Search results for: quantum key distribution systems

Authors: Hafiz Muhammad Tayyab Bhatti, Munazza Usmani

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One of nature's most destructive occurrences, floods do immense damage to communities and economic losses. Nigeria country, specifically southern Nigeria, is known for being prone to flooding. Even though periodic flooding occurs in Nigeria frequently, the floods of 2022 were the worst since those in 2012. Flood vulnerability analysis and mapping are still lacking in this region due to the very limited historical hydrological measurements and surveys on the effects of floods, which makes it difficult to develop and put into practice efficient flood protection measures. Remote sensing and Geographic Information Systems (GIS) are useful approaches to detecting, determining, and estimating the flood extent and its impacts. In this study, NOAA VIIR has been used to extract the flood extent using the flood water fraction data and afterward fused with GIS data for some zonal statistical analysis. The estimated possible flooding areas are validated using satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS). The goal is to map and studied flood extent, flood hazards, and their effects on the population, schools, and health facilities for each state of Nigeria. The resulting flood hazard maps show areas with high-risk levels clearly and serve as an important reference for planning and implementing future flood mitigation and control strategies. Overall, the study demonstrated the viability of using the chosen GIS and remote sensing approaches to detect possible risk regions to secure local populations and enhance disaster response capabilities during natural disasters.

Keywords: flood hazards, remote sensing, damage assessment, GIS, geospatial analysis

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Authors: Ankit Sharma, Mudit Sachan, Anurag Prakash

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Rapid growth in data acquisition technologies through drones, have led to advances and interests in collecting high-resolution images of geological fields. Being advantageous in capturing high volume of data in short flights, a number of challenges have to overcome for efficient analysis of this data, especially while data acquisition, image interpretation and processing. We introduce a method that allows effective mapping of geological fields using photogrammetric data of surfaces, drainage area, water bodies etc, which will be captured by airborne vehicles like UAVs, we are not taking satellite images because of problems in adequate resolution, time when it is captured may be 1 yr back, availability problem, difficult to capture exact image, then night vision etc. This method includes advanced automated image interpretation technology and human data interaction to model structures and. First Geological structures will be detected from the primary photographic dataset and the equivalent three dimensional structures would then be identified by digital elevation model. We can calculate dip and its direction by using the above information. The structural map will be generated by adopting a specified methodology starting from choosing the appropriate camera, camera’s mounting system, UAVs design ( based on the area and application), Challenge in air borne systems like Errors in image orientation, payload problem, mosaicing and geo referencing and registering of different images to applying DEM. The paper shows the potential of using our method for accurate and efficient modeling of geological structures, capture particularly from remote, of inaccessible and hazardous sites.

Keywords: digital elevation model, mapping, photogrammetric data analysis, geological structures

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Authors: Anne Gesare Timu, Megan Sheahan, Andrew Gache Mude, Rupsha Banerjee

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Women headed households are among the most vulnerable to negative climatic shocks and are often left poorer as a result. Credit provision has been recognized as one way of alleviating rural poverty and developing poor rural households’ resilience to shocks. Much has been documented about credit demand in small-holder agriculture settings in Kenya. However, little is known about demand for credit among pastoral women. This paper analyzes the determinants of demand for credit in the pastoral regions of Marsabit District of Northern Kenya. Using a five wave balanced panel data set of 820 households, a double hurdle model is employed to analyze if shocks, financial literacy and risk aversion affect credit demand among female and male headed households differently. The results show that borrowing goods on credit and monetary credit from informal market segments are the most common sources of credit in the study area. The impact of livestock loss and financial literacy on the decision to borrow and how much to borrow vary with gender. While the paper suggests that provision of credit is particularly valuable in the aftermath of a negative shock and more so for female-headed households, it also explores alternatives to the provision of credit where credit access is a constraint. It recommends further understanding of systems and institutions which could enhance access to credit, and particularly during times of stress, to enable households in the study area in particular and Northern Kenya in general to invest, engage in meaningful development and growth, and be resilient to persistent shocks.

Keywords: female headed households, pastoralism, rural financing, double hurdle model

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Authors: Ousmane Sarr

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The Senegalese coasts have high productivity of fishery resources due to the frequency of intense up-welling system that occurs along its coast, caused by the maritime trade winds making its waters nutrients rich. Fishing plays a primordial role in Senegal's socioeconomic plans and food security. However, a global diagnosis of the Senegalese maritime fishing sector has highlighted the challenges this sector encounters. Among these concerns, some significant stocks, a priority target for artisanal fishing, need further assessment. If no efforts are made in this direction, most stock will be overexploited or even in decline. It is in this context that this research was initiated. This investigation aimed to apply a multi-modal approach (LBB, Catch-only-based CMSY model and its most recent version (CMSY++); JABBA, and JABBA-Select) to assess the stock of Atlantic bonito, Sarda sarda (Bloch, 1793) in the Senegalese Exclusive Economic Zone (SEEZ). Available catch, effort, and size data from Atlantic bonito over 15 years (2004-2018) were used to calculate the nominal and standardized CPUE, size-frequency distribution, and length at retentions (50 % and 95 % selectivity) of the species. These relevant results were employed as input parameters for stock assessment models mentioned above to define the stock status of this species in this region of the Atlantic Ocean. The LBB model indicated an Atlantic bonito healthy stock status with B/BMSY values ranging from 1.3 to 1.6 and B/B0 values varying from 0.47 to 0.61 of the main scenarios performed (BON_AFG_CL, BON_GN_Length, and BON_PS_Length). The results estimated by LBB are consistent with those obtained by CMSY. The CMSY model results demonstrate that the SEEZ Atlantic bonito stock is in a sound condition in the final year of the main scenarios analyzed (BON, BON-bt, BON-GN-bt, and BON-PS-bt) with sustainable relative stock biomass (B2018/BMSY = 1.13 to 1.3) and fishing pressure levels (F2018/FMSY= 0.52 to 1.43). The B/BMSY and F/FMSY results for the JABBA model ranged between 2.01 to 2.14 and 0.47 to 0.33, respectively. In contrast, The estimated B/BMSY and F/FMSY for JABBA-Select ranged from 1.91 to 1.92 and 0.52 to 0.54. The Kobe plots results of the base case scenarios ranged from 75% to 89% probability in the green area, indicating sustainable fishing pressure and an Atlantic bonito healthy stock size capable of producing high yields close to the MSY. Based on the stock assessment results, this study highlighted scientific advice for temporary management measures. This study suggests an improvement of the selectivity parameters of longlines and purse seines and a temporary prohibition of the use of sleeping nets in the fishery for the Atlantic bonito stock in the SEEZ based on the results of the length-base models. Although these actions are temporary, they can be essential to reduce or avoid intense pressure on the Atlantic bonito stock in the SEEZ. However, it is necessary to establish harvest control rules to provide coherent and solid scientific information that leads to appropriate decision-making for rational and sustainable exploitation of Atlantic bonito in the SEEZ and the Eastern Atlantic Ocean.

Keywords: multi-model approach, stock assessment, atlantic bonito, healthy stock, sustainable, SEEZ, temporary management measures

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Authors: Zohreh Moghaddas, Morteza Yazdani, Farhad Hosseinzadeh

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Nowadays, data envelopment analysis (DEA) models featuring network structures have gained widespread usage for evaluating the performance of production systems and activities (Decision-Making Units (DMUs)) across diverse fields. By examining the relationships between the internal stages of the network, these models offer valuable insights to managers and decision-makers regarding the performance of each stage and its impact on the overall network. To further empower system decision-makers, the inverse data envelopment analysis (IDEA) model has been introduced. This model allows the estimation of crucial information for estimating parameters while keeping the efficiency score unchanged or improved, enabling analysis of the sensitivity of system inputs or outputs according to managers' preferences. This empowers managers to apply their preferences and policies on resources, such as inputs and outputs, and analyze various aspects like production, resource allocation processes, and resource efficiency enhancement within the system. The results obtained can be instrumental in making informed decisions in the future. The top result of this study is an analysis of infeasibility and incorrect estimation that may arise in the theory and application of the inverse model of data envelopment analysis with network structures. By addressing these pitfalls, novel protocols are proposed to circumvent these shortcomings effectively. Subsequently, several theoretical and applied problems are examined and resolved through insightful case studies.

Keywords: inverse models of data envelopment analysis, series network, estimation of inputs and outputs, efficiency, resource allocation, sensitivity analysis, infeasibility

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Authors: Cristian Pauna

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Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: Muhammad Izzam Bin Mohd Khazar, Ruqayyah Binti Mohamad Ali, Nurul Atiqah Binti Yusri

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Value-based intermediation (VBI) has been introduced by Bank Negara Malaysia (BNM) as the next strategic direction and growth driver for Islamic banking institutions. The aim of VBI is to deliver the intended outcome of Shariah through practices, conducts, and offerings that generate positive and sustainable impact to the economy, community and environment which is aligned to Maqasid Shariah in preserving the common interest of society by preventing harm and maximizing benefit. Hence, upon its implementation, VBI will experiment the current Shariah compliance treatment and revolutionise new policies and systems that can meritoriously entrench and convey the objectives of Shariah. However, discussion revolving VBI in the light of Maqasid analysis is still scarce hence further research needs to be undertaken. The idea of implementation of VBI vision into quantifiable Maqasid Shariah measurement is yet to be explored due to the nature of Maqasid that is variable. The contemporary scholars also have different views on the implementation of VBI. This paper aims to discuss on the importance of Maqasid Shariah in the current Islamic finance transactions by providing Shariah index measurement in the application of VBI. This study also intends to explore basic Shariah guidelines and parameters based on the objectives of Shariah; preservation of the five pillars (religion, life, progeny, intellect and wealth) with further elaboration on preservation of wealth under five headings: rawaj (circulation and marketability); wuduh (transparency); hifz (preservation); thabat (durability and tranquillity); and ‘adl (equity and justice). In alignment with these headings, Islamic finance can be innovated for VBI implementation, particularly in Maybank Islamic being a significant leader in the IFI market.

Keywords: Islamic Financial Institutions, Maqasid Index, Maqasid Shariah, sustainability, value-based intermediation

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Authors: Yue Sun, Yuan Wang

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The Grand Canal of China has been in use for more than two thousand years. It runs through the central and eastern regions of China and communicates with the five major river systems of Haihe River, Yellow River, Huaihe River, Yangtze River and Qiantang River from north to south. It is a complex, systematic and comprehensive water conservancy project in the period of agricultural civilization and includes the three parts of the Beijing-Hangzhou Canal, the Sui and Tang Dynasties Canal and the Eastern Zhejiang Canal. It covers eight provinces and cities including Beijing, Tianjin, Hebei, Shandong, Jiangsu, Zhejiang, Henan and Anhui. The Grand Canal is an important channel connecting the Central Plains and the Beijing-Hangzhou Canal, and it is also an important waterway trade channel. Nowadays, although the Grand Canal no longer bears the burden of communicating water transportation between the north and the south, the site of the Grand Canal is still a “historical museum” of the lifestyle of people who lived on the canal from the Ming and Qing Dynasties to the Republic of China. By means of literature reading and field investigation, this paper compares the different protection strategies of the Grand Canal in the region between the ancient villages of Huaxian and Xunxian, which witness the vicissitudes of canal water transport, to explore whether the protective renovation of historical and cultural routes is “protection” or “destruction”, and puts forward some protection suggestions.

Keywords: The Grand Canal, heritage conservation, cultural route, ancient villages, strategies

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Authors: Kaaryn Cater

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People vary in how they perceive, process, and react to internal, external, social, and emotional environmental factors; some are more sensitive than others. Compassionate people have a highly reactive nervous system and are more impacted by positive and negative environmental conditions (Differential Susceptibility). Further, some sensitive individuals are disproportionately able to benefit from positive and supportive environments without necessarily suffering negative impacts in less supportive environments (Vantage Sensitivity). Environmental sensitivity is underpinned by physiological, genetic, and personality/temperamental factors, and the phenotypic expression of high sensitivity is Sensory Processing Sensitivity. The hallmarks of Sensory Processing Sensitivity are deep cognitive processing, emotional reactivity, high levels of empathy, noticing environmental subtleties, a tendency to observe new and novel situations, and a propensity to become overwhelmed when over-stimulated. Several educational advantages associated with high sensitivity include creativity, enhanced memory, divergent thinking, giftedness, and metacognitive monitoring. High sensitivity can also lead to some educational challenges, particularly managing multiple conflicting demands and negotiating low sensory thresholds. A mixed methods study was undertaken. In the first quantitative study, participants completed the Perceived Success in Study Survey (PSISS) and the Highly Sensitive Person Scale (HSPS-12). Inclusion criteria were current or previous postsecondary education experience. The survey was presented on social media, and snowball recruitment was employed (n=365). The Excel spreadsheets were uploaded to the statistical package for the social sciences (SPSS)26, and descriptive statistics found normal distribution. T-tests and analysis of variance (ANOVA) calculations found no difference in the responses of demographic groups, and Principal Components Analysis and the posthoc Tukey calculations identified positive associations between high sensitivity and three of the five PSISS factors. Further ANOVA calculations found positive associations between the PSISS and two of the three sensitivity subscales. This study included a response field to register interest in further research. Respondents who scored in the 70th percentile on the HSPS-12 were invited to participate in a semi-structured interview. Thirteen interviews were conducted remotely (12 female). Reflexive inductive thematic analysis was employed to analyse data, and a descriptive approach was employed to present data reflective of participant experience. The results of this study found that compassionate students prioritize work-life balance; employ a range of practical metacognitive study and self-care strategies; value independent learning; connect with learning that is meaningful; and are bothered by aspects of the physical learning environment, including lighting, noise, and indoor environmental pollutants. There is a dearth of research investigating sensitivity in the educational context, and these studies highlight the need to promote widespread education sector awareness of environmental sensitivity, and the need to include sensitivity in sector and institutional diversity and inclusion initiatives.

Keywords: differential susceptibility, highly sensitive person, learning, neurosensitivity, sensory processing sensitivity, vantage sensitivity

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Authors: Dachuan Shi, M. Hecht, Y. Ye

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With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

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Authors: Gabriel Sunday Ayayia

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Food waste has reached alarming levels worldwide, contributing to food insecurity, resource depletion, and environmental degradation. While numerous strategies exist to mitigate this issue, the role of traditional practices and indigenous knowledge remains underexplored. There is a need to investigate how these age-old practices can contribute to sustainable food waste reduction, particularly in the African context. This study explores the potential of traditional practices and indigenous knowledge in Africa to address this challenge sustainably. The study examines traditional African food management practices and indigenous knowledge related to food preservation and utilization; assess the impact of traditional practices on reducing food waste and its broader implications for sustainable development, and identify key factors influencing the continued use and effectiveness of traditional practices in contemporary African societies. Thus, the study argues that traditional practices and indigenous knowledge in Africa offer valuable insights and strategies for sustainable food waste reduction that can be adapted and integrated into global initiatives This research will employ a mixed-methods approach, combining qualitative and quantitative research techniques. Data collection will involve in-depth interviews, surveys, and participant observations in selected African communities. Moreover, a comprehensive review of literature on traditional food management practices and their impact on food waste reduction will be conducted. The significance of this study lies in its potential to bridge the gap between traditional knowledge and modern sustainability efforts. By uncovering the value of traditional practices in reducing food waste, this research can inform policies, interventions, and awareness campaigns aimed at achieving sustainable food systems worldwide.

Keywords: traditional practices, indigenous knowledge, food waste reduction, sustainability

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Authors: S. S. Adamu, H. S. Muhammad, D. S. Shuaibu

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Poor power quality in electrical power systems can lead to medical equipment at healthcare centres to malfunction and present wrong medical diagnosis. Equipment such as X-rays, computerized axial tomography, etc. can pollute the system due to their high level of harmonics production, which may cause a number of undesirable effects like heating, equipment damages and electromagnetic interferences. The conventional approach of mitigation uses passive inductor/capacitor (LC) filters, which has some drawbacks such as, large sizes, resonance problems and fixed compensation behaviours. The current trends of solutions generally employ active power filters using suitable control algorithms. This work focuses on assessing the level of Total Harmonic Distortion (THD) on medical facilities and various ways of mitigation, using radiology unit of an existing hospital as a case study. The measurement of the harmonics is conducted with a power quality analyzer at the point of common coupling (PCC). The levels of measured THD are found to be higher than the IEEE 519-1992 standard limits. The system is then modelled as a harmonic current source using MATLAB/SIMULINK. To mitigate the unwanted harmonic currents a shunt active filter is developed using synchronous detection algorithm to extract the fundamental component of the source currents. Fuzzy logic controller is then developed to control the filter. The THD without the active power filter are validated using the measured values. The THD with the developed filter show that the harmonics are now within the recommended limits.

Keywords: power quality, total harmonics distortion, shunt active filters, fuzzy logic

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Authors: Onur Kaya, Halit Bayer

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Global grocery and supermarket sales are among the largest markets in the world and perishable products such as fresh produce, dairy and meat constitute the biggest section of these markets. Due to their deterioration over time, the demand for these products depends highly on their freshness. They become totally obsolete after a certain amount of time causing a high amount of wastage and decreases in grocery profits. In addition, customers are asking for higher product variety in perishable product categories, leading to less predictable demand per product and to more out-dating. Effective management of these perishable products is an important issue since it is observed that billions of dollars’ worth of food is expired and wasted every month. We consider coordinated inventory and pricing decisions for perishable products with a time and price dependent random demand function. We use stochastic dynamic programming to model this system for both periodically-reviewed and continuously-reviewed inventory systems and prove certain structural characteristics of the optimal solution. We prove that the optimal ordering decision scenario has a monotone structure and the optimal price value decreases by time. However, the optimal price changes in a non-monotonic structure with respect to inventory size. We also analyze the effect of 1 different parameters on the optimal solution through numerical experiments. In addition, we analyze simple-to-implement heuristics, investigate their effectiveness and extract managerial insights. This study gives valuable insights about the management of perishable products in order to decrease wastage and increase profits.

Keywords: age-dependent demand, dynamic programming, perishable inventory, pricing

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Authors: Jalal Maqbool, Gyu Myoung Lee

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Using smart devices for context aware mobile learning is becoming increasingly popular. This has led to mobile learning technology becoming an indispensable part of today’s learning environment and platforms. However, some fundamental issues remain - namely, mobile learning still lacks the ability to truly understand human reaction and user behaviour. This is due to the fact that current mobile learning systems are passive and not aware of learners’ changing contextual situations. They rely on static information about mobile learners. In addition, current mobile learning platforms lack the capability to incorporate dynamic contextual situations into learners’ preferences. Thus, this thesis aims to address these issues highlighted by designing a context aware framework which is able to sense learner’s contextual situations, handle data dynamically, and which can use contextual information to suggest bespoke learning content according to a learner’s preferences. This is to be underpinned by a robust recommendation system, which has the capability to perform these functions, thus providing learners with a truly context-aware mobile learning experience, delivering learning contents using smart devices and adapting to learning preferences as and when it is required. In addition, part of designing an algorithm for the recommendation engine has to be based on learner and application needs, personal characteristics and circumstances, as well as being able to comprehend human cognitive processes which would enable the technology to interact effectively and deliver mobile learning content which is relevant, according to the learner’s contextual situations. The concept of this proposed project is to provide a new method of smart learning, based on a capable recommendation engine for providing an intuitive mobile learning model based on learner actions.

Keywords: aware, context, learning, mobile

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Authors: Abbas Jafarizad, Duygu Ekinci

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In this work targeted drug delivery is proposed to decrease adverse effect of drugs with concomitant reduces in consumption and treatment outgoings. Nanoparticles (NPs) can be prepared from a variety of materials such as lipid, biodegradable polymer that prevent the drugs cytotoxicity in healthy cells, etc. One of the most important drugs used in chemotherapy is mitoxantrone (MTX) which prevents cell proliferation by inhibition of topoisomerase II and DNA repair; however, it is not selective and has some serious side effects. In this study, mentioned aim is achieved by using several nanocarriers like magnetite (Fe3O4) and their composites with magnetic graphene oxide (Fe3O4@GO). Also, cytotoxic potential of Fe3O4, Fe3O4-MTX, and Fe3O4@GO-MTX nanocomposite were evaluated on mesenchymal stem cells (MSCs). In this study, we reported the synthesis of monodisperse Fe3O4 NPs and Fe3O4@GO nanocomposite and their structures were investigated by using field emission scanning electron microscope (FESEM), Fourier transform infrared (FTIR) spectra, atomic force microscopy (AFM), Brauneur Emmet Teller (BET) isotherm and contact angle studies. Moreover, we used 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay to evaluate cytotoxic effects of MTX, Fe3O4 NPs, Fe3O4-MTX and Fe3O4@GO-MTX nanocomposite on MSCs. The in-vitro MTT results indicated that all concentrations of MTX and Fe3O4@GO-MTX nanocomposites showed cytotoxic effects while all concentrations of Fe3O4 NPs and Fe3O4-MTX NPs did not show any cytotoxic effect on stem cells. The results from this study indicated that using Fe3O4 NPs as anticancer drug delivery systems could be a trustworthy method for cancer treatment. But for reaching excellent and accurate results, further investigation is necessary.

Keywords: mitoxantrone, magnetite, magnetic graphene oxide, MTT assay, mesenchymal stem cells

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Authors: Chin-Chih Chang

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Many manufacturing systems suffer failures due to complex degradation processes and various environment conditions such as random shocks. Consider an operating system is subject to random shocks and works at random times for successive jobs. When successive jobs often result in production losses and performance deterioration, it would be better to do maintenance or replacement at a planned time. A preventive replacement (PR) policy is presented to replace the system before a failure occurs at a continuous time T. In such a policy, the failure characteristics of the system are designed as follows. Each job would cause a random amount of additive damage to the system, and the system fails when the cumulative damage has exceeded a failure threshold. Suppose that the deteriorating system suffers one of the two types of shocks with age-dependent probabilities: type-I (minor) shock is rectified by a minimal repair, or type-II (catastrophic) shock causes the system to fail. A corrective replacement (CR) is performed immediately when the system fails. In summary, a generalized maintenance model to scheduling replacement plan for an operating system is presented below. PR is carried out at time T, whereas CR is carried out when any type-II shock occurs and the total damage exceeded a failure level. The main objective is to determine the optimal continuous schedule time of preventive replacement through minimizing the mean cost rate function. The existence and uniqueness of optimal replacement policy are derived analytically. It can be seen that the present model is a generalization of the previous models, and the policy with preventive replacement outperforms the one without preventive replacement.

Keywords: preventive replacement, working time, cumulative damage model, minimal repair, imperfect maintenance, optimization

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Authors: Ingy Ibrahim El-Darwish, Neveen Youssef Azmy

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The endowment of a healthy environment can be implemented by endorsing sustainable fundamentals. Design of sustainable buildings through recycling of waste, can reduce health problems, provide good environments and contribute to the aesthetically pleasing entourage. Such environments can help in providing energy-saving alternatives to consolidate the principles of sustainability. The poor community awareness and the absence of laws and legislation in Egypt for waste management specifically in residential areas have led to an inability to provide an integrated system for waste management in urban and rural areas. Many problems and environmental challenges face the Egyptian urban environments. From these problems, is the lack of a cohesive vision for waste collection and recycling for energy-saving. The second problem is the lack public awareness of the short term and long term vision of waste management. Bad practices have adversely affected the efficiency of environmental management systems due to lack of urban legislations that codify collection and recycling of residential communities in Egyptian urban environments. Hence, this research tries to address residents on waste management matters to facilitate legislative process on waste collection and classification within residential units and outside them in a preparation phase for recycling in the Egyptian urban environments. In order to achieve this goal, one of the Egyptian communities has been addressed, analyzed and studied. Waste collection, classification, separation and access to recycling places in the urban city are proposed in preparation for a legislation ruling and regulating the process. Hence, sustainable principles are to be achieved.

Keywords: recycling, residential buildings, sustainability, waste

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Authors: Khawla Askri, Mohamed Haythem Msaddek, AbdelAziz Sebei

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Climate change combined with the increase in anthropogenic activities will affect coastal groundwater systems around the world and, more particularly, the Cap Bon region in the North East of Tunisia. This study aims to study the impact of climate change and human stress on the salinization and quantification of groundwater in the Wadi Chiba watershed. In this regard, a hydro-climatological study and a hydrogeological study were carried out based on the characterization of the aquifer system of the eastern coast at the level of the watershed of Wadi Chiba in order to seek to identify, first of all, the degradation of the state of the aquifer on the quantitative level by the study of the piezometric and its evolution over time. Secondly, we sought to identify the degradation of the state of the aquifer qualitatively by using the geochemical method, in particular the major elements, to assess the mineralization of the aquifer water and understand its hydrogeochemical functioning. The study of the Na + / Cl- and Ca2 + / Mg2 + chemical relationships confirmed the presence of a marine intrusion downstream of the Wadi Chiba watershed northeast of Cap-Bon accompanied by a piezometric depression. For this purpose, we proceeded to: 1) Mapping of both piezometric data and salinity. 2) The interpretation of the mapping results. 3)Identification of the origin of the localized deterioration in the quality of the aquifer water. Finally, the analysis of the results showed that the scarcity of water is already forcing human actions in the Chiba watershed due to the irrigation of agricultural lands and the overexploitation of the water table in the study area.

Keywords: climate change, human activities, water table, Wadi Chiba watershed, piezometric depression, marine intrusion

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Authors: Mukisa Daphine Letisha

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Plea bargaining, a practice often associated with more developed legal systems, has emerged as a significant tool within Uganda's criminal justice system despite its absence in formal legal structures inherited from its colonial past. Initiated in 2013 with the aim of reducing case backlogs, expediting trials, and addressing prison congestion, plea bargaining reflects a pragmatic response to systemic challenges. While rooted in international statutes and domestic constitutional provisions, its implementation relies heavily on the Judicature (Plea Bargain) Rules of 2016, which outline procedural requirements and safeguards. Advocates argue that plea bargaining has yielded tangible benefits, including a reduction in case backlog and efficient allocation of resources, with notable support from judicial and prosecutorial authorities. Case examples demonstrate successful outcomes, with accused individuals benefitting from reduced sentences in exchange for guilty pleas. However, challenges persist, including procedural irregularities, inadequate statutory provisions, and concerns about coercion and imbalance of power between prosecutors and accused individuals. To enhance efficacy, recommendations focus on establishing monitoring mechanisms, stakeholder training, and public sensitization campaigns. In conclusion, while plea bargaining offers potential advantages in streamlining Uganda's criminal justice system, addressing its challenges requires careful consideration of procedural safeguards and stakeholder engagement to ensure fairness and integrity in the administration of justice.

Keywords: plea-bargaining, criminal-justice system, uganda, efficacy

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Authors: Gabrielle Robertson, Matthew Downs, Joseph Dagher

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Iron deposition in the brain has been linked with a host of neurological disorders such as Alzheimer’s, Parkinson’s, and Multiple Sclerosis. While some treatment options exist, there are no objective measurement tools that allow for the monitoring of iron levels in the brain in vivo. An emerging Magnetic Resonance Imaging (MRI) method has been recently proposed to deduce iron concentration through quantitative measurement of magnetic susceptibility. This is a multi-step process that involves repeated modeling of physical processes via approximate numerical solutions. For example, the last two steps of this Quantitative Susceptibility Mapping (QSM) method involve I) mapping magnetic field into magnetic susceptibility and II) mapping magnetic susceptibility into iron concentration. Process I involves solving an ill-posed inverse problem by using regularization via injection of prior belief. The end result from Process II highly depends on the model used to describe the molecular content of each voxel (type of iron, water fraction, etc.) Due to these factors, the accuracy and repeatability of QSM have been an active area of research in the MRI and medical imaging community. This work aims to estimate iron concentration in the brain via a single step. A synthetic numerical model of the human head was created by automatically and manually segmenting the human head on a high-resolution grid (640x640x640, 0.4mm³) yielding detailed structures such as microvasculature and subcortical regions as well as bone, soft tissue, Cerebral Spinal Fluid, sinuses, arteries, and eyes. Each segmented region was then assigned tissue properties such as relaxation rates, proton density, electromagnetic tissue properties and iron concentration. These tissue property values were randomly selected from a Probability Distribution Function derived from a thorough literature review. In addition to having unique tissue property values, different synthetic head realizations also possess unique structural geometry created by morphing the boundary regions of different areas within normal physical constraints. This model of the human brain is then used to create synthetic MRI measurements. This is repeated thousands of times, for different head shapes, volume, tissue properties and noise realizations. Collectively, this constitutes a training-set that is similar to in vivo data, but larger than datasets available from clinical measurements. This 3D convolutional U-Net neural network architecture was used to train data-driven Deep Learning models to solve for iron concentrations from raw MRI measurements. The performance was then tested on both synthetic data not used in training as well as real in vivo data. Results showed that the model trained on synthetic MRI measurements is able to directly learn iron concentrations in areas of interest more effectively than other existing QSM reconstruction methods. For comparison, models trained on random geometric shapes (as proposed in the Deep QSM method) are less effective than models trained on realistic synthetic head models. Such an accurate method for the quantitative measurement of iron deposits in the brain would be of important value in clinical studies aiming to understand the role of iron in neurological disease.

Keywords: magnetic resonance imaging, MRI, iron deposition, machine learning, quantitative susceptibility mapping

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Authors: Alisher Ibragimov, Sofya Senotrusova, Aleksandra Beliaeva, Egor Ushakov, Yuri Markin

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Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification.

Keywords: breast cancer, computer-aided diagnosis, deep learning, multi-view mammogram, siamese neural network

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Authors: Saurabh Shukla, G. N. Pandey

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In recent times, oil exploration and monitoring in on-shore areas have gained much importance considering the fact that in India the oil import is 62 percent of the total imports. Thus, architectural model like wireless sensor network to monitor on-shore deep sea oil well is being developed to get better estimate of the oil prospects. The problem we are facing nowadays that we have very few restricted areas of oil left today. Countries like India don’t have much large areas and resources for oil and this problem with most of the countries that’s why it has become a major problem when we are talking about oil exploration in on-shore areas also the increase of oil prices has further ignited the problem. For this the use of wireless network system having relative simplicity, smallness in size and affordable cost of wireless sensor nodes permit heavy deployment in on-shore places for monitoring oil wells. Deployment of wireless sensor network in large areas will surely reduce the cost it will be very much cost effective. The objective of this system is to send real time information of oil monitoring to the regulatory and welfare authorities so that suitable action could be taken. This system architecture is composed of sensor network, processing/transmission unit and a server. This wireless sensor network system could remotely monitor the real time data of oil exploration and monitoring condition in the identified areas. For wireless sensor networks, the systems are wireless, have scarce power, are real-time, utilize sensors and actuators as interfaces, have dynamically changing sets of resources, aggregate behaviour is important and location is critical. In this system a communication is done between the server and remotely placed sensors. The server gives the real time oil exploration and monitoring conditions to the welfare authorities.

Keywords: sensor, wireless sensor network, oil, sensor, on-shore level

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Authors: Abdul Azarrudin M. A., Pothiraj K., Kandasamy Satish

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In the current automobile scenario, there comes a demand on more sophistication and comfort drive feel on passenger segments. The clutch pedal effort is one such customer touch feels in manual transmission vehicles, where the driver continuous to operate the clutch pedal in his entire the driving maneuvers. Hence optimum pedal efforts at green condition and over mileage to be ensured for fatigue free the driving. As friction is one the predominant factor and its tendency to challenge the technicality by causing the function degradation. One such semi-hydraulic systems shows load efficiency of about 70-75% over lifetime only due to the increase in friction which leads to the increase in pedal effort and cause fatigue to the vehicle driver. This work deals with the study of friction with different interfaces and its influence in the fulcrum points over mileage, with the objective of understanding the trend over mileage and determining the alternative ways of resolving it. In that one way of methodology is the reduction of friction by experimental investigation of various friction reduction interfaces like metal-to-metal interface and it has been tried out and is detailed further. Also, the specific attention has been put up considering the fulcrum load and its contact interfaces to move on with this study. The main results of the experimental data with the influence of three different contact interfaces are being presented with an ultimate intention of ending up into less fatigue with longer consistent pedal effort, thus smoothens the operation of the end user. The Experimental validation also has been done through rig-level test setup to depict the performance at static condition and in-parallel vehicle level test has also been performed to record the additional influences if any.

Keywords: automobile, clutch, friction, fork

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Authors: Ratri Werdiningtyas, Yongping Wei, Andrew Western

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The status quo of social-ecological systems is the results of not only natural processes but also the accumulated consequence of policies applied in the past. Often water management objectives are challenging and are only achieved to a limited degree on the ground. In choosing water management approaches, it is important to account for current conditions and important differences due to varied histories. Since the mid-nineteenth century, Victorian water management has evolved through a series of policy regime shifts. The main goal of this research to explore and identify the stages of the evolution of the water policy instruments as practiced in Victoria from 1890-2016. This comparative historical analysis has identified four stages in Victorian policy instrument development. In the first stage, the creation of policy instruments aimed to match the demand and supply of the resource (reserve condition). The second stage begins after natural system alone failed to balance supply and demand. The focus of the policy instrument shifted to an authority perspective in this stage. Later, the increasing number of actors interested in water led to another change in policy instrument. The third stage focused on the significant role of information from different relevant actors. The fourth and current stage is the most advanced, in that it involved the creation of a policy instrument for synergizing the previous three focal factors: reserve, authority, and information. When considering policy in other jurisdiction, these findings suggest that a key priority should be to reflect on the jurisdictions current position among these four evolutionary stages and try to make improve progressively rather than directly adopting approaches from elsewhere without understanding the current position.

Keywords: policy instrument, policy transformation, socio-ecolgical system, water management

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Authors: M. Taheri Tehrani, H. Ajorloo

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In this paper, an intelligent multi-agent framework is developed for each router in which agents have two vital functionalities, traffic shaping and buffer allocation and are positioned in the ports of the routers. With traffic shaping functionality agents shape the traffic forward by dynamic and real time allocation of the rate of generation of tokens in a Token Bucket algorithm and with buffer allocation functionality agents share their buffer capacity between each other based on their need and the conditions of the network. This dynamic and intelligent framework gives this opportunity to some ports to work better under burst and more busy conditions. These agents work intelligently based on Reinforcement Learning (RL) algorithm and will consider effective parameters in their decision process. As RL have limitation considering much parameter in its decision process due to the volume of calculations, we utilize our novel method which invokes Principle Component Analysis (PCA) on the RL and gives a high dimensional ability to this algorithm to consider as much as needed parameters in its decision process. This implementation when is compared to our previous work where traffic shaping was done without any sharing and dynamic allocation of buffer size for each port, the lower packet drop in the whole network specifically in the source routers can be seen. These methods are implemented in our previous proposed intelligent simulation environment to be able to compare better the performance metrics. The results obtained from this simulation environment show an efficient and dynamic utilization of resources in terms of bandwidth and buffer capacities pre allocated to each port.

Keywords: principal component analysis, reinforcement learning, buffer allocation, multi- agent systems

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Authors: Thanthrige Thiunuwan Priyathilaka, Don Anushka Sandaruwan Elvitigala, Bong-Soo Lim, Hyung-Bok Jeong, Jehee Lee

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Toll like receptors (TLRs) are a phylogeneticaly conserved family of pattern recognition receptors, which participates in the host immune responses against various pathogens and pathogen derived mitogen. TLR21, a non-mammalian type, is almost restricted to the fish species even though those can be identified rarely in avians and amphibians. Herein, this study was carried out to identify and characterize TLR21 from rock bream (Oplegnathus fasciatus) designated as RbTLR21, at transcriptional and genomic level. In this study, the full length cDNA and genomic sequence of RbTLR21 was identified using previously constructed cDNA sequence database and BAC library, respectively. Identified RbTLR21 sequence was characterized using several bioinformatics tools. The quantitative real time PCR (qPCR) experiment was conducted to determine tissue specific expressional distribution of RbTLR21. Further, transcriptional modulation of RbTLR21 upon the stimulation with Streptococcus iniae (S. iniae), rock bream iridovirus (RBIV) and Edwardsiella tarda (E. tarda) was analyzed in spleen tissues. The complete coding sequence of RbTLR21 was 2919 bp in length which can encode a protein consisting of 973 amino acid residues with molecular mass of 112 kDa and theoretical isoelectric point of 8.6. The anticipated protein sequence resembled a typical TLR domain architecture including C-terminal ectodomain with 16 leucine rich repeats, a transmembrane domain, cytoplasmic TIR domain and signal peptide with 23 amino acid residues. Moreover, protein folding pattern prediction of RbTLR21 exhibited well-structured and folded ectodomain, transmembrane domain and cytoplasmc TIR domain. According to the pair wise sequence analysis data, RbTLR21 showed closest homology with orange-spotted grouper (Epinephelus coioides) TLR21with 76.9% amino acid identity. Furthermore, our phylogenetic analysis revealed that RbTLR21 shows a close evolutionary relationship with its ortholog from Danio rerio. Genomic structure of RbTLR21 consisted of single exon similar to its ortholog of zebra fish. Sevaral putative transcription factor binding sites were also identified in 5ʹ flanking region of RbTLR21. The RBTLR 21 was ubiquitously expressed in all the tissues we tested. Relatively, high expression levels were found in spleen, liver and blood tissues. Upon induction with rock bream iridovirus, RbTLR21 expression was upregulated at the early phase of post induction period even though RbTLR21 expression level was fluctuated at the latter phase of post induction period. Post Edwardsiella tarda injection, RbTLR transcripts were upregulated throughout the experiment. Similarly, Streptococcus iniae induction exhibited significant upregulations of RbTLR21 mRNA expression in the spleen tissues. Collectively, our findings suggest that RbTLR21 is indeed a homolog of TLR21 family members and RbTLR21 may be involved in host immune responses against bacterial and DNA viral infections.

Keywords: rock bream, toll like receptor 21 (TLR21), pattern recognition receptor, genomic characterization

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Authors: Jixiang Lei, Alexander Fuchs, Franz Pernkopf, Katrin Ellermann

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Within the Space@Sea project, funded by the Horizon 2020 program, an island consisting of multiple platforms was designed. The platforms are connected by ropes and fenders. The connection is critical with respect to the safety of the whole system. Therefore, fault detection systems are investigated, which could detect early warning signs for a possible failure in the connection elements. Previously, a model-based method called Extended Kalman Filter was developed to detect the reduction of rope stiffness. This method detected several types of faults reliably, but some types of faults were much more difficult to detect. Furthermore, the model-based method is sensitive to environmental noise. When the wave height is low, a long time is needed to detect a fault and the accuracy is not always satisfactory. In this sense, it is necessary to develop a more accurate and robust technique that can detect all rope faults under a wide range of operational conditions. Inspired by this work on the Space at Sea design, we introduce a fault diagnosis method based on deep neural networks. Our method cannot only detect rope degradation by using the acceleration data from each platform but also estimate the contributions of the specific acceleration sensors using methods from explainable AI. In order to adapt to different operational conditions, the domain adaptation technique DANN is applied. The proposed model can accurately estimate rope degradation under a wide range of environmental conditions and help users understand the relationship between the output and the contributions of each acceleration sensor.

Keywords: fault diagnosis, deep learning, domain adaptation, explainable AI

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Authors: Monica Plechero

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Many firms belonging to Italian industrial districts faced a crisis starting from 2000 and upsurging during 2008-2014. To remain competitive in the global market, these firms and their local systems need to renovate their traditional competitive advantages, strengthen their link with global flows of knowledge. This may be particularly relevant in sectors such as the mechatronics, that combine traditional knowledge domain with new knowledge domains (e.g. mechanics, electronics, and informatics). This sector is nowadays one of the key sectors within the so-called ‘smart specialization strategy’ that can lead part of the Italian traditional industry towards new economic developmental opportunities. This paper, by investigating the mechatronics district of the Veneto region, wants to shed new light on how firms of a local system can gain from the globalization of innovation and innovation networks. Methodologically, the paper relies on primary data collected through a survey targeting firms of the local system, as well as on a number of qualitative case studies. The relevant role of medium size companies in the district emerges as evident, as they have wider opportunities to be involved in different processes of globalization of innovation. Indeed, with respect to small companies, the size of medium firms allows them to exploit strategically international markets and globally distributed knowledge. Supporting medium firms’ global innovation strategies, and incentivizing their role as district gatekeepers, may strengthen the competitive capability of the local system and provide new opportunities to positively face global competition.

Keywords: global innovation network, industrial district, internationalization, innovation, mechatronics, Veneto region

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Authors: Babatunde James Falaye, Shi Hai Dong, Kayode John Oyewumi

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We study the effects of oblateness up to J4 of the primaries and power-law density profile (PDP) on the linear stability of libration location of an innitesimal mass within the framework of restricted three body problem (R3BP), by using a more realistic model in which a disc with PDP is rotating around the common center of the system mass with perturbed mean motion. The existence and stability of triangular equilibrium points have been explored. It has been shown that triangular equilibrium points are stable for 0 < μ < μc and unstable for μc ≤ μ ≤ 1/2, where c denotes the critical mass parameter. We find that, the oblateness up to J2 of the primaries and the radiation reduces the stability range while the oblateness up to J4 of the primaries increases the size of stability both in the context where PDP is considered and ignored. The PDP has an eect of about ≈0:01 reduction on the application of c to Earth-Moon and Jupiter-Moons systems. We find that the comprehensive effects of the perturbations have a stabilizing proclivity. However, the oblateness up to J2 of the primaries and the radiation of the primaries have tendency for instability, while coecients up to J4 of the primaries have stability predisposition. In the limiting case c = 0, and also by setting appropriate parameter(s) to zero, our results are in excellent agreement with the ones obtained previously. Libration points play a very important role in space mission and as a consequence, our results have a practical application in space dynamics and related areas. The model may be applied to study the navigation and station-keeping operations of spacecraft (innitesimal mass) around the Jupiter (more massive) -Callisto (less massive) system, where PDP accounts for the circumsolar ring of asteroidal dust, which has a cloud of dust permanently in its wake.

Keywords: libration points, oblateness, power-law density profile, restricted three-body problem

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Authors: Gebrekiros Welegebriel Asfaw

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The management of the fragile, unique and irreplaceable cultural heritage from different perspectives is becoming a major challenge as important elements of culture are vanishing throughout the globe. The major purpose of this study is to assess how the cultural heritages of Aksum are managed for their future sustainability from heritage conservation and interpretation perspectives. Descriptive type of research design inculcating both quantitative and qualitative research methods is employed. Primary quantitative data was collected from 189 respondents (19 professionals, 88 tourism service providers and 82 tourists) and interview was conducted with 33 targeted informants from heritage and related professions, security employees, local community, service providers and church representatives by applying probability and non probability sampling methods. Findings of the study reveal that the overall sustainable management status of the cultural heritage of Aksum is below average. It is found that the sustainability of cultural heritage management in Aksum is facing a lot of unfavorable factors like lack of long term planning, incompatible system of heritage administration, limited capacity and number of professionals, scant attention to community based heritage and tourism development, dirtiness and drainage problems, problems with stakeholder involvement and cooperation, lack of organized interpretation and presentation systems and others. So, re-organization of the management system, creating platform for coordination among stakeholders and developing appropriate interpretation system can be good remedies. Introducing community based heritage and tourism development concept is also recommendable for a long term win-win success in Aksum.

Keywords: Aksum, conservation, interpretation, Sustainable Cultural Heritage Management

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