Vitamin C in different citrus juice samples were investigated; Citrus Limon, Citrus reticulate, Citrus paradise, and Citrus sinensis. Iodine titration was utilized for the determination of vitamin C in the different samples. The highest amount of vitamin C concentration observed in Citrus sinensis was 95 mg / 100 ml whereas Citrus reticulate showed low levels of 82 mg / 100 ml. The same tendency was observed in packed citrus juice, with the effect of cold temperature as though the content of vitamin C was less than in fresh citrus juice. Results revealed that; there is a loss of vitamin C content in all four storage conditions (days), Vitamin C reduces differently in citrus juice samples stored in different conditions. On the Fourth day Table 1. The rate at which vitamin C is lost depends on the temperature and time methods employed. It has been found that the hot environment provides the conditions for fast decay of vitamin C content and the room environment provides the conditions for slow decay of vitamin C content. In present analyzed samples values are different because of the fact that the composition of content vitamin c depends upon environmental factors. Hence it is always suggested to consume fresh juice rather than packed and stored one.

In recent decades, the Internet of Things (IoT) has grown rapidly, attracting the attention of scientists and businesspeople. In extreme conditions, autonomously scattered sensor nodes pose a high risk of failure and intrusion into the IoT, skewing sensor values. Abnormal data, anomalies, or outliers are sensor values that depart from norms. When abnormalities are factored into data analytics, the ultimate judgment is affected. Using data-driven algorithms for IoT outlier detection is a cutting-edge tactic in Machine Learning (ML). However, evaluating the effectiveness of implemented ML techniques for outlier detection in IoT, which have the minimal processing power and power sources to ensure data quality, raises several difficulties that have just recently begun to be addressed in the academic literature. This paper analyses the cutting-edge architecture, type, degree, technique, and detection mode of AI and statistical outlier detection strategies in IoTs. Also, each of the ways to find outliers is talked about in detail, along with ways to make them better.

Black soldier fly larvae feed on different organic materials. The nutritional composition of these larvae depends on the feeding substrate. The objective of this study is to determine the composition of macronutrients and micronutrients of fruits and vegetables used as substrate for the production of larvae. To do this, various fruits and vegetables were obtained from fruit and vegetable retailers of Carena in the commune of Plateau (Abidjan). These are the releases of sweet banana, watermelon, avocado, cucumber, papaya and orange. They were packed in a cooler, closed and transported to the experiment site. Samples of these fruits and vegetables were taken and chemically analyzed. It was determined metabolizable energy, the content of moisture, protein, fat, carbohydrate, cellulose, ash, minerals (potassium, phosphorus, calcium, sodium, and magnesium) and vitamin C. The results showed that sweet banana and watermelon are rich in protein respectively 2.20% ± 0.00 and 1.93% ± 0.00. The sweet banana is also rich in ash (4.80% ± 0.00) and mineral elements such as sodium (17.35 mg ± 3.68) and magnesium (30.8 mg ± 4.40). Avocado is energetic (161 ± 1.02 kcal). It is rich in fat (43.5% ± 0.01), potassium (339 mg ± 22.3) and phosphorus (52 mg ± 3.02). Orange and papaya are rich in vitamin C respectively 70 mg ± 3.00 and 64 mg ± 2.00. Watermelon and cucumber have a high water respectively 93.91% ± 1.00 and 91.4% ± 1.01. The have also a high cellulose content respectively 11.41% ± 0.01 and 11.1% ± 0.01. This study has shown that the fruits and vegetables used are excellent substrates. They are rich in macronutrients and micronutrients useful for the growth of black soldier fly larvae.

As a replacement for porcelain and glass, two fundamental polymer compositions—silicone rubber (SiR) and ethylene propylene diene monomer (EPDM)—as well as a combination of these two compositions—have been used for outdoor electrical insulation applications. There is widespread agreement that improving a composite's insulating properties by adding micron-sized particles is possible. To enhance these composites when used for outdoor electrical insulation, various fillers such as alumina trihydrate and silica are used. In this study, the effect of micro filler Silica with various contents on the electrical and mechanical properties of blending of SiR and EPDM with different weight ratios was studied. Test samples were prepared using two roll mill. To analyze the electrical characteristics, AC breakdown tests were conducted following IEC 60243-1 Standard. Tensile strength and elongation at break were carried out according to ASTM D-412 for assessing the mechanical properties. This experiment demonstrates an increase in dielectric breakdown strength of all EPDM/SiR composites filled with Silica micron particles till 30 Phr filling content behind this value the breakdown strength was decreased. Regarding mechanical parameters, the micro silica composite exhibited enhanced Tensile Strength to till only 10 Phr, contrarily, doping micro silica deteriorates the Elongation at break for all microcomposite samples.

The goals of "smart cities" include relieving the burden of growing urbanisation, cutting down on energy use, protecting the environment, boosting the local economy and people's standard of living, and facilitating wider access to and use of cutting-edge Information and Communication Technologies (ICT). In smart cities, ICT is essential for policymaking, decision making, plan execution, and the delivery of useful services. The primary goal of this analysis is to investigate the part that AI and machine learning play. Examples of ed tech include Deep Reinforcement Learning (DRL) and Machine Learning (ML). In a complicated smart city setting, the aforementioned methods can be used to develop the best possible rules. Smart transportation, cyber-security, energy-efficient usage of smart grids (SG), efficient use of Unmanned Aerial Vehicles (UAVs) to guarantee the best 5G and beyond 5G (B5G) communications, and a smart health monitoring are all discussed in length in this paper. Finally, we discuss the research challenges that have yet to be met and potential future research directions that could bring the concept of a "smart city" closer to fruition.

The government seeks to improve public services through the emergence of innovative ideas, one of which is with the "Gancang Aron" program at a hospital in Banyuwangi. This study aims to provide a particular system design integrated with the primary program system to minimize errors in the pharmacy department. This research uses a descriptive qualitative approach with case studies—data obtained through observations, interviews, and documentation. The analysis technique used is the input-process-output-based information system development. This system's development will provide optimization of services, especially in the medicine section, to avoid errors that will impact reports and decision-making. This research implies that the system design results can serve as a basis or model for the development of information systems in various organizations and a guide in making policies and decision making.

Due to the increasingly expanding medical profession, big data analytics has begun to play a crucial role in advancing healthcare execution and research. It has enabled the collection, management, analysis, and assimilation of huge volumes of unique, structured, and unstructured information generated by contemporary medical service systems. It has provided devices for gathering, directing, analysing, and storing vast quantities of unique, structured, and unstructured data generated by contemporary medicinal administration systems. It produces information in exponentially varied configurations. The medical services division has been well ahead of the curve in adopting this new technology, and it is producing this data at an exponential rate. Consequently, the medical services information contains a substantial amount of information originating from internal and external sources. Payers (claims and cost data), consumers and marketers (patient conduct and feeling data), providers (medical information, government population and general wellbeing information), developers (Pharmacy and therapeutic device research and development), and researchers and scientists (academic and independent) are among the information sources. Because data isn't always the same, each of these data storage facilities is also becoming more diverse, as shown by the four Vs: volume, velocity, variety, and veracity.