Head Module Research Articles

Pre-implementation of electronic patient-reported outcomes at reference centers for head and neck oncology : Aroadmap towards patient-centered digitalization

Electronically captured patient reports (electronic patient-reported outcomes, ePROs) are digital questionnaires filled out by cancer patients. Despite indications of improved clinical care, the integration of ePROs into clinical head and neck oncology is uncharted territory. This work outlines the implementation process for ePROs at the University Hospital for Otorhinolaryngology at the Medical University of Innsbruck (ENT Innsbruck). The implementation is carried out by aproject team in apre-implementation phase (needs assessment, implementation planning, identification of intra-hospital barriers, prototype development, testing and adaptation, and user training), an implementation phase (implementation and user training), and apost-implementation phase (quality control and project expansion). The project team at ENT Innsbruck, consisting of 10members, identified the need for digitalization in cancer follow-up. Ahybrid implementation solution (Computer-Based Health Evaluation System, CHES; Evaluation Software Development, ESD, Innsbruck, Austria) was chosen. ePROs (European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - 30 items, EORTC-QLQ-C30; Head and Neck Functional Integrity Scale, HNC-FIT scale; and EORTC Head and Neck Cancer Module, EORTC H&N43) will be collected 12times over 5.5years. Atotal of 25users rated the prototype as user friendly (patient perspective: 8.1 ± 1.6, 3-10; user perspective: 8.6 ± 1.1, 6-10). The main advantage was faster medical history taking (72%), and the main disadvantages were alack of personnel, time, and motivation (52%). The feedback on the ePRO prototype at ENT Innsbruck was positive. The implementation phase commenced in the first quarter of 2024. Goal achievement will be evaluated in the post-implementation phase in the fourth quarter of 2024.

Distress and health-related quality of life in post-treatment nasopharyngeal carcinoma patients: a two-centre cross-sectional study

Abstract Background Nasopharyngeal carcinoma (NPC) patients often face significant physical and psychological challenges. While disease control remains a clinical priority, the impact on distress and health-related quality of life (HRQOL) is often overlooked. This study will assess distress and the HRQOL prevalence, severity, and relationship among post-treatment nasopharyngeal carcinoma (NPC) patients and investigate its associated factors. Method A cross-sectional study at Hospital Melaka (HM) and Hospital Canselor Tuanku Muhriz (HCTM) involved post-treatment NPC patients with at least six months of follow-up. Distress was assessed using the Distress Thermometer (DT). Quality of life (QoL) and symptomatology were assessed with the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire (QLQ-C30) and EORTC Head and Neck Module (QLQ-H&N 35). We conducted statistical analyses using Kruskal–Wallis and Mann–Whitney U tests for comparisons and Spearman Correlation for analyzing relationships. Results One hundred forty-four patients with a median age of 60.5 years (range 18–92) were recruited and completed the questionnaires. The median NPC surveillance follow-up time was seven years (6 months to 36 years). 84% of the patients received concurrent chemoradiotherapy. The majority of the patients were in late stage 57.6% (Stage III, n = 51[35.4%]; Stage IV, n = 32 [22.2%]). The median distress score is 4 (range, 1–8), and the median HRQOL score is 75 (range of 50–100). A significant negative correlation was found between distress and the HRQOL score. (r = -0.53, p < 0.01). Factors influencing distress and HRQOL scores were sticky saliva, dry mouth, fatigue, and less sexuality, with a significance of P < 0.01. Conclusion Overall distress and HRQOL scores among post-treatment NPC patients were good. Factors such as sticky saliva, dry mouth, fatigue, and decreased sexuality substantially impacted distress and HRQOL. Addressing these factors alongside cancer treatment may enhance HRQOL outcomes.

RLGS-YOLO: an improved algorithm for metro station passenger detection based on YOLOv8

Abstract Passenger detection is a key component of guaranteeing the safe operation of the subway. Nevertheless, the issue of varying target sizes across subway scenarios impedes passenger detection. Additionally, there is the issue of occlusion overlap between passengers. To resolve these concerns, an improved model based on YOLOv8n is proposed. It consists of Reduced Channel Spatial Object Attention (RCSOSA) module, Large Separable Kernel Attention (LSK) module, Group Shuffle Convolution (GSConv) convolution, and other modules such as Small Detect Head (S-detect), and named RLGS-YOLO. Designed to facilitate the deployment of a variety of platforms, S-detect lightweight detection head module minimizes the number of parameters and the amount of model computation. A re-parameterized structure based on Re-param Visual Geometry Group (RepVGG) is proposed to address the issue of mutual occlusion between passengers. This structure is achieved by integrating RCSOSA module, which substantially improves the information exchange between various channels. The dynamic sensory field module of LSK has been incorporated. It improved the issue of model misdetection by whole-heartedly incorporating the background information. The model's sophistication is de-creased by integrating GSConv lightweight convolution. The experimental findings indicate that in comparison to YOLOv8n. In the passenger data set of Nanning Metro Line 1, RLGS-YOLO indicates superior performance. The Mean Average Precision (mAP) improved by 2.2%. RLGS-YOLO, on the other hand, attains superior performance on the public dataset VOC2007. This contains a 1.1% increase in mAP, a 13.79% increase in Frames Per Second (FPS), and a 0.5G Floating Point Operations (FLOP) reduction in computation. In comparison to other prominent detection models, RLGS-YOLO also exhibits superior performance. The improved RLGS-YOLO model provides a precise and efficient solution for passenger detection methods.

Structural Design and Kinematic Modeling of Highly Biomimetic Flapping-Wing Aircraft with Perching Functionality.

Birds use their claws to perch on branches, which helps them to recover energy and observe their surroundings; however, most biomimetic flapping-wing aircraft can only fly, not perch. This study was conducted on the basis of bionic principles to replicate birds' claw and wing movements in order to design a highly biomimetic flapping-wing aircraft capable of perching. First, a posture conversion module with a multi-motor hemispherical gear structure allows the aircraft to flap, twist, swing, and transition between its folded and unfolded states. The perching module, based on helical motion, converts the motor's rotational movement into axial movement to extend and retract the claws, enabling the aircraft to perch. The head and tail motion module has a dual motor that enables the aircraft's head and tail to move as flexibly as a bird's. Kinematic models of the main functional modules are established and verified for accuracy. Functional experiments on the prototype show that it can perform all perching actions, demonstrating multi-modal motion capabilities and providing a foundation upon which to develop dynamics models and control methods for highly biomimetic flapping-wing aircraft with perching functionality.

Automatic pine wilt disease detection based on improved YOLOv8 UAV multispectral imagery

The pine wilt disease (PWD) can cause destructive death to pine trees in a short period. Utilizing unmanned aerial vehicle (UAV) remote sensing technology to promptly identify PWD-infected trees has become an effective and feasible method for precise PWD monitoring. In this study, UAV multispectral imagery was used to analyze the sensitive spectral bands and different vegetation indices for PWD discriminability. A dataset of optimal spectral combinations from visible light and multispectral images was constructed, along with an improved YOLOv8 deep learning model for rapid and accurate identification of PWD-infected trees. The improved YOLOv8 model used omni-dimensional dynamic convolution (ODConv) to enhance the performance of convolutional networks, designed a dynamic head (DyHead) module to capture PWD features more accurately, and applied MPDioU to improve the regression accuracy and model runtime efficiency. Experimental results showed that the mAP@0.5 of the improved YOLOv8 model increased to 89.1 %, with a user accuracy of 90 % and a recall rate of 93.1 %. This achieved rapid and accurate detection of PWD-infected trees, providing effective technical support for automatic identification of PWD epidemic areas and control of PWD outbreaks based on UAV multispectral imagery.

Integrated Anti-Aliasing and Fully Shared Convolution for Small-Ship Detection in Synthetic Aperture Radar (SAR) Images

Synthetic Aperture Radar (SAR) imaging plays a vital role in maritime surveillance, yet the detection of small vessels poses a significant challenge when employing conventional Constant False Alarm Rate (CFAR) techniques, primarily due to the limitations in resolution and the presence of clutter. Deep learning (DL) offers a promising alternative, yet it still struggles with identifying small targets in complex SAR backgrounds because of feature ambiguity and noise. To address these challenges, our team has developed the AFSC network, which combines anti-aliasing techniques with fully shared convolutional layers to improve the detection of small targets in SAR imagery. The network is composed of three key components: the Backbone Feature Extraction Module (BFEM) for initial feature extraction, the Neck Feature Fusion Module (NFFM) for consolidating features, and the Head Detection Module (HDM) for final object detection. The BFEM serves as the principal feature extraction technique, with a primary emphasis on extracting features of small targets, The NFFM integrates an anti-aliasing element and is designed to accentuate the feature details of diminutive objects throughout the fusion procedure, HDM is the detection head module and adopts a new fully shared convolution strategy to make the model more lightweight. Our approach has shown better performance in terms of speed and accuracy for detecting small targets in SAR imagery, surpassing other leading methods on the SSDD dataset. It attained a mean Average Precision (AP) of 69.3% and a specific AP for small targets (APS) of 66.5%. Furthermore, the network’s robustness was confirmed using the HRSID dataset.

Research on Target Hybrid Recognition and Localization Methods Based on an Industrial Camera and a Depth Camera in Complex Scenes

In order to improve the target visual recognition and localization accuracy of robotic arms in complex scenes with similar targets, hybrid recognition and localization methods based on an industrial camera and depth camera are proposed. First, according to the speed and accuracy requirements of target recognition and localization, YOLOv5s is introduced as the basic algorithm model for target hybrid recognition and localization. Then, in order to improve the accuracy of target recognition and coarse localization based on an industrial camera (eye-to-hand), the AFPN feature fusion module, simple and parameter-free attention module (SimAM), and soft non-maximum suppression (Soft NMS) are introduced. In order to improve the accuracy of target recognition and fine localization based on a depth camera (eye-in-hand), the SENetV2 backbone network structure, dynamic head module, deformable attention mechanism, and chain-of-thought prompted adaptive enhancer network are introduced. After that, on the basis of constructing a dual camera platform for target hybrid recognition and localization, the hand–eye calibration, collection and production of image datasets required for model training are completed. Finally, for the docking of the oil filling port, the hybrid recognition and localization experimental tests are completed in sequence. The test results show that in target recognition and coarse localization based on the industrial camera, the recognition accuracy of the designed model reaches 99%, and the average localization errors in the horizontal and vertical directions are 2.22 mm and 3.66 mm, respectively. In target recognition and fine localization based on the depth camera, the recognition accuracy of the designed model reaches 98%, and the average errors in depth, horizontal, and vertical directions are 0.12 mm, 0.28 mm, and 0.16 mm, respectively. These not only verify the effectiveness of the target hybrid recognition and localization methods based on dual cameras, but also demonstrate that they meet the high-precision recognition and localization requirements in complex scenes.

Finnish translation and linguistic validation of the FACE-Q Head and Neck Cancer Module.

Head and neck cancer (HNC) and its treatment can result in permanent changes to a patient's appearance, speaking, eating, and psychosocial well-being. To better assess the impact of the disease on HNC patients, the FACE-Q Head and Neck Cancer Module, a health-related quality-of-life instrument, was developed. The aim of this study was to produce and linguistically validate a Finnish version of the module. The module was translated into Finnish following the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) guidelines. A total of 51 patients who had undergone tumour resection and reconstruction of the oral cavity, tonsil, or tongue area between 2019 and 2021 were approached for a pilot study. They completed the translated module and provided feedback on any linguistic issues. Adjustments were made based on the pilot study comments. The FACE-Q Head and Neck Cancer Module translated well into Finnish. Twenty-one (41%) patients participated in the survey, 12 men (57%) and nine women (43%) with a median age of 66 years (range 48-89 years). The median time since surgery was 3 years (range 1-4 years). Based on the feedback from the pilot study participants, one word was changed, and one question was rewritten. Otherwise, no deficiencies were identified in the language of the module. In summary, this study produced a linguistically valid Finnish version of the FACE-Q Head and Neck Cancer Module, enabling its application in evaluating the health-related quality-of-life among Finnish HNC patients who have undergone reconstructive surgery.

Clinical Outcomes of Scapular versus Fibular free flaps in Head and Neck Reconstructions: A Retrospective Study of 120 patients".

The scapular free flap has increasingly gained popularity as an alternative to the fibular free flap in osseous head and neck reconstruction. The present study aimed to evaluate their use in maxillomandibular reconstructions and examine surgical and patient outcomes. Osseous head and neck defects reconstructed with an angular artery-based scapular flap or fibular flap from 2016 to 2022 at two Swedish University Hospitals were evaluated for their intraoperative execution (osseous and soft tissue combinations) and postoperative outcomes. Facial quality of life (QoL) was assessed using the FACEQ Head and Neck Cancer Module. Donor-site morbidity was assessed using the DASH and SEFAS questionnaires for scapular- and fibular-reconstructed patients, respectively. A total of 120 patients were recruited: 86 mandibular reconstructions (26 scapulas, 60 fibulas) and 34 maxillary reconstructions (32 scapulas, 2 fibulas). The concave scapula facilitated fewer osteotomies for curved reconstructions, particularly for lateral mandibular defects (p=0.039). Intraoral lining was primarily achieved with muscle accompanying the scapula and skin accompanying the fibula. Using the latissimus dorsi muscle instead of only teres major decreased fistulas, plate/bone exposure and non-unions (p=0.039) in scapular reconstructions. Both flaps demonstrated comparable facial QoL, but the scapula presented superior donor site outcomes (p=0.001). Donor limb outcomes were unaffected by harvesting the latissimus dorsi with the scapula (p=0.64), and by re-suturing the detached muscles to the remaining scapula. (p=0.35). The scapular free flap can be advantageous in osseous head and neck reconstructions, enabling non-osteotomized and chimeric reconstructions without compromising surgical outcomes or quality of life.

The european organisation for research and treatment of cancer head and neck cancer module (EORTC QLQ-HN43): Estimates for minimal important difference and minimal important change

IntroductionMinimal important change estimates (MIC) are useful for interpreting results of clinical research with quality of life (QoL) as an endpoint. For the European Organisation for Research and Treatment of Cancer head and neck cancer module, the EORTC QLQ-HN43, no such thresholds are established. MethodsHead and neck cancer patients under active treatment (n = 503) from 15 countries completed the EORTC QLQ-HN43 three times (t1: before treatment, t2: three months after t1, t3: six months after t1). A subgroup completed a Subjective Significance Questionnaire (SSQ), indicating experienced change from the previous time point in four QoL domains. QoL was assumed to deteriorate after t1 and improve again until t3. The MIC was established using the average of mean differences in SSQ groups (MICmean) and estimates based on logistic regressions (MICpredict). Additionally, minimal detectable changes (MDC) were computed using 0.5 standard deviation and standard error of the mean. ResultsFor swallowing, speech, dry mouth, and global QoL, the MIC for deterioration were 13, 14, 26, and 10 respectively. The MIC for improvement were 8 (swallowing), 6 (dry mouth), and 5 (global QoL); no MIC for speech improvement can be presented because of insufficient correlation between change score and anchor. The MDC estimates for deterioration were 15, 14, 15, and 11. For improvement, the MDC estimates were 13, 14, 14, and 11. ConclusionsOur results underline that no single MIC or MDC can be applied to all EORTC QLQ-HN43 scales, and that the MIC for deterioration seems larger than those for improvement.

Wildlife target detection based on improved YOLOX-s network

To addresse the problem of poor detection accuracy or even false detection of wildlife caused by rainy environment at night. In this paper, a wildlife target detection algorithm based on improved YOLOX-s network is proposed. Our algorithm comprises the MobileViT-Pooling module, the Dynamic Head module, and the Focal-IoU module.First, the MobileViT-Pooling module is introduced.It is based on the MobileViT attention mechanism, which uses a spatial pooling operator with no parameters as a token mixer module to reduce the number of network parameters. This module performs feature extraction on three feature layers of the backbone network output respectively, senses the global information and strengthens the weight of the effective information. Second, the Dynamic Head module is used on the downstream task of network detection, which fuses the information of scale sensing, spatial sensing, and task sensing and improves the representation ability of the target detection head. Lastly, the Focal idea is utilized to improve the IoU loss function, which balances the learning of high and low quality IoU for the network. Experimental results reveal that our algorithm achieves a notable performance boost with mAP@0.5 reaching 87.8% (an improvement of 7.9%) and mAP@0.5:0.95 reaching 62.0% (an improvement of 5.3%). This advancement significantly augments the night-time wildlife detection accuracy under rainy conditions, concurrently diminishing false detections in such challenging environments.

Health-Related Quality of Life and Psychological Distress Status in Survivors of Nasopharyngeal Carcinoma: A Retrospective Study.

Introduction Nasopharyngeal carcinoma (NPC) is an uncommon cancer globally, with a low incidence in Western countries. However, this rate increases significantly in some endemic regions, including the Middle East. Although studies have evaluated the physical health outcomes, treatment efficacy, and short-term effects, long-term effects - especially quality of life (QoL) - have been neglected. Therefore, we aimed to assess the QoL and psychological distress status of survivors of NPC and investigate the demographic, clinical, temporal, and therapeutic factors that impact the well-being of such patients. Methodology This cross-sectional analysis was performed in 2023 at the King Abdulaziz University Hospital (KAUH), a major tertiary care facility located in Jeddah, Kingdom of Saudi Arabia. We retrospectively reviewed all reported cases of NPC at KAUH from 2010 to 2023 and identified 34 patients who met our inclusion criteria. Data on the QoL and psychological distress status were collected using telephone interviews, during which three questionnaires were administered: the "European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 Version 3.0" (EORTC QLQ-C30), the "European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Head and Neck Module 43" (EORTC QLQ-HN43), an additional questionnaire designed to be utilized alongside the EORTC QLQ-C30, and the "Hospital Anxiety and Depression Scale." Demographic and clinical information were also obtained. Results The patients had a mean age of 49.88 ± 2.27 years (range: 18-70 years). The mean survival duration was 5.88 years (range: 1-14 years). From the pooled data, abnormal borderline depression, borderline anxiety, and anxiety were identified in five patients (14.7%), four patients (11.76%), and two patients (5.88%), respectively. Patient age showed a significant positive correlation with both "fatigue" (r = 0.367, p = 0.033) and "social contact" (r = 0.423, p = 0.013), where higher scores on both symptom scales indicate worse symptoms (i.e., greater fatigue and more difficulties with social interactions). The mean "global health status/QoL" was significantly lower in patients with comorbidities (p = 0.039) and "oral dryness" (p = 0.027) compared to those without. The tumor stage showed a significant negative correlation with the "global health status/QoL" (r = -0.664, p = 0.001). Conclusion A significant number of survivors of NPC experienced borderline depression and anxiety. "Global health status/QoL" was low in advanced tumor stages. The mean "global health status/QoL" was low in the presence of certain factors, including coexisting comorbidities and treatment-related side effects. Our findings suggest that patients diagnosed with NPC should undergo assessments for anxiety and depression before, during, and after treatment. Moreover, symptomatic treatment of therapeutic side effects, especially "oral dryness," should be undertaken. "Oral dryness," experienced by most of the patients, was significantly correlated with a low mean "global health status/QoL."

Assessing Postoperative Quality of Life and Psychological Impact of Free Flap Reconstruction in Advanced Head and Neck Cancer Patients.

Introduction Head and neck cancer (HNC) significantly affects patients worldwide, often resulting in physical disfigurement, functional impairment, and diminished quality of life (QoL). Advanced HNC may necessitate reconstructive surgeries using free tissue transfer, which, despite improving survival, can exacerbate emotional and social challenges such as anxiety, depression, and social isolation. This study aims to evaluate the well-being of patients undergoing free flap surgery for advanced HNC using validated tools. Methods We conducted a prospective longitudinal study of 30 patients with advanced HNC (≥T3, stage ΙΙΙ-IV) who underwent free flap reconstructive surgery at the General Oncological Anticancer Hospital of Athens 'Agios Savvas' between January 2018 and January 2024. Patient-reported outcomes were assessed using three validated questionnaires in Greek: the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Head and Neck Module (EORTC QLQ-H&N35), the Hospital Anxiety and Depression Scale (HADS), and the Brief Pain Inventory (BPI). Statistical analysis was performed using IBM SPSS Statistics for Windows, version 26.0 (IBM Corp., Armonk, NY), with statistical significance set at p < 0.05. Results Among the 30 patients (10 women and 20 men; mean age = 56.6 years), post-surgical assessments indicated significant worsening of symptoms, including increased pain (p < 0.001), swallowing difficulties (p < 0.001), and speech impairments (p < 0.001). Nearly all patients reported increased analgesic use post surgery (p = 0.003), and a significant correlation was found between pain severity and diminished daily functioning, particularly life enjoyment (correlation coefficient = 0.788, p < 0.001). Additionally, 63% of patients (n = 19) were diagnosed with depression and 67% with anxiety (n = 20), with men showing a higher prevalence of depression (p = 0.001). No significant differences in anxiety rates were found between the sexes. Overall, pain severity had a broad impact on both physical and psychological status, contributing to social withdrawal and isolation. Conclusions Patients with HNC who undergo free flap reconstructive surgery face significant challenges in emotional, social, and physical well-being. Depression and anxiety are common but often undetected, especially in men. Pain remains a significant factor affecting QoL and social engagement. Comprehensive care models, including standardized pre- and post-treatment assessments, early mental health screenings, and personalized rehabilitation strategies, are essential to improve outcomes and enhance QoL for HNC patients.

YOLO-EF: A lightweight YOLOv7 PCB defect detection algorithm

Abstract This study addresses the challenge of accurate PCB defect detection, considering the computational complexity and excessive model parameters associated with existing methods. To mitigate these issues, we propose an enhanced PCB surface defect detection method called YOLO-EF, which is built upon the YOLOv7 architecture. Firstly, we replace the backbone of YOLOv7 with the lightweight Efficient FormerV2 module. This replacement reduces both the model volume and computational requirements. Secondly, the dynamic head module is introduced to unify the target detection head and improve its performance. Finally, the Wise-IoUv3 function is used to suppress the influence of low-quality samples on the model performance. The experimental results show that the proposed model improves by 2.85% in mAP over the original model. Model computation is reduced from 106.5 GFLOPs to 44.3 GFLOPs. Our YOLO-EF model achieves an optimal balance in terms of precision, computational efficiency, and detection speed.

Research on the Behavior Recognition of Beef Cattle Based on the Improved Lightweight CBR-YOLO Model Based on YOLOv8 in Multi-Scene Weather.

In modern animal husbandry, intelligent digital farming has become the key to improve production efficiency. This paper introduces a model based on improved YOLOv8, Cattle Behavior Recognition-YOLO (CBR-YOLO), which aims to accurately identify the behavior of cattle. We not only generate a variety of weather conditions, but also introduce multi-target detection technology to achieve comprehensive monitoring of cattle and their status. We introduce Inner-MPDIoU Loss and we have innovatively designed the Multi-Convolutional Focused Pyramid module to explore and learn in depth the detailed features of cattle in different states. Meanwhile, the Lightweight Multi-Scale Feature Fusion Detection Head module is proposed to take advantage of deep convolution, achieving a lightweight network architecture and effectively reducing redundant information. Experimental results prove that our method achieves an average accuracy of 90.2% with a reduction of 3.9 G floating-point numbers, an increase of 7.4%, significantly better than 12 kinds of SOTA object detection models. By deploying our approach on monitoring computers on farms, we expect to advance the development of automated cattle monitoring systems to improve animal welfare and farm management.

M-YOLOv8s: An improved small target detection algorithm for UAV aerial photography

The object of UAV target detection usually means small target with complicated backgrounds. In this paper, an object detection model M-YOLOv8s based on UAV aerial photography scene is proposed. Firstly, to solve the problem that the YOLOv8s model cannot adapt to small target detection, a small target detection head (STDH) module is introduced to fuse the location and appearance feature information of the shallow layers of the backbone network. Secondly, Inner-Wise intersection over union (Inner-WIoU) is designed as the boundary box regression loss, and auxiliary boundary calculation is used to accelerate the regression speed of the model. Thirdly, the structure of multi-scale feature pyramid network (MS-FPN) can effectively combine the shallow network information with the deep network information and improve the performance of the detection model. Furthermore, a multi-scale cross-spatial attention (MCSA) module is proposed to expand the feature space through multi-scale branch, and then achieves the aggregation of target features through cross-spatial interaction, which improves the ability of the model to extract target features. Finally, the experimental results show that our model does not only possess fewer parameters, but also the values of mAP0.5 are 6.6% and 5.4% higher than the baseline model on the Visdrone2019 validation dataset and test dataset, respectively. Then, as a conclusion, the M-YOLOv8s model achieves better detection performance than some existing ones, indicating that our proposed method can be more suitable for detecting the small targets.

Lightweight Substation Equipment Defect Detection Algorithm for Small Targets.

Substation equipment defect detection has always played an important role in equipment operation and maintenance. However, the task scenarios of substation equipment defect detection are complex and different. Recent studies have revealed issues such as a significant missed detection rate for small-sized targets and diminished detection precision. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. In view of the above problems, a small target and lightweight substation main scene equipment defect detection algorithm is proposed: Efficient Attentional Lightweight-YOLO (EAL-YOLO), which detection accuracy exceeds the current mainstream model, and the number of parameters and floating point operations (FLOPs) are also advantageous. Firstly, the EfficientFormerV2 is used to optimize the model backbone, and the Large Separable Kernel Attention (LSKA) mechanism has been incorporated into the Spatial Pyramid Pooling Fast (SPPF) to enhance the model's feature extraction capabilities; secondly, a small target neck network Attentional scale Sequence Fusion P2-Neck (ASF2-Neck) is proposed to enhance the model's ability to detect small target defects; finally, in order to facilitate deployment on resource-constrained devices, a lightweight shared convolution detection head module Lightweight Shared Convolutional Head (LSCHead) is proposed. Experiments show that compared with YOLOv8n, EAL-YOLO has improved its accuracy by 2.93 percentage points, and the mAP50 of 12 types of typical equipment defects has reached 92.26%. Concurrently, the quantity of FLOPs and parameters has diminished by 46.5% and 61.17% respectively, in comparison with YOLOv8s, meeting the needs of substation defect detection.

GFI-YOLOv8: Sika Deer Posture Recognition Target Detection Method Based on YOLOv8.

As the sika deer breeding industry flourishes on a large scale, accurately assessing the health of these animals is of paramount importance. Implementing posture recognition through target detection serves as a vital method for monitoring the well-being of sika deer. This approach allows for a more nuanced understanding of their physical condition, ensuring the industry can maintain high standards of animal welfare and productivity. In order to achieve remote monitoring of sika deer without interfering with the natural behavior of the animals, and to enhance animal welfare, this paper proposes a sika deer individual posture recognition detection algorithm GFI-YOLOv8 based on YOLOv8. Firstly, this paper proposes to add the iAFF iterative attention feature fusion module to the C2f of the backbone network module, replace the original SPPF module with AIFI module, and use the attention mechanism to adjust the feature channel adaptively. This aims to enhance granularity, improve the model's recognition, and enhance understanding of sika deer behavior in complex scenes. Secondly, a novel convolutional neural network module is introduced to improve the efficiency and accuracy of feature extraction, while preserving the model's depth and diversity. In addition, a new attention mechanism module is proposed to expand the receptive field and simplify the model. Furthermore, a new pyramid network and an optimized detection head module are presented to improve the recognition and interpretation of sika deer postures in intricate environments. The experimental results demonstrate that the model achieves 91.6% accuracy in recognizing the posture of sika deer, with a 6% improvement in accuracy and a 4.6% increase in mAP50 compared to YOLOv8n. Compared to other models in the YOLO series, such as YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv9, and YOLOv10, this model exhibits higher accuracy, and improved mAP50 and mAP50-95 values. The overall performance is commendable, meeting the requirements for accurate and rapid identification of the posture of sika deer. This model proves beneficial for the precise and real-time monitoring of sika deer posture in complex breeding environments and under all-weather conditions.

An Intelligent Fishery Detection Method Based on Cross-Domain Image Feature Fusion

Target detection technology plays a crucial role in fishery ecological monitoring, fishery diversity research, and intelligent aquaculture. Deep learning, with its distinct advantages, provides significant convenience to the fishery industry. However, it still faces various challenges in practical applications, such as significant differences in image species and image blurring. To address these issues, this study proposes a multi-scale, multi-level, and multi-stage cross-domain feature fusion model. In order to train the model more effectively, a new data set called Fish52 (multi-scene fish data set, a data set containing 52 fish species) was constructed, on which the model achieved an mAP (mean average precision is a key measure of model performance) of 82.57%. Furthermore, we compared prevalent one-stage and two-stage detection methods on the Lahatan (single-scene fish data set) and Fish30 data set (a data set containing 30 fish species) and tested them on the F4k (Fish4Knowledge (F4K) is a data set focused on fish detection and identification) and FishNet data set (it is a data set containing 94,532 images from 17,357 aquatic species). The mAP of our proposed model on the Fish30, Lahatan, F4k, and FishNet data sets reaches 91.72%, 98.7%, 88.6%, and 81.5%, respectively, outperforming existing mainstream models. Comprehensive empirical analysis indicates that our model possesses a high generalization ability and reaches advanced performance levels. In this study, the depth of the model backbone is deepened, a novel neck structure is proposed, and a new module is embedded therein. To enhance the fusion ability of the model, a new attention mechanism module is introduced. In addition, in the adaptive decoupling detection head module, introducing classes with independent parameters and regression adapters reduces interaction between different tasks. The proposed model can better monitor fishery resources and enhance aquaculture efficiency. It not only provides an effective approach for fish detection but also has certain reference significance for the identification of similar targets in other environments and offers assistance for the construction of smart fisheries and digital fisheries.

Associations between hypopharyngeal defect closure and quality of life in long-term total laryngectomy survivors.

Few studies have examined health-related quality of life (HRQOL) outcomes in long-term total laryngectomy survivors in relation to the type of hypopharyngeal defect. A cross-sectional study was performed in long-term total laryngectomy survivors, treated between 2000 and 2020. The primary outcome was HRQOL, assessed using the FACE-Q Head and Neck Cancer Module, in relation to the type of hypopharyngeal closure (primary closure, partial or circumferential reconstruction). Seventy-nine survivors were included with a median follow-up of 92.1 months (IQR 75.6-140.2 months). Patients requiring partial hypopharyngeal reconstruction (n = 18) scored significantly worse than patients with primary closure (n = 51) on 4 of 13 FACE-Q domains: functional domains of eating (p = 0.03), speech (p = 0.05), and swallowing (p = 0.03), and the psychological domain of speaking-related distress (p = 0.02). No statistically significant differences were found between the circumferential hypopharyngeal defect reconstruction group (n = 10). Stricture occurrence was the only clinical factor associated with worse eating, speaking, swallowing, eating-related distress, and cancer worry in multivariable analyses. Several functional and psychological domains were significantly worse following partial hypopharyngeal reconstruction than in patients who received primary closure. Efforts to reduce stricture rates to enhance reconstructive outcomes following total laryngectomy merit further research.