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High damage was observed lately for green onion production in Marawi City condition. Majority of the damage was suspected to cause by an insect but was not identified. This study was carried with the following objectives: (1) assess the arthropods and weed population that can be found on the different types of mulching materials and (2) determine the relationships that exist between organic mulching materials, insect pests, insect damage and weeds in green onion. This was conducted in January to April 2018 during dry season at Plant Science Experimental Area, Mindanao State University Main Campus, Marawi City, Lanao del Sur, Philippines. Results of the study showed that Lepidopterans caused most of the damage on green onion and high on grass clipping mulches. The weed population was effectively controlled by organic mulches as of evidence of weed population decline from 42 Days After Planting (DAP) to 90 DAP. The relationship of weeds, insect damage and arthropods is more evident at 90 DAP. Hymenopterans were considered to be good natural enemies of Lepidopterans, causing significantly lowered its population, thus lowering the damage. Lepidopterans population increases with increasing sedges population, hence sedges should be controlled at early stage of the crop. Hymenopterans preferred grasses as habitat. Overall, organic mulches control the population of weeds and become the habitat of arthropods in the soil.

Nurses are health care professionals whose duty it is to protect patients from acquiring infections while hospitalized or while in a health care setup. By maintaining an infection-free environment, the patient’s recovery will be promoted and high-quality nursing care will be delivered. Aims and Objectives: The aim of the study was to determine the knowledge, attitudes, and practices of nurses regarding infection prevention and control. Material and Methods: A quantitative descriptive study was conducted at a tertiary hospital in Multan. Stratified random sampling was performed. Result and Conclusion: A total of n= 196 nurses of all categories were recruited in the study. Most of the participants were female; 84.7% (n= 166) while 15.3 % (n= 30) were male. The majority of participants had good knowledge of infection prevention and control with the mean score of 83.21. The attitude towards infection prevention and control was good with a mean score of 81.37. The practice in infection prevention and control was poor with a mean score of 48.88. Based on the findings of the current study, it can be concluded that, despite performing well in knowledge and showing a positive attitude towards infection prevention and control, nurses had unsatisfactory practice levels regarding infection prevention and control, exposing the pat1ients to infection-related diseases.

Lassa fever is a zoonotic disease caused by Lassa virus, which is a single-stranded RNA virus. Even though the virus was first described in the 1950s, it was not identified until 19691 and was subsequently named after a town in the present Borno state of Nigeria where the first case of the disease was recorded. The primary host of Lassa virus is a rodent of the genus. Mastomys, also referred to as ‘multimammate rat’. Once infected, Mastomys rats do not become ill but can shed the virus in their urine and faeces. Humans become infected from contact with the urine and faeces of infected rats. The infection can also occur in the process of hunting and processing rats for consumption. The virus is spread between humans through direct contact with blood, urine, faeces or other secretions from the infected person. As the world becomes increasingly connected, viral diseases, such as Lassa fever, once endemic to a region can be easily transmitted to other parts of the world, thus increasing the likelihood of a global pandemic. To reduce the probabilities of such events playing out, deliberate and concerted efforts must be applied towards both understanding Lassa fever and limiting infection rates. Therefore, measures that are limited in keeping of rodent out of homes and food supplies, as well as maintaining effective personal hygiene should be employed. Gloves, face masks, laboratory coats, and goggles are advised while in contact with an infected person.

The design and implementation of a fire prevention and control system using ATmega328P was successfully designed and implemented to curb the frequent fire outbreak in Africa. The system was designed to trigger alarm ON whenever gas leakage or smoke is detected and as well triggers ON fire extinguishing fan ON to suck the gas away from the premise. The Liquid Crystal Display (LCD) as well indicates the system performance measure of the premises at any point in time. The Arduino UNO is used as the main controller of the system and the buzzer for notification when there is danger. The system will detect the leakage using a gas sensor MQ-5 and use the buzzer for alarm to alert the public/authorities that there is danger in the premises. The system uses two indicator LED Lights; the Green LED light indicates that there was no gas leakage detected in the premises whereas the Red LED light indicate that there was gas leakage detected and needs immediate intervention. This designed and implemented device has 15% performance efficiency compared to the existing ones and will be of a very good help to African countries as it has the ability to prevent fire outbreak in promises.

Background: Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance, impaired glucose tolerance, and progressive ?-cell dysfunction, leading to persistent hyperglycemia. Traditionally, individuals with T2DM are advised to limit fruit consumption due to concerns over natural sugars. However, emerging evidence suggests that whole fruits may support metabolic health by improving insulin sensitivity and glycemic regulation. Objective: This study evaluated the short-term effects of an exclusively fruit-based diet on glycemic control, body weight, and overall metabolic well-being in individuals with T2DM. Methods: Thirty-two adults with type 2 diabetes participated in a 7-day all-fruit dietary intervention. Participants consumed 3–4 mono-meals of fruit daily, with individual meals consisting of 500 to 1000 grams of fruit. Self-reported data on dietary intake, symptoms, fasting blood glucose (FBG), and body weight (BW) were collected pre- and post-intervention for analysis. Results: The intervention resulted in a mean weight loss of 2.7 kg and an average decrease in FBG of 1.02 mmol/L. No serious adverse effects were reported. Conclusion: A short-term, all-fruit diet appears to be safe and effective in improving glycemic control and reducing body weight in people with T2DM. Further research with larger sample sizes and controlled conditions is warranted to validate these findings and assess long-term outcomes.

Traffic congestion at urban roundabouts represents a critical challenge in developing cities such as N’Djamena, the capital of Chad, where rapid urbanization, heterogeneous traffic composition, informal driving behaviors, and limited road infrastructure significantly degrade mobility and traffic efficiency [1], [2]. In such environments, conventional fixed-time and rule-based traffic signal control systems lack the adaptability required to respond effectively to highly dynamic and uncertain traffic conditions. This paper proposes a reinforcement learning–based intelligent traffic signal control framework specifically designed for signalized roundabouts in developing-city contexts, with a particular focus on the traffic characteristics of N’Djamena. The proposed system models traffic signal control as a sequential decision-making problem, in which an autonomous reinforcement learning agent continuously observes real-time traffic states—such as queue lengths, vehicle waiting times, and traffic density—and learns optimal red-light and green-light timing policies through direct interaction with the traffic environment. Unlike traditional approaches, the proposed method does not rely on predefined signal plans or prior traffic flow models, enabling it to adapt effectively to fluctuating and unbalanced traffic demand typical of N’Djamena’s urban road network. The effectiveness of the proposed approach is evaluated through traffic simulation experiments configured to reflect realistic traffic conditions observed in N’Djamena, including heterogeneous vehicle types and variable demand patterns. Performance comparisons with conventional fixed-time signal control demonstrate substantial reductions in average vehicle waiting time, queue length, and overall congestion. These results confirm the potential of reinforcement learning–based traffic signal control as a scalable and cost-effective solution for adaptive traffic management in resource-constrained urban environments and highlight its applicability to intelligent transportation systems in developing cities.