, which leads to low output. So, this research studied sunflower oil production technical efficiency in the Singida region. The study examined the social and economic factors that affect technical efficiency in small-scale sunflower oil producers. It applied cross-sectional design and secondary data for the years 2016–2017, which were collected from the Central Zone Sunflower Oil Processors Association and Small Industrial Development Organization based on Singida sunflower oil producers. It also used stochastic frontier analysis and multiple linear regressions to find the empirical economic relationship between the variables. Sunflower oil producers averaged 30.1% less technical efficiency, as the average was 69.9%. It also revealed that labor, seed, capital, and electricity affect production, while age, experience, education, and locality also matter. Finally, processing techniques, socio-economic, and demographic differences affect sunflower oil production. So, producers must increase capital inputs to be technically efficient. Consider boosting producer capacity and abilities and observing a minimum number of productive staff.

Uncertainty-driven active developmental learning

Existing machine learning models can well handle common classes but struggle to detect unfamiliar or unknown classes due to environmental variations. To address this challenge, we propose a new task called active developmental learning (ADL), which empowers models to actively determine what to learn in the open world, thereby progressively enhancing the capability of detecting unfamiliar and unknown classes. Considering the uncertain essence of the task, we design an uncertainty-driven method for ADL (UADL) that measures and utilizes uncertainty to evaluate unfamiliar known classes and unknown classes separately, which consists of two stages: (1) unfamiliar detection of known classes and (2) unknown detection of novel classes. In the first stage, UADL identifies unfamiliar samples of known classes via known-class uncertainty calculated by GMMs on detectors’ heads. In the second stage, UADL identifies samples containing unknown classes via unknown-class uncertainty computed by class-specific GMMs in feature space. In both stages, uncertainty is used to select a minimal number of unlabeled samples for manual labeling, facilitating the model’s active self-development. Experiments on multiple object detection benchmark datasets demonstrate the feasibility and significant performance of UADL and show its effectiveness against the ADL task compared to other state-of-the-art approaches.

Optimization method of water-quality observation positions in offshore CCS sites using the adjoint marginal sensitivity method

Carbon dioxide (CO2) Capture and Storge (CCS) is recognized as a promising method to mitigate the global warming. In Japan, sub-seabed CO2 storage is considered to be commercialized in a decade. However, offshore CO2 storage has a risk of leakage. Since CO2 seepage into seawater may affect the marine environment, CO2 concentration must be monitored in CO2 storage sea sites under a Japanese law. However, there is no scientifically logical base to determine observation positions. In this study, we therefore developed a method of determining optimal observation positions in CO2 storage sea sites. First, we defined optimal observation positions as those that can estimate seepage positions with high accuracy with the minimal number using the adjoint marginal sensitivity method, even if the seepage positions locate anywhere in a target sea site. Second, non-dimensional parameters were proposed to determine whether a datum at a candidate observation position can be successfully used to estimate a possible seepage position using the adjoint method. Then, we defined a coverage index that indicates how much of a target sea site is “covered” in total by observation points and proposed an algorithm to optimally place observation positions. Finally, we validated the developed method numerically and indicated that, to perform highly accurate estimation, it is desirable that a seepage position be covered by at least three observation points.

Do the associations of daily steps with mortality and incident cardiovascular disease differ by sedentary time levels? A device-based cohort study

ObjectivesThis study aims to examine the associations of daily step count with all-cause mortality and incident cardiovascular disease (CVD) by sedentary time levels and to determine if the minimal and...

Nine level switched capacitor inverter with level shifted pulse width modulation approach

This article proposes a nine-level switched capacitor inverter (NLSCI) with a minimum number of switches. In recent years, switching capacitor (SC) multilevel inverters (MLIs) have become one of the most common inverter topologies. These proposed nine level switched capacitor inverter (NLSCI) do not deserve any external control unit for capacitor control. Since, the charging and discharging of the capacitors are controlled by the on and off states of switches. Furthermore, by employing fewer switches and DC voltage sources, the suggested design produces a greater amount of resultant voltage. Additionally, pulse width modulation (PWM) is recommended as a method to enhance output quality and power level quality. The switched-capacitor two-output multilevel inverter (SCMLI) structure's viability and effectiveness have been demonstrated using MATLAB simulation.

Learning curve of achieving competency in emergency endoscopy in upper gastrointestinal bleeding: how much experience is necessary?

ObjectivesThe management of upper gastrointestinal bleeding (UGIB) has seen rapid advancements with revolutionising innovations. However, insufficient data exist on the necessary number of emergency endoscopies needed to achieve competency in...

Establishing a fingerprinting method for fast catheter identification in HDR brachytherapy in vivo dosimetry

To use quantities measurable during in vivo dosimetry to build unique channel identifiers, that enable detection of brachytherapy errors. Treatment plan of 360 patients with prostate cancer who underwent high-dose-rate brachytherapy (range, 16-25 catheters; mean, 17) were used. A single point virtual dosimeter was placed at multiple positions within the treatment geometry, and the source-dosimeter distance and dwell time were determined for each dwell position in each catheter. These values were compared across all catheters, dwell position by dwell position, simulating a treatment delivery. A catheter was considered uniquely identified if, for a given dwell position, no other catheters had the same measured values. The minimum number of dwell positions needed to identify a specific catheter and the optimal dosimeter location uniquely were determined. The radial (r) and vertical (z) dimensions of the source-dosimeter distance were also examined for their utility in discriminating catheters. Using a virtual dosimeter with no uncertainties, all catheters were identified in 359 of the 360 cases with 9 dwell position measurements. When only the dwell time were measured, all catheters were uniquely identified after 1 dwell position. With a 2-mm spatial accuracy (r,z), all catheters were identified in 94% of the plans. Simultaneous measurement of source-dosimeter distance and dwell time ensured full catheter identification in all plans ranging from 2 to 6 dwell positions. The number of dwell positions needed to uniquely identify all catheters was lower when the distance from the implant center was higher. The most efficient fingerprinting approach involved combining source-dosimeter distance (i.e., source tracking) and dwell time. The further the dosimeter is placed from the center of the implant the better it can uniquely identify catheters.

Optimization design of configurable standard-type coils based on singular value decomposition for transcranial magnetic stimulation

Objective: This study aims to introduce a configurable standard-type (CST) coil design methodology that can accommodate different stimulation needs through the overlapping of standard coil components, while ensuring the performance of CST coil approaches the existing optimal physical limit as closely as possible. Approach: The study utilizes planar, spherical, and hemispherical stream function to represent the spatial geometry of the coils. Singular value decomposition is employed to design the standard coil components while minimizing the number of coil components. Initially, this study delineates the relationship between singular vectors and coil geometry, defining a multi-objective optimization problem for the research. By contrasting the decay characteristics of singular values in the inductance and electric field matrices of the stream functions, the singular vectors of the electric field matrix are utilized as the foundation for CST coil optimization. Subsequently, the relationships between the number of singular vectors and the coil’s Pareto fronts are determined through the optimization algorithms, and the geometric patterns of the singular vectors, i.e., the standard coil components, are analyzed. Main results: By constructing the CST coil components using seven singular vectors and comparing with the Pareto front of the performance of over 50 existing coil types, the stimulation depth range of the CST coil based on the planar stream function decreased by only 7.7%; the focality performance of the CST coil based on the spherical stream function improved by 22.4%; the focality performance of the CST coil based on the hemispherical stream function decreased by only 1.6%. Significance: The construction of CST coils with a minimal number of components is explored to approximate the performance at any discrete point on the physical limit curve. This approach overcomes the limitation of one coil associating with one performance point in traditional designs, presenting a novel perspective on coil design.

Equivalence of Generalised Feistel Networks

This paper focuses on equivalences between Generalised Feistel Networks (GFN) of type-II. We introduce a new definition of equivalence which captures the concept that two GFNs are identical up to re-labelling of the inputs/outputs, and give a procedure to test this equivalence relation. Such two GFNs are therefore cryptographically equivalent for several classes of attacks. It induces a reduction o the space of possible GFNs: the set of the (k!)2 possible even-odd GFNs with 2k branches can be partitioned into k! different classes.This result can be very useful when looking for an optimal GFN regarding specific computationally intensive properties, such as the minimal number of active S-boxes in a differential trail. We also show that in several previous papers, many GFN candidates are redundant as they belong to only a few classes. Because of this reduction of candidates, we are also able to suggest better permutations than the one of WARP: they reach 64 active S-boxes in one round less and still have the same diffusion round that WARP. Finally, we also point out a new family of permutations with good diffusion properties.

Of bats and robots

Some bat species feature joint adaptations to their biosonar sensing and flight systems that allow them to navigate and hunt in dense vegetation. Understanding how biosonar sensing and flight are connected in these species poses a challenge: Kinematics recordings of bat-flight can document the system outputs, but due to various limitations, especially on processing, analysis of bat flight has been limited to minimal numbers of flights and hence has had difficulty to capture the natural variability in flight maneuvers. On the input side of the bats' flight-control system, it is necessary to understand the stimulus ensemble that guides flight in dense vegetation. Navigation in these cases must be based on clutter echoes, i.e., signals consisting of many unresolved components. Since the biosonar inputs into a bat are difficult to record without heavy interference with the animals' behaviors. Hence, biomimetic sonar robots can be used to collect stimuli from natural environments and their recreations. With their superior abilities to find patterns, deep-learning offer an opportunity to cut through the complexity of the input and output data of bat flight control and elucidate how clutter echoes are interfaced with the high-dimensional flight kinematics of bats to create the animals' exceptional maneuvering capabilities.

A new solution of the nonlinear fractional logistic differential equations utilizing efficient techniques

The formulation of models and solutions for various physical problems are the primary goals of scientific achievements in engineering and physics. Our paper focuses on using the Caputo fractional derivative operator to solve nonlinear fractional logistic differential equations. In order to solve general nonlinear fractional differential equations, we first introduce a novel numerical methodology termed the Homotopy perturbation transform method. The perturbation approach and the Yang transform method are combined to create the suggested strategy. Second, we introduce a new hybrid method that uses the time-fractional Caputo derivative to approximate and analytically solve nonlinear fractional logistic differential equations. This method combines the Yang transform with the decomposition method. To validate the analysis, we offer three numerical cases of nonlinear fractional logistic differential equations employing the Caputo fractional derivative operator. The resulting solutions exhibit rapid convergence and are presented in series form. In order to verify the efficacy and relevance of the suggested methodologies, the investigated issues were assessed through the implementation of different fractional orders. We examine and show that, under the specified initial conditions, the solution approaches under evaluation are accurate and effective. Graphs in two and three dimensions show the results that were obtained. Numerical simulations are presented to confirm the efficacy of the strategies. The numerical results show that an accurate, reliable, and efficient approximation can be obtained with a minimal number of terms. The results obtained demonstrate that the new analytical solution method is easy to apply and very successful in solving difficult fractional problems that occur in relevant engineering and scientific domains.

Temporal Analysis of Cutaneous Leishmaniasis Incidence in an Endemic Area of Southeast Iran.

Cutaneous leishmaniasis (CL) is the most common type of leishmaniasis in tropical and subtropical areas. This study investigated the trend of CL changes from 2009 to 2022, and predicted the number of leishmaniasis cases until 2024. This ecological study was performed on new monthly confirmed CL cases from 2009 to 2022 from the leishmaniasis registration system in southeast Iran. The time series method was used to investigate the trend of changes in CL from 2009 to 2022. SARIMA model was run to predict the number of leishmaniasis cases until 2024 by controlling the effect of climatic variables on the disease process. The analysis showed a significant increase in CL cases in 2015 and from 2021 to 2022. The minimum number of registered cases was observed in 2018, with 81 cases. The maximum number was also observed in 2021, with 318 patients. The leishmaniasis cases decreased from January to June and increased from July to December. According to the results of SARIMA (1, 0, 0) (1, 0, 0) multivariate analysis, the temperature in log 12 has a significant negative correlation with the number of leishmaniasis cases. This model predicted a decreasing trend in leishmaniasis cases until 2024. The southeast region of Fars province is one of the hyper-endemic regions of the disease, and it is prone to periodic outbreaks. An active surveillance system must investigate the CL incidence trend and evaluate the effectiveness of interventions to prevent the occurrence of new outbrea.

Platelet-Rich Plasma Versus Alternative Injections for Osteoarthritis of the Knee: A Systematic Review and Statistical Fragility Index-Based Meta-analysis of Randomized Controlled Trials.

Based in part on the results of randomized controlled trials (RCTs) that suggest a beneficial effect over alternative treatment options, the use of platelet-rich plasma (PRP) for the management of knee osteoarthritis (OA) is widespread and increasing. However, the extent to which these studies are vulnerable to slight variations in the outcomes of patients remains unknown. To evaluate the statistical fragility of conclusions from RCTs that reported outcomes of patients with knee OA who were treated with PRP versus alternative nonoperative management strategies. Systematic review and meta-analysis; Level of evidence, 2. All RCTs comparing PRP with alternative nonoperative treatment options for knee OA were identified. The fragility index (FI) and reverse FI were applied to assess the robustness of conclusions regarding the efficacy of PRP for knee OA. Meta-analyses were performed to determine the minimum number of patients from ≥1 trials included in the meta-analysis for which a modification on the event status would change the statistical significance of the pooled treatment effect. In total, this analysis included outcomes from 1993 patients with a mean ± SD age of 58.0 ± 3.8 years. The mean number of events required to reverse significance of individual RCTs (FI) was 4.57 ± 5.85. Based on random-effects meta-analyses, PRP demonstrated a significantly higher rate of successful outcomes when compared with hyaluronic acid (P = .002; odds ratio [OR], 2.19; 95% CI, 1.33-3.62), as well as higher rates of patient-reported symptom relief (P = .019; OR, 1.55; 95% CI, 1.07-2.24), not requiring a reintervention after the initial injection treatment (P = .002; OR, 2.17; 95% CI, 1.33-3.53), and achieving the minimal clinically important difference (MCID) for pain improvement (P = .007; OR, 6.19; 95% CI, 1.63-23.42) when compared with all alternative nonoperative treatments. Overall, the mean number of events per meta-analysis required to change the statistical significance of the pooled treatment effect was 8.67 ± 4.50. Conclusions drawn from individual RCTs evaluating PRP for knee OA demonstrated slight robustness. On meta-analysis, PRP demonstrated a significant advantage over hyaluronic acid as well as improved symptom relief, lower rates of reintervention, and more frequent achievement of the MCID for pain improvement when compared with alternative nonoperative treatment options. Statistically significant pooled treatment effects evaluating PRP for knee OA are more robust than approximately half of all comparable meta-analyses in medicine and health care. Future RCTs and meta-analyses should consider reporting FIs and fragility quotients to facilitate interpretation of results in their proper context.