Search results for: convolutional long short-term memory

Authors: Same Noel Ngando, Yakub Abdulfatai Olatunji

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Renewable energy micro-grids, such as those powered by solar or wind energy, are often intermittent in nature. This means that the amount of energy generated by these systems can vary depending on weather conditions or other factors, which can make it difficult to ensure a steady supply of power. To address this issue, energy storage systems have been developed to increase the reliability of renewable energy micro-grids. Battery systems have been the dominant energy storage technology for renewable energy micro-grids. Batteries can store large amounts of energy in a relatively small and compact package, making them easy to install and maintain in a micro-grid setting. Additionally, batteries can be quickly charged and discharged, allowing them to respond quickly to changes in energy demand. However, the process involved in recycling batteries is quite costly and difficult. An alternative energy storage system that is gaining popularity is hydrogen storage. Hydrogen is a versatile energy carrier that can be produced from renewable energy sources such as solar or wind. It can be stored in large quantities at low cost, making it suitable for long-distance mass storage. Unlike batteries, hydrogen does not degrade over time, so it can be stored for extended periods without the need for frequent maintenance or replacement, allowing it to be used as a backup power source when the micro-grid is not generating enough energy to meet demand. When hydrogen is needed, it can be converted back into electricity through a fuel cell. Energy consumption data is got from a particular residential area in Daegu, South Korea, and the data is processed and analyzed. From the analysis, the total energy demand is calculated, and different hybrid energy system configurations are designed using HOMER Pro (Hybrid Optimization for Multiple Energy Resources) and MATLAB software. A techno-economic and environmental comparison and life cycle assessment (LCA) of the different configurations using battery and hydrogen as storage systems are carried out. The various scenarios included PV-hydrogen-grid system, PV-hydrogen-grid-wind, PV-hydrogen-grid-biomass, PV-hydrogen-wind, PV-hydrogen-biomass, biomass-hydrogen, wind-hydrogen, PV-battery-grid-wind, PV- battery -grid-biomass, PV- battery -wind, PV- battery -biomass, and biomass- battery. From the analysis, the least cost system for the location was the PV-hydrogen-grid system, with a net present cost of about USD 9,529,161. Even though all scenarios were environmentally friendly, taking into account the recycling cost and pollution involved in battery systems, all systems with hydrogen as a storage system produced better results. In conclusion, hydrogen is becoming a very prominent energy storage solution for renewable energy micro-grids. It is easier to store compared with electric power, so it is suitable for long-distance mass storage. Hydrogen storage systems have several advantages over battery systems, including flexibility, long-term stability, and low environmental impact. The cost of hydrogen storage is still relatively high, but it is expected to decrease as more hydrogen production, and storage infrastructure is built. With the growing focus on renewable energy and the need to reduce greenhouse gas emissions, hydrogen is expected to play an increasingly important role in the energy storage landscape.

Keywords: renewable energy systems, microgrid, hydrogen production, energy storage systems

Procedia PDF Downloads 94

Authors: Lucia Carrion Gordon, Jaime Santiago Sanchez Reinoso

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According to the relation of Digital Preservation and the Health field as a case of study, the architectural model help us to explain that definitions. .The principal goal of Data Preservation is to keep information for a long term. Regarding of Mediacal information, in order to perform a heart transplant, physicians need to preserve this organ in an adequate way. This approach between the two perspectives, the medical and the technological allow checking the similarities about the concepts of preservation. Digital preservation and medical advances are related in the same level as knowledge improvement.

Keywords: medical management, digital, data, heritage, preservation

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Authors: Asma Mehan

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Under the thrust of technological changes, population growth and vehicular traffic, Iranian historical squares have lost their significance and they are no longer the main social nodes of the society. This research focuses on how historical public squares can inspire designers to enhance social interactions among citizens in Iranian urban context. Moreover, the recent master plan of Tehran demonstrates the lack of public spaces designed for the purpose of people’s social gatherings. For filling this gap, first the current situation of 7 selected primary historical public squares in Tehran including Sabze Meydan, Arg, Topkhaneh, Baherstan, Mokhber-al-dole, Rah Ahan and Hassan Abad have been compared. Later, the influencing elements on social interactions of the public squares such as subjective factors (human relationships and memories) and objective factors (natural and built environment) have been investigated. As a conclusion, some strategies are proposed for improving social interactions in historical public squares like; holding cultural, national, athletic and religious events, defining different and new functions in public squares’ surrounding, increasing pedestrian routs, reviving the collective memory, demonstrating the historical importance of square, eliminating visual obstacles across the square, organization the natural elements of the square, appropriate pavement for social activities. Finally, it is argued that the combination of all influencing factors which are: human interactions, natural elements and built environment criteria will lead to enhance the historical public squares’ potential for social interaction.

Keywords: historical square, Iranian public square, social interaction, Tehran

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Authors: Evangelia-Chrysanthi Kouklari, Stella Tsermentseli, Claire P. Monks

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Executive function (EF) refers to a set of future-oriented and goal-directed cognitive skills that are crucial for problem solving and social behaviour, as well as the ability to organise oneself. It has been suggested that EF could be conceptualised as two distinct but interrelated constructs, one emotional (hot) and one cognitive (cool), as it facilitates both affective and cognitive regulation. Cool EF has been found to be strongly related to Theory of Mind (ToM) that is the ability to infer mental states, but research has not taken into account the association between hot EF and ToM in Autism Spectrum Disorder (ASD) to date. The present study investigates the associations between both hot and cool EF and ToM in school-aged children with ASD. This cross-sectional study assesses 79 school-aged children with ASD (7-15 years) and 91 controls matched for age and IQ, on tasks tapping cool EF (working memory, inhibition, planning), hot EF (effective decision making, delay discounting), and ToM (emotional understanding and false/no false belief). Significant group differences in each EF measure support a global executive dysfunction in ASD. Strong associations between hot EF and ToM in ASD are reported for the first time (i.e. ToM emotional understanding and delay discounting). These findings highlight that hot EF also makes a unique contribution to the developmental profile of ASD. Considering the role of both hot and cool EF in association with ToM in individuals with ASD may aid in gaining a greater understanding not just of how these complex multifaceted cognitive abilities relate to one another, but their joint role in the distinct developmental pathway followed in ASD.

Keywords: ASD, executive function, school age, theory of mind

Procedia PDF Downloads 291

Authors: Mohamad R. Moshtagh, Ahmad Bagheri

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Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime.

Keywords: fault detection, gearbox, machine learning, wiener method

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Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

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Authors: S. M. C. Kelly, M. Goulden

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Introduction: Chronic Pancreatitis is an increasing problem worldwide. Pain is the main symptom and the main reason for hospital readmission following diagnosis, despite the use of strong analgesics including opioids. Ketamine infusions reduce pain in complex regional pain syndrome and other neuropathic pain conditions. Our centre has trialed the use of ketamine infusions in patients with chronic pancreatitis. We have evaluated this service to assess whether ketamine reduces emergency department admissions and analgesia requirements. Methods: This study collected retrospective data from 2010 in all patients who received a ketamine infusion for chronic pain secondary to a diagnosis of chronic pancreatitis. The day-case ketamine infusions were initiated in theatre by an anaesthetist, with standard monitoring and the assistance of an anaesthetic practitioner. A bolus dose of 0.5milligrams/kilogram was given in theatre. The infusion of 0.5 milligrams/kilogram per hour was then administered over a 6 hour period in the theatre recovery area. A study proforma detailed the medical history, analgesic use and admissions to hospital. Patients received a telephone follow up consultation. Results: Over the last eight years, a total of 30 patients have received intravenous ketamine infusions, with a total of 92 ketamine infusions being administered. 53% of the patients were male with the average age of 47. A total of 27 patients participated with the telephone consultation. A third of patients reported a reduction in hospital admissions with pain following the ketamine infusion. Analgesia requirements were reduced by an average of 48.3% (range 0-100%) for an average duration of 69.6 days (range 0-180 days.) Discussion: This service evaluation illustrates that ketamine infusions can reduce analgesic requirements and the number of hospital admissions in patients with chronic pancreatitis. In the light of increasing pressures on Emergency departments and the increasing evidence of the dangers of long-term opioid use, this is clearly a useful finding. We are now performing a prospective study to assess the long-term effectiveness of ketamine infusions in reducing analgesia requirements and improving patient’s quality of life.

Keywords: acute-on-chronic pain, intravenous analgesia infusion, ketamine, pancreatitis

Procedia PDF Downloads 136

Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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Diabetic nephropathy (DN) is a major complication of diabetes, with fibrosis and propionate metabolism playing critical roles in its progression. Identifying biomarkers linked to these pathways may provide novel insights into DN diagnosis and treatment. This study aims to identify biomarkers associated with fibrosis and propionate metabolism in DN. Analyze the biological pathways and regulatory mechanisms of these biomarkers. Develop a machine learning model to predict DN-related biomarkers and validate their functional roles. Publicly available transcriptome datasets related to DN (GSE96804 and GSE104948) were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds), and 924 propionate metabolism-related genes (PMRGs) and 656 fibrosis-related genes (FRGs) were identified. The analysis began with the extraction of DN-differentially expressed genes (DN-DEGs) and propionate metabolism-related DEGs (PM-DEGs), followed by the intersection of these with fibrosis-related genes to identify key intersected genes. Instead of relying on traditional models, we employed a combination of deep neural networks (DNNs) and ensemble methods such as Gradient Boosting Machines (GBM) and XGBoost to enhance feature selection and biomarker discovery. Recursive feature elimination (RFE) was coupled with these advanced algorithms to refine the selection of the most critical biomarkers. Functional validation was conducted using convolutional neural networks (CNN) for gene set enrichment and immunoinfiltration analysis, revealing seven significant biomarkers—SLC37A4, ACOX2, GPD1, ACE2, SLC9A3, AGT, and PLG. These biomarkers are involved in critical biological processes such as fatty acid metabolism and glomerular development, providing a mechanistic link to DN progression. Furthermore, a TF–miRNA–mRNA regulatory network was constructed using natural language processing models to identify 8 transcription factors and 60 miRNAs that regulate these biomarkers, while a drug–gene interaction network revealed potential therapeutic targets such as UROKINASE–PLG and ATENOLOL–AGT. This integrative approach, leveraging deep learning and ensemble models, not only enhances the accuracy of biomarker discovery but also offers new perspectives on DN diagnosis and treatment, specifically targeting fibrosis and propionate metabolism pathways.

Keywords: diabetic nephropathy, deep neural networks, gradient boosting machines (GBM), XGBoost

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Authors: Antonio Montes, Antonio Cozar, Clara Pereyra, Diego Valor, Enrique Martinez de la Ossa

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Tissues and organs can be damaged because of traumatism, congenital illnesses, or cancer and the traditional therapeutic alternatives, such as surgery, cannot usually completely repair the damaged tissues. Tissue engineering allows regeneration of the patient's tissues, reducing the problems caused by the traditional methods. Scaffolds, polymeric structures with interconnected porosity, can be promoted the proliferation and adhesion of the patient’s cells in the damaged area. Furthermore, by means of impregnation of the scaffold with beneficial active substances, tissue regeneration can be induced through a drug delivery process. The objective of the work is the fabrication of a PVA scaffold coated with Gallic Acid and polypyrrole through a one-step foaming and impregnation process using the SSI technique (Supercritical Solvent Impregnation). In this technique, supercritical CO₂ penetrates into the polymer chains producing the plasticization of the polymer. In the depressurization step a CO₂ cellular nucleation and growing to take place to an interconnected porous structure of the polymer. The foaming process using supercritical CO₂ as solvent and expansion agent presents advantages compared to the traditional scaffolds’ fabrication methods, such as the polymer’s high solubility in the solvent or the possibility of carrying out the process at a low temperature, avoiding the inactivation of the active substance. In this sense, the supercritical CO₂ avoids the use of organic solvents and reduces the solvent residues in the final product. Moreover, this process does not require long processing time that could cause the stratification of substance inside the scaffold reducing the therapeutic efficiency of the formulation. An experimental design has been carried out to optimize the SSI technique operating conditions, as well as a study of the morphological characteristics of the scaffold for its use in tissue engineerings, such as porosity, conductivity or the release profiles of the active substance. It has been proved that the obtained scaffolds are partially porous, conductors of electricity and are able to release Gallic Acid in the long term.

Keywords: scaffold, foaming, supercritical, PVA, polypyrrole, gallic acid

Procedia PDF Downloads 182

Authors: Maryam Farzaneh, Seyyedeh Nafiseh Hassani, Hossein Baharvand

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Objective: Chicken gonadal tissue has a two population such primordial germ cells (PGCs) and stromal cells (somatic cells). PGCs and embryonic germ cells (EGCs) that is a pluripotent type of PGCs in long-term culture are suitable sources for the production of chicken pluripotent stem cell lines, transgenic birds, vaccine and recombinant protein production. In general, the effect of growth factors such bFGF and mouse LIF on derivation of PGCs in vitro are important and in this study we could see the unique effect of small molecules such PD032 and SB43 as a chemical, in comparison to growth factors. Materials and Methods: After incubation of fertilized chicken egg up to 6 days and isolation of primary gonadal tissues and culture of mixed cells like PGCs and stromal cells. PGCs proliferate in the present of fetal calf serum (FCS) and small molecules and in another group bFGF, that these factors are important for PGCs culture and derivation. Somatic cells produce a multilayer feeder under the PGCs in primary culture and PGCs make a small cluster under these cells. Results: In present of small molecules and high volume of FCS (15%), the present of EGCs as a pluripotent stem cells were clear four weeks, that they had a positive immune-staining and periodic acid-Schiff staining (PAS), but in present of growth factors like bFGF without any chemicals, the present of PGCs were clear but after 7 until 10 days, there were disappear. Conclusion: Until now we have seen many researches about derivation and maintenance of chicken PGCs, in the hope of understanding the mechanisms that occur during germline development and production of a therapeutic product by transgenic birds. There are still many unknowns in this area and this project will try to have efficient conditions for identification of suitable culture medium for long-term culture of PGCs in vitro without serum and feeder cells.

Keywords: chicken gonadal primordial germ cells, pluripotent stem cells, growth factors, small molecules, transgenic birds

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Authors: Hsin Lee, Hsuan Lee

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Legal artificial intelligence (LegalAI) has been increasing applications within legal systems, propelled by advancements in natural language processing (NLP). Compared with general documents, legal case documents are typically long text sequences with intrinsic logical structures. Most existing language models have difficulty understanding the long-distance dependencies between different structures. Another unique challenge is that while the Judiciary of Taiwan has released legal judgments from various levels of courts over the years, there remains a significant obstacle in the lack of labeled datasets. This deficiency makes it difficult to train models with strong generalization capabilities, as well as accurately evaluate model performance. To date, models in Taiwan have yet to be specifically trained on judgment data. Given these challenges, this research proposes a Longformer-based pre-trained language model explicitly devised for retrieving similar judgments in Taiwanese legal documents. This model is trained on a self-constructed dataset, which this research has independently labeled to measure judgment similarities, thereby addressing a void left by the lack of an existing labeled dataset for Taiwanese judgments. This research adopts strategies such as early stopping and gradient clipping to prevent overfitting and manage gradient explosion, respectively, thereby enhancing the model's performance. The model in this research is evaluated using both the dataset and the Average Entropy of Offense-charged Clustering (AEOC) metric, which utilizes the notion of similar case scenarios within the same type of legal cases. Our experimental results illustrate our model's significant advancements in handling similarity comparisons within extensive legal judgments. By enabling more efficient retrieval and analysis of legal case documents, our model holds the potential to facilitate legal research, aid legal decision-making, and contribute to the further development of LegalAI in Taiwan.

Keywords: legal artificial intelligence, computation and language, language model, Taiwanese legal cases

Procedia PDF Downloads 72

Authors: Seyedmohammad Mousavian

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Technological advancements are becoming unprecedentedly dominant everywhere. This dominance requires drastic chTechnological advancements are becoming unprecedentedly dominant everywhere. This dominance requires drastic changes in traditional thinking, procedures, and responsibilities. Chief Financial Officers (CFOs) have long played a key role in every organization around the globe and must adapt themselves to the disruptive technology which brings positive and negative points. This paper will discuss the shift of the traditional role of CFOs from just reporting toward more innovative roles like “Storytelling”, business partnering, and strategic planning.

Keywords: accounting information system, technology, data, CFO, finance

Procedia PDF Downloads 140

Authors: J. S. Dhillon, K. K. Dhaliwal

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In almost all the digital filtering applications the digital infinite impulse response (IIR) filters are preferred over finite impulse response (FIR) filters because they provide much better performance, less computational cost and have smaller memory requirements for similar magnitude specifications. However, the digital IIR filters are generally multimodal with respect to the filter coefficients and therefore, reliable methods that can provide global optimal solutions are required. The artificial bee colony (ABC) algorithm is one such recently introduced meta-heuristic optimization algorithm. But in some cases it shows insufficiency while searching the solution space resulting in a weak exchange of information and hence is not able to return better solutions. To overcome this deficiency, the opposition based learning strategy is incorporated in ABC and hence a modified version called oppositional artificial bee colony (OABC) algorithm is proposed in this paper. Duplication of members is avoided during the run which also augments the exploration ability. The developed algorithm is then applied for the design of optimal and stable digital IIR filter structure where design of low-pass (LP) and high-pass (HP) filters is carried out. Fuzzy theory is applied to achieve maximize satisfaction of minimum magnitude error and stability constraints. To check the effectiveness of OABC, the results are compared with some well established filter design techniques and it is observed that in most cases OABC returns better or atleast comparable results.

Keywords: digital infinite impulse response filter, artificial bee colony optimization, opposition based learning, digital filter design, multi-parameter optimization

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Authors: Zelikha Labbani

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Many biotechnology methods have been used in plant breeding programs. The in vitro isolated microspore culture is the one of these methods. For durum wheat, the use of this technology has been limited for a long time due to the low number of embryos produced and also most regeneration plants are albina. The objective of this paper is to show that using isolated microspores culture on durum wheat is possible due to the development of the new methods using the new pretreatment of the microspores before their isolation and cultivation.

Keywords: isolated microspore culture, pretreatments, in vitro embryogenesis, plant breeding program

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Authors: Bashayer AlRobayaan, Munira Saad, Alaa AlBawab, Fatma AlHamad, Sara AlAwadhi, Sherif Fahmy

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This paper addresses the problem of long waiting times in government hospitals emergency departments (ED). It aims at finding feasible and simple ways of reducing waiting times that do not require a lot of resources and/or expenses. AlAdan Hospital in Kuwait was chosen to be understudy to further understand and capture the problem.

Keywords: healthcare, hospital, Kuwait, waiting times, emergency department

Procedia PDF Downloads 489

Authors: Sungmin Park, Gyungmok Nam, Seungpyo Woo, Young Choi, Sangheon Park, Sang-Hee Yoon

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Puncturing human skin with microneedles is critically important for microneedle-mediate drug delivery. Despite of extensive efforts in the past decades, the scale-up fabrication of sharp-tipped and high-aspect-ratio microneedles, especially made of biodegradable polymers, is still a long way off. Here, we present a UV dose insensitive and cost-effective microfabrication method for the biodegradable polymer microneedles with sharp tips and long lengths which can pierce human skin with low insertion force. The biodegradable polymer microneedles are fabricated with the polymer solution casting where a poly(lactic-co-glycolic acid) (PLGA, 50:50) solution is coated onto a SU-8 mold prepared with a reverse, ramped, and rotational (R3) UV exposure. The R3 UV exposure is modified from the multidirectional UV exposure both to suppress UV reflection from the bottom surface without anti-reflection layers and to optimize solvent concentration in the SU-8 photoresist, therefore achieving robust (i.e., highly insensitive to UV dose) and cost-effective fabrication of biodegradable polymer microneedles. An optical model for describing the spatial distribution of UV irradiation dose of the R3 UV exposure is also developed to theoretically predict the microneedle geometry fabricated with the R3 UV exposure and also to demonstrate the insensitiveness of microneedle geometry to UV dose. In the experimental characterization, the microneedles fabricated with the R3 UV exposure are compared with those fabricated with a conventional method (i.e., multidirectional UV exposure). The R3 UV exposure-based microfabrication reduces the end-tip radius by a factor of 5.8 and the deviation from ideal aspect ratio by 74.8%, compared with conventional method-based microfabrication. The PLGA microneedles fabricated with the R3 UV exposure pierce full-thickness porcine skins successfully and are demonstrated to completely dissolve in PBS (phosphate-buffered saline). The findings of this study will lead to an explosive growth of the microneedle-mediated drug delivery market.

Keywords: R³ UV exposure, optical model, UV dose, reflection, solvent concentration, biodegradable polymer microneedle

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Authors: Ali Razmi, Saeed Golian

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Climate change is known to have the potential to impact adversely hydrologic patterns for variables such as rainfall, maximum and minimum temperature and sea level rise. Long-term average of these climate variables could possibly change over time due to climate change impacts. In this study, trend analysis was performed on rainfall, maximum and minimum temperature and water level data of a coastal area in Manhattan, New York City, Central Park and Battery Park stations to investigate if there is a significant change in the data mean. Partial Man-Kendall test was used for trend analysis. Frequency analysis was then performed on data using common probability distribution functions such as Generalized Extreme Value (GEV), normal, log-normal and log-Pearson. Goodness of fit tests such as Kolmogorov-Smirnov are used to determine the most appropriate distributions. In flood frequency analysis, rainfall and water level data are often separately investigated. However, in determining flood zones, simultaneous consideration of rainfall and water level in frequency analysis could have considerable effect on floodplain delineation (flood extent and depth). The present study aims to perform flood frequency analysis considering joint probability distribution for rainfall and storm surge. First, correlation between the considered variables was investigated. Joint probability distribution of extreme water level and temperature was also investigated to examine how global warming could affect sea level flooding impacts. Copula functions were fitted to data and joint probability of water level with rainfall and temperature for different recurrence intervals of 2, 5, 25, 50, 100, 200, 500, 600 and 1000 was determined and compared with the severity of individual events. Results for trend analysis showed increase in long-term average of data that could be attributed to climate change impacts. GEV distribution was found as the most appropriate function to be fitted to the extreme climate variables. The results for joint probability distribution analysis confirmed the necessity for incorporation of both rainfall and water level data in flood frequency analysis.

Keywords: climate change, climate variables, copula, joint probability

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Authors: Hasti Abbasi

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Creative writers have long followed the tradition of romantic exile, looking inward in an attempt to construct new viewpoints through the power of imagination. The writer, who attempts to resist uncertainty and locate her place in the new country through writing, resists creativity itself. For a writer, certain satisfaction can be achieved through producing a creative art away from the anxiety of the sense of dislocation. Dislocation, whether enforced or self-inflicted, could in many ways be a disaster but it could also cultivate a greater creative capacity and be a source of creative expression. This paper will investigate the idea of the creative writer as exiled self through reflections on the relationship between dislocation and writing.

Keywords: dislocation, creative writing, remaking identity, exile literature

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Authors: Prasamsha Panta, Da Yeon Kim, Ja Yong Jang, Min Jae Kim, Jae Ho Kim, Moon Suk Kim

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Introduction: Among the many anticancer drugs used clinically, doxorubicin (Dox), was one of widely used drugs to treat many types of solid tumors such as liver, colon, breast, or lung. Intratumoral injection of chemotherapeutic agents is a potentially more effective alternative to systemic administration because direct delivery of the anticancer drug to the target may improve both the stability and efficacy of anticancer drugs. Injectable in situ-forming gels have attracted considerable attention because they can achieve site specific drug delivery, long term action periods, and improved patient compliance. Objective: Objective of present study is to confirm clinical benefit of intratumoral chemotherapy using injectable in situ-forming poly(ethylene glycol)-b-polycaprolactone diblock copolymer (MP) and Dox with increase in efficacy and reducing the toxicity in patients with cancer diseases. Methods and methodology: We prepared biodegradable MP hydrogel and measured viscosity for the evaluation of thermo-sensitive property. In vivo antitumor activity was performed with normal saline, MP only, single free Dox, repeat free Dox, and Dox-loaded MP gel. The remaining amount of Dox drug was measured using HPLC after the mouse was sacrified. For cytotoxicity studies WST-1 assay was performed. Histological analysis was done with H&E and TUNEL processes respectively. Results: The works in this experiment showed that Dox-loaded MP have biodegradable drug depot property. Dox-loaded MP gels showed remarkable in vitro cytotoxicity activities against cancer cells. Finally, this work indicates that injection of Dox-loaded MP allowed Dox to act effectively in the tumor and induced long-lasting supression of tumor growth. Conclusion: This work has examined the potential clinical utility of intratumorally injected Dox-loaded MP gel, which shows significant effect of higher local Dox retention compared with systemically administered Dox.

Keywords: injectable in-situ forming hydrogel, anticancer, doxorubicin, intratumoral injection

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Authors: Antonella Cartoni, Mattea Carmen Castrovilli

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A biosensor for lactate detection has been developed using an environmentally friendly approach. The biosensor is based on lactate oxidase (LOX) and has remarkable capabilities for reuse and storage at room temperature. The manufacturing technique employed is ambient electrospray deposition (ESD), which enables efficient and sustainable immobilization of the LOX enzyme on a cost-effective com-mercial screen-printed Prussian blue/carbon electrode (PB/C-SPE). The study demonstrates that the ESD technology allows the biosensor to be stored at ambient pressure and temperature for extended periods without affecting the enzymatic activity. The biosensor can be stored for up to 90 days without requiring specific storage conditions, and it can be reused for up to 24 measurements on both freshly prepared electrodes and electrodes that are three months old. The LOX-based biosensor exhibits a lin-ear range of lactate detection between 0.1 and 1 mM, with a limit of detection of 0.07±0.02 mM. Ad-ditionally, it does not exhibit any memory effects. The immobilization process does not involve the use of entrapment matrices or hazardous chemicals, making it environmentally sustainable and non-toxic compared to current methods. Furthermore, the application of a electrospray deposition cycle on previously used biosensors rejuvenates their performance, making them comparable to freshly made biosensors. This highlights the excellent recycling potential of the technique, eliminating the waste as-sociated with disposable devices.

Keywords: green friendly, reuse, storage performance, immobilization, matrix-free, electrospray deposition, biosensor, lactate oxidase, enzyme

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Authors: Sulemana Ibrahim

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Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

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Authors: E. Channol, O. Collet, N. Kostyuchyk, T. Mesbah, Quoc Hoang Long Nguyen

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In this paper, a pricing model is proposed for co-integrated commodities extending Larsson model. The futures formulae have been derived and tests have been performed with non-constant volatility. The model has been applied to energy commodities (gas, CO2, energy) and soft commodities (corn, wheat). Results show that non-constant volatility leads to more accurate short term prices, which provides better evaluation of value-at-risk and more generally improve the risk management.

Keywords: co-integration, soft commodities, risk management, value-at-risk

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Authors: Silke Bustamante, Andrea Pelzeter, Andreas Deckmann, Rudi Ehlscheidt, Franziska Freudenberger

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CSR is associated with a great number of positive effects. This also includes the positive impact of CSR on the commitment of its employees. The internal CSR communication here takes the essential function as a mediator of the CSR performance of a company to the employees. The importance of CSR communication is, therefore, essential. Employees can usually only perceive the CSR efforts of a company if it is also communicated to them. Only if the employees perceive the CSR of their employer positively, the employer-CSR can also have a positive impact on their affective commitment. Therefore, organizational and individual factors are crucial and thus need to consider. This relationship between the organizational and individual factors was investigated in a qualitative case study in six companies of the German service sector. Expert interviews and focus group interviews were conducted and questionnaire-based ratings by company representatives were raised. Among the individual factors, in terms of CSR, the expectations and relevance of its employees, as well as the perception of CSR by the staff, are included. The organizational factors include the actual CSR performance and its communication. Ultimately, the impact of CSR on the commitment is examined with this holistic approach. The results show that the individual CSR perception does not always match the corporate CSR performance and its depiction in internal communication. Furthermore, employees have given suggestions on how CSR should be communicated by their employer. Knowledge memory systems (e.g. wiki) on the on hand and media-based information, on the other hand, were highlighted. Primarily the employee-related CSR is most important for the employees, whereas ecological CSR activities hardly play a role. The findings indicate the importance of CSR communication in the CSR concept as it provides the missing link between CSR performance and appreciation by an increase in commitment. It should only be communicated, what is done. CSR communication should also be carried out in a plausible and transparent way.

Keywords: CSR, employee commitment, employer brand, internal communication

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Authors: Amina Merghad, Cherif Abdennour

Abstract:

Solvents are widely used in paint industry, where humans are highly exposed, especially from inhalation. A case report describes how paint affects reproductive markers and the health of workers. Sixty four subjects were chosen and divided into two groups; a control and an exposed group. A questionnaire was given to male workers from similar socio-economic status in order to know their ages, working conditions, clinical symptoms, working period, smoking history, shift, medical history and nutrition. Blood was withdrawn in the morning from volunteers. The measurement of blood testosterone and prolactin concentrations was then carried out. Results showed that the ages of the two groups were almost similar and were up to 47 and 43 years. The period of employment was 17 years and 14 years for the control and the exposed workers, respectively. Concerning clinical symptoms, the frequency of neuropsychological symptoms of the two groups are presented. It is clear that the symptom of memory loss, headaches are the highest among exposed workers followed by poor coordination, poor concentration and insomnia. On the other hand, the symptoms’ frequency in the control was less than that of the exposed group. Testosterone concentration has significantly decreased in group 2 (4.61±2,005 ng/ml) and group 3 (4.25±1.67 ng/ml) of exposed workers. On the other hand, prolactin concentration was higher in group 3 compared to other groups. To conclude, paint industry has disturbed reproductive markers and created high frequency of neuropsychological symptoms.

Keywords: blood, paint, prolactin, occupational exposure, organic solvent, reproductive toxicity, testosterone

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Authors: Sukhwinder Singh Jolly

Abstract:

Metal spinning is one such process in which the stresses are localized to a small area and the material is made to flow or move over the mandrel with the help of spinning tool. Spinning of tubular products can be performed by two techniques, forward spinning and backward spinning. Many researchers have studied the process both experimentally and analytically. An effort has been made to apply the process to the spinning of thin wall, highly precision, small bore long tube in hard-to-work materials such as titanium.

Keywords: metal spinning, hard-to-work materials, roller diameter, power consumption

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Authors: Hamid Vahidkia

Abstract:

Central to the World Court’s mission is the assurance of universal custom “as prove of a common hone acknowledged as law.” Understudies of the Court’s law have long been mindful that the Court has been superior at applying standard law than characterizing it. However until Nicaragua v. Joined together States, small hurt was done. For within the strongly challenged cases earlier to Nicaragua, the Court overseen to inspire commonalities in factious structure that floated its decisions toward the standard standards certain in state hone. The Court’s need of hypothetical unequivocality basically implied that a career opportunity emerged for a few eyewitnesses like me to endeavor to supply the lost hypothesis of custom.

Keywords: law, international law, jurisdication, customary

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Authors: Kyllie Cripps

Abstract:

In Australia, not dissimilar to other jurisdictions with indigenous populations, indigenous women are more likely to experience violence than any other section of society. In recent years in response to horrific examples of Indigenous women’s deaths, Australian Coronial courts have investigated, wanting to know more about the circumstances that led to the deaths. This paper critically examined 12 Coronial Court investigations from around Australia, analyzing them thematically. The analysis highlighted the differential vulnerability of indigenous women to intimate partner homicides. In all the cases reviewed, it was evident that the women’s deaths, in most instances were entirely preventable. Evidence was also presented demonstrating that services were aware of the women’s heightened risks but were unable to sufficiently coordinate themselves to provide wrap around support to minimise the risk of violence and to maximise the women’s safety. Consequently, putting the women in environments where their deaths were both predictable and inevitable. The profound system failings at the intersections of law, policy, and practice have ultimately cost indigenous women their lives. This paper firstly explores the nuances of the Coronial Court findings – demonstrating the similarities and differences present within the cases. Part two interrogates the reported system failings, and part three considers potential improvements in system integration to prevent future deaths. The paper concludes recognizing that Indigenous women play important valued roles in indigenous communities, their loss has profound costs and consequences, and to honor their memory, we must learn from their deaths and improve responses to intimate partner violence.

Keywords: homicide, intimate partner violence, indigenous women

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Authors: Rajiv Jash, Himangshusekhar Maji

Abstract:

Phytomolecules have long been associated with the effective treatment of various disorders since ages. This study focuses on identifying the immunomodulatory pure molecules isolated from plants, which can be studied for their effect in alleviating IgAN. All the phytomolecules mentioned here have inflammation-reducing properties, and IgAN, being an autoimmune disease, can be a good target of these phytomolecules. Various pathological pathways of IgA nephropathy can be targeted with these phytomolecules, and this study is an effort to find out the rationale behind the choice of the molecules based on their ability to target the effector molecules of those pathological pathways.

Keywords: IgAN, fibrosis, inflammation, ESRD, TGFβ

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Authors: J. K. Karapetyan

Abstract:

In this paper seismological-statistical analysis of actual instrumental data on the main tremor of the Great Japan earthquake 11.03.2011 is implemented for finding out the dependence between maximal values of peak ground accelerations (PGA) and epicentric distances. A number of peculiarities of manifestation of accelerations' maximum values at the interval of long epicentric distances are revealed which do not correspond with current scales of seismic intensity.

Keywords: earthquakes, instrumental records, seismic hazard, Japan

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Authors: Mohammadreza Hedyehzadeh, Mahdi Yousefi

Abstract:

Colon cancer is one of the most common cancer, which predicted to increase its prevalence due to the bad eating habits of peoples. Nowadays, due to the busyness of people, the use of fast foods is increasing, and therefore, diagnosis of this disease and its treatment are of particular importance. To determine the best treatment approach for each specific colon cancer patients, the oncologist should be known the stage of the tumor. The most common method to determine the tumor stage is TNM staging system. In this system, M indicates the presence of metastasis, N indicates the extent of spread to the lymph nodes, and T indicates the size of the tumor. It is clear that in order to determine all three of these parameters, an imaging method must be used, and the gold standard imaging protocols for this purpose are CT and PET/CT. In CT imaging, due to the use of X-rays, the risk of cancer and the absorbed dose of the patient is high, while in the PET/CT method, there is a lack of access to the device due to its high cost. Therefore, in this study, we aimed to estimate the tumor size and the extent of its spread to the lymph nodes using MR images. More than 1300 MR images collected from the TCIA portal, and in the first step (pre-processing), histogram equalization to improve image qualities and resizing to get the same image size was done. Two expert radiologists, which work more than 21 years on colon cancer cases, segmented the images and extracted the tumor region from the images. The next step is feature extraction from segmented images and then classify the data into three classes: T0N0، T3N1 و T3N2. In this article, the VGG-16 convolutional neural network has been used to perform both of the above-mentioned tasks, i.e., feature extraction and classification. This network has 13 convolution layers for feature extraction and three fully connected layers with the softmax activation function for classification. In order to validate the proposed method, the 10-fold cross validation method used in such a way that the data was randomly divided into three parts: training (70% of data), validation (10% of data) and the rest for testing. It is repeated 10 times, each time, the accuracy, sensitivity and specificity of the model are calculated and the average of ten repetitions is reported as the result. The accuracy, specificity and sensitivity of the proposed method for testing dataset was 89/09%, 95/8% and 96/4%. Compared to previous studies, using a safe imaging technique (MRI) and non-use of predefined hand-crafted imaging features to determine the stage of colon cancer patients are some of the study advantages.

Keywords: colon cancer, VGG-16, magnetic resonance imaging, tumor size, lymph node metastasis

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