Continuous Monitoring Research Articles

Perioperative Fully Closed-loop versus Usual Care Glucose Management in Adults Undergoing Major Abdominal Surgery - A Two-centre Randomised Controlled Trial.

To assess the efficacy and safety of fully closed-loop (FCL) compared with usual care (UC) glucose control in patients experiencing major abdominal surgery-related stress hyperglycaemia. Major abdominal surgery-related stress and periprocedural interventions predispose to perioperative hyperglycaemia, both in diabetes and non-diabetes patients. Insulin corrects hyperglycaemia effectively, but its safe use remains challenging. In this two-centre randomised controlled trial, we contrasted subcutaneous FCL with UC glucose management in patients undergoing major abdominal surgery anticipated to experience prolonged hyperglycaemia. FCL (CamAPS HX, Dexcom G6, mylife YpsoPump 1.5x) or UC treatment was used from hospital admission to discharge (max 20d). Glucose control was assessed using continuous glucose monitoring (masked in the UC group). The primary outcome was the proportion of time with sensor glucose values in target range 5.6-10.0mmol/L. Thirty-seven surgical patients (54% pancreas, 22% liver, 19% upper gastrointestinal, 5% lower gastrointestinal), of whom 18 received FCL and 19 UC glucose management, were included in the analysis. Mean±SD percentage time with sensor glucose in target range was 80.1±10.0% in the FCL and 53.7±19.7% in the UC group (P<0.001). Mean±SD glucose was 7.5±0.5mmol/L in the FCL and 9.1±2.4mmol/L in the UC group (P=0.015). Time in hypoglycaemia (<3.0mmol/L) was low in either group. No study-related serious adverse events occurred. The FCL approach resulted in significantly better glycaemic control compared to UC management, without increasing the risk of hypoglycaemia. Automated glucose-responsive insulin delivery is a safe and effective strategy to minimise hyperglycaemia in complex surgical populations.

Management Strategies for Children and Adolescents with Diabetes Mellitus

The principles of treatment for diabetes in children and adolescents cannot simply be derived from care routinely provided to adults with diabetes. The major consideration is that the epidemiology, pathophysiology, developmental considerations, and response to treatment of pediatric diabetes are often different from those of adult diabetes. Second, recommended treatments for children and adolescents with type 1 diabetes (T1DM), type 2 diabetes (T2DM), and other pediatric conditions such as monogenic diabetes (neonatal diabetes and MODY [maturity-onset diabetes of the young]) also differ. HbA1c goals in T1DM and T2DM must be individualized and reassessed over time. A HbA1c &lt; 7% is appropriate for many children and adolescents with T1DM. In a case with hypoglycemia, hypoglycemic unawareness, lack of access to analog insulins, advanced insulin delivery technology and/or continuous glucose monitoring, a less stringent HbA1c &lt; 7.5% will be required. A reasonable HbA1c goal for T2DM is &lt; 7%. If possible, a strict HbA1c target of &lt; 6.5% can be implemented. Metformin is the first-line treatment of choice in T2DM. In a case with ketosis or HbA1c &gt; 8.5%, insulin will be required with once daily basal insulin (0.25~0.5 IU/kg). If the glycemic goal is not attained, the addition of a second agent is considered in adult patients but might not be applicable or safe in pediatric cases. Therefore, the efficacy and safety of these drugs used in adult patients, including glucagon-like peptide-1 receptor agonists and sodium glucose cotransporter 2 inhibitors, should be evaluated in pediatric patients worldwide.

Patient Education in Type 2 Diabetes Mellitus through Continuous Glucose Monitoring Reports

The number of patients with type 2 diabetes mellitus using continuous glucose monitoring (CGM) continues to increase. The combined effects of CGM and patient education have been demonstrated to improve glycemic control more effectively than CGM alone. Therefore, healthcare providers should analyze CGM reports and offer structured, personalized education to patients with type 2 diabetes mellitus. Various features are frequently updated in CGM reports. Thus, healthcare providers should be skilled in using these features to improve the quality of patient care.

Criteria for Personalised Choice of a Continuous Glucose Monitoring System: An Expert Opinion.

Despite the growing evidence supporting the outpatient use of continuous glucose monitoring (CGM) for improving glycaemic control and reducing hypoglycaemia, there is a need for a detailed understanding of the specific features of CGM devices that best meet individual patient needs. This expert opinion, based on a comprehensive literature review and the personal perspectives of clinicians, aims to provide thehealthcare professionals (HCPs) with a comprehensive framework for selecting CGM devices. It evaluates the current state of CGM technology, categorizing features into essential features, major drivers of choice, and additional/useful features. Moreover, the practical model presented outlines a patient's journey with CGM, emphasising the importance of aligning device features with patient needs. This includes understanding the patient's lifestyle, clinical conditions, and personal preferences to optimize CGM use and improve diabetes management outcomes.

Driving Wi-Fi IoT Sensors by a Hybrid Magneto-Mechano-Electric Energy Generator Extracting a Power of over 50mW.

Energy harvesting technology is mainly used as a power source for driving Internet of Things (IoT) devices. However, the output power of conventional harvesting devices are limited, suitable only for low-power-consumption IoT sensors based on Bluetooth communication. In contrast to Bluetooth, wireless fidelity (Wi-Fi) communication offers superior real-time monitoring and transmission capabilities, but requires more power in the range of hundreds of milliwatts or higher. Therefore, the hybridization of three energy conversion devices, namely, piezoelectric magneto-mechano-electric (MME) generator, electromagnetic (EM) induction coil, and triboelectric nanogenerator (TENG) is proposed as a standalone power source for Wi-Fi communication sensors. By integrating these three mechanisms, the hybrid MME energy harvester can achieve an output power exceeding 50mW at the second harmonic resonance condition under the alternating current (AC) magnetic field of 10Oe. Furthermore, it can successfully drive the Wi-Fi sensor, enabling continuous real-time monitoring without the degradation of charged power in a supercapacitor. These results highlight that energy harvesting technology is not limited to low-power devices but can also be applied to Wi-Fi communication sensors and beyond.

A Position Statement on Diabetes with β-Cell Failure

Diabetes mellitus is a heterogeneous disease that encompasses a wide range of conditions, from mild cases to severe conditions where survival depends on insulin therapy. The Korean Diabetes Association Task Force Team for Diabetes with β-Cell Failure has established the term to classify severe refractory disease with β-cell failure. Individuals with β-cell failure are at high risk of diabetes-related complications. We propose that diabetes with β-cell failure can be diagnosed when individuals treated with multiple daily insulin injections or insulin pumps meet at least one of the following criteria: fasting C-peptide ≤ 0.6 ng/mL, non-fasting C-peptide ≤ 1.8 ng/mL, 24-hour urine C-peptide &lt; 30 μg/day, or spot urine C-peptide/creatinine ratio ≤ 0.6 nmol/mmol. Among cases of diabetes with β-cell failure, β-cell failure with absolute insulin deficiency can be diagnosed when at least one of the following criteria is met: fasting C-peptide &lt; 0.24 ng/mL, non-fasting C-peptide &lt; 0.6 ng/mL, or spot urine C-peptide/ creatinine ratio &lt; 0.2 nmol/mmol. Multiple daily insulin injections with long-acting insulin analogs and rapid-acting insulin analogs or insulin pumps are required for treatment of diabetes with β-cell failure. Continuous glucose monitoring and an automated insulin delivery system, sensor-augmented pump, or smart insulin pen, along with structured education, are necessary. We call for improvements in the relevant systems to ensure that such treatments can be provided.

Frequency and genotyping of group A rotavirus among Egyptian children with acute gastroenteritis: a hospital-based cross-sectional study

BackgroundThis hospital-based cross-sectional study aims to investigate the epidemiologic and clinical characteristics of rotavirus group A (RVA) infection among children with acute gastroenteritis and to detect the most common G and P genotypes in Egypt.MethodsA total of 92 stool samples were collected from children under five who were diagnosed with acute gastroenteritis. RVA in stool samples was identified using ELISA and nested RT-PCR. Common G and P genotypes were identified utilizing multiplex nested RT-PCR assays.ResultsRVA was detected at a rate of 24% (22 /92) using ELISA and 26.1% (24 /92) using VP6 nested RT-PCR. The ELISA test demonstrated diagnostic sensitivity, specificity, and accuracy of 91.7%, 100%, and 97.8%, respectively. G3 was the most prevalent G type (37.5%), followed by G1 (12.5%), whereas the most commonly detected P type were P[8] (41.7%) and P[6] (8.2%). RVA-positive samples were significantly associated with younger aged children (p = 0.026), and bottle-fed (p = 0.033) children. In addition, RVA-positive samples were more common during cooler seasons (p = 0.0001). Children with rotaviral gastroenteritis had significantly more frequent episodes of diarrhea (10.87 ± 3.63 times/day) and vomiting (8.79 ± 3.57 times/day) per day (p = 0.013 and p = 0.011, respectively). Moreover, they had a more severe Vesikari clinical score (p = 0.049).ConclusionRVA is a prevalent cause of acute gastroenteritis among Egyptian children in our locality. The discovery of various RVA genotypes in the local population, as well as the identification of common G and P untypeable strains, highlights the significance of implementing the rotavirus vaccine in Egyptian national immunization programs accompanied by continuous monitoring of strains.

Traditional Chinese Medicine (TCM)-Inspired Fully Printed Soft Pressure Sensor Array with Self-Adaptive Pressurization for Highly Reliable Individualized Long-Term Pulse Diagnostics.

Reliable, non-invasive, continuous monitoring of pulse and blood pressure is essential for the prevention and diagnosis of cardiovascular diseases. However, the pulse wave varies drastically among individuals or even over time in the same individual, presenting significant challenges for the existing pulse sensing systems. Inspired by pulse diagnosis methods in traditional Chinese medicine (TCM), this work reports a self-adaptive pressure sensing platform (PSP) that combines the fully printed flexible pressure sensor array with an adaptive wristband-style pressure system can identify the optimal pulse signal. Besides the detected pulse rate/width/length, "Cun, Guan, Chi" position, and "floating, moderate, sinking" pulse features, the PSP combined with a machine learning-based linear regression model can also accurately predict blood pressure such as systolic, diastolic, and mean arterial pressure values. The developed diagnostic platform is demonstrated for highly reliable long-term monitoring and analysis of pulse and blood pressure across multiple human subjects over time. The design concept and proof-of-the-concept demonstrations also pave the way for the future developments of flexible sensing devices/systems for adaptive individualized monitoring in the complex practical environments for personalized medicine, along with the support for the development of digital TCM.

Comprehensive data security and compliance framework for SMEs

Small and Medium-sized Enterprises (SMEs) are increasingly relying on cloud platforms to support critical business operations, making effective disaster recovery (DR) strategies essential for ensuring business continuity. This review proposes a robust disaster recovery framework tailored for SMEs, designed to minimize downtime and data loss in the event of a system failure, cyberattack, or natural disaster. The framework integrates advanced cloud technologies to create a cost-effective, scalable solution that aligns with the resource constraints of SMEs while providing enterprise-grade resilience. Key components of the disaster recovery framework include cloud-based data replication, automated backup solutions, and geo-redundant storage to ensure that data is continuously available and recoverable. This model employs real-time data synchronization and incremental backups to minimize Recovery Point Objectives (RPO), ensuring that critical data is not lost during an unexpected outage. Additionally, the framework leverages automated failover mechanisms to achieve low Recovery Time Objectives (RTO), allowing businesses to restore operations quickly after an interruption. Cloud orchestration tools such as AWS Elastic Disaster Recovery or Azure Site Recovery are utilized to automate disaster recovery processes, reducing manual intervention and improving the speed of recovery. The framework also incorporates regular testing of disaster recovery plans, using simulation tools to identify weaknesses and optimize response times. For SMEs, cost-effectiveness and ease of management are crucial. The framework emphasizes a pay-as-you-go model for cloud resources, allowing businesses to scale their disaster recovery solutions as they grow without incurring excessive upfront costs. By providing continuous monitoring and proactive threat detection, this disaster recovery framework ensures that SMEs can maintain uninterrupted business operations on cloud platforms, thereby enhancing resilience and mitigating the financial and operational risks associated with data loss and system downtime.

End-to-end AI pipeline optimization: Benchmarking and performance enhancement techniques for recommendation systems

End-to-end AI pipeline optimization is critical for improving the efficiency and performance of recommendation systems, which play a pivotal role in personalizing user experiences across various domains. This review explores benchmarking and performance enhancement techniques tailored to recommendation systems within AI pipelines. The objective is to streamline the processes involved in data ingestion, feature engineering, model training, and deployment to achieve optimal system performance and user satisfaction. Recommendation systems typically involve complex workflows that require continuous optimization. Benchmarking serves as a foundational step, enabling the identification of bottlenecks and inefficiencies within the pipeline. By establishing clear performance metrics, such as precision, recall, and latency, benchmarking allows for the comparative analysis of different algorithms, data processing methods, and system configurations. These metrics guide the selection of the most suitable models and techniques, thereby enhancing overall system effectiveness. Performance enhancement techniques are then applied to various stages of the AI pipeline. Advanced methods in feature engineering, such as automated feature selection and dimensionality reduction, can significantly improve model accuracy while reducing computational overhead. In the model training phase, techniques like hyperparameter tuning, gradient-based optimization, and distributed training are employed to accelerate convergence and improve model generalization. Additionally, optimization strategies at the deployment stage, including model compression, quantization, and the use of specialized hardware, are crucial for minimizing latency and resource consumption. This review also highlights the importance of continuous monitoring and feedback loops to maintain the effectiveness of recommendation systems in dynamic environments. By integrating real-time analytics and adaptive algorithms, systems can adjust to changing user behaviors and preferences, ensuring sustained performance improvements. In conclusion, optimizing end-to-end AI pipelines for recommendation systems involves a multifaceted approach that includes benchmarking, feature engineering, model training, and deployment enhancements. These efforts collectively contribute to more efficient, scalable, and accurate recommendation systems, ultimately leading to better user experiences and operational efficiencies. This paper will focus on optimizing AI pipelines for recommendation systems, covering the entire process from data extraction and feature engineering to model deployment and performance benchmarking. It will discuss techniques for identifying and mitigating performance bottlenecks in different computing environments, providing valuable insights for enhancing the efficiency of recommendation systems, which are crucial for various applications.

CLASSIFYING CYBERCRIME NETWORKS: ORGANIZED AND TRANSNATIONAL SCHEMES IN THE DIGITAL ERA

The purpose of this study is to analyze and classify the main schemes of organized and transnational cybercriminal activity, as well as to identify the methods and technologies used by cybercriminals to carry out criminal activities in the digital space. The article explores current trends in cybercrime, such as the rise in financial fraud, ransomware attacks, phishing, and DDoS attacks. The study shows that the use of the latest technologies, particularly cryptocurrencies and the Internet of Things (IoT), significantly complicates the detection and prosecution of cyber criminals, necessitating the constant improvement of legal and technical tools to combat cybercrime. Key findings indicate that the transnational nature of cybercrime often makes it difficult to prosecute criminals across jurisdictions, requiring greater international coordination. It was also found that cybercriminals are using increasingly sophisticated methods to conceal their activities, incorporating modern technological solutions into criminal schemes. This underscores the need for continuous monitoring of new threats and the updating of protection methods for both organizations and individual users. The main conclusions of the study emphasize the importance of global cooperation in the fight against cybercrime, the need to enhance technological protection, and the development of legal mechanisms to counter modern cyber threats.

Assessing anthropogenic influences on the water quality of Gomati River using an innovative weighted fuzzy soft set based water pollution rating system

The increasing pollution of river systems poses a significant challenge to water quality management and public health. This study was conducted to evaluate the water quality of the Gomati River in India, focusing on the impact of anthropogenic activities on pollution levels across different seasons. The primary objective was to develop a comprehensive assessment model using an effective weighted fuzzy soft expert system (WFSES) to derive a weighted water pollution score (WWP-score) for rating water pollution levels based on various water quality parameters. To achieve this, we employed a fuzzy soft set (FSS) methodology integrated with a weighted fuzzy soft set (WFSS) model to analyze water quality indices at six sampling stations along the Gomati River, the largest river in Tripura, India. Originating from the Raima and Sarma streams, the river flows westward through key regions, providing 249.39 million cubic meters of water annually, essential for drinking, agriculture, fishing, and transportation. The study utilized crisp data summaries of key water quality parameters, including pH, total dissolved solids, total suspended solids, electrical conductivity, biochemical oxygen demand, dissolved oxygen, total hardness, chloride, total alkalinity, and total coliform, across the pre-monsoon, monsoon, and post-monsoon seasons from May 2022 to April 2023, guided by the distribution of waste discharge points. Fuzzy membership functions were defined for these parameters, and the root mean square (RMSQR) operation was used to combine multiple FSSs. The results revealed that the WWP-scores, a measure of water quality, ranged from 0.744 to 0.872 across the sampling stations, with Srimantapur exhibiting the highest level of pollution and Udaipur showing comparatively better quality. These scores indicate that water quality at all sites falls into the poor category, with Srimantapur experiencing the most severe contamination due to high levels of coliform bacteria and organic pollutants. The analysis highlights the impact of domestic wastewater and agricultural runoff on the river's health. This research offers a novel application of FSS and WFSS theory in environmental management, providing a robust framework for evaluating and ranking water quality across multiple parameters. The findings are valuable to the scientific community as they offer detailed insights into the pollution dynamics of the Gomati River and propose a methodological approach for assessing water quality in other river systems facing similar challenges. The study's results underscore the need for targeted pollution control measures and continuous monitoring to safeguard water resources and public health.

AI-Driven Realtime Monitoring of Early Indicators for Ichthyophthirius multifiliis Infection of Rainbow Trout.

A novel video-based real-time system based on AI (artificial intelligence) was developed to detect clinical signs in fish exposed to pathogens. We selected a White Spot Disease model involving rainbow trout as the experimental animal and the parasitic ciliate Ichthyophthirius multifiliis as a pathogen. We compared two identical fish tank systems: one tank was infected by co-habitation, whereas the other tank was kept non-infected (sham infection). The two fish tanks were separately video monitored (full top and side view) during the course of infection, during which fish were removed whenever they developed clinical signs (direct visual inspection by the observer). Image analysis (object detection, classification and tracking) was used to track behavioural changes in fish (in every recorded video frame), focusing on movement patterns and spatial localisation. Initially, the two fish groups (infected and non-infected) exhibited similar behaviour and non-infected fish did not change behaviour during the 15 d observation period (from 5 d before infection until 10 dpi). At 4, 7, 8, 9 and 10 dpi some infected fish showed clinical signs (equilibrium disturbance, gasping and lethargy) and were removed from the experiment. Anorexia occurred from 5 dpi and a gradual progression of gasping behaviour was noted, whereas the frequency of fish flashing (rubbing/scratching against objects) was low. Equilibrium disturbances and the development of white spots in the skin appeared to be a much later (8-10 dpi at this temperature) indicator of infection. The video analysis showed a general distribution of non-infected fish in all parts of the fish tank during the entire experiment, whereas infected fish already at 4-5 dpi moved towards higher water currents in the top and bottom positions. This change of fish positioning within the tank appeared as a promising early indicator of infection. The study suggests that continuous monitoring of fish behaviour using AI can potentially optimise the timing of humane endpoints, indicate disease signs earlier and thereby improve animal welfare in both animal experimentation and in aquaculture settings.

Influenza Vaccine Effectiveness against Influenza A-Associated Outpatient and Emergency-Department-Attended Influenza-like Illness during the Delayed 2022–2023 Season in Beijing, China

Background: During the 2022–2023 influenza season, the influenza activities in most regions of China were postponed, including Beijing. The unusually delayed influenza epidemic posed a challenge to the effectiveness of the influenza vaccine. Methods: Using the test-negative design, we evaluated influenza vaccine effectiveness (VE) during the 2022–2023 influenza season against influenza A-associated outpatient and emergency-department-attended influenza-like illness (ILI) in Beijing, China, from 9 January to 30 April 2023. Results: The analysis included 8301 medically attended ILI patients, of which 1342 (46.2%) had influenza A(H1N1)pdm09, 1554 (53.4%) had influenza A(H3N2), and 11 (0.4%) had co-infection of the two viruses. VE against influenza A-associated ILI patients was 23.2% (95% CI: −6.5% to 44.6%) overall, and 23.1%, 9.9%, and 33.8% among children aged 6 months to 17 years, adults aged 18–59 years, and adults aged ≥60 years, respectively. VE against influenza A(H1N1)pdm09 and against influenza A(H3N2) were 36.2% (95% CI: −1.9% to 60.1%) and 9.5% (95% CI: −34.1% to 39.0%), respectively. VE of the group with vaccination intervals of 14–90 days (70.1%, 95% CI: −145.4 to 96.4) was higher than that of the groups with a vaccination interval of 90–149 days (18.7%, 95% CI: −42.4% to 53.6%) and ≥150 days (21.2%, 95% CI: −18.8% to 47.7%). Conclusions: A moderate VE against influenza A(H1N1)pdm09 and a low VE against influenza A(H3N2) were observed in Beijing during the 2022–2023 influenza season, a season characterized with a delayed and high-intensity influenza epidemic. VE appears to be better within three months after vaccination. Our findings indicate a potential need for the optimization of vaccination policies and underscore the importance of continuous monitoring of influenza to enhance vaccines and optimizing vaccination timing.

Impact of Landfill Leachate on Groundwater Quality and Health Risk Assessment in Mohammedia Prefecture, Morocco

Several studies have been conducted to identify the potential impact of landfills on groundwater resources. This study evaluates the impact of landfills on groundwater resources in Mohammedia prefecture, Morocco. The groundwater was analysed from 2015 to 2022. The groundwater quality was evaluated based on electrical conductivity, pH, biological oxygen demand, chemical oxygen demand, total Kjeldahl nitrogen, phosphate, suspended solids, dissolved oxygen, ammonia, and total hydrocarbon, aluminium, iron, cadmium, chromium, copper, iron-nickel, zing, and mercury. The assessment was based on the water quality index, leachate pollution index, non-carcinogenic risk assessment, and carcinogenic risk assessment. A leachate pollution index &lt;5 indicates that it poses a severe risk to groundwater resources. The non-carcinogenic risk HQ was determined to be &lt;1, which infers no potential risk. The carcinogenic risk index value of 10-4 indicated that it is within the threshold of acceptable limit. The current study concludes that leachate from the analysed landfills does not infiltrate the groundwater resources of Mohammedia prefecture. However, the leachate pollution, even though it varies, is increasing over time. This is validated by the fact that the landfill is protected with a membrane covering the ground, which inhibits any possible infiltration of soil or water resources. Hence, this study calls for continuous monitoring of groundwater resources in the region. Future studies are required to investigate the groundwater in Mohammedia prefecture in terms of emerging pollutants to identify any potential risk.

Development and implementation of cost control strategies in oil and gas engineering projects

This review delves into the development and implementation of cost control strategies in oil and gas engineering projects, an area critical for maintaining project viability and profitability amidst fluctuating market conditions. Effective cost control is essential in the oil and gas sector, where projects are often capital-intensive and subject to economic and operational uncertainties. This review examines various cost control methodologies and their application throughout the project lifecycle, from initial planning and budgeting to execution and closure. The paper highlights key cost control strategies, including detailed project cost estimation, budget forecasting, and variance analysis. It discusses the use of advanced project management tools and technologies, such as software for real-time cost tracking and predictive analytics, which enhance decision-making and financial oversight. The review also covers the importance of risk management practices in identifying and mitigating potential cost overruns, emphasizing the role of contingency planning and risk assessment in maintaining financial control. Case studies demonstrate how successful implementation of these strategies can lead to significant cost savings, improved project efficiency, and enhanced financial performance. The challenges associated with cost control, including dealing with unexpected price fluctuations, scope changes, and regulatory compliance issues, are also addressed. Solutions to these challenges, such as adopting flexible budgeting techniques and leveraging data-driven insights, are discussed. The review concludes with recommendations for refining cost control practices in oil and gas engineering projects, including the need for continuous monitoring and adjustment of cost control measures, integrating cost management with overall project strategy, and fostering collaboration among project stakeholders to ensure alignment and accountability.

SUSTAINABLE STRATEGIES FOR INTEGRATED MANAGEMENT OF BREVICORYNE BRASSICAE IN CABBAGE CULTURE: ECOLOGICAL APPROACHES AND IMPACT ON BIOLOGICAL CONTROL IN MOZAMBIQUE

Cabbage (Brassica oleracea) is a vegetable widely cultivated due to its nutritional and economic value, especially for small farmers. However, kale production faces significant challenges, mainly due to attacks by aphids (Brevicoryne brassicae), a pest that causes wilting and deformations in plants and acts as a virus vector. In regions such as Southern Africa, inadequate control of this pest has resulted in considerable losses in production, reaching 40% in Mozambique.The adult stage of Brevicoryne brassicae is the most damaging, especially affecting the cabbage crop during its vegetative phase, when the plants are most vulnerable. Effective management of this pest involves continuous monitoring and the use of Integrated Pest Management (IPM) practices, which include cultural, biological and chemical methods. Applying these techniques in an integrated way, adapted to local conditions, can significantly reduce damage and improve agricultural productivity, promoting food security and sustainability for small Mozambican farmers.

Analysing Indoor Air Pollution: A Study on Pollutant Levels and Air Quality Assessment in Social Housing Properties

Amid growing concerns over Indoor Air Quality (IAQ), this paper presents a comprehensive analysis of pollutants to assess the environmental health in a few Case Study Buildings (CSBs). The main objectives were to evaluate the levels of key Indoor Air Pollutants (IAPs) (PM10, PM2.5, CO, CO2, TVOCs) and relative humidity across multiple social housing properties and to analyze how occupant behaviormay affect IAQ. Utilizing quantitative measurements, the study investigates the range and exposure of the building occupants to IAPs. The methodology involves the systematic measurement of IAPs through the installation of data loggers, focusing on pollutant levels to evaluate air quality across multiple CSBs. Findings show fluctuation in pollutant levels, with some IAPs demonstrating consistently within the acceptable range while others exhibit sporadic spikes in pollutant concentrations. The results indicate that significant attention should be given to PM10, CO, and VOCs. This study underscores the importance of continuous monitoring and targeted interventions to mitigate poor IAQ and ensure healthier indoor environments for building occupants. Further research and implementation of effective measures are imperative to safeguard public health and well-being.

Self-consuming solar-powered campus monitoring drone

Drones are utilised for a variety of purposes and are a regular sight these days. Selfies, insecticide application, and military monitoring. The issue with surveillance and monitoring is that a lot of applications call for continuous surveillance. Although they have a major disadvantage, drones do offer a good view for surveillance and monitoring. This is the battery life of the drone. The main worry a drone operator has when operating a surveillance drone is that the battery might die and the drone might crash into a building, tree, or other unreachable place from which it cannot be recovered and hence cannot be charged. This is also true for drones used in military surveillance; the risk of a drone's battery dying and becoming unusable places restrictions on drone operators while they are conducting surveillance or monitoring. Here, we've developed a drone that solves these issues by using solar power to continuously charge the device, extending its flying time. It can also land anywhere and remotely recharge its battery so it can take off later. The development and deployment of a solar-powered, self-charging campus surveillance drone are suggested in this research. By offering continual monitoring capabilities without requiring manual charge, the drone seeks to answer the demand for increased security measures on campus. The drone can recharge itself during the day because to the solar panels in its construction, which capture energy from sunshine. The drone can recharge itself during the day because to the solar panels in its construction, which capture energy from sunshine. This maximizes security coverage while minimizing downtime and guarantees continuous operation. Drone capabilities are further enhanced by the incorporation of cutting-edge surveillance technology, which enable detection of threats and constant surveillance. With possible applications in educational institutions, corporate campuses, and large-scale facilities, the suggested method provides an effective and long-lasting answer to campus security concerns.

Barley yellow dwarf virus in winter barley: Control in light of resistance issues and loss of neonicotinoid insecticides

AbstractBarley yellow dwarf virus (BYDV) is the most widespread viral disease of cereal crops vectored by aphids, causing stunting of the crop and significant yield loss. Management recommendations for autumn sown cereals has centred on the timely control of aphid vectors, particularly their management before the crop has reached Growth Stage 31 (stem elongation). In recent years two separate but related issues have emerged, (i) the detection of pyrethroid resistance in a single Sitobion avenae SA3 clone and (ii) the withdrawal of neonicotinoid insecticide seed dressing; widely used to manage aphids in the early development of cereal crops. A series of replicated field studies were conducted from 2016 to 2019 to ascertain if the pyrethroid insecticide is still effective in light of the SA3 clones' presence in aphid populations and if withdrawal of neonicotinoid seed dressings has negatively affected aphid management. Both disease levels and yields confirmed that the neonicotinoid, clothianidin, applied as a seed treatment significantly contributed to the management of BYDV in the different trials investigated. Results of this study demonstrate application of a foliar (lambda‐cyhalothrin or a sulfoxaflor) insecticide has a significant positive impact on yield. Foliar pyrethroid insecticides were as effective in protecting yield from BYDV as the neonicotinoid seed treatments. In the absence of neonicotinoid seed dressing, alternative insecticides still offer protection for winter barley crops against the aphid vectors of BYDV and associated yield loss. Furthermore, the presence of a pyrethroid resistant Sitobion avenae SA3 clone had no observable impact on field efficacy of the pyrethroid insecticide, lambda‐cyhalothrin in these trials. However, this needs continuous monitoring because of potential further increases in resistance levels or emergence of additional resistance mechanisms, which could render pyrethroid insecticides ineffective.