Entity Alignment (EA) is vital for integrating diverse knowledge graph (KG) data, playing a crucial role in data-driven AI applications. Traditional EA methods primarily rely on comparing entity embeddings, but their effectiveness is constrained by the limited input KG data and the capabilities of the representation learning techniques. Against this backdrop, we introduce ChatEA, an innovative framework that incorporates large language models (LLMs) to improve EA. To address the constraints of limited input KG data, ChatEA introduces a KG-code translation module that translates KG structures into a format understandable by LLMs, thereby allowing LLMs to utilize their extensive background knowledge to improve EA accuracy. To overcome the over-reliance on entity embedding comparisons, ChatEA implements a two-stage EA strategy that capitalizes on LLMs' capability for multi-step reasoning in a dialogue format, thereby enhancing accuracy while preserving efficiency. Our experimental results affirm ChatEA's superior performance, highlighting LLMs' potential in facilitating EA tasks.

Interacting with a contemporary LLM-based conversational agent can create an illusion of being in the presence of a thinking creature. Yet, in their very nature, such systems are fundamentally not like us.

Air pollution is a result of multiple sources including both natural and anthropogenic activities. The rapid urbanization of the cities such as Bujumbura economic capital of Burundi, is one of these factors. The very first characterization of the spatio-temporal variability of PM2.5 in Bujumbura and the forecasting of PM2.5 concentration have been conducted in this paper using data collected during a year, from august 2022 to august 2023, by low cost sensors installed in Bujumbura city. For each commune, an hourly, daily and seasonal analysis were carried out and the results showed that the mass concentrations of PM2.5 in the three municipalities differ from one commune to another. The average hourly and annual PM2.5 concentrations exceed the World Health Organization standards. The range is between 28.3 and 35.0 microgram/m3 . In order to make prediction of PM2.5 concentration, an investigation of RNN with Long Short Term Memory (LSTM) has been undertaken.

Malaria continues to be a major public health problem on the African continent, particularly in Sub-Saharan Africa. Nonetheless, efforts are ongoing, and significant progress has been made. In Burundi, malaria is among the main public health concerns. In the literature, there are limited prediction models for Burundi. We know that such tools are much needed for interventions design. In our study, we built machine-learning based models to estimates malaria cases in Burundi. The forecast of malaria cases was carried out at province level and national scale as well. Long short term memory (LSTM) model, a type of deep learning model has been used to achieve best results using climate-change related factors such as temperature, rainfal, and relative humidity, together with malaria historical data and human population. With this model, the results showed that at country level different tuning of parameters can be used in order to determine the minimum and maximum expected malaria cases. The univariate version of that model (LSTM) which learns from previous dynamics of malaria cases give more precise estimates at province-level, but both models have same trends overall at provnce-level and country-level

Thanks to rapid progress in artificial intelligence, we have entered an era when technology and philosophy intersect in interesting ways. Sitting squarely at the centre of this intersection are large language models (LLMs). The more adept LLMs become at mimicking human language, the more vulnerable we become to anthropomorphism, to seeing the systems in which they are embedded as more human-like than they really are. This trend is amplified by the natural tendency to use philosophically loaded terms, such as "knows", "believes", and "thinks", when describing these systems. To mitigate this trend, this paper advocates the practice of repeatedly stepping back to remind ourselves of how LLMs, and the systems of which they form a part, actually work. The hope is that increased scientific precision will encourage more philosophical nuance in the discourse around artificial intelligence, both within the field and in the public sphere.

… involved in finding solutions to minimize the impact of a devastating banana virus in Burundi have developed an Artificial Intelligence.

A clearer picture has emerged of what may have happened to the young members of Burundi's robotics team who vanished last week. The team's six teenage members disappeared after a robotics competition in Washington, D.C. -- but recent reports revealed their parents may have known of their plan not to return home. The principal of Iteletique High School, where two of the students were enrolled, said the teens told their parents of their plans in advance. The parents themselves have not yet made any public statements regarding the disappearances. "Talking with parents, they told us that once the kids arrived there, they told them they may not come back," the principal, Esperance Niyonzima, told VOA's Central Africa news service Thursday.

Six teenage members of a robotics team from Burundi, Africa -- who went missing last week after a competition in Washington, D.C. -- may have planned their disappearance, their coach said Monday. Burundi Police confirmed at least two of the missing teenagers were in Canada, and the remaining four were not in danger. Esperence Niyonzima, the director of Iteletique, the school which sent two of the teens said the students probably left the East African nation to get a better life, the Associated Press (AP) reported. Niyonzima mentioned that many Burundians, even those who travel outside for official or government assignments, remain in western countries. Read: What Happened To Burundi Robotics Team?

Six members of a robotics team from Burundi, Africa remained missing Monday after vanishing from an international competition in Washington, D.C. last week. The teenagers were last seen Tuesday evening at the competition's closing ceremonies. Police said two of the teens, identified as Audrey Mwamikazi, 17, and Don Charu Ingabire, 16, were in Canada. No further details were released regarding their exact whereabouts. The remaining four teens, Richard Irakoze, 18, Kevin Sabumukiza, 17, Nice Munezero, 17, and Aristide Irambona, 18, were still missing.

We have been in touch with the Metropolitan Police Department; they told us that they are doing what they can to find those teens,