Search Results (12)

The strategic importance of forest biomass as a renewable energy source is growing across the EU, driven by climate goals, energy security, and the abundance of logging residues. While logging waste offers considerable potential for bioenergy production, its life cycle environmental impacts remain insufficiently understood. This study evaluates the impacts of utilizing Scots pine (Pinus sylvestris) logging residues for energy production in Lithuania using a comparative life cycle assessment (LCA). Two harvesting scenarios were assessed at midpoint and endpoint levels: one excluding and one including logging residues. The results show that about 173.2 tons of biofuels can be produced from one hectare of Scots pine forest over a 100-year cycle, generating up to 513.6 MWh of energy when residues are utilized. The LCA revealed improvements in 9 of 18 impact categories, with greenhouse gas avoidance increasing from –52 to –89.5 t CO₂ eq, and overall endpoint impacts decreasing by nearly 39%. The novelty of this study lies in applying established LCA methods with region- and species-specific data, partly obtained through monitoring, for Scots pine residues in Lithuania, while extending system boundaries to include soil degradation, storage losses, and ash management—providing a more holistic and Northern Europe-relevant perspective. Full article

In-situ chemical oxidation (ISCO) is a common method to remediate chlorinated ethene contaminants in groundwater. Monitoring the effectiveness of ISCO can be hindered because of insufficient observations to assess oxidant delivery. Advantageously, potassium permanganate, one type of oxidant, provides the opportunity to use its strong electrical signal as a surrogate to track oxidant delivery using time-series borehole geophysical methods, like electromagnetic (EM) induction logging. Here we report a passive ISCO (P-ISCO) experiment, using potassium permanganate cylinders emplaced in boreholes, at a chlorinated ethene contamination site, Naval Air Station Pensacola, Florida. The contaminants are found primarily at the base of a shallow sandy aquifer in contact with an underlying silty-clay confining bed. We used results of the time-series borehole logging collected between 2017 and 2022 in 4 monitoring wells to track oxidant delivery. The EM-induction logs from the monitoring wells showed an increase in EM response primarily along the contact, likely from pooling of the oxidant, during P-ISCO treatment in 2021. Interestingly, concurrent natural gamma-ray (NGR) logging showed a decrease in NGR response at 3 of the 4 wells possibly from the formation of manganese precipitates coating sediments. The coupling of time-series logging and well-chemistry data allowed for an improved assessment of passive ISCO treatment effectiveness. Full article

The indoor microbiome is a dynamic ecosystem including pathogens that can impact human health. In this regard, the school environment represents the main living space of humans for many years, and an unhealthy environment can significantly condition students’ health. School rooms can suffer from insufficient ventilation and the use of building materials that may favor pathogen contamination, mostly sanitized by conventional chemical-based methods, which can impact pollution, have temporary effects, and induce the selection of antimicrobial resistance (AMR) in persistent microbes. In the search for sustainable and effective methods to improve the healthiness of the classroom environment, a pre–post case–control study was performed in an Italian high school. Over a year, different interventions were sequentially placed and evaluated for their impact on bioburden and air quality, including the introduction of plants, a mechanical ventilation system, and probiotic-based sanitation (PBS) in substitution for chemical sanitation. Through continuous microbial monitoring of the enrolled school rooms, via culture-dependent and -independent methods, a remarkable bioburden level was detected at baseline (around 12,000 and 20,000 CFU/m², before and after classes, respectively), composed mostly of Staphylococcus spp. and fungi. Some decrease in fungal contamination was observed following the introduction of plants. Still, the most significant decrease in pathogens and associated AMR was detected following the introduction of ventilation and PBS, which decreased pathogen level by >80% (p < 0.001) and AMR by up to 3 Log₁₀ (p < 0.001) compared to controls. Collected data support the use of combined strategies to improve indoor microbial quality and confirm that PBS can effectively control bioburden and AMR spread not only in sanitary environments. Full article

After transplantation, systematically monitoring and assessing the risk of transplanted organ rejection is crucial. Current methods involving immunosuppressant monitoring, the assessment of organ function, and biopsies are insufficient for predicting rejection. However, regular determination of torque teno virus (TTV) load after transplantation may prove to be a useful parameter for monitoring immunosuppression efficacy. Therefore, we aimed to evaluate TTV load in patients before and after lung transplantation and the kinetics of TTV growth in relation to immunosuppression strength. We included 14 patients (mean age: 49.4 ± 14.0 years) undergoing lung transplantation and determined TTV copy numbers using the commercial ARGENE TTV-R-GENE kit from BioMerieux from the day of transplantation to 180 days post-transplantation. We also developed an empirical immunosuppression unit scale to calculate immunosuppression strength. We observed an average positive correlation between log₁₀ TTV and immunosuppression strength, with significant increases in log₁₀ TTV depending on the duration of immunosuppression. These results indicate the potential of TTV as a new parameter to assess the possibility of transplanted organ rejection. Full article

As gas wells enter the middle and late stages of production, they will become low-yielding gas wells due to fluid loading and insufficient formation pressure. For many years, there has been a lack of effective dynamic monitoring methods for low-yielding gas wells, and it is difficult to determine the production of each phase in each production layer, which makes further development face great uncertainty and a lack of basis for measurement adjustment. In order to solve this problem, this paper proposes an intelligent dynamic monitoring method suitable for low-yielding gas wells, which uses an ultrasonic Doppler logging instrument and machine learning algorithm as the core to obtain the output contribution of each production layer of the gas well. The intelligent dynamic monitoring method is based on the HGWO-SVR algorithm to predict the flow of each phase. The experimental data are selected for empirical analysis, and the effectiveness and accuracy of the method are verified. The research shows that this method has good application prospects and can provide strong technical support for gas reservoir production stability and development adjustment. Full article

Crop classification of large-scale agricultural land is crucial for crop monitoring and yield estimation. Hyperspectral image classification has proven to be an effective method for this task. Most current popular hyperspectral image classification methods are based on image classification, specifically on convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In contrast, this paper focuses on methods based on semantic segmentation and proposes a new transformer-based approach called HyperSFormer for crop hyperspectral image classification. The key enhancement of the proposed method is the replacement of the encoder in SegFormer with an improved Swin Transformer while keeping the SegFormer decoder. The entire model adopts a simple and uniform transformer architecture. Additionally, the paper introduces the hyper patch embedding (HPE) module to extract spectral and local spatial information from the hyperspectral images, which enhances the effectiveness of the features used as input for the model. To ensure detailed model processing and achieve end-to-end hyperspectral image classification, the transpose padding upsample (TPU) module is proposed for the model’s output. In order to address the problem of insufficient and imbalanced samples in hyperspectral image classification, the paper designs an adaptive min log sampling (AMLS) strategy and a loss function that incorporates dice loss and focal loss to assist model training. Experimental results using three public hyperspectral image datasets demonstrate the strong performance of HyperSFormer, particularly in the presence of imbalanced sample data, complex negative samples, and mixed sample classes. HyperSFormer outperforms state-of-the-art methods, including fast patch-free global learning (FPGA), a spectral–spatial-dependent global learning framework (SSDGL), and SegFormer, by at least 2.7% in the mean intersection over union (mIoU). It also improves the overall accuracy and average accuracy values by at least 0.9% and 0.3%, respectively, and the kappa coefficient by at least 0.011. Furthermore, ablation experiments were conducted to determine the optimal hyperparameter and loss function settings for the proposed method, validating the rationality of these settings and the fusion loss function. Full article

Modern distributed systems that operate concurrently generate interleaved logs. Identifiers (ID) are always associated with active instances or entities in order to track them in logs. Consequently, log messages with similar IDs can be categorized to aid in the localization and detection of anomalies. Current methods for achieving this are insufficient for overcoming the following obstacles: (1) Log processing is performed in a separate component apart from log mining. (2) In modern software systems, log format evolution is ongoing. It is hard to detect latent technical issues using simple monitoring techniques in a non-intrusive manner. Within the scope of this paper, we present a reliable and consistent method for the detection and localization of anomalies in interleaved unstructured logs in order to address the aforementioned drawbacks. This research examines Log Sequential Anomalies (LSA) for potential performance issues. In this study, IDs are used to group log messages, and ID relation graphs are constructed between distributed components. In addition to that, we offer a data-driven online log parser that does not require any parameters. By utilizing a novel log parser, the bundled log messages undergo a transformation process involving both semantic and temporal embedding. In order to identify instance–granularity anomalies, this study makes use of a heuristic searching technique and an attention-based Bi-LSTM model. The effectiveness, efficiency, and robustness of the paper are supported by the research that was performed on real-world datasets as well as on synthetic datasets. The neural network improves the F1 score by five percent, which is greater than other cutting-edge models. Full article

The Western Balkans represent a priority area for improving forest legality monitoring systems in line with the European Union Timber Regulation (EUTR). However, research on EUTR implementation in Western Balkan countries is still sporadic with a limited geographical scope; therefore, the preparedness of forestry sector actors for the EUTR in the region is largely unknown. The main objective of this study is to determine to what extent the forest policy frameworks of Montenegro and the Republic of Srpska (Bosnia and Herzegovina) are aligned with the EUTR requirements. To achieve this aim, we applied a qualitative content analysis of policy documents identified via an expert-based approach. Our results show that both countries have well-developed policy frameworks addressing illegal logging and preventing illegal activities in forestry, especially through dedicated action plans. Key actors in both countries are public, including the ministries responsible for forestry, public forest enterprises, and forestry inspectorates. The forestry sector in Montenegro is facing significant changes due to the termination of forest concessions and the reorganization of the management of state forests, including forest certification. The Republic of Srpska has relatively well-established institutional bodies for EUTR implementation but, in some cases, insufficient exchange of information and cooperation among them. Our findings indicate that the forestry sectors in Montenegro and the Republic of Srpska (as well as in Serbia, Croatia, and Slovenia, as per previous research) are dynamic, undergoing various changes, so there is room for improvement in terms of capacities (e.g., human, technological, infrastructural), legal responsibilities, and information access and availability. With an increasing focus on “deforestation-free” commodities within the EU and global policy arena, a new, more demanding, and broader regulation is expected at the EU level, replacing the EUTR. The incoming regulation will expand existing EUTR requirements, likely posing severe challenges to many EU member countries. This could be even more challenging for countries with less developed or advanced systems to enforce legality requirements, including Western Balkan countries. Full article

In the petroleum industry, artificial intelligence has been applied in seismic and logging interpretation, accurate modeling, optimized drilling operations, well dynamics prediction, safety warning, etc. However, field-scale application and deployment remain a challenge due to the lack of sufficiently powerful algorithms for the integration of multi-granularity data in the time and space domain, for the construction of a deep-learning network able to represent the evolution of well and reservoir dynamics, and finally the lack of investment in surveillance data acquisition. This paper offers a concise review of smart field deployment for mature waterflood reservoirs, including the current status of data foundation construction, and the research progress for applied AI algorithms, as well as application scenarios and overall deployment. With respect to data, the domestic and international oil and gas industry has completed or at least started the large-scale construction and deployment of lake data. However, the data isolation phenomenon is serious in China. Preparation for the integration of new monitoring data for the overall research of reservoirs is insufficient. With respect to algorithms, data-based and model-based AI algorithms have been emerging recently, but the development of the overall proxy model for rapid prediction and automatic model calibration is still in the preliminary period. For application scenarios, relatively simple and independent applications related to geophysical interpretation and production engineering are continuing to emerge, while large-scale reservoir and field application require substantial investment in data acquisition, game-changing algorithms with cloud-based computing architecture, and top-down deployment. Full article

The growing use of the internet has resulted in an exponential rise in the use of web applications. Businesses, industries, financial and educational institutions, and the general populace depend on web applications. This mammoth rise in their usage has also resulted in many security issues that make these web applications vulnerable, thereby affecting the confidentiality, integrity, and availability of associated information systems. It has, therefore, become necessary to find vulnerabilities in these information system resources to guarantee information security. A publicly available web application vulnerability scanner is a computer program that assesses web application security by employing automated penetration testing techniques that reduce the time, cost, and resources required for web application penetration testing and eliminates test engineers’ dependency on human knowledge. However, these security scanners possess various weaknesses of not scanning complete web applications and generating wrong test results. Moreover, intensive research has been carried out to quantitatively enumerate web application security scanners’ results to inspect their effectiveness and limitations. However, the findings show no well-defined method or criteria available for assessing their results. In this research, we have evaluated the performance of web application vulnerability scanners by testing intentionally defined vulnerable applications and the level of their respective precision and accuracy. This was achieved by classifying the analyzed tools using the most common parameters. The evaluation is based on an extracted list of vulnerabilities from OWASP (Open Web Application Security Project). Full article

The use of Sentinel-1 (S1) radar for wide-area, near-real-time (NRT) tropical-forest-change monitoring is discussed, with particular attention to forest degradation and deforestation. Since forest change can relate to processes ranging from high-impact, large-scale conversion to low-impact, selective logging, and can occur in sites having variable topographic and environmental properties such as mountain slopes and wetlands, a single approach is insufficient. The system introduced here combines time-series analysis of small objects identified in S1 data, i.e., segments containing linear features and apparent small-scale disturbances. A physical model is introduced for quantifying the size of small (upper-) canopy gaps. Deforestation detection was evaluated for several forest landscapes in the Amazon and Borneo. Using the default system settings, the false alarm rate (FAR) is very low (less than 1%), and the missed detection rate (MDR) varies between 1.9% ± 1.1% and 18.6% ± 1.0% (90% confidence level). For peatland landscapes, short radar detection delays up to several weeks due to high levels of soil moisture may occur, while, in comparison, for optical systems, detection delays up to 10 months were found due to cloud cover. In peat swamp forests, narrow linear canopy gaps (road and canal systems) could be detected with an overall accuracy of 85.5%, including many gaps barely visible on hi-res SPOT-6/7 images, which were used for validation. Compared to optical data, subtle degradation signals are easier to detect and are not quickly lost over time due to fast re-vegetation. Although it is possible to estimate an effective forest-cover loss, for example, due to selective logging, and results are spatiotemporally consistent with Sentinel-2 and TerraSAR-X reference data, quantitative validation without extensive field data and/or large hi-res radar datasets, such as TerraSAR-X, remains a challenge. Full article

This case study analyzed the performance of a start-up cable yarding crew in southern China through operational monitoring by consecutive time studies, long-term log book recordings and efficiency evaluation by stochastic frontier analysis (SFA). The crew, which used a KOLLER K303 H mobile tower yarder, was monitored for two years. During this period, detailed data recordings of 687 yarding cycles of 12 yarding corridors as well as log book recordings of an additional 1122 scheduled system hours (SSH, including all delays) were generated. Mean extraction productivity of the system ranged between 5.23 and 6.40 m³ per productive system hour (PSH₀, excluding all delays), mostly depending on slope yarding distance and lateral distance. Corresponding gross-productivity ranged from 1.91 to 2.24 m³/SSH, with an overall mean machine utilization rate of 31%. Unproductive yarding times and delays associated with the relative low utilization rate were mainly caused by lengthy rigging processes, as well as organizational deficiencies and not yet fully developed skill sets of the operating crew. The latter was reflected in a mean efficiency effect frontier value of 0.62 based on evaluation of data sets of individual yarding cycles recorded during detailed assessments, suggesting a mean improvement potential of 38% based on the SFA, translating in a potentially achievable gross-productivity of 2.64 to 3.09 m³/SSH. We conclude that current local operating conditions including insufficient planning, implementation and logistics and in particular, frequent discontinuations of system operations of up to three months all resulting in generally low operation hours per shift and per year, inhibit efficient operations and rapid skill development. These circumstances also inhibit an economic utilization of the equipment. Nevertheless, from a technical perspective, yarding systems have a promising potential in southern China. Full article