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New plantations can either cause deforestation by replacing natural forests or avoid this by using previously cleared areas. The extent of these two situations is contested in tropical biodiversity hotspots where objective data are limited. Here, we explore delays between deforestation and the establishment of industrial tree plantations on Borneo using satellite imagery. Between 1973 and 2015 an estimated 18.7 Mha of Borneo’s old-growth forest were cleared (14.4 Mha and 4.2 Mha in Indonesian and Malaysian Borneo). Industrial plantations expanded by 9.1 Mha (7.8 Mha oil-palm; 1.3 Mha pulpwood). Approximately 7.0 Mha of the total plantation area in 2015 (9.2 Mha) were old-growth forest in 1973, of which 4.5–4.8 Mha (24–26% of Borneo-wide deforestation) were planted within five years of forest clearance (3.7–3.9 Mha oil-palm; 0.8–0.9 Mha pulpwood). This rapid within-five-year conversion has been greater in Malaysia than in Indonesia (57–60% versus 15–16%). In Indonesia, a higher proportion of oil-palm plantations was developed on already cleared degraded lands (a legacy of recurrent forest fires). However, rapid conversion of Indonesian forests to industrial plantations has increased steeply since 2005. We conclude that plantation industries have been the principle driver of deforestation in Malaysian Borneo over the last four decades. In contrast, their role in deforestation in Indonesian Borneo was less marked, but has been growing recently. We note caveats in interpreting these results and highlight the need for greater accountability in plantation development.

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.

The links between plantation expansion and deforestation in Borneo are debated. We used satellite imagery to map annual loss of old‐growth forests, expansion of industrial plantations (oil palm and pulpwood), and their overlap in Borneo from 2001 to 2017. In 17 years, forest area declined by 14% (6.04 Mha), including 3.06 Mha of forest ultimately converted into industrial plantations. Plantations expanded by 170% (6.20 Mha: 88% oil palm; 12% pulpwood). Most forests converted to plantations were cleared and planted in the same year (92%; 2.83 Mha). Annual forest loss generally increased before peaking in 2016 (0.61 Mha) and declining sharply in 2017 (0.25 Mha). After peaks in 2009 and 2012, plantation expansion and associated forest conversion have been declining in Indonesia and Malaysia. Annual plantation expansion is positively correlated with annual forest loss in both countries. The correlation vanishes when we consider plantation expansion versus forests that are cleared but not converted to plantations. The price of crude palm oil is positively correlated with plantation expansion in the following year in Indonesian (not Malaysian) Borneo. Low palm oil prices, wet conditions, and improved fire prevention all likely contributed to reduced 2017 deforestation. Oversight of company conduct requires transparent concession ownership.

Annual deforestation rates in the Brazilian Amazon fell by 77% between 2004 and 2011, yet have stabilized since 2009 at 5,000–7,000 km ². We provide the first submunicipality assessment, to our knowledge, of actor-specific contributions to the deforestation slowdown by linking agricultural census and remote-sensing data on deforestation and forest degradation. Almost half (36,158 km ²) of the deforestation between 2004 and 2011 occurred in areas dominated by larger properties (>500 ha), whereas only 12% (9,720 km ²) occurred in areas dominated by smallholder properties (<100 ha). In addition, forests in areas dominated by smallholders tend to be less fragmented and less degraded. However, although annual deforestation rates fell during this period by 68–85% for all actors, the contribution of the largest landholders (>2,500 ha) to annual deforestation decreased over time (63% decrease between 2005 and 2011), whereas that of smallholders went up by a similar amount (69%) during the same period. In addition, the deforestation share attributable to remote areas increased by 88% between 2009 and 2011. These observations are consistent across the Brazilian Amazon, regardless of geographical differences in actor dominance or socioenvironmental context. Our findings suggest that deforestation policies to date, which have been particularly focused on command and control measures on larger properties in deforestation hotspots, may be increasingly limited in their effectiveness and fail to address all actors equally. Further reductions in deforestation are likely to be increasingly costly and require actor-tailored approaches, including better monitoring to detect small-scale deforestation and a shift toward more incentive-based conservation policies. Significance The Brazilian Amazon is at a critical juncture after the recent stabilization of deforestation rates. Identifying opportunities for continued deforestation reductions requires an understanding of the contribution of different actors to overall deforestation. We provide the first such assessment, to our knowledge, that reports on two headline findings. First, between 2004 and 2011, areas dominated by properties larger than 500 ha accounted for 48% of the deforestation compared with only 12% for smallholders (<100 ha). Second, the deforestation share attributed to the largest properties (≥2,500 ha) declined by 63% from a peak in 2005, whereas that of smallholders increased by 69%. Further reductions in deforestation are likely to require a shift toward more incentive-based policies that are tailored toward different actors.

A major reduction in global deforestation is needed to mitigate climate change and biodiversity loss. Recent private sector commitments aim to eliminate deforestation from a company’s operations or supply chain, but they fall short on several fronts. Company pledges vary in the degree to which they include time-bound interventions with clear definitions and criteria to achieve verifiable outcomes. Zero-deforestation policies by companies may be insufficient to achieve broader impact on their own due to leakage, lack of transparency and traceability, selective adoption and smallholder marginalization. Public–private policy mixes are needed to increase the effectiveness of supply-chain initiatives that aim to reduce deforestation. We review current supply-chain initiatives, their effectiveness, and the challenges they face, and go on to identify knowledge gaps for complementary public–private policies.

Early fault diagnosis in induction motors is important to maintain correct operation in terms of energy and efficiency, as well as to achieve a reduction in costs associated with maintenance or unexpected stoppages in production processes. These motors are widely used in industry due to their reliability, low cost, and great robustness; however, over time, they may be exposed to wear that can affect their performance, endanger the integrity of operators, or cause unexpected shutdowns that generate economic losses. Corrosion in the bearings is one of the most common failures, which is mainly triggered by high humidity in combination with high temperatures. However, despite its relevance, it has not been widely explored as a cause of failure in induction motors. Unlike failures that occur in specific or localized areas, corrosion in bearings does not manifest through specific frequencies associated with the phenomenon, since the corrosion occurs extensively on the surface of the raceway, making early diagnosis difficult with conventional techniques based on spectral analysis. Therefore, this work proposes an approach for the analysis of magnetic stray flux and vibration signals under different levels of corrosion using statistical and non-statistical parameters to capture variations in the dynamic behavior of the motors while employing genetic algorithms to select the most relevant parameters for each signal and optimize the configuration of an ensemble learning algorithm. The classification of the bearing condition is achieved using support vector machines in combination with the bagging method, which increases the robustness and accuracy of the model in the presence of signal variability. A classification accuracy between the healthy state and two gradualities greater than 99% was obtained, indicating that the proposed approach is reliable and efficient for corrosion diagnosis.

In this study, thermoplastic starch (TPS) biofilms were developed using starch isolated from the seeds of Melicoccus bijugatus (huaya) and reinforced with bentonite clays at concentrations of 1%, 3%, and 5% by weight. Novelty of this research lies in utilizing a non-conventional starch source and enhancing properties of TPS through clay reinforcement. FTIR analysis verified bentonite’s nature of clays, while SEM analysis provided insights into morphology and agglomeration behavior. Key findings include a notable increase in biofilm thickness and elastic modulus with higher clay content. Specifically, tensile strength of biofilms improved from 2.5 MPa for pure TPS to 5.0 MPa with 5% clay reinforcement. The elastic modulus increased from 25 MPa (TPS) to 60 MPa (5% clay). Thermal stability also showed enhancement, with initial degradation temperature increasing from 110 °C for pure TPS to 130 °C for TPS with 5% clay. Water vapor permeability (WVP) tests demonstrated a decrease in WVP values from 4.11 × 10 −10  g m −1  s −1  Pa −1 for pure TPS to 2.09 × 10 −10  g m −1  s −1 ·Pa −1 for TPS with 5% clay, indicating a significant barrier effect due to clay dispersion. These results suggest that biofilms based on huaya starch and reinforced with bentonite clay have considerable potential for sustainable food packaging applications, offering enhanced mechanical and barrier properties.

The computer numerical control (CNC) machine has recently taken a fundamental role in the manufacturing industry, which is essential for the economic development of many countries. Current high quality production standards, along with the requirement for maximum economic benefits, demand the use of tool condition monitoring (TCM) systems able to monitor and diagnose cutting tool wear. Current TCM methodologies mainly rely on vibration signals, cutting force signals, and acoustic emission (AE) signals, which have the common drawback of requiring the installation of sensors near the working area, a factor that limits their application in practical terms. Moreover, as machining processes require the optimal tuning of cutting parameters, novel methodologies must be able to perform the diagnosis under a variety of cutting parameters. This paper proposes a novel non-invasive method capable of automatically diagnosing cutting tool wear in CNC machines under the variation of cutting speed and feed rate cutting parameters. The proposal relies on the sensor information fusion of spindle-motor stray flux and current signals by means of statistical and non-statistical time-domain parameters, which are then reduced by means of a linear discriminant analysis (LDA); a feed-forward neural network is then used to automatically classify the level of wear on the cutting tool. The proposal is validated with a Fanuc Oi mate Computer Numeric Control (CNC) turning machine for three different cutting tool wear levels and different cutting speed and feed rate values.

Laguna del Sauce, one of the main drinking water sources in Uruguay, is an eutrophic shallow lake with high temporal variation of inorganic turbidity caused by extreme wind events. During low turbidity periods, high phytoplankton biomass can be reached, frequently associated to cyanobacteria blooms, which can cause interferences in the water supply. In this study, we assessed the environmental drivers of cyanobacteria dominance, composition, and nitrogen-fixation behavior. For this, we analyzed the spatial and temporal phytoplankton composition, physical and chemical variables performing weekly samplings during two summers: 2015–2016 and 2016–2017. When inorganic turbidity was high (above 30 NTU), phytoplankton biomass was controlled, below this threshold, temperature, secchi depth and nutrients played key factors controlling cyanobacteria biomass and composition. Blooms of N2-fixing cyanobacteria (Dolichospermum crassum, Aphanizomenon gracile, and Cuspidothrix issatschenkoi) were promoted by low N:P ratios (average 11.5) and wide TN range (286–1300 µg l−1). Non-heterocystous cyanobacteria blooms occurred above TN 1000–1200 µg l−1. The N2-fixing behavior (heterocytes:vegetative cell ratio) depended on TN, it was highest at low TN (< 700 µg l−1) but null above ca. 1000 µg l−1. While low inorganic turbidity allowed cyanobacteria blooms in Laguna del Sauce, its composition and N2-fixation behavior depended on the TN and TP levels.

Abstract Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory infections in children. By the age of 1 year, 60%–70% of children have been infected by RSV. In addition, early-life RSV infection is associated with the development of recurrent wheezing and asthma in infancy and childhood. The need for precise epidemiologic data regarding RSV as a worldwide pathogen has been growing steadily as novel RSV therapeutics are reaching the final stages of development. To optimize the prevention, diagnosis, and treatment of RSV infection in a timely manner, knowledge about the differences in the timing of the RSV epidemics worldwide is needed. Previous analyses, based on literature reviews of individual reports obtained from medical databases, have failed to provide global country seasonality patterns. Until recently, only certain countries have been recording RSV incidence through their own surveillance systems. This analysis was based on national RSV surveillance reports and medical databases from 27 countries worldwide. This is the first study to use original-source, high-quality surveillance data to establish a global, robust, and homogeneous report on global country-specific RSV seasonality. Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory infections in children. This is the first study to use original-source, high-quality surveillance data to establish a global, robust, and homogeneous report on global country-specific RSV seasonality.