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With the implementation of garbage classification, perishable waste has become increasingly concentrated. This has led to a significant change in the VOC release characteristics at residential garbage collection points, posing a potential risk with unknown characteristics. This study investigated the release characteristics, odor pollution, and health risks of VOCs at garbage collection points under different classification effectiveness, seasons, garbage drop-off periods, and garbage collection point types. The results showed that the average concentration of VOCs released from the garbage sorting collection points (SPs) was 341.43 ± 261.16 µg/m3, and oxygenated compounds (e.g., ethyl acetate and acetone) were the main VOC components. The VOC concentration increased as the community classification effectiveness improved, and it was higher in summer (followed by spring, autumn, and winter). Moreover, the VOC concentrations were higher in the evenings than in the mornings and at centralized garbage collection points (CPs) than at SPs. Further, odor activity value (OAV) assessments indicated that acrolein, styrene, and ethyl acetate were the critical odorous components, with an average OAV of 0.87 ± 0.85, implying marginal odor pollution in some communities. Health risk assessments further revealed that trichloroethylene, benzene, and chlorotoluene were the critical health risk substances, with an average carcinogenic risk (CR) value of 10-6-10-4, and a non-carcinogenic risk (HI) value < 1. These results indicated that HIs were acceptable, but potential CRs existed in the communities. Therefore, VOC pollution prevention and control measures should be urgently strengthened at the garbage collection points in high pollution risk scenarios.

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Aim: To perform an exposure assessment of arsenic, manganese, mercury and lead levels in hair samples from children from poor neighborhoods. Materials & methods: A total of 38 Caucasian children were recruited with the consent of their parents or tutors. Determinations were performed by atomic absorption spectrometry. Results & conclusion: Results were 0.045-0.12 µg/g-1 (arsenic), 0.56-2.05 µg/g-1 (manganese) and 0.34-27.8 µg/g-1 (lead). Lead results did not correlate with those previously reported in blood from the same individuals, suggesting that hair is not useful for exposure assessment of this contaminant. Mercury was determined for the first time in Uruguayan children showing levels <0.083 µg/g-1. Results revealed low-to-moderate metal exposure, except for some high lead findings.

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Autonomous underwater garbage grasping and collection pose a great challenge to underwater robots. To assist underwater robots in locating and recognizing underwater garbage objects efficiently, a modified U-Net-based architecture consisting of a deeper contracting path and an expansive path is proposed to accomplish end-to-end image semantic segmentation. In addition, a dataset for underwater garbage semantic segmentation is established. The proposed architecture is further verified in the underwater garbage dataset and the effects of different hyperparameters, loss functions, and optimizers on the performance of refining the predicted segmented mask are examined. It is confirmed that the focal loss function will lead to a boost in solving the target-background unbalance problem. Eventually, the obtained results offer a solid foundation for fast and precise underwater target recognition and operations.

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IoT plays an important role in the overall development and advancement of the country as it is the key ingredient for the development of the smart environment. IoT is a network of physical objects, devices that contain embedded technologies such as sensors, controllers, etc., which can sense, communicate, and interact with the system to carry out desired operations. The advancement in technology over the past years has provided a new era for computational processing and sensing to facilitate the vision of a smart environment. Researchers have put several efforts to use IoT to facilitate our lives. This paper purposes on an integrated smart environment using IoT. Various sectors such as agriculture, transportation, garbage collection, security issues, sensors, etc. are discussed along with the key technologies including RFID, IP, EPC, Wi-Fi, Bluetooth, and ZigBee. This paper will provide a complete insight into the one who wants to research in the field of IoT. It also highlights the unprecedented opportunities brought by IoT-based technologies to human life. Finally, we have discussed the future enhancements in IoT.

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In this paper, based on an improved swarm optimization algorithm, a new site selection model of a municipal solid waste (MSW) incineration plant is proposed. First, the whale optimization algorithm and particle swarm optimization algorithm are combined according to certain rules to improve the performance of the hybrid algorithm. Through a verification of the single and multi-peak functions, the results show that the algorithm achieves a good performance. The location model of the MSW incineration plant is based on many factors, including the economy, environmental protection, population scale, and operation cost. Finally, based on a sample analysis, a new location model of an MSW incineration plant is used to select the location of an Anshan MSW incineration plant, and a reasonable location is obtained.

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The recent development of 3D flash memories has promoted the widespread application of SSDs in modern storage systems by providing large storage capacity and low cost. Garbage collection (GC) as a time-consuming but necessary operation in flash memories largely affects the performance. In this paper, we perform a comprehensive experimental study on how garbage collection impacts the performance of flash-based SSDs, in the view of performance cliff that closely relates to Quality of Service (QoS). According to the study results using real-world workloads, we first observe that GC occasionally causes response time spikes, which we call the performance cliff problem. Then, we find that 3D SSDs exacerbate the situation by inducing a much higher number of page migrations during GC. To relieve the performance cliff problem, we propose PreGC to assist normal GC. The key idea is to distribute the page migrations into the period before normal GC, thus leading to a reduction in page migrations during the GC period. Comprehensive experiments with real-world workloads have been performed on the SSDsim simulator. Experimental results show that PreGC can efficiently relieve the performance cliff by reducing the tail latency from the 90th to 99.99th percentiles while inducing a little extra write amplification.

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BACKGROUND: elPrep is an established multi-threaded framework for preparing SAM and BAM files in sequencing pipelines. To achieve good performance, its software architecture makes only a single pass through a SAM/BAM file for multiple preparation steps, and keeps sequencing data as much as possible in main memory. Similar to other SAM/BAM tools, management of heap memory is a complex task in elPrep, and it became a serious productivity bottleneck in its original implementation language during recent further development of elPrep. We therefore investigated three alternative programming languages: Go and Java using a concurrent, parallel garbage collector on the one hand, and C++17 using reference counting on the other hand for handling large amounts of heap objects. We reimplemented elPrep in all three languages and benchmarked their runtime performance and memory use. RESULTS: The Go implementation performs best, yielding the best balance between runtime performance and memory use. While the Java benchmarks report a somewhat faster runtime than the Go benchmarks, the memory use of the Java runs is significantly higher. The C++17 benchmarks run significantly slower than both Go and Java, while using somewhat more memory than the Go runs. Our analysis shows that concurrent, parallel garbage collection is better at managing a large heap of objects than reference counting in our case. CONCLUSIONS: Based on our benchmark results, we selected Go as our new implementation language for elPrep, and recommend considering Go as a good candidate for developing other bioinformatics tools for processing SAM/BAM data as well.

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Resumo O objetivo desse estudo foi identificar quais categorias de lixo urbano apresentam associação com casos de dengue e, em seguida, avaliar o impacto da coleta de lixo sobre os casos da doença na cidade do Recife. Foram utilizados dados da pesagem categorizada de lixo, juntamente com os casos confirmados de dengue no município. Os dados foram analisados através do coeficiente de correlação de Pearson para as treze categorias de lixo, seguido pela Regressão Linear Multivariada, selecionando as variáveis pelo método de "stepwise". Identificou-se a existência de correlação negativa entre o total de casos de dengue em sete categorias: lixo domiciliar (r = -0,835), resíduos diferenciados (r = -0,835), resíduos de operações especiais (r = -0,711), entulhos (r = -0,687), coleta seletiva (r = -0,425) e pneus (r = -0,423). O modelo de regressão foi capaz de explicar 75% da variação, apontando que um incremento de 1.000 toneladas na coleta de lixo doméstico proporciona uma redução de 0,032 casos de dengue enquanto que o mesmo incremento na coleta de pneus é capaz de reduzir 0,465 casos da doença. Os resultados demonstram que a coleta de lixo possui um forte impacto negativo nos casos de dengue e podem ser adotados como estratégia de prevenção pelos governos municipais.

Abstract The scope of this study was to identify which categories of urban waste are associated with cases of dengue and to evaluate the impact of garbage collection on dengue infection in the City of Recife (Brazil). Data from categorized waste weighing and the confirmed cases of dengue in the city were used. The data were analyzed using Pearson's correlation coefficient for the 13 categories of urban garbage, followed by Multivariate Linear Regression, selecting the variables by the stepwise method. A negative correlation between dengue infections in seven categories was identified: household garbage (r = -0.835), differentiated residues (r = -0.835), special operations residues (r = -0.711), building rubble (r = -0.687), selective waste collection (r = -0.425) and tires (r = -0.423). The regression model was able to explain 75% of the variation, indicating that an increase of 1,000 tons in household garbage collection provides a decrease of 0.032 in cases of dengue, while the same increase in tire collection esults in a decrease of 0.465. The results show that garbage collection has a strong negative impact on dengue cases and can be adopted as a prevention strategy by municipal governments.

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