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

Rolando Toro’s Biodanza (SRT) is a therapeutic strategy that uses movement, music, and emotions to induce integrative living experiences. The present study aims to explore the efficacy of a three-month SRT intervention on motor, cognitive, and behavioral symptoms in patients with Parkinson’s disease (PD). This study employed a randomized between-group design. Twenty-eight non-demented PD patients were enrolled in this study. Out of these, fourteen patients were assigned to the active treatment group using the Biodanza SRT system and fourteen to the untreated control group. The study group attended 2 h SRT classes once a week, completing twelve lessons in twelve weeks. All patients underwent: (i) a neurological examination to measure the severity of motor symptoms, balance, mobility, and risk of falls, and (ii) a neuropsychological battery to assess cognitive status, apathy, depressive symptomatology, and perceived quality of life (QoL), at study entry (T0) and at twelve weeks (T1, end of dance training). At T1, we observed a significant improvement in motor (i.e., severity of motor symptoms and balance) and cognitive parameters (i.e., working memory and delayed verbal memory) in all treated patients compared with the controls. Furthermore, a significant improvement in the social support dimension was found in all treated patients compared to the controls. A trend toward increased apathy was found in untreated patients at T1. The three-month Biodanza intervention significantly ameliorated the motor parameters of PD patients, with a parallel improvement in cognitive and QoL status. Hence, Biodanza intervention can, in the short term, represent a useful personalized medical intervention for the management of Parkinson’s disease. Full article

The precise identification of railway subgrade defects remains a significant challenge for the railway industry globally. Due to the limitations of individual monitoring techniques, comprehensive information on subgrade defects cannot be obtained. In fact, the presence of subgrade defects can significantly increase the risk of traffic accidents during high-speed train operations, which may affect the safety of train operations and economic development. The monitoring of subgrade health status is used as a pre-disaster planning method that is urgently required to avoid accidents and guide the maintenance strategy. Therefore, a novel “integrated” holistic monitoring approach for subgrade structures is presented based on satellite remote sensing, a comprehensive inspection vehicle, and a ground-based testing technique. Additionally, the monitoring content is more clearly defined during the service life of the subgrade. The method is used to investigate the location, development trend, and the cause of subgrade defects on the Shanghai–Nanjing high-speed railway. Some new viewpoints are put forward: First, the monitoring content for assessing the health status of the subgrade should encompass the foundation settlement, the track geometry status, and the monitoring of deformation and defects within the subgrade. Second, the mileage points K235 and K299 of the subgrade, as well as K236 and K237 of the bridge–subgrade transition sections, are estimated to be locations with potential defects based on the differential InSAR and track quality index. Third, the result of settlement monitoring and ground-penetrating radar analysis illustrates that sections K235 +540 to +680 and K299 +680 to +750 are diagnosed as defect positions triggered by the rapid drop of water level and engineering activity, respectively. Fourth, the “integrated” holistic monitoring technique for subgrade service status might be expected to be a promising method that can be useful in developing maintenance plans and implementing fault recovery for railway infrastructure. Full article

Simulation models are used in various areas of agriculture to better understand the system and assist in decision making. In the beef production sector, a variety of simulation research focusing on various dimensions of the system is available. However, an overview of the available research is lacking. Therefore, a systematic review was conducted to provide an overview of simulation studies of beef production and create an understanding of the simulation approaches used. Scopus, Web of Science, and ProQuest Central research databases were used to search the relevant articles, with the last search conducted in June 2023. Studies that developed or used simulation strategies and used beef cattle as a primary focus of the study were included. The 105 studies included in this review were examined thoroughly to record the authors, year of publication, country of study, type of study, focus area of the study, simulated scenarios, validation methods, and software programs used. There has been growing research interest in simulating beef production systems worldwide, with most studies conducted in North America and Europe. Among these studies, the majority (84.76%, n = 89) are biophysical or bioeconomic study types and use deterministic approaches (n = 42). Additionally, most studies have a whole-farm scope (38.09%, n = 40) and focus on productivity (51.43%, n = 54). Since only less than half of the studies mentioned the validation techniques and software programs used, there is a need to improve the availability of this information to ensure that the models are adopted effectively in decision making. Full article

Fabry disease is an invalidating multisystemic disorder affecting α-Galactosidase, a rate-limiting hydrolase dedicated to lipid catabolism. Non-metabolized substrates, such as Globotriaosylceramide and its derivatives trigger the direct or indirect activation of inflammatory events and endothelial dysfunction. In spite of the efficacy demonstrated by enzyme replacement therapy or pharmacological chaperones in delaying disease progression, few studies have analyzed whether these treatments can improve the pro-inflammatory state of FD patients. Therefore, the aim of this work was to assess cytokines and cardiovascular risk-related proteins detectable in plasma from FD patients, whether treated or not with ERT, to evaluate the reliability of these markers in monitoring disease stage and treatment effects. We identified inflammatory and endothelial dysfunction markers (ADAMTS-13, TNF-α, GDF-15, MIP-1β, VEGFA, MPO, and MIC-1) that cooperate in a common pathway and are increased in FD patients’ plasma samples. As shown by the assessment of these proteins over time, they can help to evaluate the risk of higher severity in FD, as well as ERT effects. Even though the analyzed proteins cannot be considered as proper biomarkers due to their non-specificity to FD, taken together they can provide a signature of reference molecules with prognostic value for early diagnosis, and evaluation of disease progression and treatment efficacy, using blood samples. Full article

To address the effect of a ship’s rolling on the fluidization quality of fluidized beds, in this study, a simulation of a rolling fluidized bed with longitudinal internal members added (R-FBLIM) was carried out and compared with that of a rolling fluidized bed without internal members added (R-FBWIM). The transient motion, as well as the behavioral characteristics of the bubbles within the R-FBLIM, was analyzed; the variation patterns of the number of bubbles, as well as the equivalent diameter of the bubbles, were compared for different apparent gas velocities, oscillation periods, and amplitudes; and the mechanism of the action of the longitudinal internal members was investigated. The results show that the structural design of the longitudinal internal members can effectively improve the gas–solid fluidization quality of the rolling fluidized bed. The horizontal support plate and the cap hole structure can effectively break the air bubbles, the cap hole structure promotes the radial mixing of the gas–solid fluid, and the internal and outer rings of the curved surface plate roll in rows, which inhibit the aggregation behavior of the gas–solid fluid to the two sides of the oscillating planes, respectively, by cooperating with the cap hole structure. Compared with R-FBWIM, the gas–solid phase within R-FBLIM is more spatially distributed, with the number of bubbles increasing by about 2–4 times and the mean diameter decreasing by about 50–60%. The number of bubbles increases with the gas velocity but decreases with the rolling amplitude; the mean diameter decreases with the gas velocity but responds less to the rolling amplitude change. Full article

Over the last few years, given the increase in the incidence and prevalence of both type 2 diabetes mellitus (T2DM) and heart failure (HF), it became crucial to develop guidelines for the optimal preventive and treatment strategies for individuals facing these coexisting conditions. In patients aged over 65, HF hospitalization stands out as the predominant reason for hospital admissions, with their prognosis being associated with the presence or absence of T2DM. Historically, certain classes of glucose-lowering drugs, such as thiazolidinediones (rosiglitazone), raised concerns due to an observed increased risk of myocardial infarction (MI) and cardiovascular (CV)-related mortality. In response to these concerns, regulatory agencies started requiring CV outcome trials for all novel antidiabetic agents [i.e., dipeptidyl peptidase-4 inhibitors (DPP-4 inhibitors), glucagon-like peptide-1 receptor agonists (GLP-1 RAs), and sodium-glucose cotransporter-2 inhibitors (SGLT2is)] with the aim to assess the CV safety of these drugs beyond glycemic control. This narrative review aims to address the current knowledge about the impact of glucose-lowering agents used in T2DM on HF prevention, prognosis, and outcome. Full article

In addressing the challenges of quantitative precipitation estimation (QPE) using weather radar, the importance of enhancing the rainfall estimates for applications such as flash flood forecasting and hydropower generation management is recognized. This study employed dual-polarization weather radar data to refine the traditional Z–R relationship, which often needs higher accuracy in areas with complex meteorological phenomena. Utilizing tree-based machine learning algorithms, such as random forest and gradient boosting, this research analyzed polarimetric variables to capture the intricate patterns within the Z–R relationship. The results highlight machine learning’s potential to improve the precision of precipitation estimation, especially under challenging weather conditions. Integrating meteorological insights with advanced machine learning techniques is a remarkable achievement toward a more precise and adaptable precipitation estimation method. Full article

The aims of the present research include (1) optimization of extraction from Vaccinium myrtillus leaf waste via investigation of plant material:medium ratio, extraction medium, and extraction period, employing extractions at room and high temperatures, or using ultrasound and microwaves (M, HAE, UAE, and MAE, respectively), (2) physicochemical characterization, and (3) investigation of extract biological potential. The statistical analysis revealed that optimal levels of parameters for the greatest polyphenolic yield were a proportion of 1:30 g/mL, ethyl alcohol 50% (v/v) during 2 min of microwave irradiation. By LC-MS analysis, 29 phenolic components were detected; HAE showed the highest richness of almost all determined polyphenols, while chlorogenic acid and quercetin 3-O-glucuronide were dominant. All extracts showed a high inhibition of Staphylococcus aureus growth. The effect of different parameters on extracts’ antioxidant capacity depended on the used tests. The extracts also showed a stimulative influence on keratinocyte viability and anti-inflammatory activity (proven in cell-based ELISA and erythrocyte stabilization assays). The extraction procedure significantly affected the extraction yield (MAE ≥ maceration ≥ UAE ≥ HAE), whereas conductivity, density, surface tension, and viscosity varied in a narrow range. The presented research provides evidence on the optimal extraction conditions and technique, chemical composition, and antioxidant, antimicrobial, anti-inflammatory, and keratinocyte viability properties of bilberry extracts for potential applications in pharmacy and cosmetics. Full article

The study aimed to assess the prevalence of COVID-19 and Klebsiella spp. coinfection across continents. Conducted following PRISMA guidelines, a systematic review utilized PubMed, Embase, SCOPUS, ScienceDirect, and Web of Science databases, searching for literature in English published from December 2019 to December 2022, using specific Health Sciences descriptors. A total of 408 records were identified, but only 50 were eligible, and of these, only 33 were included. Thirty-three references were analyzed to evaluate the correlation between COVID-19 and Klebsiella spp. infections. The tabulated data represented a sample group of 8741 coinfected patients. The findings revealed notable disparities in co-infection rates across continents. In Asia, 23% of individuals were infected with Klebsiella pneumoniae, while in Europe, the proportion of co-infected patients stood at 15%. Strikingly, on the African continent, 43% were found to be infected with Klebsiella pneumoniae, highlighting significant regional variations. Overall, the proportion of Klebsiella pneumoniae co-infections among COVID-positive individuals were determined to be 19%. Particularly concerning was the observation that 1 in 6 ICU coinfections was attributed to Klebsiella pneumoniae, indicating its substantial impact on patient outcomes and healthcare burden. The study underscores the alarming prevalence of co-infection between COVID-19 and Klebsiella pneumoniae, potentially exacerbating the clinical severity of patients and posing challenges to treatment strategies. These findings emphasize the importance of vigilant surveillance and targeted interventions to mitigate the adverse effects of bacterial coinfections in the context of the COVID-19 pandemic. Full article