Search results for: post classification change detection

Authors: Mohamed M. Diab, Fahmy E. Mohamed, Alaa Balbaa

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Background: Myofascial pain syndrome a common type of non-articular musculoskeletal pain, is a condition associated with regional pain and muscle tenderness characterized by the presence of hypersensitive nodules. Objectives: the purpose of this study is to compare between the effects of manual progressive ischemic pressure versus the effect of post isometric facilitation in the treatment of Rhomboid latent myofascial trigger points. Methods: six patients had participated in this study. Patients divided into two groups. Group A treated by manual progressive ischemic pressure and traditional physical therapy program. Group B treated by post isometric facilitation and traditional physical therapy program. Treatment program was for 6 sessions over two week’s period. Result: Statistical analysis revealed that there is no significant difference in post treatment from pretreatment in pain severity (VAS) in myofascial trigger points with Rhomboid muscles) and Pain pressure threshold (PPT) for tenderness at both groups (A,B). Conclusion: ischemic pressure technique appear to be no more effective than post isometric facilitation in treatment of rhomboids latent myofacial trigger point.

Keywords: Rhmoiboid trigger point, myofacila trigger point, ischemic pressure, post isometric facilitation

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Authors: Amirali Mahouti, Hooshang Katebi

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Enlargement of Tabriz metropolis to the east and north-east caused urban construction to be built on Marl layers and because of increase in excavations depth, further information of this layer is inescapable. Looking at geotechnical investigation shows there is not enough information about Tabriz Marl and this soil has been classified only by color. Tabriz Marl is lacustrine carbonate sediment outcrops, surrounds eastern, northern and southern region of city in the East Azerbaijan Province of Iran and is known as bed rock of city under alluvium sediments. This investigation aims to characterize geotechnical parameters of this soil to identify and set it in classification system of carbonated soils. For this purpose, specimens obtained from 80 locations over the city and subjected to physical and mechanical tests, such as Atterberg limits, density, moisture content, unconfined compression, direct shear and consolidation. CaCO3 content, organic content, PH, XRD, XRF, TGA and geophysical downhole tests also have been done on some of them.

Keywords: carbonated soils, classification of soils, mineralogy, physical and mechanical tests for Marls, Tabriz Marl

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Authors: I. Parkova, A. Vališevskis, A. Viļumsone

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Nocturnal enuresis or bed-wetting is intermittent incontinence during sleep of children after age 5 that may precipitate wide range of behavioural and developmental problems. One of the non-pharmacological treatment methods is the use of a bed-wetting alarm system. In order to improve comfort conditions of nocturnal enuresis alarm system, modular moisture sensor should be replaced by a textile sensor. In this study behaviour and moisture detection speed of woven and sewn sensors were compared by analysing change in electrical resistance after solution (salt water) was dripped on sensor samples. Material of samples has different structure and yarn location, which affects solution detection rate. Sensor system circuit was designed and two sensor tests were performed: system activation test and false alarm test to determine the sensitivity of the system and activation threshold. Sewn sensor had better result in system’s activation test – faster reaction, but woven sensor had better result in system’s false alarm test – it was less sensitive to perspiration simulation. After experiments it was found that the optimum switching threshold is 3V in case of 5V input voltage, which provides protection against false alarms, for example – during intensive sweating.

Keywords: conductive yarns, moisture textile sensor, industry, material

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Authors: Dede Basturk, Metin Kaya, Halil Taskin, Nurtekin Erkmen

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In total, 29 students studying in Selçuk University Physical Training and Sports School who are recreationally active participated voluntarilyin this study which was carried out in order to examine effects of Vertimax trainings on agility, quickness and acceleration. 3 groups took their parts in this study as Vertimax training group (N=10), Ordinary training group (N=10) and Control group (N=9). Measurements were carried out in performance laboratory of Selçuk University Physical Training and Sports School. A training program for quickness and agility was followed up for subjects 3 days a week (Monday, Wednesday, Friday) for 8 weeks. Subjects taking their parts in vertimax training group and ordinary training group participated in the training program for quickness and agility. Measurements were applied as pre-test and post-test. Subjects of vertimax training group followed the training program with vertimax device and subjects of ordinary training group followed the training program without vertimax device. As to control group who are recreationally active, they did not participate in any program. 4 gate photocells were used for measuring and measurement of distances was carried out in m. Furthermore, single gate photocell and honi were used for agility test. Measurements started with 15 minutes of warm-up. Acceleration, quickness and agility tests were applied on subjects. 3 measurements were made for each subject at 3 minutes resting intervals. The best rating of three measurements was recorded. 5 m quickness pre-test value of vertimax training groups has been determined as 1,11±0,06 s and post-test value has been determined as 1,06 ± 0,08 s (P<0,05). 5 m quickness pre-test value of ordinary training group has been determined as 1,11±0,06 s and post-test value has been determined as 1,07±0,07 s (P<0,05).5 m quickness pre-test value of control group has been determined as 1,13±0,08 s and post-test value has been determined as 1,10 ± 0,07 s (P>0,05). Upon examination of 10 m acceleration value before and after the training, 10 m acceleration pre-test value of vertimax training group has been determined as 1,82 ± 0,07 s and post-test value has been determined as 1,76±0,83 s (P>0,05). 10 m acceleration pre-test value of ordinary training group has been determined as 1,83±0,05 s and post-test value has been determined as 1,78 ± 0,08 s (P>0,05).10 m acceleration pre-test value of control group has been determined as 1,87±0,11 s and post-test value has been determined as 1,83 ± 0,09 s (P>0,05). Upon examination of 15 m acceleration value before and after the training, 15 m acceleration pre-test value of vertimax training group has been determined as 2,52±0,10 s and post-test value has been determined as 2,46 ± 0,11 s (P>0,05).15 m acceleration pre-test value of ordinary training group has been determined as 2,52±0,05 s and post-test value has been determined as 2,48 ± 0,06 s (P>0,05). 15 m acceleration pre-test value of control group has been determined as 2,55 ± 0,11 s and post-test value has been determined as 2,54 ± 0,08 s (P>0,05).Upon examination of agility performance before and after the training, agility pre-test value of vertimax training group has been determined as 9,50±0,47 s and post-test value has been determined as 9,66 ± 0,47 s (P>0,05). Agility pre-test value of ordinary training group has been determined as 9,99 ± 0,05 s and post-test value has been determined as 9,86 ± 0,40 s (P>0,05). Agility pre-test value of control group has been determined as 9,74 ± 0,45 s and post-test value has been determined as 9,92 ± 0,49 s (P>0,05). Consequently, it has been observed that quickness and acceleration features were developed significantly following 8 weeks of vertimax training program and agility features were not developed significantly. It is suggested that training practices used for the study may be used for situations which may require sudden moves and in order to attain the maximum speed in a short time. Nevertheless, it is also suggested that this training practice does not make contribution in development of moves which may require sudden direction changes. It is suggested that productiveness and innovation may come off in terms of training by using various practices of vertimax trainings.

Keywords: vertimax, training, quickness, agility, acceleration

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Authors: Margarida Porfírio Tomás, Paula Pereira, José Palma Oliveira

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Robotic grasping is advancing rapidly, but transferring techniques from rigid to deformable objects remains a challenge. Deformable and flexible items, such as food containers, demand nuanced handling due to their changing shapes. Bridging this gap is crucial for applications in food processing, surgical robotics, and household assistance. AGILEHAND, a Horizon project, focuses on developing advanced technologies for sorting, handling, and packaging soft and deformable products autonomously. These technologies serve as strategic tools to enhance flexibility, agility, and reconfigurability within the production and logistics systems of European manufacturing companies. Key components include intelligent detection, self-adaptive handling, efficient sorting, and agile, rapid reconfiguration. The overarching goal is to optimize work environments and equipment, ensuring both efficiency and safety. As new technologies emerge in the food industry, there will be some implications, such as labour force, safety problems and acceptance of the new technologies. To overcome these implications, AGILEHAND emphasizes the integration of social sciences and humanities, for example, the application of the Technology Acceptance Model (TAM). The project aims to create a change management manual, that will outline strategies for developing digital skills and managing health and safety transitions. It will also provide best practices and models for organizational change. Additionally, AGILEHAND will design effective training programs to enhance employee skills and knowledge. This information will be obtained through a combination of case studies, structured interviews, questionnaires, and a comprehensive literature review. The project will explore how organizations adapt during periods of change and identify factors influencing employee motivation and job satisfaction. This project received funding from European Union’s Horizon 2020/Horizon Europe research and innovation program under grant agreement No101092043 (AGILEHAND).

Keywords: change management, technology acceptance model, organizational change, health and safety

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Authors: Raphael M. Ritti-Dias, Marilia A. Correia, Wagner J. R. Domingues, Aline C. Palmeira, Paulo Longano, Nelson Wolosker, Lauro C. Vianna, Gabriel G. Cucato

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Patients with peripheral arterial disease (PAD) have a high prevalence of hypertension, which contributes to a high risk of acute cardiovascular events and cardiovascular mortality. Strategies to reduce cardiovascular risk of these patients are needed. Meta-analysis studies have shown that isometric handgrip training promotes reductions in clinical blood pressure in normotensive, pre-hypertensive and hypertensive individuals. However, the effect of this exercise training on other cardiovascular function indicators in PAD patients remains unknown. Thus, the aim of this study was to analyze the effects of isometric handgrip training on blood pressure in patients with PAD. In this clinical trial, 28 patients were randomly allocated into two groups: isometric handgrip training (HG) and control (CG). The HG conducted the unilateral handgrip training three days per week (four sets of two minutes, with 30% of maximum voluntary contraction with an interval of four minutes between sets). CG was encouraged to increase their physical activity levels. At baseline and after eight weeks blood pressure and heart rate were obtained. ANOVA two-way for repeated measures with the group (GH and GC) and time (pre- and post-intervention) as factors was performed. After 8 weeks of training there were no significant changes in systolic blood pressure (HG pre 141 ± 24.0 mmHg vs. HG post 142 ± 22.0 mmHg; CG pre 140 ± 22.1 mmHg vs. CG post 146 ± 16.2 mmHg; P=0.18), diastolic blood pressure (HG pre 74 ± 10.4 mmHg vs. HG post 74 ± 11.9 mmHg; CG pre 72 ± 6.9 mmHg vs. CG post 74 ± 8.0 mmHg; P=0.22) and heart rate (HG pre 61 ± 10.5 bpm vs. HG post 62 ± 8.0 bpm; CG pre 64 ± 11.8 bpm vs. CG post 65 ± 13.6 bpm; P=0.81). In conclusion, our preliminary data indicate that isometric handgrip training did not modify blood pressure and heart rate in patients with PAD.

Keywords: blood pressure, exercise, isometric, peripheral artery disease

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Authors: Ronak Khosravi, Mahmood Abbasi Layegh, Siamak Haghipour, Avin Esmaili

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In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function.

Keywords: coefficient features, relevance vector machines, spectral features, support vector machines, temporal features

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Authors: Abdulkader Helwan

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Iris cancer, so called intraocular melanoma is a cancer that starts in the iris; the colored part of the eye that surrounds the pupil. There is a need for an accurate and cost-effective iris cancer detection system since the available techniques used currently are still not efficient. The combination of the image processing and artificial neural networks has a great efficiency for the diagnosis and detection of the iris cancer. Image processing techniques improve the diagnosis of the cancer by enhancing the quality of the images, so the physicians diagnose properly. However, neural networks can help in making decision; whether the eye is cancerous or not. This paper aims to develop an intelligent system that stimulates a human visual detection of the intraocular melanoma, so called iris cancer. The suggested system combines both image processing techniques and neural networks. The images are first converted to grayscale, filtered, and then segmented using prewitt edge detection algorithm to detect the iris, sclera circles and the cancer. The principal component analysis is used to reduce the image size and for extracting features. Those features are considered then as inputs for a neural network which is capable of deciding if the eye is cancerous or not, throughout its experience adopted by many training iterations of different normal and abnormal eye images during the training phase. Normal images are obtained from a public database available on the internet, “Mile Research”, while the abnormal ones are obtained from another database which is the “eyecancer”. The experimental results for the proposed system show high accuracy 100% for detecting cancer and making the right decision.

Keywords: iris cancer, intraocular melanoma, cancerous, prewitt edge detection algorithm, sclera

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Authors: Aisultan Shoiynbek, Darkhan Kuanyshbay, Paulo Menezes, Akbayan Bekarystankyzy, Assylbek Mukhametzhanov, Temirlan Shoiynbek

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Speech emotion recognition (SER) has received increasing research interest in recent years. It is a common practice to utilize emotional speech collected under controlled conditions recorded by actors imitating and artificially producing emotions in front of a microphone. There are four issues related to that approach: emotions are not natural, meaning that machines are learning to recognize fake emotions; emotions are very limited in quantity and poor in variety of speaking; there is some language dependency in SER; consequently, each time researchers want to start work with SER, they need to find a good emotional database in their language. This paper proposes an approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describes the sequence of actions involved in the proposed approach. One of the first objectives in the sequence of actions is the speech detection issue. The paper provides a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To investigate the working capacity of the developed model, an analysis of speech detection and extraction from real tasks has been performed.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Mohammad Zare, Mahbubeh Sheikh

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Many of the impact of climate change will material through change in soil erosion which were rarely addressed in Iran. This paper presents an investigation of the impacts of climate change soil erosin for the Kasilian basin. LARS-WG5 was used to downscale the IPCM4 and GFCM21 predictions of the A2 scenarios for the projected periods of 1985-2030 and 2080-2099. This analysis was carried out by means of the dataset the International Centre for Theoretical Physics (ICTP) of Trieste. Soil loss modeling using Revised Universal Soil Loss Equation (RUSLE). Results indicate that soil erosion increase or decrease, depending on which climate scenarios are considered. The potential for climate change to increase soil loss rate, soil erosion in future periods was established, whereas considerable decreases in erosion are projected when land use is increased from baseline periods.

Keywords: Kasilian watershed, climatic change, soil erosion, LARS-WG5 Model, RUSLE

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Authors: Mohammed Mustapha Alhaji, Sadiku Salawu

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A clay soil which classified under A-7-6 soil according to AASHTO soil classification system and CH according to the unified soil classification system was stabilized using A-3 soil (AASHTO soil classification system). The clay soil was replaced with 0%, 10%, 20% to 100% A-3 soil, compacted at both the BSL and BSH compaction energy level and using unconfined compressive strength as evaluation criteria. The MDD of the compactions at both the BSL and BSH compaction energy levels showed increase in MDD from 0% A-3 soil replacement to 40% A-3 soil replacement after which the values reduced to 100% A-3 soil replacement. The trend of the OMC with varied A-3 soil replacement is similar to that of MDD but in a reversed order. The OMC reduced from 0% A-3 soil replacement to 40% A-3 soil replacement after which the values increased to 100% A-3 soil replacement. This trend was attributed to the observed reduction in the void ratio from 0% A-3 soil replacement to 40% A-3 soil replacement after which the void ratio increased to 100% A-3 soil replacement. The maximum UCS for clay at varied A-3 soil replacement increased from 272 and 770kN/m2 for BSL and BSH compaction energy level at 0% A-3 soil replacement to 295 and 795kN/m2 for BSL and BSH compaction energy level respectively at 10% A-3 soil replacement after which the values reduced to 22 and 60kN/m2 for BSL and BSH compaction energy level respectively at 70% A-3 soil replacement. Beyond 70% A-3 soil replacement, the mixture cannot be moulded for UCS test.

Keywords: A-3 soil, clay minerals, pozzolanic action, stabilization

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Authors: M. Khouil, N. Saber, M. Mestari

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This study aimed to propose, a different architecture of a Path Planning using the NECMOP. where several nonlinear objective functions must be optimized in a conflicting situation. The ability to detect and avoid collision is very important for mobile intelligent machines. However, many artificial vision systems are not yet able to quickly and cheaply extract the wealth information. This network, which has been particularly reviewed, has enabled us to solve with a new approach the problem of collision detection between two convex polyhedra in a fixed time (O (1) time). We used two types of neurons linear and threshold logic, which simplified the actual implementation of all the networks proposed. This article represents a comprehensive algorithm that determine through the AMAXNET network a measure (a mini-maximum point) in a fixed time, which allows us to detect the presence of a potential collision.

Keywords: path planning, collision detection, convex polyhedron, neural network

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Authors: Zixian Zhang, Shanliang Yao, Zile Huang, Zhaodong Wu, Xiaohui Zhu, Yong Yue, Jieming Ma

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Unmanned Surface Vehicles (USVs) are valuable due to their ability to perform dangerous and time-consuming tasks on the water. Object detection tasks are significant in these applications. However, inherent challenges, such as the complex distribution of obstacles, reflections from shore structures, water surface fog, etc., hinder the performance of object detection of USVs. To address these problems, this paper provides a fusion method for USVs to effectively detect objects in the inland surface environment, utilizing vision sensors and 3+1D Millimeter-wave radar. MMW radar is complementary to vision sensors, providing robust environmental information. The radar 3D point cloud is transferred to 2D radar pseudo image to unify radar and vision information format by utilizing the point transformer. We propose a multi-source object detection network (RV-YOLOX )based on radar-vision fusion for inland waterways environment. The performance is evaluated on our self-recording waterways dataset. Compared with the YOLOX network, our fusion network significantly improves detection accuracy, especially for objects with bad light conditions.

Keywords: inland waterways, YOLO, sensor fusion, self-attention

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Authors: Carlos Huertas, Reyes Juarez-Ramirez

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Public health is one of the most critical issues today; therefore, there is great interest to improve technologies in the area of diseases detection. With machine learning and feature selection, it has been possible to aid the diagnosis of several diseases such as cancer. In this work, we present an extension to the Heat Map Based Feature Selection algorithm, this modification allows automatic threshold parameter selection that helps to improve the generalization performance of high dimensional data such as mass spectrometry. We have performed a comparison analysis using multiple cancer datasets and compare against the well known Recursive Feature Elimination algorithm and our original proposal, the results show improved classification performance that is very competitive against current techniques.

Keywords: biomarker discovery, cancer, feature selection, mass spectrometry

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Anita Khadka, Gregory Epiphaniou, Carsten Maple

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There is a dramatic surge in the adoption of machine learning (ML) techniques in many areas, including the nuclear industry (such as fault diagnosis and fuel management in nuclear power plants), autonomous systems (including self-driving vehicles), space systems (space debris recovery, for example), medical surgery, network intrusion detection, malware detection, to name a few. With the application of learning methods in such diverse domains, artificial intelligence (AI) has become a part of everyday modern human life. To date, the predominant focus has been on developing underpinning ML algorithms that can improve accuracy, while factors such as resiliency and robustness of algorithms have been largely overlooked. If an adversarial attack is able to compromise the learning method or data, the consequences can be fatal, especially but not exclusively in safety-critical applications. In this paper, we present an in-depth analysis of five adversarial attacks and three defence methods on a crack detection ML model. Our analysis shows that it can be dangerous to adopt machine learning techniques in security-critical areas such as the nuclear industry without rigorous testing since they may be vulnerable to adversarial attacks. While common defence methods can effectively defend against different attacks, none of the three considered can provide protection against all five adversarial attacks analysed.

Keywords: adversarial machine learning, attacks, defences, nuclear industry, crack detection

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Authors: Bishwa Nath Oli

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Nepal is highly impacted by climate change and adaptation has been a major focus. This paper investigates the gaps and coherence in national policies across water, forestry, local development and agriculture sectors, identifies their links to climate change adaptation and national development plans and analyzes the effectiveness of climate change policy on adaptation. The study was based on a content analysis of relevant policy documents on the level of attention given to adaptation and key informant interviews. Findings show that sectoral policies have differing degrees of cross thematic coherence, often with mismatched priorities and differing the paths towards achieving climate change goal. They are somewhat coherent in addressing immediate disaster management issues rather than in climate adaptation. In some cases, they are too broad and complicated and the implementation suffers from barriers and limits due to lack of capacity, investment, research and knowledge needed for evidence-based policy process. They do not adequately provide operational guidance in supporting communities in adapting to climate change. The study recommends to a) embrace longer-term cross-sectoral planning within government structures to foster greater policy coherence and integrated adaptation planning, b) increase awareness and flow of information on the potential role of communities in climate change, c) review the existing development sectors from the climate change perspectives, and d) formulate a comprehensive climate change legislation based on the need to implement the new Constitution.

Keywords: agriculture, climate change adaptation, forestry, policies

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Authors: Chimey Lhamo, Ngawang Tsering

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Traditional Tibetan medicine, known as Sowa Rigpa (Science of healing), originated more than 2500 years ago with an insightful background, and it has been growing significant attention in many Asian countries like China, India, Bhutan, and Nepal. Particularly, the Indian government has targeted Traditional Tibetan medicine as its major Indian medical system, including Ayurveda. Although Traditional Tibetan medicine has been growing interest and has a long history, it is not easily recognized worldwide because it exists only in the Tibetan language and it is neither accessible nor understood by patent examiners at the international patent office, data about Traditional Tibetan medicine is not yet broadly exist in the Internet. There has also been the exploitation of traditional Tibetan medicine increasing. The Traditional Knowledge Digital Library is a database aiming to prevent the patenting and misappropriation of India’s traditional medicine knowledge by using India’s Traditional knowledge Digital Library on Sowa Rigpa in order to prevent its exploitation at international patent with the help of information technology tools and an innovative classification systems-traditional knowledge resource classification (TKRC). As of date, more than 3000 Sowa Rigpa formulations have been transcribed into a Traditional Knowledge Digital Library database. In this paper, we are presenting India's Traditional Knowledge Digital Library for Traditional Tibetan medicine, and this database system helps to preserve and prevent the exploitation of Sowa Rigpa. Gradually it will be approved and accepted globally.

Keywords: traditional Tibetan medicine, India's traditional knowledge digital library, traditional knowledge resources classification, international patent classification

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Authors: Manpreet Kaur

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When legitimate credit card users are mistakenly labelled as fraudulent in numerous financial delated applications, there are numerous ethical problems. The innovative machine learning approach we have suggested in this research outperforms the current models and shows how to model a data set for credit card fraud detection while minimizing false positives. As a result, we advise using random forests as the best machine learning method for predicting and identifying credit card transaction fraud. The majority of victims of these fraudulent transactions were discovered to be credit card users over the age of 60, with a higher percentage of fraudulent transactions taking place between the specific hours.

Keywords: automated fraud detection, isolation forest method, local outlier factor, ML algorithm, credit card

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Authors: Nada R. Abuknesha, John P. Morgan, Andrew J. Searle

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Information regarding post-mortem interval (PMI) in criminal investigations is vital to establish a time frame when reconstructing events. PMI is defined as the time period that has elapsed between the occurrence of death and the discovery of the corpse. Adipocere, commonly referred to as ‘grave-wax’, is formed when post-mortem adipose tissue is converted into a solid material that is heavily comprised of fatty acids. Adipocere is of interest to forensic anthropologists, as its formation is able to slow down the decomposition process. Therefore, analysing the changes in the patterns of fatty acids during the early decomposition process may be able to estimate the period of burial, and hence the PMI. The current study concerned the investigation of the fatty acid composition and patterns in buried pig fat tissue. This was in an attempt to determine whether particular patterns of fatty acid composition can be shown to be associated with the duration of the burial, and hence may be used to estimate PMI. The use of adipose tissue from the abdominal region of domestic pigs (Sus-scrofa), was used to model the human decomposition process. 17 x 20cm piece of pork belly was buried in a shallow artificial grave, and weekly samples (n=3) from the buried pig fat tissue were collected over an 11-week period. Marker fatty acids: palmitic (C16:0), oleic (C18:1n-9) and linoleic (C18:2n-6) acid were extracted from the buried pig fat tissue and analysed as fatty acid methyl esters using the gas chromatography system. Levels of the marker fatty acids were quantified from their respective standards. The concentrations of C16:0 (69.2 mg/mL) and C18:1n-9 (44.3 mg/mL) from time zero exhibited significant fluctuations during the burial period. Levels rose (116 and 60.2 mg/mL, respectively) and fell starting from the second week to reach 19.3 and 18.3 mg/mL, respectively at week 6. Levels showed another increase at week 9 (66.3 and 44.1 mg/mL, respectively) followed by gradual decrease at week 10 (20.4 and 18.5 mg/mL, respectively). A sharp increase was observed in the final week (131.2 and 61.1 mg/mL, respectively). Conversely, the levels of C18:2n-6 remained more or less constant throughout the study. In addition to fluctuations in the concentrations, several new fatty acids appeared in the latter weeks. Other fatty acids which were detectable in the time zero sample, were lost in the latter weeks. There are several probable opportunities to utilise fatty acid analysis as a basic technique for approximating PMI: the quantification of marker fatty acids and the detection of selected fatty acids that either disappear or appear during the burial period. This pilot study indicates that this may be a potential semi-quantitative methodology for determining the PMI. Ideally, the analysis of particular fatty acid patterns in the early stages of decomposition could be an additional tool to the already available techniques or methods in improving the overall processes in estimating PMI of a corpse.

Keywords: adipocere, fatty acids, gas chromatography, post-mortem interval

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Authors: Sharon Baoas, Toni Beninato, Michael Zenilman, Gokhan Ozuner

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Background An enhanced recovery after surgery (ERAS) protocol was implemented to improve quality and cost effectiveness of surgical care in elective colorectal procedures. Results A total of 109 patients, 55 (pre-ERAS) and 54 (post-ERAS) are included in the final analysis. There were no differences in complications were recorded (p = 0.37) and 30-day readmissions (p = 0.785). The mean hospital stay was 5.89 ± 2.62 days in pre-ERAS and 4.94 ± 2.27 days in post-ERAS group which was statistically significant (p = 0.047). Conclusions An ERAS protocol for colorectal surgery harmonised perioperative care and decreased length of stay.

Keywords: 30-day readmission, lenght of stay, Enhanced Recovery after surgery, Surgical site infection

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Authors: K. A. El-Battawy, W. S. El-Nattat

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The aim of the present investigation was to evaluate the impact of methionine on the preservation, acrosomal integrity, DNA integrity and post thawing motility of extended goat semen. Semen samples were diluted with a Tris-based extender containing the additive methionine 1.5, 2.5 and 5mM then the diluted samples were kept in glass tubes and cooled from 37°C to 5°C in a cold cabinet, and maintained at 5°C. Sperm motility (SM%), alive sperm (AS%), sperm abnormalities (SA%) acrosomal integrity and DNA integrity were determined at 5°C for periods of 0,24, 48and 72 h of liquid storage. Furthermore, the influence of methionine on post-thawing motility was assessed. The results elaborated that the addition of methionine and L-tyrosine particularly 2.5mM of methionine significantly improved SM% and reduced dead sperm %. Furthermore, the addition of 2.5mM methionine improved post-thawing motility (43.75 ± 1.25% vs. 32.50 ± 3.23 in the control group). Moreover, the frequency of acrosomal defects was lower in treated groups than in control. In conclusion, the addition of methionine induced remarkable physiological effects on goat semen quality during conservation for 7-days-long period at 5°C and improved its freezability.

Keywords: methionine, acrosome, semen, cryopreservation

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Authors: Zouhir Gabsi

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The Arab Spring has had noticeable effects on Tunisia in socio-economic, political, and cultural terms. Few have predicted that the music of hip-hop and rap could engage with the socio-political situation in Tunisia, especially after the downfall of Ben Ali’s regime. Having survived as underground music since the year 2000, the genre of hip-hop and rap remains an aberration from the folkloric tradition. By adhering to the socio-economic reality of the Tunisian street, rappers attempt to claim authenticity mainly in both thematic and language uses, and by usurping the power of ‘space’ from the regime’s control. With the songs’ fast-paced rhythms, catchy phrases, puns, vulgarisms, and linguistic innovations using metaphors, hip-hop, and rap have struck a chord with Tunisia’s youth. Tunisia’s new social reality has allowed Tunisian rappers to express dissent and voice people’s despair over the socio-economic and political situation. This paper argues that rap artists use language as a vehicle to claim the authenticity of their message. It also explores how the performative nature of the language of hip-hop and rap interacts with the Tunisian culture and argues the power of music in the context of political and socio-economic grievances in post-Arab Spring Tunisia.

Keywords: Arab Spring, hip-hop, eevolution, Tunisia, Tunisian Arabic

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

Abstract:

Document Analysis is an important research field that aims to gather the information by analyzing the data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enriching the lexicon with adapting their content based on semantic patterns extraction. The proposed approach has been presented, and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.

Keywords: document analysis, sentimental analysis, emotion detection, WEKA tool, NRC lexicon

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

Abstract:

Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

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Authors: Aslihan Cetinbas Genc, Meral Unal

Abstract:

This study presents the structural and cytochemical alterations of stigma at the stages of pre-anthesis, anthesis and post-anthesis in Vibirnum tinus. Capitate stigma continues with a closed style. The receptive surface of stigma is composed of unicellular papillae which are short and flattened at pre-anthesis stage. The papillae in this stage have dense cytoplasm with small vacuoles and a centrally located nucleus. With the start of anthesis, the stigma widens, papillae lengthen and become cylindrical. At anthesis stage, vacuoles enlarge, and nucleus moves to the base of the cell. At post-anthesis stage, the boundaries of the papillae become less noticeable. As proved by Periodic Acid Schiff procedure, the cytoplasm of papillae is rich in insoluble polysaccharides at all stages of development but it becomes remarkable at post-anthesis, particularly at the sub-papillar area. Although there is no significant difference in the content of protein in all stages of the development, it is more abundant at post-anthesis stage, as in Coomassie Brillant Blue stained sections. The surface of papillae is covered by a cuticle which becomes thicker at post-anthesis, and it gives positive reaction with Sudan Black B and Auramine O. The cuticle is covered by a pellicle stained by Coomassie Brillant Blue, indicating dry type of stigma.

Keywords: develeopmental features, histochemistry, stigma, Vibirnum tinus

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Authors: Omaima Jbara, Mohame Amine Omrani, Mounir Zrigui

Abstract:

The detection of violent content in animated cartoons is anessential step toward safeguarding young audiences and promoting responsible media consumption. This study introduces an automated approach to identify violent scenes in cartoons using advanced object detection models. A custom dataset comprising 1,200 frames was curated from various animated sources, focusing on four key classes: Explosion, Blood, Fight, and Gunshot. Data augmentation techniques, including rotation, scaling, and color adjustments, expanded the dataset to 2,000 frames, enhancing diversity and model generalization. YOLO versions 8, 9, and 10 were trained and evaluated on this dataset. Among these, YOLOv9 achieved the highest performance with a mean Average Precision (mAP) of 94%, demonstrating superior accuracy and robustness. These findings highlight YOLOv9’s potential as a reliable tool for detecting violent content in animated media, contributing to the development of effective content moderation systems.

Keywords: cartoon violence detection, YOLO model, computer Vi sion, Real-time content analysis

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Authors: Dilip Saikia, Suparna Bhattacharjee, Nirab Adhikary

Abstract:

In this piece of work, we have presented the synthesis and characterization of CdTe/ZnS core/shell (CS) quantum dots (QD). CS QDs are used as a fluorescence probe to design a simple cost-effective and ultrasensitive sensor for the detection of toxic Hg(II) in an aqueous medium. Mercaptosuccinic acid (MSA) has been used as a capping agent for the synthesis CdTe/ZnS CS QD. Photoluminescence quenching mechanism has been used in the detection experiment of Hg(II). The designed sensing technique shows a remarkably low detection limit of about 1 picomolar (pM). Here, the CS QDs are synthesized by a simple one-pot aqueous method. The synthesized CS QDs are characterized by using advanced diagnostics tools such as UV-vis, Photoluminescence, XRD, FTIR, TEM and Zeta potential analysis. The interaction between CS QDs and the Hg(II) ions results in the quenching of photoluminescence (PL) intensity of QDs, via the mechanism of excited state electron transfer. The proposed mechanism is explained using cyclic voltammetry and zeta potential analysis. The designed sensor is found to be highly selective towards Hg (II) ions. The analysis of the real samples such as drinking water and tap water has been carried out and the CS QDs show remarkably good results. Using this simple sensing method we have designed a prototype low-cost electronic device for the detection of Hg(II) in an aqueous medium. The findings of the experimental results of the designed sensor is crosschecked by using AAS analysis.

Keywords: photoluminescence, quantum dots, quenching, sensor

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Authors: Changhwan Choi, Yongwan Park

Abstract:

In this paper, we propose a method that allows faster and more accurate detection of traffic lights by a vision sensor during driving, DGPS is used to obtain physical location of a traffic light, extract from the image information of the vision sensor only the traffic light area at this location and ascertain if the sign is in operation and determine its form. This method can solve the problem in existing research where low visibility at night or reflection under bright light makes it difficult to recognize the form of traffic light, thus making driving unstable. We compared our success rate of traffic light recognition in day and night road environments. Compared to previous researches, it showed similar performance during the day but 50% improvement at night.

Keywords: traffic light, intelligent vehicle, night, detection, DGPS

Procedia PDF Downloads 325

Authors: Danielle Klassen, Reiko Yeap, Margo Schmitt-Boshnick, Scott Oddie

Abstract:

The development of the 7-week Alberta Happiness Basics program was initiated in 2010 in response to the need for community mental health programming. This provincial wide program aims to increase overall happiness and reduce negative thoughts and feelings through a positive psychology intervention. While the 7-week program has proven effective, a shortened 4-week program has additionally been developed to address client needs. In this study, participants were interviewed to determine if the 4- and 7-week programs had similar success of producing lasting behavior change at 3, 6, and 9 months post-program. A health quality of life (HQOL) measure was also used to compare the two programs and examine patient outcomes. Quantitative and qualitative analysis showed significant improvements in HQOL and sustainable behavior change for both programs. Findings indicate that the shorter, patient-centered program was effective in increasing happiness and reducing negative thoughts and feelings.

Keywords: primary care, mental health, depression, short duration

Procedia PDF Downloads 271