Prostate cancer lung metastasis represents a clinical conundrum due to its implications for advanced disease progression and the complexities it introduces in treatment planning. As the disease progresses to distant sites such as the lung, the clinical management becomes increasingly intricate, requiring tailored therapeutic strategies to address the unique characteristics of metastatic lesions. This review seeks to synthesize the current state of knowledge surrounding prostate cancer metastasis to the lung, shedding light on the diverse array of clinical presentations encountered, ranging from subtle radiological findings to overt symptomatic manifestations. By examining the diagnostic modalities utilized in identifying this metastasis, including advanced imaging techniques and histopathological analyses, this review aims to provide insights into the diagnostic landscape and the challenges associated with accurately characterizing lung metastatic lesions in prostate cancer patients. Moreover, this review delves into the nuances of therapeutic interventions employed in managing prostate cancer lung metastasis, encompassing systemic treatments such as hormonal therapies and chemotherapy, as well as metastasis-directed therapies including surgery and radiotherapy. Full article

Several engineering systems currently use microchannel heat sinks. In order to increase the performance of these devices, optimisation according to the first and second law of thermodynamics is employed. One way to achieve the goal is to modify the geometry of the cross-section, as is done in this paper for square ducts, having the walls at a uniform temperature which is higher than that of the bulk fluid at the inlet. The effects of both the thermal entry region of the duct and the heat generation due to viscous dissipation are considered. The resulting Graetz–Brinkman problem is solved numerically to obtain the velocity and temperature fields. It is demonstrated that non-negligible viscous heating eventually causes the heat flux to reverse (from fluid to walls), and that, only after this condition is achieved, can the flow become fully developed, which makes the entry region the only useful stretch for real-life applications. The length after which the direction of the heat flux reverses due to viscous heating in the fluid is obtained as a function of the Brinkman number and of the smoothing radius. Optimisation with performance evaluation criteria and entropy generation minimisation was carried out separately, and the results were combined into a single objective function. A comparison with published models highlights how neglecting the entry region and viscous heating yields misleading results. It turns out that smoothing the corners is always profitable in the case of the constrained heated perimeter or area of the cross-section but seldom when the characteristic length or the hydraulic diameter is fixed. With few exceptions, viscous heating amplifies the trends experienced for zero-Brinkman flows. The results are in non-dimensional form, yet they have been obtained starting from plausible dimensional values and are applicable to real-life devices. Full article

While unsupervised denoising models have shown progress in recent years, their noise reduction capabilities still lag behind those of supervised denoising models. This limitation can be attributed to the lack of effective constraints during training, which only utilizes noisy images and hinders further performance improvements In this work, we propose a novel dual-branch self-boosting network called DBSNet, which offers a straightforward and effective approach to image denoising. By leveraging task-dependent features, we exploit the intrinsic relationships between the two branches to enhance the effectiveness of our proposed model. Initially, we extend the classic Noise2Noise (N2N) architecture by adding a new branch for noise component prediction to the existing single-branch network designed for content prediction. This expansion creates a dual-branch structure, enabling us to simultaneously decompose a given noisy image into its content (clean) and noise components. This enhancement allows us to establish stronger constraint conditions and construct more powerful loss functions to guide the training process. Furthermore, we replace the UNet structure in the N2N network with the proven DnCNN (Denoising Convolutional Neural Network) sequential network architecture, which enhances the nonlinear mapping capabilities of the DBSNet. This modification enables our dual-branch network to effectively map a noisy image to its content (clean) and noise components simultaneously. To further improve the stability and effectiveness of training, and consequently enhance the denoising performance, we introduce a feedback mechanism where the network’s outputs, i.e., content and noise components, are fed back into the dual-branch network. This results in an enhanced loss function that ensures our model possesses excellent decomposition ability and further boosts the denoising performance. Extensive experiments conducted on both synthetic and real-world images demonstrate that the proposed DBSNet outperforms the unsupervised N2N denoising model as well as mainstream supervised models trained with supervised methods. Moreover, the evaluation results on real-world noisy images highlight the desirable generalization ability of DBSNet for practical denoising applications. Full article

The present research points to an alternative concern against the mainstream research of future ports’ development by taking a transdisciplinary approach of a Living Lab (LL) concept for a better sustainability and innovation record in Small and Medium-Sized Ports (SMSPs). Deploying qualitative research for the examination of this new phenomenon of aggregating LLs into SMSPs, this research builds upon stakeholder workshops, in-depth interviews, and designed port pilots as case studies dedicated to innovation and sustainability transition in the Baltic Sea Region (BSR) at the turn of 2030. Given its rich and significant empirical foundation, the present research substantially contributes to sustainability orientation and transitions in ports. The key original elements of this study are fourfold: (1) the research provides a theoretical and practical LL framework enabling innovation and sustainability to be grasped in ports in times of technological, social, and political disruption; (2) this research increases the minimal number of existing previous efforts studying SMSPs in the transitional discourse; (3) the paper addresses not only hard technological innovation concerns but also aspects of social acceptance and the role of social interactions; (4) the research goes beyond geographical boundaries of a single port, thus providing a joint and collaborative approach towards sustainability rather than an individual perception on sustainability transition, existing networks, and clusters. Full article

As the world’s largest emerging market country, not only has China faced the contradiction between its huge population size and per capita energy scarcity for a long time, but the rigid constraints brought by energy poverty have also plagued the lives and production of Chinese residents. Based on panel data from 30 provinces (except Tibet) in mainland China from 2009 to 2021, this study employs double machine learning and spatial difference-in-difference for causal inference to explore the impact of a medium- to long-term regional innovation pilot policy in China—the new policy for innovative transformation in regional industrial chains—on energy poverty alleviation. This study also introduces China’s conversion of new and old kinetic energy into this quasi-natural experiment. This study presents the following findings: (1) The new policy for innovative transformation in regional industrial chains and the concept of the conversion of new and old kinetic energy can both significantly promote energy poverty alleviation. (2) The mechanism pathway of “the new policy for innovative transformation in regional industrial chains → the conversion of new and old kinetic energy → the energy poverty alleviation in heating/household electricity/transportation segments” has proved to be an effective practice in China. (3) Based on the spatial double difference model, the spatial direct effect of the new regional industrial chain innovation and change policy on energy poverty alleviation is significantly positive, while the spatial direct effect and spatial spillover effect of the new and old kinetic energy transformation on energy poverty alleviation are both significantly positive. (4) Based on the counterfactual framework analysis, in addition to the causal mediating mechanism of the demand-side conversion of new and old kinetic energy being impeded, both the supply-side and the structural-side conversion of new and old kinetic energy are able to play a significant positive causal mediating role in both the treatment and control groups. Full article

Recreational team sports have received great acceptance lately, in different populations, indicating encouraging results in health-related quality of life. This study examined the efficacy of a 3-month basketball exercise program on selected indices of health (body mass—BM, body fat—BF, blood pressure—BP), functional capacity (flexibility of lower and upper limbs, balance), and physical fitness (maximum strength of lower limbs, trunk and handgrip, aerobic capacity) in middle-aged individuals. Forty middle-aged individuals (males and females; 40–55 years old) were randomly divided into (a) exercise (EG; n = 20) and (b) control groups (CG; n = 20). The EG followed a 3-month modified basketball exercise program (2 times/week; 24 training units), including different basketball drills with and without the ball (dribbling, passing, pivot, stops, etc.), to improve participants’ health and physical fitness. Repeated measures ANOVA showed that the EG significantly increased their flexibility (17.23–74.88%; p < 0.001), static balance (44.76–54.69%; p < 0.001), and strength of lower limbs and trunk (11.67–13.13%; p < 0.001), while reducing BP (7.31–12%; p < 0.001), heart rate and RPE (5.30–34.37%; p < 0.001), and time during time-up-and-go test (−10.91%; p < 0.001). Handgrip strength, BM, and BF did not change following the program in the EG (p > 0.05). In the CG, the above variables remained stable. In conclusion, this program may be used to eliminate the detrimental effects of aging on health, functional capacity, and physical fitness parameters. Full article

As an important research focus in radar detection and localization, direction-of-arrival (DOA) estimation has advanced significantly owing to deep learning techniques with powerful fitting and classifying abilities in recent years. However, deep learning inevitably requires substantial data to ensure learning and generalization abilities and lacks reasonable interpretability. Recently, a deep unfolding technique has attracted widespread concern due to the more explainable perspective and weaker data dependency. More importantly, it has been proven that deep unfolding enables convergence acceleration when applied to iterative algorithms. On this basis, we rigorously deduce an iterative sparse Bayesian learning (SBL) algorithm and construct a Bayesian deep unfolded network in a one-to-one correspondence. Moreover, the common but intractable off-grid errors, caused by grid mismatch, are directly considered in the signal model and computed in the iterative process. In addition, minimum hole array, little considered in deep unfolding, is adopted to further improve estimation performance owing to the maximized array degrees of freedom (DOFs). Extensive simulation results are presented to illustrate the superiority of the proposed method beyond other state-of-the-art methods. Full article

Jeryak is the F1 generation of the cross between Gannan yak and Jersey cattle, which has the advantages of fast growth and high adaptability. The growth and development of skeletal muscle is closely linked to meat production and the quality of meat. However, the molecular regulatory mechanisms of muscle growth differences between Gannan yak and Jeryak analyzed from the perspective of chromatin opening have not been reported. In this study, ATAC-seq was used to analyze the difference of chromatin openness in longissimus muscle of Gannan yak and Jeryak. It was found that chromatin accessibility was more enriched in Jeryak compared to Gannan yak, especially in the range of the transcription start site (TSS) ± 2 kb. GO and KEGG enrichment analysis indicate that differential peak-associated genes are involved in the negative regulation of muscle adaptation and the Hippo signaling pathway. Integration analysis of ATAC-seq and RNA-seq revealed overlapping genes were significantly enriched during skeletal muscle cell differentiation and muscle organ morphogenesis. At the same time, we screened FOXO1, ZBED6, CRY2 and CFL2 for possible involvement in skeletal muscle development, constructed a genes and transcription factors network map, and found that some transcription factors (TFs), including YY1, KLF4, KLF5 and Bach1, were involved in skeletal muscle development. Overall, we have gained a comprehensive understanding of the key factors that impact skeletal muscle development in various breeds of cattle, providing new insights for future analysis of the molecular regulatory mechanisms involved in muscle growth and development. Full article

The family Beggiatoaceae is currently represented by 25 genera in the Genome Taxonomy Database, of which only 6 have a definite taxonomic status. Two metagenome-assembled genomes (MAGs), WS_Bin1 and WS_Bin3, were assembled from metagenomes of the sulfur mats coating laminaria remnants in the White Sea. Using the obtained MAGs, we first applied phylogenetic analysis based on whole-genome sequences to address the systematics of Beggiatoaceae, which clarify the taxonomy of this family. According to the average nucleotide identity (ANI) and average amino acid identity (AAI) values, MAG WS_Bin3 was assigned to a new genus and a new species in the family Beggiatoaceae, namely, ‘Candidatus Albibeggiatoa psychrophila’ gen. nov., sp. nov., thus providing the revised taxonomic status of the candidate genus ‘BB20’. Analysis of 16S rRNA gene homology allowed us to identify MAG WS_Bin1 as the only currently described species of the genus ‘Candidatus Parabeggiatoa’, namely, ‘Candidatus Parabeggiatoa communis’, and consequently assign the candidate genus ‘UBA10656’, including four new species, to the genus ‘Ca. Parabeggiatoa’. Using comparative whole-genome analysis of the members of the genera ‘Candidatus Albibeggiatoa’ and ‘Ca. Parabeggiatoa’, we expanded information on the central pathways of carbon, sulfur and nitrogen metabolism in the family Beggiatoaceae. Full article

Energy transition is one of the basic actions taken to counteract and prevent climate change. The basic assumption of energy-related changes is its sustainable use according to the closed-loop model, as well as moving away from fossil fuels, in particular from coal, the combustion of which contributes to excessive harmful carbon dioxide emissions. One of the most popular solutions towards green energy is nuclear energy. Its use allows for a significant reduction in greenhouse gas emissions harmful to the environment and climate, but it also involves the generation of radioactive waste that requires appropriate processing. This paper presents the results of the flotation removal of barium(II) ions from a dilute aqueous solution using ionized acyclic polyethers. The basic factors determining the efficiency and kinetics of the process were defined. It has been shown that as the acidity of the attached polyether molecules increases: the flotation rate constant 1 (0.1667 min⁻¹) < 3 (0.2468 min⁻¹) < 2 (0.3616 min⁻¹) and the separation degree Ba²⁺: 1 (86.8%) < 3 (99.3%) < 2 (99.4%). The presented results of ion flotation tests may facilitate the collective or selective separation of radioactive isotopes, i.e., Cs-137, Sr-90, Ba-133 and Co-60, from radioactive wastewater in the future. The results of the experimental work described in the article can also be used to develop individual processes for separating mixtures of radioactive isotopes (radioactive wastewater) into individual components (isotopes) and subjecting them to subsequent transformation processes. The obtained results allow us to claim that the tested organic compounds can be used in the future in the selective treatment of hazardous wastewater, which will translate into a reduction in unit costs of industrial processes. The selective recovery of individual pollutants is the basis for the next step in waste management, i.e., designing a cheap method of waste disposal, which also directly affects the economics of the process and its use in industrial conditions. Full article

The rapid advancement of technology, alongside state-of-the-art techniques is at an all-time high. However, this unprecedented growth of technological prowess also brings forth potential threats, as oftentimes the security encompassing these technologies is imperfect. Particularly within the automobile industry, the recent strides in technology have brought about increased complexity. A notable flaw lies in the CAN-FD protocol, which lacks robust security measures, making it vulnerable to data theft, injection, replay, and flood data attacks. With the rising complexity of in-vehicular networks and the widespread adoption of CAN-FD, the imperative to safeguard the protocol has never been more crucial. This paper aims to provide a comprehensive review of the existing in-vehicle communication protocol, CAN-FD. It explores existing security approaches designed to fortify CAN-FD, demonstrating multiple multi-layer solutions that leverage modern techniques including Physical Unclonable Function (PUF), Elliptical Curve Cryptography (ECC), Ethereum Blockchain, and Smart contracts. The paper highlights existing multi-layer security measures that offer minimal overhead, optimal performance, and robust security. Moreover, it identifies areas where these security measures fall short and discusses ongoing research along with suggestions for implementing software and hardware-level modifications. These proposed changes aim to streamline complexity, reduce overhead while ensuring forward compatibility. In essence, the methods outlined in this study are poised to excel in real-world applications, offering robust protection for the evolving landscape of in-vehicular communication systems. Full article

As deep learning has evolved, larger and deeper neural networks are currently a popular trend in both natural language processing tasks and computer vision tasks. With the increasing parameter size and model complexity in deep neural networks, it is also necessary to have more data available for training to avoid overfitting and to achieve better results. These facts demonstrate that training deep neural networks takes more and more time. In this paper, we propose a training acceleration method based on gradually freezing the parameters during the training process. Specifically, by observing the convergence trend during the training of deep neural networks, we freeze part of the parameters so that they are no longer involved in subsequent training and reduce the time cost of training. Furthermore, an adaptive freezing algorithm for the control of freezing speed is proposed in accordance with the information reflected by the gradient of the parameters. Concretely, a larger gradient indicates that the loss function changes more drastically at that position, implying that there is more room for improvement with the parameter involved; a smaller gradient indicates that the loss function changes less and the learning of that part is close to saturation, with less benefit from further training. We use ViTDet as our baseline and conduct experiments on three remote sensing target detection datasets to verify the effectiveness of the method. Our method provides a minimum speedup ratio of 1.38×, while maintaining a maximum accuracy loss of only 2.5%. Full article

Advancements in machining technology demand higher speeds and precision, necessitating improved control systems in equipment like CNC machine tools. Due to lead errors, structural vibrations, and thermal deformation, commercial CNC controllers commonly use rotary encoders in the motor side to close the position loop, aiming to prevent insufficient stability and premature wear and damage of components. This paper introduces a multivariable iterative learning control (MILC) method tailored for flexible feed drive systems, focusing on enhancing dynamic positioning accuracy. The MILC employs error data from both the motor and table sides, enhancing precision by injecting compensation commands into both the reference trajectory and control command through a norm-optimization process. This method effectively mitigates conflicts between feedback control (FBC) and traditional iterative learning control (ILC) in flexible structures, achieving smaller tracking errors in the table side. The performance and efficacy of the MILC system are experimentally validated on an industrial biaxial CNC machine tool, demonstrating its potential for precision control in modern machining equipment. Full article

This paper offers paradigmatic insights from an international workshop on Ecological Legacies: Bridge Between Science and Community, in Ecuador, in the summer of 2023. The conference brought together foreign and local scholars, tour operators, village community, and Indigenous leaders in the upper Amazonia region of Ecuador with the goal of developing a vision for a sustainable and regenerative future of the upper Amazon. The conference offered three epistemological contributions to the existing literature in the emergent field of Montology, including addressing issues of (a) understanding the existing linguistic hegemony in describing tropical environments, (b) the redress of mistaken notions on pristine jungle environments, and (c) the inclusion of traditional knowledge and transdisciplinary approaches to understand the junglescape from different perspectives and scientific traditions. Methodologically, the conference bridged the fields of palaeoecological and ethnobotanical knowledge (as part of a wider conversation between science and local communities). Results show that local knowledge should be incorporated into the study of the junglescape and its conservation, with decolonial approaches for tourism, sharing language, methodology, tradition, and dissemination of the forest’s attributes. Our research helped co-create and formulate the “Coca Declaration” calling for a philosophical turn in research, bridging science and ethnotourism in ways that are local, emancipatory, and transdisciplinary. We conclude that facilitating new vocabulary by decolonial heightening of Indigenous perspectives of the junglescape helps to incorporate the notion of different Amazons, including the mountainscape of the Andean–Amazonian flanks. We also conclude that we can no consider Ecuador the country of “pure nature” since we helped demystify pristine nature for foreign tourists and highlighted local views with ancestral practices. Finally, we conclude that ethnotourism is a viable alternative to manage heritagization of the junglescape as a hybrid territory with the ecological legacies of the past and present inhabitants of upper Amazonia. Full article

Total knee arthroplasty (TKA) is a gold standard surgical procedure to improve pain and restore function in patients affected by moderate-to-severe severe gonarthrosis refractory to conservative treatments. Indeed, millions of these procedures are conducted yearly worldwide, with their number expected to increase in an ageing and more demanding population. Despite the progress that has been made in optimizing surgical techniques, prosthetic designs, and durability, up to 20% of patients are dissatisfied by the procedure or still report knee pain. From this perspective, the introduction of robotic TKA (R-TKA) in the late 1990s represented a valuable instrument in performing more accurate bone cuts and improving clinical outcomes. On the other hand, prolonged operative time, increased complications, and high costs of the devices slow down the diffusion of R-TKA. The advent of newer technological devices, including those using navigation systems, has made robotic surgery in the operatory room more common since the last decade. At present, many different robots are available, representing promising solutions to avoid persistent knee pain after TKA. We hereby describe their functionality, analyze potential benefits, and hint at future perspectives in this promising field. Full article

Road transport and traffic congestion significantly contribute to dust pollution, which negatively impacts the growth of roadside plants in urban areas. This study aims to quantify the air pollution tolerance index (APTI) and analyze the impacts of dust deposition on different plant species and trees planted along a busy urban roadside in Lahore, Pakistan by considering seasonal variations. The APTI of each species is determined based on inputs of various biochemical parameters (leaf extract pH, ascorbic acid content, relative water content, and total chlorophyll levels), including dust deposition. In this study, laboratory analysis techniques are employed to assess these factors in selected plant species such as Mangifera indica, Saraca asoca, Cassia fistula, and Syzygium cumini. A statistical analysis is conducted to understand the pairwise correlation between various parameters and the APTI at significant and non-significant levels. Additionally, uncertainties in the inputs and APTI are addressed through a probabilistic analysis using the Monte Carlo simulation method. This study unveils seasonal variations in key parameters among selected plant species. Almost all biochemical parameters exhibit higher averages during the rainy season, followed by the summer and winter. Conversely, dust deposition on plants follows an inverse trend, with values ranging from 0.19 to 4.8 g/cm², peaking during winter, notably in Mangifera indica. APTI values, ranging from 9.39 to 14.75, indicate varying sensitivity levels across species, from sensitive (Syzygium cumini) to intermediate tolerance (Mangifera indica). Interestingly, plants display increased tolerance during regular traffic hours, reflecting a 0.9 to 5% difference between the APTI at peak and regular traffic hours. Moreover, a significant negative correlation (−0.86 at p < 0.05 level) between APTI values and dust deposition suggests a heightened sensitivity to pollutants during the winter. These insights into the relationship between dust pollution and plant susceptibility will help decision makers in the selection of resilient plants for urban areas and improve air quality. Full article

This article compares potential revenue from electric storage in retail and wholesale electric markets. The retail value can be extracted when storage responds to time-of-day retail prices. The wholesale value is enabled by the recent US Federal Energy Regulatory Commission Order 2222, which requires regional transmission operators (RTOs) to allow distributed storage behind the meter to participate in wholesale electric markets. To quantify the value of these markets, we use realistic time-of-day rates and market prices in one RTO’s ancillary service market. Formulae are developed to estimate the value of behind-the-meter storage in wholesale and retail markets, using an example electric vehicle in a fleet setting. The formulae are also used to compare whether or not net metering is available and different charging rates. The aggregate national storage behind the retail meter is very large, given the projected growth of electric vehicles. Our findings indicate the revenue from wholesale markets can be significantly more than that of retail opportunities. However, the potential in either retail or wholesale markets is currently limited by both state policy and incomplete RTO implementation of FERC orders. Full article

Link prediction is recognized as a crucial means to analyze dynamic social networks, revealing the principles of social relationship evolution. However, the complex topology and temporal evolution characteristics of dynamic social networks pose significant research challenges. This study introduces an innovative fusion framework that incorporates entropy, causality, and a GCN model, focusing specifically on link prediction in dynamic social networks. Firstly, the framework preprocesses the raw data, extracting and recording timestamp information between interactions. It then introduces the concept of “Temporal Information Entropy (TIE)”, integrating it into the Node2Vec algorithm’s random walk to generate initial feature vectors for nodes in the graph. A causality analysis model is subsequently applied for secondary processing of the generated feature vectors. Following this, an equal dataset is constructed by adjusting the ratio of positive and negative samples. Lastly, a dedicated GCN model is used for model training. Through extensive experimentation in multiple real social networks, the framework proposed in this study demonstrated a better performance than other methods in key evaluation indicators such as precision, recall, F1 score, and accuracy. This study provides a fresh perspective for understanding and predicting link dynamics in social networks and has significant practical value. Full article

Women’s representation in Science, Technology, Engineering and Mathematics (STEM) is a powerful resource to motivate girls to study STEM degrees and fulfill the growing demands for professionals in these fields. From their youth, positive attitudes toward mathematics are characteristic of girls and boys who study STEM degrees. This research aims to identify the association between gender stereotypes and attitudes toward mathematics. The 6° grade generation from a middle school in Monterrey, Mexico, first answered tests on attitudes toward mathematics and gender stereotypes in mathematics. Afterwards, a sample group underwent a 4-week intervention during which students saw videos of STEM professionals and answered a questionnaire on student’s self-perception in STEM careers. Finally, the tests were reapplied with a questionnaire on the use and ease of mathematics. Quasi-statistical and discourse analysis were used to obtain the results. These are presented through a model that highlights the mediating role that the mathematical self-concept and the interest/enjoyment for mathematics have in the association between gender stereotypes and attitudes toward mathematics. The role of gender on female’s lower mathematical self-concept is also exposed, suggesting subsequent lines of research on improving self-concept as an approach to equitably increase students’ interests in STEM degrees from their youth. Full article

Considering previous research indicating the presence of biases based on gender and accent in AI-based tools such as virtual assistants or automatic speech recognition (ASR) systems, this paper examines these potential biases in both Alexa and Whisper for the major Spanish accent groups. The Mozilla Common Voice dataset is employed for testing, and after evaluating tens of thousands of audio fragments, descriptive statistics are calculated. After analyzing the data disaggregated by gender and accent, it is observed that, for this dataset, in terms of means and medians, Alexa performs slightly better for female voices than for male voices, while the opposite is true for Whisper. However, these differences in both cases are not considered significant. In the case of accents, a higher Word Error Rate (WER) is observed among certain accents, suggesting bias based on the spoken Spanish accent. Full article

Accurately detecting spine vertebrae plays a crucial role in successful orthopedic surgery. However, identifying and classifying lumbar vertebrae from arbitrary spine X-ray images remains challenging due to their similar appearance and varying sizes among individuals. In this paper, we propose a novel approach to enhance vertebrae detection accuracy by leveraging both global and local spatial relationships between neighboring vertebrae. Our method incorporates a two-stage detector architecture that captures global contextual information using an intermediate heatmap from the first stage. Additionally, we introduce a detection head in the second stage to capture local spatial information, enabling each vertebra to learn neighboring spatial details, visibility, and relative offset. During inference, we employ a fusion strategy that combines spatial offsets of neighboring vertebrae and heatmap from a conventional detection head. This enables the model to better understand relationships and dependencies between neighboring vertebrae. Furthermore, we introduce a new representation of object centers that emphasizes critical regions and strengthens the spatial priors of human spine vertebrae, resulting in an improved detection accuracy. We evaluate our method using two lumbar spine image datasets and achieve promising detection performance. Compared to the baseline, our algorithm achieves a significant improvement of 13.6% AP in the CM dataset and surpasses 6.5% and 4.8% AP in the anterior and lateral views of the BUU dataset, respectively. Full article