Search Results (11,087)

Eutrophication, a growing environmental concern, exacerbates algal blooms and alters the physical and chemical properties of water, thereby diminishing biodiversity, water quality, and ecosystem services. While various control strategies have been developed, most are designed for temperate regions and may not be applicable to tropical systems, which differ ecologically and climatically. This study reviewed 84 articles published between 2000 and 2024, focusing on eutrophication management in tropical and subtropical lakes. The studies were categorized into physical (8), chemical (17), and biological (59) approaches. Over time, research activity has increased, with Asia leading in publication output. Among biological strategies, biomanipulation—especially the use of macrophytes—emerged as the most common and effective strategy. Macrophytes are preferred due to their strong antagonistic interaction with algae, ease of implementation, cost-effectiveness, and minimal ecological risks. While the review also addresses the limitations of each method, it concludes that macrophyte-based biomanipulation remains a promising tool for mitigating eutrophication in tropical and subtropical freshwater ecosystems. In this context, effective lake restoration requires balancing ecological goals with human needs, supported by stakeholder engagement, community education, and multi-sectoral governance. Full article

Lygus pratensis is a major pest of cotton, causing serious damage to cotton production. This study designed species-specific PCR detection primers for L. pratensis, established a detection system to identify L. pratensis DNA in the intestinal contents of predatory natural enemies, and investigated the control potential of four species’ predatory natural enemies against L. pratensis. The results indicated that 826 predatory natural enemies were collected from cotton fields belonging to two classes, five orders, and twelve families. Among these, 9 species of insecta natural enemies accounted for 54.12% of the total number of predatory natural enemies collected, while 14 species of arachnida predatory natural enemies comprised 45.88%. Of the 806 natural enemies tested, 5.58% were found to be positive for L. pratensis, all of which were arachnid predators, specifically Ebrechtella tricuspidata, Xysticus ephippiatus, Hylyphantes graminicola, and Oxyopes sertatus. The predation response of these four spider species to the fourth to fifth instar nymphs and adults of L. pratensis adhered to the Holling II model. The theoretical predation (a′/Th), daily maximum predation rate (T/Th), and searching effect for the fourth to fifth instar nymphs and adults of L. pratensis of the four spider species were assessed. According to the results, the species can be ranked in terms of their predatory and searching efficiency as follows: O. sertatus > E. tricuspidata > X. ephippiatus > H. graminicola. Four species of spiders had the highest theoretical predation against L. pratensis nymphs, ranging from 23.71 to 60.86, and adults, ranging from 22.14 to 50.25. Therefore, these four spider species could be utilized for L. pratensis management. This study identified the main predatory natural enemies of L. pratensis and their pest control capabilities, providing a scientific basis for selecting and utilizing natural enemies in integrated pest management (IPM) strategies. This will help promote ecological and green pest control of L. pratensis in cotton-growing areas. Full article

Two-component systems (TCSs) are ubiquitous in bacteria and are central to their ability to sense and respond to diverse environmental and intracellular cues. Classically composed of a sensor histidine kinase and a cognate response regulator, TCSs control processes ranging from metabolism and development to virulence and antibiotic resistance. In addition to their biological roles, TCSs are garnering attention in synthetic biology and antimicrobial drug development. While canonical architectures have been extensively studied, increasing evidence highlights the remarkable diversity in their organization and regulation. Despite substantial progress, key questions remain regarding the prevalence and physiological relevance of non-canonical TCSs, the mechanisms ensuring signal fidelity, and the potential for engineering these systems. This review explores non-typical TCSs, focusing on their varied transcriptional regulation, alternative response regulator activities, varied control by phosphorylation, and negative control mechanisms. We discuss how bacteria manage signaling specificity among numerous TCSs through cross-talk, hierarchical interactions, and phosphorelay systems and how these features shape adaptive responses. By synthesizing current understanding and highlighting still existing knowledge gaps, this review offers a novel perspective on TCS diversity, indicating directions for future research and potential translational applications in biotechnology and medicine. Full article

Grain storage is one of the important means of national macro-control, significantly impacting people’s livelihood and social stability. In emergencies, grain storage enhances disaster relief efficiency and victim resettlement. Currently, developing countries primarily use government storage and government–enterprise joint storage. In response to the speculative behavior caused by the profit-seeking tendencies of agent storage enterprises in the process of joint government–enterprise grain storage, this study considers the current status of digital governance reform by the government and takes the government–enterprise emergency joint grain storage mechanism as its research object. We construct an evolutionary game model between the government and agent storage enterprises, analyze the evolutionary stability of the strategy choices of the two parties, explore the impact of various factors on the strategy choices of both parties, and discuss different stable strategy combinations. Through simulation analysis of the cost–benefit systems of both sides, initial strategy probabilities, key factor sensitivity, and the impact of digital governance levels, we propose a number of management recommendations that can effectively reduce speculative behavior and provide guidance for the government to improve its emergency grain storage system. Full article

Electric vehicles (EVs) provide a feasible solution for the electrification of the transportation sector. However, the large-scale deployment of EVs over wide working conditions is limited by the temperature sensitivity of lithium-ion batteries (LIBs). Therefore, an efficient and reliable battery thermal management system (BTMS) becomes essential to achieve precise temperature control of batteries and prevent potential thermal runaway. Owing to their high heat-transfer efficiency and controllability, liquid-based cooling technologies have become a key research focus in the field of BTMS. In both design and operation, BTMSs are required to comprehensively consider thermal characteristics, energy consumption, economics, and environmental impact, which demands more scientific and rational evaluation criteria. This paper reviews the latest research progress on liquid-based cooling technologies, with a focus on indirect-contact and direct-contact cooling. In addition, existing evaluation methods are summarized. This work proposes insights for future research on liquid-cooled BTMS development in EVs. Full article

This paper presents a case study on the integration of embedded IoT hardware with a modern Android application, demonstrated through the development of a compact autoclave system for small-scale food sterilization. The device is controlled by an ESP8266-based module and communicates securely with a Kotlin-based Android app via MQTT using HiveMQ. The app incorporates advanced Android design patterns such as coroutines, LiveData, Navigation UI, and DataStore. Each device is uniquely addressable and fully configurable from the mobile interface. The work highlights Android’s role as a powerful interface for managing embedded IoT systems. Full article

(1) Background: Gestational diabetes mellitus (GDM) is a glucose metabolism disorder that typically develops in the second half of pregnancy, transforming a normal pregnancy into a high-risk condition, with both short- and long-term complications for the mother and the fetus. Achieving optimal glycaemic control during pregnancy is essential for preventing these outcomes and could be realized using continuous glucose monitoring systems (CGMSs). This systematic review aims to evaluate the role of the CGMS as a potential diagnostic aid and predictor of maternal and fetal outcomes in GDM. (2) Methods: Following the PRISMA guidelines (protocol ID: CRD42024559169), we performed a literature search using the terms “(continuous glucose monitoring system OR CGMS) AND (gestational diabetes mellitus OR GDM)” in the PubMed, Web of Science, and Scopus databases. (3) Results: Twelve studies were included, all reporting data on CGMS use in pregnancies complicated by GDM. The data included in our analysis are heterogeneous, the results suggesting that the CGMS may offer several advantages such as improved glycaemic control (by avoiding hyper- and hypoglycaemia), better gestational weight management, timely initiation of pharmacologic treatment, lower rates of preeclampsia, and improved neonatal outcomes. (4) Conclusions: the CGMS offers a more detailed assessment of both maternal and fetal exposure to high glucose levels, which could lead to earlier detection of those at risk for GDM complications and better guide treatment regimens, especially timely pharmacological intervention. While the current data are heterogeneous, reporting both limited or no benefits and superior benefits compared to the classic monitoring, larger longitudinal studies are mandatory to validate these findings and to better refine the role of CGMS in the monitoring and management of GDM. Full article

In this study, a prototype system was developed as a potential alternative to lockdown measures against the spread of airborne infectious diseases such as COVID-19. The system integrates real-time estimation functions for respiratory flow and mask leakage into a low-cost powered air-purifying respirator (PAPR) designed for the general public. Using only a single differential-pressure sensor (SDP810) and a controller (Arduino UNO R4 WiFi), the respiratory flow (Q_3e) is estimated from the differential pressure (ΔP) and battery voltage (V_b), and both the wearing status and leak status are transmitted to and displayed on a smartphone application. For evaluation, a testbench called the Respiratory Airflow Testbench was constructed by connecting a cylinder–piston drive to a mannequin head to simulate realistic wearing conditions. The estimated respiratory flow Q_3e, calculated solely from ΔP and V_b, showed high agreement with the measured flow Q_3m obtained from a reference flow sensor, confirming the effectiveness of the estimation algorithm. Furthermore, an automatic leak detection method based on the time-integrated value of Q_3e was implemented, enabling the detection of improper wearing. This system thus achieves respiratory flow estimation and leakage detection based only on ΔP and V_b. In the future, it is expected to be extended to applications such as pressure control synchronized with breathing activity and health monitoring based on respiratory and coughing analysis. This platform also has the potential to serve as the foundation of a PAPR Wearing Status Network Management System, which will contribute to societal-level infection control through the networked sharing of wearing status information. Full article

Vegetables such as lettuce, tomato, carrot, and beet are vital to the global food industry, providing essential nutrients and supporting sustainable agriculture. Their cultivation in greenhouses across diverse climatic zones (temperate, Mediterranean, tropical, subtropical, and arid) has gained prominence due to controlled environments that enhance yield and quality. However, these crops face significant threats from climate change, including rising temperatures, erratic light availability, and resource constraints, which challenge optimal growth and nutritional content. This study investigates the influence of microclimatic conditions—temperature, light intensity, and CO₂ concentration—on the growth, physiology, and biochemistry of these vegetables under varying greenhouse types and climatic zones, addressing these threats through a systematic review. The methodology followed the PRISMA guidelines, synthesizing peer-reviewed articles from 1995 to 2025 sourced from Web of Science, Pub Med, Scopus, Science Direct, Springer Link, and Google Scholar. Search terms included “greenhouse microclimate”, “greenhouse types”, “Climatic Zones, “and crop-specific keywords, with data extracted on microclimatic parameters and analyzed across growth stages and climatic zones. Eligibility criteria ensured focus on quantitative data from greenhouse studies, excluding pre-1995 or non-peer-reviewed sources. The results identified the following optimal conditions: lettuce and beet thrive at 15–22 °C, 200–250 μmol·m⁻²·s⁻¹, and 600–1100 ppm CO₂ in temperate zones; tomatoes at 18–25 °C, 200–300 μmol·m⁻²·s⁻¹, and 600–1100 ppm in Mediterranean and arid zones; and carrots at 15–20 °C, 150–250 μmol·m⁻²·s⁻¹, and 600–1000 ppm in subtropical zones. Greenhouse types (e.g., glasshouses, polytunnels) modulate these optima, with high-tech systems enhancing resilience. Conclusively, tailored microclimatic management, integrating AI-driven technologies and advanced greenhouse designs, is recommended to mitigate threats and optimize production across climatic zones. Full article

The relationship between sleep and epilepsy involves complex interactions between thalamocortical circuits, circadian mechanisms, and sleep architecture that fundamentally influence seizure susceptibility and cognitive outcomes. Epileptic activity disrupts essential sleep oscillations, particularly sleep spindles generated by thalamic circuits. Thalamic epileptic spikes actively compete with physiological sleep spindles, impairing memory consolidation and contributing to cognitive dysfunction in epileptic encephalopathy. This disruption explains why patients with epilepsy often experience learning difficulties despite adequate seizure control. Sleep stages show differential seizure susceptibility. REM sleep provides robust protection through enhanced GABAergic inhibition and motor neuron suppression, while non-REM sleep, particularly slow-wave sleep, increases seizure risk. These observations reveal fundamental mechanisms of seizure control within normal brain physiology. Circadian clock genes (BMAL1, CLOCK, PER, CRY) play crucial roles in seizure modulation. Dysregulation of these molecular timekeepers creates permissive conditions for seizure generation while being simultaneously disrupted by epileptic activity, establishing a bidirectional relationship. These mechanistic insights are driving chronobiological therapeutic approaches, including precisely timed antiseizure medications, sleep optimization strategies, and orexin/hypocretin system interventions. This understanding enables a paradigm shift from simple seizure suppression toward targeted restoration of physiological brain rhythms, promising transformative epilepsy management through sleep-informed precision medicine. Full article

Background and Objectives: Rheumatoid arthritis (RA) is a systemic autoimmune inflammatory disease, and progressive arthritis is its primary clinical manifestation. The role of human parvovirus B19 (B19V) infection in the progression of RA remains unclear. This study aims to investigate the association between B19V infection and viral genetic distribution in Vietnamese RA patients. Materials and Methods: 115 Vietnamese RA patients and 86 healthy controls (HCs) were enrolled in this observational study at the Thai Nguyen National Hospital from January 2019 to December 2021. B19V DNA was examined in serum and synovial fluid samples from RA patients using nested PCR and real-time PCR. B19V antibodies were detected in serum samples using ELISA. Results: B19V DNA was detected in the serum of 2 out of 115 (1.74%) RA patients but not in any HCs. Interestingly, B19V DNA was present in 12 out of 68 (17.65%) RA patients with knee effusion in their synovial fluid. Anti-B19V-IgG and anti-B19V-IgM were detected in the serum of 42.61% and 2.61% of RA patients, respectively, and in 24.42% and 12.79% of HCs, respectively. Anti-B19V-IgG levels were significantly higher in the serum of RA patients than in the serum of HCs (p = 0.007). However, anti-B19V-IgM was more commonly detected in HC serum than in RA patient serum (p = 0.006). Phylogenetic analysis showed that all B19V strains belonged to genotype 1 and subgenotype 1A. Conclusions: B19V infection is frequent in RA patients and suggests a contribution of B19V to the progression of RA, particularly in a B19V genotype-1- and subgenotype-1A-dependent manner and emphasises the need for early detection and management of B19V infection in RA patients. Full article

In this article, the development of the software system—a virtual simulator for FATEK programmable controllers for industrial processes—is proposed. The main purpose of the development is to create a virtual tool that emulates the operation of FATEK controllers, with the primary task being the receiving and sending of data related to the controller’s resources, using the FACON communication protocol. The simulator implements a protocol that is described in the FACON documentation. The simulator works as a slave device and returns a response only to a received request from the master device (this can be any program—SCADA or HM). The communication is asynchronous, i.e., receiving messages occurs independently of the operation of the simulator itself. The simulator is implemented as a desktop GUI application using the C++ programming language and the C++ Builder platform. This simulator can be used to manage tested SCADA and HMI programs for technological processes, etc. The main part of this work is the correct reading/writing of controller memory data (inputs/outputs/memory bits and registers). Through the developed simulator, you are fully tested under conditions of impossibility of using a real programmable controller. Full article

Biological control serves as a crucial strategy for crop disease management. The biocontrol potential and plant growth-promoting effects of the strain YTBTa14 were investigated. Genetic sequencing confirmed YTBTa14 as Pseudomonas chlororaphis, which exhibited broad-spectrum antifungal activity against multiple pathogens affecting grapevine, apple, cherry, and wheat. YTBTa14 significantly enhanced the growth of wheat and grapevine, specifically increasing wheat seed germination rates and improving root and coleoptile development. In grapevine plant, significant increases in root length, stem length, and fresh weight were observed. The strain demonstrated robust adaptability and stable antagonism under varying sodium chloride (NaCl) concentrations, pH levels, and temperatures. YTBTa14 modulated plant hormone levels, elevating the content of indole-3-acetic acid (IAA), gibberellins (GA), and cytokinins (CTK). Furthermore, it effectively stimulated the production of key plant defense enzymes, including superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). Pretreatment of grape leaves with YTBTa14 triggered plant cell defense response and upregulated the expression of defense-related genes PR1 (pathogenesis-related protein 1) and PAL1 (phenylalanine ammonia-lyase 1), thereby mitigating the severity of downy mildew disease and inducing systemic resistance. These findings demonstrate that YTBTa14 is a highly promising candidate for development as a multifunctional agricultural biocontrol agent. Full article

The metropolitan agglomeration in and around Bucharest, Romania’s capital and largest city, has experienced significant growth in recent decades, both economically and demographically. With over two million residents in its metropolitan area, Bucharest faces urban mobility challenges characterized by congested roads, overcrowded public transport routes, limited parking, and air pollution. This study evaluates the potential of AI-driven adaptive traffic signal control to address these challenges using an agent-based simulation approach. The authors focus on Bucharest’s north-western part, a critical congestion area. A detailed road network was derived from OpenStreetMap and calibrated with empirical traffic data from TomTom Junction Analytics and Route Monitoring (corridor-level speeds and junction-level turn ratios). Using the MATSim framework, the authors implemented and compared fixed-time and adaptive signal control scenarios. The adaptive approach uses a decentralized, demand-responsive algorithm to minimize delays and queue spillback in real time. Simulation results indicate that adaptive signal control significantly improves network-wide average speeds, reduces congestion peaks, and flattens the number of en-route agents throughout the day, compared to fixed-time plans. While simplifications remain in the model, such as generalized signal timings and the exclusion of pedestrian movements, these findings suggest that deploying adaptive traffic management systems could deliver substantial operational benefits in Bucharest’s urban context. This work demonstrates a scalable methodology combining open geospatial data, commercial traffic analytics, and agent-based simulation to rigorously evaluate AI-based traffic management strategies, offering evidence-based guidance for urban mobility planning and policy decisions. Full article

This paper presents a novel electro-thermal modeling approach for a lithium-ion battery pack in an electric vehicle (EV), along with parameter identification using controller area network (CAN) data collected from chassis dynamometer and real-world driving tests. The proposed electro-thermal model consists of a first-order equivalent circuit model (ECM) and a lumped-parameter thermal network in considering a simplified cooling circuit layout and temperature distributions across four distinct zones within the battery pack. This model captures the nonuniform heat transfer between the pack modules and the coolant, as well as variations in coolant temperature and flow rates. Model parameters are identified directly from vehicle-level test data without relying on laboratory-level measurements. Validation results demonstrate that the model can predict terminal voltage with an RMSE of less than 6 V (normalized root mean square error of less than 2%), and battery module surface temperatures with root mean square errors of less than 2 °C for over 90% of the test cases. The proposed approach provides a cost-effective and accurate solution for predicting electro-thermal behavior of EV battery systems, making it a valuable tool for battery design and management to optimize performance and ensure the safety of EVs. Full article