Search results for: efficiency classification

Authors: Majid Javanmard

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Edible mushrooms are those agricultural products which contain high quantity of protein and can have special role in human diet. So search for methods to increase their shelf life is important. One of these strategies can be use of active packaging for absorb the ethylene which has been studied in present study. In this study, initially, production of impregnating zeolite with potassium permanganate has been studied with zeolite clinoptiolite available in iran. After that, these ethylene scavengers were placed in the package of edible mushrooms and then transferred to the refrigerator with temperature 4c for a period of 20 days. Each 5 days, several experiments accomplished on edible mushrooms such as weight loss, moisture content, color, texture, bacterial experiments and sensory evaluation. After production of impregnating zeolite with potassium permanganate (with a concentration of %2.5, %5, %7.5, %10 and %12.5) by zeolite type clinoptiolite (with mesh 35 and 60), samples have been analyzed with gas chromatography and titration with sodium oxalate. The results showed that zeolite by concentration of %5, %7.5 and %10 potassium permanganate and mesh 60 have a higher efficiency. Results from the experiments on edible mushrooms proved that impregnated zeolite with potassium permanganate have a meaningful influence in prevent the weight loss, decrease of moisture content and L-value, increase of a-value and overall color change (ΔE) and decrease of firmness texture of mushrooms. In addition, these absorbents can influence on decrease microbial load (mesophilic bacteria) rather than control. Generally, concluded that the impregnated zeolite with 10% permanganate potassium has a high efficiency on increase the shelf life of fresh edible mushrooms.

Keywords: active packaging, ethylene scavenger, zeolite clinoptiolite, permanganate potassium, shelf life

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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Authors: Ngonidzashe Mangoma, Caroline Marigold Sebata

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The release of cyanide-rich effluents from gold mines, and other industries, into the environment, is a global concern considering the well-known metabolic effects of cyanide in all forms of life. Such effluents need to be treated to remove cyanide, among other pollutants, before their disposal. This study aimed at investigating the possible use of bacteria in the biological removal of cyanide from cyanide-rich effluents. Firstly, cyanide-degrading bacteria were isolated from gold mine effluents and characterised. The isolates were then tested for their ability to grow in the presence of cyanide and their tolerance to increasing levels of the compound. To evaluate each isolate’s cyanide-degrading activities, isolates were grown in the simulated and actual effluent, and a titrimetric method was used to quantify residual cyanide over a number of days. Cyanide degradation efficiency (DE) was then calculated for each isolate. Identification of positive isolates involved 16S rRNA gene amplification and sequence analysis through BLAST. Six cyanide-utilising bacterial strains were isolated. Two of the isolates were identified as Klebsiella spp. while the other two were shown to be different strains of Clostridium bifermentans. All isolates showed normal growth in the presence of cyanide, with growth being inhibited at 700 mg/L cyanide and beyond. Cyanide degradation efficiency for all isolates in the simulated effluent ranged from 79% to 97%. All isolates were able to remove cyanide from actual gold mine effluent with very high DE values (90 – 94%) being recorded. Isolates obtained in this study were able to efficiently remove cyanide from both simulated and actual effluent. This observation clearly demonstrates the feasibility of the biological removal of cyanide from cyanide-rich gold mine effluents and should, therefore, motivate research towards the possible large-scale application of this technology.

Keywords: cyanide effluent, bioremediation, Clostridium bifermentans, Klebsiella spp, environment

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Authors: Manisha Bal, Amit Verma, B. C. Meikap

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Carbon dioxide (CO₂) is the most abundant waste produced by human activities. It is estimated to be one of the major contributors of greenhouse effect and also considered as a major air pollutant formed by burning of fossil fuels. The main sources of emissions are flue gas from thermal power plants and process industries. It is also a contributor of acid rain. Its exposure through inhalation can lead to health risks. Therefore, control of CO₂ emission in the environment is very necessary. The main focus of this study is on the removal of carbon dioxide from off gases using a self-priming venturi scrubber in submerged conditions using sodium hydroxide as the scrubbing liquid. A self-priming submerged venturi scrubber is an efficient device to remove gaseous pollutants. In submerged condition, venturi scrubber remains submerged in the liquid tank and the liquid enters at the throat section of venturi scrubber due to the pressure difference which includes the hydrostatic pressure of the liquid and static pressure of the gas. The inlet polluted air stream enters through converging section which moves at very high velocity in the throat section and atomizes the liquid droplets. This leads to absorption of CO₂ from the off gases in scrubbing liquid which resulted in removal of CO₂ gas from the off gases. Detailed investigation on the scrubbing of carbon dioxide has been done in this literature. Experiments were conducted at different throat gas velocities, liquid levels in outer cylinder and CO₂ inlet concentrations to study the carbon dioxide removal efficiency. Experimental results give more than 95% removal efficiency of CO₂ in the self priming venturi scrubber which can meet the environmental emission limit of CO₂ to save the human life.

Keywords: carbon dioxide, scrubbing, pollution control, self-priming venturi scrubber

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Authors: Bin Liu

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Prokaryotic promoter, consisted of two short DNA sequences located at in -35 and -10 positions, is responsible for controlling the initiation and expression of gene expression. Different types of promoters have different functions, and their consensus sequences are similar. In addition, their consensus sequences may be different for the same type of promoter, which poses difficulties for promoter identification. Unfortunately, all existing computational methods treat promoter identification as a binary classification task and can only identify whether a query sequence belongs to a specific promoter type. It is desired to develop computational methods for effectively identifying promoters and their types. Here, a two-layer predictor is proposed to try to deal with the problem. The first layer is designed to predict whether a given sequence is a promoter and the second layer predicts the type of promoter that is judged as a promoter. Meanwhile, we also analyze the importance of feature and sequence conversation in two aspects: promoter identification and promoter type identification. To the best knowledge of ours, it is the first computational predictor to detect promoters and their types.

Keywords: promoter, promoter type, random forest, sequence information

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Authors: Lee Yan Nian

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Safety investigation is different from an administrative investigation in that the former is conducted by an independent agency and the purpose of such investigation is to prevent accidents in the future and not to apportion blame or determine liability. Before October 2018, Taiwan railway occurrences were investigated by local supervisory authority. Characteristics of this kind of investigation are that enforcement actions, such as administrative penalty, are usually imposed on those persons or units involved in occurrence. On October 21, 2018, due to a Taiwan Railway accident, which caused 18 fatalities and injured another 267, establishing an agency to independently investigate this catastrophic railway accident was quickly decided. The Taiwan Transportation Safety Board (TTSB) was then established on August 1, 2019 to take charge of investigating major aviation, marine, railway and highway occurrences. The objective of this study is to assess the effectiveness of safety investigations conducted by the TTSB. In this study, the major railway occurrence investigation reports published by the TTSB are used for modeling and analysis. According to the classification of railway occurrences investigated by the TTSB, accident types of Taiwan railway occurrences can be categorized into: derailment, fire, Signal Passed at Danger and others. A Causal Factor Analysis System (CFAS) developed by the TTSB is used to identify the influencing causal factors and their causal relationships in the investigation reports. All terminologies used in the CFAS are equivalent to the Human Factors Analysis and Classification System (HFACS) terminologies, except for “Technical Events” which was added to classify causal factors resulting from mechanical failure. Accordingly, the Bayesian network structure of each occurrence category is established based on the identified causal factors in the CFAS. In the Bayesian networks, the prior probabilities of identified causal factors are obtained from the number of times in the investigation reports. Conditional Probability Table of each parent node is determined from domain experts’ experience and judgement. The resulting networks are quantitatively assessed under different scenarios to evaluate their forward predictions and backward diagnostic capabilities. Finally, the established Bayesian network of derailment is assessed using investigation reports of the same accident which was investigated by the TTSB and the local supervisory authority respectively. Based on the assessment results, findings of the administrative investigation is more closely tied to errors of front line personnel than to organizational related factors. Safety investigation can identify not only unsafe acts of individual but also in-depth causal factors of organizational influences. The results show that the proposed methodology can identify differences between safety investigation and administrative investigation. Therefore, effective intervention strategies in associated areas can be better addressed for safety improvement and future accident prevention through safety investigation.

Keywords: administrative investigation, bayesian network, causal factor analysis system, safety investigation

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Authors: Ana Paula Esteves, Diego S. Caetano, Louise L. B. Lomardo

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This paper presents an approach to the performance of the natural lighting, when the use of appropriated solar lighting systems on the roof is applied in cultural buildings such as museums and foundations. The roofs, as a part of contact between the building and the external environment, require special attention in projects that aim at energy efficiency, being an important element for the capture of natural light in greater quantity, but also for being the most important point of generation of photovoltaic solar energy, even semitransparent, allowing the partial passage of light. Transparent elements in roofs, as well as superior protection of the building, can also play other roles, such as: meeting the needs of natural light for the accomplishment of the internal tasks, attending to the visual comfort; to bring benefits to the human perception and about the interior experience in a building. When these resources are well dimensioned, they also contribute to the energy efficiency and consequent character of sustainability of the building. Therefore, when properly designed and executed, a roof light system can bring higher quality natural light to the interior of the building, which is related to the human health and well-being dimension. Furthermore, it can meet the technologic, economic and environmental yearnings, making possible the more efficient use of that primordial resource, which is the light of the Sun. The article presents the analysis of buildings that used zenith light systems in search of better lighting performance in museums and foundations: the Solomon R. Guggenheim Museum in the United States, the Iberê Camargo Foundation in Brazil, the Museum of Fine Arts in Castellón in Spain and the Pinacoteca of São Paulo.

Keywords: natural lighting, roof lighting systems, natural lighting in museums, comfort lighting

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Authors: Hanan Al-Khalaifah, Afaf Al-Nasser

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Probiotics and prebiotics claimed to serve as effective alternatives to antibiotics in the poultry. This study aims to investigate the effect of different probiotics and prebiotics on the economic impact analysis of the use of probiotics and prebiotics in broiler feed. The study involved four broiler cycles, two during winter and two during summer. In the first two cycles (summer and winter), different types of prebiotics and probiotics were used. The probiotics were Bacillus coagulans (1 g/kg dried culture) and Lactobacillus (1 g/kg dried culture of 12 commercial strains), and prebiotics included fructo-oligosaccharides (FOS) (5 g/kg) and mannan-oligosaccharide (MOS) derived from Saccharomyces cerevisiae (5 g/kg). Based on the results obtained, the best treatment was chosen to be FOS, from which different ratios were used in the last two cycles during winter and summer. The levels of FOS chosen were 0.3, 0.5, and 0.7% of the diet. From an economic point of view, it was generally concluded that in all dietary treatments, food was consumed less in cycle 1 than in cycle 2, the total body weight gain was more in cycle 1 than cycle 2, and the average feed efficiency was less in cycle l than cycle 2. This indicates that the weather condition affected better in cycle 1. Also, there were very small differences between the dietary treatments in each cycle. In cycle 1, the best total feed consumption was for the FOS treatment, the highest total body weight gain and average feed efficiency were for B. coagulans. In cycle 2, all performance was better in FOS treatment. FOS significantly reduced the Salmonella sp. counts in the intestine, where the environment was driven towards acidity. FOS was the best on the average taste panel study of the produced meat. Accordingly, FOS prebiotic was chosen to be the best treatment to be used in cycles 3 and 4. The economic impact analysis generally revealed that there were no big differences between the treatments in all of the studied indicators, but there was a difference between the cycles.

Keywords: antibiotic, economic impact, prebiotic, probiotic, broiler

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Authors: Mohsin Raza, Rizwan Latif, Syed Adnan Qasim, Imran Shafi

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High torque low-speed diesel engine has a wide range of industrial and commercial applications. In literature, it’s found that lot of work has been done for the high-speed diesel engine and research on High Torque low-speed is rare. The fuel injection plays a key role in the efficiency of engine and reduction in exhaust emission. The fuel breakup plays a critical role in air-fuel mixture and spray combustion. The current study explains numerically an important phenomenon in spray combustion which is deformation and breakup of liquid drops in compression ignition internal combustion engine. The secondary breakup and its influence on spray and characteristics of compressed gas in-cylinder have been calculated by using simulation software in the backdrop of high torque low-speed diesel like conditions. The secondary spray breakup is modeled with KH - RT instabilities. The continuous field is described by turbulence model and dynamics of the dispersed droplet is modeled by Lagrangian tracking scheme. The results by using KH - RT model are compared against other default methods in OpenFOAM and published experimental data from research and implemented in CFD (Computational Fluid Dynamics). These numerical simulation, done in OpenFoam and Matlab, results are analyzed for the complete 720- degree 4 stroke engine cycle at a low engine speed, for favorable agreement to be achieved. Results thus obtained will be analyzed for better evaporation in near nozzle region. The proposed analyses will further help in better engine efficiency, low emission and improved fuel economy.

Keywords: diesel fuel, KH-RT, Lagrangian , Open FOAM, secondary breakup

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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Authors: Yaser S. Kishawi, Sadi R. Ali

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Part of Palestine, Gaza Strip (365 km2 and 1.8 million inhabitants) is considered a semi-arid zone relies solely on the Coastal Aquifer. The coastal aquifer is only source of water with only 5-10% suitable for human use. This barely cover the domestic and agricultural needs of Gaza Strip. Palestinian Water Authority Strategy is finding non-conventional water resource from treated wastewater to cover agricultural requirements and serve the population. A new WWTP project is to replace the old-overloaded Biet Lahia WWTP. The project consists of three parts; phase A (pressure line & infiltration basins - IBs), phase B (a new WWTP) and phase C (Recovery and Reuse Scheme – RRS – to capture the spreading plume). Currently, only phase A is functioning. Nearly 23 Mm3 of partially treated wastewater were infiltrated into the aquifer. Phase B and phase C witnessed many delays and this forced a reassessment of the RRS original design. An Environmental Management Plan was conducted from Jul 2013 to Jun 2014 on 13 existing monitoring wells surrounding the project location. This is to measure the efficiency of the SAT system and the spread of the contamination plume with relation to the efficiency of the proposed RRS. Along with the proposed location of the 27 recovery wells as part of the proposed RRS. The results of monitored wells were assessed compared with PWA baseline data. This was put into a groundwater model to simulate the plume to propose the best suitable solution to the delays. The redesign mainly manipulated the pumping rate of wells, proposed locations and functioning schedules (including wells groupings). The proposed simulations were examined using visual MODFLOW V4.2 to simulate the results. The results of monitored wells were assessed based on the location of the monitoring wells related to the proposed recovery wells locations (200m, 500m and 750m away from the IBs). Near the 500m line (the first row of proposed recovery wells), an increase of nitrate (from 30 to 70mg/L) compare to a decrease in Chloride (1500 to below 900mg/L) was found during the monitoring period which indicated an expansion of plume to this distance. On this rate with the required time to construct the recovery scheme, keeping the original design the RRS will fail to capture the plume. Based on that many simulations were conducted leading into three main scenarios. The scenarios manipulated the starting dates, the pumping rate and the locations of recovery wells. A simulation of plume expansion and path-lines were extracted from the model monitoring how to prevent the expansion towards the nearby municipal wells. It was concluded that the location is the most important factor in determining the RRS efficiency. Scenario III was adopted and showed an effective results even with a reduced pumping rates. This scenario proposed adding two additional recovery wells in a location beyond the 750m line to compensate the delays and effectively capture the plume. A continuous monitoring program for current and future monitoring wells should be in place to support the proposed scenario and ensure maximum protection.

Keywords: soil aquifer treatment, recovery and reuse scheme, infiltration basins, north gaza

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Authors: Javier Carroquino, Nieves García-Casarejos, Pilar Gargallo

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Climate change, depletion of non-renewable resources in the current energies, pollution from them, the greater ecological awareness of the population, are factors that suggest the change of energy sources in business. The agri-food industry is a growth sector, concerned about product innovation, process and with a clear awareness of what climate change may mean for it. This sector is supposed to have a high receptivity to the implementation of clean energy, as this favors not only the environment but also the essence of its business. This work, through surveys, aims to know the willingness of Spanish wineries to implement renewable energies in their vineyards, as well as the limitations they perceive for their implementation. This questionnaire allows the characterization of the sector in terms of its geographical typologies, their activity levels, their perception of environmental issues, the degree of implementation of measures to mitigate climate change and improve energy efficiency, and its uses and energy consumption. The analysis of data proves that the penetration of renewable energies is still at low levels, being the most used energies, solar thermal, photovoltaic and biomass. The initial investment seems to be at the origin of the lack of implantation of this type of energy in the wineries, and not so much the costs of operations and maintenance. The environmental management of the wineries is still at an embryonic stage within the company's organization chart, because these services are either outsourced or, if technicians are available, they are not exclusively dedicated to these tasks. However, there is a strong environmental awareness, as evidenced by the number of climate change mitigation and energy efficiency measures already adopted. The gap between high awareness and low achievement is probably due to the lack of knowledge about how to do it or the perception of a high cost.

Keywords: survey, renewable energy, winery, Spanish case

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Dorian Audot, Isobel Margaret Thompson, Dominic Hudson, Joseph Banks, Martin Warner

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In competitive swimming, after dives and turns, athletes perform underwater undulatory swimming (UUS), copying marine mammals’ method of locomotion. The body, performing this wave-like motion, accelerates the fluid downstream in its vicinity, generating propulsion with minimal resistance. Through this technique, swimmers can maintain greater speeds than surface swimming and take advantage of the overspeed granted by the dive (or push-off). Almost all previous work has considered UUS when performed at maximum effort. Critical parameters to maximize UUS speed are frequently discussed; however, this does not apply to most races. In only 3 out of the 16 individual competitive swimming events are athletes likely to attempt to perform UUS with the greatest speed, without thinking of the cost of locomotion. In the other cases, athletes will want to control the speed of their underwater swimming, attempting to maximise speed whilst considering energy expenditure appropriate to the duration of the event. Hence, there is a need to understand how swimmers adapt their underwater strategies to optimize the speed within the allocated energetic cost. This paper develops a consistent methodology that enables different sets of UUS kinematics to be investigated. These may have different propulsive efficiencies and force generation mechanisms (e.g.: force distribution along with the body and force magnitude). The developed methodology, therefore, needs to: (i) provide an understanding of the UUS propulsive mechanisms at different speeds, (ii) investigate the key performance parameters when UUS is not performed solely for maximizing speed; (iii) consistently determine the propulsive efficiency of a UUS technique. The methodology is separated into two distinct parts: kinematic data acquisition and computational fluid dynamics (CFD) analysis. For the kinematic acquisition, the position of several joints along the body and their sequencing were either obtained by video digitization or by underwater motion capture (Qualisys system). During data acquisition, the swimmers were asked to perform UUS at a constant depth in a prone position (facing the bottom of the pool) at different speeds: maximum effort, 100m pace, 200m pace and 400m pace. The kinematic data were input to a CFD algorithm employing a two-dimensional Large Eddy Simulation (LES). The algorithm adopted was specifically developed in order to perform quick unsteady simulations of deforming bodies and is therefore suitable for swimmers performing UUS. Despite its approximations, the algorithm is applied such that simulations are performed with the inflow velocity updated at every time step. It also enables calculations of the resistive forces (total and applied to each segment) and the power input of the modeled swimmer. Validation of the methodology is achieved by comparing the data obtained from the computations with the original data (e.g.: sustained swimming speed). This method is applied to the different kinematic datasets and provides data on swimmers’ natural responses to pacing instructions. The results show how kinematics affect force generation mechanisms and hence how the propulsive efficiency of UUS varies for different race strategies.

Keywords: CFD, efficiency, human swimming, hydrodynamics, underwater undulatory swimming

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Authors: Beenish Urooj, Ubaid Ullah, Sidra Riasat

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National Testing Service-Pakistan (NTS) is an agency in Pakistan that conducts student success appraisal examinations. In this research paper, we must present a security model for the NTS organization. The security model will depict certain security countermeasures for a better defense against certain types of breaches and system malware. We will provide a security roadmap, which will help the company to execute its further goals to maintain security standards and policies. We also covered multiple aspects in securing the environment of the organization. We introduced the processes, architecture, data classification, auditing approaches, survey responses, data handling, and also training and awareness of risk for the company. The primary contribution is the Risk Survey, based on the maturity model meant to assess and examine employee training and knowledge of risks in the company's activities.

Keywords: NTS, risk assessment, threat factors, security, services

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Authors: Francesca Carta, Giovanna Linfante, Laura Agneni, Debora Radicchia, Camilla Micheletta, Angelo Del Cimmuto

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Overtaking competence mismatches and fostering career pathways congruent with the individual skills profile would significantly contribute to fighting unemployment. The aim of this paper is to examine the usefulness and efficiency of qualitative tools in supporting and improving the quality of caseworkers’ activities during the jobseekers’ profile analysis and career guidance process. The selected target groups are long-term and middle term unemployed, job seekers, young people at the end of the vocational training pathway and unemployed woman with social disadvantages. The experimentation is conducted in Italy at public employment services in 2017. In the framework of Italian labour market reform, the experimentation represents the first step to develop a customized qualitative model profiling; the final general object is to improve the public employment services quality. The experimentation tests the transferability of an OECD self-assessment competences tool in the Italian public employment services. On one hand, the first analysis results will indicate the user’s perception concerning the tool’s application and their different competence levels (literacy, numeracy, problem solving, career interest, subjective well-being and health, behavioural competencies) with reference to the specific target. On the other hand, the experimentation outcomes will show caseworkers understanding regarding the tool’s usability and efficiency for career guidance and reskilling and upskilling programs.

Keywords: career guidance, evaluation competences, reskilling pathway, unemployment

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Authors: Mohammed A. Elhaj, Jamal S. Yassin

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In the present time, energy crises are considered a severe problem across the world. For the protection of global environment and maintain ecological balance, energy saving is considered one of the most vital issues from the view point of fuel consumption. As the industrial sectors everywhere continue efforts to improve their energy efficiency, recovering waste heat losses provides an attractive opportunity for an emission free and less costly energy resource. In the other hand the using of solar energy has become more insistent particularly after the high gross of prices and running off the conventional energy sources. Therefore, it is essential that we should endeavor for waste heat recovery as well as solar energy by making significant and concrete efforts. For these reasons this investigation is carried out to study and analyze the performance of a power plant working by a combined cycle in which Heat Recovery System Generator (HRSG) gets its energy from the waste heat of a gas turbine unit. Evaluation of the performance of the plant is based on different thermal efficiencies of the main components in addition to the second law analysis considering the exergy destructions for the whole components. The contribution factors including the solar as well as the wasted energy are considered in the calculations. The final results have shown that there is significant exergy destruction in solar concentrator and the combustion chamber of the gas turbine unit. Other components such as compressor, gas turbine, steam turbine and heat exchangers having insignificant exergy destruction. Also, solar energy can contribute by about 27% of the input energy to the plant while the energy lost with exhaust gases can contribute by about 64% at maximum cases.

Keywords: solar energy, environment, efficiency, waste heat, steam generator, performance, exergy destruction

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Authors: C. F. Glover, J. Marinaccio, A. Barnes, I. Mabbett, G. Williams

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The aim of the current study is to optimise formulations, in terms of charging efficiency, of a printed Ni(OH)2 precursor coating of a battery anode. Through the assessment of the current densities during charging, the efficiency of a range of formulations are compared. The Scanning vibrating electrode technique (SVET) is used extensively in the field of corrosion to measure area-averaged current densities of freely-corroding metal surfaces when fully immersed in electrolyte. Here, a Ni(OH)2 electrode is immersed in potassium hydroxide (30% w/v solution) electrolyte and charged using a range of applied currents. Samples are prepared whereby multiple coatings are applied to one substrate, separated by a non-conducting barrier, and charged using a constant current. With a known applied external current, electrode efficiencies can be calculated based on the current density outputs measured using SVET. When fully charged, a green Ni(OH)2 is oxidised to a black NiOOH surface. Distinct regions displaying high current density, and hence a faster oxidising reaction rate, are located using the SVET. This is confirmed by a darkening of the region upon transition to NiOOH. SVET is a highly effective tool for assessing homogeneity of electrodes during charge/discharge. This could prove particularly useful for electrodes where there are no visible surface appearance changes. Furthermore, a scanning Kelvin probe technique, traditionally used to assess underfilm delamination of organic coatings for the protection of metallic surfaces, is employed to study the change in phase of oxides, pre and post charging.

Keywords: battery, electrode, nickel hydroxide, SVET, printed

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Authors: Bartlomiej Przybyszewski, Rafal Kozera, Katarzyna Zolynska, Anna Boczkowska, Daria Pakula

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The build-up and accumulation of dirt, ice, and snow on structural elements and vehicles is an unfavorable phenomenon, leading to economic losses and often also posing a threat to people. This problem occurs wherever the use of polymer coatings has become a standard, among others in photovoltaic farms, aviation, wind energy, and civil engineering. The accumulated pollution on the photovoltaic modules can reduce their efficiency by several percent, and snow stops power production. Accumulated ice on the blades of wind turbines or the wings of airplanes and drones disrupts the airflow by changing their shape, leading to increased drag and reduced efficiency. This results in costly maintenance and repairs. The goal of the work is to reduce or completely eliminate the accumulation of dirt, snow, and ice build-up on polymer coatings by achieving self-cleaning and icephobic properties. It is done by the use of a multi-step surface modification of the polymer nanocoatings. For this purpose, two methods of surface structuring and the preceding volumetric modification of the chemical composition with proprietary organosilicon compounds and/or mineral additives were used. To characterize the surface topography of the modified coatings, light profilometry was utilized. Measurements of the wettability parameters (static contact angle and contact angle hysteresis) on the investigated surfaces allowed to identify their wetting behavior and determine relation between hydrophobic and anti-icing properties. Ice adhesion strength was measured to assess coatings' anti-icing behavior.

Keywords: anti-icing properties, self-cleaning, polymer coatings, icephobic coatings

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Authors: Samira-Sadat JamaliDinan, Haidar Almohri, Mohammad-Reza Nazem-Zadeh

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Lateralization of temporal lobe epilepsy (TLE) is very important for positive surgical outcomes. We propose a machine learning framework to ultimately identify the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Unlike most studies that use classification algorithms, we propose an effective clustering approach to distinguish between normal and TLE cases. We apply the famous Minkowski weighted K-Means (MWK-Means) technique as the clustering framework. To overcome the problem of poor initialization of K-Means, we use particle swarm optimization (PSO) to effectively select the initial centroids of clusters prior to applying MWK-Means. We demonstrate that compared to K-means and MWK-means independently, this approach is able to improve the result of a benchmark data set.

Keywords: temporal lobe epilepsy, machine learning, clustering, magnetoencephalography

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Authors: Sheldon McCall, Miao Yu, Liyun Gong, Shigang Yue, Stefanos Kollias

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Falls are a significant health concern for older adults globally, and prompt identification is critical to providing necessary healthcare support. Our study proposes a new fall detection method using computer vision based on modern deep learning techniques. Our approach involves training a trans- former model on a large 2D pose dataset for general action recognition, followed by transfer learning. Specifically, we freeze the first few layers of the trained transformer model and train only the last two layers for fall detection. Our experimental results demonstrate that our proposed method outperforms both classical machine learning and deep learning approaches in fall/non-fall classification. Overall, our study suggests that our proposed methodology could be a valuable tool for identifying falls.

Keywords: healthcare, fall detection, transformer, transfer learning

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Authors: Nitish Suvarna, Anjali Awasthi

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In an era marked by rapid technological advancements, the integration of Autonomous Vehicles into supply chain networks represents a transformative shift, promising to redefine the paradigms of logistics and transportation. This thesis delves into a comprehensive assessment of the impact of autonomous vehicles on supply chain performance, with a particular focus on network design, operational efficiency, and environmental sustainability. Employing the advanced simulation capabilities of anyLogistix (ALX), the study constructs a digital twin of a conventional supply chain network, encompassing suppliers, production facilities, distribution centers, and customer endpoints. The research methodically integrates Autonomous Vehicles into this intricate network, aiming to unravel the multifaceted effects on transportation logistics including transit times, cost-efficiency, and sustainability. Through simulations and scenarios analysis, the study scrutinizes the operational resilience and adaptability of supply chains in the face of dynamic market conditions and disruptive technologies like Autonomous Vehicles. Furthermore, the thesis undertakes carbon footprint analysis, quantifying the environmental benefits and challenges associated with the adoption of Autonomous Vehicles in supply chain operations. The insights from this research are anticipated to offer a strategic framework for industry stakeholders, guiding the adoption of Autonomous Vehicles to foster a more efficient, responsive, and sustainable supply chain ecosystem. The findings aim to serve as a cornerstone for future research and practical implementations in the realm of intelligent transportation and supply chain management.

Keywords: autonomous vehicle, agri-food supply chain, ALX simulation, anyLogistix

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Authors: Dayo Samuel Banjo, Connice Trimmingham, Niloofar Yousefi, Nitin Agarwal

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Technological advancement and its omnipresent connection have pushed humans past the boundaries and limitations of a computer screen, physical state, or geographical location. It has provided a depth of avenues that facilitate human-computer interaction that was once inconceivable such as audio and body language detection. Given the complex modularities of emotions, it becomes vital to study human-computer interaction, as it is the commencement of a thorough understanding of the emotional state of users and, in the context of social networks, the producers of multimodal information. This study first acknowledges the accuracy of classification found within multimodal emotion detection systems compared to unimodal solutions. Second, it explores the characterization of multimedia content produced based on their emotions and the coherence of emotion in different modalities by utilizing deep learning models to classify emotion across different modalities.

Keywords: affective computing, deep learning, emotion recognition, multimodal

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Authors: Helida T. G. Soares, Antonio J. P. da Silva

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As the society develops, the accumulation of solid waste in landfills, in the environment, and the depletion of the raw material increases. In this way, there is relevance in researching the interaction between the environmental management and civil construction; therefore, this project has for scope the analysis and the effects of the rubber microparticles use as a small aggregate added to the sand, producing an ecological mortar for the pavement constitution, from the mixture of a paste, composed of Portland cement and water, and its application in sports courts. It was used the detailed reutilization of micro rubber in its most primordial, micro form, highlighting the powder pattern as the additional balancing of the mortar, analyzing the evolution of the mechanical properties. Percentages of 5, 10 and 15% rubber were used based on the total mass of the trace, where there is no removal of aggregates or cement, only increment of the rubber. The results obtained through the mechanical test of simple compression showed that the rubber, added to the mortar, presents low mechanical resistance compared to the reference trait, the study of this subject is vast of possibilities to be explored. In this sense, we seek sustainability and innovation from the use of an ecological material, thus adding value and reducing the impact of this material on the environment. The manufacturing process takes place from the direct mixing of cement paste and rubber, whether manually, mechanically or industrially. It results in the production of a low-cost mortar, through the use of recycled rubber, with high efficiency in general properties, such as compressive strength and friction coefficient, allowing its use for the construction of floors for sports courts with high durability. Thus, it is possible to reuse this micro rubber residue in other applications in simple concrete artifacts.

Keywords: civil construction, ecological mortar, high efficiency, rubber

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Authors: Zainal A. Muchlisin, Muhammad Iqbal, Abdullah A. Muhammadar

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The objective of the present study was to determine the optimum dose of papain enzyme in the diet for growing, survival rate and feed efficacy of climbing perch (Anabas testudineus). The study was conducted at the Laboratory of Aquatic of Faculty of Veterinary, Syiah Kuala University from January to March 2016. The completely randomized design was used in this study. Six dosages level of papain enzyme were tested with 4 replications i.e. 0 g kg-1 of feed, 20.0 g kg-1 feed, 22.5 g kg-1 of feed, 25.0 g kg-1 of feed, 27.5 g kg-1 of feed, and 30.0 g kg-1 of feed. The experimental fish fed twice a day at feeding level of 5% for 60 days. The results showed that weight gain ranged from 2.41g to 7.37g, total length gain ranged from 0.67cm to 3.17cm, specific growth rate ranged from 1.46 % day to 3.41% day, daily growth rate ranged from 0.04 g day to 0.13 g day, feed conversion ratio ranged from 1.94 to 3.59, feed efficiency ranged from 27.99% to 51.37%, protein retention ranged from 3.38% to 28.28%, protein digestibility ranged from 50.63% to 90.38%, and survival rate ranged from 88.89% to 100%. The highest rate for all parameters was found in the dosage of 3.00% papain enzyme kg feed. The ANOVA test showed that enzyme papain gave a significant effect on the weight gain, total length gain, daily growth rate, specific growth rate, feed conversion ratio, feed efficiency, protein retention, protein digestibility, and survival rate of the climbing perch (Anabas testudieus). The best enzyme papain dosage was 3.0%.

Keywords: betok, feed conversion ratio, freshwater fish, nutrition, feeding

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Authors: Yaser S. Kishawi, Sadi R. Ali

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Part of Palestine, Gaza Strip (365 km2 and 1.8 million habitants) is considered a semi-arid zone relies solely on the Coastal Aquifer. The coastal aquifer is only source of water with only 5-10% suitable for human use. This barely covers the domestic and agricultural needs of Gaza Strip. Palestinian Water Authority Strategy is finding non-conventional water resource from treated wastewater to cover agricultural requirements and serve the population. A new WWTP project is to replace the old-overloaded Biet Lahia WWTP. The project consists of three parts; phase A (pressure line and infiltration basins-IBs), phase B (a new WWTP) and phase C (Recovery and Reuse Scheme–RRS– to capture the spreading plume). Currently, only phase A is functioning. Nearly 23 Mm3 of partially treated wastewater were infiltrated into the aquifer. Phase B and phase C witnessed many delays and this forced a reassessment of the RRS original design. An Environmental Management Plan was conducted from Jul 2013 to Jun 2014 on 13 existing monitoring wells surrounding the project location. This is to measure the efficiency of the SAT system and the spread of the contamination plume with relation to the efficiency of the proposed RRS. Along with the proposed location of the 27 recovery wells as part of the proposed RRS. The results of monitored wells were assessed compared with PWA baseline data. This was put into a groundwater model to simulate the plume to propose the best suitable solution to the delays. The redesign mainly manipulated the pumping rate of wells, proposed locations and functioning schedules (including wells groupings). The proposed simulations were examined using visual MODFLOW V4.2 to simulate the results. The results of monitored wells were assessed based on the location of the monitoring wells related to the proposed recovery wells locations (200m, 500m, and 750m away from the IBs). Near the 500m line (the first row of proposed recovery wells), an increase of nitrate (from 30 to 70mg/L) compare to a decrease in Chloride (1500 to below 900mg/L) was found during the monitoring period which indicated an expansion of plume to this distance. On this rate with the required time to construct the recovery scheme, keeping the original design the RRS will fail to capture the plume. Based on that many simulations were conducted leading into three main scenarios. The scenarios manipulated the starting dates, the pumping rate and the locations of recovery wells. A simulation of plume expansion and path-lines were extracted from the model monitoring how to prevent the expansion towards the nearby municipal wells. It was concluded that the location is the most important factor in determining the RRS efficiency. Scenario III was adopted and showed effective results even with a reduced pumping rates. This scenario proposed adding two additional recovery wells in a location beyond the 750m line to compensate the delays and effectively capture the plume. A continuous monitoring program for current and future monitoring wells should be in place to support the proposed scenario and ensure maximum protection.

Keywords: soil aquifer treatment, recovery reuse scheme, infiltration basins, North Gaza

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Authors: Ebenezer Obaloluwa Olaniyi

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In this paper, an intelligent system has been designed to grade apple based on either its defective or healthy for production in food processing. This paper is segmented into two different phase. In the first phase, the image processing techniques were employed to extract the necessary features required in the apple. These techniques include grayscale conversion, segmentation where a threshold value is chosen to separate the foreground of the images from the background. Then edge detection was also employed to bring out the features in the images. These extracted features were then fed into the neural network in the second phase of the paper. The second phase is a classification phase where neural network employed to classify the defective apple from the healthy apple. In this phase, the network was trained with back propagation and tested with feed forward network. The recognition rate obtained from our system shows that our system is more accurate and faster as compared with previous work.

Keywords: image processing, neural network, apple, intelligent system

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Authors: Abeeb Abiodun Bakare

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The advent of blockchain technology has birthed a revolutionary innovation: smart contracts. These self-executing contracts, encoded within the immutable ledger of a blockchain, hold the potential to transform the landscape of traditional contractual agreements. This research paper embarks on a comprehensive exploration of the legal implications surrounding smart contracts, delving into their enforceability and their profound impact on traditional contract law. The first section of this paper delves into the foundational principles of smart contracts, elucidating their underlying mechanisms and technological intricacies. By harnessing the power of blockchain technology, smart contracts automate the execution of contractual terms, eliminating the need for intermediaries and enhancing efficiency in commercial transactions. However, this technological marvel raises fundamental questions regarding legal enforceability and compliance with traditional legal frameworks. Moving beyond the realm of technology, the paper proceeds to analyze the legal validity of smart contracts within the context of traditional contract law. Drawing upon established legal principles, such as offer, acceptance, and consideration, we examine the extent to which smart contracts satisfy the requirements for forming a legally binding agreement. Furthermore, we explore the challenges posed by jurisdictional issues as smart contracts transcend physical boundaries and operate within a decentralized network. Central to this analysis is the examination of the role of arbitration and dispute resolution mechanisms in the context of smart contracts. While smart contracts offer unparalleled efficiency and transparency in executing contractual terms, disputes inevitably arise, necessitating mechanisms for resolution. We investigate the feasibility of integrating arbitration clauses within smart contracts, exploring the potential for decentralized arbitration platforms to streamline dispute resolution processes. Moreover, this paper explores the implications of smart contracts for traditional legal intermediaries, such as lawyers and judges. As smart contracts automate the execution of contractual terms, the role of legal professionals in contract drafting and interpretation may undergo significant transformation. We assess the implications of this paradigm shift for legal practice and the broader legal profession. In conclusion, this research paper provides a comprehensive analysis of the legal implications surrounding smart contracts, illuminating the intricate interplay between code and law. While smart contracts offer unprecedented efficiency and transparency in commercial transactions, their legal validity remains subject to scrutiny within traditional legal frameworks. By navigating the complex landscape of smart contract law, we aim to provide insights into the transformative potential of this groundbreaking technology.

Keywords: smart-contracts, law, blockchain, legal, technology

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Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

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Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT

Procedia PDF Downloads 276