Search results for: tensor deep stacking neural networks

Authors: Ali J. Ghandour, Houssam A. Krayem, Abedelkarim A. Jezzini

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High-resolution satellite images and remote sensing can provide global information in a fast way compared to traditional methods of data collection. Under such high resolution, a road is not a thin line anymore. Objects such as cars and trees are easily identifiable. Automatic vehicles enumeration can be considered one of the most important applications in traffic management. In this paper, autonomous vehicle detection and classification approach in highway environment is proposed. This approach consists mainly of three stages: (i) first, a set of preprocessing operations are applied including soil, vegetation, water suppression. (ii) Then, road networks detection and delineation is implemented using built-up area index, followed by several morphological operations. This step plays an important role in increasing the overall detection accuracy since vehicles candidates are objects contained within the road networks only. (iii) Multi-level Otsu segmentation is implemented in the last stage, resulting in vehicle detection and classification, where detected vehicles are classified into cars and trucks. Accuracy assessment analysis is conducted over different study areas to show the great efficiency of the proposed method, especially in highway environment.

Keywords: remote sensing, object identification, vehicle and road extraction, vehicle and road features-based classification

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Authors: Mohamed Abdalla Saleem

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Around one hundred wells were drilled in the Cyrenaica platform north-east Libya, and almost all of them were dry. Although the drilled samples reveal good oil shows and good source rock maturity. Most of the upper Cretaceous age and the above deposit successions are outcrops in different places. We have a thorough understanding and mapping of the structures related to the Cretaceous and above Cenozoic Era. But the subsurface beneath these outcrops still needs more investigation and delineation. This study aims to give answers to some questions about the tectonic history and the types of structures that are distributed in the area using gravity, magnetic, and well data. According to the information that has been obtained from groups of wells drilled in concessions 31, 35, and 37, one can note that the depositional sections become ticker and deeper southward. The topography map of the study area shows that the area is highly elevated at the north, about 300 m above the sea level, while the minimum elevation (16–18 m) exists nearly in the middle (lat. 30°). South to this latitude, the area is started elevated again (more than 100 m). The third-order residual gravity map, which was constructed from the Bouguer gravity map, reveals that the area is dominated by a large negative anomaly working as a sub-basin (245 km x 220 km), which means a very thick depositional section, and the basement is very deep. The mentioned depocenter is surrounded by four high gravity anomalies (12-37 mGal), which means a shallow basement and a relative thinner succession of sediments. The highest gravity values are located beside the coast line. The total horizontal gradient (THG) map reveals various systems of structures, the first system where the structures are oriented NE-SW, which is crosscut by the second regime extending NW-SE. This second system is distributed through the whole area, but it is very strong and shallow near the coast line and at the south part, while it is relatively deep at the middle depocenter area.

Keywords: cyrenaica platform, gravity, structures, basement, tectonic history

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Authors: A. Khan, H. Mahmood

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In Underwater Wireless Sensor Networks (UWSNs), sensor nodes close to water surface (final destination) are often preferred for selection as forwarders. However, their frequent selection makes them depleted of their limited battery power. In consequence, these nodes die during early stage of network operation and create energy holes where forwarders are not available for packets forwarding. These holes severely affect network throughput. As a result, system performance significantly degrades. In this paper, a routing protocol is proposed to avoid energy holes during packets forwarding. The proposed protocol does not require the conventional position information (localization) of holes to avoid them. Localization is cumbersome; energy is inefficient and difficult to achieve in underwater environment where sensor nodes change their positions with water currents. Forwarders with the lowest water pressure level and the maximum number of neighbors are preferred to forward packets. These two parameters together minimize packet drop by following the paths where maximum forwarders are available. To avoid interference along the paths with the maximum forwarders, a packet holding time is defined for each forwarder. Simulation results reveal superior performance of the proposed scheme than the counterpart technique.

Keywords: energy holes, interference, routing, underwater

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Authors: Bhagwant Singh, Sikander Singh Cheema

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This paper examines firewall mechanisms routinely implemented for network security in depth. A firewall can't protect you against all the hazards of unauthorized networks. Consequently, many kinds of infrastructure are employed to establish a secure network. Firewall strategies have already been the subject of significant analysis. This study's primary purpose is to avoid unnecessary connections by combining the capability of the firewall with the use of additional firewall mechanisms, which include packet filtering and NAT, VPNs, and backdoor solutions. There are insufficient studies on firewall potential and combined approaches, but there aren't many. The research team's goal is to build a safe network by integrating firewall strength and firewall methods. The study's findings indicate that the recommended concept can form a reliable network. This study examines the characteristics of network security and the primary danger, synthesizes existing domestic and foreign firewall technologies, and discusses the theories, benefits, and disadvantages of different firewalls. Through synthesis and comparison of various techniques, as well as an in-depth examination of the primary factors that affect firewall effectiveness, this study investigated firewall technology's current application in computer network security, then introduced a new technique named "tight coupling firewall." Eventually, the article discusses the current state of firewall technology as well as the direction in which it is developing.

Keywords: firewall strategies, firewall potential, packet filtering, NAT, VPN, proxy services, firewall techniques

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Authors: Kwan-Ho Kim, Jin-Hong Park

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The multi-valued logic (MVL) circuits, which can handle more than two logic states, are one of the promising solutions to overcome the bit density limitations of conventional binary logic systems. Recently, tunneling devices such as Esaki diode and resonant tunneling diode (RTD) have been extensively explored to construct the MVL circuits. These tunneling devices present a negative differential resistance (NDR) phenomenon in which a current decreases as a voltage increases in a specific applied voltage region. Due to this non-monotonic current behavior, the tunneling devices have more than two threshold voltages, consequently enabling construction of MVL circuits. Recently, the emergence of two dimensional (2D) van der Waals (vdW) crystals has opened up the possibility to fabricate such tunneling devices easily. Owing to the defect-free surface of the 2D crystals, a very abrupt junction interface could be formed through a simple stacking process, which subsequently allowed the implementation of a high-performance tunneling device. Here, we report a vdW heterostructure based tunneling device with multiple threshold voltages, which was fabricated with black phosphorus (BP) and hafnium diselenide (HfSe₂). First, we exfoliated BP on the SiO₂ substrate and then transferred HfSe₂ on BP using dry transfer method. The BP and HfSe₂ form type-Ⅲ heterojunction so that the highly doped n+/p+ interface can be easily implemented without additional electrical or chemical doping process. Owing to high natural doping at the junction, record high peak to valley ratio (PVCR) of 16 was observed to the best our knowledge in 2D materials based NDR device. Furthermore, based on this, we first demonstrate the feasibility of the ternary latch by connecting two multi-threshold voltage devices in series.

Keywords: two dimensional van der Waals crystal, multi-valued logic, negative differential resistnace, tunneling device

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Nasrud Din, Fawad Saeed, Sajid Hussain, Rai Muhammad Dawood Sultan, Premkumar Sellan, Qasim Khan, Wei Lei

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The continuously increasing external quantum efficiencies of Perovskite light-emitting diodes (LEDs) have received significant interest in the scientific community. The need for monitoring and medical diagnostics has experienced a steady growth in recent years, primarily caused by older people and an increasing number of heart attacks, tumors, and cancer disorders among patients. The application of Perovskite near-infrared light-emitting diode (PeNIRLEDs) has exhibited considerable efficacy in bioimaging, particularly in the visualization and examination of blood arteries, blood clots, and tumors. PeNIRLEDs exhibit exciting potential in the field of blood vessel imaging because of their advantageous attributes, including improved depth penetration and less scattering in comparison to visible light. In this study, we synthesized FAPbI₃ Perovskite doped with different concentrations of 5-Aminovaleric acid (5-AVA) 1-6 mg. The incorporation of 5-AVA as a dopant during the FAPbI₃ Perovskite formation influences the FAPbI3 Perovskite’s structural and optical properties, improving its stability, photoluminescence efficiency, and charge transport characteristics. We found a resulting PL emission peak wavelength of 850 nm and bandwidth of 44 nm, along with a calculated quantum yield of 75%. The incorporation of 5-AVA-modified FAPbI₃ Perovskite into LEDs will show promising results, enhancing device efficiency, color purity, and stability. Making it suitable for various medical applications, including subcutaneous deep vein imaging, blood flow visualization, and tumor illumination.

Keywords: perovskite light-emitting diodes, deep vein imaging, blood flow visualization, tumor illumination

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Authors: Ali Shareef, Aliha Shareef, Yifeng Zhu

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Energy minimization is of great importance in wireless sensor networks in extending the battery lifetime. One of the key activities of nodes in a WSN is communication and the routing of their data to a centralized base-station or sink. Routing using the shortest path to the sink is not the best solution since it will cause nodes along this path to fail prematurely. We propose a cross-layer energy efficient routing protocol Optrix that utilizes a convex formulation to maximize the lifetime of the network as a whole. We further propose, Optrix-BW, a novel convex formulation with bandwidth constraint that allows the channel conditions to be accounted for in routing. By considering this key channel parameter we demonstrate that Optrix-BW is capable of congestion control. Optrix is implemented in TinyOS, and we demonstrate that a relatively large topology of 40 nodes can converge to within 91% of the optimal routing solution. We describe the pitfalls and issues related with utilizing a continuous form technique such as convex optimization with discrete packet based communication systems as found in WSNs. We propose a routing controller mechanism that allows for this transformation. We compare Optrix against the Collection Tree Protocol (CTP) and we found that Optrix performs better in terms of convergence to an optimal routing solution, for load balancing and network lifetime maximization than CTP.

Keywords: wireless sensor network, Energy Efficient Routing

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Authors: Mahmoud S. Zaghloul, Mohammed M. Khalaf, Wael N. Thabet, Haidy N. Asham

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Background. Hypertrophic scarring is a difficult problem for burn patients, and scar management is an essential aspect of outpatient burn therapy. Post-burn pathologic scars involve functional and aesthetic limitations that have a dramatic influence on the patient’s quality of life. The aim was to investigate the use of extracorporeal shock wave therapy (ESWT), which targets the fibroblasts in scar tissue, as an effective modality for scar treatment in burn patients. Subjects and methods: forty patients with post-burn scars were assigned randomly into two equal groups; their ages ranged from 20-45 years. The study group received ESWT and traditional physical therapy program (deep friction massage, stretching exercises). The control group received traditional physical therapy program (deep friction massage, stretching exercises). All groups received two sessions per week for six successful weeks. The data were collected before and after the same period of treatment for both groups. Evaluation procedures were carried out to measure scar thickness using ultrasonography and Vancouver Scar Scale (VSS) was completed before and after treatment. Results: Post-treatment results showed that there was a significant improvement difference in scar thickness in both groups in favor of the study group. Percentage of improvement in scar thickness in the study group was 42.55%, while it was 12.15% in the control group. There was also a significant improvement difference between results obtained using VSS in both groups in favor of the study group. Conclusion: ESWT is effective in management of pathologic post burn scars.

Keywords: extracorporeal shock wave therapy, post-burn scars, ultrasonography, Vancouver scar scale

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Authors: Min Kyung An

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In Wireless Sensor Networks which consist of tiny wireless sensor nodes with limited battery power, one of the most fundamental applications is data aggregation which collects nearby environmental conditions and aggregates the data to a designated destination, called a sink node. Important issues concerning the data aggregation are time efficiency and energy consumption due to its limited energy, and therefore, the related problem, named Minimum Latency Aggregation Scheduling (MLAS), has been the focus of many researchers. Its objective is to compute the minimum latency schedule, that is, to compute a schedule with the minimum number of timeslots, such that the sink node can receive the aggregated data from all the other nodes without any collision or interference. For the problem, the two interference models, the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), have been adopted with different power models, uniform-power and non-uniform power (with power control or without power control), and different antenna models, omni-directional antenna and directional antenna models. In this survey article, as the problem has proven to be NP-hard, we present and compare several state-of-the-art approximation algorithms in various models on the basis of latency as its performance measure.

Keywords: data aggregation, convergecast, gathering, approximation, interference, omni-directional, directional

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Authors: Fatima Babar, Majid I. Khan, Malik Najmus Saqib, Muhammad Tahir

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This article provides range based improvements over a well-known single-hop range free localization scheme, Approximate Point in Triangulation (APIT) by proposing an energy efficient Barycentric coordinate based Point-In-Triangulation (PIT) test along with PIT based trilateration. These improvements result in energy efficiency, reduced localization error and improved localization coverage compared to APIT and its variants. Moreover, we propose to embed Received signal strength indication (RSSI) based distance estimation in DV-Hop which is a multi-hop localization scheme. The proposed localization algorithm achieves energy efficiency and reduced localization error compared to DV-Hop and its available improvements. Furthermore, a hybrid multi-hop localization scheme is also proposed that utilize Barycentric coordinate based PIT test and both range based (Received signal strength indicator) and range free (hop count) techniques for distance estimation. Our experimental results provide evidence that proposed hybrid multi-hop localization scheme results in two to five times reduction in the localization error compare to DV-Hop and its variants, at reduced energy requirements.

Keywords: Localization, Trilateration, Triangulation, Wireless Sensor Networks

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Fuyuan Ma, Ying Wang, Xin Wang

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Social networks have become breeding grounds for the rapid spread of rumors and malicious information, posing threats to societal stability and causing significant public harm. Existing research focuses on simulating the spread of information and its impact on users through propagation dynamics and applies methods such as greedy approximation strategies to approximate the optimal control solution at the global scale. However, the greedy strategy at the global scale may fall into locally optimal solutions, and the approximate simulation of information spread may accumulate more errors. Therefore, we propose a multi-scale control model for network group behavior, introducing individual and group scales on top of the greedy strategy’s global scale. At the individual scale, we calculate the propagation influence of nodes based on their structural attributes to alleviate the issue of local optimality. At the group scale, we conduct precise propagation simulations to avoid introducing cumulative errors from approximate calculations without increasing computational costs. Experimental results on three real-world datasets demonstrate the effectiveness of our proposed multi-scale model in controlling network group behavior.

Keywords: influence blocking maximization, competitive linear threshold model, social networks, network group behavior

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Authors: Redvan Ghasemlounia, Elham Ansari, Hikmet Kerem Cigizoglu

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Hydrological modeling in arid and semi-arid regions is very important. Iran has many regions with these climate conditions such as Chaharmahal and Bakhtiari province that needs lots of attention with an appropriate management. Forecasting of hydrological parameters and estimation of hydrological events of catchments, provide important information that used for design, management and operation of water resources such as river systems, and dams, widely. Discharge in rivers is one of these parameters. This study presents the application and comparison of some estimation methods such as Feed-Forward Back Propagation Neural Network (FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) to predict the daily flow discharge of the Soolegan River, located at Chaharmahal and Bakhtiari province, in Iran. In this study, Soolegan, station was considered. This Station is located in Soolegan River at 51° 14՜ Latitude 31° 38՜ longitude at North Karoon basin. The Soolegan station is 2086 meters higher than sea level. The data used in this study are daily discharge and daily precipitation of Soolegan station. Feed Forward Back Propagation Neural Network(FFBPNN), Multi Linear Regression (MLR), Gene Expression Programming (GEP) and Bayesian Network (BN) models were developed using the same input parameters for Soolegan's daily discharge estimation. The results of estimation models were compared with observed discharge values to evaluate performance of the developed models. Results of all methods were compared and shown in tables and charts.

Keywords: ANN, multi linear regression, Bayesian network, forecasting, discharge, gene expression programming

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Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

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Authors: Hermalina Sinay, Estri L. Arumingtyas

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The research objective was to determine the expression level of dehydration responsive element binding/DREB gene of local corn cultivars from Kisar Island Maluku. The study design was a randomized block design with single factor consist of six local corn cultivars obtained from farmers in Kisar Island and one reference varieties wich has been released by the government as a drought-tolerant varieties and obtained from Cereal Crops Research Institute (ICERI) Maros South Sulawesi. Leaf samples were taken is the second leaf after the flag leaf at the 65 days after planting. Isolation of total RNA from leaf samples was carried out according to the protocols of the R & A-BlueTM Total RNA Extraction Kit and was used as a template for cDNA synthesis. The making of cDNA from total RNA was carried out according to the protocol of One-Step Reverse Transcriptase PCR Premix Kit. Real Time-PCR was performed on cDNA from reverse transcription followed the procedures of Real MODTM Green Real-Time PCR Master Mix Kit. Data obtained from the real time-PCR results were analyzed using relative quantification method based on the critical point / Cycle Threshold (CP / CT). The results of gene expression analysis of DREB gene showed that the expression level of the gene was highest obtained at Deep Yellow local corn cultivar, and the lowest one was obtained at the Rubby Brown Cob cultivar. It can be concluded that the expression level of DREB gene of Deep Yellow local corn cultivar was highest than other local corn cultivars and Srikandi variety as a reference variety.

Keywords: expression, level, DREB gene, local corn cultivars, Kisar Island, Maluku

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Authors: Mohammad Hossein Mohammadi Sanjani, Ashknaz Oraee, Arian Amirnia, Atena Taheri, Mohammadreza Arabi, Mahmud Fotuhi-Firuzabad

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Ferroresonance effects cause overvoltage in medium voltage transformers and isolators used in electrical networks. Ferroresonance effects are nonlinear and occur between the network capacitor and the nonlinear inductance of the voltage transformer during saturation. This phenomenon is unwanted for transformers since it causes overheating, introduction of high dynamic forces in primary coils, and rise of voltage in primary coils for the voltage transformer. Furthermore, it results in electrical and thermal failure of the transformer. Expansion of distribution lines, design of the transformer in smaller sizes, and the increase of harmonics in distribution networks result in an increase of ferroresonance. There is limited literature available to improve the effects of ferroresonance; therefore, optimizing its effects for voltage transformers is of great importance. In this study, comprehensive modeling of a medium voltage block-type voltage transformer is performed. In addition, a recent model is proposed to improve the performance of voltage transformers during the occurrence of ferroresonance using damping oscillations. Also, transformer design optimization is presented in this study to show further improvements in the performance of the voltage transformer. The recently proposed model is experimentally tested and verified on a medium voltage transformer in the laboratory, and simulation results show a large reduction of the effects of ferroresonance.

Keywords: optimization, voltage transformer, ferroresonance, modeling, damper

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Authors: Hussein Bizimana, Osman Sönmez

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Constructed wetlands are current the most widely recognized waste water treatment option, especially in developing countries where they have the potential for improving water quality and creating valuable wildlife habitat in ecosystem with treatment requirement relatively simple for operation and maintenance cost. Lack of grey waste water treatment facilities in Kigali İnstitute of Science and Technology in Rwanda, causes pollution in the surrounding localities of Rugunga sector, where already a problem of poor sanitation is found. In order to treat grey water produced at Kigali İnstitute of Science and Technology, with high BOD concentration, high nutrients concentration and high alkalinity; a Horizontal Sub-surface Flow pilot-scale constructed wetland was designed and can operate in Kigali İnstitute of Science and Technology. The study was carried out in a sedimentation tank of 5.5 m x 1.42 m x 1.2 m deep and a Horizontal Sub-surface constructed wetland of 4.5 m x 2.5 m x 1.42 m deep. The grey waste water flow rate of 2.5 m3/d flew through vegetated wetland and sandy pilot plant. The filter media consisted of 0.6 to 2 mm of coarse sand, 0.00003472 m/s of hydraulic conductivity and cattails (Typha latifolia spp) were used as plants species. The effluent flow rate of the plant is designed to be 1.5 m3/ day and the retention time will be 24 hrs. 72% to 79% of BOD, COD, and TSS removals are estimated to be achieved, while the nutrients (Nitrogen and Phosphate) removal is estimated to be in the range of 34% to 53%. Every effluent characteristic will meet exactly the Rwanda Utility Regulatory Agency guidelines primarily because the retention time allowed is enough to make the reduction of contaminants within effluent raw waste water. Treated water reuse system was developed where water will be used in the campus irrigation system again.

Keywords: constructed wetlands, hydraulic conductivity, grey waste water, cattails

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Authors: Tariq Ahamad, Mohammed S. Al-Kahtani, Taisir Eldos

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Optical communications technology has made enormous and steady progress for several decades, providing the key resource in our increasingly information-driven society and economy. Much of this progress has been in finding innovative ways to increase the data carrying capacity of a single optical fiber. In this research article we have explored basic issues in terms of security and reliability for secure and reliable information transfer through the fiber infrastructure. Conspicuously, one potentially enormous source of improvement has however been left untapped in these systems: fibers can easily support hundreds of spatial modes, but today’s commercial systems (single-mode or multi-mode) make no attempt to use these as parallel channels for independent signals. Bandwidth, performance, reliability, cost efficiency, resiliency, redundancy, and security are some of the demands placed on telecommunications today. Since its initial development, fiber optic systems have had the advantage of most of these requirements over copper-based and wireless telecommunications solutions. The largest obstacle preventing most businesses from implementing fiber optic systems was cost. With the recent advancements in fiber optic technology and the ever-growing demand for more bandwidth, the cost of installing and maintaining fiber optic systems has been reduced dramatically. With so many advantages, including cost efficiency, there will continue to be an increase of fiber optic systems replacing copper-based communications. This will also lead to an increase in the expertise and the technology needed to tap into fiber optic networks by intruders. As ever before, all technologies have been subject to hacking and criminal manipulation, fiber optics is no exception. Researching fiber optic security vulnerabilities suggests that not everyone who is responsible for their networks security is aware of the different methods that intruders use to hack virtually undetected into fiber optic cables. With millions of miles of fiber optic cables stretching across the globe and carrying information including but certainly not limited to government, military, and personal information, such as, medical records, banking information, driving records, and credit card information; being aware of fiber optic security vulnerabilities is essential and critical. Many articles and research still suggest that fiber optics is expensive, impractical and hard to tap. Others argue that it is not only easily done, but also inexpensive. This paper will briefly discuss the history of fiber optics, explain the basics of fiber optic technologies and then discuss the vulnerabilities in fiber optic systems and how they can be better protected. Knowing the security risks and knowing the options available may save a company a lot embarrassment, time, and most importantly money.

Keywords: in-house networks, fiber optics, security risk, money

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Authors: Milton Jusu, Moses Koroma

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Efforts by the Ministry of Fisheries and Marine Resources and its development partners to introduce “modern” aquaculture in Sierra Leone since the 1970s have not been successful. A number of reasons have been hypothesized, including the suggestion that the market infrastructure and demand for farmed fish were inadequate to stimulate large-scale and widespread aquaculture production in the country. We have assessed the size, structure, networks and opportunities in fish markets using a combination of Participatory Rural Appraisals (PRAs) and questionnaire surveys conducted in a sample of 29 markets (urban, weekly, wholesale and retail) and two hundred traders. The study showed that the local fish markets were dynamic, with very high variations in demand and supply. The markets sampled supplied between 135.2 and 9947.6 tonnes/year. Mean prices for fresh fish varied between US$1.12 and US$3.89/kg depending on species, with smoked catfish and shrimps commanding prices as high as US$7.4/kg. It is unlikely that marine capture fisheries can increase their current production levels, and these may, in fact, already be over-exploited and declining. Marine fish supplies are particularly low between July and September. More careful attention to the timing of harvests (rainy season, not dry season) and to species (catfish, not tilapia) (could help in the successful adoption of aquaculture.

Keywords: fisheries and aquaculture, fish market, marine fish supplies, harvests

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Authors: Sergio A. Navarro Tuch, Martin Rogelio Bustamante Bello, Leopoldo Julian Lechuga Lopez

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In recent years, Mexico’s population has seen a rise of different physiological and mental negative states. Two main consequences of this problematic are deficient work performance and high levels of stress generating and important impact on a person’s physical, mental and emotional health. Several approaches, such as the use of audiovisual stimulus to induce emotions and modify a person’s emotional state, can be applied in an effort to decreases these negative effects. With the use of different non-invasive physiological sensors such as EEG, luminosity and face recognition we gather information of the subject’s current emotional state. In a controlled environment, a subject is shown a series of selected images from the International Affective Picture System (IAPS) in order to induce a specific set of emotions and obtain information from the sensors. The raw data obtained is statistically analyzed in order to filter only the specific groups of information that relate to a subject’s emotions and current values of the physical variables in the controlled environment such as, luminosity, RGB light color, temperature, oxygen level and noise. Finally, a neural network based control algorithm is given the data obtained in order to feedback the system and automate the modification of the environment variables and audiovisual content shown in an effort that these changes can positively alter the subject’s emotional state. During the research, it was found that the light color was directly related to the type of impact generated by the audiovisual content on the subject’s emotional state. Red illumination increased the impact of violent images and green illumination along with relaxing images decreased the subject’s levels of anxiety. Specific differences between men and women were found as to which type of images generated a greater impact in either gender. The population sample was mainly constituted by college students whose data analysis showed a decreased sensibility to violence towards humans. Despite the early stage of the control algorithm, the results obtained from the population sample give us a better insight into the possibilities of emotional domotics and the applications that can be created towards the improvement of performance in people’s lives. The objective of this research is to create a positive impact with the application of technology to everyday activities; nonetheless, an ethical problem arises since this can also be applied to control a person’s emotions and shift their decision making.

Keywords: data analysis, emotional domotics, performance improvement, neural network

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Authors: K. P. Valdés García, E. I. Rodríguez Fonseca, L. G. Juárez Cantú

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Suicide is a global, multidimensional and dynamic problem of mental health, which requires a constant study for its understanding and prevention. When research of this phenomenon is done, it is necessary to consider the different characteristics it may have because of the individual and sociocultural variables, the importance of this consideration is related to the generation of effective treatments and interventions. Adolescents are a vulnerable population due to the characteristics of the development stage. The investigation was carried out with the objective of identifying and describing the conceptualization of adolescents of suicide, and in this process, we find possible differences between men and women. The study was carried out in Saltillo, Coahuila, Mexico. The sample was composed of 418 volunteer students aged between 11 and 18 years. The ethical aspects of the research were reviewed and considered in all the processes of the investigation with the participants, their parents and the schools to which they belonged, psychological attention was offered to the participants and preventive workshops were carried in the educational institutions. Natural semantic networks were the instrument used, since this hybrid method allows to find and analyze the social concept of a phenomenon; in this case, the word suicide was used as an evocative stimulus and participants were asked to evoke at least five words and a maximum 10 that they thought were related to suicide, and then hierarchize them according to the closeness with the construct. The subsequent analysis was carried with Excel, yielding the semantic weights, affective loads and the distances between each of the semantic fields established according to the words reported by the subjects. The results showed similarities in the conceptualization of suicide in adolescents, men and women. Seven semantic fields were generated; the words were related in the discourse analysis: 1) death, 2) possible triggering factors, 3) associated moods, 4) methods used to carry it out, 5) psychological symptomatology that could affect, 6) words associated with a rejection of suicide, and finally, 7) specific objects to carry it out. One of the necessary aspects to consider in the investigations of complex issues such as suicide is to have a diversity of instruments and techniques that adjust to the characteristics of the population and that allow to understand the phenomena from the social constructs and not only theoretical. The constant study of suicide is a pressing need, the loss of a life from emotional difficulties that can be solved through psychiatry and psychological methods requires governments and professionals to pay attention and work with the risk population.

Keywords: adolescents, psychological construct, semantic networks, suicide

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Authors: Stefan Papastefanou

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Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

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Authors: Abbas Raza Ali

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Big Data technology is gradually becoming a dire need of large enterprises. These enterprises are generating massively large amount of off-line and streaming data in both structured and unstructured formats on daily basis. It is a challenging task to effectively extract useful insights from the large scale datasets, even though sometimes it becomes a technology constraint to manage transactional data history of more than a few months. This paper presents a framework to efficiently manage massively large and complex datasets. The framework has been tested on a communication service provider producing massively large complex streaming data in binary format. The communication industry is bound by the regulators to manage history of their subscribers’ call records where every action of a subscriber generates a record. Also, managing and analyzing transactional data allows service providers to better understand their customers’ behavior, for example, deep packet inspection requires transactional internet usage data to explain internet usage behaviour of the subscribers. However, current relational database systems limit service providers to only maintain history at semantic level which is aggregated at subscriber level. The framework addresses these challenges by leveraging Big Data technology which optimally manages and allows deep analysis of complex datasets. The framework has been applied to offload existing Intelligent Network Mediation and relational Data Warehouse of the service provider on Big Data. The service provider has 50+ million subscriber-base with yearly growth of 7-10%. The end-to-end process takes not more than 10 minutes which involves binary to ASCII decoding of call detail records, stitching of all the interrogations against a call (transformations) and aggregations of all the call records of a subscriber.

Keywords: big data, communication service providers, enterprise data warehouse, stream computing, Telco IN Mediation

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Authors: Jae-Hyung Jin, Kyung-Tae Lee, Yee-Seul So, Eunji Jeong, Yeonseon Lee, Dongpil Lee, Dong-Gi Lee, Yong-Sun Bahn

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Cryptococcus neoformans causes cryptococcal meningoencephalitis mainly in immunocompromised patients as well as immunocompetent people. But therapeutic options are limited to treat cryptococcosis. Some signaling pathways including cyclic AMP pathway, MAPK pathway, and calcineurin pathway play a central role in the regulation of the growth, differentiation, and virulence of C. neoformans. To understand signaling networks regulating the virulence of C. neoformans, we selected the 114 putative phosphatase genes, one of the major components of signaling networks, in the genome of C. neoformans. We identified putative phosphatases based on annotation in C. neoformans var. grubii genome database provided by the Broad Institute and National Center for Biotechnology Information (NCBI) and performed a BLAST search of phosphatases of Saccharomyces cerevisiae, Aspergillus nidulans, Candida albicans and Fusarium graminearum to Cryptococcus neoformans. We classified putative phosphatases into 14 groups based on InterPro phosphatase domain annotation. Here, we constructed 170 signature-tagged gene-deletion strains through homologous recombination methods for 91 putative phosphatases. We examined their phenotypic traits under 30 different in vitro conditions, including growth, differentiation, stress response, antifungal resistance and virulence-factor production.

Keywords: human fungal pathogen, phosphatase, deletion library, functional genomics

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Przemysław Adamczyk, Mirosław Wyczesany, Aleksandra Domagalik, Artur Daren, Kamil Cepuch, Piotr Błądziński, Tadeusz Marek, Andrzej Cechnicki

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The present study aimed at evaluation of neural correlates of humor comprehension impairments observed in schizophrenia. To investigate the nature of this deficit in schizophrenia and to localize cortical areas involved in humor processing we used functional magnetic resonance imaging (fMRI). The study included chronic schizophrenia outpatients (SCH; n=20), and sex, age and education level matched healthy controls (n=20). The task consisted of 60 stories (setup) of which 20 had funny, 20 nonsensical and 20 neutral (not funny) punchlines. After the punchlines were presented, the participants were asked to indicate whether the story was comprehensible (yes/no) and how funny it was (1-9 Likert-type scale). fMRI was performed on a 3T scanner (Magnetom Skyra, Siemens) using 32-channel head coil. Three contrasts in accordance with the three stages of humor processing were analyzed in both groups: abstract vs neutral stories - incongruity detection; funny vs abstract - incongruity resolution; funny vs neutral - elaboration. Additionally, parametric modulation analysis was performed using both subjective ratings separately in order to further differentiate the areas involved in incongruity resolution processing. Statistical analysis for behavioral data used U Mann-Whitney test and Bonferroni’s correction, fMRI data analysis utilized whole-brain voxel-wise t-tests with 10-voxel extent threshold and with Family Wise Error (FWE) correction at alpha = 0.05, or uncorrected at alpha = 0.001. Between group comparisons revealed that the SCH subjects had attenuated activation in: the right superior temporal gyrus in case of irresolvable incongruity processing of nonsensical puns (nonsensical > neutral); the left medial frontal gyrus in case of incongruity resolution processing of funny puns (funny > nonsensical) and the interhemispheric ACC in case of elaboration of funny puns (funny > neutral). Additionally, the SCH group revealed weaker activation during funniness ratings in the left ventro-medial prefrontal cortex, the medial frontal gyrus, the angular and the supramarginal gyrus, and the right temporal pole. In comprehension ratings the SCH group showed suppressed activity in the left superior and medial frontal gyri. Interestingly, these differences were accompanied by protraction of time in both types of rating responses in the SCH group, a lower level of comprehension for funny punchlines and a higher funniness for absurd punchlines. Presented results indicate that, in comparison to healthy controls, schizophrenia is characterized by difficulties in humor processing revealed by longer reaction times, impairments of understanding jokes and finding nonsensical punchlines more funny. This is accompanied by attenuated brain activations, especially in the left fronto-parietal and the right temporal cortices. Disturbances of the humor processing seem to be impaired at the all three stages of the humor comprehension process, from incongruity detection, through its resolution to elaboration. The neural correlates revealed diminished neural activity of the schizophrenia brain, as compared with the control group. The study was supported by the National Science Centre, Poland (grant no 2014/13/B/HS6/03091).

Keywords: communication skills, functional magnetic resonance imaging, humor, schizophrenia

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Authors: Clotilde Guyon, Nada Jmari, Yen-Chin Li, Jean Denoyel, Noriyuki Fujikado, Christophe Blanchet, David Root, Matthieu Giraud

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Aire induces ectopic expression of a large repertoire of tissue-specific antigen (TSA) genes in thymic medullary epithelial cells (MECs), driving immunological self-tolerance in maturing T cells. Although important mechanisms of Aire-induced transcription have recently been disclosed through the identification and the study of Aire’s partners, the fine transcriptional functions underlied by a number of them and conferred to Aire are still unknown. Alternative cleavage and polyadenylation (APA) is an essential mRNA processing step regulated by the termination complex consisting of 85 proteins, 10 of them have been related to Aire. We evaluated APA in MECs in vivo by microarray analysis with mRNA-spanning probes and RNA deep sequencing. We uncovered the preference of Aire-dependent transcripts for short-3’UTR isoforms and for proximal poly(A) site selection marked by the increased binding of the cleavage factor Cstf-64. RNA interference of the 10 Aire-related proteins revealed that Clp1, a member of the core termination complex, exerts a profound effect on short 3’UTR isoform preference. Clp1 is also significantly upregulated in the MECs compared to 25 mouse tissues in which we found that TSA expression is associated with longer 3’UTR isoforms. Aire-dependent transcripts escape a global 3’UTR lengthening associated with MEC differentiation, thereby potentiating the repressive effect of microRNAs that are globally upregulated in mature MECs. Consistent with these findings, RNA deep sequencing of actinomycinD-treated MECs revealed the increased stability of short 3’UTR Aire-induced transcripts, resulting in TSA transcripts accumulation and contributing for their enrichment in the MECs.

Keywords: Aire, central tolerance, miRNAs, transcription termination

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Authors: E. Nakamachi, S. Tanaka, K. Yamamoto, Y. Morita

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In this study, we developed a three-dimensional (3D) direct current electric field (DCEF) stimulation bio-reactor for axonal outgrowth enhancement to generate the neural network of the central nervous system (CNS). By using our newly developed 3D DCEF stimulation bio-reactor, we cultured the rat pheochromocytoma cells (PC12) and investigated the effects on the axonal extension enhancement and network generation. Firstly, we designed and fabricated a 3D bio-reactor, which can load DCEF stimulation on PC12 cells embedded in the collagen gel as extracellular environment. The connection between the electrolyte and the medium using salt bridges for DCEF stimulation was introduced to avoid the cell death by the toxicity of metal ion. The distance between the salt bridges was adopted as the design variable to optimize a structure for uniform DCEF stimulation, where the finite element (FE) analyses results were used. Uniform DCEF strength and electric flux vector direction in the PC12 cells embedded in collagen gel were examined through measurements of the fabricated 3D bio-reactor chamber. Measurement results of DCEF strength in the bio-reactor showed a good agreement with FE results. In addition, the perfusion system was attached to maintain pH 7.2 ~ 7.6 of the medium because pH change was caused by DCEF stimulation loading. Secondly, we disseminated PC12 cells in collagen gel and carried out 3D culture. Finally, we measured the morphology of PC12 cell bodies and neurites by the multiphoton excitation fluorescence microscope (MPM). The effectiveness of DCEF stimulation to enhance the axonal outgrowth and the neural network generation was investigated. We confirmed that both an increase of mean axonal length and axogenesis rate of PC12, which have been exposed 5 mV/mm for 6 hours a day for 4 days in the bioreactor. We found following conclusions in our study. 1) Design and fabrication of DCEF stimulation bio-reactor capable of 3D culture nerve cell were completed. A uniform electric field strength of average value of 17 mV/mm within the 1.2% error range was confirmed by using FE analyses, after the structure determination through the optimization process. In addition, we attached a perfusion system capable of suppressing the pH change of the culture solution due to DCEF stimulation loading. 2) Evaluation of DCEF stimulation effects on PC12 cell activity was executed. The 3D culture of PC 12 was carried out adopting the embedding culture method using collagen gel as a scaffold for four days under the condition of 5.0 mV/mm and 10mV/mm. There was a significant effect on the enhancement of axonal extension, as 11.3% increase in an average length, and the increase of axogenesis rate. On the other hand, no effects on the orientation of axon against the DCEF flux direction was observed. Further, the network generation was enhanced to connect longer distance between the target neighbor cells by DCEF stimulation.

Keywords: PC12, DCEF stimulation, 3D bio-reactor, axonal extension, neural network generation

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Authors: Hardisiswo Soedjana, Inne Caroline

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Deep dermal or full thickness burns are inevitably lead to post-burn contractures. These contractures remain to be one of the most concerning late complications of burn injuries. Surgical management includes releasing the contracture followed by resurfacing the defect accompanied by post-operative rehabilitation. Optimal treatment of post-burn contractures depends on the characteristics of the contractures. This study is aimed to describe clinical characteristics, problems, and management of post-burn contractures in adults and children. A retrospective analysis was conducted from medical records of patients suffered from contractures after burn injuries admitted to Hasan Sadikin general hospital between January 2016 and January 2018. A total of 50 patients with post burn contractures were included in the study. There were 17 adults and 33 children. Most patients were male, whose age range within 15-59 years old and 5-9 years old. Educational background was mostly senior high school among adults, while there was only one third of children who have entered school. Etiology of burns was predominantly flame in adults (82.3%); whereas flame and scald were the leading cause of burn injury in children (11%). Based on anatomical regions, hands were the most common affected both in adults (35.2%) and children (48.5%). Contractures were identified in 6-12 months since the initial burns. Most post-burn hand contractures were resurfaced with full-thickness skin graft (FTSG) both in adults and children. There were 11 patients who presented with recurrent contracture after previous history of contracture release. Post-operative rehabilitation was conducted for all patients; however, it is important to highlight that it is still challenging to control splinting and exercise when patients are discharged and especially the compliance in children. In order to improve quality of life in patients with history of deep burn injuries, prevention of contractures should begin right after acute care has been established. Education for the importance of splinting and exercise should be administered as comprehensible as possible for adult patients and parents of pediatric patients.

Keywords: burn, contracture, education, exercise, splinting

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