Search results for: forest law and regulation

Authors: Jinesh Kumar Jain, Faishal Pathan

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The concept of sustainable development and effective environmental regulation has led to the emergence of a new field of study and practise that is the Green Supply Chain Management. GSCM has become a subject of great importance for both the developed and developing countries to achieve the desired and much-awaited goals of the firm within the environmental and sustainable framework. Its merits are comprised of good financial pay off and competitiveness to the firms in a long lasting and sustainable manner. The purpose of the paper is to briefly review the recent literature of the GSCM and also determines the new direction area of this emerging field. A detailed study has helped to enlighten the minute details and develop the research direction of the study. The GSCM has gained popularity with both academic and practitioners. The items for the study were developed based on the extent literature. Here we found that the state of adoption of GSCM practices by Indian Firms was still in its infancy, the awareness of environmental sustainability was quite low among consumers and the regulatory frameworks were also lacking in terms promoting environmental sustainability. The present paper is an attempt to emphasize much attention on the above-mentioned issues and present a conclusive summary to make its use widespread and for reaching.

Keywords: environmental management, environmental performance, financial performance, green supply chain management

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Authors: Leila Anoosheh

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Aim: The aim of this study was to assess The effects of aerobic training on microRNA let-7a expression and levels of tumor tissue IL-6 in mice with breast cancer. Method: Twenty BALB/c c mice (4-5 weeks,17 gr mass) were cancerous by injection of estrogen-dependent receptor breast cancer cells MC4-L2 and divided into two groups: tumor-training(TT) and tumor-control(TC) group. Then TT group completed aerobic training for 6 weeks, 5 days per week (14-18 m/min). After tumor emersion, tumor width and length were measured by digital caliper every week. 48 hours after the last exercise subjects were killed. Tissue sampling were collected and stored in -70ᵒ. Tumor tissue was homogenized and let-7a expression and IL-6 levels were accounted with Real time-PCR and ELISA Kit respectively. Statistical analysis of let-7a was conducted by the REST software. Repeated measures and independent tests were used to assess tumor size and IL-6, respectively. Results: Tumor size and IL-6 levels were significantly decreased in TT group compare with TC group (p<0.05). microRNA let-7a was increased significantly in TT against control group respectively (p=0/000). Conclusion: Reduction in tumor size, followed by aerobic exercise can be attributed to the loss of inflammatory factors such as IL-6; It seems that regarding to up regulation effects of aerobic exercise training on let-7a and down regulation effects of that on IL-6 in mice with breast cancer, This type of training can be used as adjuvant therapy in conjunction with other therapies for breast cancer.

Keywords: breast cancer, aerobic training, microRNA let-7a, IL-6

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Authors: Kever Bruno Paradelo Gomes, Rosana Carvalho Cristo Martins

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Payment for Environmental Services (PSA) has been a strategy used since the late 1990s by Latin American countries to finance environmental conservation. Payment for Environmental Services has been absorbing a growing amount of time in the discussions around environmentally sustainable development strategies in the world. In Brazil, this theme has permeated the discussions since the publication of the new Forest Code. The objective of this work was to verify the perception of the resident farmers in the region of Ponte Alta, Gama, Federal District, Brazil, on environmental legislation and Payments for Environmental Services. The work was carried out in 99 rural properties of the family farmers of the Rural Nucleus Ponte Alta, Administrative Region of Gama, in the city of Brasília, Federal District, Brazil. The present research is characterized methodologically as a quantitative, exploratory, and descriptive nature. The data treatment was performed through descriptive statistical analysis and hypothesis testing. The perceptions about environmental legislation in the rural area of Ponte Alta, Gama, DF respondents were positive. Although most of the family farmers interviewed have some knowledge about environmental legislation, it is perceived that in practice, the environmental adequacy of property is ineffective given the current situation of sustainable rural development; there is an abyss between what is envisaged by legislation and reality in the field. Thus, as in the reports of other researchers, it is verified that the majority of respondents are not aware of PSA (62.62%). Among those interviewed who were aware of the subject, two learned through the course, three through the university, two through TV and five through other people. The planting of native forest species on the rural property was the most informed practice by farmers if they received some Environmental Service Payment (PSA). Reflections on the environment allow us to infer that the effectiveness and fulfillment of the incentives and rewards in the scope of public policies to encourage the maintenance of environmental services, already existing in all spheres of government, are of great relevance to the process of environmental sustainability of rural properties. The relevance of the present research is an important tool to promote the discussion and formulation of public policies focused on sustainable rural development, especially on payments for environmental services; it is a space of great interest for the strengthening of the social group dedicated to production. Public policies that are efficient and accessible to the small rural producers become decisive elements for the promotion of changes in behavior in the field, be it economic, social, or environmental.

Keywords: forest code, public policy, rural development, sustainable agriculture

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Authors: Valeria Arcucci, Musarat Ishaq, Steven A. Stacker, Greg J. Goodall, Marc G. Achen

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Metastasis is the lethal aspect of cancer for most patients. Remodelling of lymphatic vessels associated with a tumour is a key initial step in metastasis because it facilitates the entry of cancer cells into the lymphatic vasculature and their spread to lymph nodes and distant organs. Although it is clear that vascular endothelial growth factors (VEGFs), such as VEGF-C and VEGF-D, are key drivers of lymphatic remodelling, the means by which many signaling pathways in endothelial cells are coordinately regulated to drive growth and remodelling of lymphatics in cancer is not understood. We seek to understand the broader molecular mechanisms that control cancer metastasis, and are focusing on microRNAs, which coordinately regulate signaling pathways involved in complex biological responses in health and disease. Here, using small RNA sequencing, we found that a specific microRNA, miR-132, is upregulated in expression in lymphatic endothelial cells (LECs) in response to the lymphangiogenic growth factors. Interestingly, ectopic expression of miR-132 in LECs in vitro stimulated proliferation and tube formation of these cells. Moreover, miR-132 is expressed in lymphatic vessels of a subset of human breast tumours which were previously found to express high levels of VEGF-D by immunohistochemical analysis on tumour tissue microarrays. In order to dissect the complexity of regulation by miR-132 in lymphatic biology, we performed Argonaute HITS-CLIP, which led us to identify the miR-132-mRNA interactome in LECs. We found that this microRNA in LECs is involved in the control of many different pathways mainly involved in cell proliferation and regulation of the extracellular matrix and cell-cell junctions. We are now exploring the functional significance of miR-132 targets in the biology of LECs using biochemical techniques, functional in vitro cell assays and in vivo lymphangiogenesis assays. This project will ultimately define the molecular regulation of lymphatic remodelling by miR-132, and thereby identify potential therapeutic targets for drugs designed to restrict the growth and remodelling of tumour lymphatics resulting in metastatic spread.

Keywords: argonaute HITS-CLIP, cancer, lymphatic remodelling, miR-132, VEGF

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Authors: Khasim Munir Bhasha Shaik

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Veligonda and adjoining hill range spread along about 170 Km North to South in Kadapa and Nellore Districts stretching a little further into Prakasam District. The latitude in general ranges up to 1000m. The forests are generally dry deciduous type. Veligonda and adjoining hill ranges comprise of Palakonda, Seshachalam, Lankamala and the terminal part of Nallamalais from mid-region of Southern Eastern Ghats. The Veligonda range which separates the Nellore district from Kadapa and Kurnool is the backbone of the Eastern Ghats, starting from Nagari promontory in Chittoor district. It runs in a northerly direction along the western border of the Nellore district, with a raising elevation of 3,626 ft at Penchalakona in Raipur thaluk. Veligonda hill ranges are high in altitude and have deep valleys. Among the Veligondas range of hills the Durgam in Venkatagiri range and Penchalakona are the most prominent and are situated 914 meters above mean sea level. It has more than 3000 species of plants along with 500 animal species. The unique specialty of this region is the presence of Pterocarpus santalinus(endangered) and Santalum album (vulnerable). In the present study, an attempt is made to assess the efforts that are going on to conserve the biodiversity of flora and fauna of this region. Various conservation strategies were suggested to protect the biodiversity and richness of Veligonda forest, hill region of Eastern Ghats of Andhra Pradesh. The major threats and the reasons for the dwindling species richness are poor rainfall, adverse climatic conditions, robbery of Red sanders and poaching of animals by the local tribals. Efforts are to be made to conserve some of the animals by both in situ and ex-situ methods. More awareness is to be developed among the local communities who are dwelling in the vicinity and importance of conservation is to be emphasized to them. Anthropogenic attachments are to be made by introducing more numbers of sacred groves. Gross enforcement of law is to be made to protect the various forest resources in this area. The important species with the medicinal values are to be identified. It was found that two important wildlife sanctuaries named Sri Lankamalleswarawildlife sanctuary and Sripenusila Narasimha wildlife sanctuary are working for the comprehensive conservation of the environment in this area. Apart from this more than 38 important sacred grooves are there where the plants and animals are protected by local Yanadi and other communities.

Keywords: biodiversity, wild life sanctuary, habitat destruction, eastern Ghats

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Authors: Nazneen Zehra

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Gir Protected Area (PA) is home to two sympatric large carnivores, the Asiatic lion and the common leopard, which share the same habitat. Understanding their interactions and coexistence is crucial for effective conservation management. From 2009 to 2012, we studied the availability and consumption of prey by these two carnivores to understand the dynamics of their interactions and coexistence. Ungulates provided approximately 3634.45 kg/km² of prey biomass, primarily composed of chital (ca. 2711.25 kg/km²), sambar (ca. 411.78 kg/km²), and nilgai (ca. 511.52 kg/km²). Other prey included peafowl (75.76 kg/km²) and langur (ca. 158.72 kg/km²). Both carnivores prioritized chital as their key prey species. The diet of Asiatic lions was predominantly composed of ungulates, with biomass contributions of chital (301.14 kg), sambar (378.75 kg), and nilgai (291.42 kg). Other prey species, such as peafowl and langur, contributed 1.36 kg and 2.40 kg, respectively, to the lions' diet. For leopards, the diet also heavily relied on chital (311.49 kg), followed by sambar (44.03 kg) and nilgai (172.78 kg). The biomass of other prey species in the leopards' diet included peafowl (2.08 kg) and langur (36.07 kg). Both species were found to primarily utilize teak-mixed forest, followed by riverine forest and teak-acacia-zizyphus habitats. The similarities in diet composition and habitat use indicate competition between these sympatric species. This competition may require one predator species to bear certain costs for the benefit of the other, which can influence conservation and management strategies. Effective conservation strategies are necessary to ensure the long-term survival of both the Asiatic lion and the common leopard equally and to maintain ecological balance in Gir PA.

Keywords: large carnivores, Gir PA, coexistence, resource utilization

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Authors: Evans Iyamu

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Genes encoding hydrophobins play distinct roles at different stages of the life cycle of fungi, and they foster hyphal attachment to surfaces. The hydrophobin Sc4 is known to provide a hydrophobic membrane lining of the gas channels within Schizophyllum commune fruiting bodies. Here, we cultivated non-fruiting, monokaryotic S. commune 12-43 on glass surfaces that could be verified by micrography. Differential gene expression profiling of nine hydrophobin genes and the hydrophobin-like sc15 gene by quantitative PCR showed significant up-regulation of sc4 when S. commune was attached to glass surfaces, also confirmed with RNA-Seq data analysis. Another silicate, namely quartz sand, was investigated, and induction of sc4 was seen as well. The up-regulation of the hydrophobin gene sc4 may indicate involvement in S. commune hyphal attachment to glass as well as quartz surfaces. We propose that the covering of hyphae by Sc4 allows for direct interaction with the hydrophobic surfaces of silicates and that differential functions of specific hydrophobin genes depend on the surface interface involved. This study could help with the clarification of the biological functions of hydrophobins in natural surroundings, including hydrophobic surface attachment. Therefore, the analysis of growth on glass serves as a basis for understanding S. commune interaction with glass surfaces while providing the possibility to visualize the interaction microscopically.

Keywords: hydrophobin, structured glass surfaces, differential gene expression, quartz sand

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Authors: Laura Helena Caicedo Lopez, Luis Miguel Contreras Medina, Ramon Gerardo Guevara Gonzales, Juan E. Andrade

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In this study, we evaluated the effect of three different hydric stress conditions: Low (LHS), medium (MHS), and high (HHS) on capsaicinoid content and enzyme regulation of C. annuum plants. Five main peaks were detected using a 2 Hz resolution vibrometer laser (Polytec-B&K). These peaks or “characteristic frequencies” were used as acoustic emissions (AEs) treatment, transforming these signals into audible sound with the frequency (Hz) content of each hydric stress. Capsaicinoids (CAPs) are the main, secondary metabolites of chili pepper plants and are known to increase during hydric stress conditions or short drought-periods. The AEs treatments were applied in two plant stages: the first one was in the pre-anthesis stage to evaluate the genes that encode the transcription of enzymes responsible for diverse metabolic activities of C. annuum plants. For example, the antioxidant responses such as peroxidase (POD), superoxide dismutase (Mn-SOD). Also, phenyl-alanine ammonia-lyase (PAL) involved in the biosynthesis of the phenylpropanoid compounds. The chalcone synthase (CHS) related to the natural defense mechanisms and species-specific aquaporin (CAPIP-1) that regulate the flow of water into and out of cells. The second stage was at 40 days after flowering (DAF) to evaluate the biochemical effect of AEs related to hydric stress on capsaicinoids production. These two experiments were conducted to identify the molecular responses of C. annuum plants to AE. Moreover, to define AEs could elicit any increase in the capsaicinoids content after a one-week exposition to AEs treatments. The results show that all AEs treatment signals (LHS, MHS, and HHS) were significantly different compared to the non-acoustic emission control (NAE). Also, the AEs induced the up-regulation of POD (~2.8, 2.9, and 3.6, respectively). The gene expression of another antioxidant response was particularly treatment-dependent. The HHS induced and overexpression of Mn-SOD (~0.23) and PAL (~0.33). As well, the MHS only induced an up-regulation of the CHs gene (~0.63). On the other hand, CAPIP-1 gene gas down-regulated by all AEs treatments LHS, MHS, and HHS ~ (-2.4, -0.43 and -6.4, respectively). Likewise, the down-regulation showed particularities depending on the treatment. LHS and MHS induced downregulation of the SOD gene ~ (-1.26 and -1.20 respectively) and PAL (-4.36 and 2.05, respectively). Correspondingly, the LHS and HHS showed the same tendency in the CHs gene, respectively ~ (-1.12 and -1.02, respectively). Regarding the elicitation effect of AE on the capsaicinoids content, additional treatment controls were included. A white noise treatment (WN) to prove the frequency-selectiveness of signals and a hydric stressed group (HS) to compare the CAPs content. Our findings suggest that WN and NAE did not present differences statically. Conversely, HS and all AEs treatments induced a significant increase of capsaicin (Cap) and dihydrocapsaicin (Dcap) after one-week of a treatment. Specifically, the HS plants showed an increase of 8.33 times compared to the NAE and WN treatments and 1.4 times higher than the MHS, which was the AEs treatment with a larger induction of Capsaicinoids among treatments (5.88) and compared to the controls.

Keywords: acoustic emission, capsaicinoids, elicitors, hydric stress, plant signaling

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Authors: Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

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Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics.

Keywords: urinary tract infection (UTI), Klebsiella pneumoniae, bacterial susceptibility, infrared spectroscopy, machine learning

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Authors: Yasuyo Makido, Vivek Shandas, David J. Sailor, M. Salim Ferwati

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Mitigating extreme urban heat is challenging in a desert climate such as Doha, Qatar, since outdoor daytime temperature area often too high for the human body to tolerate. Recent studies demonstrate that cities in arid and semiarid areas can exhibit ‘urban cool islands’ - urban areas that are cooler than the surrounding desert. However, the variation of temperatures as a result of the time of day and factors leading to temperature change remain at the question. To address these questions, we examined the spatial and temporal variation of air temperature in Doha, Qatar by conducting multiple vehicle-base local temperature observations. We also employed three statistical approaches to model surface temperatures using relevant predictors: (1) Ordinary Least Squares, (2) Regression Tree Analysis and (3) Random Forest for three time periods. Although the most important determinant factors varied by day and time, distance to the coast was the significant determinant at midday. A 70%/30% holdout method was used to create a testing dataset to validate the results through Pearson’s correlation coefficient. The Pearson’s analysis suggests that the Random Forest model more accurately predicts the surface temperatures than the other methods. We conclude with recommendations about the types of development patterns that show the greatest potential for reducing extreme heat in air climates.

Keywords: desert cities, tree-structure regression model, urban cool Island, vehicle temperature traverse

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Authors: D. T. Chabalala, J. M. Ndambuki, M. F. Ilunga

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Sedimentation includes the processes of erosion, transportation, deposition, and the compaction of sediment. Sedimentation in reservoir results in a decrease in water storage capacity, downstream problems involving aggregation and degradation, blockage of the intake, and change in water quality. A study was conducted in Caledon River catchment in the upstream of Welbedacht Reservoir located in the South Eastern part of Free State province, South Africa. The aim of this research was to investigate and develop a model for an Integrated Catchment Modelling of Sedimentation processes and management for the Welbedacht reservoir. Revised Universal Soil Loss Equation (RUSLE) was applied to determine sources of sediment at different areas of the catchment. The model has been also used to determine the impact of changes from management practice on erosion generation. The results revealed that the main sources of sediment in the watershed are cultivated land (273 ton per hectare), built up and forest (103.3 ton per hectare), and grassland, degraded land, mining and quarry (3.9, 9.8 and 5.3 ton per hectare) respectively. After application of soil conservation practices to developed Revised Universal Soil Loss Equation model, the results revealed that the total average annual soil loss in the catchment decreased by 76% and sediment yield from cultivated land decreased by 75%, while the built up and forest area decreased by 42% and 99% respectively. Thus, results of this study will be used by government departments in order to develop sustainable policies.

Keywords: Welbedacht reservoir, sedimentation, RUSLE, Caledon River

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Authors: Dijana Sulejmanović

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Cognitive-behavioral modification (CBM) is a combination of cognitive and behavioral learning principles to shape and encourage the desired behaviors. A crucial element of cognitive-behavioral modification is that a change the behavior precedes awareness of how it affects others. CBM is oriented toward changing inner speech and learning to control behaviors through self-regulation techniques. It aims to teach individuals how to develop the ability to recognize, monitor and modify their thoughts, feelings, and behaviors. The review of literature emphasizes the efficiency the CBM approach in the treatment of children's hyperactivity and negative emotions such as anger. The results of earlier research show how impulsive and hyperactive behavior, agitation, and aggression may slow down and block the child from being able to actively monitor and participate in regular classes, resulting in the disruption of the classroom and the teaching process, and the children may feel rejected, isolated and develop long-term poor image of themselves and others. In this article, we will provide how the use of CBM, adapted to child's age, can incorporate measures of cognitive and emotional functioning which can help us to better understand the children’s cognitive processes, their cognitive strengths, and weaknesses, and to identify factors that may influence their behavioral and emotional regulation. Such a comprehensive evaluation can also help identify cognitive and emotional risk factors associated with aggressive behavior, specifically the processes involved in modulating and regulating cognition and emotions.

Keywords: aggressive behavior, cognitive behavioral modification, cognitive behavioral theory, modification

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Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: Sempiga Olivier, Dominika Latusek-Jurczak

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Climate change has allegedly been attributed to a high consumption of fossil fuels, leading to severe environmental problems. The energy sector has been among the most polluting sectors for many decades. Consequently, there is a lack of trust in several energy firms, especially those in fossil fuels and nuclear energy. A robust regulatory framework is needed, and more investment in renewable energy sources is paramount for a better environmental outcome. Given the significant environmental impact of energy production and consumption in the energy sector, sustainable marketing practices have become increasingly important. Although the latter has had the lion’s share in polluting the environment, much effort has been made recently to move away from fossil fuels and privilege renewable energy sources. How this shift would help rebuild trust in the energy industry is unclear. For the shift to have lasting effects, it may be essential that regulatory agencies examine how energy firms engage in sustainable investment. There is little empirical evidence on whether adopting regulating marketing practices and investment initiatives can help different organizations reduce their environmental impact and promote sustainable development. Little is known about how and whether the environmental value in firms goes beyond rhetoric, greenwashing and publicity to translate into economic gains and environmental performance. The study investigates how regulatory agencies can help energy firms invest sustainably and take sustainable initiatives even amid the energy crisis caused by the Russia-Ukraine conflict and how these sustainable practices relate to renewed consumer trust. Using data from Corporate Knights, the study, through time series, analyses the relationship between sustainable regulation, sustainable practices of energy firms from around the world and their relation to consumer trust and environmental performance over the past 20 years. It examines how their sustainable investment, energy, and carbon productivity relate to environmental sustainability and consumer trust. This longitudinal study provides empirical evidence of the interplay between regulation, trust and environmental performance. The research is grounded in institutional trust theory, which emphasizes the role of regulatory frameworks and organizational practices in shaping public perceptions of fairness, transparency, and legitimacy. Results show that organizations in the energy sector, supported by robust regulatory tools, can overcome the negative image of polluters and compete with other companies in the fight against climate change and global warming. However, to do so, energy firms should consider investing more in renewable energy sources and implementing sustainable strategies and practices that go beyond greenwashing to improve their environmental performance, thereby rebuilding consumer trust in the energy sector. Results allow regulatory regimes and organizations to learn why it is crucial for energy firms to invest in renewable energy sources and engage in various sustainable initiatives and practices to contribute to better environmental outcomes and higher levels of trust.

Keywords: consumer trust, energy, environmental performance, regulation, renewable energy sources, sustainable practices

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Authors: A. Alyami, S. Christmas, K. Neeltje, G. Pollakis, B. Paxton, Z. Al-Bayati

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The Human leukocyte antigen-G (HLA-G) molecule plays an important role in immunomodulation. To date, 16 untranslated regions (UTR) HLA-G haplotypes have been previously defined by sequenced SNPs in the coding region. From these, UTR-1, UTR-2, UTR-3, UTR-4, UTR-5, UTR-6 and UTR-7 are the most frequent 3’UTR haplotypes at the global level. UTR-1 is associated with higher levels of soluble HLA-G and HLA-G expression, whereas UTR-5 and UTR-7 are linked with low levels of soluble HLA-G and HLA-G expression. Human immunodeficiency virus type 1 (HIV-1) infection results in the progressive loss of immune function in infected individuals. The virus escape mechanism typically includes T lymphocytes and NK cell recognition and lyses by classical HLA-A and B down-regulation, which has been associated with non-classical HLA-G molecule up-regulation, respectively. We evaluated the haplotypes of the HLA-G 3′ untranslated region frequencies observed in three HIV-1 groups from the Netherlands and their susceptibility to develop infection. The three groups are made up of mainly men who have sex with men (MSM), injection drug users (IDU) and a high-risk-seronegative (HRSN) group. DNA samples were amplified with published primers prior sequencing. According to our results, the low expresser frequencies show higher in HRSN compared to other groups. This is indicating that 3’UTR polymorphisms may be identified as potential prognostic biomarkers to determine susceptibility to HIV.

Keywords: Human leukocyte antigen-G (HLA-G) , men who have sex with men (MSM), injection drug users (IDU), high-risk-seronegative (HRSN) group, high-untranslated region (UTR)

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Authors: Faiza Babur Khan, Sohail Asghar

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With an emergence of distributed environment, cloud computing is proving to be the most stimulating computing paradigm shift in computer technology, resulting in spectacular expansion in IT industry. Many companies have augmented their technical infrastructure by adopting cloud resource sharing architecture. Cloud computing has opened doors to unlimited opportunities from application to platform availability, expandable storage and provision of computing environment. However, from a security viewpoint, an added risk level is introduced from clouds, weakening the protection mechanisms, and hardening the availability of privacy, data security and on demand service. Issues of trust, confidentiality, and integrity are elevated due to multitenant resource sharing architecture of cloud. Trust or reliability of cloud refers to its capability of providing the needed services precisely and unfailingly. Confidentiality is the ability of the architecture to ensure authorization of the relevant party to access its private data. It also guarantees integrity to protect the data from being fabricated by an unauthorized user. So in order to assure provision of secured cloud, a roadmap or model is obligatory to analyze a security problem, design mitigation strategies, and evaluate solutions. The aim of the paper is twofold; first to enlighten the factors which make cloud security critical along with alleviation strategies and secondly to propose an intrusion detection model that identifies the attackers in a preventive way using machine learning Random Forest classifier with an accuracy of 99.8%. This model uses less number of features. A comparison with other classifiers is also presented.

Keywords: cloud security, threats, machine learning, random forest, classification

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Authors: Zhongyang Sun, Shu Zhang, Manjiang Xie

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Emerging evidence indicates that microRNAs (miRNAs) play important roles in modulating osteoblast function and bone formation. However, the influence of miRNA on osteoblast proliferation and the possible mechanisms underlying remain to be defined. In this study, we aimed to investigate whether miR-103 regulates osteoblast proliferation under simulated microgravity condition through regulating Cav1.2, the primary subunit of L-type voltage sensitive calcium channels (LTCCs). We first investigated the effect of simulated microgravity on osteoblast proliferation and the outcomes clearly demonstrated that the mechanical unloading inhibits MC3T3-E1 osteoblast-like cells proliferation. Using quantitative Real-Time PCR (qRT-PCR), we provided data showing that miR-103 was up-regulated in response to simulated microgravity. In addition, we observed that up-regulation of miR-103 inhibited and down-regulation of miR-103 promoted osteoblast proliferation under simulated microgravity condition. Furthermore, knocking-down or over-expressing miR-103, respectively, up- or down-regulated the level of Cav1.2 expression and LTCCs currents, suggesting that miR-103 acts as an endogenous attenuator of Cav1.2 in osteoblasts under the condition of simulated microgravity. More importantly, we showed that the effect of miR-103 on osteoblast proliferation was diminished in simulated microgravity, when co-transfecting miR-103 mimic or inhibitor with Cav1.2 siRNA. Taken together, our data suggest that miR-103 inhibits osteoblast proliferation mainly through suppression of Cav1.2 expression under simulated microgravity condition. This work may provide a novel mechanism of microgravity-induced detrimental effects on osteoblast, identifying miR-103 as a novel possible therapeutic target in bone remodeling disorders in this mechanical unloading.

Keywords: microRNA, osteoblasts, cell proliferation, Cav1.2, simulated microgravity

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Yusuf Adebayo Oduwole

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The evaluation of blockchain technology's potential effects on the accounting and auditing fields is the main objective of this essay. It also adds to the existing body of work by examining how these practices alter technological concerns, including cryptocurrency accounting, regulation, governance, accounting practices, and technical challenges. Examples of this advancement include the growth of the concept of blockchain and its application in accounting. This technology is being considered one of the digital revolutions that could disrupt the world and civilization as it can transfer large volumes of virtual currencies like cryptocurrencies with the help of a third party. The basis for this research is a systematic review of the articles using Vosviewer to display and reflect on the bibliometric information of the articles accessible on the Scopus database. Also, as the practice of using blockchain technology in the field of accounting and auditing is still in its infancy, it may be useful to carry out a more thorough analysis of any implications for accounting and auditing regarding aspects of governance, regulation, and cryptocurrency that have not yet been discussed or addressed to any significant extent. The main findings on the relationship between blockchain and accounting show that the application of smart contracts, such as triple-entry accounting, has increased the quality of accounting records as well as reliance on the information available. This results in fewer cyclical assignments, no need for resolution, and real-time accounting, among others. Thereby, to integrate blockchain through a computer system, one must continuously learn and remain naive when using blockchain-integrated accounting software. This includes learning about how cryptocurrencies are accounted for and regulated. In this study, three original and contributed efforts are presented. To offer a transparent view of the state of previous relevant studies and research works in accounting and auditing that focus on blockchain, it begins by using bibliographic visibility analysis and a Scopus narrative analysis. Second, it highlights legislative, governance, and ethical concerns, such as education, where it tackles the use of blockchain in accounting and auditing. Lastly, it examines the impact of blockchain technologies on the accounting recognition of cryptocurrencies. Users of the technology should, therefore, take their time and learn how it works, as well as keep abreast of the different developments. In addition, the accounting industry must integrate blockchain certification and practice, most likely offline or as part of university education for those intending to become auditors or accountants.

Keywords: blockchain, crypto assets, governance, regulation & smart contracts

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Authors: Azzeddine Atibi, Khadija El Kababi, Salim Ahmed, Mohamed Radid

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Teaching and learning occupy a significant position globally, as the sustainable development of all sectors is intrinsically linked to the improvement of the educational system. The COVID-19 pandemic demonstrated that the integration of Information and Communication Technology (ICT) in the learning process is not optional but essential, and that proficiency in computer tools is an asset that will enhance pedagogy and ensure the continuity of learning under any circumstances. The objective of our study is to evaluate the impact of introducing computer tools on the quality of learning and the realization of conceptual change in learners. To this end, a learning situation was meticulously prepared, targeting first-year baccalaureate students in experimental sciences at a public high school, "Khadija Oum Almouminin," focusing on the chapter on glycemia regulation in the Moroccan Life and Earth Sciences (LES) curriculum. The learning situation was implemented with a pilot group that utilized computer tools and a control group that studied the same chapter without using ICT. The analysis and comparison of the results allowed us to verify the research question posed and to propose perspectives to ensure conceptual change in learners.

Keywords: information and communication technology, conceptual change, continuity of learning, life and earth sciences, glycemia regulation

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Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

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Authors: Junichi Hosoi

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Epidermal Langerhans cells reside in upper layer of epidermis and play a role in immune surveillance. The finding of the close association of nerve endings to Langerhans cells triggered the research on systemic regulation of Langerhans cells. They disappear from epidermis after exposure to environmental and internal stimuli and reappear about a week later. Myeloid progenitor cells are assumed to be one of the sources of Langerhans cells. We examined the effects of cortisol on the reappearance of Langerhans cells in vitro. Cord-blood derived CD34-positive cells were cultured in the medium supplemented with stem cell factor/Flt3 ligand/granulocyte macrophage-colony stimulating factor/tumor necrosis factor alpha/bone morphologic protein 7/transforming growth factor beta in the presence or absence of cortisol. Cells were analyzed by flow cytometry for CD1a (cluster differentiation 1a), a marker of Langerhans cells and dermal dendritic cells, and CD39 (cluster differentiation factor 39), extracellular adenosine triphosphatase. Both CD1a-positive cells and CD39-positive cells were decreased by treatment with cortisol (suppression by 35% and 22% compared to no stress hormone, respectively). Differentiated Langerhans cells are attracted to epidermis by chemokines that are secreted from keratinocytes. Epidermal keratinocytes were cultured in the presence or absence of cortisol and analyzed for the expression of CCL2 (C-C motif chemokine ligand 2) and CCL20 (C-C motif chemokine ligand 20), which are typical attractants of Langerhans cells, by quantitative reverse transcriptase polymerase chain reaction. The expression of both chemokines, CCL2 and CCL20, were suppressed by treatment with cortisol (suppression by 38% and 48% compared to no stress hormone, respectively). We examined the possible regulation of the suppression by cortisol with plant extracts. The extracts of Ganoderma lucidum and Iris protected the suppression of the differentiation to CD39-positive cells and also the suppression of the gene expression of LC-chemoattractants. These results suggest that cortisol, which is either systemic or locally produced, blocks the supply of epidermal Langerhans cells at 2 steps, differentiation from the precursor and attraction to epidermis. The suppression is possibly blocked by some plant extracts.

Keywords: Langerhans cell, stress, CD39, chemokine

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: Scott R. Butler

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Ontario, Canada’s largest province, is in the midst of an interesting experiment in mandated asset management planning for local governments. At the beginning of 2021, Ontario’s 444 municipalities were responsible for the management of 302,864 lane kilometers of roads that have a replacement cost of $97.545 billion CDN. Roadways are by far the most complex, expensive, and extensive assets that a municipality is responsible for overseeing. Since adopting Ontario Regulation 588/47: Asset Management Planning for Municipal Infrastructure in 2017, the provincial government has established prescriptions for local road authorities regarding asset category and levels of service being provided. This provincial regulation further stipulates that asset data such as extent, condition, and life cycle costing are to be captured in manner compliant with qualitative descriptions and technical metrics. The Ontario Good Roads Association undertook an exercise to aggregate the road-related data contained within the 444 asset management plans that municipalities have filed with the provincial government. This analysis concluded that collectively Ontario municipal roadways have a $34.7 billion CDN in deferred maintenance. The ill-state of repair of Ontario municipal roads has lasting implications for province’s economic competitiveness and has garnered considerable political attention. Municipal efforts to address the maintenance backlog are stymied by the extremely limited fiscal parameters municipalities must operate within in Ontario. Further exacerbating the program are provincially designed programs that are ineffective, administratively burdensome, and not necessarily aligned with local priorities or strategies. This paper addresses how municipal asset management plans – and more specifically, the data contained in these plans – can be used to design innovative policy frameworks, flexible funding programs, and new levels of service that respond to these funding challenges, as well as emerging issues such as local economic development and climate change. To fully unlock the potential that Ontario Regulation 588/17 has imposed will require a resolute commitment to data standardization and horizontal collaboration between municipalities within regions.

Keywords: transportation, municipal asset management, subnational policy design, subnational funding program design

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Authors: Alvin Saputra, Felix Wijovi

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Background: Adults with Autism Spectrum Disorder (ASD) often experience emotional dysregulation and heightened suicidality. Dialectical Behavior Therapy (DBT) and Radically Open DBT (RO-DBT) have shown promise in addressing these challenges, though research on their effectiveness in ASD populations remains limited. This systematic review aims to evaluate the impact of DBT and RO-DBT on emotional regulation, depression, and suicidality in adults with ASD. Methods: A systematic review was conducted by searching databases such as PubMed, PsycINFO, and Scopus for studies published on DBT and RO-DBT interventions in adults with Autism Spectrum Disorder (ASD). Inclusion criteria were peer-reviewed studies that reported on emotional regulation, suicidality, or depression outcomes. Data extraction focused on sample characteristics, intervention details, and outcome measures. Quality assessment was performed using standard systematic review criteria to ensure reliability and relevance of findings. Results: 4 studies comprising a total of 343 participants were included in this study. DBT and RO-DBT interventions demonstrated a medium effect size (Cohen's d = 0.53) in improving emotional regulation for adults with ASD, with ASD participants achieving significantly better outcomes than non-ASD individuals. RO-DBT was particularly effective in reducing maladaptive overcontrol, though high attrition and a predominantly White British sample limited generalizability. At end-of-treatment, DBT significantly reduced suicidal ideation (z = −2.24; p = 0.025) and suicide attempts (z = −3.15; p = 0.002) compared to treatment as usual (TAU), although this effect did not sustain at 12 months. Depression severity decreased with DBT (z = −1.99; p = 0.046), maintaining significance at follow-up (z = −2.46; p = 0.014). No significant effects were observed for social anxiety, and two suicides occurred in the TAU group. Conclusions: DBT and RO-DBT show potential efficacy in reducing emotional dysregulation, suicidality, and depression in adults with ASD, though the effects on suicidality may diminish over time. High dropout rates and limited sample diversity suggest further research is needed to confirm long-term benefits and improve applicability across broader populations.

Keywords: dialectical behaviour therapy, emotional dysregulation, autism spectrum disorder, suicidality

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Abdulrahman Fahim M. Alsulami

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The current research seeks to explore the regulation of vaccine-related IP rights in light of the areas of divergence between the Trade-Related Aspects of Intellectual Property Rights (TRIPS) Agreement and investment treaties. The study is conducted in the context of the COVID-19 pandemic; therefore, it seems natural that a specific chapter is devoted to the examination of vaccine arrangements related to vaccine supplies. The chapter starts with the examination of a typical vaccine from the perspective of IP rights. It presents the distinctive features of vaccines as pharmaceutical products and investments, reviews the basics of their patent protection, reviews vaccines’ components, and discusses IPR protection of different components of vaccines. The subsection that focuses on vaccine development and licensing reviews vaccine development stages investigates differences between vaccine licensing in different countries and presents barriers to vaccine licensing. The third subsection, at the same time, introduces the existing arrangements related to COVID-19 vaccine supplies, including COVAX arrangements, international organizations’ assistance, and direct negotiations between governments and vaccine manufacturers.

Keywords: bilateral investment treaties, COVID-19 vaccine, IP rights, TRIPs agreement

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Authors: Alexandra Serra, Nadia Torrão, Rui G. Serôdio, José A. Lima

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The present study aimed to evaluate the incidence and the prevalence of domestic violence legitimatory beliefs, emotional regulation difficulties and, early maladaptive schemas regarding intimidate partner violence in a sample of 50 Portuguese inmates. As expected, results show high levels of legitimatory beliefs, significant difficulties of emotional regulation and a set of high levels of early maladaptive schemas that clearly compromise the inmates affective-cognitive functioning. The most prevalent set of maladaptive schemas are associated with depression, anxiety, hostility, reduced ability to empathize and, dependence on the approval of others, which, combined, may trigger aggressive responses towards the intimate’s partner. Being victimized in their childhood and having committing murder are not differentiating factors on the measures we analyzed, but alcohol consumption may be associated with an intensification of domestic violence legitimatory beliefs. In the discussion of our findings, we compare the pattern of the psychosocial measures we used with the equivalent results obtained with convicted individuals that attend a community compulsory program, specifically designed for domestic violence perpetrators. We also highlight the importance of implementing specialized interventions in prison settings focusing on an evidence-based-practice.

Keywords: affective-cognitive functioning, intimate partner offenders, psychological research with inmates

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Authors: Yuval Reinfeld

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This paper explores the European Union’s expanding influence as a global regulatory power, particularly in the realms of legal, security, and privacy challenges within the digital landscape. As digital regulation becomes increasingly vital, the EU has positioned itself as a leading exporter of privacy and cybersecurity standards through landmark frameworks like the General Data Protection Regulation (GDPR), the Artificial Intelligence Act (AIA), and the Digital Services Act (DSA). These regulations have set global benchmarks, extending their influence well beyond Europe’s borders by shaping legal frameworks in third countries and guiding the development of global digital governance. Central to this regulatory diffusion is the European Court of Justice (CJEU), whose rulings consistently reinforce and extend the reach of EU standards on an international scale. Through mechanisms such as trade agreements, adequacy decisions, and multilateral cooperation, the EU has constructed a regulatory ecosystem that other jurisdictions increasingly adopt. This paper investigates key CJEU cases to illustrate how the EU’s legal instruments in privacy, security, and AI contribute to its role as a global standard-setter. By examining the intersection of digital governance, international law, and normative power, this research provides a thorough analysis of the EU’s regulatory impact on global privacy, cybersecurity, and AI frameworks.

Keywords: digital privacy, cybersecurity, GDPR, European Union Law, artificial intelligence, global normative power

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Authors: Musarat Ishaq, Tara Karnezis, Ramin Shayan

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Lipedema, a poorly understood chronic disease of adipose hyper-deposition, is often mistaken for obesity and causes significant impairment to mobility and quality-of-life. To identify molecular mechanisms underpinning lipedema, we employed comprehensive omics-based comparative analyses of whole tissue, adipocyte precursors (adipose-derived stem cells (ADSCs)), and adipocytes from patients with or without lipedema. Transcriptional profiling revealed significant differences in lipedema tissue, adipocytes, and ADSCs, with altered levels of mRNAs involved inproliferation and cell adhesion. One highly up-regulated gene in lipedema adipose tissue, adipocytes and ADSCs, ZIC4, encodes Zinc Finger Protein ZIC 4, a class of transcription factor which may be involved in regulating metabolism and adipogenesis. ZIC4 inhibition impaired the adipogenesis of ADSCs into mature adipocytes. Epigenetic regulation study revealed overexpression of ZIC4 is involved in decreased promoter DNA methylation and subsequent decrease in adipogenesis. These epigenetic modifications can alter adipocytes microenvironment and adipocytes differentiation. Our study show that epigenetic events regulate the ability of ADSCs to commit and differentiate into mature adipocytes by modulating ZIC4.

Keywords: lipedema, adipose-derived stem cells, adipose tisue, adipocytes, zinc finger protein, epigenetic

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