Search results for: multi pass weld

Authors: Tran Le Na, Didit Novianto, Yoshiaki Ushifusa, Weijun Gao

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This paper introduces the characteristics of Japanese residential lifestyle and Japanese Architectural housing design, meanwhile, summarizes the results from an analysis of energy use of 12 households in electric-only multi dwellings in Higashida Smart Community, Kitakyushu, Japan. Using hourly load and daily load data collected from smart meter, we explore correlations of energy use in households according to the incentive of different levels of architectural characteristics and lifestyle, following three factors: Space (Living room, Kitchen, Bedroom, Bathroom), Time (daytime and night time, weekdays and weekend) and User (Elderly, Parents, Kids). The energy consumption reports demonstrated that the essential demand of household’s response to variable factors. From that exploratory analysis, we can define the role of housing equipment layout and spatial layout in residential housing design. Likewise, determining preferred spaces and time use can help to optimize energy consumption in households. This paper contributes to the application of Smart Home Energy Management System in Smart Community in Japan and provides a good experience to other countries.

Keywords: smart community, energy efficiency, architectural housing design, residential lifestyle

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Authors: Arne Hitzmann, Philipp Wentscher, Alexander Gabel, Reinhard Gerndt

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Autonomous mobile robots can be found in a wide field of applications. Their types range from household robots over workshop robots to autonomous cars and many more. All of them undergo a number of testing steps during development, production and maintenance. This paper describes an approach to improve testing of robot behavior. It was inspired by the RoboCup @work competition that itself reflects a robotics benchmark for industrial robotics. There, scaled down versions of mobile industrial robots have to navigate through a workshop-like environment or operation area and have to perform tasks of manipulating and transporting work pieces. This paper will introduce an approach of automated vision-based testing of the behavior of the so called youBot robot, which is the most widely used robot platform in the RoboCup @work competition. The proposed system allows automated testing of multiple tries of the robot to perform a specific missions and it allows for the flexibility of the robot, e.g. selecting different paths between two tasks within a mission. The approach is based on a multi-camera setup using, off the shelf cameras and optical markers. It has been applied for test-driven development (TDD) and maintenance-like verification of the robot behavior and performance.

Keywords: supervisory control, testing, markers, mono vision, automation

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Authors: Muhammad Saqlain, Poom Kumam

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Language being an abstract system and creative act, is quite complicated as its meaning varies depending on the context. The context is determined by the empirical knowledge of a person, which is derived from observation and experience. About further subdivided attributes, the decision-making challenges may entail quantitative and qualitative factors. However, because there is no norm for putting a numerical value on language, existing approaches cannot carry out the operations of linguistic knowledge. The assigning of mathematical values (fuzzy, intuitionistic, and neutrosophic) to any decision-making problem; without considering any rule of linguistic knowledge is ambiguous and inaccurate. Thus, this paper aims to provide a generic model for these issues. This paper provides the linguistic set structure of the fuzzy hypersoft set (FLHSS) to solve decision-making issues. We have proposed the definition some basic operations like AND, NOT, OR, AND, compliment, negation, etc., along with Topology and examples, and properties. Secondly, the operational laws for the fuzzy linguistic hypersoft set have been proposed to deal with the decision-making issues. Implementing proposed aggregate operators and operational laws can be used to convert linguistic quantifiers into numerical values. This will increase the accuracy and precision of the fuzzy hypersoft set structure to deal with decision-making issues.

Keywords: linguistic quantifiers, aggregate operators, multi-criteria decision making (mcdm)., fuzzy topology

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Authors: Naveed Ghani

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With the arrival of higher speed communication links and mature application running over the internet, the requirement for reliable, efficient and robust network designs rising day by day. Multi-Protocol Label Switching technology (MPLS) Virtual Private Networks (VPNs) have committed to provide optimal network services. They are gaining popularity in industry day by day. Enterprise customers are moving to service providers that offer MPLS VPNs. The main reason for this shifting is the capability of MPLS VPN to provide built in security features and any-to-any connectivity. MPLS VPNs improved the network performance due to fast label switching as compare to traditional IP Forwarding but traffic classification and policing was still required on per hop basis to enhance the performance of real time traffic which is delay sensitive (particularly voice and video). QoS (Quality of service) is the most important factor to prioritize enterprise networks’ real time traffic such as voice and video. This thesis is focused on the study of QoS parameters (e.g. delay, jitter and MOS (Mean Opinion Score)) for the real time traffic over MPLS VPNs. DiffServ (Differentiated Services) QoS model will be used over MPLS VPN network to get end-to-end service quality.

Keywords: network, MPLS, VPN, DiffServ, MPLS VPN, DiffServ QoS, QoS Model, GNS2

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Authors: Gaoyuan Wang, Tian Chen, Junnan Liu

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Facing a period of major change that’s rarely seen in a century, China formulates the 14th Five-Year Plan and places emphasis on promoting high-quality development. In this context, the overall urban design has become a crucial and systematic tool for high-quality urban development. However, there are bottlenecks in the nature definition, content scope and transmission mechanisms of the current overall urban design in China. The paper interprets the emerging demands of the 14th Five-Year Plan on urban design in terms of new value-quality priority, new dynamic-space performance, new target-region coordination and new path-refined governance. Based on the new trend and appeal, the multi-dimensional thinking integrated with the major tasks of urban design are proposed accordingly, which is the biomass thinking in ecological, production and living element, the strategic thinking in spatial structure, the systematic thinking in the cityscape, the low-carbon thinking in urban form, the governance thinking in public space, the user thinking in design implementation. The paper explores the possibility of transforming the value thinking and technical system of urban design in China and provides a breakthrough path for the urban planning and design industry to better respond to the propositions of the country’s 14th Five-Year Plan.

Keywords: China’s 14th five-year plan, overall urban design, urban design thinking, transformation of urban design

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Abdulkarim Mohammed Iliyasu, Mahmud Abba Tahir

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Freezing and thawing are considered to be one of the major causes of concrete deterioration in the cold regions. This study aimed at assessing the freezing and thawing of concrete within the cover zone by monitoring the formation of ice and melting at different temperatures using electrical measurement technique. A multi-electrode array system was used to obtain the resistivity of ice formation and melting at discrete depths within the cover zone of the concrete. A total number of four concrete specimens (250 mm x 250 mm x 150 mm) made of ordinary Portland cement concrete and ordinary Portland cement replaced by 65% ground granulated blast furnace slag (GGBS) is investigated. Water/binder ratios of 0.35 and 0.65 were produced and ponded with water to ensure full saturation and then subjected to freezing and thawing process in a refrigerator within a temperature range of -30 ⁰C and 20 ⁰C over a period of time 24 hours. The data were collected and analysed. The obtained results show that the addition of GGBS changed the pore structure of the concrete which resulted in the decrease in conductance. It was recommended among others that, the surface of the concrete structure should be protected as this will help to prevent the instantaneous propagation of ice trough the rebar and to avoid corrosion and subsequent damage.

Keywords: concrete, conductance, deterioration, freezing and thawing

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Authors: Olufemi Adedamola Oyedele

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Infrastructure provision in Nigeria is now at its lowest ebb in spite of its being critical to the socio-economic and political development of any nation. This is partly because the policy that will ensure its adequate provisioning is missing and partly because politics is affecting its provision. Policy is the basic principles by which a government is guided. Infrastructural development is the basis for measuring the performance of governments and it is the foundation of good governance. Demand for infrastructural development is higher and resources used in its provision are limited. Ethnic-interest agitation and lobbying for infrastructure provision are common things in multi-ethnic state like Nigeria. Most infrastructures are now decayed and need repair or replacement. Government is the system that organizes, control and sensitizes the people in a society in other for all to have an acceptable level of living. Governments have the power to put in place all measures that they deem fit will make an environment conducive for living for everybody. Infrastructure development in any environment requires needs assessment, feasibility and viability studies and carrying out physical development of the project. The challenge in Nigeria is largely carrying out development where they are not needed but where the people are loyal. There are numerous abandoned projects because they were started due to politics and not because they are feasible. Policies and politics greatly affect infrastructure provisioning in Nigeria and this is the premise of this paper.

Keywords: infrastructure challenges, infrastructure development, policy making, politics, project finance

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Authors: Tarik Ouslimane, Abdenour Lami, Salaheddine Aoudj, Mouna Hecini, Ouahiba Bouchelaghem, Nadjib Drouiche

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Slurry waste is a byproduct generated from the slicing process of multi-crystalline silicon ingots. This waste can be used as a secondary resource to recover high purity silicon which has a great economic value. From the management perspective, the ever increasing generation of kerf slurry waste loss leads to significant challenges for the photovoltaic industry due to the current low use of slurry waste for silicon recovery. Slurry waste, in most cases, contains silicon, silicon carbide, metal fragments and mineral-oil-based or glycol-based slurry vehicle. As a result, of the global scarcity of high purity silicon supply, the high purity silicon content in slurry has increasingly attracted interest for research. This paper presents a critical overview of the current techniques employed for high purity silicon recovery from kerf slurry waste. Hydrometallurgy is continuously a matter of study and research. However, in this review paper, several new techniques about the process of high purity silicon recovery from slurry waste are introduced. The purpose of the information presented is to improve the development of a clean and effective recovery process of high purity silicon from slurry waste.

Keywords: Kerf-loss, slurry waste, silicon carbide, silicon recovery, photovoltaic, high purity silicon, polyethylen glycol

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Authors: Mahmut Yildirim

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This paper proposed the bidirectional long short-term memory (Bi-LSTM) network-aided deep learning (DL)-based signal detection for Orthogonal frequency division multiplexing with all index modulation (OFDM-AIM), namely Bi-DeepAIM. OFDM-AIM is developed to increase the spectral efficiency of OFDM with index modulation (OFDM-IM), a promising multi-carrier technique for communication systems beyond 5G. In this paper, due to its strong classification ability, Bi-LSTM is considered an alternative to the maximum likelihood (ML) algorithm, which is used for signal detection in the classical OFDM-AIM scheme. The performance of the Bi-DeepAIM is compared with LSTM network-aided DL-based OFDM-AIM (DeepAIM) and classic OFDM-AIM that uses (ML)-based signal detection via BER performance and computational time criteria. Simulation results show that Bi-DeepAIM obtains better bit error rate (BER) performance than DeepAIM and lower computation time in signal detection than ML-AIM.

Keywords: bidirectional long short-term memory, deep learning, maximum likelihood, OFDM with all index modulation, signal detection

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Authors: Putul Haldar, Yogendra Singh, D. K. Paul

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Un-Reinforced Masonry (URM) infilled RC framed buildings are the most common construction practice for modern multi-storey buildings in India like many other parts of the world. Although the behavior and failure pattern of the global structure changes significantly due to infill-frame interaction, the general design practice is to treat them as non-structural elements and their stiffness, strength and interaction with frame is often ignored, as it is difficult to simulate. Indian Standard, like many other major national codes, does not provide any explicit guideline for modeling of infills. This paper takes a stock of controlling design provisions in some of the major national seismic design codes (BIS 2002; CEN 2004; NZS-4230 2004; ASCE-41 2007) to ensure the desired seismic performance of infilled frame. Most of the national codes on seismic design of buildings still lack in adequate guidelines on modeling and design of URM infilled frames results in variable assumption in analysis and design. This paper, using nonlinear pushover analysis, also presents the effect of one of such assumptions of conventional ‘simultaneous’ analysis procedure of infilled frame on the seismic performance of URM infilled RC frame buildings.

Keywords: URM infills, RC frame, seismic design codes, construction sequence of infilled frame

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Authors: Telesphore Tiendrebeogo, Oumarou Sié

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Cloud computing technologies have attracted considerable interest in recent years. Thus, these latters have become more important for many existing database applications. It provides a new mode of use and of offer of IT resources in general. Such resources can be used “on demand” by anybody who has access to the internet. Particularly, the Cloud platform provides an ease to use interface between providers and users, allow providers to develop and provide software and databases for users over locations. Currently, there are many Cloud platform providers support large scale database services. However, most of these only support simple keyword-based queries and can’t response complex query efficiently due to lack of efficient in multi-attribute index techniques. Existing Cloud platform providers seek to improve performance of indexing techniques for complex queries. In this paper, we define a new cloud computing architecture based on a Distributed Hash Table (DHT) and design a prototype system. Next, we perform and evaluate our cloud computing indexing structure based on a hyperbolic tree using virtual coordinates taken in the hyperbolic plane. We show through our experimental results that we compare with others clouds systems to show our solution ensures consistence and scalability for Cloud platform.

Keywords: virtual coordinates, cloud, hyperbolic plane, storage, scalability, consistency

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Authors: Niels Grataloup, Micael S. Couceiro, Manousos Valyrakis, Javier Escudero, Patricia A. Vargas

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This paper benchmarks the possible underwater communication technologies that can be integrated into a swarm of underwater robots by proposing an underwater robot simulator named CORSICA (Cross platfORm wireleSs communICation simulator). Underwater exploration relies increasingly on the use of mobile robots, called Autonomous Underwater Vehicles (AUVs). These robots are able to reach goals in harsh underwater environments without resorting to human divers. The introduction of swarm robotics in these scenarios would facilitate the accomplishment of complex tasks with lower costs. However, swarm robotics requires implementation of communication systems to be operational and have a non-deterministic behaviour. Inter-robot communication is one of the key challenges in swarm robotics, especially in underwater scenarios, as communication must cope with severe restrictions and perturbations. This paper starts by presenting a list of the underwater propagation models of acoustic and electromagnetic waves, it also reviews existing transmitters embedded in current robots and simulators. It then proposes CORSICA, which allows validating the choices in terms of protocol and communication strategies, whether they are robot-robot or human-robot interactions. This paper finishes with a presentation of possible integration according to the literature review, and the potential to get CORSICA at an industrial level.

Keywords: underwater simulator, robot-robot underwater communication, swarm robotics, transceiver and communication models

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Authors: Aleksander Nawrat, Karol Jędrasiak, Artur Ryt, Dawid Sobel

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The goal of the article is to present a novel Multimedia Firearms Training System. The system was developed in order to compensate for major problems of existing shooting training systems. The designed and implemented solution can be characterized by five major advantages: algorithm for automatic geometric calibration, algorithm of photometric recalibration, firearms hit point detection using thermal imaging camera, IR laser spot tracking algorithm for after action review analysis, and implementation of ballistics equations. The combination of the abovementioned advantages in a single multimedia firearms training system creates a comprehensive solution for detecting and tracking of the target point usable for shooting training systems and improving intervention tactics of uniformed services. The introduced algorithms of geometric and photometric recalibration allow the use of economically viable commercially available projectors for systems that require long and intensive use without most of the negative impacts on color mapping of existing multi-projector multimedia shooting range systems. The article presents the results of the developed algorithms and their application in real training systems.

Keywords: firearms shot detection, geometric recalibration, photometric recalibration, IR tracking algorithm, thermography, ballistics

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Authors: Kanika Taneja, V. K. Soni, S. D. Attri, Kafeel Ahmad, Shamshad Ahmad

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Aerosols represent an important component of earth-atmosphere system and have a profound impact on the global and regional climate. With the growing population and urbanization, the aerosol load in the atmosphere over the Indian region is found to be increasing. Several studies have reported that the aerosol optical depth over the northern part of India is higher as compared to the southern part. The northern India along the Indo-Gangetic plain is often influenced with dust transported from the Thar Desert in northwestern India and from Arabian Peninsula during the pre-monsoon season. Seasonal variations in aerosol optical and radiative properties were examined using data retrieved from ground based multi-wavelength Prede Sun/sky radiometer (POM-02) over Delhi, Rohtak, Jodhpur and Varanasi for the period April 2011-April 2013. These stations are part of the Skynet-India network of India Meteorological Department. The Sun/sky radiometer (POM-02) has advantage over other instruments that it can be calibrated on-site. These aerosol optical properties retrieved from skyradiometer observations are further used to analyze the Direct Aerosol Radiative Forcing (DARF) over the study locations.

Keywords: aerosol optical properties, indo- gangetic plain, radiative forcing, sky radiometer

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Authors: Amirhossein Mohajerzadeh, Abolghasem Mohajerzadeh

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In this paper, target parameter is estimated with desirable precision in hierarchical wireless sensor networks (WSN) while the proposed algorithm also tries to prolong network lifetime as much as possible, using efficient data collecting algorithm. Target parameter distribution function is considered unknown. Sensor nodes sense the environment and send the data to the base station called fusion center (FC) using hierarchical data collecting algorithm. FC builds underlying phenomena based on collected data. Considering the aggregation level, x, the goal is providing the essential infrastructure to find the best value for aggregation level in order to prolong network lifetime as much as possible, while desirable accuracy is guaranteed (required sample size is fully depended on desirable precision). First, the sample size calculation algorithm is discussed, second, the average queue length based on M/M[x]/1/K queue model is determined and it is used for energy consumption calculation. Nodes can decrease transmission cost by aggregating incoming data. Furthermore, the performance of the new algorithm is evaluated in terms of lifetime and estimation accuracy.

Keywords: aggregation, estimation, queuing, wireless sensor network

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Authors: Beatrice Adeoye

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Studies have associated social and demographic characteristics as strong determinants of utilization of health care services; however, not much has been done to explore the dynamics of these variables in Nigeria. This empirical study explores the link between demographic factors and the future use of health care services in Nsukka, southeast Nigeria. A total of 543 respondents were selected using multi-stage sampling technique. The findings of the study showed that majority (56.9%) of the respondents were female while 43.1% were male. More of the respondents were married (50.3%) while 41.80/0 of the respondents were between ages 26-35. Testing the demographic characteristics regarding where people will prefer to go first for treatment with multiple regression, It is only Sex as a demographic variable that indicates positive association for future occurrence to where people will prefer to go first for treatment with 0.08 significance. Age and education indicates no association considering their level of significance. This result shows that sex is one of the determinant factors of where and when people will go for treatment. This is pointing out the realities regarding African society where in the family setting, it is the father that dictates the cause of action. Also to buttress these findings, cross tabulating age with who determines where and when to go for treatment, findings show that majority (58.9%) within age 26-35 said their spouses decide on where and when to go for treatment. Findings showed that patriarchy still plays an important role in the utilization of health care delivery among the people studied.

Keywords: Demographic characters, Determinant, Health Care, treatment, self-medication, symptom,

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Authors: Fatma El-Zahraa El-Taher, Ayman Taha, Jane Courtney, Susan Mckeever

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Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated.

Keywords: satellite images, remote sensing images, data acquisition, autonomous vehicles

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Authors: Gousilin Leandra Rocha Da Silva, Stéfani T. A. Dantas, Bruna F. Rossi, Erika R. Bonsaglia, Ivana G. Castilho, Terue Sadatsune, Ary Fernandes Júnior, Vera l. M. Rall

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Kidney failure causes decreased diuresis and accumulation of nitrogenous substances in the body. To increase patient survival, hemodialysis is used as a partial substitute for renal function. However, contamination of the water used in this treatment, causing bacteremia in patients, is a worldwide concern. The Burkholderia cepacia complex (Bcc), a group of bacteria with more than 20 species, is frequently isolated from hemodialysis water samples and comprises opportunistic bacteria, affecting immunosuppressed patients, due to its wide variety of virulence factors, in addition to innate resistance to several antimicrobial agents, contributing to the permanence in the hospital environment and to the pathogenesis in the host. The objective of the present work was to characterize molecularly and phenotypically Bcc isolates collected from the water and dialysate of the Hemodialysis Unit and from the blood of patients at a Public Hospital in Botucatu, São Paulo, Brazil, between 2019 and 2021. We used 33 Bcc isolates, previously obtained from blood cultures from patients with bacteremia undergoing hemodialysis treatment (2019-2021) and 24 isolates obtained from water and dialysate samples in a Hemodialysis Unit (same period). The recA gene was sequenced to identify the specific species among the Bcc group. All isolates were tested for the presence of some genes that encode virulence factors such as cblA, esmR, zmpA and zmpB. Considering the epidemiology of the outbreak, the Bcc isolates were molecularly characterized by Multi Locus Sequence Type (MLST) and by pulsed-field gel electrophoresis (PFGE). The verification and quantification of biofilm in a polystyrene microplate were performed by submitting the isolates to different incubation temperatures (20°C, average water temperature and 35°C, optimal temperature for group growth). The antibiogram was performed with disc diffusion tests on agar, using discs impregnated with cefepime (30µg), ceftazidime (30µg), ciprofloxacin (5µg), gentamicin (10µg), imipenem (10µg), amikacin 30µg), sulfametazol/trimethoprim (23.75/1.25µg) and ampicillin/sulbactam (10/10µg). The presence of ZmpB was identified in all isolates, while ZmpA was observed in 96.5% of the isolates, while none of them presented the cblA and esmR genes. The antibiogram of the 33 human isolates indicated that all were resistant to gentamicin, colistin, ampicillin/sulbactam and imipenem. 16 (48.5%) isolates were resistant to amikacin and lower rates of resistance were observed for meropenem, ceftazidime, cefepime, ciprofloxacin and piperacycline/tazobactam (6.1%). All isolates were sensitive to sulfametazol/trimethoprim, levofloxacin and tigecycline. As for the water isolates, resistance was observed only to gentamicin (34.8%) and imipenem (17.4%). According to PFGE results, all isolates obtained from humans and water belonged to the same pulsotype (1), which was identified by recA sequencing as B. cepacia¸, belonging to sequence type ST-767. By observing a single pulse type over three years, one can observe the persistence of this isolate in the pipeline, contaminating patients undergoing hemodialysis, despite the routine disinfection of water with peracetic acid. This persistence is probably due to the production of biofilm, which protects bacteria from disinfectants and, making this scenario more critical, several isolates proved to be multidrug-resistant (resistance to at least three groups of antimicrobials), turning the patient care even more difficult.

Keywords: hemodialysis, burkholderia cepacia, PFGE, MLST, multi drug resistance

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Authors: Antonio Gioiosa, Simone Doanti, Eric Flumerfelt, Luca Morescalchi, Elena Pedreschi, Gianantonio Pezzullo, Ryan A. Rivera, Franco Spinella

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The Mu2e experiment at Fermilab aims to measure the charged-lepton flavour violating neutrino-less conversion of a negative muon into an electron in the field of an aluminum nucleus. With the expected experimental sensitivity, Mu2e will improve the previous limit of four orders of magnitude. The Mu2e data acquisition (DAQ) system provides hardware and software to collect digitized data from the tracker, calorimeter, cosmic ray veto, and beam monitoring systems. Mu2e’s trigger and data acquisition system (TDAQ) uses otsdaq as its solution. developed at Fermilab, otsdaq uses the artdaq DAQ framework and art analysis framework, under-the-hood, for event transfer, filtering, and processing. Otsdaq is an online DAQ software suite with a focus on flexibility and scalability while providing a multi-user, web-based interface accessible through the Chrome or Firefox web browser. The detector read out controller (ROC) from the tracker and calorimeter stream out zero-suppressed data continuously to the data transfer controller (DTC). Data is then read over the PCIe bus to a software filter algorithm that selects events which are finally combined with the data flux that comes from a cosmic ray veto system (CRV).

Keywords: trigger, daq, mu2e, Fermilab

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Accurate Liver, spleen and kidneys segmentation in abdominal CT images is one of the most important steps for computer aided abdominal organs pathology diagnosis. In this paper, we have proposed a new semi-automatic algorithm for Liver, spleen and kidneys area extraction in abdominal CT images. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. The algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, multi-abdominal organ segmentation, mosaic image, the watershed algorithm

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: M. Sumida, S. Morita

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In this research, the effect of unsteady flows acting on road vehicles was experimentally investigated, using an advanced and recently introduced wind tunnel. The aims of this study were to extract the characteristics of fluid forces acting on road vehicles under unsteady wind conditions and obtain new information on drag forces in a practical on-road test. We applied pulsating wind as a representative example of the atmospheric fluctuations that vehicles encounter on the road. That is, we considered the case where the vehicles are moving at constant speed in the air, with large wind oscillations. The experimental tests were performed on the Ahmed-type test model, which is a simplified vehicle model. This model was chosen because of its simplicity and the data accumulated under steady wind conditions. The experiments were carried out with a time-averaged Reynolds number of Re = 4.16x10⁵ and a pulsation period of T = 1.5 s, with amplitude of η = 0.235. Unsteady fluid forces of drag and lift were obtained utilizing a multi-component load cell. It was observed that the unsteady aerodynamic forces differ significantly from those under steady wind conditions. They exhibit a phase shift and an enhanced response to the wind oscillations. Furthermore, their behavior depends on the slant angle of the rear shape of the model.

Keywords: Ahmed body, automotive aerodynamics, unsteady wind, wind tunnel test

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Authors: Naouelle Azzag, Nadia Haddad, Benoit Durand, Elisabeth Petit, Ali Ammouche, Bruno Chomel, Henri J. Boulouis

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Bartonella henselae is a small, gram negative, arthropod-borne bacterium that has been shown to cause multiple clinical manifestations in humans including cat scratch disease, bacillary angiomatosis, endocarditis, and bacteremia. In this research, we report the results of a cross sectional study of Bartonella henselae bacteremia in stray cats from Algiers. Whole blood of 227 stray cats from Algiers was tested for the presence of Bartonella species by culture and for the evaluation of the genetic diversity of B. henselae strains by multi-locus variable number of tandem repeats assay (MLVA). Bacteremia prevalence was 17% and only B. henselae was identified. Type I was the predominant type (64%). MLVA typing of 259 strains from 30 bacteremic cats revealed 52 different profiles. 51 of these profiles were specific to Algerian cats/identified for the first time. 20/30 cats (67%) harbored 2 to 7 MLVA profiles simultaneously. The similarity of MLVA profiles obtained from the same cat, neighbor-joining clustering and structure-neighbor clustering showed that such a diversity likely results from two different mechanisms occurring either independently or simultaneously independent infections and genetic drift from a primary strain.

Keywords: Bartonella, cat, MLVA, genetic

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Authors: Diogo José Morgado Rebelo, Francisco António Carneiro Pacheco de Andrade, Paulo Jorge Freitas de Oliveira Novais

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Not everything in AI-power consumer credit scoring turns out to be a wonder. When using AI in Creditworthiness Assessment (CWA), opacity and unfairness ‘sins’ must be considered to the task be deemed Responsible. AI software is not always 100% accurate, which can lead to misclassification. Discrimination of some groups can be exponentiated. A hetero personalized identity can be imposed on the individual(s) affected. Also, autonomous CWA sometimes lacks transparency when using black box models. However, for this intended purpose, human analysts ‘on-the-loop’ might not be the best remedy consumers are looking for in credit. This study seeks to explore the legality of implementing a Multi-Agent System (MAS) framework in consumer CWA to ensure compliance with the regulation outlined in Article 14(4) of the Proposal for an Artificial Intelligence Act (AIA), dated 21 April 2021 (as per the last corrigendum by the European Parliament on 19 April 2024), Especially with the adoption of Art. 18(8)(9) of the EU Directive 2023/2225, of 18 October, which will go into effect on 20 November 2026, there should be more emphasis on the need for hybrid oversight in AI-driven scoring to ensure fairness and transparency. In fact, the range of EU regulations on AI-based consumer credit will soon impact the AI lending industry locally and globally, as shown by the broad territorial scope of AIA’s Art. 2. Consequently, engineering the law of consumer’s CWA is imperative. Generally, the proposed MAS framework consists of several layers arranged in a specific sequence, as follows: firstly, the Data Layer gathers legitimate predictor sets from traditional sources; then, the Decision Support System Layer, whose Neural Network model is trained using k-fold Cross Validation, provides recommendations based on the feeder data; the eXplainability (XAI) multi-structure comprises Three-Step-Agents; and, lastly, the Oversight Layer has a 'Bottom Stop' for analysts to intervene in a timely manner. From the analysis, one can assure a vital component of this software is the XAY layer. It appears as a transparent curtain covering the AI’s decision-making process, enabling comprehension, reflection, and further feasible oversight. Local Interpretable Model-agnostic Explanations (LIME) might act as a pillar by offering counterfactual insights. SHapley Additive exPlanation (SHAP), another agent in the XAI layer, could address potential discrimination issues, identifying the contribution of each feature to the prediction. Alternatively, for thin or no file consumers, the Suggestion Agent can promote financial inclusion. It uses lawful alternative sources such as the share of wallet, among others, to search for more advantageous solutions to incomplete evaluation appraisals based on genetic programming. Overall, this research aspires to bring the concept of Machine-Centered Anthropocentrism to the table of EU policymaking. It acknowledges that, when put into service, credit analysts no longer exert full control over the data-driven entities programmers have given ‘birth’ to. With similar explanatory agents under supervision, AI itself can become self-accountable, prioritizing human concerns and values. AI decisions should not be vilified inherently. The issue lies in how they are integrated into decision-making and whether they align with non-discrimination principles and transparency rules.

Keywords: creditworthiness assessment, hybrid oversight, machine-centered anthropocentrism, EU policymaking

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Authors: Netsanet Kebede Hundessa

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Substrate modification of thin film composite (TFC) membranes with various crosslinkers is typically necessary for organic solvent nanofiltration (OSN) applications. This modification is aimed at enhancing membrane stability and solvent resistance, but it often results in a decline in permeance. This study introduces a distinct approach by developing a coordination-crosslinked polyimide substrate, which differs from the covalently-crosslinked substrates traditionally used. This developed substrate achieves enhanced solvent resistance, improved hydrophilicity, and optimized porous microstructure simultaneously. The study investigates the effects of an alkaline coagulation bath, subsequent ion exchange, and further solvent activation. The resulting TFC membrane successfully overcomes the typical permeability-selectivity trade-off of OSN membranes. It demonstrates significantly improved solvent permeance (1.5–2 times higher than previously reported data) with values of 65.2 LMH/bar for methanol, 33.1 LMH/bar for ethanol, and 59.1 LMH/bar for acetone while maintaining competitive solute rejection (>98% for Rose Bengal). This research is expected to provide a new direction for developing high-performance OSN composite membranes and other separation applications.

Keywords: metal coordinatiom, thin film composite membrane, organic solvent nanofiltration, solvent activation

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Authors: Agnieszka E. Laudy, Karolina Stępien, Sergiusz Lulinski, Krzysztof Durka, Stefan Tyski

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1,3-dihydro-1-hydroxy-2,1-benzoxaborole and its derivatives are a particularly interesting group of synthetic agents and were successfully employed in supramolecular chemistry medicine. The first important compounds, 5-fluoro-1,3-dihydro-1-hydroxy-2,1-benzoxaborole and 5-chloro-1,3-dihydro-1-hydroxy-2,1-benzoxaborole were identified as potent antifungal agents. In contrast, (S)-3-(aminomethyl)-7-(3-hydroxypropoxy)-1-hydroxy-1,3-dihydro-2,1-benzoxaborole hydrochloride is in the second phase of clinical trials as a drug for the treatment of Gram-negative bacterial infections of the Enterobacteriaceae family and Pseudomonas aeruginosa. Equally important and difficult task is to search for compounds active against Gram-negative bacilli, which have multi-drug-resistance efflux pumps actively removing many of the antibiotics from bacterial cells. We have examined whether halogen-substituted benzoxaborole-based derivatives and their analogues possess antibacterial activity and are substrates for multi-drug-resistance efflux pumps. The antibacterial activity of 1,3-dihydro-3-hydroxy-1,1-dimethyl-1,2,3-benzosiloxaborole and 10 halogen-substituted its derivatives, as well as 1,2-phenylenediboronic acid and 3 synthesised fluoro-substituted its analogs, were evaluated. The activity against the reference strains of Gram-positive (n=5) and Gram-negative bacteria (n=10) was screened by the disc-diffusion test (0.4 mg of tested compounds was applied onto paper disc). The minimal inhibitory concentration values and the minimal bactericidal concentration values were estimated according to The Clinical and Laboratory Standards Institute and The European Committee on Antimicrobial Susceptibility Testing recommendations. During the minimal inhibitory concentration values determination with or without phenylalanine-arginine beta-naphthylamide (50 mg/L) efflux pump inhibitor, the concentrations of tested compounds ranged 0.39-400 mg/L in the broth medium supplemented with 1 mM magnesium sulfate. Generally, the studied benzosiloxaboroles and benzoxadiboroles showed a higher activity against Gram-positive cocci than against Gram-negative rods. Moreover, benzosiloxaboroles have the higher activity than benzoxadiboroles compounds. In this study, we demonstrated that substitution (mono-, di- or tetra-) of 1,3-dihydro-3-hydroxy-1,1-dimethyl-1,2,3-benzosiloxaborole with halogen groups resulted in an increase in antimicrobial activity as compared to the parent substance. Interestingly, the 6,7-dichloro-substituted parent substance was found to be the most potent against Gram-positive cocci: Staphylococcus sp. (minimal inhibitory concentration 6.25 mg/L) and Enterococcus sp. (minimal inhibitory concentration 25 mg/L). On the other hand, mono- and dichloro-substituted compounds were the most actively removed by efflux pumps present in Gram-negative bacteria mainly from Enterobacteriaceae family. In the presence of efflux pump inhibitor the minimal inhibitory concentration values of chloro-substituted benzosiloxaboroles decreased from 400 mg/L to 3.12 mg/L. Of note, the highest increase in bacterial susceptibility to tested compounds in the presence of phenylalanine-arginine beta-naphthylamide was observed for 6-chloro-, 6,7-dichloro- and 6,7-difluoro-substituted benzosiloxaboroles. In the case of Escherichia coli, Enterobacter cloacae and P. aeruginosa strains at least a 32-fold decrease in the minimal inhibitory concentration values of these agents were observed. These data demonstrate structure-activity relationships of the tested derivatives and highlight the need for further search for benzoxaboroles and related compounds with significant antimicrobial properties. Moreover, the influence of phenylalanine-arginine beta-naphthylamide on the susceptibility of Gram-negative rods to studied benzosiloxaboroles indicate that some tested agents are substrates for efflux pumps in Gram-negative rods.

Keywords: antibacterial activity, benzosiloxaboroles, efflux pumps, phenylalanine-arginine beta-naphthylamide

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Authors: Amel Hannech, Mehdi Adda, Hamid Mcheick

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In computer science, the purpose of finding useful information is still one of the most active and important research topics. The most popular application of information retrieval (IR) are Search Engines, they meet users' specific needs and aim to locate the effective information in the web. However, these search engines have some limitations related to the relevancy of the results and the ease to explore those results. In this context, we proposed in previous works a Multi-Space Search Engine model that is based on a multidimensional interpretation universe. In the present paper, we integrate an additional dimension that allows to offer users new research experiences. The added component is based on creating user profiles and calculating the similarity between them that then allow the use of collaborative filtering in retrieving search results. To evaluate the effectiveness of the proposed model, a prototype is developed. The experiments showed that the additional dimension has improved the relevancy of results by predicting the interesting items of users based on their experiences and the experiences of other similar users. The offered personalization service allows users to approve the pertinent items, which allows to enrich their profiles and further improve research.

Keywords: information retrieval, v-facets, user behavior analysis, user profiles, topical ontology, association rules, data personalization

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Authors: Raqiqa Tur Rasool, Ghulam Abbas Ashraf

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This study presents the synthesis and application of Carbon@NiCoFeS nanoparticles as a photocatalyst for the degradation of organic pollutants through peroxymonosulfate (PMS) activation. The Carbon@NiCoFeS nanoparticles, synthesized via a hydrothermal method, exhibit a highly crystalline and uniformly distributed nanostructure, as confirmed by XRD, SEM, TEM, and FTIR analyses. The photocatalytic performance was tested using ibuprofen (IBU) as a model pollutant under visible light, demonstrating remarkable efficiency across various conditions, including different concentrations of photocatalyst and PMS and a range of pH values. The enhanced activity is attributed to the synergistic effects of Ni, Co, and Fe, promoting effective electron-hole separation and reactive radical generation, primarily SO4•− and •OH. Quenching experiments highlighted sulfate radicals' predominant role in the degradation process. The Carbon@NiCoFeS photocatalyst also showed excellent reusability and stability over multiple cycles, and its versatility in degrading various organic pollutants underscores its potential for practical wastewater treatment applications. This research offers significant insights into multi-metal sulfide photocatalyst design, showcasing Carbon@NiCoFeS nanoparticles' promising role in environmental remediation via efficient PMS activation.

Keywords: NiCoFeS nanoparticles, photocatalytic degradation, peroxymonosulfate activation, organic pollutant removal, wastewater treatment

Procedia PDF Downloads 47

Authors: Giovanni Menichini, Salvatore Giacomo Morano, Gloria Terenzi, Luca Salvatori, Maurizio Orlando

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The “Guidelines for Risk Classification, Safety Assessment, and Structural Health Monitoring of Existing Bridges” were recently approved by the Italian Government to define technical standards for managing the national network of existing bridges. These guidelines provide a framework for risk mitigation and safety assessment of bridges, which are essential elements of the built environment and form the basis for the operation of transport systems. Within the guideline framework, a workflow based on three main points was proposed: (1) risk-based, i.e., based on typical parameters of hazard, vulnerability, and exposure; (2) multi-level, i.e., including six assessment levels of increasing complexity; and (3) multirisk, i.e., assessing structural/foundational, seismic, hydrological, and landslide risks. The paper focuses on applying the Italian Guidelines to specific case studies, aiming to identify the parameters that predominantly influence the determination of the “class of attention”. The significance of each parameter is determined via sensitivity analysis. Additionally, recommendations for enhancing the process of assigning the class of attention are proposed.

Keywords: bridge safety assessment, Italian guidelines implementation, risk classification, structural health monitoring

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