Medication errors in patients are a global problem. They can negatively affect patients and be costly for hospitals and medical clinics. In 2021, a 28-year-old man with heart problems was admitted to a hospital in Porto Alegre. Due to a pharmacy error and insufficient monitoring in the administration, he received a dose 10 times higher than prescribed. This caused serious and probably irreversible damage to the patient. Reading the news and following the case in the media has encouraged research in scientific databases, searching for information and data on medication errors, as well as emerging technologies to reduce the occurrence of adverse medication events. Based on the findings of an English study that proved that errors occur at the drug prescription stage, the first stage of this research focused on drug dosage errors. The aim of this study is to develop an application based on artificial intelligence that can recognise these errors and help prevent them. The application uses a neural network to analyse prescriptions and warn of possible cases of incorrect dosage. The computer program was developed using a neural network and the drug dosage error recognition system using Python and Keras. The system was trained with 10 drugs and correct and incorrect dosage cases. A graphical interface was created to input and display new case data. Neural networks with different configurations were tested to obtain high accuracy with the training and validation data. A confusion matrix was used to assess the accuracy of the network for cases not used for training. The accuracy was approximately 96%, but problems were found in certain intervals. The errors are due to the need for more training, higher processing capacity and a cloud server. The results of the first stage of the research indicate the feasibility of using a neural network to recognise medication dosage errors and thus preventing the associated risks. Such a method could prevent cases like the one in Porto Alegre. Future studies could incorporate more types of drugs, allergies, drug interactions, pre-existing illnesses and other relevant factors into the system.

Examination of the predation behavior of polyclad flatworms is extremely rare. This study collects Paraplanocera oligoglena, the most common species in Taiwan. Tank-based feeding experiments reveal that Paraplanocera oligoglena can prey on several species of gastropods, such as sea snails and sea hares. Predation behavior encompasses attack, invasion and ingestion periods. This research pioneers the use of stained clam and static image analysis to observe the highly branched digestive system of flatworms. The sequentially bidirectional flow of gastrovascular cavity is first found in polyclad flatworms by the post-stain active tracking technique. Measuring peristalsis movement in inward and outward directions and segmented movement, the contraction frequencies are roughly the same in subsequent order of given branches. Confirmation is provided that the circular membrane-like muscles within the digestive tract are the main driving force for transporting and mixing food. The food dyeing technology used in this experiment also provides the possibility of future research on food chains in the wild.

Infections by the Dengue virus (DENV) cause a disease amonghumansreferred to as Dengue fever, which causes thousands of fatalities globally. There is no existing treatment as of yet that successfully targets DENV. Among the factors thatdeterminetheentry of the virus and severity of the disease is the envelope(E) protein of DENV. This study aimed to examine forty antiviral phytochemicals enumeratedinpaststudiesaspossibleinhibitorstotheEprotein of DENV to provide candidates to aid in drug discovery against DENV. The phytochemicals were screened for their likelihood of inhibition of the E protein using AutoDock Suite and LigPlot+. Seven phytochemicals produced favorable binding affinities to the E protein, which are based on the interactions between the phytochemicals and amino acidsintheactivesiteoftheEprotein.Lipinski’s rule of 5 was then used to screen the seven phytochemicals for oral bioavailability. Glabridin has a binding affinity of -7.6 kcal/mol and was predicted to be orally bioavailable. This phytochemical interacts with amino acids in the E protein active site through hydrogen bonds to Asn355, andPhe337, as well as ten hydrophobic interactions. These interactions ensure that glabridin is able to specifically target and fit intotheactivesiteoftheEprotein, preventing its binding to the host cell and activating its viral proliferation. Glabridin is known to be found in the roots of licorice plants, providing anatural source for a possible cure for Dengue fever.

Formaldehyde is an air-borne, carcinogenic indoor pollutant. It may cause adverse effects on human health such as irritation of eyes and respiratory system. Shells of hermetia illucens, Black Soldier Flies (BSF) are leftovers when the insects mature from pupae to adults. BSF shells are rich in chitin which can be converted into chitosan by demineralisation and deacetylation. Chitosan and its ammonium salt (chitogel) can remove formaldehyde via condensation of water. In this investigation, the efficiency of removal of formaldehyde by different substrates were compared including shells of BSF before and after demineralization, deacetylation and action of vinegar; and common commercial products and Anti-Forma Chitogels made from shells of BSF and some crustaceans. Anti-Forma Chitogel of BSF was found to be effective in removing (91.2%) formaldehyde (1:20 by mass) among shells of BSF with different treatments and its efficiency was better than all commercial products tested. Concentration of formaldehyde in the container with deacetylated Anti-Forma Chitogel is 0.54 mg/m3. It removed 74.8% of formaldehyde compared to the control (2.14 mg/m3). Concentration of formaldehyde in the container with Anti-Forma Chitogel without deacetylation is 0.76 mg/m3 . It removed 64.5% of formaldehyde compared to the control (2.14 mg/m3). The Anti-forma Chitogel of BSF was found to be eco-friendly with high formaldehyde removal efficiency when placed in a drawer (removal of 54.8% of in 24 hours), the chamber of a newly renovated room (removal of 84.9% in 30 minutes reducing the conc. of formaldehyde from 0.53 mg/m3 to 0.08 mg/m3; cf. the safety limit of formaldehyde <0.125mg/m3) and drawers of a new wardrobe (removal of 83.7% at 20.2oC in 1 day reducing the conc. of formaldehyde from 0.49 mg/m3 to 0.08 mg/m3 & kept the conc. of drawers below 0.125mg/m3 most of the time over a month when temperature was below 21oC). Conc. of formaldehyde in air-tight boxes (5g of construction adhesive in 9.3 dm3) with air purifiers with and without Anti-forma Chitogel as filter before and after 3 hours was reduced by 44.5% (from 6.25mg/m3 to 3.47mg/m3 ) and 27.7% respectively showing that Anti-forma Chitogel as filter in air purifier outperformed that without by 160%. Besides, anti-forma Chitogel is antibacterial, so it would also kill bacteria when used in air purifiers. [1] proving that Anti-forma Chitogel is effective in removal of formaldehyde on the spot and can be applied to households. It can also help achieve Target 3.9 and 12.5 of the Sustainable Development Goals of the United Nations.

In recent years, lower inter-limb asymmetries have become a topic of increasing interest in sports research. Numerous studies have investigated the occurrence of between-limb differences in a variety of physical tests, ranging from strength, sprinting, and change of direction speed to jumping tasks. The main focus has been the association of asymmetries to either enhanced injury risk or reduced physical performance. Sport specific aspects and differences of lower limb asymmetries have not often been analysed. Additionally, most studies have been performed with male athletes. Women, however, present higher prevalence of lower limb asymmetries in strength, coordination, and postural control than men. These two aspects were therefore addressed in the present study. Female youth soccer (n=18, age: 16.7 ± 0.8 years) and floorball (n=18, age: 17.6 ± 0.9 years) players completed a test battery consisting of six unilateral jumping tasks in horizontal and lateral direction to detect sport specificity in inter-limb asymmetries. The test comprised the following hops: (1) Single Leg Hop for Distance and (2) a newly created version of it, (3) Single Leg Triple Hop, (4) Single Leg Crossover Hop, (5) Side Hop and (6) Single Leg 6-meters Timed Hop. The scores of every jump were calculated into Limb Symmetry Indexes for each participant. A linear mixed effect (LME) model (using function lmer in program R) was applied to evaluate the effects of sport and jump type on asymmetries. In terms of the whole test battery, there was a significantly higher magnitude of asymmetries in soccer compared to floorball (p=0.0067) with a mean difference of 1.9%. Three significant differences between the effects of different jump types were detected (5>2: p=0.027; 5>3: p6: p=0.014). Moreover, the results showed no significant effect on leg dominance. According to the findings of this study, soccer appears to be more asymmetric than floorball, leading to the suggestion that inter-limb asymmetries may not only be task-specific, but also sportspecific. Since no significant correlations were detected, this study suggests that inter-limb asymmetries are independent of leg dominance. Considering the possible reduction in athletic performance and increasing injury risk, strength and conditioning coaches are advised to assess athletes' inter-limb asymmetries using a broad, sport specific test battery and decrease them.

Over 30 million souls live in a world of darkness, a number greater than the populations of both Norway and Sweden combined. Every individual deserves the chance to embark on a journey across our magnificent blue planet. Yet, regrettably, little has been done to assist them. With this project, we’re lighting the way for the blind to explore our beautiful world independently, breaking free from dependence and embracing boundless horizons. In order to put our theory of the project into practice & explore the use of artificial intelligence & computer science, we started by collecting the required materials for our project such as micro-controllers, sensors, a pair of glasses, a laptop, and a miniature camera. Then we moved onto creating the project itself in which the digital software programmed onto the hardware plays the key-role, as the sensors and the camera will record the details and information from the surroundings and send it to the laptop for further processing. The camera would be the backbone of our project, as it will stream real-time footage to the laptop which will be analyzed by an open-source object detection model ‘YOLOv8’ for identifying objects. After finishing the base model of our project, we tested it in-front of objects such as toy cars, bikes, people, etc, and the results of the object-detection would be shown on the laptop. To observe this data, we created a device which has different modules and integrations for different functions. For example, we will use our camera and then stream it onto a laptop so the reading and the data can be processed on the laptop by AI using YOLOv8. As mentioned in the start, many people do not possess the ability to see, to assist them we have thought of this device which uses all readings and its analytical skills to analyze data and help them navigate, travel or simply, live a better life.

為研究花青素在不同因素影響下的抗 UV 能力，選用矮仙丹作為研究對象。以矮仙丹花瓣的花青素，研究不同濃度及 pH 值的環境下，花青素的抗 UV 能力，以及在照射 UV 後， 矮仙丹 SOD、POD 之濃度變化。由實驗得知，紅花的花青素含量大於粉花。對比了紅花及粉花的 UV-Vis 光譜圖後發現：在相同濃度下，紅花的吸光度大於粉花且兩者的最大吸收高峰皆位於約 510nm~520nm 處，由此推知矮仙丹的花瓣含有大量的花青素。而花青素在不同pH 值環境下，會因為結構改變而呈現不同的顏色。我們測試在不同 pH 值緩衝液的花青素是否具有不同的抗 UV 能力，發現鹼性高於酸性。植物在代謝過程中容易產生具有毒害作用的活性氧，由於活性氧會對細胞造成傷害，於是演化出抗氧化酶，如 SOD、POD 等，用以清除活性氧，降低對植物的傷害。

Cardiac Arrhythmia is one of the conditions in the group of heart and blood vessel diseases that can lead to sudden cardiac arrest (sudden death) and other conditions if not diagnosed quickly and accurately. According to research, heart and blood vessel diseases are the most common diseases and have a mortality rate of one-half of all non-communicable diseases. According to WHO statistics in 2012, it was found that there were 7.4 million deaths from heart and blood vessel diseases, and in 2017, the number of deaths increased to 177 million people, or about 94,444 people per day. Diagnosis of heart and blood vessel diseases can be done by measuring the electrical activity of the heart, and after the examination, a specialized physician will read and analyze the graph to find abnormal patterns. Currently, the shortage of qualified heart specialists to read the graph and screen for heart disease is a medical position shortage, which requires transferring data to hospitals with specialists, resulting in delays in diagnosis and treatment and even death. The project "EIPCA: Electrocardiogram Interpretation Pattern for Cardiovascular Abnormalities prediction" is an application program that assists in screening for fatal diseases that arise from abnormal heart rhythm. It employs artificial intelligence to aid in the screening and analysis of the electrical waveforms generated by an ECG machine, thus reducing diagnosis time and addressing the shortage of cardiology experts. EIPCA is comprised of two systems: (1) a system for screening and analyzing ECG waveforms using artificial intelligence to solve the problem of a shortage of specialized cardiology physicians, and (2) a system for risk assessment of fatal diseases by analyzing the ECG waveform data. The target group of the project is Rural hospitals, as well as health-related agencies. The project team hopes that the development of this project will significantly improve the efficiency and speed of screening for heart-related diseases, ultimately reducing the mortality rate from these diseases in the future.

Agro-guard aims to revolutionize disease identification and community-based projects in the field of agriculture. Integrating Machine learning, Computer vision, clustering, and community-based technology, this project helped farmers to detect their plant disease with their solution and for early warning of plant disease which was spreading in their community which helped in crop management. The research project is divided into three parts.First,Integrating Machine learning to detect and classify plant disease with their solutions.Second,Integrating Density-Based Spatial Clustering of Applications with Noise (DBSCAN),to identify disease and analyze the pattern within agricultural regions.Third,Establishing notification system to notify real-time alerts to farmers about disease spreading in particular region.The research is crucial because it solve one of the crucial problem of our community which is untimely detection of disease.The finding of the research highlight the effectiveness of Agro-Guard framework in early disease detection and community detection.The machine learning models achieved high accuracy in identifying common plant disease and clustering results the pattern in diseases that were very important for notifying the community.The significance of these findings is that it can build powerful system which will overall grow the production of crops and plants due to timely update of the disease prevailing in the community.It contributes in sustainability production of crops and plants which ultimately ensure the good livelihood of farmer.

Recently,thefeedpricesforlivestockfarmshavebeencontinuouslyincreasing, while the prices of calves have been declining, leading to many livestock and dairy farms facing financial losses. If livestock farms are affected by diseases such as foot-and-mouth disease, they suffer significant losses. This is because reducedproductivityinlivestocknotonlyaffectstheirproductionbutalsoentails substantial costs for vaccines and treatments. Therefore, there is a need to explore how to effectively prevent diseases, focusing on common diseases in cattle such as subclinical mastitis and mastitis, and alleviate the burden on farms economically and environmentally. This study aims to investigate the production of bacterial growth-inhibiting straw bedding using substances such as illite and charcoal to contribute to disease prevention.