Suitable Metrics Research Articles

Optimal transport with nonlinear mobilities: A deterministic particle approximation result

Abstract We study the discretisation of generalised Wasserstein distances with nonlinear mobilities on the real line via suitable discrete metrics on the cone of N ordered particles, a setting which naturally appears in the framework of deterministic particle approximation of partial differential equations. In particular, we provide a Γ-convergence result for the associated discrete metrics as N → ∞ {N\to\infty} to the continuous one and discuss applications to the approximation of one-dimensional conservation laws (of gradient flow type) via the so-called generalised minimising movements, proving a convergence result of the schemes at any given discrete time step τ > 0 {\tau>0} . This the first work of a series aimed at sheding new lights on the interplay between generalised gradient-flow structures, conservation laws, and Wasserstein distances with nonlinear mobilities.

Audio Generation with Multiple Conditional Diffusion Model

Text-based audio generation models have limitations as they cannot encompass all the information in audio, leading to restricted controllability when relying solely on text. To address this issue, we propose a novel model that enhances the controllability of existing pre-trained text-to-audio models by incorporating additional conditions including content (timestamp) and style (pitch contour and energy contour) as supplements to the text. This approach achieves fine-grained control over the temporal order, pitch, and energy of generated audio. To preserve the diversity of generation, we employ a trainable control condition encoder that is enhanced by a large language model and a trainable Fusion-Net to encode and fuse the additional conditions while keeping the weights of the pre-trained text-to-audio model frozen. Due to the lack of suitable datasets and evaluation metrics, we consolidate existing datasets into a new dataset comprising the audio and corresponding conditions and use a series of evaluation metrics to evaluate the controllability performance. Experimental results demonstrate that our model successfully achieves fine-grained control to accomplish controllable audio generation.

How a student becomes a teacher: learning and forgetting through spectral methods

In theoretical machine learning, the teacher–student paradigm is often employed as an effective metaphor for real-life tuition. A student network is trained on data generated by a fixed teacher network until it matches the instructor’s ability to cope with the assigned task. The above scheme proves particularly relevant when the student network is overparameterized (namely, when larger layer sizes are employed) as compared to the underlying teacher network. Under these operating conditions, it is tempting to speculate that the student ability to handle the given task could be eventually stored in a sub-portion of the whole network. This latter should be to some extent reminiscent of the frozen teacher structure, according to suitable metrics, while being approximately invariant across different architectures of the student candidate network. Unfortunately, state-of-the-art conventional learning techniques could not help in identifying the existence of such an invariant subnetwork, due to the inherent degree of non-convexity that characterizes the examined problem. In this work, we take a decisive leap forward by proposing a radically different optimization scheme which builds on a spectral representation of the linear transfer of information between layers. The gradient is hence calculated with respect to both eigenvalues and eigenvectors with negligible increase in terms of computational and complexity load, as compared to standard training algorithms. Working in this framework, we could isolate a stable student substructure, that mirrors the true complexity of the teacher in terms of computing neurons, path distribution and topological attributes. When pruning unimportant nodes of the trained student, as follows a ranking that reflects the optimized eigenvalues, no degradation in the recorded performance is seen above a threshold that corresponds to the effective teacher size. The observed behavior can be pictured as a genuine second-order phase transition that bears universality traits. Code is available at: https://github.com/Jamba15/Spectral-regularization-teacher-student/tree/master.

Defining Data Model Quality Metrics for Data Vault 2.0 Model Evaluation

Designing a database is a crucial step in providing businesses with high-quality data for decision making. The quality of a data model is the key to the quality of its data. Evaluating the quality of a data model is a complex and time-consuming task. Having suitable metrics for evaluating the quality of a data model is an essential requirement for automating the design process of a data model. While there are metrics available for evaluating data warehouse data models to some degree, there is a distinct lack of metrics specifically designed to assess how well a data model conforms to the rules and best practices of Data Vault 2.0. The quality of a Data Vault 2.0 data model is considered suboptimal if it fails to adhere to these principles. In this paper, we introduce new metrics that can be used for evaluating the quality of a Data Vault 2.0 data model, either manually or automatically. This methodology involves defining a set of metrics based on the best practices of Data Vault 2.0, evaluating five representative data models using both metrics and manual assessments made by a human expert. Finally, a comparative analysis of both evaluations was conducted to validate the consistency of the metrics with the judgments made by a human expert.

A comparative analysis of lexical-based automatic evaluation metrics for different Indic language pairs

<p>With the rise of machine translation systems, it has become essential to evaluate the quality of translations produced by these systems. However, the existing evaluation metrics designed for English and other European languages may not always be suitable or apply to other Indic languages due to their complex morphology and syntax. Machine translation evaluation (MTE) is a process of assessing the quality and accuracy of the machine-translated text. MTE involves comparing the machine-translated output with the reference translation to calculate the level of similarity and correctness. Therefore, this study evaluates different metrics, namely, BLEU, METEOR, and TER to identify the most suitable evaluation metric for Indic languages. The study uses datasets for Indic languages and evaluates the metrics on various translation systems. The study contributes to the field of MT by providing insights into suitable evaluation metrics for Indic languages. This research paper aims to study and compare several lexical automatic machine translation evaluation metrics for Indic languages. For this research analysis, we have selected five language pairs of parallel corpora from the low-resource domain, such as English–Hindi, English-Punjabi, English-Gujarati, English-Marathi, and English-Bengali. All these languages belong to the Indo-Aryan language family and are resource-poor. A comparison of the state of art MT is presented and shows which translator works better on these language pairs. For this research work, the natural language toolkit tokenizers are used to assess the analysis of the experimental results. These results have been performed by taking two different datasets for all these language pairs using fully automatic MT evaluation metrics. The research study explores the effectiveness of these metrics in assessing the quality of machine translations between various Indic languages. Additionally, this dataset and analysis will make it easier to do future research in Indian MT evaluation.</p>

Enhanced detection of obfuscated malware in memory dumps: a machine learning approach for advanced cybersecurity

In the realm of cybersecurity, the detection and analysis of obfuscated malware remain a critical challenge, especially in the context of memory dumps. This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware. Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps, encompassing a wide array of obfuscated malware types including Spyware, Ransomware, and Trojan Horses with their sub-categories. We begin by employing rigorous data preprocessing methods, including the normalization of memory dumps and encoding of categorical data. To tackle the issue of class imbalance, a Synthetic Minority Over-sampling Technique is utilized, ensuring a balanced representation of various malware types. Feature selection is meticulously conducted through Chi-Square tests, mutual information, and correlation analyses, refining the model’s focus on the most indicative attributes of obfuscated malware. The heart of our framework lies in the deployment of an Ensemble-based Classifier, chosen for its robustness and effectiveness in handling complex data structures. The model’s performance is rigorously evaluated using a suite of metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC) with other evaluation metrics to assess the model’s efficiency. The proposed model demonstrates a detection accuracy exceeding 99% across all cases, surpassing the performance of all existing models in the realm of malware detection.

Graph neural networks are promising for phenotypic virtual screening on cancer cell lines.

Artificial intelligence is increasingly driving early drug design, offering novel approaches to virtual screening. Phenotypic virtual screening (PVS) aims to predict how cancer cell lines respond to different compounds by focusing on observable characteristics rather than specific molecular targets. Some studies have suggested that deep learning may not be the best approach for PVS. However, these studies are limited by the small number of tested molecules as well as not employing suitable performance metrics and dissimilar-molecules splits better mimicking the challenging chemical diversity of real-world screening libraries. Here we prepared 60 datasets, each containing approximately 30 000-50 000 molecules tested for their growth inhibitory activities on one of the NCI-60 cancer cell lines. We conducted multiple performance evaluations of each of the five machine learning algorithms for PVS on these 60 problem instances. To provide even a more comprehensive evaluation, we used two model validation types: the random split and the dissimilar-molecules split. Overall, about 14 440 training runs aczross datasets were carried out per algorithm. The models were primarily evaluated using hit rate, a more suitable metric in VS contexts. The results show that all models are more challenged by test molecules that are substantially different from those in the training data. In both validation types, the D-MPNN algorithm, a graph-based deep neural network, was found to be the most suitable for building predictive models for this PVS problem.

Nonlinear dimensionality reduction based visualization of single-cell RNA sequencing data

Single-cell multi-omics technology has catalyzed a transformative shift in contemporary cell biology, illuminating the nuanced relationship between genotype and phenotype. This paradigm shift hinges on the understanding that while genomic structures remain uniform across cells within an organism, the expression patterns dictate physiological traits. Leveraging high throughput sequencing, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool, enabling comprehensive transcriptomic analysis at unprecedented resolution. This paper navigates through a landscape of dimensionality reduction techniques essential for distilling meaningful insights from the scRNA-seq datasets. Notably, while foundational, Principal Component Analysis may fall short of capturing the intricacies of diverse cell types. In response, nonlinear techniques have garnered traction, offering a more nuanced portrayal of cellular relationships. Among these, Pairwise Controlled Manifold Approximation Projection (PaCMAP) stands out for its capacity to preserve local and global structures. We present an augmented iteration, Compactness Preservation Pairwise Controlled Manifold Approximation Projection (CP-PaCMAP), a novel advancement for scRNA-seq data visualization. Employing benchmark datasets from critical human organs, we demonstrate the superior efficacy of CP-PaCMAP in preserving compactness, offering a pivotal breakthrough for enhanced classification and clustering in scRNA-seq analysis. A comprehensive suite of metrics, including Trustworthiness, Continuity, Mathew Correlation Coefficient, and Mantel test, collectively validate the fidelity and utility of proposed and existing techniques. These metrics provide a multi-dimensional evaluation, elucidating the performance of CP-PaCMAP compared to other dimensionality reduction techniques.

Pneumonia Detection Using Machine Learning

An enormous amount of morbidity and mortality cases are caused by pneumonia, which is still a major global health concern. Pneumonia must be accurately and quickly detected in order to manage patients effectively and achieve better results. Machine learning (ML) algorithms have become effective instruments in recent years for automating the detection and diagnosis of pneumonia from medical imaging data. The goal of this review paper is to give a thorough overview of recent developments in ML-based pneumonia detection. It includes the various ML algorithms used, the training and testing datasets, and the evaluation metrics used to rate the effectiveness of these models. Additionally, this review highlights the difficulties encountered in the field and suggests possible directions for improvement in order to create a more reliable and robust pneumonia detection system. Healthcare professionals place a high value on pneumonia detection, and machine learning (ML)-based automation of There's been a lot of attention paid to this process. The importance of pneumonia detection and the part that ML techniques play in automating this process are highlighted in the introduction to this review paper. In the following section, it examines different machine learning (ML) The various system used for the discernment of pneumonia. Such include supervised understanding algorithms like logistic statistics, vector machine and randomization. forests, and convolutional neural networks. The review also discusses pneumonia detection using unsupervised learning techniques like clustering, dimensionality reduction, and autoencoders. In order to develop them, an assessment of pneumonia detection models is essential. The study has examined several appraisal metrics which are commonly used for that purpose, such as sensitivity, specificity, precision and the operational status of receivers. characteristic (ROC) curve, recall, precision, and F1-score. The selection of suitable metrics, which considers specific requirements for pneumonia detection, is main factor to be taken into consideration. The main obstacles is that there are no annotation data. to creating reliable pneumonia detection models. Accurate ML algorithms must be trained on high-quality labelled datasets. However, since chest X-ray images must be annotated by qualified radiologists, obtaining a sizable annotated dataset for pneumonia is frequently challenging. The creation of efficient ML models for pneumonia detection is hampered by the limited availability of annotated data.

An ultra-fast and energy-efficient CNTFET-based image corner detection hardware for real-time image processing applications

In image processing and machine vision, corner detection is pivotal in diverse applications, including computer vision, 3D reconstruction, face detection, object tracking, and video technologies. Despite the wide usage, the real-time and energy-efficient hardware implementation of corner detection algorithms remains a critical challenge because of the computational resource limitations. On the other hand, owing to the complicated nature of corner detection algorithms, their hardware implementation has been limited to the graphics processing unit (GPU) and field programmable gate arrays (FPGA) platforms. In this regard, this work aims to propose a novel and ultra-efficient carbon nanotube field-effect transistor (CNTFET)-based hardware for image corner detection. Thanks to the proposed corner detection algorithm, the designed hardware has been realized using 2742 transistors with competitive accuracy. The proposed corner detection hardware indicated a remarkable salt-and-pepper noise immunity without using any noise reduction circuit. Our comprehensive simulations demonstrate 78%, 87%, and 94.5% total average improvements in delay, power, and energy compared to the other related corner detection hardware. Moreover, the proposed CNTFET-based corner detection hardware shows a 43 ps propagation delay, demonstrating its real-time operation. The proposed corner detection algorithm at the system level shows suitable accuracy metrics such as Recall, Precision, and error of detection (EoD) compared to the other well-known corner detectors. Our method has established a new pathway for real-time circuit-level hardware design for image processing and machine vision applications.

Ecosystem functions of plant diversity: Comparisons from a large‐scale marsh restoration experiment in California, USA

AbstractAlthough the promotion of biodiversity has been recognized as an important conservation goal, salt marsh restoration typically focuses on reestablishing dominant foundation species. Salt marsh restoration projects that add or remove sediment to optimize marsh elevation often result in a bare landscape following construction. Restoration managers must decide whether to plant and, if so, which species. This decision can be difficult because few studies have examined the ecological functions of individual species, especially those that are less abundant. Within a major salt marsh restoration project in Elkhorn Slough, California, where 17,000 plants of five high marsh species were planted, we investigated how rarer species and the naturally recruiting dominant (Salicornia pacifica) differ in ecosystem function. We evaluated 31 different metrics related to blue carbon, plant productivity, environmental effects, and community interactions. No single species had the greatest ecological function across this suite of metrics, and measured effects were mainly independent, with only 16 of 435 pairwise comparisons revealing a strong correlation. We found significant differences among species for 18 metrics, revealing key contrasts in ecosystem function, with significant effects of marsh elevation and interaction between effects of species and elevation on some of these functions. S. pacifica scored highest for metrics such as recruitment and canopy height but other species outperformed Salicornia in other metrics. Frankenia salina had the greatest ecological function in the highest number of metrics, including cover and belowground biomass carbon content, but other species had higher rates of photosynthesis and harbored fewer individuals of invasive arthropods. We recommend planting a suite of less common species at restoration sites to provide more diverse functions across the landscape. F. salina in particular is recommended for its tolerance of hypersalinity and low moisture conditions. Our demonstration of the value of complementing restoration of the dominant foundation species with restoration of less common species is applicable to restoration of other ecosystems beyond salt marshes. The approach we implemented, evaluating a large suite of functions for multiple species across a restored landscape, can serve as a model for investigations of the importance of biodiversity for enhancing multifunctionality in other restored ecosystems.

A multivariable analysis to predict variations in hospital mortality using systems-based factors of healthcare delivery to inform improvements to healthcare design within the English NHS.

Over the last decade, the strain on the English National Health Service (NHS) has increased. This has been especially felt by acute hospital trusts where the volume of admissions has steadily increased. Patient outcomes, including inpatient mortality, vary between trusts. The extent to which these differences are explained by systems-based factors, and whether they are avoidable, is unclear. Few studies have investigated these relationships. A systems-based methodology recognises the complexity of influences on healthcare outcomes. Rather than clinical interventions alone, the resources supporting a patient's treatment journey have near-equal importance. This paper first identifies suitable metrics of resource and demand within healthcare delivery from routinely collected, publicly available, hospital-level data. Then it proceeds to use univariate and multivariable linear regression to associate such systems-based factors with standardised mortality. Three sequential cross-sectional analyses were performed, spanning the last decade. The results of the univariate regression analyses show clear relationships between five out of the six selected predictor variables and standardised mortality. When these five predicators are included within a multivariable regression analysis, they reliably explain approximately 36% of the variation in standardised mortality between hospital trusts. Three factors are consistently statistically significant: the number of doctors per hospital bed, bed occupancy, and the percentage of patients who are placed in a bed within four hours after a decision to admit them. Of these, the number of doctors per bed had the strongest effect. Linear regression assumption testing and a robustness analysis indicate the observations have internal validity. However, our empirical strategy cannot determine causality and our findings should not be interpreted as established causal relationships. This study provides hypothesis-generating evidence of significant relationships between systems-based factors of healthcare delivery and standardised mortality. These have relevance to clinicians and policymakers alike. While identifying causal relationships between the predictors is left to the future, it establishes an important paradigm for further research.

An Explorative Review of the Constructs, Metrics, Models, and Methods for Evaluating e-Learning Performance in Medical Education

The performance evaluation of e-learning in medical education has been the subject of much research lately. Researchers are yet to achieve a consensus on the definition of performance or the suitable constructs, metrics, models, and methods to help understand student performance. Through a systematic review, this study put forward a working definition of what constitutes performance evaluation to reduce the ambiguity, arbitrariness, and multiplicity surrounding performance evaluation of e-learning in medical education. A systematic review of published articles on performance evaluation of e-learning in medical education was performed on the SCOPUS, Web of Science, PubMed, and EBSCOHost databases using search terms deduced from the PICOS model. Following the PRISMA guidelines relevant published papers were searched and exported to Endnote. Screening and quality appraisal were done on Rayyan. Three thousand four hundred and thirty-nine published studies were retrieved and screened using predetermined inclusion and exclusion criteria. One hundred and three studies passed all the criteria and were reviewed. The reviewed literature used 30 constructs to operationalize performance. The leading constructs are knowledge and effectiveness. Both constructs were used by 60% of the authors of the reviewed literature to define student performance. Knowledge gain, satisfaction, and learning outcome are the most common metrics used by 81%, 26%, and 15% of the reviewed literature to measure student performance. The study discovered that most researchers forget to evaluate the “e” or electronic component of e-learning when evaluating performance. The constructs operationalized and metrics measured were primarily focused on learning outcomes with minimal focus on technology-related metrics or the influence of the electronic mode of delivery on the learning process or evaluation outcome. Only 6% of the reviewed literature applied evaluation models to guide their evaluation process - mostly the Kirkpatrick evaluation model. Also, most of the included studies used randomization as an experimental control method, mainly using pre-and post-test surveys. Modern evaluation methods were rarely used. Only 1% of the reviewed literature used Google Analytics, and 2% used data from a learning management system. This study increments the existing body of knowledge in performance evaluation of e-learning in medical education by providing a convergence of constructs, metrics, models, and methods and proposing a roadmap to guide students’ performance evaluation process from the synthesis of findings and the gaps identified through the systematic review of existing literature in the domain. This roadmap will assist in informing researchers of grey areas to consider when evaluating performance to ensure more quality research outputs in the domain.

Evaluation of complexity and deliverability of IMRT treatment plans for breast cancer

This study aimed to predict the outcome of patient specific quality assurance (PSQA) in IMRT for breast cancer using complexity metrics, such as MU factor, MAD, CAS, MCS. Several breast cancer plans were considered, including LBCS, RBCS, LBCM, RBCM, left breast, right breast and the whole breast for both Edge and TrueBeam LINACS. Dose verification was completed by Portal Dosimetry (PD). The receiver operating characteristic (ROC) curve was employed to determine whether the treatment plans pass or failed. The area under the curve (AUC) was used to assess the classification performance. The correlation of PSQA and complexity metrics was examined using Spearman’s rank correlation coefficient (Rs). For LINACS, the most suitable complexity metric was found to be the MU factor (Edge Rs = − 0.608, p < 0.01; TrueBeam Rs = − 0.739, p < 0.01). Regarding the specific breast cancer categories, the optimal complexity metrics were as follows: MAD (AUC = 0.917) for LBCS, MCS (AUC = 0.681) for RBCS, MU factor (AUC = 0.854) for LBCM and MAD (AUC = 0.731) for RBCM. On the Edge LINAC, the preferable method for breast cancers was MCS (left breast, AUC = 0.938; right breast, AUC = 0.813), while on the TrueBeam LINAC, it became MU factor (left breast, AUC = 0.950) and MCS (right breast, AUC = 0.806), respectively. Overall, there was no universally suitable complexity metric for all types of breast cancers. The choice of complexity metric depended on different cancer types, locations and treatment LINACs. Therefore, when utilizing complexity metrics to predict PSQA outcomes in IMRT for breast cancer, it was essential to select the appropriate metric based on the specific circumstances and characteristics of the treatment.

USE-Evaluator: Performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging.

Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of these models in order for results to be comparable. However, there is a mismatch between the distributions of cases and the difficulty level of segmentation tasks in public data sets compared to clinical practice. Common metrics used to assess performance fail to capture the impact of this mismatch, particularly when dealing with datasets in clinical settings that involve challenging segmentation tasks, pathologies with low signal, and reference annotations that are uncertain, small, or empty. Limitations of common metrics may result in ineffective machine learning research in designing and optimizing models. To effectively evaluate the clinical value of such models, it is essential to consider factors such as the uncertainty associated with reference annotations, the ability to accurately measure performance regardless of the size of the reference annotation volume, and the classification of cases where reference annotations are empty. We study how uncertain, small, and empty reference annotations influence the value of metrics on a stroke in-house data set regardless of the model. We examine metrics behavior on the predictions of a standard deep learning framework in order to identify suitable metrics in such a setting. We compare our results to the BRATS 2019 and Spinal Cord public data sets. We show how uncertain, small, or empty reference annotations require a rethinking of the evaluation. The evaluation code was released to encourage further analysis of this topic https://github.com/SophieOstmeier/UncertainSmallEmpty.git.

Propagation of chaos for topological interactions by a coupling technique

We consider a system of particles that interact through a jump process. The jump intensities are functions of the proximity rank of the particles, a type of interaction referred to as topological in the literature. Such interactions have been shown relevant for the modelling of bird flocks. We show that, in the large number of particles limit and under minimal smoothness assumptions on the data, the model converges to a kinetic equation which was derived in earlier works both formally and rigorously under more stringent regularity assumptions. The proof relies on the coupling method which assigns to the particle and limiting processes a joint process posed on the cartesian product of the two configuration spaces of the former processes. By appropriate estimates in a suitable Wasserstein metric, we show that the distance between the two processes tends to zero as the number of particles tends to infinity, with an error typical of the law of large numbers.

Cisco population characteristics in Wisconsin lakes in relation to lake‐ and landscape‐level factors

AbstractObjectiveDeclines in Cisco Coregonus artedi populations in some inland lakes have prompted assessments of Cisco occurrence and extirpation risk in relation to various stressors to identify refuge lakes and factors that promote Cisco persistence. However, most previous assessments have focused on presence–absence of Cisco rather than examining how population characteristics, such as relative abundance or growth, might change in relation to lake‐ and landscape‐level environmental factors. Consequently, our specific objectives were to identify important environmental factors explaining variation in Cisco relative abundance and growth and to determine whether population metrics describing size and age distributions were related to relative abundance in Wisconsin inland lakes.MethodsCisco were collected from 48 inland Wisconsin lakes during 2011–2015 using vertical monofilament gill nets and population‐specific relative abundance estimates (catch per unit effort [CPUE]) were quantified as the number of individuals per gill‐net night. Sagittal otoliths were removed from a subsample of Cisco for age estimation and growth was indexed as mean total length (TL; mm) at age 2. Length and age data were used to develop a suite of metrics describing size and age distributions of each population. Random forest models were used to evaluate relationships between 10 biologically relevant predictor variables representing variation in physical, climatic, catchment, and limnological characteristics and Cisco CPUE and growth. Pearson correlations were used to determine whether population characteristics were related to CPUE.ResultCisco populations exhibited large variation in relative abundance, growth, and size and age distributions. Best‐fit random forest models explained approximately 25% of the variation in Cisco CPUE and 46% of the variation in growth. Growing degree‐days and variables associated with availability, quality, and quantity of suitable oxythermal conditions were identified as important predictors of both Cisco CPUE and growth; CPUE was also identified as an important predictor of growth. Mean TL and mean TL at age 2 were negatively related to Cisco CPUE, whereas mean age, number of age‐classes present, and maximum observed age were positively related to CPUE.ConclusionOur results suggest that maintenance of suitable oxythermal habitat conditions may be critical to conserving abundant Cisco populations. Our assessment also provides insights on how Cisco populations may respond to environmental and anthropogenic stressors, which could aid ongoing and future conservation and management efforts in Wisconsin and elsewhere.

Sink Node Placement and Partial Connectivity in Wireless Sensor Networks.

This research delves into the aspects of communication and connectivity problems within random Wireless Sensor Networks (WSNs). It takes into account the distinctive role of the sink node, its placement, and application-specific requirements for effective communication while conserving valuable network resources. Through mathematical modeling, theoretical analysis, and simulation evaluations, we derive, compare, and contrast the probabilities of partial and full connectivity within a random WSN, factoring in network parameters and the maximum allowable hop distance/count hmax. hmax captures the diverse range of delay-sensitive requirements encountered in practical scenarios. Our research underscores the significant impact of the sink node and its placement on network connectivity and the sensor connection rate. The results exemplify a noteworthy decline in the sensor connection rate, dropping from 98.8% to 72.5%, upon relocating the sink node from the network center to the periphery. Moreover, as compared with full connectivity, partial connectivity and the sensor connection rate are more suitable metrics for assessing the communication capability of random WSNs. The results illustrate that 1.367 times more energy is required to connect less than 4% of the remote sensors, based on the examined network settings. Additionally, to increase the sensor connection rate slightly from 96% to 100%, an additional 538% more energy is required in multipath fading based on the widely adopted energy consumption model. This research and its outcomes contribute to establishing appropriate performance metrics and determining critical network parameters for the practical design and implementation of real-world wireless sensor networks.

Improvement of an order-to-cash business process by deploying lean six sigma tools: a case study

PurposeThis study is focused to support an ongoing development project of the case company's current state and the challenges of the order-to-cash (O2C) process. The O2C process is the most visible process to the customer, and therefore, its punctual and fluent order management is vital. It is observed that the high degree of manual work in the O2C process causes mistakes, delays and rework in the process. The purpose of this article is therefore to analyze the case company's current state of the O2C process as well as to identify the areas of development in this process by deploying the means of Lean Six Sigma tools such as value stream mapping (VSM).Design/methodology/approach The study was conducted as a mix of quantitative and qualitative analysis. Based on both the quantitative and qualitative data, a workshop on VSM was organized to analyze the current state of the O2C process of a case company, engaged in the energy and environment sector in Finland.Findings The results found that excessive manual work was highly connected to inadequate or incorrect data in pricing and invoicing activities, which resulted in canceled invoices. Canceled invoices are visible to the customer and have a negative impact on the customer experience. This study found that by improving the performance of the O2C process activities and improving communication among the internal and external stakeholders, the whole O2C process can perform more effectively and provide better customer value.Originality/value The O2C process is the most visible process to the customer and therefore its punctual and fluent order management is vital. To ensure that the O2C process is operating as desired, suitable process performance metrics need to be aligned and followed. The results gathered from the case company's data, questionnaire interviews, and the VSM workshop are all highlighted in this study. The main practical and managerial implications were to understand the real-time O2C process performance, which is necessary to ensure strong performance and enhance continuous improvement of the O2C process that leads to operational excellence and commercial competitiveness of the studied case company.

Twistedly hydrophobic basis with suitable aromatic metrics in covalent organic networks govern micropollutant decontamination

The pre-designable structure and unique architectures of covalent organic frameworks (COFs) render them attractive as active and porous medium for water crisis. However, the effect of functional basis with different metrics on the regulation of interfacial behavior in advanced oxidation decontamination remains a significant challenge. In this study, we pre-design and fabricate different molecular interfaces by creating ordered π skeletons, incorporating different pore sizes, and engineering hydrophilic or hydrophobic channels. These synergically break through the adsorption energy barrier and promote inner-surface renewal, achieving a high removal rate for typical antibiotic contaminants (like levofloxacin) by BTT-DATP-COF, compared with BTT-DADP-COF and BTT-DAB-COF. The experimental and theoretical calculations reveal that such functional basis engineering enable the hole-driven levofloxacin oxidation at the interface of BTT fragments to occur, accompanying with electron-mediated oxygen reduction on terphenyl motif to active radicals, endowing it facilitate the balanced extraction of holes and electrons.