Distinct Objects Research Articles

Exercise Across the Phases of Cancer Survivorship: A Narrative Review.

Exercise has long been recognized as an important component of treatment for various diseases. However, the benefits and risks of exercise interventions must be carefully evaluated to ensure the former outweighs the latter. As cancer patients undergo diverse treatment modalities with distinct objectives, a systematic approach partitioning the cancer journey into distinct phases is necessary to inform tailored exercise prescriptions. This narrative review summarizes exercise benefits and mechanisms for cancer patients and survivors across four distinct survivorship periods-before surgery, after surgery and before adjuvant treatment, during nonsurgical treatment (adjuvant and neoadjuvant), and during extended survival. In summary, exercise reduces the risks of complications and declines in physical functioning while improving fatigue, quality of life, and the ability to manage treatment effects. Although additional research is warranted, existing evidence is sufficient to integrate exercise into clinical oncology practice and cancer survivorship programs.

Concurrent Heat and Air Pollution Exposures among People Experiencing Homelessness.

Extreme heat and air pollution are important human health concerns; exposure can affect mental and physical well-being, particularly during periods of co-occurrence. Yet, the impacts on people are largely determined by underlying health conditions, coupled with the length and intensity of exposure. Preexisting adverse health conditions and prolonged exposure times are more common for people experiencing homelessness, particularly those with intersectional identity characteristics (e.g., disease, ability, age, etc.). Partially due to methodological limitations, such as data scarcity, there is a lack of research at the intersection of this at-risk population within the climate-health domain. We have three distinct objectives throughout this article: a) to advance critical discussions around the state of concurrent high heat and air pollution exposure research as it relates to people experiencing homelessness; b) to assert the importance of heat and air pollution exposure research among a highly vulnerable, too-often homogenized population-people experiencing homelessness; and c) to underline challenges in this area of study while presenting potential ways to address such shortcomings. The health insights from concurrent air pollution and heat exposure studies are consequential when studying unhoused communities who are already overexposed to harmful environmental conditions. Without holistic data sets and more advanced methods to study concurrent exposures, appropriate and targeted prevention and intervention strategies cannot be developed to protect this at-risk population. We highlight that a) concurrent high heat and air pollution exposure research among people experiencing homelessness is significantly underdeveloped considering the pressing human health implications; b) the severity of physiological responses elicited by high heat and air pollution are predicated on exposure intensity and time, and thus people without means of seeking climate-controlled shelter are most at risk; and c) collaboration among transdisciplinary teams is needed to resolve data resolution issues and enable targeted prevention and intervention strategies. https://doi.org/10.1289/EHP13402.

Gain Scheduled PI controller design using Multi-Objective Reinforcement Learning

Gain scheduling, a widely adopted control design approach, performs tasks by decomposing into sub-problems. It has found successful applications across diverse fields, including aerospace and industrial process control (Leith and Leithead (2000)). Reinforcement Learning (RL) has become increasingly significant in process control applications, thanks to the advancement of sophisticated algorithms and the rise in computational speeds. An attempt has been made to enhance the performance of a Proportional-Integral (PI) controller by integrating the benefits of both gain scheduling and RL. The manuscript focuses on designing a Gain Scheduled PI controller using Multi-Objective RL (GS-MORL). Proposing the training of multiple RL-based PI controllers with distinct objectives with customized reward functions to derive tuning parameters for each goal. The switching between these parameter sets is facilitated by a gain schedule variable. The controller parameters are adjusted based on the error between actual and desired responses. The aim is to enhance overall performance across various performance indices, including the integral of absolute error and time response specifications. The performance improvement from the proposed work has been demonstrated using two simulation case studies, one involving an underdamped second-order system and an integrating type steam turbine system. Comparative analysis against existing literature revealed superior performance across all indices.

An iterative approach for the solution of fully fuzzy linear fractional programming problems via fuzzy multi-objective optimization

<abstract><p>The primary goal of optimization theory is to formulate solutions for real-life challenges that play a fundamental role in our daily lives. One of the most significant issues within this framework is the Linear Fractional Programming Problem (LFrPP). In practical situations, such as production planning and financial decision-making, it is often feasible to express objectives as a ratio of two distinct objectives. To enhance the efficacy of these problems in representing real-world scenarios, it is reasonable to utilize fuzzy sets for expressing variables and parameters. In this research, we have worked on the Fully Fuzzy Linear Fractional Linear Programming Problem (FFLFrLPP). In our approach to problem-solving, we simplified the intricate structure of the FFLFrLPP into a crisp Linear Programming Problem (LPP) while accommodating the inherent fuzziness. Notably, unlike literature, our proposed technique avoided variable transformation, which is highly competitive in addressing fuzzy-based problems. Our methodology also distinguishes itself from the literature in preserving fuzziness throughout the process, from problem formulation to solution. In this study, we conducted a rigorous evaluation of our proposed methodology by applying it to a selection of numerical examples and production problems sourced from the existing literature. Our findings revealed significant improvements in performance when compared to established solution approaches. Additionally, we presented comprehensive statistical analyses showcasing the robustness and effectiveness of our algorithms when addressing large-scale problem instances. This research underscores the innovative contributions of our methods to the field, further advancing the state-of-the-art in problem-solving techniques.</p></abstract>

Optimization of offset and cant angle winglet on remote control airplane using Taguchi – Particle swarm optimization

Incorporating winglets into aircraft has been empirically proven to notably improve aerodynamic efficiency by reducing vortex-induced effects at the wingtips. However, conducting comprehensive investigations necessitates the exploration of numerous winglet factors and value variations. This study pertains to remote control aircraft winglets, focusing on manipulating cant angle and offset factors across four distinct values. Two primary objectives guide this research: firstly, the maximization Cl/Cdmax, and secondly, the minimization Cd₀. The Taguchi experimental design is employed to randomize the variations in offset and cant angle values systematically. These variations are then used to generate pivotal regression values in the subsequent particle swarm optimization (PSO). The variance analysis evaluates the impact of winglet-related variables on each research goal. Additionally, the winglet design incorporates the open-source XFLR5 software, an accessible resource for aeromodelling clubs in Indonesia. XFLR5 enables the investigation of aerodynamic parameters across various angles of incidence and plays a crucial role in this research. The results of this study reveal that the Taguchi method yields two distinct combinations of factor values, aligning with the two primary research objectives. Conversely, particle swarm optimization generates a combination that effectively addresses both objectives. A comparative analysis of the winglet factor combinations from Taguchi and PSO underscores the greater efficiency of the PSO method in optimizing winglet variations for two distinct objectives.

Process evaluations for the scale-up of complex interventions – A scoping review

Background: Process evaluation (PE) is an essential part of testing and adapting complex health interventions (CHIs) and involves evaluating what is implemented (and how), mechanisms of change, and how context affects implementation and health outcomes. To address complex health needs, the focus is on developing new CHIs, rather than on adapting and scaling up existing interventions to promote stronger integration, increased population coverage, or comprehensiveness. Therefore, more attention is needed to process evaluations conducted during the scale-up process as they may have distinctive features, stakeholders, or objectives. We aim to describe the current practice of process evaluations conducted during the scale-up of CHIs.
 Who is it for? The results of this review are relevant to all stakeholders involved in the scale-up of CHI.
 Who did you involve and engage with? All authors of this review have been involved in the scale-up of integrated care across multiple contexts.
 What did you do? Eight primary data sources (PubMed, Embase, CENTRAL, Web of Science, CINAHL, Global Health, Scielo and African Index Medicus) and grey literature were searched. Studies were screened following gold standard review procedures. Study demographics, scale-up dimensions (i.e., integration, coverage, comprehensiveness), features of the complex intervention and PE were charted. Process evaluation was further assessed in terms of stakeholders involved and proposed objectives of the PE. For this review, the United Kingdom Medical Research Council’s (MRC) definition of PE was adjusted in terms of “scale-up” instead of “delivery and functioning.”
 What results did you get? We identified 10303 unique records of which 34 were included after abstract (n=223) and full-text (n=91) screening. Twenty of the 34 studies were conducted in one or more low-middle income countries (LMICs). Most CHIs studied looked at scale-up of interventions for patient empowerment through digital health, strengthening primary care, or person-centered care models. In the absence of a guiding framework for PEs in the context of scale-up, a variety of other frameworks were often used including the RE-AIM framework (n=5), Consolidated Framework for Implementation Research (n=3), or MRC framework for the PE of CHIs (n=1). Some of these frameworks were subsequently adapted to facilitate unexpected pathways to impact or interactions with contextual factors to emerge as well. Interestingly, in 12 out of 34 studies, beneficiaries (e.g., patients) were not stakeholders in the PE.
 What is the learning for the international audience? Process evaluations specific to the process of scaling up is advertently different from implementing a CHI. In the absence of a gold standard framework to guide PE during scale-up, the development of such guidance could be considered.
 What are the next steps? We plan to seek further engagement, including with those funded under the “Global Alliance of Chronic Disease” scale-up program, to determine the value and content of a process evaluation framework for scale-up.

Deep Learning for Enhanced Marine Vision: Object Detection in Underwater Environments

This study leverages the Semantic Segmentation of Underwater Imagery (SUIM) dataset, encompassing over 1,500 meticulously annotated images that delineate eight distinct object categories. These categories encompass a diverse array, ranging from vertebrate fish and invertebrate reefs to aquatic vegetation, wreckage, human divers, robots, and the seafloor. The use of this dataset involves a methodical synthesis of data through extensive oceanic expeditions and collaborative experiments, featuring both human participants and robots. The research extends its scope to evaluating cutting-edge semantic segmentation techniques, employing established metrics to gauge their performance comprehensively. Additionally, we introduce a fully convolutional encoder-decoder model designed with a dual purpose: delivering competitive performance and computational efficiency. Notably, this model boasts a remarkable accuracy of 88%, underscoring its proficiency in underwater image segmentation. Furthermore, this model's integration within the autonomy pipeline of visually-guided underwater robots presents its tangible applicability. Its rapid end-to-end inference capability addresses the exigencies of real-time decision-making, vital for autonomous systems. This study elucidates the model's practical benefits across diverse applications like visual serving, saliency prediction, and intricate scene comprehension. Crucially, the utilization of the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) elevates image quality, enriching the foundation upon which our model's success rests. This research establishes a solid groundwork for future exploration in underwater robot vision by presenting the model and the benchmark dataset.

Retina image segmentation using the three-path Unet model

Unsupervised image segmentation is a technique that divides an image into distinct regions or objects without prior labeling. This approach offers flexibility and adaptability to various types of image data. Particularly for large datasets, it eliminates the need for manual labeling, thereby it presents advantages in terms of time and labor costs. However, when applied to retinal image segmentation, challenges arise due to variations in data, presence of noise, and manual threshold adjustments, which can lead to over-segmentation or under-segmentation of small blood vessel boundaries and endpoints. In order to enhance the precision and accuracy of retinal image segmentation, we propose a novel image supervised segmentation network based on three-path Unet model.Firstly, the Haar wavelet transform is employed to extract high-frequency image information, which forms the foundation for the proposed HaarNet, a Unet-inspired architecture. Next, the HaarNet is integrated with the Unet and SegNet frameworks to develop a three-path Unet model, referred to as TP-Unet. Finally, the model is further refined into TP-Unet+AE+DSL by incorporating the advantages of auto-encoding (AE) and deep supervised learning (DSL) techniques, thereby enhancing the overall performance of the system. To evaluate the effectiveness of our proposed model, we conduct experiments using the DRIVE and CHASE public datasets. On the DRIVE dataset, our recommended model achieves a Dice coefficient of 0.8291 and a sensitivity index of 0.8184. These results significantly outperform the Unet model by 1.34%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.34\\%$$\\end{document} and 2.60%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.60\\%$$\\end{document}, respectively. Furthermore, our model demonstrates excellent performance on the CHASE dataset, with a Dice coefficient of 0.8162, a sensitivity of 0.8242, and an accuracy of 0.9664. These metrics surpass the Unet model by 3.20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.20\\%$$\\end{document}, 6.66%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.66\\%$$\\end{document}, and 0.42%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.42\\%$$\\end{document}, respectively. Our proposed model provides more accurate and reliable results for retinal vessel segmentation, which holds significant potential for assisting doctors in their diagnosis.

Enhancing English For Nursing: A Comprehensive Needs Analysis For Effective Communication In Emergency Nursing Study Programs

students enrolled in nursing study programs with a focus on emergency characteristics. The investigation encompasses both target needs and learning needs, emphasizing the specific language requirements in the nursing field. English for Nursing falls under the domain of English for Specific Purposes (ESP), designed to cater to learners' specific language needs, fostering more effective and comprehensive teaching and learning. By aligning English instruction with the demands of the nursing discipline, non-English program students will gain familiarity with English tailored to nursing contexts. The ESP approach aims to address the specific language acquisition challenges faced by learners with distinct objectives. Data collection involves questionnaires administered to 45 students and direct interviews with emergency nursing lecturers. Findings reveal the significance of learning English for improved communication within the context of conversations or practical applications. The identified English materials concentrate on terms relevant to nursing study programs, particularly those associated with emergency characteristics, becoming the focal points of instruction. Additionally, materials focusing on enhancing listening and speaking skills emerge as key priorities in the learning process. The outcomes of this study are anticipated to serve as a valuable reference for the design and development of syllabi, specifically tailored to English for Nursing courses.

Is distinct location evidence of distinct objects? Multilocation and the problem of parsimony

AbstractFor an object to be multilocated is for it to wholly occupy disjoint spatial regions simultaneously. If multilocation is possible, it is possible that a multilocated particle is wholly located at 1080 distinct locations, such that it constitutes a particle‐for‐particle duplicate of the actual universe. Such a universe would presumably be perceptually identical to the actual universe. If we take multilocation as possible, we are thus presented with two accounts between which our perceptual evidence cannot adjudicate: one wherein the universe is constituted by many particles and another wherein it is constituted by one radically multilocated particle. Parsimony concerns dictate that the latter is the more rational to accept. Since this is absurd, we should reject that multilocation is possible. Mooney responds to the problem by arguing that distinct location is evidence of non‐identity, even if acceptance of the possibility of multilocation entails that this evidence is not decisive. If this is right, then the evidence favors a theory featuring many particles. In this paper, I contend that our commitment to taking distinct location as evidence of nonidentity is motivated by a more fundamental intuition that does not apply in the relevant context.

Synchrony in auditory 40-Hz gamma oscillations increases in older age and correlates with hearing abilities and cortical GABA levels

Abstract Synchronized 40-Hz gamma oscillations in specific sensory and higher-order thalamocortical networks provide a neural mechanism for feature binding. Aging-related changes in gamma oscillations may cause deficits in auditory feature binding, contributing to impaired speech-in-noise perception. Gamma synchrony is controlled through inhibitory mechanisms mediated by the neurotransmitter γ-aminobutyric acid (GABA), which has been shown to decline in aging. This study investigated aging-related changes in gamma oscillations and how they relate to auditory function and cortical GABA levels. Magnetoencephalograms of 40-Hz auditory steady-state responses (ASSRs) were recorded in young and older adults by presenting amplitude-modulated tones in quiet and mixed with concurrent multi-talker babble noise. Responses in the quiet condition had longer latencies and more prominent amplitudes, indicating the 40-Hz ASSRs in noise were dominated by a sensory component and in quiet by a component involved in higher-order processing. The ASSR amplitudes increased in older adults under both stimulus conditions. However, larger ASSR amplitudes were associated with more severe hearing and speech-in-noise loss only in the noise condition. This suggests the aging-related increase in synchrony of sensory gamma oscillations has a detrimental effect on auditory processing. It may cause increased interference between competing sounds in the central auditory system, making it difficult for the aging auditory system to separate speech features from noise and bind them into a distinct perceptual object. Also in older adults, larger amplitudes of the 40-Hz ASSRs in the quiet condition were associated with higher left auditory cortex GABA concentrations measured with magnetic resonance spectroscopy, supporting GABA’s role in internally generated gamma synchrony in aging.

Topological spin textures in electronic non-Hermitian systems

Non-Hermitian systems have been discussed mostly in the context of open systems and nonequilibrium. Recent experimental progress is much from optical, cold-atomic, and classical platforms due to the vast tunability and clear identification of observables. However, their counterpart in solid-state electronic systems in equilibrium remains unmasked although highly desired, where a variety of materials are available, calculations are solidly founded, and accurate spectroscopic techniques can be applied. We demonstrate that, in the surface state of a topological insulator with spin-dependent relaxation due to magnetic impurities, highly nontrivial topological soliton spin textures appear in momentum space. Such spin-channel phenomena are delicately related to the type of non-Hermiticity and correctly reveal the most robust non-Hermitian features detectable spectroscopically. Moreover, the distinct topological soliton objects can be deformed to each other, mediated by topological transitions driven by tuning across a critical direction of doped magnetism. These results not only open a solid-state avenue to exotic spin patterns via spin- and angle-resolved photoemission spectroscopy, but also inspire non-Hermitian dissipation engineering of spins in solids.

TOWARDS ACCURATE INSTANCE SEGMENTATION IN LARGE-SCALE LIDAR POINT CLOUDS

Abstract. Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy.

Self-Improved Learning for Salient Object Detection

Salient Object Detection (SOD) aims at identifying the most visually distinctive objects in a scene. However, learning a mapping directly from a raw image to its corresponding saliency map is still challenging. First, the binary annotations of SOD impede the model from learning the mapping smoothly. Second, the annotator's preference introduces noisy labeling in the SOD datasets. Motivated by these, we propose a novel learning framework which consists of the Self-Improvement Training (SIT) strategy and the Augmentation-based Consistent Learning (ACL) scheme. SIT aims at reducing the learning difficulty, which provides smooth labels and improves the SOD model in a momentum-updating manner. Meanwhile, ACL focuses on improving the robustness of models by regularizing the consistency between raw images and their corresponding augmented images. Extensive experiments on five challenging benchmark datasets demonstrate that the proposed framework can play a plug-and-play role in various existing state-of-the-art SOD methods and improve their performances on multiple benchmarks without any architecture modification.

Introducing Greenery into a Building Based on its Impact on Human Wellbeing – Review of Theories and Methods in Architecture

Abstract The presence of greenery around and within buildings is an integral aspect of architectural space. Although scientific knowledge regarding the impact of greenery on human well-being is expanding, there remains a scarcity of guidelines for implementing this knowledge in architectural design. This review article aims to lay the foundation for further research dedicated to supporting architectural design in the seamless integration of greenery as a co-creative element within human living space. Existing review literature is diverse and dispersed across various scientific disciplines, addressing distinct objectives, approaches, and research methodologies. Consequently, this review initially concentrates on an in-depth exploration of the broad topic of perception in architecture, highlighting significant theories and methods. Subsequently, the analysis extends to the current state of research specifically centred on well-being and the perception of greenery in the built environment. The evaluation emphasizes criteria conducive to obtaining objective, quantitative results. Finally, considering their stages of development, the available research methods are categorized and those most pertinent to the field of architecture are identified.

LFTransNet: Light Field Salient Object Detection via a Learnable Weight Descriptor

Light Field Salient Object Detection (LF SOD) aims to segment the visually distinctive objects out of surroundings. Since light field images provide a multi-focus stack (many focal slices in different depth levels) and an all-focus image for the same scene, they record comprehensive but redundant information. Existing methods exploit the useful cue by long short-term memory with attention mechanism, 3D convolution, and graph learning. However, the importance of intra-slice and inter-slice in the focal stack is not well investigated. In the paper, we propose a learnable weight descriptor to simultaneously exploit different weights in slice, spatial region, and channel dimensions, and therefore propose an LF SOD method based on the learnable descriptor. The method extracts slice features and all-focus features from a weight-shared backbone and another backbone, respectively. A transformer decoder is used to learn the weight descriptor which both emphasizes the importance of each slice (inter-slice) and discriminates the spatial and channel importance of each slice (intra-slice). The learnt descriptor serves as the weight to make slice features attend to important slices, regions, and channels. Furthermore, we propose the hierarchical multi-modal fusion which aggregates high-layer features by modelling the long-range dependency to fully excavate common salient semantics and combines low-layer features by spatial constraint to eliminate the blurring effect of slice features. The experimental result exceeds the state-of-the-art methods at least 25% in terms of mean absolute error evaluation metric. It demonstrates a significant improvement in LF SOD performance via the designed learnable weight descriptor. https://github.com/liuzywen/LFTransNet.

Bi-criteria set covering problem with conflict constraints

In this article, we consider the set covering problem with conflict constraints, i.e., from each conflicting pair of columns, at most one column can appear in the feasible solution, and we seek to identify (select) a subset of columns so as to achieve two distinct objectives. The first objective is to maximize the number of rows covered by the selected subset of columns, and the second objective is to minimize the number of selected columns. In this context, we present a bi-criteria integer programming (IP) model and two corresponding epsilon-constraint methods to find the non-dominated frontier for this bi-criteria optimization problem. We then propose several families of valid inequalities for the corresponding IP models. Finally, through computational experiments, we demonstrate the effectiveness of the proposed methods.

The influence of biofuel energy production on the exports of the palm oil industry in Malaysia: A panel GMM and robust standard error approach

In recent years, there has been a consistent and significant increase in the consumption of biofuels by countries across the globe. Numerous governments have implemented distinct quotas and objectives aimed at promoting and governing the augmented utilization of biofuels. The demand for biofuel is growing in European countries, which increases the export of biofuel energy in parent countries such as Malaysia. The Malaysian government has suggested receiving different sustainability certifications that are relevant to the biofuel industry. The Malaysian government has made it compulsory to attain environmental sustainability for oil-producing certification. The purpose of this article is to examine the role of biofuel energy production on the exports of the palm oil industry in Malaysia. This study uses the annual data of Malaysian palm oil exporters from 2009 to 2018 and employs the generalized method of moments (GMM) and the robust standard error methodologies. The findings showed that the production of palm oil plays a positive role in the exports of Malaysia’s palm oil industry. This study will serve as a guide for future research in this area and for the authorities when formulating policies regarding exports. It is recommended that these authorities should put extra emphasis on the productivity of the palm oil industry to further enhance Malaysia’s exports of biofuel, and the government needs to implement regulations to protect the environment.

Microorganism Detection in Activated Sludge Microscopic Images Using Improved YOLO

Wastewater has detrimental effects on the natural environment. The activated sludge method, a widely adopted approach for wastewater treatment, has proven highly effective. Within this process, microorganisms play a pivotal role, necessitating continuous monitoring of their quantity and diversity. Conventional methods, such as microscopic observation, are time-consuming. With the widespread integration of computer vision technologies into object detection, deep learning-based object detection algorithms, notably the You Only Look Once (YOLO) model, have garnered substantial interest for their speed and precision in detection tasks. In this research, we applied the YOLO model to detect microorganisms in microscopic images of activated sludge. Furthermore, addressing the irregular shapes of microorganisms, we developed an improved YOLO model by incorporating deformable convolutional networks and an attention mechanism to enhance its detection capabilities. We conducted training and testing using a custom dataset comprising five distinct objects. The performance evaluations used in this study utilized metrics such as the mean average precision at intersections over a union threshold of 0.5 (mAP@0.5), with the improved YOLO model achieving a mAP@0.5 value of 93.7%, signifying a 4.3% improvement over the YOLOv5 model. Comparative analysis of the improved YOLO model and other object detection algorithms on the same dataset revealed a higher accuracy for the improved YOLO model. These results demonstrate the superior performance of the improved YOLO model in the task of detecting microorganisms in activated sludge, providing an effective auxiliary method for wastewater treatment monitoring.

Nonextensive Behavior of Stellar Rotation in the Galactic Disk Components

The solar neighborhood is a unique stellar astrophysical laboratory formed by a variety of stars from different origins. In particular, two of the most notable populations known are the thick and thin disk stars, each characterized by distinct chemical compositions, ages, kinematics, and origins. Based on Tsallis nonextensive statistics, we investigate the observed distribution of the projected rotational velocity of the thin and thick disk component stars. Through Bayesian inference, our results show that the distributions of the Galactic disk populations selected from both kinematic and chemical criteria follow a nonextensive behavior, where non-Gaussian statistics provide a more accurate representation. We also observed an anticorrelation between the entropic index q and the age of disk components confirming the interpretation of initial angular momentum memory loss scaled by the parameter q. In contrast, a subextensivity case with q > 1 was found for the old high-α metal-rich subgroup hαmr, and due to their distinguished rotational behavior and atypical subextensive regime, we infer that thick disk and hαmr stars are, in fact, distinct objects. Our results also suggest that the rotational velocities of stars are defined not only by their evolutionary spin-down processes but also by their birth sites.