Search results for: palm kernel cake

Authors: Masoume Ehsani, Ning Zhu, Huu Doan, Ali Lohi, Amira Abdelrasoul

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Membrane fouling poses severe challenges in membrane-based wastewater treatment applications. Ultrasound (US) has been considered an effective fouling remediation technique in filtration processes. Bubble cavitation in the liquid medium results from the alternating rarefaction and compression cycles during the US irradiation at sufficiently high acoustic pressure. Cavitation microbubbles generated under US irradiation can cause eddy current and turbulent flow within the medium by either oscillating or discharging energy to the system through microbubble explosion. Turbulent flow regime and shear forces created close to the membrane surface cause disturbing the cake layer and dislodging the foulants, which in turn improve the cleaning efficiency and filtration performance. Therefore, the number, size, velocity, and oscillation pattern of the microbubbles created in the liquid medium play a crucial role in foulant detachment and permeate flux recovery. The goal of the current study is to gain in depth understanding of the influence of the US power intensity and frequency on the microbubble dynamics and its characteristics generated under US irradiation. In comparison with other imaging techniques, the synchrotron in-line Phase Contrast Imaging technique at the Canadian Light Source (CLS) allows in-situ observation and real-time visualization of microbubble dynamics. At CLS biomedical imaging and therapy (BMIT) polychromatic beamline, the effective parameters were optimized to enhance the contrast gas/liquid interface for the accuracy of the qualitative and quantitative analysis of bubble cavitation within the system. With the high flux of photons and the high-speed camera, a typical high projection speed was achieved; and each projection of microbubbles in water was captured in 0.5 ms. ImageJ software was used for post-processing the raw images for the detailed quantitative analyses of microbubbles. The imaging has been performed under the US power intensity levels of 50 W, 60 W, and 100 W, in addition to the US frequency levels of 20 kHz, 28 kHz, and 40 kHz. For the duration of 2 seconds of imaging, the effect of the US power and frequency on the average number, size, and fraction of the area occupied by bubbles were analyzed. Microbubbles’ dynamics in terms of their velocity in water was also investigated. For the US power increase of 50 W to 100 W, the average bubble number and the average bubble diameter were increased from 746 to 880 and from 36.7 µm to 48.4 µm, respectively. In terms of the influence of US frequency, a fewer number of bubbles were created at 20 kHz (average of 176 bubbles rather than 808 bubbles at 40 kHz), while the average bubble size was significantly larger than that of 40 kHz (almost seven times). The majority of bubbles were captured close to the membrane surface in the filtration unit. According to the study observations, membrane cleaning efficiency is expected to be improved at higher US power and lower US frequency due to the higher energy release to the system by increasing the number of bubbles or growing their size during oscillation (optimum condition is expected to be at 20 kHz and 100 W).

Keywords: bubble dynamics, cavitational bubbles, membrane fouling, ultrasonic cleaning

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Authors: Ayodeji Oluleye

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This study used Total Ozone Mapping Spectrometer (TOMS) Aerosol Index (AI) and reanalysis dataset of thirty years (1983-2012) to investigate the influence of the atmospheric circulation on dust transport during the Harmattan period over WestAfrica using TOMS data. The Harmattan dust mobilization and atmospheric circulation pattern were evaluated using a kernel density estimate which shows the areas where most points are concentrated between the variables. The evolution of the Inter-Tropical Discontinuity (ITD), Sea surface Temperature (SST) over the Gulf of Guinea, and the North Atlantic Oscillation (NAO) index during the Harmattan period (November-March) was also analyzed and graphs of the average ITD positions, SST and the NAO were observed on daily basis. The Pearson moment correlation analysis was also employed to assess the effect of atmospheric circulation on Harmattan dust transport. The results show that the departure (increased) of TOMS AI values from the long-term mean (1.64) occurred from around 21st of December, which signifies the rich dust days during winter period. Strong TOMS AI signal were observed from January to March with the maximum occurring in the latter months (February and March). The inter-annual variability of TOMSAI revealed that the rich dust years were found between 1984-1985, 1987-1988, 1997-1998, 1999-2000, and 2002-2004. Significantly, poor dust year was found between 2005 and 2006 in all the periods. The study has found strong north-easterly (NE) trade winds were over most of the Sahelianregion of West Africa during the winter months with the maximum wind speed reaching 8.61m/s inJanuary.The strength of NE winds determines the extent of dust transport to the coast of Gulf of Guinea during winter. This study has confirmed that the presence of the Harmattan is strongly dependent on theSST over Atlantic Ocean and ITD position. The locus of the average SST and ITD positions over West Africa could be described by polynomial functions. The study concludes that the evolution of near surface wind field at 925 hpa, and the variations of SST and ITD positions are the major large scale atmospheric circulation systems driving the emission, distribution, and transport of Harmattan dust aerosols over West Africa. However, the influence of NAO was shown to have fewer significance effects on the Harmattan dust transport over the region.

Keywords: atmospheric circulation, dust aerosols, Harmattan, West Africa

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Authors: Teresa Wanjiru Mbatia

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On 7th June 2011, the government Minister of Roads in Kenya announced the proposed construction of a major highway known as a southern bypass to run on the northern border of the Nairobi National Park. The following day on 8th June 2011, the chairperson of the Friends of Nairobi National Park (FONNAP) posted a protest statement on their website, with the heading, ‘Nairobi Park is Not a cake’ alerting its members and conservation groups, with the aim of getting support to the campaign against the government’s intention to hive off a section of the park for road construction. This was the first and earliest statement that led to a series of other events that culminated in conservationists and some other members of the public campaign against the government’s plan to hive off sections of the park to build road and railway infrastructure in or around the park. Together with other non-state actors, mostly non-governmental organisations in conservation/environment and tourism businesses, FoNNAP issued a series of other statements on social, print and electronic media to battle against road and railway construction. This paper examined the strategies, outcomes and interests of actors involved in opposing/proposing the development of transport infrastructure in and around the Nairobi National Park. Specifically, the objectives were to analyse the: (1) Arguments put forward by the eco-warriors to protest infrastructure development; (2) Background and interests of the eco-warriors; (3) Needs/interests and opinions of ordinary common citizens on transport infrastructural development, particularly in and around the urban nature reserve and (4) Final outcomes of the eco-politics surrounding infrastructure development in and around Nairobi National Park. The methodological approach used was environmental history and the social construction of nature. The study collected combined qualitative data using four main approaches, the grounded theory approach, narratives, case studies and a phenomenological approach. The information collected was analysed using critical discourse analysis. The major findings of the study were that under the guise of “public participation,” influential non-state actors have the capacity to perpetuate social-spatial inequalities in the form of curtailing the majority from accessing common public goods. A case in point in this study is how the efforts of powerful conservationists, environmentalists, and tourism businesspersons managed to stall the construction of much-needed road and railway infrastructure severally through litigations in lengthy environmental court processes involving injunctions and stop orders to the government bodies in charge. Moreover, powerful non-state actors were found to have formed informal and sometimes formal coalitions with politicians with selfish interests, which serves to deepen the exclusionary practices and the common good. The study concludes that mostly composed of certain types of elites (NGOs, business communities, politicians and privileged social-cultural groups), non-state actors have used participatory policies to advance their own interests at the expense of the majority whom they claim to represent. These practices are traced to the historically unjust social, political, and economic forces involved in the production of space in Nairobi.

Keywords: eco-politics, exclusion, infrastructure, Nairobi national park, non-state actors, protests

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Authors: Pompilia Buzatu, Hazim Qiblawey, Albert Odai, Jana Jamaleddin, Mustafa Nasser, Simon J. Judd

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Membrane bioreactor (MBR) technology is challenged by the tendency for the membrane permeability to decrease due to ‘clogging’. Clogging includes ‘sludging’, the filling of the membrane channels with sludge solids, and ‘ragging’, the aggregation of short filaments to form long rag-like particles. Both sludging and ragging demand manual intervention to clear out the solids, which is time-consuming, labour-intensive and potentially damaging to the membranes. These factors impact on costs more significantly than membrane surface fouling which, unlike clogging, is largely mitigated by the chemical clean. However, practical evaluation of MBR clogging has thus far been limited. This paper presents the results of recent work attempting to quantify sludging and clogging based on simple bench-scale tests. Results from a novel ragging simulation trial indicated that rags can be formed within 24-36 hours from dispersed < 5 mm-long filaments at concentrations of 5-10 mg/L under gently agitated conditions. Rag formation occurred for both a cotton wool standard and samples taken from an operating municipal MBR, with between 15% and 75% of the added fibrous material forming a single rag. The extent of rag formation depended both on the material type or origin – lint from laundering operations forming zero rags – and the filament length. Sludging rates were quantified using a bespoke parallel-channel test cell representing the membrane channels of an immersed flat sheet MBR. Sludge samples were provided from two local MBRs, one treating municipal and the other industrial effluent. Bulk sludge properties measured comprised mixed liquor suspended solids (MLSS) concentration, capillary suction time (CST), particle size, soluble COD (sCOD) and rheology (apparent viscosity μₐ vs shear rate γ). The fouling and sludging propensity of the sludge was determined using the test cell, ‘fouling’ being quantified as the pressure incline rate against flux via the flux step test (for which clogging was absent) and sludging by photographing the channel and processing the image to determine the ratio of the clogged to unclogged regions. A substantial difference in rheological and fouling behaviour was evident between the two sludge sources, the industrial sludge having a higher viscosity but less shear-thinning than the municipal. Fouling, as manifested by the pressure increase Δp/Δt, as a function of flux from classic flux-step experiments (where no clogging was evident), was more rapid for the industrial sludge. Across all samples of both sludge origins the expected trend of increased fouling propensity with increased CST and sCOD was demonstrated, whereas no correlation was observed between clogging rate and these parameters. The relative contribution of fouling and clogging was appraised by adjusting the clogging propensity via increasing the MLSS both with and without a commensurate increase in the COD. Results indicated that whereas for the municipal sludge the fouling propensity was affected by the increased sCOD, there was no associated increased in the sludging propensity (or cake formation). The clogging rate actually decreased on increasing the MLSS. Against this, for the industrial sludge the clogging rate dramatically increased with solids concentration despite a decrease in the soluble COD. From this was surmised that sludging did not relate to fouling.

Keywords: clogging, membrane bioreactors, ragging, sludge

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Authors: Temitayo Abayomi Ewemoje, Oluwamayowa Oluwafemi Oluwaniyi

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Agriculture and food processing, industry are among the largest industrial sectors that uses large amount of energy. Thus, a larger amount of gases from their fuel combustion technologies is being released into the environment. The choice of input energy supply not only directly having affects the environment, but also poses a threat to human health. The study was therefore designed to assess each unit production processes in order to identify hotspots using life cycle assessments (LCA) approach in South-western Nigeria. Data such as machine power rating, operation duration, inputs and outputs of shea butter materials for unit processes obtained at site were used to modelled Life Cycle Impact Analysis on GaBi6 (Holistic Balancing) software. Four scenarios were drawn for the impact assessments. Material sourcing from Kaiama, Scenarios 1, 3 and Minna Scenarios 2, 4 but different heat supply sources (Liquefied Petroleum Gas ‘LPG’ Scenarios 1, 2 and 10.8 kW Diesel Heater, scenarios 3, 4). Modelling of shea butter production on GaBi6 was for 1kg functional unit of shea butter produced and the Tool for the Reduction and Assessment of Chemical and other Environmental Impacts (TRACI) midpoint assessment was tool used to was analyse the life cycle inventories of the four scenarios. Eight categories in all four Scenarios were observed out of which three impact categories; Global Warming Potential (GWP) (0.613, 0.751, 0.661, 0.799) kg CO2¬-Equiv., Acidification Potential (AP) (0.112, 0.132, 0.129, 0.149) kg H+ moles-Equiv., and Smog (0.044, 0.059, 0.049, 0.063) kg O3-Equiv., categories had the greater impacts on the environment in Scenarios 1-4 respectively. Impacts from transportation activities was also seen to contribute more to these environmental impact categories due to large volume of petrol combusted leading to releases of gases such as CO2, CH4, N2O, SO2, and NOx into the environment during the transportation of raw shea kernel purchased. The ratio of transportation distance from Minna and Kaiama to production site was approximately 3.5. Shea butter unit processes with greater impacts in all categories was the packaging, milling and with the churning processes in ascending order of magnitude was identified as hotspots that may require attention. From the 1kg shea butter functional unit, it was inferred that locating production site at the shortest travelling distance to raw material sourcing and combustion of LPG for heating would reduce all the impact categories assessed on the environment.

Keywords: GaBi6, Life cycle assessment, shea butter production, TRACI

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Authors: Inyoung Park, Youngjoo Woo, Euiseong Seo

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With the advent of WebGL, Web apps are now able to provide high quality graphics by utilizing the underlying graphic processing units (GPUs). Despite that the Web apps are becoming common and popular, the current power management schemes, which were devised for the conventional native applications, are suboptimal for Web apps because of the additional layer, the Web browser, between OS and application. The Web browser running on a CPU issues GL commands, which are for rendering images to be displayed by the Web app currently running, to the GPU and the GPU processes them. The size and number of issued GL commands determine the processing load of the GPU. While the GPU is processing the GL commands, CPU simultaneously executes the other compute intensive threads. The actual user experience will be determined by either CPU processing or GPU processing depending on which of the two is the more demanded resource. For example, when the GPU work queue is saturated by the outstanding commands, lowering the performance level of the CPU does not affect the user experience because it is already deteriorated by the retarded execution of GPU commands. Consequently, it would be desirable to lower CPU or GPU performance level to save energy when the other resource is saturated and becomes a bottleneck in the execution flow. Based on this observation, we propose a power management scheme that is specialized for the Web app runtime environment. This approach incurs two technical challenges; identification of the bottleneck resource and determination of the appropriate performance level for unsaturated resource. The proposed power management scheme uses the CPU utilization level of the Window Manager to tell which one is the bottleneck if exists. The Window Manager draws the final screen using the processed results delivered from the GPU. Thus, the Window Manager is on the critical path that determines the quality of user experience and purely executed by the CPU. The proposed scheme uses the weighted average of the Window Manager utilization to prevent excessive sensitivity and fluctuation. We classified Web apps into three categories using the analysis results that measure frame-per-second (FPS) changes under diverse CPU/GPU clock combinations. The results showed that the capability of the CPU decides user experience when the Window Manager utilization is above 90% and consequently, the proposed scheme decreases the performance level of CPU by one step. On the contrary, when its utilization is less than 60%, the bottleneck usually lies in the GPU and it is desirable to decrease the performance of GPU. Even the processing unit that is not on critical path, excessive performance drop can occur and that may adversely affect the user experience. Therefore, our scheme lowers the frequency gradually, until it finds an appropriate level by periodically checking the CPU utilization. The proposed scheme reduced the energy consumption by 10.34% on average in comparison to the conventional Linux kernel, and it worsened their FPS by 1.07% only on average.

Keywords: interactive applications, power management, QoS, Web apps, WebGL

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Authors: Huayuan Lu, Chunfang Yang, Ma Zhu, Baojun Qi, Yaqiong Qiao, Jiangqian Xu

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Image geolocalization has great application prospects in fields such as autonomous driving and virtual/augmented reality. In practical application scenarios, the size of the image to be located is not fixed; it is impractical to train different networks for all possible sizes. When its size does not match the size of the input of the descriptor extraction model, existing image geolocalization methods usually directly scale or crop the image in some common ways. This will result in the loss of some information important to the geolocalization task, thus affecting the performance of the image geolocalization method. For example, excessive down-sampling can lead to blurred building contour, and inappropriate cropping can lead to the loss of key semantic elements, resulting in incorrect geolocation results. To address this problem, this paper designs a learnable image resizer and proposes an arbitrary-sized image geolocation method. (1) The designed learnable image resizer employs the self-attention mechanism to enhance the geometric features of the resized image. Firstly, it applies bilinear interpolation to the input image and its feature maps to obtain the initial resized image and the resized feature maps. Then, SKNet (selective kernel net) is used to approximate the best receptive field, thus keeping the geometric shapes as the original image. And SENet (squeeze and extraction net) is used to automatically select the feature maps with strong contour information, enhancing the geometric features. Finally, the enhanced geometric features are fused with the initial resized image, to obtain the final resized images. (2) The proposed image geolocalization method embeds the above image resizer as a fronting layer of the descriptor extraction network. It not only enables the network to be compatible with arbitrary-sized input images but also enhances the geometric features that are crucial to the image geolocalization task. Moreover, the triplet attention mechanism is added after the first convolutional layer of the backbone network to optimize the utilization of geometric elements extracted by the first convolutional layer. Finally, the local features extracted by the backbone network are aggregated to form image descriptors for image geolocalization. The proposed method was evaluated on several mainstream datasets, such as Pittsburgh30K, Tokyo24/7, and Places365. The results show that the proposed method has excellent size compatibility and compares favorably to recently mainstream geolocalization methods.

Keywords: image geolocalization, self-attention mechanism, image resizer, geometric feature

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Authors: Omar Shafqat, Jaime Arias, Cristian Bogdan, Björn Palm

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Heating constitutes a major part of the overall energy consumption in Sweden. In 2013 heating and hot water accounted for about 55% of the total energy use in the housing sector. Historically, the end users have not been able to make a significant impact on their consumption on account of traditional control systems that do not facilitate interaction and control of the heating systems. However, in recent years internet connected home energy management systems have become increasingly available which allow users to visualize the indoor temperatures as well as control the heating system. However, the adoption of these systems is still in its nascent stages. This paper presents the outcome of a study carried out in a community of single-family houses in Stockholm. Heating in the area is provided through district heating, and the neighbourhood is connected through a local micro thermal grid, which is owned and operated by the local community. Heating in the houses is accomplished through a hydronic system equipped with radiators. The system installed offers the households to control the indoor temperature through a mobile application as well as through a physical thermostat. It was also possible to program the system to, for instance, lower the temperatures during night time and when the users were away. The users could also monitor the indoor temperatures through the application. It was additionally possible to create different zones in the house with their own individual programming. The historical heating data (in the form of billing data) was available for several previous years and has been used to perform quantitative analysis for the study after necessary normalization for weather variations. The experiment involved 30 households out of a community of 178 houses. The area was selected due to uniform construction profile in the area. It was observed that despite similar design and construction period there was a large variation in the heating energy consumption in the area which can for a large part be attributed to user behaviour. The paper also presents qualitative analysis done through survey questions as well as a focus group carried out with the participants. Overall, considerable energy savings were accomplished during the trial, however, there was a considerable variation between the participating households. The paper additionally presents recommendations to improve the impact of home energy management systems for heating in terms of improving user engagement and hence the energy impact.

Keywords: energy efficiency in buildings, energy behavior, heating control system, home energy management system

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Authors: Hamza Benzerrouk, Alexander Nebylov

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In classical GNSS/INS integration designs, the loosely coupled approach uses the GNSS derived position and the velocity as the measurements vector. This design is suboptimal from the standpoint of preventing GNSSoutliers/outages. The tightly coupled GPS/INS navigation filter mixes the GNSS pseudo range and inertial measurements and obtains the vehicle navigation state as the final navigation solution. The ultra‐tightly coupled GNSS/INS design combines the I (inphase) and Q(quadrature) accumulator outputs in the GNSS receiver signal tracking loops and the INS navigation filter function intoa single Kalman filter variant (EKF, UKF, SPKF, CKF and HCKF). As mentioned, EKF and UKF are the most used nonlinear filters in the literature and are well adapted to inertial navigation state estimation when integrated with GNSS signal outputs. In this paper, it is proposed to move a step forward with more accurate filters and modern approaches called Cubature and High Degree cubature Kalman Filtering methods, on the basis of previous results solving the state estimation based on INS/GNSS integration, Cubature Kalman Filter (CKF) and High Degree Cubature Kalman Filter with (HCKF) are the references for the recent developed generalized Cubature rule based Kalman Filter (GCKF). High degree cubature rules are the kernel of the new solution for more accurate estimation with less computational complexity compared with the Gauss-Hermite Quadrature (GHQKF). Gauss-Hermite Kalman Filter GHKF which is not selected in this work because of its limited real-time implementation in high-dimensional state-spaces. In ultra tightly or a deeply coupled GNSS/INS system is dynamics EKF is used with transition matrix factorization together with GNSS block processing which is well described in the paper and assumes available the intermediary frequency IF by using a correlator samples with a rate of 500 Hz in the presented approach. GNSS (GPS+GLONASS) measurements are assumed available and modern SPKF with Cubature Kalman Filter (CKF) are compared with new versions of CKF called high order CKF based on Spherical-radial cubature rules developed at the fifth order in this work. Estimation accuracy of the high degree CKF is supposed to be comparative to GHKF, results of state estimation are then observed and discussed for different initialization parameters. Results show more accurate navigation state estimation and more robust GNSS receiver when Ultra Tightly Coupled approach applied based on High Degree Cubature Kalman Filter.

Keywords: GNSS, INS, Kalman filtering, ultra tight integration

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Authors: Nathalie Stephenne, Mathieu Veschkens, Stéphane Palm, Christophe Charlemagne, Jacques Defoux

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According to Europe’s first River basin Management Plans (RBMPs), 56% of European rivers failed to achieve the good status targets of the Water Framework Directive WFD. In Central European countries such as Belgium, even more than 80% of rivers failed to achieve the WFD quality targets. Although the RBMP’s should reduce the stressors and improve water body status, their potential to address multiple stress situations is limited due to insufficient knowledge on combined effects, multi-stress, prioritization of measures, impact on ecology and implementation effects. This paper describes a webGis prototype developed for the Walloon administration to improve the communication and the management of sediment dredging actions carried out in rivers and lakes in the frame of RBMPs. A large number of stakeholders are involved in the management of rivers and lakes in Wallonia. They are in charge of technical aspects (client and dredging operators, organizations involved in the treatment of waste…), management (managers involved in WFD implementation at communal, provincial or regional level) or policy making (people responsible for policy compliance or legislation revision). These different kinds of stakeholders need different information and data to cover their duties but have to interact closely at different levels. Moreover, information has to be shared between them to improve the management quality of dredging operations within the ecological system. In the Walloon legislation, leveling dredged sediments on banks requires an official authorization from the administration. This request refers to spatial information such as the official land use map, the cadastral map, the distance to potential pollution sources. The production of a collective geodatabase can facilitate the management of these authorizations from both sides. The proposed internet system integrates documents, data input, integration of data from disparate sources, map representation, database queries, analysis of monitoring data, presentation of results and cartographic visualization. A prototype of web application using the API geoviewer chosen by the Geomatic department of the SPW has been developed and discussed with some potential users to facilitate the communication, the management and the quality of the data. The structure of the paper states the why, what, who and how of this communication tool.

Keywords: sediments, web application, GIS, rivers management

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Authors: Michal Pravenec, Miroslava Simakova, Jan Silhavy

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Brown adipose tissue (BAT) plays an important role in lipid and glucose metabolism in rodents and possibly also in humans. Recently, using systems genetics approach in the BAT from BXH/HXB recombinant inbred strains, derived from the SHR (spontaneously hypertensive rat) and BN (Brown Norway) progenitors, we identified Cd36 (fatty acid translocase) as the hub gene of co-expression module associated with BAT relative weight and function. An important aspect of BAT biology is to better understand the mechanisms regulating the uptake and utilization of fatty acids and glucose. Accordingly, BAT function in the SHR that harbors mutant nonfunctional Cd36 variant (hereafter referred to as SHR-Cd36⁻/⁻) was compared with SHR transgenic line expressing wild type Cd36 under control of a universal promoter (hereafter referred to as SHR-Cd36⁺/⁺). BAT was incubated in media containing insulin and 14C-U-glucose alone or 14C-U-glucose together with palmitate. Incorporation of glucose into BAT lipids was significantly higher in SHR-Cd36⁺/⁺ versus SHR-Cd36⁻/⁻ rats when incubation media contained glucose alone (SHR-Cd36⁻/⁻ 591 ± 75 vs. SHR-Cd36⁺/⁺ 1036 ± 135 nmol/gl./2h; P < 0.005). Adding palmitate into incubation media had no effect in SHR-Cd36⁻/⁻ rats but significantly reduced glucose incorporation into BAT lipids in SHR-Cd36⁺/⁺ (SHR-Cd36⁻/⁻ 543 ± 55 vs. SHR-Cd36⁺/⁺ 766 ± 75 nmol/gl./2h; P < 0.05 denotes significant Cd36 x palmitate interaction determined by two-way ANOVA). This Cd36-dependent reduced glucose uptake in SHR-Cd36⁺/⁺ BAT was likely secondary to increased palmitate incorporation and utilization due to the presence of wild type Cd36 fatty acid translocase in transgenic rats. This possibility is supported by increased incorporation of 14C-U-palmitate into BAT lipids in the presence of both palmitate and glucose in incubation media (palmitate alone: SHR-Cd36⁻/⁻ 870 ± 21 vs. SHR-Cd36⁺/⁺ 899 ± 42; glucose+palmitate: SHR-Cd36⁻/⁻ 899 ± 47 vs. SHR-Cd36⁺/⁺ 1460 ± 111 nmol/palm./2h; P < 0.05 denotes significant Cd36 x glucose interaction determined by two-way ANOVA). It is possible that addition of glucose into the incubation media increased palmitate incorporation into BAT lipids in SHR-Cd36⁺/⁺ rats because of glucose availability for glycerol phosphate production and increased triglyceride synthesis. These changes in glucose and palmitate incorporation into BAT lipids were associated with significant differential expression of Irs1, Irs2, Slc2a4 and Foxo1 genes involved in insulin signaling and glucose metabolism only in SHR-Cd36⁺/⁺ rats which suggests Cd36-dependent effects on insulin action. In conclusion, these results provide compelling evidence that Cd36 plays an important role in BAT insulin signaling and energy substrate utilization.

Keywords: brown adipose tissue, Cd36, energy substrate utilization, insulin signaling, spontaneously hypertensive rat

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Authors: Leslie Nobler

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African fabrics based specifically on the Dutch Wax Print, or Ankara, popularized during Africa's colonial era, have had an enormous impact on western fashion (especially in the US and UK), in the last half-decade. The trend has had an effect on the world of visual arts as well, which circuitously, also heavily impacts fashion design. In fashion, and notably in celebrity apparel choices, this is in part due to ‘identity’ and taking pride in one's African roots; in the visual arts, artists such as Yinka Shonibare and Njideka Akunyili Crosby are making statements about identity politics, colonialism up through post-colonialism, and racism. The ‘global village’ brought on by the internet has driven this proliferation, as have improvements in the printing technology with which the Ankara print is made, combining wax-resist with roller printing. The newest patterns can now be designed authentically in western African and easily sent electronically to Europe for printing. Examples of Ankara's new reach across the Atlantic abound. They have taken several paths, which the paper will detail. Briefly, the first is its greater utilization in the fashion world, from authentic textile shops in African American neighborhoods to copied (knocked-off) low-end reproductions in discount chains. Secondly, we are seeing far more uses of these textiles/patterns in important works of fine arts from major museums, in Philadelphia to Palm Beach to the Mass MOCA (in the US), all the way to the Israel Museum in Jerusalem, and everywhere in between. And lastly, but quite significantly, we see this trend throughout social media thanks to Instagram, Pinterest and celebrity photos –even at the recent royal wedding. What shall sustain this major new design direction is that Ankara changes with and adapts to the times. Some of it is now printed in West Africa, often in the Nigeria area. And some may be designed in Europe or even at knock-off apparel studios in NY or Asia. But it stays utterly relevant because the motifs are based on objects and scenes in everyday life. In my design studio and university design classes, this idea is first and foremost, from our big spiritual eye motifs to drawings of our art supplies to the ‘politically-loaded’ chain patterns. This first-hand creativity experience becomes part of the research of this paper, along with historic and contemporary sources of inquiry, both through a literature/image search and anecdotal experience into what is behind this exciting and surprising trend.

Keywords: African wax print, Ankara, identity (politics), textile design, surface design

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Authors: Aprajeeta Jha, Punyadarshini P. Tripathy

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Drying technologies for various food processing operations shares an inevitable linkage with energy, cost and environmental sustainability. Hence, solar drying of food grains has become imperative choice to combat duo challenges of meeting high energy demand for drying and to address climate change scenario. But performance and reliability of solar dryers depend hugely on sunshine period, climatic conditions, therefore, offer a limited control over drying conditions and have lower efficiencies. Solar drying technology, supported by Photovoltaic (PV) power plant and hybrid type solar air collector can potentially overpower the disadvantages of solar dryers. For development of such robust hybrid dryers; to ensure quality and shelf-life of paddy grains the optimization of process parameter becomes extremely critical. Investigation of the moisture distribution profile within the grains becomes necessary in order to avoid over drying or under drying of food grains in hybrid solar dryer. Computational simulations based on finite element modeling can serve as potential tool in providing a better insight of moisture migration during drying process. Hence, present work aims at optimizing the process parameters and to develop a 3-dimensional (3D) finite element model (FEM) for predicting moisture profile in paddy during solar drying. COMSOL Multiphysics was employed to develop a 3D finite element model for predicting moisture profile. Furthermore, optimization of process parameters (power level, air velocity and moisture content) was done using response surface methodology in design expert software. 3D finite element model (FEM) for predicting moisture migration in single kernel for every time step has been developed and validated with experimental data. The mean absolute error (MAE), mean relative error (MRE) and standard error (SE) were found to be 0.003, 0.0531 and 0.0007, respectively, indicating close agreement of model with experimental results. Furthermore, optimized process parameters for drying paddy were found to be 700 W, 2.75 m/s at 13% (wb) with optimum temperature, milling yield and drying time of 42˚C, 62%, 86 min respectively, having desirability of 0.905. Above optimized conditions can be successfully used to dry paddy in PV integrated solar dryer in order to attain maximum uniformity, quality and yield of product. PV-integrated hybrid solar dryers can be employed as potential and cutting edge drying technology alternative for sustainable energy and food security.

Keywords: finite element modeling, moisture migration, paddy grain, process optimization, PV integrated hybrid solar dryer

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Authors: Elia Favarelli, Enrico Testi, Andrea Giorgetti

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Autonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by density-based time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., mean value, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one class classifier (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, a new anomaly detector strategy is proposed, namely one class classifier neural network two (OCCNN2), which exploit the classification capability of standard classifiers in an anomaly detection problem, finding the standard class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimation. The coarse estimation uses classics OCC techniques, while the fine estimation is performed through a feedforward neural network (NN) trained that exploits the boundaries estimated in the coarse step. The detection algorithms vare then compared with known methods based on principal component analysis (PCA), kernel principal component analysis (KPCA), and auto-associative neural network (ANN). In many cases, the proposed solution increases the performance with respect to the standard OCC algorithms in terms of F1 score and accuracy. In particular, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 96% with the proposed method.

Keywords: anomaly detection, frequencies selection, modal analysis, neural network, sensor network, structural health monitoring, vibration measurement

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Authors: Bharti Singh, Shri K. Singh

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Child malnutrition is a multifaceted issue that transcends geographical boundaries. Malnutrition not only stunts physical growth but also leads to a spectrum of morbidities and child mortality. It is one of the leading causes of death (~50 %) among children under age five. Despite economic progress and advancements in healthcare, child malnutrition remains a formidable challenge for India. The objective is to investigate the impact of women's empowerment on child nutrition outcomes in India from 2006 to 2021. A composite index of women's empowerment was constructed using Confirmatory Factor Analysis (CFA), a rigorous technique that validates the measurement model by assessing how well-observed variables represent latent constructs. This approach ensures the reliability and validity of the empowerment index. Secondly, kernel density plots were utilised to visualise the distribution of key nutritional indicators, such as stunting, wasting, and overweight. These plots offer insights into the shape and spread of data distributions, aiding in understanding the prevalence and severity of malnutrition. Thirdly, linear polynomial graphs were employed to analyse how nutritional parameters evolved with the child's age. This technique enables the visualisation of trends and patterns over time, allowing for a deeper understanding of nutritional dynamics during different stages of childhood. Lastly, multilevel analysis was conducted to identify vulnerable levels, including State-level, PSU-level, and household-level factors impacting undernutrition. This approach accounts for hierarchical data structures and allows for the examination of factors at multiple levels, providing a comprehensive understanding of the determinants of child malnutrition. Overall, the utilisation of these statistical methodologies enhances the transparency and replicability of the study by providing clear and robust analytical frameworks for data analysis and interpretation. Our study reveals that NFHS-4 and NFHS-5 exhibit an equal density of severely stunted cases. NFHS-5 indicates a limited decline in wasting among children aged five, while the density of severely wasted children remains consistent across NFHS-3, 4, and 5. In 2019-21, women with higher empowerment had a lower risk of their children being undernourished (Regression coefficient= -0.10***; Confidence Interval [-0.18, -0.04]). Gender dynamics also play a significant role, with male children exhibiting a higher susceptibility to undernourishment. Multilevel analysis suggests household-level vulnerability (intra-class correlation=0.21), highlighting the need to address child undernutrition at the household level.

Keywords: child nutrition, India, NFHS, women’s empowerment

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Authors: Yik Sin Chan, Bee Ling Chuah, Wei Quan Chan, Ri Jin Cheng, Yan Hang Oon, Kong Soo Khoo, Nam Weng Sit

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Traditionally, medicinal plants have been used to treat different kinds of ailments including infectious diseases. They serve as a good source of lead compounds for the development of new and safer anti-infective agents. This study aimed to investigate the antimicrobial potential of the leaves of three medicinal plants, namely Catunaregam spinosa (Rubiaceae; Mountain pomegranate), Houttuynia cordata (Saururaceae; "fishy-smell herb") and Rhapis excelsa (Arecaceae; “broadleaf lady palm”). The leaves extracts were obtained by sequential extraction using hexane, chloroform, ethyl acetate, ethanol, methanol and water. The antibacterial and antifungal activities were assessed using a colorimetric broth microdilution method against a panel of human pathogenic bacteria (Gram-positive: Bacillus cereus and Staphylococcus aureus; Gram-negative: Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa) and fungi (yeasts: Candida albicans, Candida parapsilosis and Cryptococcus neoformans; Moulds: Aspergillus fumigatus and Trichophyton mentagrophytes) respectively; while antiviral activity was evaluated against the Chikungunya virus on monkey kidney epithelial (Vero) cells by neutral red uptake assay. All the plant extracts showed bacteriostatic activity, however, only 72% of the extracts (13/18) were found to have bactericidal activity. The lowest minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were given by the hexane extract of C. spinosa against S. aureus with the values of 0.16 and 0.31 mg/mL respectively. All the extracts also possessed fungistatic activity. Only the hexane, chloroform and ethyl acetate extracts of H. cordata exerted inhibitory activity against A. fumigatus, giving the lowest fungal susceptibility index of 16.7%. In contrast, only 61% of the extracts (11/18) showed fungicidal activity. The ethanol extract of R. excelsa exhibited the strongest fungicidal activity against C. albicans, C. parapsilosis and T. mentagrophytes with minimum fungicidal concentration (MFC) values of 0.04–0.08 mg/mL, in addition to its methanol extract against T. mentagrophytes (MFC=0.02 mg/mL). For anti-Chikungunya virus activity, only chloroform and ethyl acetate extracts of R. excelsa showed significant antiviral activity with 50% effective concentrations (EC50) of 29.9 and 78.1 g/mL respectively. Extracts of R. excelsa warrant further investigations into their active principles responsible for antifungal and antiviral properties.

Keywords: bactericidal, Chikungunya virus, extraction, fungicidal

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: Ching Shya Lee, Mohamed Kheireddine Aroua, Wan Mohd Ashri Wan Daud, Patrick Cognet, Yolande Peres, Mohammed Ajeel

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In recent years, with the decreasing supply of fossil fuel, renewable energy has received a significant demand. Biodiesel which is well known as vegetable oil based fatty acid methyl ester is an alternative fuel for diesel. It can be produced from transesterification of vegetable oils, such as palm oil, sunflower oil, rapeseed oil, etc., with methanol. During the transesterification process, crude glycerol is formed as a by-product, resulting in 10% wt of the total biodiesel production. To date, due to the fast growing of biodiesel production in worldwide, the crude glycerol supply has also increased rapidly and resulted in a significant price drop for glycerol. Therefore, extensive research has been developed to use glycerol as feedstock to produce various added-value chemicals, such as tartronic acid, mesoxalic acid, glycolic acid, glyceric acid, propanediol, acrolein etc. The industrial processes that usually involved are selective oxidation, biofermentation, esterification, and hydrolysis. However, the conversion of glycerol into added-value compounds by electrochemical approach is rarely discussed. Currently, the approach is mainly focused on the electro-oxidation study of glycerol under potentiostatic mode for cogenerating energy with other chemicals. The electro-organic synthesis study from glycerol under galvanostatic mode is seldom reviewed. In this study, the glycerol was converted into various added-value compounds by electrochemical method under galvanostatic mode. This work aimed to study the possible compounds produced from glycerol by electrochemical technique in a one-pot electrolysis cell. The electro-organic synthesis study from glycerol was carried out in a single compartment reactor for 8 hours, over the platinum cathode and anode electrodes under acidic condition. Various parameters such as electric current (1.0 A to 3.0 A) and reaction temperature (27 °C to 80 °C) were evaluated. The products obtained were characterized by using gas chromatography-mass spectroscopy equipped with an aqueous-stable polyethylene glycol stationary phase column. Under the optimized reaction condition, the glycerol conversion achieved as high as 95%. The glycerol was successfully converted into various added-value chemicals such as ethylene glycol, glycolic acid, glyceric acid, acetaldehyde, formic acid, and glyceraldehyde; given the yield of 1%, 45%, 27%, 4%, 0.7% and 5%, respectively. Based on the products obtained from this study, the reaction mechanism of this process is proposed. In conclusion, this study has successfully converted glycerol into a wide variety of added-value compounds. These chemicals are found to have high market value; they can be used in the pharmaceutical, food and cosmetic industries. This study effectively opens a new approach for the electrochemical conversion of glycerol. For further enhancement on the product selectivity, electrode material is an important parameter to be considered.

Keywords: biodiesel, glycerol, electrochemical conversion, galvanostatic mode

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Authors: A. Chatel, I. S. Torreguitart, T. Verstraete

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The paper deals with a single point optimization of the LS89 turbine using an adjoint optimization and defining the design variables within a CAD system. The advantage of including the CAD model in the design system is that higher level constraints can be imposed on the shape, allowing the optimized model or component to be manufactured. However, CAD-based approaches restrict the design space compared to node-based approaches where every node is free to move. In order to preserve a rich design space, we develop a methodology to refine the CAD model during the optimization and to create the best parameterization to use at each time. This study presents a methodology to progressively refine the design space, which combines parametric effectiveness with a differential evolutionary algorithm in order to create an optimal parameterization. In this manuscript, we show that by doing the parameterization at the CAD level, we can impose higher level constraints on the shape, such as the axial chord length, the trailing edge radius and G2 geometric continuity between the suction side and pressure side at the leading edge. Additionally, the adjoint sensitivities are filtered out and only smooth shapes are produced during the optimization process. The use of algorithmic differentiation for the CAD kernel and grid generator allows computing the grid sensitivities to machine accuracy and avoid the limited arithmetic precision and the truncation error of finite differences. Then, the parametric effectiveness is computed to rate the ability of a set of CAD design parameters to produce the design shape change dictated by the adjoint sensitivities. During the optimization process, the design space is progressively enlarged using the knot insertion algorithm which allows introducing new control points whilst preserving the initial shape. The position of the inserted knots is generally assumed. However, this assumption can hinder the creation of better parameterizations that would allow producing more localized shape changes where the adjoint sensitivities dictate. To address this, we propose using a differential evolutionary algorithm to maximize the parametric effectiveness by optimizing the location of the inserted knots. This allows the optimizer to gradually explore larger design spaces and to use an optimal CAD-based parameterization during the course of the optimization. The method is tested on the LS89 turbine cascade and large aerodynamic improvements in the entropy generation are achieved whilst keeping the exit flow angle fixed. The trailing edge and axial chord length, which are kept fixed as manufacturing constraints. The optimization results show that the multilevel optimizations were more efficient than the single level optimization, even though they used the same number of design variables at the end of the multilevel optimizations. Furthermore, the multilevel optimization where the parameterization is created using the optimal knot positions results in a more efficient strategy to reach a better optimum than the multilevel optimization where the position of the knots is arbitrarily assumed.

Keywords: adjoint, CAD, knots, multilevel, optimization, parametric effectiveness

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Authors: Alinka Molnár-Tóth, Tímea Tánczos, Regina Barna, Katalin Jakab, Péter Klivényi

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Supported by neuroscientific findings, the so-called Hub-and-Spoke model of the human semantic system is based on two subcomponents of semantic cognition, namely the semantic control process and semantic representation. Our semantic knowledge is multimodal in nature, as the knowledge system stored in relation to a conception is extensive and broad, while different aspects of the conception may be relevant depending on the purpose. The motivation of our research is to develop a new diagnostic measurement procedure based on the preservation of semantic representation, which is appropriate to the specificities of the Hungarian language and which can be used to compare the non-verbal semantic knowledge of healthy and aphasic persons. The development of the test will broaden the Hungarian clinical diagnostic toolkit, which will allow for more specific therapy planning. The sample of healthy persons (n=480) was determined by the last census data for the representativeness of the sample. Based on the concept of the Pyramids and Palm Tree Test, and according to the characteristics of the Hungarian language, we have elaborated a test based on different types of semantic information, in which the subjects are presented with three pictures: they have to choose the one that best fits the target word above from the two lower options, based on the semantic relation defined. We have measured 5 types of semantic knowledge representations: associative relations, taxonomy, motional representations, concrete as well as abstract verbs. As the first step in our data analysis, we examined the normal distribution of our results, and since it was not normally distributed (p < 0.05), we used nonparametric statistics further into the analysis. Using descriptive statistics, we could determine the frequency of the correct and incorrect responses, and with this knowledge, we could later adjust and remove the items of questionable reliability. The reliability was tested using Cronbach’s α, and it can be safely said that all the results were in an acceptable range of reliability (α = 0.6-0.8). We then tested for the potential gender differences using the Mann Whitney-U test, however, we found no difference between the two (p < 0.05). Likewise, we didn’t see that the age had any effect on the results using one-way ANOVA (p < 0.05), however, the level of education did influence the results (p > 0.05). The relationships between the subtests were observed by the nonparametric Spearman’s rho correlation matrix, showing statistically significant correlation between the subtests (p > 0.05), signifying a linear relationship between the measured semantic functions. A margin of error of 5% was used in all cases. The research will contribute to the expansion of the clinical diagnostic toolkit and will be relevant for the individualised therapeutic design of treatment procedures. The use of a non-verbal test procedure will allow an early assessment of the most severe language conditions, which is a priority in the differential diagnosis. The measurement of reaction time is expected to advance prodrome research, as the tests can be easily conducted in the subclinical phase.

Keywords: communication disorders, diagnostic toolkit, neurorehabilitation, semantic knowlegde

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Authors: Dmitriy Babenko, Sergey Yegorov, Ilya Korshukov, Aidana Sultanbekova, Valentina Barkhanskaya, Tatiana Bashirova, Yerzhan Zhunusov, Yevgeniya Li, Viktoriya Parakhina, Svetlana Kolesnichenko, Yeldar Baiken, Aruzhan Pralieva, Zhibek Zhumadilova, Matthew S. Miller, Gonzalo H. Hortelano, Anar Turmuhambetova, Antonella E. Chesca, Irina Kadyrova

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Introduction: The rapidly evolving COVID-19 pandemic, along with the re-emergence of pathogens causing acute respiratory infections (ARI), has necessitated the development of novel diagnostic tools to differentiate various causes of ARI. MALDI-MS, due to its wide usage and affordability, has been proposed as a potential instrument for diagnosing SARS-CoV-2 versus non-SARS-CoV-2 ARI. The aim of this study was to investigate the potential of MALDI-MS in conjunction with a machine learning model to accurately distinguish between symptomatic infections caused by SARS-CoV-2 and non-SARS-CoV-2 during both the early and later phases of the pandemic. Furthermore, this study aimed to analyze mass spectrometry (MS) data obtained from nasal swabs of healthy individuals. Methods: We gathered mass spectra from 252 samples, comprising 108 SARS-CoV-2-positive samples obtained in 2020 (Covid 2020), 7 SARS-CoV- 2-positive samples obtained in 2023 (Covid 2023), 71 samples from symptomatic individuals without SARS-CoV-2 (Control non-Covid ARVI), and 66 samples from healthy individuals (Control healthy). All the samples were subjected to RT-PCR testing. For data analysis, we employed the caret R package to train and test seven machine-learning algorithms: C5.0, KNN, NB, RF, SVM-L, SVM-R, and XGBoost. We conducted a training process using a five-fold (outer) nested repeated (five times) ten-fold (inner) cross-validation with a randomized stratified splitting approach. Results: In this study, we utilized the Covid 2020 dataset as a case group and the non-Covid ARVI dataset as a control group to train and test various machine learning (ML) models. Among these models, XGBoost and SVM-R demonstrated the highest performance, with accuracy values of 0.97 [0.93, 0.97] and 0.95 [0.95; 0.97], specificity values of 0.86 [0.71; 0.93] and 0.86 [0.79; 0.87], and sensitivity values of 0.984 [0.984; 1.000] and 1.000 [0.968; 1.000], respectively. When examining the Covid 2023 dataset, the Naive Bayes model achieved the highest classification accuracy of 43%, while XGBoost and SVM-R achieved accuracies of 14%. For the healthy control dataset, the accuracy of the models ranged from 0.27 [0.24; 0.32] for k-nearest neighbors to 0.44 [0.41; 0.45] for the Support Vector Machine with a radial basis function kernel. Conclusion: Therefore, ML models trained on MALDI MS of nasopharyngeal swabs obtained from patients with Covid during the initial phase of the pandemic, as well as symptomatic non-Covid individuals, showed excellent classification performance, which aligns with the results of previous studies. However, when applied to swabs from healthy individuals and a limited sample of patients with Covid in the late phase of the pandemic, ML models exhibited lower classification accuracy.

Keywords: SARS-CoV-2, MALDI-TOF MS, ML models, nasopharyngeal swabs, classification

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Amina Sa'id Muhammad, Asmau Ishaq Alhassan, Beba Raymond, Fatima Bello

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Severe acute malnutrition (SAM) remains a major killer of children under five years of age. Nigeria has the second highest burden of stunted children in the world, with a national prevalence rate of 32 percent of children under five. An estimated 2 million children in Nigeria suffer from severe acute malnutrition (SAM), and 3.9% of children in northwest Nigeria suffer from SAM, which is significantly higher than the national average of 2.1%. Community-Based Management of Acute Malnutrition (CMAM) has proven to be an effective intervention in the treatment of SAM in children using Ready-to-Use Therapeutic Food (RUTF). Ready-to-use therapeutic food (RUTF) is a key component for the treatment of Severe Acute Malnutrition. It contains all the energy and nutrients required for rapid catch-up growth and used particularly in the treatment of children over 6 months of age with SAM without medical complications. However, almost all RUTFs are currently imported to Nigeria from other countries. Shortages of RUTF due to logistics (shipping costs, delays, donor fatigue etc) and funding issues present a threat to the achievement of the 2030 World Health Assembly (WHA) targets for reducing malnutrition in addition to 2030 SDGs 2 (Zero Hunger), 3 (Good Health and Wellbeing), 12 (Responsible Consumption and Production), and 17 (Partnerships for the Goals), thus undermining its effectiveness in combating malnutrition On the other hand, the availability of human and material resources that will aid local production of RUTF presents an opportunity to fill in the gap in regular RUTF supply. About one thousand Nigerian children die of malnutrition-related causes every day, reaching a total of 361,000 each year. Owing to the high burden of malnutrition in Nigeria, the local production of RUTF is a logical step, that will ensure increased availability, acceptability, access, and efficiency in supply, and at lower costs. Objective(s): The objectives of this study were therefore, to formulate RUTF from locally available resources and to determine its energy and nutrients composition, incommensurate with the standard/commercial RUTF. Methods: Three samples of RUTF were formulated using locally available resources (soya beans, wheat, rice, baobab, brown-sugar, date palm and soya oil); which were subjected to various analysis to determine their energy/proximate composition, vitamin and mineral contents and organoleptic properties were also determined using sensory evaluation. Results: The energy values of the three samples of locally produced RUTF were found to be in conformity with WHO recommendation of ≥ 500 kcal per 100g. The energy values of the three RUTF samples produced in the current study were found to be 563.08, 503.67 and 528.98 kcal respectively. Sample A, B and C had protein content of 13.56% 16.71% and 14.62% respectively, which were higher than that of commercial RUTF (10.9%). Conclusions/recommendations: The locally formulated RUTF samples had energy value of more than 500 kcal per 100g; with an appreciable amount of macro and micro nutrients. The appearance, taste, flavor and general acceptability of the formulated RUTF samples were also commendable.

Keywords: energy, malnutrition, nutrients, RUTF

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: Sigal Portnoy, Yael Kaufman-Cohen, Yafa Levanon

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Introduction: Rehabilitation following wrist fractures often includes exercising flexion-extension movements with a dynamic splint. However, during daily activities, we combine most of our wrist movements with radial and ulnar deviations. Also, the multi-plane wrist motion, named the ‘dart throw motion’ (DTM), was found to be a more stable motion in healthy individuals, in term of the motion of the proximal carpal bones, compared with sagittal wrist motion. The aim of this study was therefore to explore the pathomechanics of the wrist in a common multi-plane movement pattern (DTM) and design a novel splint for rehabilitation following distal radius fractures. Methods: First, a multi-axis electro-goniometer was used to quantify the plane angle of motion of the dominant and non-dominant wrists during various activities, e.g. drinking from a glass of water and answering a phone in 43 healthy individuals. The following protocols were then implemented with a population following distal radius fracture. Two dynamic scans were performed, one of the sagittal wrist motion and DTM, in a 3T magnetic resonance imaging (MRI) device, bilaterally. The scaphoid and lunate carpal bones, as well as the surface of the distal radius, were manually-segmented in SolidWorks and the angles of motion of the scaphoid and lunate bones were calculated. Subsequently, a patient-specific splint was designed using 3D scans of the hand. The brace design comprises of a proximal attachment to the arm and a distal envelope of the palm. An axle with two wheels is attached to the proximal part. Two wires attach the proximal part with the medial-palmar and lateral-ventral aspects of the distal part: when the wrist extends, the first wire is released and the second wire is strained towards the radius. The opposite occurs when the wrist flexes. The splint was attached to the wrist using Velcro and constrained the wrist movement to the desired calculated multi-plane of motion. Results: No significant differences were found between the multi-plane angles of the dominant and non-dominant wrists. The most common daily activities occurred at a plane angle of approximately 20° to 45° from the sagittal plane and the MRI studies show individual angles of the plane of motion. The printed splint fitted the wrist of the subjects and constricted movement to the desired multi-plane of motion. Hooks were inserted on each part to allow the addition of springs or rubber bands for resistance training towards muscle strengthening in the rehabilitation setting. Conclusions: It has been hypothesized that activation of the wrist in a multi-plane movement pattern following distal radius fractures will accelerate the recovery of the patient. Our results show that this motion can be determined from either the dominant or non-dominant wrists. The design of the patient-specific dynamic splint is the first step towards assessing whether splinting to induce combined movement is beneficial to the rehabilitation process, compared to conventional treatment. The evaluation of the clinical benefits of this method, compared to conventional rehabilitation methods following wrist fracture, are a part of a PhD work, currently conducted by an occupational therapist.

Keywords: distal radius fracture, rehabilitation, dynamic magnetic resonance imaging, dart throw motion

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Authors: Rohit T. P., Sahil Athrij, Sasi Gopalan

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Currently, the content entertainment industry is dominated by mobile devices. As the trends slowly shift towards Augmented/Virtual Reality applications the computational demands on these devices are increasing exponentially and we are already reaching the limits of hardware optimizations. This paper proposes a software solution to this problem. By leveraging the capabilities of cloud computing we can offload the work from mobile devices to dedicated rendering servers that are way more powerful. But this introduces the problem of latency. This paper introduces a protocol that can achieve high-performance low latency Augmented/Virtual Reality experience. There are two parts to the protocol, 1) In-flight compression The main cause of latency in the system is the time required to transmit the camera frame from client to server. The round trip time is directly proportional to the amount of data transmitted. This can therefore be reduced by compressing the frames before sending. Using some standard compression algorithms like JPEG can result in minor size reduction only. Since the images to be compressed are consecutive camera frames there won't be a lot of changes between two consecutive images. So inter-frame compression is preferred. Inter-frame compression can be implemented efficiently using WebGL but the implementation of WebGL limits the precision of floating point numbers to 16bit in most devices. This can introduce noise to the image due to rounding errors, which will add up eventually. This can be solved using an improved interframe compression algorithm. The algorithm detects changes between frames and reuses unchanged pixels from the previous frame. This eliminates the need for floating point subtraction thereby cutting down on noise. The change detection is also improved drastically by taking the weighted average difference of pixels instead of the absolute difference. The kernel weights for this comparison can be fine-tuned to match the type of image to be compressed. 2) Dynamic Load distribution Conventional cloud computing architectures work by offloading as much work as possible to the servers, but this approach can cause a hit on bandwidth and server costs. The most optimal solution is obtained when the device utilizes 100% of its resources and the rest is done by the server. The protocol balances the load between the server and the client by doing a fraction of the computing on the device depending on the power of the device and network conditions. The protocol will be responsible for dynamically partitioning the tasks. Special flags will be used to communicate the workload fraction between the client and the server and will be updated in a constant interval of time ( or frames ). The whole of the protocol is designed so that it can be client agnostic. Flags are available to the client for resetting the frame, indicating latency, switching mode, etc. The server can react to client-side changes on the fly and adapt accordingly by switching to different pipelines. The server is designed to effectively spread the load and thereby scale horizontally. This is achieved by isolating client connections into different processes.

Keywords: 2D kernelling, augmented reality, cloud computing, dynamic load distribution, immersive experience, mobile computing, motion tracking, protocols, real-time systems, web-based augmented reality application

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Authors: Jatinder Pal Singh, Harpreet Singh, Gagandeep Singh Digra, Mandeep Kaur Sidhu, Pawan Kumar

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Introduction: Ganglion cyst is a benign condition in which there is a cystic lesion in relation to a joint or a tendon sheath arising from myxoid degeneration of fibrous connective tissue. These can be unilocular or multilocular. In rare cases, there may be multiple ganglion cysts, known as cystic ganglionosis. They can occur at any age but are commonly seen in adults. Clinically they may be asymptomatic or present as swelling or mass effect in adjacent structures. These are common in extremities such as hands and feet. Case Presentation: 11-year-old female child presented with slowly progressive painless swelling of her right hand since the age of 4. Antenatal and perinatal history was unremarkable. Her family history was negative. She denies fever, malaise, morning stiffness, weight loss, fatigue, restriction of joint movements, or any sensory and motor deficit. Lab parameters were negative for inflammatory or infectious etiology. No other joint or extremity involvement was present. On physical examination, the swelling was present on the dorsum and palmer aspect of the right hand and wrist. They were non-tender on palpation without any motor or sensory deficit. MRI hand revealed multiple well-defined fluid signal intensity cystic appearing lesions in periarticular/intraarticular locations in relation to distal radio-ulnar, radio-carpal, intercarpal, carpometacarpal, metacarpophalangeal and interphalangeal joints as well as peritendinous location around flexor tendons more so in the region of wrist, palm, 1st and 5th digit and along extensor tendons in the region of wrist, largest one noted along flexor pollicis longus tendon in thenar region and along 1st digit measuring approx. 4.6 x 1.2 x 1.2 centimeter. Pressure erosions and bone remodelling were noted in the bases of the 2nd to 5th metacarpals, capitate, trapezoid, the distal shaft of 1st metacarpal, and proximal phalanx of 1st digit. Marrow edema was noted in the base and proximal shaft of the 4th metacarpal and proximal shaft of the 3rd metacarpal – likely stress or pressure related. The patient was advised of aspiration, but the family refused the procedure. Therefore the patient was kept on conservative treatment. Conclusion: Cystic ganglionosis is a rare condition with very few cases reported in the medical literature. Its prevalence and association are not known because of the rarity of this condition. It should be considered as an important differential in patients presenting with soft tissue swelling in extremities. Treatment option includes conservative management, aspiration, and surgery. Aspiration has a high recurrence rate. Although surgery has a low recurrence rate, it carries a high rate of complications. Imaging with MRI is essential for confirmation of the cystic nature of lesions and their relation with the joint capsules or tendons. This helps in differentiating from other soft tissue lesions and presurgical planning.

Keywords: radiology, rare, cystic ganglionosis, child

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Linna Li

Abstract:

The capability to move around places is doubtless very important for individuals to maintain good health and social functions. People’s activities in space and time have long been a research topic in behavioral and socio-economic studies, particularly focusing on the highly dynamic urban environment. By analyzing groups of people who share similar activity patterns, many socio-economic and socio-demographic problems and their relationships with individual behavior preferences can be revealed. Los Angeles, known for its large population, ethnic diversity, cultural mixing, and entertainment industry, faces great transportation challenges such as traffic congestion, parking difficulties, and long commuting. Understanding people’s travel behavior and movement patterns in this metropolis sheds light on potential solutions to complex problems regarding urban mobility. This project visualizes people’s trajectories in Greater Los Angeles (L.A.) Area over a period of two months using Twitter data. A Python script was used to collect georeferenced tweets within the Greater L.A. Area including Ventura, San Bernardino, Riverside, Los Angeles, and Orange counties. Information associated with tweets includes text, time, location, and user ID. Information associated with users includes name, the number of followers, etc. Both aggregated and individual activity patterns are demonstrated using various geovisualization techniques. Locations of individual Twitter users were aggregated to create a surface of activity hot spots at different time instants using kernel density estimation, which shows the dynamic flow of people’s movement throughout the metropolis in a twenty-four-hour cycle. In the 3D geovisualization interface, the z-axis indicates time that covers 24 hours, and the x-y plane shows the geographic space of the city. Any two points on the z axis can be selected for displaying activity density surface within a particular time period. In addition, daily trajectories of Twitter users were created using space-time paths that show the continuous movement of individuals throughout the day. When a personal trajectory is overlaid on top of ancillary layers including land use and road networks in 3D visualization, the vivid representation of a realistic view of the urban environment boosts situational awareness of the map reader. A comparison of the same individual’s paths on different days shows some regular patterns on weekdays for some Twitter users, but for some other users, their daily trajectories are more irregular and sporadic. This research makes contributions in two major areas: geovisualization of spatial footprints to understand travel behavior using the big data approach and dynamic representation of activity space in the Greater Los Angeles Area. Unlike traditional travel surveys, social media (e.g., Twitter) provides an inexpensive way of data collection on spatio-temporal footprints. The visualization techniques used in this project are also valuable for analyzing other spatio-temporal data in the exploratory stage, thus leading to informed decisions about generating and testing hypotheses for further investigation. The next step of this research is to separate users into different groups based on gender/ethnic origin and compare their daily trajectory patterns.

Keywords: geovisualization, human mobility pattern, Los Angeles, social media

Procedia PDF Downloads 102