Search results for: amazon forest

Authors: Joyce Onoromhenre Agofure

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Research has shown how fictional work and films reflect the destruction of the environment due to the exploitation of oil, gas, gold, and forest products by multinational companies for profits but overlook discussions on conflict resolution and peacebuilding. However, this paper examines the manner art forms project peace and conflict resolution, thereby contributing to mediation and stability geared towards changing appalling situations in the resource extraction regions of Nigeria. This paper draws from selected Nigerian films- Blood and Oil (2019), directed by Curtis Graham, Black November (2012), directed by Jeta Amata, and a novel- Death of Eternity (2007), by Adamu Kyuka Usman. The study seeks to show that the disruptions caused in the natural resource regions of Nigeria have not only left adverse effects on the social well-being of the people but require resolutions through means of peacebuilding. By adopting the theoretical insights of Social Conflict, this paper focuses on artistic processes that enhance peacebuilding and conflict resolution in non-violent ways by using scenes, visual effects, themes, and images that can educate by shaping opinions, influencing attitudes, and changing ideas and behavioral patterns of individuals and communities. Put together; the research will open up critical perceptions brought about by the artists of study to shed light on the dire need to sustain peace and actively participate in conflict resolution in natural resource extraction spaces.

Keywords: natural resource, extraction, conflict resolution, peace building

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Authors: Junyu Chen, Peng Xu

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In the context of global efforts to enhance building energy efficiency, accurate thermal load forecasting is crucial for both device sizing and predictive control. Variable Refrigerant Flow (VRF) systems are widely used in buildings around the world, yet VRF zonal load prediction has received limited attention. Due to differences between VRF zones in building-level prediction methods, zone-level load forecasting could significantly enhance accuracy. Given that modern VRF systems generate high-quality data, this paper introduces transfer learning to leverage this data and further improve prediction performance. This framework also addresses the challenge of predicting load for building zones with no historical data, offering greater accuracy and usability compared to pure white-box models. The study first establishes an initial variable set of VRF zonal building loads and generates a foundational white-box database using EnergyPlus. Key variables for VRF zonal loads are identified using methods including SRRC, PRCC, and Random Forest. XGBoost and LSTM are employed to generate pre-trained black-box models based on the white-box database. Finally, real-world data is incorporated into the pre-trained model using transfer learning to enhance its performance in operational buildings. In this paper, zone-level load prediction was integrated with transfer learning, and a framework was proposed to improve the accuracy and applicability of VRF zonal load prediction.

Keywords: zonal load prediction, variable refrigerant flow (VRF) system, transfer learning, energyplus

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Authors: Lavanya Annadi, Fahimeh Jafari

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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.

Keywords: road safety, crash prediction, exploratory analysis, machine learning

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Authors: N. Tumenbayeva

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At the present time in Kazakhstan, the area for saxaul growing is strongly depopulateddue to anthropogenic and other factors. To prevent further reduction of natural haloxylon forest area their artificial crops are offered. Seed germination and survival of young plants in such haloxylon crops are very low. Insects, as one of the most important nutrient factors have appreciable effect on seed germination and saxaul productivity at the all stages of its formation. Insects, feeding on leaves, flowers, seeds and developing inside the trunk, branches, twigs, roots have a change in its formation and influence on the lifespan of saxaul. Representatives of Lepidoptera troop (Lepidopteraare the most harmful pests forsaxaul. As a result of our research we have identified 15 species of Lepidoptera living on haloxylon which display very different cycles and different types of food relations. It allows them to inhabit a variety of habitats, and feeding on various parts of saxaul. Some of them cause significant and sometimes very heavy damage for saxaul. There are 17identified species of Lepidoptera from the Coleophoridaefamily - 1, Gelechidae - 5, Pyralidae - 4, Noctuidae - 4, Lymantridae- 1, Cossidae - 2 species. At the same time we found 8 species for the first time, which have not been mentioned in the literature before. According to food specialization they are divided into monophages (2 types), oligophages (6 species) and polyphages (3 species). By affinity to plant parts, leaves and seeds are fed by 8 species, shoots by 1 specie, scions by 5 species, flowers, scions, seeds by 1, and 2species damage the roots and trunks. In whole installed seasonal groups of Lepidoptera - saxaul pests in the desert area, confined to the certain parts of the year, as well as certain parts of the plant for feeding. Harmfulness, depending on their activity appear during the growing season is also different.

Keywords: saxaul, Lepidoptera, insecta, haloxylon

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Authors: Jovana M. Ćirković, Aleksandar M. Radojković, Sanja Z. Perać, Jelena N. Jovanović, Zorica M. Branković, Slobodan D. Milanović, Ivan Lj. Milenković, Jovan N. Dobrosavljević, Nemanja V. Simović, Vanja M. Tadić, Ana R. Žugić, Goran O. Branković

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The protection of forests is a national interest and of strategic importance in every country. The spongy moth (Lymantria dispar) is a damaging invasive pest that can weaken and destroy trees by defoliating them. Chemical pesticides commonly used to protect forests against spongy moths not only have a negative impact on terrestrial and aquatic organisms/ecosystems but also often fail to provide significant protection. Therefore, many eco-friendly alternatives have been considered. Within this research, a new biopesticide was developed based on the method of nanoencapsulation of plant extracts in a biopolymer matrix, which provides a slow release of the active components during a substantial time period. The antifeedant activity of plant extracts of common (Fraxinus excelsior L.), manna (F. ornus L.) ash tree, and the tree of heaven Ailanthus altissima (Mill.) was tested on the spongy moth (Lymantria dispar L, 1758) larvae. To test the antifeedant activity of these compounds, the choice and non-choice tests in laboratory conditions for different plant extract concentrations (0.01, 0.1, 0.5, and 1 % v/v) were carried out. In both cases, the best results showed formulations based on the tree of heaven and common ash for the concentration of 1%, with deterioration indices of 163 and 132, respectively. The main benefit of these formulations is their versatility, effectiveness, prolonged effect, and because they are completely environmentally acceptable. Therefore, they can be considered for suppression of the spongy moth in forest ecosystems.

Keywords: Ailanthus altissima (Mill.), Fraxinus excelsior L., encapsulation, Lymantria dispar

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Authors: Pablo Chico De Guzman, Cesar Sanchez

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Microservice architectures are increasingly in distributed cloud applications due to the advantages on the software composition, development speed, release cycle frequency and the business logic time to market. On the other hand, these architectures also introduce some challenges on the testing and release phases of applications. Container technology solves some of these issues by providing reproducible environments, easy of software distribution and isolation of processes. However, there are other issues that remain unsolved in current container technology when dealing with multiple machines, such as networking for multi-host communication, service discovery, load balancing or data persistency (even though some of these challenges are already solved by traditional cloud vendors in a very mature and widespread manner). Container cluster management tools, such as Kubernetes, Mesos or Docker Swarm, attempt to solve these problems by introducing a new control layer where the unit of deployment is the container (or the pod — a set of strongly related containers that must be deployed on the same machine). These tools are complex to configure and manage and they do not follow a pure immutable infrastructure approach since servers are reused between deployments. Indeed, these tools introduce dependencies at execution time for solving networking or service discovery problems. If an error on the control layer occurs, which would affect running applications, specific expertise is required to perform ad-hoc troubleshooting. As a consequence, it is not surprising that container cluster support is becoming a source of revenue for consulting services. This paper presents i2kit, a deployment tool based on the immutable infrastructure pattern, where the virtual machine is the unit of deployment. The input for i2kit is a declarative definition of a set of microservices, where each microservice is defined as a pod of containers. Microservices are built into machine images using linuxkit —- a tool for creating minimal linux distributions specialized in running containers. These machine images are then deployed to one or more virtual machines, which are exposed through a cloud vendor load balancer. Finally, the load balancer endpoint is set into other microservices using an environment variable, providing service discovery. The toolkit i2kit reuses the best ideas from container technology to solve problems like reproducible environments, process isolation, and software distribution, and at the same time relies on mature, proven cloud vendor technology for networking, load balancing and persistency. The result is a more robust system with no learning curve for troubleshooting running applications. We have implemented an open source prototype that transforms i2kit definitions into AWS cloud formation templates, where each microservice AMI (Amazon Machine Image) is created on the fly using linuxkit. Even though container cluster management tools have more flexibility for resource allocation optimization, we defend that adding a new control layer implies more important disadvantages. Resource allocation is greatly improved by using linuxkit, which introduces a very small footprint (around 35MB). Also, the system is more secure since linuxkit installs the minimum set of dependencies to run containers. The toolkit i2kit is currently under development at the IMDEA Software Institute.

Keywords: container, deployment, immutable infrastructure, microservice

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Authors: Franklin N. Mabe, Samuel A. Donkoh, Seidu Al-Hassan

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In order to increase and bridge the differences in rice yield, many farmers have resorted to adopting Farmer Innovation Systems (FISs) and Improved Agricultural Technologies (IATs). This study econometrically analysed the impacts of adoption of FISs and IATs on rice yield using multinomial endogenous switching regression (MESR). Nine-hundred and seven (907) rice farmers from Guinea Savannah Zone (GSZ), Forest Savannah Transition Zone (FSTZ) and Coastal Savannah Zone (CSZ) were used for the study. The study used both primary and secondary data. FBO advice, rice farming experience and distance from farming communities to input markets increase farmers’ adoption of only FISs. Factors that increase farmers’ probability of adopting only IATs are access to extension advice, credit, improved seeds and contract farming. Farmers located in CSZ have higher probability of adopting only IATs than their counterparts living in other agro-ecological zones. Age and access to input subsidy increase the probability of jointly adopting FISs and IATs. FISs and IATs have heterogeneous impact on rice yield with adoption of only IATs having the highest impact followed by joint adoption of FISs and IATs. It is important for stakeholders in rice subsector to champion the provision of improved rice seeds, the intensification of agricultural extension services and contract farming concept. Researchers should endeavour to researched into FISs.

Keywords: farmer innovation systems, improved agricultural technologies, multinomial endogenous switching regression, treatment effect

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Authors: Mudasir Gani, Rakesh Kumar Gupta, Kamlesh Bali, Abdul Rouf Wani

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The gypsy moth (Lymantria obfuscata) and tent caterpillar (Malacosoma indicum) are serious pests that attack a wide range of fruit and forest trees in Jammu & Kashmir range of North-Western Himalayas in India. Investigations were carried out to isolate and bioprospect naturally occurring nucleopolyhedroviruses (NPVs) as potent biopesticides against these pests. The biological and molecular characterization of NPV isolates from different ecosystems was conducted, and the polh, lef-8 and lef-9 genes were sequenced and subjected to phylogenetic analysis. The L. obfuscata NPV was more closely related to the L. dispar NPV, whereas M. indicum NPV was more closely related to the M. californicum NPV in the NCBI taxonomy database. Among different isolates, Bhaderwah isolates exhibited highest virus activity (LD₅₀ = 250 POBs/larvae) and speed of kill (ST₅₀ = 6.80 days) against L. obfuscata whereas Mahor isolates proved most virulent against M. indicum, with lowest LD₅₀ (257 POBs/larva) and ST₅₀ (6.80 days). The in vivo mass production for highest productivity and quality revealed that the optimum yield was obtained when 3rd instar larvae were inoculated with a viral dose of 1.44 × 105 POBs/larva and allowed to incubate for nine days for L. obfuscata. However, for M. indicum larvae, a viral dose of 2.88 × 10⁶ POBs/larva and incubation period of 10 days were found optimum. It was found that harvesting of moribund larvae yields good quality NPV. The field application of L. obfuscata NPV and M. indicum NPV against the respective host populations on apple and willow with the pre-standardized dosage of 1 × 10¹² POBs/acre reduced the larval population density up to 25-63%.

Keywords: baculoviruses, biopesticides, Lymantria obfuscata, Malacosoma indicum

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Authors: Pinal Patel

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The study defines the problems and issues of women in Maoist groups, also referred as ‘Naxalites’, in Chhattisgarh, India. It analyses the causes and consequences of increasing number of women joining Maoists groups and measures taken by the central and state government to retreat them. The main aspect of the study is, how to counter the challenges to resolve the issues and restore normalcy in the life of women Maoists to resettle them in mainstream once they become physically inactive and wish to become part of the society. The rationale behind this study is that women Maoists once inactive, has no place either with Maoist camps/rebel groups or particularly in society. The problems faced by the women Maoists, in society as well as in Maoists camps, can be studied through social, economic, cultural, political and humanitarian aspects. The methodology of the study is dependent on primary sources of information which includes a research survey in majorly affected areas, statistical analysis. Secondary sources of information are helpful for understanding the background of the problem. Government’s strategy of rewarding with cash and providing resettlement and rehabilitation benefits including houses and jobs to ex-women Maoists and their families is a well formulated and feasible policy and effectively implemented by the concerned authorities. But, the survey results show that the policy has not been able to have impacts as it was intended. Because inactive and physically disabled women are still left deserted in deep forests to die and police or authorities are not able to reach them and bring them back. The difficult terrain and dense forest areas are major hurdles to reach to Maoists camps. Moreover, to make people aware of government’s surrendering and rehabilitation schemes and policies as communication networks are very poor due to the lack of development in the state.

Keywords: maoists, women, government, policy

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Authors: T. S. Desak Ketut, A.n. Isna, A.a. Ayu, D. P. Ririn, Suharjono Hadiatullah

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The requirement of paper from year to year rise rapidly. The raising of cellulose requirement in paper production caused increasing of wood requirement with the effect that limited forest areal because of deforestation. Alternative cellulose that can be used for making paper is microbial cellulose. The objective of this research are to know the effectivity fermentation media Spirogyra sp. by Gluconacetobacter xylinum for cellulose production as material for the making of paper and to know effect composition bacterial cellulose composite product of Gluconacetobacter xylinum in Spirogyra sp. The method, was used, is as follow, 1) the effect assay from variation composition of fermentation media to bacterial cellulose production by Gluconacetobacter xylinum. 2) The effect assay of composition bacterial cellulose fermentation producted by Gluconacetobacter xylinum in extracted Spirogyra media to paper quality. The result of this research is variation fermentation media Spirogyra sp. affect to production of cellulose by Gluconacetobacter xylinum. Thus, result showed by the highest value and significantly different in thickness parameter, dry weight and wet weight of nata in sucrose concentration 7,5 % and urea 0,75 %. Composition composite of bacterial cellulose from fermentation product by Gluconacetobacter xylinum in media Spirogyra sp. affect to paper quality from wet nata and dry nata. Parameters thickness, weight, water absorpsion, density and gramatur showed highest result in sucrose concentration 7,5 % and urea concentration 0,75 %, except paper density from dry nata had highest result in sucrose and urea concentration 0%.

Keywords: cellulose, fermentation media, , Gluconacetobacter xylinum, paper, Spirogyra sp.

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Authors: Ashwinder K. Dhaliwal, G. P. S. Dhillon

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Eucalyptus is one of the most important forest tree species that can tolerate and grow well on degraded and unfertile soils which are not suitable for other tree species. Besides this, these trees have a short rotation and good economic value. However, the gall inducing wasp Leptocybe invasa Fisher and La Salle has been reported from many countries throughout the world. The spread of L. invasa is of huge economic concern as more than 20,000 ha of young Eucalyptus trees have already been affected in southern states of India. The host plant resistance being the first line of defense against insect pests demands the screening of different germplasm source against L. invasa. Keeping this in view, fourteen different clones of Eucalyptus spp. were evaluated for their susceptibility to L. invasa from a replicated clonal trial planted at Punjab Agricultural University, Ludhiana. The degree of gall infestation was recorded from three plants of each clone in each replication. Three branches selected from the lower, middle and upper canopy of the trees were selected for recording the total number of galls induced by L. invasa. The statistical analysis was done as per the procedure laid down for completely randomised block design (CRBD), analysis of variance (ANOVA), critical difference (CD) and variance components using Proc GLM (SAS software 9.3, SAS Institute Ltd. U.S.A). All possible treatment means were compared with Duncan’s multiple range test (DMRT) at 1 % probability level. The results showed that the clones C-9, C-45 and C-42 were completely free from the infestation of L. invasa. However, there was minor infestation of L. invasa on C-2135, C-413, C-407, C-35, C-72 and C-37 clones. The clone C-6 was severely infested by L. invasa followed by C-11, C-12, F-316 and C-25 clones. The information generated by this study will be helpful for future breeding and use in afforestation programmes.

Keywords: eucalyptus clones, gall wasp, Leptocybe invasa, screening, susceptibility

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: Tahmidul Islam

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Propensity Score Matching (PSM) technique has been widely used for estimating causal effect of treatment in observational studies. One major step of implementing PSM is estimating the propensity score (PS). Logistic regression model with additive linear terms of covariates is most used technique in many studies. Logistics regression model is also used with cubic splines for retaining flexibility in the model. However, choosing the functional form of the logistic regression model has been a question since the effectiveness of PSM depends on how accurately the PS been estimated. In many situations, the linearity assumption of linear logistic regression may not hold and non-linear relation between the logit and the covariates may be appropriate. One can estimate PS using machine learning techniques such as random forest, neural network etc for more accuracy in non-linear situation. In this study, an attempt has been made to compare the efficacy of Generalized Additive Model (GAM) in various linear and non-linear settings and compare its performance with usual logistic regression. GAM is a non-parametric technique where functional form of the covariates can be unspecified and a flexible regression model can be fitted. In this study various simple and complex models have been considered for treatment under several situations (small/large sample, low/high number of treatment units) and examined which method leads to more covariate balance in the matched dataset. It is found that logistic regression model is impressively robust against inclusion quadratic and interaction terms and reduces mean difference in treatment and control set equally efficiently as GAM does. GAM provided no significantly better covariate balance than logistic regression in both simple and complex models. The analysis also suggests that larger proportion of controls than treatment units leads to better balance for both of the methods.

Keywords: accuracy, covariate balances, generalized additive model, logistic regression, non-linearity, propensity score matching

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Authors: Diptimayee Nayak

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Ecotourism has been recognised as a popular component of alternative tourism, which claims to guard host local environment and economy. This concept of ecological tourism (eco-tourism) has become more meaningful in evaluating the recreational function and services of any pristine ecosystem in context of ‘The Economics of Ecosystem and Biodiversity (TEEB)’. This ecotourism is said to be a local solution to the global problem of conserving ecosystems and optimising the utilisations of their services. This paper takes a case of recreational services of an Indian protected area ecosystems ‘Bhitarakanika mangrove protected area’ discussing how ecotourism is functioning taking the perspectives of different stakeholders. Specific stakeholders are taken for analysis, viz., tourists and local people, as they are believed to be the major beneficiaries of ecotourism. The stakeholders’ analysis is evaluated on the basis of travel cost techniques (by using truncated Poisson distribution model) for tourists and descriptive and analytical tools for local people. The evaluation of stakeholders’ analysis of ecotourism has gained its impetus after the formulation of Ecotourism guidelines by the Ministry of Environment and Forest (MoEF), Government of India. The paper concludes that ecotourism issues and challenges are site-specific and region-specific; without critically focussing challenges of ecotourism faced at local level the discourses of ecotourism at global level cannot be tackled. Mere integration and replication of policies at global level to be followed at local level will not be successful (top down policies). Rather mainstreaming the decision making process at local level with the global policy stature helps to solve global issues to a bigger extent (bottom up).

Keywords: ecosystem services, ecotourism, TEEB, economic valuation, stakeholders, travel cost techniques

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Authors: Carolyn Hatch, Laurent Simon

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Urban mobility and the transportation industry are undergoing a transformation, shifting from an auto production-consumption model that has dominated since the early 20th century towards new forms of personal and shared multi-modality [1]. This is shaped by key forces such as climate change, which has induced a shift in production and consumption patterns and efforts to decarbonize and improve transport services through, for instance, the integration of vehicle automation, electrification and mobility sharing [2]. Advanced innovation practices and platforms for experimentation and validation of new mobility products and services that are increasingly complex and multi-stakeholder-oriented are shaping this new world of mobility. Transportation hubs – such as airports - are emblematic of these disruptive forces playing out in the mobility industry. Airports are emerging as the core of innovation ecosystems on and around contemporary mobility issues, and increasingly recognized as complex public/private nodes operating in many societal dimensions [3,4]. These include urban development, sustainability transitions, digital experimentation, customer experience, infrastructure development and data exploitation (for instance, airports generate massive and often untapped data flows, with significant potential for use, commercialization and social benefit). Yet airport innovation practices have not been well documented in the innovation literature. This paper addresses this gap by proposing a model of airport innovation that aims to equip airport stakeholders to respond to these new and complex innovation needs in practice. The methodology involves: 1 – a literature review bringing together key research and theory on airport innovation management, open innovation and innovation ecosystems in order to evaluate airport practices through an innovation lens; 2 – an international benchmarking of leading airports and their innovation practices, including such examples as Aéroports de Paris, Schipol in Amsterdam, Changi in Singapore, and others; and 3 – semi-structured interviews with airport managers on key aspects of organizational practice, facilitated through a close partnership with the Airport Council International (ACI), a major stakeholder in this research project. Preliminary results find that the most successful airports are those that have shifted to a multi-stakeholder, platform ecosystem model of innovation. The recent entrance of new actors in airports (Google, Amazon, Accor, Vinci, Airbnb and others) have forced the opening of organizational boundaries to share and exchange knowledge with a broader set of ecosystem players. This has also led to new forms of governance and intermediation by airport actors to connect complex, highly distributed knowledge, along with new kinds of inter-organizational collaboration, co-creation and collective ideation processes. Leading airports in the case study have demonstrated a unique capacity to force traditionally siloed activities to “think together”, “explore together” and “act together”, to share data, contribute expertise and pioneer new governance approaches and collaborative practices. In so doing, they have successfully integrated these many disruptive change pathways and forced their implementation and coordination towards innovative mobility outcomes, with positive societal, environmental and economic impacts. This research has implications for: 1 - innovation theory, 2 - urban and transport policy, and 3 - organizational practice - within the mobility industry and across the economy.

Keywords: airport management, ecosystem, innovation, mobility, platform, transport hubs

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Authors: Arun Jyothi Bheemagani, Chakrapani Pullagummi, Anupalli Roja Rani

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Exploration of the chemical constituents of the plants and pharmacological screening may provide us the basis for the development of novel agents. Plants have provided us some of the very important life saving drugs used in the modern medicine. The aim of our work was to screen the phytochemical constituents and antimicrobial and antioxidant activities of methanol extract of leaves of Annona cherimola Mill plant from Tirumala forest, Tirupathi. It was originally called Chirimuya by the Inca people who lived where it was growing in the Andes of South America, is an edible fruit-bearing species of the genus Annona from the family Annonaceae. Annona cherimola Mill is a multipurpose tree with edible fruits and is one of the sources of the medicinal products. The antibacterial activity was measured by agar well diffusion method; the diameter of the zone of bacterial growth inhibition was measured after incubation of plates. The inhibitory effect was studied against the pathogenic bacteria (Klebsiella pneumonia, Bacillus subtilis, Staphylococcus aureus and Escherichia coli (E. coli). Antioxidant assays were also performed for the same extracts by spectrophotometric methods using known standard antioxidants as reference. The studied plant extracts were found to be very effective against the pathogenic microorganisms tested. The methanolic extract of Annona cherimola Mill from showed maximum activity against Escherichia coli and Staphylococcus aureus and the least concentration required showing the activity was 0.5mg/ml. Phytochemical screening of the plants revealed the presence of flavonoids, alkaloids, steroids and carbohydrates. Good presence of antioxidants was also found in the methanolic extracts.

Keywords: annona cherimola, phytochemicals, antioxidant and antibacterial activity, methanol extract

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Authors: Sahil Patel

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Increasing urbanization leads to a concrete forest. The effects of new development practices occur in the natural hydrologic cycle. Here the concerns have been raised about the groundwater recharge in sufficient quantity. With further development, porous surfaces reduce rapidly. A city like Ahmedabad, with a non-perennial river, is 100% dependent on groundwater. The Ahmedabad city receives its domestic use water from the Narmada river, located about 200 km away. The expenses to bring water is much higher. Ahmedabad city receives annually 800 mm rainfall, and mostly this water increases the local level waterlogging problems; after that, water goes to the Sabarmati river and merges into the sea. The existing developed area of Ahmedabad city is very dense, and does not offer many chances to change the built form and increase porous surfaces to absorb storm-water. Therefore, there is a need to plan upcoming areas with more effective solutions to manage storm-water. This paper is focusing on the management of stormwater for new development by retaining natural hydrology. The Low Impact Development (LID) concept is used to manage storm-water efficiently. Ahmedabad city has a tool called the “Town Planning Scheme,” which helps the local body drive new development by land pooling mechanism. This paper gives a detailed analysis of the selected area (proposed Town Planning Scheme area by the local authority) in Ahmedabad. Here the development control regulations for individual developers and some physical elements for public places are presented to manage storm-water. There is a different solution for the Town Planning scheme than that of the conventional way. A local authority can use it for any area, but it can be site-specific. In the end, there are benefits to locals with some financial analysis and comparisons.

Keywords: water management, green field development, low impact development, town planning scheme

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Authors: S. Begum, T. Biswas, M. A. Islam

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The present research investigates the distribution and contamination of heavy metals in core sediments collected from three locations of the Sundarbans mangrove forest. In this research, quality of the analysis is evaluated by analyzing certified reference materials IAEA-SL-1 (lake sediment), IAEA-Soil-7, and NIST-1633b (coal fly ash). Total concentrations of 28 heavy metals (Na, Al, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Zn, Ga, As, Sb, Cs, La, Ce, Sm, Eu, Tb, Dy, Ho, Yb, Hf, Ta, Th, and U) have determined in core sediments of the Sundarbans mangrove by neutron activation analysis (NAA) technique. When compared with upper continental crustal (UCC) values, it is observed that mean concentrations of K, Ti, Zn, Cs, La, Ce, Sm, Hf, and Th show elevated values in the research area is high. In this research, the assessments of metal contamination levels using different environmental contamination indices (EF, Igeo, CF) indicate that Ti, Sb, Cs, REEs, and Th have minor enrichment of the sediments of the Sundarbans. The modified degree of contamination (mCd) of studied samples of the Sundarbans ecosystem show low contamination. The pollution load index (PLI) values for the cores suggested that sampling points are moderately polluted. The possible sources of the deterioration of the sediment quality can be attributed to the different chemical carrying cargo accidents, port activities, ship breaking, agricultural and aquaculture run-off of the area. Pearson correlation matrix (PCM) established relationships among elements. The PCM indicates that most of the metal's distributions have been controlled by the same factors such as Fe-oxy-hydroxides and clay minerals, and also they have a similar origin. The poor correlations of Ca with most of the elements in the sediment cores indicate that calcium carbonate has a less significant role in this mangrove sediment. Finally, the data from this research will be used as a benchmark for future research and help to quantify levels of metal pollutions, as well as to manage future ecological risks of the vulnerable mangrove ecosystem, the Sundarbans.

Keywords: contamination, core sediment, trace element, sundarbans, vulnerable

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Authors: Nico Rosamilia

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The study evaluates the importance of non-financial disclosure practices for regulators, investors, businesses, and markets. It aims to create a sector-specific set of indicators for environmental, social, and governance (ESG) performances alternative to the ratings of the agencies. The existing literature extensively studies the implementation of ESG rating systems. Conversely, this study has a twofold outcome. Firstly, it should generalize incentive systems and governance policies for ESG and sustainable principles. Therefore, it should contribute to the EU Sustainable Finance Disclosure Regulation. Secondly, it concerns the market and the investors by highlighting successful sustainable investing. Indeed, the study contemplates the effect of ESG adoption practices on corporate value. The research explores the asset pricing angle in order to shed light on the fragmented argument on the finance of ESG. Investors may be misguided about the positive or negative effects of ESG on performances. The paper proposes a different method to evaluate ESG performances. By comparing the results of a traditional econometric approach (Lasso) with a machine learning algorithm (Random Forest), the study establishes a set of indicators for ESG performance. Therefore, the research also empirically contributes to the theoretical strands of literature regarding model selection and variable importance in a finance framework. The algorithms will spit out sector-specific indicators. This set of indicators defines an alternative to the compounded scores of ESG rating agencies and avoids the possible offsetting effect of scores. With this approach, the paper defines a sector-specific set of indicators to standardize ESG disclosure. Additionally, it tries to shed light on the absence of a clear understanding of the direction of the ESG effect on corporate value (the problem of endogeneity).

Keywords: ESG ratings, non-financial information, value of firms, sustainable finance

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Authors: F. O. Danquah, F. Frimpong, E. Owusu Danquah, T. Frimpong, J. Adu, S. K. Amposah, P. Amankwaa-Yeboah, N. E. Amengor

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In the face of global concerns such as population increase, climate change, and limited natural resources, sustainable agriculture practices are critical for ensuring food security and environmental stewardship. The study was conducted in the Forest zones of Ghana during the major and minor seasons of 2023 cropping seasons to evaluate maize yield productivity improvement and profitability of integrating Cajanus cajan (pigeonpea) into a maize production system described as a pigeonpea-maize cropping system. This is towards an integrated soil fertility management (ISFM) with a legume shrub pigeonpea for sustainable maize production while improving smallholder farmers' resilience to climate change. A split-plot design with maize-pigeonpea (Pigeonpea-Maize intercrop – MPP and No pigeonpea/ Sole maize – NPP) and inorganic fertilizer rate (250 kg/ha of 15-15-15 N-P2O5-K2O + 250 kg/ha Sulphate of Ammonia (SoA) – Full rate (FR), 125 kg/ha of 15-15-15 N-P2O5-K2O + 125 kg/ha Sulphate of Ammonia (SoA) – Half rate (HR) and no inorganic fertilizer (NF) as control) was used as the main plot and subplot treatments respectively. The results indicated a significant interaction of the pigeonpea-maize cropping system and inorganic fertilizer rate on the growth and yield of the maize with better and similar maize productivity when HR and FR were used with pigeonpea biomass. Thus, the integration of pigeonpea and its biomass would result in the reduction of recommended fertiliser rate to half. This would improve farmers’ income and profitability for sustainable maize production in the face of climate change.

Keywords: agroforestry tree, climate change, integrated soil fertility management, resource use efficiency

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Authors: Syed Asif Hassan, Syed Atif Hassan

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Multidrug-resistant Tuberculosis (MDR-TB) is an infection caused by the resistant strains of Mycobacterium tuberculosis that do not respond either to isoniazid or rifampicin, which are the most important anti-TB drugs. The increase in the occurrence of a drug-resistance strain of MTB calls for an intensive search of novel target-based therapeutics. In this context LprG (Rv1411c) a lipoprotein from MTB plays a pivotal role in the immune evasion of Mtb leading to survival and propagation of the bacterium within the host cell. Therefore, a machine learning method will be developed for generating a computational model that could predict for a potential anti LprG activity of the novel antituberculosis compound. The present study will utilize dataset from PubChem database maintained by National Center for Biotechnology Information (NCBI). The dataset involves compounds screened against MTB were categorized as active and inactive based upon PubChem activity score. PowerMV, a molecular descriptor generator, and visualization tool will be used to generate the 2D molecular descriptors for the actives and inactive compounds present in the dataset. The 2D molecular descriptors generated from PowerMV will be used as features. We feed these features into three different classifiers, namely, random forest, a deep neural network, and a recurring neural network, to build separate predictive models and choosing the best performing model based on the accuracy of predicting novel antituberculosis compound with an anti LprG activity. Additionally, the efficacy of predicted active compounds will be screened using SMARTS filter to choose molecule with drug-like features.

Keywords: antituberculosis drug, classifier, machine learning, molecular descriptors, prediction

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Authors: Hongmei Gu, Shaobo Liang, Richard Bergman

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With current worldwide trend in designs to have net-zero emission buildings to mitigate climate change, widespread use of mass timber products, such as Cross Laminated Timber (CLT), or Nail Laminated Timber (NLT) or Dowel Laminated Timber (DLT) in buildings have been proposed as one approach in reducing Greenhouse Gas (GHG) emissions. Consequentially, mass timber building designs are being adopted more and more by architectures in North America, especially for mid- to high-rise buildings where concrete and steel buildings are currently prevalent, but traditional light-frame wood buildings are not. Wood buildings and their associated wood products have tended to have lower environmental impacts than competing energy-intensive materials. It is common practice to conduct life cycle assessments (LCAs) and life cycle cost analyses on buildings with traditional structural materials like concrete and steel in the building design process. Mass timber buildings with lower environmental impacts, especially GHG emissions, can contribute to the Net Zero-emission goal for the world-building sector. However, the economic impacts from CLT mass timber buildings still vary from the life-cycle cost perspective and environmental trade-offs associated with GHG emissions. This paper quantified the Total Life Cycle Cost and cradle-to-grave GHG emissions of a pre-designed CLT mass timber building and compared it to a functionally-equivalent concrete building. The Total life cycle Eco-cost-efficiency is defined in this study and calculated to discuss the trade-offs for the net-zero emission buildings in a holistic view for both environmental and economic impacts. Mass timber used in buildings for the United States is targeted to the materials from the nation’s sustainable managed forest in order to benefit both national and global environments and economies.

Keywords: GHG, economic impact, eco-cost-efficiency, total life-cycle costs

Procedia PDF Downloads 140

Authors: Maria Luiza D. Ramiento, Maria Lissette D. Lucas

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Electricity is said to serve as the backbone of modern technology. Considering this, electricity consumption has dynamically grown due to the continuous demand. An alternative producer of energy concerning electricity must therefore be given focus. Microbial fuel cell wholly characterizes a new method of renewable energy recovery: the direct conversion of organic matter to electricity using bacteria. Electricity is produced as fuel or new food is given to the bacteria. The study concentrated in determining the feasibility of electricity production from local benthic zones. Microbial fuel cells were constructed to harvest the possible electricity and to test the presence of electricity producing microorganisms. Soil samples were gathered from Calumpang River, Palawan Mangrove Forest, Rosario River and Batangas Port. Eleven modules were constructed for the different trials of the soil samples. These modules were made of cathode and anode chambers connected by a salt bridge. For 85 days, the harvested voltage was measured daily. No parameter is added for the first 24 days. For the next 61 days, acetic acid was included in the first and second trials of the modules. Each of the trials of the soil samples gave a positive result in electricity production.There were electricity producing microbes in local benthic zones. It is observed that the higher the organic content of the soil sample, the higher the electricity harvested from it. It is recommended to identify the specific species of the electricity-producing microorganism present in the local benthic zone. Complement experiments are encouraged like determining the kind of soil particles to test its effect on the amount electricity that can be harvested. To pursue the development of microbial fuel cells by building a closed circuit in it is also suggested.

Keywords: microbial fuel cell, benthic zone, electricity, reduction-oxidation reaction, bacteria

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Authors: Bhenjamin Jordan L. Ona

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Weather modification is an act of altering weather systems that catches interest on scientific studies. Cloud seeding is a common form of weather alteration. On the same principle, tropical cyclone mitigation experiment follows the methods of cloud seeding with intensity to account for. This study will present the effects of aerosol to tropical cyclone cloud microphysics and intensity. The framework of Weather Research and Forecasting (WRF) model incorporated with Thompson aerosol-aware scheme is the prime host to support the aerosol-cloud microphysics calculations of cloud condensation nuclei (CCN) ingested into the tropical cyclones before making landfall over the Philippine coast. The coupled microphysical and radiative effects of aerosols will be analyzed using numerical data conditions of Tropical Storm Ketsana (2009), Tropical Storm Washi (2011), and Typhoon Haiyan (2013) associated with varying CCN number concentrations per simulation per typhoon: clean maritime, polluted, and very polluted having 300 cm-3, 1000 cm-3, and 2000 cm-3 aerosol number initial concentrations, respectively. Aerosol species like sulphates, sea salts, black carbon, and organic carbon will be used as cloud nuclei and mineral dust as ice nuclei (IN). To make the study as realistic as possible, investigation during the biomass burning due to forest fire in Indonesia starting October 2015 as Typhoons Mujigae/Kabayan and Koppu/Lando had been seeded with aerosol emissions mainly comprises with black carbon and organic carbon, will be considered. Emission data that will be used is from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). The physical mechanism/s of intensification or deintensification of tropical cyclones will be determined after the seeding experiment analyses.

Keywords: aerosol, CCN, IN, tropical cylone

Procedia PDF Downloads 296

Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

Procedia PDF Downloads 87

Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

Procedia PDF Downloads 147

Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Hamid Ebrahimy

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Large-scale land cover mapping has become a new challenge in land change and remote sensing field because of involving a big volume of data. Moreover, selecting the right classification method, especially when there are different types of landscapes in the study area is quite difficult. This paper is an attempt to compare the performance of different machine learning (ML) algorithms for generating a land cover map of the China-Central Asia–West Asia Corridor that is considered as one of the main parts of the Belt and Road Initiative project (BRI). The cloud-based Google Earth Engine (GEE) platform was used for generating a land cover map for the study area from Landsat-8 images (2017) by applying three frequently used ML algorithms including random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The selected ML algorithms (RF, SVM, and ANN) were trained and tested using reference data obtained from MODIS yearly land cover product and very high-resolution satellite images. The finding of the study illustrated that among three frequently used ML algorithms, RF with 91% overall accuracy had the best result in producing a land cover map for the China-Central Asia–West Asia Corridor whereas ANN showed the worst result with 85% overall accuracy. The great performance of the GEE in applying different ML algorithms and handling huge volume of remotely sensed data in the present study showed that it could also help the researchers to generate reliable long-term land cover change maps. The finding of this research has great importance for decision-makers and BRI’s authorities in strategic land use planning.

Keywords: land cover, google earth engine, machine learning, remote sensing

Procedia PDF Downloads 113

Authors: Hachaichi Mohamed Nour El-Islem, Baouni Tahar

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Environmental sustainability rather than a trans-disciplinary and a scientific issue, is the main problem that characterizes all modern cities nowadays. In developing countries, this concern is expressed in a plethora of critical urban ills: traffic congestion, air pollution, noise, urban decay, increase in energy consumption and CO₂ emissions which blemish cities’ landscape and might threaten citizens’ health and welfare. As in the same manner as developing world cities, the rapid growth of Algiers’ human population and increasing in city scale phenomena lead eventually to increase in daily trips, energy consumption and CO₂ emissions. In addition, the lack of proper and sustainable planning of the city’s infrastructure is one of the most relevant issues from which Algiers suffers. The aim of this contribution is to estimate the carbon deficit of the City of Algiers, Algeria, using the Ecological Footprint Model (carbon footprint). In order to achieve this goal, the amount of CO₂ from fuel combustion has been calculated and aggregated into five sectors (agriculture, industry, residential, tertiary and transportation); as well, Algiers’ biocapacity (CO₂ uptake land) has been calculated to determine the ecological overshoot. This study shows that Algiers’ transport system is not sustainable and is generating more than 50% of Algiers total carbon footprint which cannot be sequestered by the local forest land. The aim of this research is to show that the Carbon Footprint Assessment might be a relevant indicator to design sustainable strategies/policies striving to reduce CO₂ by setting in motion the energy consumption in the transportation sector and reducing the use of fossil fuels as the main energy input.

Keywords: biocapacity, carbon footprint, ecological footprint assessment, energy consumption

Procedia PDF Downloads 147

Authors: Fatemeh Parhizkar, Mojtaba Yamani, Abdolla Behboodi, Masoomeh Hashemi

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The mangrove forests are natural and valuable gifts that exist in some parts of the world, including Iran. Regarding the threats faced by these forests and the declining area of them all over the world, as well as in Iran, it is very necessary to manage and monitor them. The current study aimed to investigate the changes in mangrove forests and the relationship between these changes and the marine hydrodynamics and coastal morphology in the area between qeshm island and the west coast of the Hormozgan province (i.e. the coastline between Mehran river and Bandar-e Pol port) in the 49-year period. After preprocessing and classifying satellite images using the SVM, MLC, and ANN classifiers and evaluating the accuracy of the maps, the SVM approach with the highest accuracy (the Kappa coefficient of 0.97 and overall accuracy of 98) was selected for preparing the classification map of all images. The results indicate that from 1972 to 1987, the area of these forests have had experienced a declining trend, and in the next years, their expansion was initiated. These forests include the mangrove forests of Khurkhuran wetland, Muriz Deraz Estuary, Haft Baram Estuary, the mangrove forest in the south of the Laft Port, and the mangrove forests between the Tabl Pier, Maleki Village, and Gevarzin Village. The marine hydrodynamic and geomorphological characteristics of the region, such as average intertidal zone, sediment data, the freshwater inlet of Mehran river, wave stability and calmness, topography and slope, as well as mangrove conservation projects make the further expansion of mangrove forests in this area possible. By providing significant and up-to-date information on the development and decline of mangrove forests in different parts of the coast, this study can significantly contribute to taking measures for the conservation and restoration of mangrove forests.

Keywords: mangrove forests, marine hydrodynamics, coastal morphology, west of strait of Hormuz, Iran

Procedia PDF Downloads 96

Authors: B. Niez, B. Moulia, J. Dlouha, E. Badel

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Trees adjust their development to the environmental conditions they experience. Storms events of last decades showed that acclimation of trees to mechanical stresses due to wind is a very important process that allows the trees to sustain for long years. In the future, trees will experience new wind patterns, namely, more often strong winds and fewer daily moderate winds. Moreover, these patterns will go along with drought periods that may interact with the capacity of trees to adjust their growth to mechanical stresses due to wind. It is necessary to understand the mechanisms of wood functional acclimations to environmental conditions in order to predict their behaviour and in order to give foresters and breeders the relevant tools to adapt their forest management. This work aims to study how trees adjust the mechanical and hydraulic functions of their wood to environmental stresses and how this acclimation may be beneficial for the tree to resist to future stresses. In this work, young poplars were grown under controlled climatic conditions that include permanent environmental stress (daily mechanical stress of the stem by bending and/or hydric stress). Then, the properties of wood formed under these stressed conditions were characterized. First, hydraulic conductivity and sensibility to cavitation were measured at the tissue level in order to evaluate the changes in water transport capacity. Secondly, bending tests and Charpy impact tests were carried out at the millimetric scale to locally measure mechanical parameters such as elastic modulus, elastic limit or rupture energy. These experimental data allow evaluating the impacts of mechanical and water stress on the wood material. At the stem level, they will be merged in an integrative model in order to evaluate the beneficial aspect of wood acclimation for trees.

Keywords: acclimation, environmental stresses, hydraulics, mechanics, wood

Procedia PDF Downloads 204