Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks

This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and Bangla-Electra by almost 3%, 4% and 10% respectively in terms of accuracy. The experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology

A multi-objective optimization approach in defining the decarbonization strategy of a refinery

Nowadays, nearly one quarter of global carbon dioxide emissions are attributable to energy use in industry, making this an important target for emission reductions. The scope of this study is hence that to define a cost-optimized decarbonization strategy for an energy and carbon intensive industry using an Italian refinery as a case study. The methodology involves the coupling of EnergyPLAN with a Multi-Objective Evolutionary Algorithm (MOEA), considering the minimization of annual cost and CO2 emissions as two potentially conflicting objectives and the energy technologies’ capacities as decision variables. For the target year 2025, EnergyPLAN+MOEA has allowed to model a range of 0-100 % decarbonization solutions characterized by optimal penetration mix of 22 technologies in the electrical, thermal, hydrogen feedstock and transport demand. A set of nine scenarios, with different land use availabilities and implementable technologies, each consisting of 100 optimal systems out of 10000 simulated ones, has been evaluated. The results show, on the one hand the possibility of achieving medium-high decarbonization solutions at costs close to current ones, on the other, how the decarbonization pathways strongly depend on the available land for solar thermal, photovoltaic and wind, as well as the presence of a biomass supply chain in the region

Urban green and blue space changes : a spatiotemporal evaluation of impacts on ecosystem service value in Bangladesh

The rapid decline in urban green (UGS) and blue space (UBS) in developing countries has led to a widespread degradation of available ecosystem services (ES). However, impacts of UGS and UBS changes on ES tend to vary over space and time, and to date these impacts have not been studied in sufficient detail in emerging economies. By comparing UGS and UBS change patterns with multitemporal Landsat data recorded during the past 30 years (1991–2021), this study has examined the impact of several factors on ES in some of the world's climate hotspots. Although obtaining relevant and accurate information on ES is difficult in many parts of the developing world, this work has developed baseline data suitable for assessing ES loss over five densely populated cities in Bangladesh – Dhaka, Chattogram, Khulna, Rajshahi, and Sylhet. ES loss was quantified in monetary terms using adjusted value coefficients. The topographic and anthropogenic factors driving spatial differences in ES degradation in these cities were analyzed with a geographical detector. The results indicated that the cities experienced a combined monetary loss of USD 628.58 million as a result of specific ES degradation, primarily due to the decline of UGS and UBS. The value of ES loss was notably higher in Dhaka and Chattogram than in the other cities due to marked differences in anthropogenic activities. Population growth, extensive urban sprawl, and the development of dense road networks were identified as the major causes of urban green and blue space loss and consequent reduction of ES. The findings of this study provide important insights which can be used to support the formulation of public policies and management plans aimed at restoring and maintaining sustainable urban ecosystems

RELEASE OF MICRO- AND NANOSCALE PLASTICS FROM SYNTHETIC TEXTILES DURING LAUNDRY AND QUANTIFICATION OF NANOSCALE PLASTICS BY SINGLE PARTICLE INDUCTIVELY COUPLED PLASMA MASS SPECTROMETRY

Plastic wastes released in the environment can produce microplastics (MPs, Size \u3c 5 mm) and nanoscale plastics (NPs, Size \u3c 100 nm) due to the environmental weathering processes. The presence of the MPs and NPs have been found worldwide in different aquatic and terrestrial environments. These tiny plastics have detrimental health impacts when they are ingested or inhaled by aquatic organisms as well as human beings. However, their occurrences including identification and quantification in the environment are still a great challenge. Particularly, quantification for NPs is a challenge, as there is no standard technique available yet that can count the NPs effectively. Therefore, this thesis was focused on two important aspects related to microplastics (MPs) and nanoscale plastics (NPs). Firstly, assess the source of MPs or NPs release and secondly, NPs quantification. Microfibers (MFs) are one of the most abundant portions of MPs in the aquatic environment, which are shed during the washing and drying of fabrics. Hence, in the first area of the study, the release pattern of MPs, in the form of acrylic MFs from portable washer and dryer during fabric washing and drying under different conditions were investigated. Additionally, the subsequent degradations behavior of these released MFs under ultraviolet light (UV-A) irradiation were explored. The results indicated that the MFs were released almost 2 times higher when the fabrics were washed for 60 min compared to 30 min due to higher mechanical stresses. In addition, MFs released were increased by 1.4 times higher when the fabrics were dried for 60 min compared to 30 min due to longer rotational forces on the fabrics. The use of detergent during washing promoted 2.7 times more MF release compared to without detergent. Moreover, MFs were released approximately 1.8 times higher from washing when washed with 40°C of water than with 20°C of water. However, subsequent washing cycles showed decreasing patterns of MF releases during washing and drying, approximately 45% less and 67% less, respectively in the 7th wash compared to the 1st wash as the fabrics approach a plateau. The released acrylic MFs were analyzed after their exposure to UV-A irradiation in the aquatic environment from 0 day to 182 days. After 182 days of UV-A irradiation, released acrylic MFs showed significant changes in the surface morphology in the form of cracks, holes, and flakes determined by scanning electron microscope (SEM). The formations of cracks, cavities, and flakes in the MF’s surface were proportional to the period of UV-A exposure. Dimensions of the formed holes and cracks on the UV-A degraded MFs suggested that MFs can turn into NPs in presence of water and UV-A exposure in the environment. Hence, a robust analytical tool must be optimized to detect these tiny degraded NPs in the aquatic environment. This brings to the second area of the study which aimed to optimize and validate a method to detect NPs through coating with synthesized gold nanoparticles (AuNPs) by Single Particle Inductively Coupled Plasma Mass Spectrometry (SP-ICP-MS). This study successfully detected the polystyrene nanoscale plastics (PS NPs, size 61 nm) by particle-by-particle analysis in single quadrupole-based SP-ICP-MS and the detection limit of particle number concentration was reached up to 8.64 × 10^7 particles/L. PS NPs were selected as a model nanoscale plastic as it is one of the most abundant plastics in the environment. The method was applied to PS NPs in deionized (DI) water which achieved a good amount of PS NP recoveries by up to 98%. This analytical technique can be further optimized and might be helpful for analyzing NPs in any environmental samples to determine their occurrences and concentrations

Development of innovative tools for multi-objective optimization of energy systems

From industrial revolution to the present day, fossil fuels are the main sources for ensuring energy supply. Fossil fuel usages have negative effects on environment that are highlighted by several local or international policy initiatives at support of the big energy transition. The effects urge energy planners to integrate renewable energies into the corresponding energy systems. However, large-scale incorporation of renewable energies into the systems is difficult because of intermittent behaviors, limited availability and economic barriers. It requires intricate balancing among different energy producing resources and the syringes among all the major energy sectors. Although it is possible to evaluate a given energy scenario (complete set of parameters describing a system) by using a simulation model, however, identifying optimal energy scenarios with respect to multiple objectives is a very difficult to accomplished. In addition, no generalized optimization framework is available that can handle all major sectors of an energy system. In this regards, we propose a complete generalized framework for identifying scenarios with respect to multiple objectives. The framework is developed by coupling a multi-objective evolutionary algorithm and EnergyPLAN. The results show that the tool has the capability to handle multiple energy sectors together; moreover, a number of optimized trade-off scenarios are identified. Furthermore, several improvements are proposed to the framework for finding better-optimized scenarios in a computationally efficient way. The framework is applied on two different real-world energy system optimization problems. The results show that the framework is capable to identify optimized scenarios both by considering recent demands and by considering projected demands. The proposed framework and the corresponding improvements make it possible to provide a complete tool for policy makers for designing optimized energy scenarios. The tool can be able to handle all major energy sectors and can be applied in short and long-term energy planning

Designing optimized energy scenarios for an Italian Alpine valley: the case of Giudicarie Esteriori

The design of future local energy scenarios, under the framework of covenant of mayors' initiative, is an important and challenging task for the energy and policy planners. Designing energy scenarios is a multi-objective optimization problem, hence, a framework that combines a multi-objective evolutionary algorithm and EnergyPLAN is employed to identify optimized scenarios. In this study, optimized scenarios for the policy makers of Giudicarie Esteriori are identified, so that they are able to face the challenges of minimizing energy costs and CO2 emissions, decreasing the dependency on foreign resources, and integrating large amount of renewable energy. The results show that economically attractive, environmental friendly and less dependent energy scenarios can be achieved by 1) increasing the capacity of photovoltaics, 2) maximizing local biomass usage through individual wood boilers, and 3) partially electrifying the thermal sector through ground source heat pumps. The modification of the transport sector by introducing electric cars is not economically viable under the current market conditions. Our kind of study can be performed for the policy makers of other regions as well, by 1) collecting energy data, 2) identifying local renewable resources, 3) modelling reference scenarios, 4) identifying optimized scenarios, 5) studying the scenarios according to the requirements

Multiple Myeloma Causing Lytic Lesions in Skull

Multiple myeloma causing lytic lesions in skull

An innovative multi-objective optimization approach for long-term energy planning

Designing future energy scenarios is an important topic to energy planners. As designing future optimized scenarios is a multi-objective optimization problem; therefore, it is required to identify trade-off scenarios (Pareto-front) in order to optimize conflicting objectives. In this study, three Pareto-fronts are identified for designing future scenarios for Val di Non (VdN) for three different time horizons. As the community has to reach different emission targets in different time horizons, it is require to select the optimized scenarios that fulfill the targets. In this regards, we propose a new approach for selecting scenarios based on maximizing decision space diversity in order to provide a diverse set of scenarios to the decision makers. The technique is tested on optimized scenarios of VdN and three sets containing 10 diverse scenarios for different time horizons are selected. Moreover, a smooth transition (in terms of decision variables) is desirable when having a transition from a scenario from one time horizon to a consecutive time horizon. A novel method is proposed to choose scenarios from the sets for a smooth transition based on minimizing distances among the scenarios. The approach is applied on VdN where transient scenarios are identified among different possible optimized scenarios

Combining multi-objective evolutionary algorithms and descriptive analytical modelling in energy scenario design

Environmental and security concerns urge energy planners to design more sustainable energy systems, reducing fossil fuel consumptions in favour of renewable solutions. The proposed scenarios typically rely on a mixing of different energy sources, thereby mitigating the availability and intermittency problems typically related to renewable technologies. Optimizing this combination is of crucial importance to cope with economic, technical, and environmental issues, which typically give rise to multiple contradictory objectives. To this purpose, this article presents a generalized framework coupling EnergyPLAN – a descriptive analytical model for medium/large-scale energy systems – with a multi-objective evolutionary algorithm – a type of optimizer widely used in the context of complex problems. By using this framework, it is possible to automatically identify a set of Pareto-optimal configurations with respect to different competing objectives. As an example, the method is applied to the case of Aalborg municipality, Denmark, by choosing cost and carbon emission minimization as contrasting goals. Results are compared with a manually identified scenario, taken from previous literature. The automatic approach, while confirming that the available manual solution is very close to optimality, yields an entire set of additional optimal solutions, showing its effectiveness in the simultaneous analysis of a wide range of combinations

Energy efficiency and sustainability assessment of about 500 small and medium-sized enterprises in Central Europe region

Nowadays more than 20 million small and medium-sized enterprises (SMEs) are located in the European Union (EU): they are a key driver for economic growth, innovation, employment and social integration. The first step towards better industrial energy and environmental performance is the acknowledgement of the savings potential that can be realized by the company ownership and management board. Following this goal, the CEEM (Central Environmental and Energy Management) project provided to about 500 Central Europe SMEs of 5 countries (Austria, the Czech Republic, Hungary, Italy and Slovenia) a user friendly and free of charge web tool called 3EMT (Eco Energy Efficiency Management Tool). Through a questionnaire the 3EMT analyzes and sums up eco-energy patterns, helps to self-assess the company environmental performance, benchmarks a company with other Central Europe’s enterprises, delivers a customized Assessment Report, suggests further services and facilities. In this paper, the large 3EMT database is analyzed, and statistics focused on SMEs eco-energy performance and future & innovation perspectives are presented and discussed. Furthermore, key challenges and key intervention points were defined by local stakeholders (policy makers, Energy Service Companies, SMEs representatives, researchers), this paper summarizes the main policy outcomes