This study aimed to investigate how stakeholders in the United States beef industry incorporate animal welfare into their sustainability programs. A survey was administered online to the U.S. Roundtable for Sustainable Beef membership. Twenty-seven surveys were analyzed. Most respondents (n = 26, 96%) had sustainability programs that incorporated animal welfare. Most respondents believed that welfare positively impacted environmental (n = 25/26, 96%), economic (n = 25/26, 96%), and social (n = 26/26, 100%) sustainability. The thematic analysis of five free response questions identified ten themes: Animal Care, Regulations and Guidelines, Responsibility, Consumers and Stakeholders, Performance and Efficiency, Financial Impact, Connectedness, Critical Component, Animal-based Outcomes, and Employees. When asked to define welfare, the most common themes were Animal Care and Regulations and Guidelines. When asked why welfare was a component of their sustainability program, the top factors from a provided list were: cattle health (n = 20, 74%), cattle performance (n = 12, 44%), and consumer perceptions (n = 12, 44%). Findings suggest a widespread recognition of animal welfare’s importance within sustainable beef production. Full article

This article is about a framework for determining the degree of realism of any given passenger ride motion discomfort measurement formula. After providing some context and reviewing evidence of deficiency in currently popular ride motion discomfort measurement formulas, the article outlines the research program that needs to be carried out in order to establish such a framework. The research begins with gathering recordings of uncomfortable ride motion episodes encountered in a chosen type of passenger transport service. It then has test subjects compare the episodes via a ride motion simulator and adjust their amplitudes pair wise until they cause equal discomfort. It explains how to take the pair wise amplitude adjustments and determine amplitude adjustments that bring all of the motion episode recordings to a common level of discomfort so that they form a normalized set. Then, the lower the scatter of the scores assigned by any given discomfort measurement formula to the members of that set, the more realistic that formula will be for the chosen service. Full article

Nanosilver is a popular nanomaterial, the potential influence of which on humans is of serious concern. Herein, we exposed male Wistar rats to two regimens: a repeated oral dose of 30 mg/kg bw silver nanoparticles (AgNPs) over 28 days and a single-dose injection of 5 mg/kg bw of AgNPs. At three different time points, we assessed antioxidant defense, oxidative stress and inflammatory parameters in the colon, as well as toxicity markers in the liver and plasma. Both experimental scenarios showed increased oxidative stress and inflammation in the colon. Oral administration seemed to be linked to increased reactive oxygen species generation and lipid peroxidation, while the effects induced by the intravenous exposure were probably mediated by silver ions released from the AgNPs. Repeated oral exposure had a more detrimental effect than the single-dose injection. In conclusion, both administration routes had a similar impact on the colon, although the underlying mechanisms are likely different. Full article

Vibrio cholerae is the etiological agent of cholera in humans. The bacterium is frequently detected in aquatic products worldwide. However, the current literature on the genome evolution of V. cholerae of aquatic animal origins is limited. Here, we firstly characterized the growth and genome features of V. cholerae isolates with different resistance phenotypes from three species of common freshwater fish. The results revealed that the non-O1/O139 V. cholerae isolates (n = 4) were halophilic and grew optimally at 2% NaCl and pH 8.0. Their draft genome sequences were 3.89 Mb–4.15 Mb with an average GC content of 47.35–47.63%. Approximately 3366–3561 genes were predicted to encode proteins, but 14.9–17.3% of them were of an unknown function. A number of strain-specific genes (n = 221–311) were found in the four V. cholerae isolates, 3 of which belonged to none of any of the known sequence types (STs). Several putative mobile genetic elements (MGEs) existed in the V. cholerae isolates, including genomic islands (n = 4–9), prophages (n = 0–3), integrons (n = 1–1), and insertion sequences (n = 0–3). Notably, CRISPR-Cas system arrays (n = 2–10) were found in the V. cholerae genomes, whereby the potential immunity defense system could be active. Comparative genomic analyses also revealed many putative virulence-associated genes (n = 106–122) and antibiotic resistance-related genes (n = 6–9). Overall, the results of this study demonstrate the bacterial broader-spectrum growth traits and fill prior gaps in the genomes of V. cholerae originating from freshwater fish. Full article

Background and Aims: Accurate recognition of endoscopic instruments facilitates quantitative evaluation and quality control of endoscopic procedures. However, no relevant research has been reported. In this study, we aimed to develop a computer-assisted system, EndoAdd, for automated endoscopic surgical video analysis based on our dataset of endoscopic instrument images. Methods: Large training and validation datasets containing 45,143 images of 10 different endoscopic instruments and a test dataset of 18,375 images collected from several medical centers were used in this research. Annotated image frames were used to train the state-of-the-art object detection model, YOLO-v5, to identify the instruments. Based on the frame-level prediction results, we further developed a hidden Markov model to perform video analysis and generate heatmaps to summarize the videos. Results: EndoAdd achieved high accuracy (>97%) on the test dataset for all 10 endoscopic instrument types. The mean average accuracy, precision, recall, and F1-score were 99.1%, 92.0%, 88.8%, and 89.3%, respectively. The area under the curve values exceeded 0.94 for all instrument types. Heatmaps of endoscopic procedures were generated for both retrospective and real-time analyses. Conclusions: We successfully developed an automated endoscopic video analysis system, EndoAdd, which supports retrospective assessment and real-time monitoring. It can be used for data analysis and quality control of endoscopic procedures in clinical practice. Full article

The judiciary is a field lacking research in relation to its administration and innovation; however, different theoretical perspectives can be followed. This work reviews this trend while adding to it. An institutional perspective is presented, as is its explanatory potential. This perspective captures the context of the public sector; however, when analyzing its interpretation in terms of innovation, it is revealed to be doubly paradoxical. From the theoretical point of view, institutionalization focuses on the maintenance of processes, while innovation, gradually or abruptly, investigates their disruptions. Nevertheless, institutionalization can be observed as part of the sedimentation of innovation. Institutionalization is presented, in the context of innovation, as a selection mechanism that shapes such innovation. This paradox is presented under the review of organizational institutionalism vis-à-vis innovation and, for its unfolding, considers the adoption of innovation as an adaptation to the prevalent rationalized elements. This presentation is paralleled with the interpretation that innovation is limited by a structure that, sometimes rationalized, forms its trajectory. Considering the social function of the judiciary that is anchored in institutionalism, historical institutionalism is thus added, centrally placing the judiciary in the current institutional matrix and associating its path dependence with the dimensions of its innovation. Based on these outlines, propositions and a suggested agenda for future research are presented. Full article

In this paper, we introduce a novel rate control scheme specifically tailored for live broadcasting scenarios. Notably, in high-definition live transmissions of sports events and video game competitions that typically exceed 1080 p resolution and run at frame rates of 60 fps or higher, the transcoding speed of encoders often becomes a limiting factor, leading to streams with substantial bitrates but unsatisfactory quality metrics. To enhance the overall Quality of Service (QoS) without increasing the bitrate, it is essential to improve the quality of Regions of Interest (ROI).Our proposed solution presents an ROI-based rate reservoir model that ingeniously leverages Convolutional Neural Networks (CNNs) to predict rate control parameters. This approach aims to optimize the bitrate allocation within high bitrate live broadcasts, thus enhancing the image quality within ROIs. Experimental outcomes demonstrate that this algorithm manages to increase the bitrate by no more than 5%, effectively redistributing the reduced bitrate across the entire Group of Pictures (GOP). As a result, it ensures a gradual decrease in the quality of Regions of Uninterest (ROU), thereby maintaining a balanced quality experience throughout the broadcasted content. Full article

Phthalates are chemical compounds, mainly used as additives in plastics, which are known to induce harmful impacts to the environment and human health due to their ability to act as hormone-mimics. Few studies have been reported on the relationship between human exposure to phthalates and the level of circulating microRNAs (miRs), especially those miRs encapsulated in extracellular vesicles/exosomes or exosome-like vesicles (ELVs). We examined the relationship of ELV-miR expression patterns and urine of adult men with five phthalate metabolites (i.e., mono isobutyl phthalate, mono-n-butyl phthalate, mono benzyl phthalate, mono-(2-ethyl-5-oxohexyl) phthalate, mono-(2-ethylhexyl) phthalate) to identify potential biomarkers and relevant pathways. We found significant positive associations which were further confirmed by multivariable analysis. Overall, our analyses showed that the Σ phthalate metabolite concentration was associated with a significant increase in the expression level of two miRs found in ELV: miR-202 and miR-543. Different pathways including cancer and immune-related responses were predicted to be involved in this relationship. Analyzing the specific downstream target genes of miR-202 and miR-543, we identified the phosphatase and tensin homolog (PTEN) as the key gene in several converging pathways. In summary, the obtained results demonstrate that exposure to environmental phthalates could be related to altered expression profiles of specific ELV-miRs in adult men, thereby demonstrating the potential of miRs carried by exosomes to act as early effect biomarkers. Full article

Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence of extensive forest fires, thereby safeguarding both forest resources and human well-being. Although drone patrols have emerged as a primary method for forest-fire prevention, the unique characteristics of forest-fire images captured from high altitudes present challenges. These include remote distances, small fire points, smoke targets with light hues, and complex, ever-changing background environments. Consequently, traditional target-detection networks frequently exhibit diminished accuracy when handling such images. In this study, we introduce a cutting-edge drone-based network designed for the detection of forest fires and smoke, named FSNet. To begin, FSNet employs the YOCO data-augmentation method to enhance image processing, thereby augmenting both local and overall diversity within forest-fire images. Next, building upon the transformer framework, we introduce the EBblock attention module. Within this module, we introduce the notion of “groups”, maximizing the utilization of the interplay between patch tokens and groups to compute the attention map. This approach facilitates the extraction of correlations among patch tokens, between patch tokens and groups, and among groups. This approach enables the comprehensive feature extraction of fire points and smoke within the image, minimizing background interference. Across the four stages of the EBblock, we leverage a feature pyramid to integrate the outputs from each stage, thereby mitigating the loss of small target features. Simultaneously, we introduce a tailored loss function, denoted as $L_{forest}$, specifically designed for FSNet. This ensures the model’s ability to learn effectively and produce high-quality prediction boxes. We assess the performance of the FSNet model across three publicly available forest-fire datasets, utilizing mAP, Recall, and FPS as evaluation metrics. The outcomes reveal that FSNet achieves remarkable results: on the Flame, Corsican, and D-Fire datasets, it attains mAP scores of 97.2%, 87.5%, and 94.3%, respectively, with Recall rates of 93.9%, 87.3%, and 90.8%, respectively, and FPS values of 91.2, 90.7, and 92.6, respectively. Furthermore, extensive comparative and ablation experiments validate the superior performance of the FSNet model. Full article

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R²) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage. Full article

With the rapid development of the emerging intelligent, flexible, transparent, and wearable electronic devices, such as quantum-dot-based micro light-emitting diodes (micro-LEDs), thin-film transistors (TFTs), and flexible sensors, numerous pixel-level printing technologies have emerged. Among them, inkjet printing has proven to be a useful and effective tool for consistently printing micron-level ink droplets, for instance, smaller than 50 µm, onto wearable electronic devices. However, quickly and accurately determining the printing quality, which is significant for the electronic device performance, is challenging due to the large quantity and micron size of ink droplets. Therefore, leveraging existing image processing algorithms, we have developed an effective method and software for quickly detecting the morphology of printed inks served in inkjet printing. This method is based on the edge detection technology. We believe this method can greatly meet the increasing demands for quick evaluation of print quality in inkjet printing. Full article

The liver fluke disease caused by Clonorchis sinensis is one of the most serious food-borne parasitic diseases in China. Many freshwater fish and shrimps can be infected with C. sinensis metacercariae as the second intermediate hosts in endemic regions. Owing to the lack of infected humans and the good administration of pet dogs and cats in cities of non-endemic regions, few fish are expected to be infected with C. sinensis metacercariae in urban lakes. To determine the infection of C. sinensis metacercariae in freshwater fish and shrimps in urban lakes, a total of 18 fish species and one shrimp species were investigated in the East Lake of Wuhan City. Metacercariae were isolated by artificial digestive juice and identified using morphology and rDNA-ITS2 sequences. Five species of fish, Pseudorasbora parva, Ctenogobius giurinus, Squalidus argentatus, Hemiculter leuciclus, and Rhodeus spp., were infected with C. sinensis metacercariae. The overall prevalence of C. sinensis was 32.5%. The highest prevalence was found in P. parva with 57.9%, while S. argentatus exhibited the highest mean abundance (13.9). Apart from the C. sinensis metacercariae, four species of other trematode metacercariae were also identified across twelve fish species in total. Owing to the consumption of undercooked fish and feeding cats with small fish caught by anglers, there is a potential risk that the small fish infected with C. sinensis metacercariae may act as an infection source to spread liver fluke. Given the complete life cycle of C. sinensis, stray cats and rats were inferred to act as the important final hosts of C. sinensis in urban lakes in non-endemic areas. Full article

The propagation of Crocus sativus L. relies exclusively on corm multiplication. As underground storage organs, corms are susceptible to a wide range of pathogens, environmental stresses, and diseases, making traditional propagation methods often ineffective with the loss of valuable material. In vitro propagation offers an alternative for the saffron culture under controlled conditions. In particular, the innovative application of the Temporary Immersion System (TIS) represents a technological advancement for enhancing biomass production with a reduction in operational costs. The current study utilized the Plantform™ bioreactor to propagate in vitro saffron corms from the ‘Abruzzo’ region (Italy), integrating machine learning models to assess its performance. The evaluation of saffron explants after 30, 60, and 90 days of culture showed a marked improvement in growth and microcorm production compared to conventional in vitro culture on semisolid medium, supported by the machine learning analysis. Indeed, the Random Forest algorithm revealed a predictive accuracy with an R² value of 0.81 for microcorm number, showcasing the capability of machine learning models to forecast propagation outcomes effectively. These results confirm that applying TIS in saffron culture could lead to economically viable, large biomass production within a controlled environment, irrespective of seasonality. This study represents the first endeavor to use TIS technology to enhance the in vitro propagation of saffron in conjunction with machine learning, suggesting an innovative approach for cultivating high-value crops like saffron. Full article

(1) Background: This study investigated the effects of sequenced electromagnetic fields, modulated at extremely low frequencies and intensities, in the treatment of drug-resistant Escherichia coli (E. coli)-induced chronic bacterial cystitis. (2) Methods: A total of 148 female participants, aged 18 to 80 years diagnosed with chronic bacterial cystitis caused by drug-resistant E. coli, were recruited for this study. Participants were randomly assigned to two groups: an experimental group (n = 74) with osteopathic palpation and assessment treated with a sequence of electromagnetic fields, and a control group (n = 74) receiving a placebo treatment. Both groups were assessed at this study’s outset, 4 weeks after eight applications, and at 12 weeks for symptomatic presentation and laboratory parameters. (3) Results: After 4 weeks of treatment, a significant difference was observed between the two groups regarding D-DIMER levels, IL-6 levels, erythrocyte levels, leukocyte levels, and E. coli levels (p < 0.001). By the 12th week, the experimental group continued to exhibit a significant reduction in the examined parameters compared to the control group (p < 0.001). Additionally, the treatment did not induce any side effects in the patients in the experimental group. (4) Conclusions: Treatment with coherently sequenced electromagnetic fields, modulated at an extremely low frequency and intensity, not only appears to provide an effective alternative for the symptoms of chronic bacterial cystitis caused by drug-resistant E. coli but also demonstrates a potent antibacterial effect. Full article

Wildfires present a significant risk to societies globally due to population growth, concentrated activities, climate change, and extreme environmental conditions. To establish effective fire suppression and management policies, it is crucial to determine whether the distance between ignition points and urban areas is increasing or decreasing. This study analyzes 101,597 fires in Spain and California between 2007 and 2015, where ignition points and all built-up areas were precisely geolocated in 2014. The study employs the Mann–Kendall test to determine trends and analyze the relative distance of wildfires to human buildings over time. The results indicate that wildfires are becoming statistically significantly closer in several areas. It is also observed that the majority of wildfires are moving away from buildings, although this is not a significant finding. These results suggest the possibility of further research into the reasons behind these observations and possible future developments and their consequences. Full article

HKUST−1 is an MOF adsorbent industrially produced in powder form and thus requires a post-shaping process for use as an adsorbent in fixed-bed separation processes. HKUST−1 is also sensitive to moisture, which degrades its crystalline structure. In this work, HKUST−1, in the form of crystalline powder, was extruded into pellets using a hydrophobic polymeric binder to improve its moisture stability. Thermoplastic polyurethane (TPU) was used for that purpose. The subsequent HKUST−1/TPU extrudate was then compared to HKUST−1/PLA extrudates synthesized with more hydrophilic polymer: polylactic acid (PLA), as the binder. The characterization of the composites was determined via XRD, TGA, SEM-EDS, and an N₂ adsorption isotherm analysis. Meanwhile, the gas-separation performances of HKUST−1/TPU were investigated and compared with HKUST−1/PLA from measurements of CO₂ and CH₄ isotherms at three different temperatures, up to 10 bars. Lastly, the moisture stability of the composite materials was investigated via an aging analysis during storage under humid conditions. It is shown that HKUST−1’s crystalline structure was preserved in the HKUST−1/TPU extrudates. The composites also exhibited good thermal stability under 523 K, whilst their textural properties were not significantly modified compared with the pristine HKUST−1. Furthermore, both extrudates exhibited larger CO₂ and CH₄ adsorption capacities in comparison to the pristine HKUST−1. After three months of storage under atmospheric humid conditions, CO₂ adsorption capacities were reduced to only 10% for HKUST−1/TPU, whereas reductions of about 25% and 54% were observed for HKUST−1/PLA and the pristine HKUST−1, respectively. This study demonstrates the interest in shaping MOF powders by extrusion using a hydrophobic thermoplastic binder to operate adsorbents with enhanced moisture stability in gas-separation columns. Full article

End-of-life electric vehicle (EV) batteries can be reused to reduce their environmental impact and economic costs. However, the growth of the second-life market is limited by the lack of information on the characteristics and performance of these batteries. As the volume of end-of-life EVs may exceed the amount of batteries needed for stationary applications, investigating the possibility of repurposing them in mobile applications is also necessary. This article presents an experimental test that can be used to collect the data necessary to fill a battery passport. The proposed procedure can facilitate the decision-making process regarding the suitability of a battery for reuse at the end of its first life. Once the battery passport has been completed, the performance and characteristics of the battery are compared with the requirements of several mobile applications. Mobile charging stations and forklift trucks were identified as relevant applications for the reuse of high-capacity prismatic cells. Finally, a definition of the state of health (SoH) is proposed to track the suitability of the battery during use in the second-life application considering not only the energy but also the power and efficiency of the battery. This SoH shows that even taking into account accelerated ageing data, a repurposed battery can have an extended life of 11 years at 25 °C. It has also been shown that energy fade is the most limiting performance factor for the lifetime and that cell-to-cell variation should be tracked as it has been shown to have a significant impact on the battery life. Full article

For most patients admitted to a hospital, it is a requirement to continuously monitor their vital signs. Among these are the waveforms from ECG and the pulmonary arterial pulse. At present, there are several electronic devices that can measure the arterial pulse waveform. However, they can be affected by electromagnetic wave radiation, and the fabrication of electronic sensors is complicated and contributes to the e-waste, among other problems. In this paper, we propose an optical method to measure arterial pulse based on a Fabry-Perot interferometer composed of two mirrors. A pulse sensor formed by an acrylic cell with a thin membrane is used to gather the vasodilatation of the wrist, forming an air pulse that is enacted by means of a tube to a metallic cell containing a mirror that is glued to a thin silicone membrane. When the air pulse arrives, a displacement of the mirror takes place and produces a shift of the interference pattern fringes given by the Fabry-Perot. A detector samples the fringe intensity. With this method, an arterial pulse waveform is obtained. We characterize this optical device as a test of concept, and its application to measuring artery pulse is presented. The optical device is compared to other electronic devices. Full article

This work presents the research results on the development of an innovative, hydrometallurgical technology for the production of manganese(II) perrhenate dihydrate from recycled waste. These wastes are scraps of Ni-based superalloys containing Re and scraps of Li–ion batteries containing Mn—specifically, solutions from the leaching of black mass. This work presents the conditions for the production of Mn(ReO₄)₂·2H₂O. Thus, to obtain Mn(ReO₄)₂·2H₂O, manganese(II) oxide was used, precipitated from the solutions obtained after the leaching of black mass from Li–ion batteries scrap and purified from Cu, Fe and Al (pH = 5.2). MnO₂ precipitation was carried out at a temperature < 50 °C for 30 min using a stoichiometric amount of KMnO₄ in the presence of H₂O₂. MnO₂ precipitated in this way was purified using a 20% H₂SO₄ solution and then H₂O. Purified MnO₂ was then added alternately with a 30% H₂O₂ solution to an aqueous HReO₄ solution. The reaction was conducted at room temperature for 30 min to obtain a pH of 6–7. Mn(ReO₄)₂·2H₂O precipitated by evaporating the solution to dryness was purified by recrystallization from H₂O with the addition of H₂O₂ at least twice. Purified Mn(ReO₄)₂·2H₂O was dried at a temperature of 100–110 °C. Using the described procedure, Mn(ReO₄)₂·2H₂O was obtained with a purity of >99.0%. This technology is an example of the green transformation method, taking into account the 6R principles. Full article

Medulloblastomas comprise a molecularly diverse set of malignant pediatric brain tumors in which patients are stratified according to different prognostic risk groups that span from very good to very poor. Metastasis at diagnosis is most often a marker of poor prognosis and the relapse incidence is higher in these children. Medulloblastoma relapse is almost always fatal and recurring cells have, apart from resistance to standard of care, acquired genetic and epigenetic changes that correlate with an increased dormancy state, cell state reprogramming and immune escape. Here, we review means to carefully study metastasis and relapse in preclinical models, in light of recently described molecular subgroups. We will exemplify how therapy resistance develops at the cellular level, in a specific niche or from therapy-induced secondary mutations. We further describe underlying molecular mechanisms on how tumors acquire the ability to promote leptomeningeal dissemination and discuss how they can establish therapy-resistant cell clones. Finally, we describe some of the ongoing clinical trials of high-risk medulloblastoma and suggest or discuss more individualized treatments that could be of benefit to specific subgroups. Full article

Image understanding plays a pivotal role in various computer vision tasks, such as extraction of essential features from images, object detection, and segmentation. At a higher level of granularity, both semantic and instance segmentation are necessary for fully grasping a scene. In recent times, the concept of panoptic segmentation has emerged as a field of study that unifies semantic and instance segmentation. This article sheds light on the pivotal role of panoptic segmentation as a visualization tool for understanding scene components, including object detection, categorization, and precise localization of scene elements. Advancements in achieving panoptic segmentation and suggested improvements to the predicted outputs through a top-down approach are discussed. Furthermore, datasets relevant to both scene recognition and panoptic segmentation are explored to facilitate a comparative analysis. Finally, the article outlines certain promising directions in image recognition and analysis by underlining the ongoing evolution in image understanding methodologies. Full article