Diverse Environments Research Articles

Automated Cloud Shadow Detection from Satellite Orthoimages with Uncorrected Cloud Relief Displacements

Clouds and their shadows significantly affect satellite imagery, resulting in a loss of radiometric information in the shadowed areas. This loss reduces the accuracy of land cover classification and object detection. Among various cloud shadow detection methods, the geometric-based method relies on the geometry of the sun and sensor to provide consistent results across diverse environments, ensuring better interpretability and reliability. It is well known that the direction of shadows in raw satellite images depends on the sun’s illumination and sensor viewing direction. Orthoimages are typically corrected for relief displacements caused by oblique sensor viewing, aligning the shadow direction with the sun. However, previous studies lacked an explicit experimental verification of this alignment, particularly for cloud shadows. We observed that this implication may not be realized for cloud shadows, primarily due to the unknown height of clouds. To verify this, we used Rapideye orthoimages acquired in various viewing azimuth and zenith angles and conducted experiments under two different cases: the first where the cloud shadow direction was estimated based only on the sun’s illumination, and the second where both the sun’s illumination and the sensor’s viewing direction were considered. Building on this, we propose an automated approach for cloud shadow detection. Our experiments demonstrated that the second case, which incorporates the sensor’s geometry, calculates a more accurate cloud shadow direction compared to the true angle. Although the angles in nadir images were similar, the second case in high-oblique images showed a difference of less than 4.0° from the true angle, whereas the first case exhibited a much larger difference, up to 21.3°. The accuracy results revealed that shadow detection using the angle from the second case improved the average F1 score by 0.17 and increased the average detection rate by 7.7% compared to the first case. This result confirms that, even if the relief displacement of clouds is not corrected in the orthoimages, the proposed method allows for more accurate cloud shadow detection. Our main contributions are in providing quantitative evidence through experiments for the application of sensor geometry and establishing a solid foundation for handling complex scenarios. This approach has the potential to extend to the detection of shadows in high-resolution satellite imagery or UAV images, as well as objects like high-rise buildings. Future research will focus on this.

Clarifying social norms which have robustness against reputation costs and defector invasion in indirect reciprocity

The evolution of cooperation through indirect reciprocity is a pivotal mechanism for sustaining large-scale societies. Because third parties return cooperative behaviour in indirect reciprocity, reputations that assess and share these third parties’ behaviour play an essential role. Studies on indirect reciprocity have predominantly focused on the costs associated with cooperative behaviour, overlooking the costs tied to the mechanisms underpinning reputation sharing. Here, we explore the robustness of social norms necessary to secure the stability of indirect reciprocity, considering both the costs of reputation and the resilience against perfect defectors. Firstly, our results replicate that only eight social norms, known as the ‘leading eight,’ can establish a cooperative regime. Secondly, we reveal the robustness of these norms against reputation costs and perfect defectors. Our analysis identifies four norms that exhibit resilience in the presence of defectors due to their neutral stance on justified defection and another four that demonstrate robustness against reputation costs through their negative evaluation of unjustified cooperation. The study underscores the need to further research how reputational information is shared within societies to promote cooperation in diverse and complex environments.

Identification of Technical Requirements for the Initial Design of an Autonomous Fruit-Harvesting Robot

Automation in agriculture is revolutionizing the management of field tasks. This paper focuses on identifying the essential needs and preliminary technical requirements for designing an autonomous robot specifically for fruit harvesting. The study examines the integration of advanced machine vision technologies and neural networks for the accurate detection of ripe fruits and obstacles, optimizing harvesting efficiency. Key features such as proximity and touch sensors are also evaluated to ensure the safe handling of fruits, minimizing damage during harvesting. The technical specifications, including traction, speed, and the robot’s ability to operate on uneven terrain, are analyzed to ensure adaptability across various crops and ground conditions. Perception systems, incorporating cameras and sensors, are crucial for crop inspection and route planning. The main findings of this review establish a set of technical requirements that serve as a foundation for developing efficient and economically viable fruit-harvesting robots. These systems offer significant potential to transform agriculture by addressing labor shortages and improving harvesting precision. However, challenges remain, particularly in adapting to diverse crops and environments, emphasizing the need for further research and development.

Theoretical investigation on crash performance of bamboo-inspired energy absorption systems based on systematic experimental and numerical analysis

The need for energy-absorbing devices with adaptable crashworthiness is increasingly urgent in various engineering fields with diverse load environments. For this aim, bamboo-inspired modular energy absorption systems have recently been proposed to integrate mechanical efficiency, tunability, and robustness. However, their mechanical responses under varying load velocities have not been systematically studied, which limits the effective prediction and optimization of their crashworthiness. In this study, quasi-static and dynamic experiments were performed on bamboo-inspired systems, discretely assembled using 3D printed steel tube specimens. Matched finite element simulations were conducted using ABAQUS/Explicit. Specific energy absorption and efficiency of these systems respectively exceeded 12.9 J/g and 47 %, surpassing existing discretely assembled systems and rivaling typical cellular materials. Efficient deformation mode with self-locking capability was observed, and sharp impact peak was avoided when the crash velocity was less than 50 m/s. Building upon these findings, a modified spherical shell theory under crashing was developed to predict energy absorption performance of bamboo-inspired system based on the traveling plastic hinge model. The average relative error between the analytical solution and finite element results was found less than 6.5 %. The mean effective stress of the system could exceed 35 MPa under thickness-to-diameter ratio of 5 %. This work presents an efficient and effective approach for optimizing and estimating the crash performance of modular protective devices.

HRTBDA: a network for post-disaster building damage assessment based on remote sensing images

ABSTRACT Efficient building damage assessment after disasters is vital for emergency response and loss evaluation, but the task is complicated by diverse building structures and complex environments. Traditional methods using Convolutional Neural Networks (CNNs) struggle to capture global contextual features, limiting damage categorization accuracy. To address this, we introduce the High-Resolution Transformer Architecture for Building Damage Assessment (HRTBDA), which enhances multi-scale feature extraction. A Cross-Attention-Based Spatial Fusion (CSF) module is proposed to utilize the attention mechanism, improving the model’s ability to identify detailed associations in damaged buildings. Additionally, we propose a deep convolution network matching optimization strategy that integrates a multilayer perceptron and expands the receptive field, enhancing global feature perception. HRTBDA’s performance was evaluated on two public datasets and compared with five recent frameworks. The model achieved an F1-score of 86.0% in building localization and 78.4% in damage assessment, with a 4.8% improvement in detecting minor damages. These results demonstrate HRTBDA’s potential for improving building damage assessment and highlight its significant advancements over existing methods.

Fertilizer nitrogen rate effects on broiler litter–fertilized corn: Grain yield and nutrient composition

AbstractNitrogen (N) plays an important role in corn (Zea mays L.) production, but little is known of optimum fertilizer N rates for corn grain yield and nutrient composition when poultry manure such as broiler litter (BL) (Gallus gallus domesticus) is applied. Typically, farmers consider BL as a soil conditioner and often do not discount the N contribution from the BL application. Field experiments were conducted in 2020 and 2021 over three diverse environments across Alabama to determine the agronomic optimum nitrogen rate (AONR) for BL–fertilized dryland corn with five side dress fertilizer N rates (0, 84, 140, 196, and 252 kg N ha−1). The study utilized a yield response curve and evaluated the effect of fertilizer N rate on grain nutrient composition. BL (4.48 Mg ha−1 equivalent to 2‐ton acre−1) was applied to the study sites before planting each year. The AONRs for grain yield across these environments varied from 114 to 223 kg N ha−1 (0.013–0.027 kg of fertilizer N per kg of grain yield ha−1). The predicted grain yield at the AONRs ranged from 4.22 to 13.27 Mg ha−1. Grain N concentration increased with increasing fertilizer N rate. However, the weak to nonexistent correlation between fertilizer N and other grain nutrient elements suggests that raising the fertilizer N rate does not necessarily lead to higher nutrient levels in corn grain. Overall, these findings could be useful for developing N management guidelines for corn fields that receive BL in Alabama.

Perturbed saliva microbiome is gender-specific in patients with oral lichen planus

ObjectiveTo understand the gender characteristics of oral lichen planus (OLP) by identifying the gender-specific salivary microbiome and its potential biomarkers. MethodsA gender-based study was undertaken, commencing with the collection of saliva samples, followed by 16S rRNA gene sequencing, to explore the differences in the composition of saliva microbiome in OLP patients (40 males and 56 females) and healthy controls (40 males and 56 females), respectively. ResultsBoth male and female OLP patients had significant differences in saliva microbiome composition from healthy controls, especially in female patients. Notably, Pseudomonas was only enriched in female patients. Rhodococcus (AUC: 0.91) and Pseudomonas (AUC: 0.97) had great potential as diagnostic biomarkers in male and female patients, respectively. The KEGG results showed metabolic dysfunction was more pronounced in female patients and a high level of microbial metabolism in diverse environments, ABC transporters, Quorum sensing and Two-component system. Capnocytophaga was negatively correlated with the erosion area in male patients. Neisseria was negatively correlated with the erosion area and Rothia was positively correlated with the pain level in female patients. ConclusionsOur study revealed gender-specific perturbation in salivary microbiome within OLP patients, suggesting that the male and female patients with OLP may have different pathogenesis.

Enhancing Grapevine Node Detection to Support Pruning Automation: Leveraging State-of-the-Art YOLO Detection Models for 2D Image Analysis.

Automating pruning tasks entails overcoming several challenges, encompassing not only robotic manipulation but also environment perception and detection. To achieve efficient pruning, robotic systems must accurately identify the correct cutting points. A possible method to define these points is to choose the cutting location based on the number of nodes present on the targeted cane. For this purpose, in grapevine pruning, it is required to correctly identify the nodes present on the primary canes of the grapevines. In this paper, a novel method of node detection in grapevines is proposed with four distinct state-of-the-art versions of the YOLO detection model: YOLOv7, YOLOv8, YOLOv9 and YOLOv10. These models were trained on a public dataset with images containing artificial backgrounds and afterwards validated on different cultivars of grapevines from two distinct Portuguese viticulture regions with cluttered backgrounds. This allowed us to evaluate the robustness of the algorithms on the detection of nodes in diverse environments, compare the performance of the YOLO models used, as well as create a publicly available dataset of grapevines obtained in Portuguese vineyards for node detection. Overall, all used models were capable of achieving correct node detection in images of grapevines from the three distinct datasets. Considering the trade-off between accuracy and inference speed, the YOLOv7 model demonstrated to be the most robust in detecting nodes in 2D images of grapevines, achieving F1-Score values between 70% and 86.5% with inference times of around 89 ms for an input size of 1280 × 1280 px. Considering these results, this work contributes with an efficient approach for real-time node detection for further implementation on an autonomous robotic pruning system.

Tailoring the Wettability of Bacterial Cellulose Magnetobots via the Assembly of In Situ Synthesized and Surfactant-Coated Magnetic Nanoparticles.

This study showcases the possibility of tailoring the wettability of magnetic bacterial cellulose (m-BC) composites by the combined effect of in situ synthesized magnetic nanoparticles (MNPs) distribution and simultaneous oleic acid (OA) coating within the BC matrix. This combined effect of MNPs and OA resulted in m-BC composites exhibiting solvent-dependent and time-dependent surface-wetting behavior, which was not observed in either individual cases of BC that have been modified with OA or BC that has MNPs adsorbed on its fibers. This tailored wettability in m-BCs was achieved via varying the concentrations of iron precursors, which governed the arrangement and morphology of MNPs (uniformly or clustered) on the BC membrane, although the same fraction of MNPs was observed in both the m-BCs. Finally, we have achieved delayed water absorption in m-BC_x (synthesized in a comparatively lower precursor concentration) and no absorption of water in m-BC_4x (synthesized in a 4-times-higher precursor concentration). The m-BC_4x composite maintained its hydrophobic characteristics in diverse environments, ranging from highly acidic conditions (pH 1.2) to physiological environments at pH 4, 5.5, and 7.4. The MNP agglomerates on the BC nanofibers in the m-BC_4x composite were found to be instrumental in attaining a stable cyclic absorption performance with structural integrity. Additionally, the magnetic-inducing heat generation efficiency of the m-BCs can be extended for evaporating low-boiling-point solvents. The present study expands the frontiers of BC-based magnetic composites by emphasizing the assembly of surfactant-coated magnetic nanostructures in their responsiveness to polar/nonpolar liquids with stable performance even in complex scenarios.

Long-Term Autotrophic Growth and Solar-to-Chemical Conversion in Shewanella Oneidensis MR-1 through Light-Driven Electron Transfer.

Members of the genus Shewanella are known for their versatile electron accepting routes, which allow them to couple decomposition of organic matter to reduction of various terminal electron acceptors for heterotrophic growth in diverse environments. Here, we report autotrophic growth of Shewanella oneidensis MR-1 with photoelectrons provided by illuminated biogenic CdS nanoparticles. This hybrid system enables photosynthetic oscillatory acetate production from CO2 for over five months, far exceeding other inorganic-biological hybrid system that can only sustain for hours or days. Biochemical, electrochemical and transcriptomic analyses reveal that the efficient electron uptake of S. oneidensis MR-1 from illuminated CdS nanoparticles supplies sufficient energy to stimulate the previously overlooked reductive glycine pathway for CO2 fixation. The continuous solar-to-chemical conversion is achieved by photon induced electric recycling in sulfur species. Overall, our findings demonstrate that this mineral-assisted photosynthesis, as a widely existing and unique model of light energy conversion, could support the sustained photoautotrophic growth of non-photosynthetic microorganisms in nutrient-lean environments and mediate the reversal of coupled carbon and sulfur cycling, consequently resulting in previously unknown environmental effects. In addition, the hybrid system provides a sustainable and flexible platform to develop a variety of solar products for carbon neutrality.

Long‐Term Autotrophic Growth and Solar‐to‐Chemical Conversion in Shewanella Oneidensis MR‐1 through Light‐Driven Electron Transfer

AbstractMembers of the genus Shewanella are known for their versatile electron accepting routes, which allow them to couple decomposition of organic matter to reduction of various terminal electron acceptors for heterotrophic growth in diverse environments. Here, we report autotrophic growth of Shewanella oneidensis MR‐1 with photoelectrons provided by illuminated biogenic CdS nanoparticles. This hybrid system enables photosynthetic oscillatory acetate production from CO2 for over five months, far exceeding other inorganic‐biological hybrid system that can only sustain for hours or days. Biochemical, electrochemical and transcriptomic analyses reveal that the efficient electron uptake of S. oneidensis MR‐1 from illuminated CdS nanoparticles supplies sufficient energy to stimulate the previously overlooked reductive glycine pathway for CO2 fixation. The continuous solar‐to‐chemical conversion is achieved by photon induced electric recycling in sulfur species. Overall, our findings demonstrate that this mineral‐assisted photosynthesis, as a widely existing and unique model of light energy conversion, could support the sustained photoautotrophic growth of non‐photosynthetic microorganisms in nutrient‐lean environments and mediate the reversal of coupled carbon and sulfur cycling, consequently resulting in previously unknown environmental effects. In addition, the hybrid system provides a sustainable and flexible platform to develop a variety of solar products for carbon neutrality.

From colon wall to tumor niche: Unraveling the microbiome's role in colorectal cancer progression.

Colorectal cancer (CRC) is influenced by perturbations in the colonic microbiota, characterized by an imbalance favoring pathogenic bacteria over beneficial ones. This dysbiosis contributes to CRC initiation and progression through mechanisms such as carcinogenic metabolite production, inflammation induction, DNA damage, and oncogenic signaling activation. Understanding the role of external factors in shaping the colonic microbiota is crucial for mitigating CRC progression. This study aims to elucidate the gut microbiome's role in CRC progression by analyzing paired tumor and mucosal tissue samples obtained from the colon walls of 17 patients. Through sequencing of the V3-V4 region of the 16S rRNA gene, we characterized the tumor microbiome and assessed its association with clinical variables. Our findings revealed a significant reduction in alpha diversity within tumor samples compared to paired colon biopsy samples, indicating a less diverse microbial environment within the tumor microenvironment. While both tissues exhibited dominance of similar bacterial phyla, their relative abundances varied, suggesting potential colon-specific effects. Fusobacteriota enrichment, notably in the right colon, may be linked to MLH1 deficiency. Taxonomy analysis identified diverse bacterial genera, with some primarily associated with the colon wall and others unique to this region. Conversely, several genera were exclusively expressed in tumor tissue. Functional biomarker analysis identified three key genes with differential abundance between tumor microenvironment and colon tissue, indicating distinct metabolic activities. Functional biomarker analysis revealed three key genes with differential abundance: K11076 (putrescine transport system) and K10535 (nitrification) were enriched in the tumor microenvironment, while K11329 (SasA-RpaAB circadian timing mediator) dominated colon tissue. Metabolic pathway analysis linked seven metabolic pathways to the microbiome. Collectively, these findings highlight significant gut microbiome alterations in CRC and strongly suggest that long-term dysbiosis profoundly impacts CRC progression.

Annotation-free prediction of microbial dioxygen utilization.

Aerobes require dioxygen (O2) to grow; anaerobes do not. However, nearly all microbes-aerobes, anaerobes, and facultative organisms alike-express enzymes whose substrates include O2, if only for detoxification. This presents a challenge when trying to assess which organisms are aerobic from genomic data alone. This challenge can be overcome by noting that O2 utilization has wide-ranging effects on microbes: aerobes typically have larger genomes encoding distinctive O2-utilizing enzymes, for example. These effects permit high-quality prediction of O2 utilization from annotated genome sequences, with several models displaying ≈80% accuracy on a ternary classification task for which blind guessing is only 33% accurate. Since genome annotation is compute-intensive and relies on many assumptions, we asked if annotation-free methods also perform well. We discovered that simple and efficient models based entirely on genomic sequence content-e.g., triplets of amino acids-perform as well as intensive annotation-based classifiers, enabling rapid processing of genomes. We further show that amino acid trimers are useful because they encode information about protein composition and phylogeny. To showcase the utility of rapid prediction, we estimated the prevalence of aerobes and anaerobes in diverse natural environments cataloged in the Earth Microbiome Project. Focusing on a well-studied O2 gradient in the Black Sea, we found quantitative correspondence between local chemistry (O2:sulfide concentration ratio) and the composition of microbial communities. We, therefore, suggest that statistical methods like ours might be used to estimate, or "sense," pivotal features of the chemical environment using DNA sequencing data.IMPORTANCEWe now have access to sequence data from a wide variety of natural environments. These data document a bewildering diversity of microbes, many known only from their genomes. Physiology-an organism's capacity to engage metabolically with its environment-may provide a more useful lens than taxonomy for understanding microbial communities. As an example of this broader principle, we developed algorithms that accurately predict microbial dioxygen utilization directly from genome sequences without annotating genes, e.g., by considering only the amino acids in protein sequences. Annotation-free algorithms enable rapid characterization of natural samples, highlighting quantitative correspondence between sequences and local O2 levels in a data set from the Black Sea. This example suggests that DNA sequencing might be repurposed as a multi-pronged chemical sensor, estimating concentrations of O2 and other key facets of complex natural settings.

Polyphenol rewiring of the microbiome reduces methane emissions.

Methane mitigation is regarded as a critical strategy to combat the scale of global warming. Currently, about 40% of methane emissions originate from microbial sources, which is causing strategies to suppress methanogens, either through direct toxic effects or by diverting their substrates and energy, to gain traction. Problematically, current microbial methane mitigation knowledge derives from rumen studies and lacks detailed microbiome-centered insights, limiting translation across ecosystems. Here we utilize genome-resolved metatranscriptomes and metabolomes to assess the impact of a proposed methane inhibitor, catechin, on greenhouse gas emissions for high-methane-emitting peatlands. In microcosms, catechin drastically reduced methane emissions by 72-84% compared to controls. Longitudinal sampling allowed for reconstruction of a novel catechin degradation pathway involving Actinomycetota and Clostridium, which break down catechin into smaller phenolic compounds within the first 21 days, followed by degradation of phenolic compounds by Pseudomonas_E from days 21 to 35. These genomes also co-expressed hydrogen-uptake genes, suggesting that hydrogenases may act as a hydrogen sink during catechin degradation, depriving methanogens of substrates. This was supported by decreased gene expression in hydrogenotrophic and hydrogen-dependent methylotrophic methanogens under catechin treatment. We also saw reduced gene expression from genomes inferred to be functioning syntrophically with hydrogen-utilizing methanogens. We propose that catechin metabolic redirection effectively starves hydrogen-utilizing methanogens, offering a potent avenue for curbing methane emissions across diverse environments including ruminants, landfills, and constructed or managed wetlands.

A Review of Strategies and Technologies for Sustainable Decentralized Wastewater Treatment

The traditional model of centralized wastewater treatment is facing substantial strain due to a confluence of global challenges. Consequently, it is imperative to evaluate the impediments and potential advantages associated with the deployment of decentralized wastewater (DW) treatment technologies and systems. Decentralized wastewater (DW) treatment represents a sustainable approach to managing and purifying wastewater across both urban and rural settings. This literature review provides a detailed examination of current advancements and challenges associated with DW treatment technologies. It specifically addresses their operational efficiency, long-term sustainability, and practical implementation across diverse environments. This review critically analyzes recent studies that highlight innovative methodologies, including the deployment of constructed wetlands, anaerobic digestion processes, and predictive models enhanced by artificial intelligence. A critical focus is placed on the ecological and economic advantages of source separation and resource recovery from wastewater streams. The issue of emerging contaminants, such as microplastics, antibiotics, and steroids, is also discussed, emphasizing the continued need for innovation in treatment technologies. Findings from various life cycle assessments are presented to illustrate the environmental impact and feasibility of decentralized systems relative to centralized alternatives. This comprehensive analysis offers valuable insights into the future trajectories of wastewater treatment research and implementation.

Optimizing Cereal Crop Performance with Nutrient Priming

The fundamental building blocks for the success of stand establishment for any crop plant are better germination and robust seedlings. Pre-treatment of the seeds is one of the best methods that may possibly improve germination and maintain the uniformity and stability of the seedlings. Primed seeds usually exhibit synchronized and early germination and sometimes greater total germination percentage over a range of environmental conditions. This enhancement has been linked to osmotic correction, metabolic repair during imbibition, and the accumulation of chemicals that promote germination. Nutri-priming is a good alternative to meet plant nutritional requirements. Seed priming with macro or micronutrient (nutripriming) application has been recently done on various agronomic crops including rice, maize and wheat; lead to better germination and establishment. It aims to combine the positive effects of hydro priming with the nutritional aspect of fertilizers. Crop seeds can be primed with either micro or macronutrients, which improves the nutrient content and increases the germination, sprouting, and water uptake efficiency. Primed seeds performed better under diverse environments especially under sub optimal conditions at sowing and resulted in higher yield compared to unprimed seeds. Priming requires specific temperature and time, if it is not followed then the seed can be deteriorated due to embryo protrusion. Due to readily available food during germination, primed seed are better able to complete the process of germination in a short time and cope with environmental stresses including low temperatures. In fact, it may be regarded as a useful method for enhancing crop establishment in regions with unfavourable agro-climatic conditions (rainfed, dry farming, and dry land farming) with increased yield, tolerance to stressful situations, resistance to diseases, crop competitiveness against weeds, and water use efficiency.

Role of Biocontrol Agents in Suppressing Plant Pathogen: A Compressive Analysis

Biocontrol agents have emerged as important tools in the quest for sustainable agriculture, offering environmentally friendly alternatives to chemical pesticides for managing plant pathogens and pests. This comprehensive review explores the advancements in biocontrol agent research, focusing on the exploration of new agents, genetic engineering, and integration into Integrated Pest Management (IPM) systems. The discovery of novel biocontrol agents from diverse environments, including plant microbiomes and marine ecosystems, has expanded the arsenal available for agricultural use, while genetic engineering and synthetic biology have enhanced the efficacy of existing agents by improving their production of antimicrobial metabolites and stress tolerance. Additionally, the development of synthetic microbial consortia and innovative delivery systems, such as encapsulation and nanotechnology, has improved the stability and application efficiency of biocontrol agents in various agricultural settings. The review also highlights the importance of sustainable and eco-friendly approaches, such as the use of organic amendments and crop rotation, to enhance the effectiveness of biocontrol strategies. These methods not only improve soil health and biodiversity but also reduce the reliance on chemical inputs. However, the widespread adoption of biocontrol agents faces challenges, including environmental variability, host specificity, and the need for supportive regulatory frameworks. Harmonizing regulatory processes, promoting public awareness, and providing incentives for sustainable practices are essential for overcoming these barriers. International cooperation and knowledge exchange are crucial for advancing research and ensuring that biocontrol agents become integral components of modern agriculture. This review underscores the potential of biocontrol agents to contribute significantly to global food security and environmental sustainability, provided that ongoing research and innovation continue to address existing challenges and expand their practical applications in diverse agricultural contexts.

Listeria in Pregnancy—The Forgotten Culprit

Listeria monocytogenes is a Gram-positive bacterium that causes listeriosis, a severe foodborne illness that is particularly dangerous during pregnancy. It thrives in diverse environments, including refrigerated conditions and food production facilities, due to its adaptability to varying temperatures, pH levels, and salt concentrations. Its virulence stems from the ability to invade host cells, particularly macrophages and epithelial cells, and avoid, or at least postpone, immune detection by utilizing virulence factors such as internalins, listeriolysin O, and actin assembly-inducing protein. This intracellular motility and biofilm formation make LM a persistent pathogen in food safety and public health. Pregnant women are at a much higher risk of listeriosis, which can result in serious fetal complications such as miscarriage, stillbirth, and preterm labor due to LM’s affinity for placental tissues. The vertical transmission of LM from mother to fetus can lead to neonatal listeriosis, which can result in sepsis and meningitis, with high mortality rates if not promptly treated. Early diagnosis and treatment with antibiotics, such as ampicillin or gentamicin, are crucial for maternal and neonatal outcomes.

A novel mobile robot with origami wheels designed for navigating sandy terrains

Abstract Sand robots play a vital role as mobile tools for human exploration of desert regions, facilitating resource transportation and exploration. However, desert areas primarily consist of beaches or dunes, resulting in a highly diverse and complex terrain environment that demands enhanced adaptability from sandy mobile robots. Traditional wheeled robots currently face challenges such as skidding, limited climbing ability, and inadequate obstacle avoidance capabilities in sandy environments. To address these issues and enable effective adaptation to the intricate sand environment, we propose a novel sandy mobile robot equipped with Kresling origami wheels. The origami wheel can dynamically adjust its width and morphology through Kresling origami folding. Experimental tests were conducted to illustrate the impact of width variation on the robot’s mobility velocity, propulsive force, climbing performance, and carrying capacity. The self-folding malleability of the origami wheel empowers the robot to efficiently accomplish diverse tasks, including swift movement on flat sand surfaces, seamless crossing of narrow channels, and intelligent obstacle avoidance. By successfully completing these multimodal tasks while adapting to varying requirements, our robot demonstrates promising prospects for practical applications of mobile robots equipped with origami wheels – paving the way for wider adaptation and utilization of sand mobile robots.

Predictors of Individual Work Performance among Private Business Companies for an Enhanced Organizational Human Resource Plan

This study aimed to examine the influence of organizational culture and multi-dimensional self-control as predictors of individual work performance among employees in private business companies. A descriptive-correlational research design was employed, involving 110 respondents from Santo Tomas, Davao del Norte, selected through stratified sampling. Data were collected using three standardized questionnaires, and statistical tools such as Mean, Pearson r, Standard Deviation, and Regression Analysis were applied. The results indicated that both organizational culture and multi-dimensional self-control had significant positive relationships with individual work performance. Specifically, organizational culture was rated with an overall mean of 4.32 (strongly agree), while multi-dimensional self-control scored 3.80 (agree), and individual work performance received an overall mean of 3.75 (agree). These findings suggest that fostering a strong organizational culture and enhancing employee self-control are key strategies for improving work performance, especially in culturally diverse environments. While Presidential Decree No. 442, Article 3, emphasizes the protection of workers' rights and labor laws, its relevance in this study lies in the broader context of promoting effective organizational practices. The study concludes that interventions to strengthen company culture and develop training programs focused on enhancing self-control may significantly boost employee performance. Future recommendations call for more specific and targeted approaches to implement these interventions within organizational frameworks.