Adequate sanitation is crucial for human health and well-being, yet billions worldwide lack access to basic facilities. This comprehensive review examines the emerging field of intelligent sanitation systems, which leverage Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies to address global sanitation challenges. The existing intelligent sanitation systems and applications is still in their early stages, marked by inconsistencies and gaps. The paper consolidates fragmented research from both academic and industrial perspectives based on PRISMA protocol, exploring the historical development, current state, and future potential of intelligent sanitation solutions. The assessment of existing intelligent sanitation systems focuses on system detection, health monitoring, and AI enhancement. The paper examines how IoT-enabled data collection and AI-driven analytics can optimize sanitation facility performance, predict system failures, detect health risks, and inform decision-making for sanitation improvements. By synthesizing existing research, identifying knowledge gaps, and discussing opportunities and challenges, this review provides valuable insights for practitioners, academics, engineers, policymakers, and other stakeholders. It offers a foundation for understanding how advanced IoT and AI techniques can enhance the efficiency, sustainability, and safety of the sanitation industry.

This study examines the challenges and needs faced by non-profit organisations (NPOs) in Colombia regarding the adopting of the International Financial Reporting Standards (IFRS) for small and medium enterprises (SMEs), particularly focusing on sections 3 and 4. Employing a mixed-method approach, the research combines qualitative and quantitative methods. Surveys were conducted with Colombia NPOs, official documents were analysed, and comparative case studies were performed. In-depth interviews and participant observation were also utilised to gain a comprehensive understanding of the obstacles and current practices within the Colombian context. The findings reveal that NPOs in Colombia encounter significant difficulties in adopting IFRS due to the complexity of the standards, lack of specialised resources, and the need for specific training. Internal challenges such as deficiencies in staff qualifications and training, resistance to change, and technological limitations were identified. Externally, ambiguities in the legal framework and donor requirements were highlighted. The case study illustrated that, while there are similarities between IFRS for SMEs and the IFR4NPO project, specific adaptations are essential to address the unique needs of NPOs. This research underscores the necessity of developing additional guidelines or modifying existing ones to enhance the interpretation and application of IFRS in Colombia NPOs. It is recommended to implement proactive strategies based on education and legislative reform to improve the transparency and comparability of financial information. Adopting a more tailored and supported accounting framework will facilitate a more relevant and sustainable implementation, benefiting Colombian NPOs in their resource management and accountability efforts.

In the current digital age, financial development has seen substantial shifts, particularly in buying and selling activities that are now facilitated by digital technology or electronic transactions (e-commerce), which offer convenience at relatively low costs. However, micro, small, and medium enterprises (MSMEs), which play a crucial role in the economy, must adapt to these advancements to sustain and grow their businesses. Despite the widespread adoption of e-commerce, many MSMEs have yet to fully capitalize on this technology. Limited knowledge often leads to hesitation in embracing e-commerce opportunities. Consequently, this study seeks to explore how innovation, information management, and e-commerce adoption impact MSME performance and its implications for business sustainability. The research targets MSME owners and managers in the Jabodetabek area (Jakarta, Bogor, Depok, Tangerang, and Bekasi) and nearby regions, with a sample of 420 individuals selected through random sampling. Data was collected through an online survey (Google Forms) administered to MSME management. The survey items were tested for validity and reliability, and the data analysis was conducted using various regression analyses with SEM-PLS and Smart-PLS3. The study’s findings highlight the following key points: 1) E-commerce adoption significantly enhances information management, which supports MSME sustainability; 2) E-commerce adoption also improves performance through better information management, further promoting MSME sustainability; 3) While technology is important, e-commerce adoption is the primary factor driving MSME sustainability, with technology serving as a secondary factor.

Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.

Important modifications are occurring in temperate forests due to climate change; in polar latitudes their distribution area is increasing, while in tropical latitudes it is decreasing due to temperature increase and droughts. One of the biotic regulators of temperate forests are the debarking insects that cause the mortality of certain trees. These insects have increased in number, favored by climate change, and the consequences on forests have not been long in coming. In recent times in the northern hemisphere, the massive mortality of conifers due to the negative synergy between climate change and debarking insects has been evident. In Mexico, we have also experienced infestations by bark stripping insects never seen before; therefore, we are trying to understand the interactions between climate change, forest health and bark stripping insects, to detect the areas with greater susceptibility to attack by these insects and propose management measures to reduce the effects.

Tomato (Solanum lycopersicon L.) is a highly valued crop in the world, particularly in Nigeria with high nutritional and economic benefits. However, its production in Iwollo, Southeast Nigeria, is constrained by unfavorable weather conditions. To address this, a study was conducted at the Teaching and Research Farm, Department of Horticultural Technology, Enugu State Polytechnic, Iwollo, Southeast Nigeria to evaluate and select the best cultivar for high tunnel production using the Rank Summation Index. Completely Randomized Design with three replications was used, and six high-yielding cultivars, namely Roma VF, BHN-1021, Supremo, Pomodro, Money maker, and Iwollo local, were evaluated. Data were collected on key agronomic characters and analyzed with Analysis of Variance (ANOVA) at a 0.05 level of probability. There were significant differences in the number of leaves per plant, plant height, number of branches per plant, days to fruit maturity, fresh fruit weight, number of harvested fresh fruits per plant, and fresh fruit yield per plant among the cultivars. These characters that showed significant differences were ranked and summed up to obtain the Rank Summation Index (RSI) score. The results revealed that the Supremo cultivar had the lowest and best score (18). This suggests Supremo as the best cultivar for high tunnel tomato production in the study area, based on its superior performance across key agronomic traits.