Problem Of Degradation Research Articles

China's Low-carbon Energy Transformation: Development of Renewable Energy

In recent years, as the world's population continues to increase, it has brought a series of pollution problems, which causes global warming intensified. In order to solve the problem of climate warming, the development trend of low carbon and environmental protection is gradually becoming the keynote of global social and economic development. At the same time, due to the excessive reliance on energy consumption for the development of China's economy in the past, the problems of energy shortage and environmental degradation have become more and more serious in China in recent years. Therefore, the carbon emission reduction index has become one of the important assessment indicators for the development of China's economic quality and an unavoidable key task for economic development. Energy is the basis of economic and social development, in the process of economic development, China will inevitably encounter energy shortages, environmental pollution problems, the development of renewable energy has become a general trend, the transformation and optimization of the traditional energy structure is an effective way to achieve this goal and an important means. This paper analyzes and puts forward suggestions on the current situation of China's energy transition, factors affecting energy transition and future development trends through the literature review method and comparative analysis method.

Real-Time On-Site Ground-Motion Prediction Using ResNet

Abstract On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023.

Internalization of Environmental Externalities: Processing Palm Waste into Renewable Energy

In 2021, the European Union issued a lawsuit at the World Trade Organization (WTO) regarding the halt in palm oil exports. The European Union Parliament considers the palm oil industry in Indonesia to be one of the triggers of deforestation, degradation and other environmental problems. Based on data from the Central Statistics Agency in 2019, the area of ??oil palm plantations in Indonesia reached 14.3 million hectares and the area of ??oil palm plantations in the Province was 2.7 million hectares (Yanti and Lestari 2020). This is based on the high demand for oil from palm oil and its derivative products which has an impact on negative externalities due to the extraction process carried out. The Palm Oil Fresh Fruit Bunches (FFB) production process produces many products such as Crude Palm Oil (CPO) and Palm Kernel Oil (PKO). Crude palm oil (CPO) production plays an important role in both the local-global environment and socio-economics. In this case, Internalization of Externalities is needed to minimize dirty oil (palm oil waste) which is detrimental to third parties from the management process carried out. Based on the case study of Panyabungan Village, the externality value for liquid palm oil waste is IDR 146,194,433,- after internalizing the externalities, we get a Total Economic Value (TEV) of IDR 627,602,359,- with the liquid waste and solid waste aspects of palm oil in three locations namely; Jambi, Bengkulu and Kalimantan. As a preventive form of assessing externalities, economic and environmental studies also include a SWOT analysis to develop strategies for the sustainability of Palm Oil in Indonesia.

The Influence of the Availability of Environmentally Friendly Food on Consumers' Green Purchasing Behavior

For a long time, scientists, intellectuals, and environmentalists around the world have advocated for adopting an eco - friendly lifestyle. Unmindful human activities over the past few decades have caused irreversible damage to the Earth's ecosystem. Green marketing, or sustainable marketing, has emerged as a highly effective tool to address the problem of environmental degradation. The present era can be seen as a time of revelation and introspection regarding the impact of human activities on the environment. With the emergence of deadly diseases, it is imperative to recognize the importance of a healthy lifestyle. It is essential to motivate people to adopt products with green attributes by employing appropriate green marketing strategies. The present research paper attempts to analyses the impact of availability of eco - friendly food products on consumer buying behaviour.

Modelling the impact of past and future land-use changes on land cover degradation at territorial level in Eastern DR Congo

AbstractLand use and land cover (LULC) changes are recognized as drivers of environmental changes at the local, regional, and global levels. Detecting these changes is essential in developing land management plans and strategies. More particularly, the LULC changes constitute one of the sub-indicators used for setting the baseline for land degradation neutrality (LDN) planning and assessing the progress toward the sustainable development goal 15.3. This study aims to determine the implication of LULC dynamics on land cover degradation in Eastern DR Congo. This region has been subjected to uncontrolled LULC changes over the last three decades but the impact of these changes on land degradation has not been assessed yet at the territorial level. To fill this gap, the territory of Kalehe has been used as a case study to monitor the LULC changes during the 1987–2020 period based on the classification of Landsat images, to forecast the future LULC for the 2030–2070 period through the hybrid Markov-Support Vector Machine modeling, to determine the extent of land cover degradation associated with these changes and to assess the impact of biophysical and socio-economic factors on the occurrence of land cover degradation using the binary logistic regression model. The results reveal that the overall trends of LULC changes during the 1987–2020 period are the increasing of built-up area, shrubland, and cropland at the expense of forestland, wetland, and grassland. This situation is expected to continue in the future and contribute to the degradation of land cover within the study area. Under the current situation of LULC changes, 34.17% of the land has been subjected to potential degradation. Furthermore, under the business-as-usual scenario 28.28%, 27.28%, and 33.65% of the land will be degraded by 2030, 2050, and 2070 respectively. This land cover degradation is more likely to occur in the proximity of roads, localities, highly populated areas, mining concessions, and high-altitudinal zones. Since the current land use system is unsustainable, there is a necessity to implement sustainable land management strategies that take into account the biophysical and socio-economic specificities of this region to reverse the problem of land cover degradation.

A novel hybrid fuzzy analytical hierarchy process–game theory model for prioritizing factors affecting the deterioration of water pipelines

Water pipes face significant aging and degradation problems due to several pipe-related, soil-related, operational, and environmental factors. Hence, the paramount objective of this research paper is to prioritize the criticality of the factors affecting the deterioration of water pipes in Hong Kong. The framework of the developed model is envisioned based on two main modules, namely weight computation and weight aggregation. The first module incorporates identifying and categorizing water deterioration factors. Then, the relative importance priorities of water deterioration factors are scrutinized using seven weight computation methods. These methods encompass analytical hierarchy process (AHP), Monte Carlo AHP, fuzzy AHP, magnitude-based fuzzy AHP, total difference-based fuzzy AHP, spherical fuzzy AHP and Pythagorean fuzzy AHP. In this regard, fuzzy-based and Monte Carlo-based methods are leveraged to circumvent the critical shortcomings of classical AHP. The performances of weight computation methods are analyzed using statistical evaluation indicators of satisfactory index (SAT) and distance between weights (WD). The second module is a hybrid meta-heuristic-based game theory model designated for compiling the importance weights of deterioration factors obtained from the first module. In this context, a set of widely acclaimed meta-heuristics are exploited and examined for optimizing the significance of deterioration factors. Analytical results exemplified that soil-related factors implicate the deterioration process more than pipe-related, operational-related, and environmental-related factors. It was also inferred that water pressure (6.64%) is the most significant factor influencing water pipe deterioration followed by internal corrosion and protection method (6.11%), and then soil corrosivity (6.05%). On the other hand, length (1.93%), rain deficit (1.97%), and street block length (2.33%) constitute the least influencers on water pipe deterioration. Results also demonstrated that spherical FAHP outperformed other variants of AHP accomplishing SAT and WD of 0.065 and 0.057, respectively. Comparative analysis revealed that particle swarm optimization-based game theory is a better mechanism than the remainder of meta-heuristic-based game theory models in obtaining a more accurate compromised-based weighting vector to the experts’ judgments. It is envisaged that this research can assist the water supplies department in identifying, assessing, and prioritizing the impairment causes of water pipelines in Hong Kong. It can also aid in establishing more accurate deterioration models and more cost-effective maintenance intervention programs.

Multivariate degradation modeling using generalized cauchy process and application in life prediction of dye-sensitized solar cells

Recently, the Generalized Cauchy (GC) process has been applied to capture a Long Memory (LM) phenomenon in product degradation modeling and life prediction. Compared with the traditional fractional Brownian motion that captures the LM using a single Hurst parameter, the GC process has two free parameters (Hurst and fractal dimension parameters) that flexibly capture both global LM and local irregularity. However, all existing GC-based degradation models are for a single Degradation Characteristic (DC). In this article, motivated by a real degradation problem of dye-sensitized solar cells that jointly exhibits multiple DCs, global LM, local irregularity and DC-wise cross-correlation, we propose a novel GC-based Multivariate Degradation Model (GC-MDM) to simultaneously capture the aforementioned effects. A maximum likelihood estimation approach is developed to estimate parameters of the GC-MDM. Subsequently, product life prediction based on the GC-MDM is developed. The proposed GC-MDM is validated through a simulation study and a physical experiment of dye-sensitized solar cells. Results show that the proposed GC-MDM fundamentally improves the life prediction accuracy in comparison with conventional degradation models which significantly misestimate the uncertainty of product life.

Interferometric phase reconstruction with temporal decorrelation model constraints for time-series InSAR surface deformation monitoring

ABSTRACT The time series interferometric synthetic aperture radar (InSAR) (TSInSAR) is an effective technique for monitoring surface deformation with wide coverage and millimeter accuracy. There are two types of scatterers in the SAR field of view, including the permanent scatterer (PS) and the distributed scatterer (DS). Compared to PS with temporarily stable scattering behavior, the filtered interferometric phases are not consistent for DS susceptible to the decorrelation effect. To overcome the phase inconsistency degradation problem of the decorrelated DSs, the phase linking (PL) theory is introduced to recover the equivalent single-reference (ESR) phases from all interferometric combinations. However, the optimization performance of the PL theory is affected by the uncontrollable estimation error of coherence magnitude. Here, we present a novel enhanced PL (EPL) by incorporating with a priori temporal decorrelation model (TDM) to enhance the weight of the short-term interferograms and suppress the abrupt coherence jump with the long temporal baseline. Real experiment selects the Daguangbao landslide triggered by the ‘5.12’ Wenchuan Mw 7.9 earthquake as the study area to verify the algorithm’s effectiveness. Compared to traditional PL methods, the new EPL method can recover the interferometric phase fringe more clearly and obtain a smoother deformation velocity spatially with higher estimation accuracy.

Synergistic Effects of BaTiO3 and MFe2O4 (M = Mn, Ni, Cu, Zn, and Co) Nanoparticles as Artificial Pinning Centers on the Performance of YBa2Cu3Oy Superconductor.

Large-scale superconductor applications necessitate a superconducting matrix with pinning sites (PSs) that immobilize vortices at elevated temperatures and magnetic fields. While previous works focused on the single addition of nanoparticles, the simultaneous inclusion of different nanoparticles into a superconducting matrix can be an effective way to achieve an improved flux pinning capacity. The purpose of this study is to explore the influence of mixed-nanoparticle pinning, with the co-addition of non-magnetic (BaTiO3; BT) and various types of magnetic spinel ferrite (MFe2O4, abbreviated as MFO, where M = Mn, Co, Cu, Zn, and Ni) nanoparticles, on the superconductivity and flux pinning performances of the high-temperature superconductor YBa2Cu3Oy (YBCO). An analysis of X-Ray diffraction (XRD) data of BT-MFe2O4-co-added YBCO samples showed the formation of an orthorhombic structure with Pmmm symmetry. According to electrical resistivity measurements, the emergence of the superconducting state below Tcoffset (zero-resistivity temperature) was proven for all samples. The highest Tcoffset value was recorded for the Y-BT-MnFO sample, while the minimum value was obtained for the Y-BT-ZnFO sample. Direct current (DC) magnetization results showed good magnetic flux pinning performance for all the co-added samples compared to the pristine sample but with some discrepancies. At 77 K, the values of the self-critical current density (self-Jcm) and maximum pinning force (Fpmax) for the Y-BT-MnFO sample were found to be eight times higher and seventeen times greater than those for the pristine sample, respectively. The results acquired suggested that mixing the BT phase with an appropriate type of spinel ferrite nanoparticles can be a practical solution to the problem of degradation of the critical current density of the YBCO material.

Isolation, Characterization, and Optimization of Culture Medium for Local Straw-Degrading Bacteria from Northeastern Black Soils of China

In order to solve the problem of low and poor straw degradation in typical black soil areas of Northeast China, the present study was carried out to screen the potential of in situ strains with cellulose degradation ability from black soils of Northeast China to play a role in the resourceful utilization of straw and the development of sustainable agriculture. The straw degradation potential of the strains was evaluated by combining sodium carboxymethyl cellulose plate screening and cellulase viability assay; the species identification of the strains was carried out by morphology, physiology, biochemistry, and molecular biology; and the basic medium formulation of the strains was optimized by Box–Behnken response surface methodology. Ten cellulose-degrading strains were identified: ZL-5, ZL-69, ZL-88, ZL-95, ZL-111, ZL-137, ZL-139, ZL-140, ZL-187, and ZL-216, of which ZL-139 had the highest cellulase production capacity, with a cellulase secretion of 7.8781 U/mL in the enzyme-producing medium. ZL-139 was identified as Bacillus cereus; the optimized best formulation was glucose—4.284 g/L, yeast extract—1.454 g/L, MgSO4—0.417 g/L, KH2PO4—0.5 g/L, KH2PO4—0.5 g/L, K2HPO4—1.5 g/L, and NaCl—1.0 g/L. In conclusion, strain ZL-139 has good potential for crop straw degradation and can be a candidate strain for a straw-rotting agent in northeast China, with promising prospects for development and utilization.

Active disturbance rejection control with adaptive RBF neural network for a permanent magnet spherical motor

In response to the issues of low tracking accuracy and poor robustness in the trajectory tracking control of a permanent magnet spherical motor (PMSpM), an active disturbance rejection control (ADRC) scheme combining neural networks is put forward in this research. The unknown total disturbance is approximated by employing a radial basis function (RBF) neural network, with weights updated by an adaptive law and compensated for through the nonlinear feedback loop. This approach addresses the problem of performance degradation of the extended state observer under severe total disturbance, thereby ensuring accurate tracking of the PMSpM. Comparative simulations are accomplished to evaluate the performance of the RBF-ADRC scheme in enhancing disturbance rejection capability and robustness. Experimental results from the planar circular motion experiment on the PMSpM test platform validate the application value of the scheme.

Land Stewardship and Development Behaviors Under an Ecological-Impact-Weighted Land Value Tax Scheme: A Proof-of-Concept Agent-Based Model

Sprawling land development patterns have exacerbated ecological degradation, social fragmentation, and public health problems. Perverse incentives arise from the ability to privatize collectively created value in land rents and socialize ecological costs. Land value taxation (LVT) has been shown to encourage urban infill development by reducing or eliminating rent-seeking behavior in land markets. However, despite its purported benefits, this tax reform is value monistic in its definition of optimal land use and, therefore, does little to address the lack of non-market information to inform land use decisions. We propose an ecological-impact-weighted land value taxation policy (ELVT) which incorporates the ecological footprint of land use into one’s land value tax burden. We test both proposed policies (LVT and ELVT) relative to a “status quo” (SQ) property tax scheme, utilizing a conceptual spatially explicit agent-based model of land use behaviors and housing development. Our findings suggest that both tax interventions can increase the capital intensity and decrease the land intensity of housing development. Furthermore, both tax interventions can lead to a net profit loss for speculators and a decrease in the average housing unit price. The ELVT scheme is shown to significantly increase urban nature provisions and dampen the loss of ecological value across a region.

Achieving land degradation neutrality: land-use planning and ecosystem approach

IntroductionThe research purpose is to scientifically substantiate an integrated approach to solving the problem of land degradation, based on the idea of land degradation neutrality (LDN), taking into account ecosystem services when planning land use to maximize the conservation of natural capital. The methodological basis of the research is the provisions and principles of the concepts of sustainable development, achieving LDN, and ecosystem services, as well as the research results revealing various aspects of land use, particularly their degradation.MethodsThe following research methods are used in the paper: dialectical – to determine the cause-and-effect conditions of land degradation; analysis – to highlight the current state of land use in Ukraine and the factors that have led to land degradation; synthesis – for global trends towards achieving LDN; deduction – to explore the possibility of introducing global experience in achieving LDN in Ukraine; structural-functional analysis – to substantiate the feasibility of introducing an ecosystem approach to land-use planning to achieve LDN.ResultsAs a result of the research, the current land degradation state in Ukraine has been analyzed, and ways of achieving LDN in land-use planning through the prism of an ecosystem approach have been substantiated. Based on statistical data, the current and potential levels of arability of the territory of Ukraine have been calculated by natural-climatic zones, and the areas of eroded arable lands in Ukraine have been determined by the erodibility factor (low-eroded, mediumeroded,and highly-eroded).DiscussionFor the first time, a structural-logical scheme has been developed for organizational-economic support for the effective use of degraded and low-productive agricultural lands in the context of implementing the idea of LDN, which is a tool for rational allocation and use of degraded lands. This scheme can serve as a basis for the development of land-use planning strategies for territorial communities, for institutions, and organizations competent in the field of land management.

A new two-stage low-light enhancement network with progressive attention fusion strategy

Low-light image enhancement is a very challenging subject in the field of computer vision such as visual surveillance, driving behavior analysis, and medical imaging . It has a large number of degradation problems such as accumulated noise, artifacts, and color distortion. Therefore, how to solve the degradation problems and obtain clear images with high visual quality has become an important issue. It can effectively improve the performance of high-level computer vision tasks. In this study, we propose a new two-stage low-light enhancement network with a progressive attention fusion strategy, and the two hallmarks of this method are the use of global feature fusion (GFF) and local detail restoration (LDR), which can enrich the global content of the image and restore local details. Experimental results on the LOL dataset show that the proposed model can achieve good enhancement effects. Moreover, on the benchmark dataset without reference images, the proposed model also obtains a better NIQE score, which outperforms most existing state-of-the-art methods in both quantitative and qualitative evaluations. All these verify the effectiveness and superiority of the proposed method.

LIVS: a tightly coupled LiDAR, IMU, and camera-based SLAM system under scene degradation

In increasingly complex environments, single-sensor-based SLAM systems face scene degradation problems such as strong light, dynamic obstacle interference, and lack of structural features in the environment, which severely constrain the localization accuracy and mapping effectiveness of SLAM systems. To address this problem, this paper proposes a tightly coupled laser-IMU-visual SLAM system (LIVS) based on factor graph optimization. LIVS consists of two subsystems: the laser inertial system (LIS) and the visual inertial system (VIS). The pose estimation of the LIS can provide an initialization for the VIS; LiDAR suffers scene degradation in environments lacking structural features, while the VIS provides better constraints for Lidar in this environment. Firstly, a method of selecting keyframes based on ORB features and the relative motion of the mobile robot is proposed as a means of improving the positioning accuracy and tracking ability of the system in the case of strenuous motion, and a strategy of local sliding window optimization is adopted to effectively improve the real-time performance and robustness of the system. Meanwhile, in order to further improve the global map consistency and positioning accuracy, a hybrid closed-loop detection method based on laser and vision is proposed, which utilizes the geometrical features of laser and the image features of vision to accomplish the hybrid closed-loop detection. Secondly, the laser inertia factor, IMU pre-integration factor, vision inertia factor, and closed-loop factor are globally optimized in a factor map framework to achieve accurate estimation of the robot pose. Finally, this paper presents experimental evaluations on both public and homemade datasets and compares the performance of the proposed algorithm with that of other algorithms. Experimental results show that the algorithm in this paper exhibits better performance in both localization accuracy and mapping effectiveness.

Construction of Co-Modified MXene/PES Catalytic Membrane for Effective Separation and Degradation of Tetracycline Antibiotics in Aqueous Solutions.

The application of antibiotics has advanced modern medicine significantly. However, the abuse and discharge of antibiotics have led to substantial antibiotic residues in water, posing great harm to natural organisms and humans. To address the problem of antibiotic degradation, this study developed a novel catalytic membrane by depositing Co catalysts onto MXene nanosheets and fabricating the polyethersulfone composite (Co@MXene/PES) using vacuum-assisted self-assembly. The dual role of MXene as both a carrier for Co atoms and an enhancer of interlayer spacing led to improved flux and catalytic degradation capabilities of the membrane. Experimental results confirmed that the Co@MXene/PES membrane effectively degraded antibiotics through peroxymonosulfate activation, achieving up to 95.51% degradation at a cobalt concentration of 0.01 mg/mL. The membrane demonstrated excellent antibacterial properties, minimal flux loss after repeated use, and robust anti-fouling performance, making it a promising solution for efficient antibiotic removal and stable water treatment.

Pelaksanaan Program REDD+ di Kalimantan Timur

The efforts made by Indonesia in overcoming the problem of deforestation and forest degradation is the REDD+ Program. REDD+ is implemented to reduce the rate of deforestation and reduce emissions and sustainable economic growth. The implementation of REDD+ in East Kalimantan has its own strengths and weaknesses. This study aims to determine the implementation of the REDD+ Program in East Kalimantan. This research uses a library research method with a qualitative approach. In answering questions about the problems of REDD+ implementation in East Kalimantan, this research uses SWOT analysis theory. The results show that the strengths of the REDD+ program are the strong commitment of the East Kalimantan local government, and the initiation of various programs. The weaknesses of the REDD+ program are the lack of long-term funding and conflicts over land ownership. The opportunity of REDD+ program is the increasing economic growth of local communities in East Kalimantan. The threat of REDD+ program is the supervision of law enforcement that has not been fair for all communities in East Kalimantan.

LDF-BNN: A Real-Time and High-Accuracy Binary Neural Network Accelerator Based on the Improved BNext.

Significant progress has been made in industrial defect detection due to the powerful feature extraction capabilities of deep neural networks (DNNs). However, the high computational cost and memory requirement of DNNs pose a great challenge to the deployment of industrial edge-side devices. Although traditional binary neural networks (BNNs) have the advantages of small storage space requirements, high parallel computing capability, and low power consumption, the problem of significant accuracy degradation cannot be ignored. To tackle these challenges, this paper constructs a BNN with layered data fusion mechanism (LDF-BNN) based on BNext. By introducing the above mechanism, it strives to minimize the bandwidth pressure while reducing the loss of accuracy. Furthermore, we have designed an efficient hardware accelerator architecture based on this mechanism, enhancing the performance of high-accuracy BNN models with complex network structures. Additionally, the introduction of multi-storage parallelism alleviates the limitations imposed by the internal transfer rate, thus improving the overall computational efficiency. The experimental results show that our proposed LDF-BNN outperforms other methods in the comprehensive comparison, achieving a high accuracy of 72.23%, an image processing rate of 72.6 frames per second (FPS), and 1826 giga operations per second (GOPs) on the ImageNet dataset. Meanwhile, LDF-BNN can also be well applied to defect detection dataset Mixed WM-38, achieving a high accuracy of 98.70%.

Role of Humic Substances in the (Bio)Degradation of Synthetic Polymers under Environmental Conditions

Information on the detection of the presence and potential for degradation of synthetic polymers (SPs) under various environmental conditions is of increasing interest and concern to a wide range of specialists. At this stage, there is a need to understand the relationship between the main participants in the processes of (bio)degradation of SPs in various ecosystems (reservoirs with fresh and sea water, soils, etc.), namely the polymers themselves, the cells of microorganisms (MOs) participating in their degradation, and humic substances (HSs). HSs constitute a macrocomponent of natural non-living organic matter of aquatic and soil ecosystems, formed and transformed in the processes of mineralization of bio-organic substances in environmental conditions. Analysis of the main mechanisms of their influence on each other and the effects produced that accelerate or inhibit polymer degradation can create the basis for scientifically based approaches to the most effective solution to the problem of degradation of SPs, including in the form of microplastics. This review is aimed at comparing various aspects of interactions of SPs, MOs, and HSs in laboratory experiments (in vitro) and environmental investigations (in situ) aimed at the biodegradation of polymers, as well as pollutants (antibiotics and pesticides) that they absorb. Comparative calculations of the degradation velocity of different SPs in different environments are presented. A special place in the analysis is given to the elemental chemical composition of HSs, which are most successfully involved in the biodegradation of SPs. In addition, the role of photo-oxidation and photoaging of polymers under the influence of the ultraviolet spectrum of solar radiation under environmental conditions on the (bio)degradation of SPs in the presence of HSs is discussed.

On the Problem of Composite Beam Degradation under Longitudinal Bending and the Solution Method for Large Displacements

On the Problem of Composite Beam Degradation under Longitudinal Bending and the Solution Method for Large Displacements