Search Results (209)

Background/Objectives: Isolated cecum necrosis (ICN) is a rare disease and there are few reports on it in the literature. We retrospectively analyzed the patients with acute abdomen who had ICN, in terms of etiology, demographic findings, surgery, and prognosis to determine the best treatment approach. Methods: Seven patients with per-operative diagnosis of isolated cecal necrosis were included in the study. Age, gender, comorbidities, symptoms, laboratory data, imaging modalities, time from symptoms to surgery, surgical procedure, hospital stay, morbidity, and mortality were retrospectively analyzed. Results: The median age of the patients and the F/M ratio were 61 years and 2/5, respectively. The most common comorbidity was chronic renal failure, followed by hypertension, diabetes mellitus, and coronary artery disease. The median time from symptom onset to surgery was 1 day. The ileocolic anastomosis was performed in 4 patients and terminal ileostomy in three patients. The morbidity and mortality rates were 28.5% and 14.3%, respectively. The median length of hospital stay was 12 days. Conclusions: ICN should be suspected in elderly patients with comorbidities presenting to the emergency department with an acute abdomen. Computed tomography is an important diagnostic tool. Diagnostic laparoscopy is a good option for definitive diagnosis and end ileostomy is better than anastomosis in patients with bleeding tendency or hypotensive patients with chronic renal failure on dialysis, coronary artery disease, and in the presence of purulent peritonitis to reduce morbidity and mortality. Full article

Microalgae have emerged as a promising renewable resource due to their biomass production and cell composition. This study aimed to evaluate a commercial nutrient source for Desmodesmus abundans L2B Bold cultivation, analyzing its metabolome, photosynthetic pigments, and biomass potential as a biostimulant for soybean crops. Samples underwent metabolomics analysis via GC/MS after derivatization. Microalgal biomass produced in a commercial fertilizer medium (CF) was applied as a biostimulant at three concentrations in soybean crops at 30, 50, and 70 days post-sowing. Pigments and dry biomass levels were similar in BG11 and CF media, but CF reduced production costs by 31.8%. Distinct metabolite profiles were observed, with major classes being organic acids (19%), amines (14%), sugars (12%), fatty acids (11%), and esters (10%). Foliar application of 20% (v/v) D. abundans L2B Bold increased stem diameter (5.88 mm), leaf fresh and dry mass, and stem mass. Additionally, there were increases in nitrogen (26%) and protein (38.1%) in seeds, alongside higher pod (10.9) and seed numbers (25.2) and seed mass (3.43 g) per plant⁻¹. These findings demonstrate the feasibility of using CF as a culture medium and the potential of D. abundans L2B Bold as a biostimulant for soybean production. Full article

The growing demand for large-scale data distribution and sharing presents significant challenges to content transmission within the current TCP/IP network architecture. To address these challenges, Information-Centric Networking (ICN) has emerged as a promising alternative, offering inherent support for multipath forwarding and in-network caching to improve data transmission performance. However, most existing ICN caching strategies primarily focus on utilizing resources along the default transmission path and its neighboring nodes, without fully exploiting the additional resources provided by multipath forwarding. To address this gap, we propose an efficient multipath-based caching strategy that optimizes cache placement by decomposing the problem into two steps, multipath selection and cache node selection along the paths. First, multipath selection considers both transmission and caching resources across multiple paths, prioritizing the caching of popular content while efficiently transmitting less popular content. Next, along the selected paths, cache node selection evaluates cache load based on cache utilization and available capacity, prioritizing nodes with the lowest cache load. Extensive simulations across diverse topologies demonstrate that the proposed strategy reduces data transmission latency by at least 12.22%, improves cache hit rate by at least 16.44%, and enhances cache node load balancing by at least 18.77%, compared to the neighborhood collaborative caching strategies. Full article

The advent of 6G networks and beyond calls for innovative paradigms to address the stringent demands of emerging applications, such as extended reality and autonomous vehicles, as well as technological frameworks like digital twin networks. Traditional cloud computing and edge computing architectures fall short in providing their required flexibility, scalability, and ultra-low latency. Cloud computing centralizes resources in distant data centers, leading to high latency and increased network congestion, while edge computing, though closer to data sources, lacks the agility to dynamically adapt to fluctuating workloads, user mobility, and real-time requirements. In-network computing (INC) offers a transformative solution by integrating computational capabilities directly into the network fabric, enabling dynamic and distributed task execution. This paper explores INC through the lens of information-centric networking (ICN), a revolutionary communication paradigm implementing routing-by-name and in-network caching, and thus emerging as a natural enabler for INC. We review state-of-the-art advancements involving INC and ICN, addressing critical topics such as service naming, executor selection strategies, compute reuse, and security. Furthermore, we discuss key challenges and propose research directions for deploying INC via ICN, thereby outlining a cohesive roadmap for future investigation. Full article

Information-Centric Networking (ICN) typically utilizes DRAM (Dynamic Random Access Memory) to build in-network cache components due to its high data transfer rate and low latency. However, DRAM faces significant limitations in terms of cost and capacity, making it challenging to meet the growing demands for cache scalability required by increasing Internet traffic. Combining high-speed but expensive memory (e.g., DRAM) with large-capacity, low-cost storage (e.g., SSD) to construct a hierarchical cache architecture has emerged as an effective solution to this problem. However, how to perform efficient cache management in such architectures to realize the expected cache performance remains challenging. This paper proposes a cache management scheme for hierarchical cache architectures in ICN, which introduces a differentiated replica replacement policy to accommodate the varying request access patterns at different cache layers, thereby enhancing overall cache performance. Additionally, a probabilistic insertion-based SSD cache admission filtering mechanism is designed to control the SSD write load, addressing the issue of balancing SSD lifespan and space utilization. Extensive simulation results demonstrate that the proposed scheme exhibits superior cache performance and lower SSD write load under various workloads and replica placement strategies, highlighting its broad applicability to different application scenarios. Additionally, it maintains stable performance improvements across different cache capacity settings, further reflecting its good scalability. Full article

Information-centric networking (ICN) changes the way data are accessed by focusing on the content rather than the location of devices. In this model, each piece of data has a unique name, making it accessible directly by name. This approach suits the Internet of Things (IoT), where data generation and real-time processing are fundamental. Traditional host-based communication methods are less efficient for the IoT, making ICN a better fit. A key advantage of ICN is in-network caching, which temporarily stores data across various points in the network. This caching improves data access speed, minimizes retrieval time, and reduces overall network traffic by making frequently accessed data readily available. However, IoT systems involve constantly updating data, which requires managing data freshness while also ensuring their validity and processing accuracy. The interactions with cached data, such as updates, validations, and replacements, are crucial in optimizing system performance. This research introduces an ICN-IoT method to manage and process data freshness in ICN for the IoT. It optimizes network traffic by sharing only the most current and valid data, reducing unnecessary transfers. Routers in this model calculate data freshness, assess its validity, and perform cache updates based on these metrics. Simulation results across four models show that this method enhances cache hit ratios, reduces traffic load, and improves retrieval delays, outperforming similar methods. The proposed method uses an artificial neural network to make predictions. These predictions closely match the actual values, with a low error margin of 0.0121. This precision highlights its effectiveness in maintaining data currentness and validity while reducing network overhead. Full article

Multipath transmission in ICN provides high transmission efficiency and stability. In an IP-ICN compatible network environment, unmodified IP terminal devices can access ICN through gateways, benefiting from these performance enhancements. This paper proposes a gateway framework for hybrid IP-ICN multipath transmission systems, enabling protocol conversion and quality of service management. A packet reordering module is integrated at the egress gateway to address complex packet disorder issues caused by ICN multipath transmission, thereby enhancing the service quality provided to IP terminals. A Reordering Buffer Management Method (RBMM) is introduced, consisting of two key components. First, RBMM employs an improved dynamic threshold scheme for reserved buffer partitioning, efficiently identifying congestion and optimizing buffer resource utilization. Second, a flow-priority-based replacement strategy is designed to enhance fairness in resource allocation by evicting packets with lower delivery probability during congestion. Experimental results demonstrate that RBMM dynamically adapts to varying traffic conditions, maintaining high transmission performance while reducing buffer resource consumption. In comparison to existing methods, RBMM significantly reduces queuing delay and flow completion time, providing more balanced resource allocation when multiple flows compete for limited buffer capacity. Full article

Understanding the growth patterns of genotypes optimizes their selection and management. The objective of this study is to investigate the seasonal variations in the morphology and biochemistry of Coffea canephora clone leaves, considering climatic conditions and the maturation cycle. Morphological characteristics and carbohydrate contents of the leaves were analyzed throughout the growth cycle. A nonlinear logistic model was applied, and critical points of the leaf emission rates of plagiotropic branches were determined. Leaf growth was greater at higher temperatures during the rainy periods and lower at milder temperatures during the dry season. Genotype 143 exhibited the largest leaf width in spring, while 104, A1, and P2 had the largest leaf width in summer. The logistic model was suitable for describing leaf emission, with the critical points of genotype 143 being earlier, while P2 displayed a longer leaf emission cycle. The peak growth period influenced the quantities of starch and total soluble sugars in the leaves. The dormancy period showed a higher availability of reducing sugars. Pearson correlation indicated significant coefficients between temperature, precipitation, photoperiod, and foliar characteristics. The results obtained serve as a reference for future investigations, particularly in response to environmental challenges. Full article

Network measurement is an efficient way to understand network behavior. Traditional measurement techniques focus on internet protocol (IP) networks, where the processing capacity of network nodes is limited and primarily dedicated to packet forwarding. As a result, these techniques typically rely on end hosts or external systems to analyze traffic and evaluate network performance. This reliance introduces several challenges, such as increased measurement latency and scalability limitations, particularly in large-scale networks. With the emergence of next-generation internet architectures, especially information-centric networking (ICN), network nodes have gained enhanced capabilities, enabling measurement tasks to be performed directly at these nodes. This paper proposes a distributed measurement scheme where network nodes collaborate to monitor the packet loss rate on the intermediate link. By setting an unused bit in the packet header, the upstream node “colors” the packets into different color blocks. The minimum duration of each block is determined by the degree of reordering on the link, and the number of packets in each block must be a power of two. The downstream node recognizes blocks, assigns packets to the right block, and deduces the original number of packets for each block to calculate packet loss. Moreover, the upstream node adjusts the number of packets in each block based on the packet transmission rate on the link, aiming to balance measurement accuracy and frequency. A P4-based implementation on a BMv2 software switch is presented to demonstrate the feasibility of the proposed scheme. Simulations show that this scheme improves measurement accuracy and is more robust against packet reordering. Additionally, the proposed scheme maintains relatively low network overhead and, at higher measurement frequencies, exhibits the lowest overhead compared to existing methods. Full article

The Computing Network is an emerging paradigm that integrates network and computing resources. One of its goals is to satisfy the requirements of delay-sensitive services through network scheduling capabilities. However, traditional TCP/IP networks are deficient in accurately being aware of requirements and performing flexible routing based on service levels. Information-Centric Networking (ICN) addresses these issues through its flexible protocol design and content-based routing mechanism. Additionally, the integration of Software-Defined Networking (SDN) technology further enhances its routing flexibility. Therefore, this paper proposes an ICN-based delay-sensitive service scheduling architecture with an SDN stateful programmable data plane. The network nodes are first layered based on the type of computing clusters they are linked with, and then within each layer, they are divided into several domains according to delay constraints. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm, combined with the Best-Worst Method (BWM) weighting method, is adopted to evaluate the candidate clusters, and the corresponding scheduling strategy is executed in the stateful programmable data plane. The simulation results show that compared with other scheduling architectures and traditional TOPSIS with the Entropy Weight Method (EWM), the proposed architecture and algorithm show significant advantages in reducing the overall delay of service requests and improving the scheduling success ratio, as well as the load balance of the computing clusters. Full article

This research proposes a model to assess the impacts of transformational environmental leadership and corporate social responsibility on sustainable performance with the mediating effect of competitive advantage in environmentally certified companies in Mexico. Based on a literature review, a measurement instrument was created to evaluate the variables in the model. The sample is composed of 150 certified companies from 29 states. We used factor analysis and path analysis for hypothesis testing. We observed that transformational environmental leadership facilitates the internal changes and decision-making necessary to implement corporate social responsibility practices, develop competitive advantages, and improve sustainable performance. We also observed a positive relationship between competitive advantage and sustainable performance. From a transformational environmental leadership perspective, this study is helpful for researchers, industry experts, policymakers, and managers interested in voluntary environmental certifications in emerging economies. The research model implies a way to strengthen companies’ competitive advantage and sustainable performance through transformational environmental leadership and corporate social responsibility. Full article

Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited attention to the multi-objective optimization techniques employed in this context. To address this gap, this paper presents a systematic literature review on multi-objective production rescheduling, examining diverse shop-floor environments. Adhering to the PRISMA guidelines, a total of 291 papers were identified. From this pool, studies meeting the inclusion criteria were selected and analyzed to provide a comprehensive overview of the problems tackled, dynamic events managed, objectives considered, and optimization approaches discussed in the literature. This review highlights the primary multi-objective optimization methods used in relation to rescheduling strategies and the dynamic disruptive events studied. Findings reveal a growing interest in this research area, with “a priori” and “a posteriori” optimization methods being the most commonly implemented and a notable rise in the use of the latter. Hybridized algorithms have shown superior performance compared to standalone algorithms by leveraging combined strengths and mitigating individual weaknesses. Additionally, “interactive” and “Pareto pruning” methods, as well as the consideration of human factors in flexible production systems, remain under-explored. Full article

Chitin, the second most abundant biopolymer after cellulose, is an important resource for biosourced materials. The global demand for chitin is rapidly increasing, however, the majority of industrial chitin is sourced from crustacean shells, which may be less accessible in regions without seafood waste. Therefore, it is crucial to explore alternative chitin sources, such as those derived from beetles and other arthropods. This study investigated chitin extraction from nine species of Curculionidae (true weevils), which are recognized as crop pests. The extraction process and yields were described, and the isolated chitin was characterized by SEM, IR spectroscopy, elemental analysis, XRD, and ash and water content measurements. This work highlights the potential of Curculionidae as an alternative chitin source. Full article

Metal-free photocatalysis that produces reactive oxygen species (ROS) shows significant promising applications for environmental remediation. Herein, we constructed iodine-doped carbon nitride (I-CN) for applications in the photocatalytic inactivation of bacteria and the heterogeneous Fenton reaction. Our findings revealed that I-CN demonstrates superior photocatalytic activity compared to pure CN, due to enhanced light adsorption and a narrowed band gap. Antibacterial tests confirmed that I-CN exhibits exceptional antibacterial activity against both Escherichia coli and Staphylococcus aureus. The results showed that I-CN effectively generates superoxide radicals and hydroxyl radicals under light irradiation, resulting in enhanced antibacterial activity. In addition, I-CN can also be applied for a heterogeneous photo-Fenton-like reaction, achieving a high performance for the degradation of sulfamethoxazole (SMX), a typical antibiotic, via the photocatalytic activation of peroxymonosulfate (PMS). These results shed new light on the fabrication of metal-free nanozymes and their applications for disinfection and water decontamination. Full article

The COVID-19 pandemic continues to constitute a public health emergency of international importance, although the state of emergency declaration has indeed been terminated worldwide, many people continue to be infected and present different symptoms associated with the illness. Undoubtedly, solutions based on divergent technologies such as machine learning have made great contributions to the understanding, identification, and treatment of the disease. Due to the sudden appearance of this virus, many works have been carried out by the scientific community to support the detection and treatment processes, which has generated numerous publications, making it difficult to identify the status of current research and future contributions that can continue to be generated around this problem that is still valid among us. To address this problem, this article shows the result of a scientometric analysis, which allows the identification of the various contributions that have been generated from the line of automatic learning for the monitoring and treatment of symptoms associated with this pathology. The methodology for the development of this analysis was carried out through the implementation of two phases: in the first phase, a scientometric analysis was carried out, where the countries, authors, and magazines with the greatest production associated with this subject can be identified, later in the second phase, the contributions based on the use of the Tree of Knowledge metaphor are identified. The main concepts identified in this review are related to symptoms, implemented algorithms, and the impact of applications. These results provide relevant information for researchers in the field in the search for new solutions or the application of existing ones for the treatment of still-existing symptoms of COVID-19. Full article