Search results for: electronic intelligence

Authors: Muhammad nor Farhan Sa'At, Xin Y. Lim, Terence Y. C. Tan, Siti Hajar M. Rosli, Syazwani S. Ali, Ami F. Syed Mohamed

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INTRODUCTION: Hemp (Cannabis sativa subsp. sativa), commonly used for industrial purposes, differs from marijuana by containing lower levels of delta-9-tetrahydronannabidiol- the principal psychoactive constituent in cannabis. Due to its non-psychoactive nature, there has been growing interest in hemp’s therapeutic potential, which has been investigated through pre-clinical and clinical study modalities. OBJECTIVE: To provide an overview of the current landscape of hemp research, through recent scientific findings specific to the pharmacological effects of the medicinal hemp plant and its derived compounds. METHODS: This review was conducted through a systematic search strategy according to the preferred reporting items for systematic review and meta-analysis-ScR (PRISMA-ScR) checklist on electronic databases including MEDLINE, OVID (OVFT, APC Journal Club, EBM Reviews), Cochrane Library Central and Clinicaltrials.gov. RESULTS: From 65 primary articles reviewed, there were 47 pre-clinical studies related to medicinal hemp. Interestingly, the hemp derivatives showed several potential activities such as anti-oxidative, anti-hypertensive, anti-inflammatory, anti-diabetic, anti-neuroinflammatory, anti-arthritic, anti-acne, and anti-microbial activities. Renal protective effects and estrogenic properties were also exhibited in vitro. CONCLUSION: Medicinal hemp possesses various pharmacological effects tested in vitro and in vivo. Information provided in this review could be used as tool to strengthen the study design of future clinical trial research.

Keywords: Preclinical, Herbal Medicine, Hemp, Cannabis

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Authors: Adesoji Tunbosun Jaiyeola, Josiah Adeyemo

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Sediment yield can be considered to be the total sediment load that leaves a drainage basin. The knowledge of the quantity of sediments present in a river at a particular time can lead to better flood capacity in reservoirs and consequently help to control over-bane flooding. Furthermore, as sediment accumulates in the reservoir, it gradually loses its ability to store water for the purposes for which it was built. The development of hydrological models to forecast the quantity of sediment present in a reservoir helps planners and managers of water resources systems, to understand the system better in terms of its problems and alternative ways to address them. The application of artificial intelligence models and technique to such real-life situations have proven to be an effective approach of solving complex problems. This paper makes an extensive review of literature relevant to the theories and applications of evolutionary algorithms, and most especially genetic programming. The successful applications of genetic programming as a soft computing technique were reviewed in sediment modelling and other branches of knowledge. Some fundamental issues such as benchmark, generalization ability, bloat and over-fitting and other open issues relating to the working principles of GP, which needs to be addressed by the GP community were also highlighted. This review aim to give GP theoreticians, researchers and the general community of GP enough research direction, valuable guide and also keep all stakeholders abreast of the issues which need attention during the next decade for the advancement of GP.

Keywords: benchmark, bloat, generalization, genetic programming, over-fitting, sediment yield

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Authors: Michael Nthabiseng Moeti, Makhulu Relebogile Langa, Joey Jansen van Vuuren

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This paper examines the utilization of personal electronic devices like laptops, tablets, and smartphones for professional duties within a financial organization. This phenomenon is known as bring your own device (BYOD). BYOD accords employees the freedom to use their personal devices to access corporate resources from anywhere in the world with Internet access. BYOD arrangements introduce significant security risks for both organizations and users. These setups change the threat landscape for enterprises and demand unique security strategies, as conventional tools tailored for safeguarding managed devices fall short in adequately protecting enterprise assets without active user cooperation. This paper applies protection motivation theory (PMT) to highlight behavioral risks from BYOD users that may impact the security of financial institutions. Thematic analysis was applied to gain a comprehensive understanding of how users perceive this phenomenon. These findings demonstrates that the existence of a security policy does not ensure that all employees will take measures to protect their personal devices. Active promotion of BYOD security policies is crucial for financial institution employees and management. This paper developed a BYOD security model which is useful for understanding compliant behaviors. Given that BYOD security is becoming a major concern across financial sector, it is important. The paper recommends that future research could expand the number of universities from which data is collected.

Keywords: BYOD, information security, protection motivation theory, security risks, thematic analysis

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Authors: Kolade Olawande Owoeye, George Weir

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Terrorism and extremism activities on the internet are becoming the most significant threats to national security because of their potential dangers. In response to this challenge, law enforcement and security authorities are actively implementing comprehensive measures by countering the use of the internet for terrorism. To achieve the measures, there is need for intelligence gathering via the internet. This includes real-time monitoring of potential websites that are used for recruitment and information dissemination among other operations by extremist groups. However, with billions of active webpages, real-time monitoring of all webpages become almost impossible. To narrow down the search domain, there is a need for efficient webpage classification techniques. This research proposed a new approach tagged: SentiPosit-based method. SentiPosit-based method combines features of the Posit-based method and the Sentistrenght-based method for classification of terrorism and extremism webpages. The experiment was carried out on 7500 webpages obtained through TENE-webcrawler by International Cyber Crime Research Centre (ICCRC). The webpages were manually grouped into three classes which include the ‘pro-extremist’, ‘anti-extremist’ and ‘neutral’ with 2500 webpages in each category. A supervised learning algorithm is then applied on the classified dataset in order to build the model. Results obtained was compared with existing classification method using the prediction accuracy and runtime. It was observed that our proposed hybrid approach produced a better classification accuracy compared to existing approaches within a reasonable runtime.

Keywords: sentiposit, classification, extremism, terrorism

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Authors: Baki Cicek, Umit Calisir

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Carbazoles have been replaced lots of studies from 1960's to present and also still continues. In 1987, the first diode device had been developed. Thanks to that study, light emitting devices have been investigated and developed and also have been used on commercial applications. Nowadays, OLED (Organic Light Emitting Diodes) technology is using on lots of electronic screen such as (mobile phone, computer monitors, televisions, etc.) Carbazoles were subject a lot of study as a semiconductor material. Although this technology is used commen and widely, it is still development stage. Metal complexes of these compounds are using at pigment dyes because of colored substances, polymer technology, medicine industry, agriculture area, preparing rocket fuel-oil, determine some of biological events, etc. Becides all of these to preparing of schiff base synthesis is going on intensely. In this study, some of novel carbazole schiff bases were synthesized starting from carbazole. For that purpose, firstly, carbazole was alkylated. After purification of N-substituted-carbazole was nitrated to sythesized 3-nitro-N-substituted and 3,6-dinitro-N-substituted carbazoles. At next step, nitro group/groups were reduced to amines. Purified with using a type of silica gel-column chromatography. At the last step of our study, with sythesized 3,6-diamino-N-substituted carbazoles and 3-amino-N-substituted carbazoles were reacted with aldehydes to condensation reactions. 3-(imino-p-hydroxybenzyl)-N-isobutyl -carbazole, 3-(imino-2,3,4-trimethoxybenzene)-N-butylcarbazole, 3-(imino-3,4-dihydroxybenzene)-N-octylcarbazole, 3-(imino-2,3-dihydroxybenzene)-N-octylkarbazole and 3,6-di(α-imino-β-naphthol) -N-hexylcarbazole compounds were synthesized. All of synthesized compounds were characterized with FT-IR, 1H-NMR, 13C-NMR, and LC-MS.

Keywords: carbazole, carbazol schiff base, condensation reactions, OLED

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Authors: Sean W. T. Bayly, Daniel Glover, Don Horrell, Simon Horrocks, Barnes Callum, Stuart Gibson, Mac Misuira

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We assess suitability of recent, ∼7B parameter, instruction-tuned Language Models Mistral-v0.3, Llama-3, and Phi-3, for Generative Named Entity Recognition (GNER). Our proposed Multi-Level Information Retrieval method achieves notable improvements over finetuned entity-level and sentence-level methods. We consider recent developments at the cross roads of prompt engineering and Retrieval Augmented Generation (RAG), such as EmotionPrompt. We conclude that language models directed toward this task are highly capable when distinguishing between positive classes (precision). However, smaller models seem to struggle to find all entities (recall). Poorly defined classes such as ”Miscellaneous” exhibit substantial declines in performance, likely due to the ambiguity it introduces to the prompt. This is partially resolved through a self verification method using engineered prompts containing knowledge of the stricter class definitions, particularly in areas where their boundaries are in danger of overlapping, such as the conflation between the location ”Britain” and the nationality ”British”. Finally, we explore correlations between model performance on the GNER task with performance on relevant academic benchmarks.

Keywords: generative named entity recognition, information retrieval, lightweight artificial intelligence, prompt engineering, personal information identification, retrieval augmented generation, self verification

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Authors: Jianghao Hu, Hongyu Wang

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The effective and efficient human pose estimation algorithm is an important task for real-time human pose estimation on mobile devices. This paper proposes a light-weight human key points detection algorithm, Light-Weight Network for Real-Time Pose Estimation (LWPE). LWPE uses light-weight backbone network and depthwise separable convolutions to reduce parameters and lower latency. LWPE uses the feature pyramid network (FPN) to fuse the high-resolution, semantically weak features with the low-resolution, semantically strong features. In the meantime, with multi-scale prediction, the predicted result by the low-resolution feature map is stacked to the adjacent higher-resolution feature map to intermediately monitor the network and continuously refine the results. At the last step, the key point coordinates predicted in the highest-resolution are used as the final output of the network. For the key-points that are difficult to predict, LWPE adopts the online hard key points mining strategy to focus on the key points that hard predicting. The proposed algorithm achieves excellent performance in the single-person dataset selected in the AI (artificial intelligence) challenge dataset. The algorithm maintains high-precision performance even though the model only contains 3.9M parameters, and it can run at 225 frames per second (FPS) on the generic graphics processing unit (GPU).

Keywords: depthwise separable convolutions, feature pyramid network, human pose estimation, light-weight backbone

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Authors: Adrian Kong, William Chang, Rolando Valdes, Alec Rodriguez, Roberto Miki

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The Pneumask consists of a custom snorkel-specific adapter that attaches a snorkel-port of the mask to a 3D-printed filter. This full-face snorkel mask was designed for use as personal protective equipment (PPE) during the COVID-19 pandemic when there was a widespread shortage of PPE for medical personnel. Various clinical validation tests have been conducted, including the sealing capability of the mask, filter performance, CO2 buildup, and clinical usability. However, data regarding the filter efficiencies of Pneumask and multiple filter types have not been determined. Using an experimental system, we evaluated the filtration efficiency across various masks and filters during inhalation. Eighteen combinations of respirator models (5 P100 FFRs, 4 Dolfino Masks) and filters (2091, 7093, 7093CN, BB50T) were evaluated for their exposure to airborne particles sized 0.3 - 10.0 microns using an electronic airborne particle counter. All respirator model combinations provided similar performance levels for 1.0-micron, 3.0-micron, 5.0-micron, 10.0-microns, with the greatest differences in the 0.3-micron and 0.5-micron range. All models provided expected performances against all particle sizes, with Class P100 respirators providing the highest performance levels across all particle size ranges. In conclusion, the modified snorkel mask has the potential to protect providers who care for patients with COVID-19 from increased airborne particle exposure.

Keywords: COVID-19, PPE, mask, filtration, efficiency

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Authors: Christos Theoharatos, Dimitris Tsourounis, Spiros Oikonomou, Andreas Makedonas

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This paper focuses on the problem of visually inspecting and recognizing the road conditions in front of moving vehicles, targeting automotive scenarios. The goal of road inspection is to identify whether the road is slippery or not, as well as to detect possible anomalies on the road surface like potholes or body bumps/humps. Our work is based on an artificial intelligence methodology for real-time monitoring of road conditions in autonomous driving scenarios, using state-of-the-art deep convolutional neural network (CNN) techniques. Initially, the road and ego lane are segmented within the field of view of the camera that is integrated into the front part of the vehicle. A novel classification CNN is utilized to identify among plain and slippery road textures (e.g., wet, snow, etc.). Simultaneously, a robust detection CNN identifies severe surface anomalies within the ego lane, such as potholes and speed bumps/humps, within a distance of 5 to 25 meters. The overall methodology is illustrated under the scope of an integrated application (or system), which can be integrated into complete Advanced Driver-Assistance Systems (ADAS) systems that provide a full range of functionalities. The outcome of the proposed techniques present state-of-the-art detection and classification results and real-time performance running on AI accelerator devices like Intel’s Myriad 2/X Vision Processing Unit (VPU).

Keywords: deep learning, convolutional neural networks, road condition classification, embedded systems

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Authors: Ranjit A. Patil, Yung Liou, Yuan-Ron Ma

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It has been observed that when the size of metals such as, Au, Zn, Ag, Cu, Te, and metal oxides is reduced to several nano-meters, it starts to show further interesting properties. These new properties boost the use of nano-structures to produce attractive functional materials or used as promising building blocks in electronic devices. Present work describes the synthesis of bismuth (Bi) nanoparticles (NP’s) having uniform morphology, high crystallinity, and single phase purity by the temperature assisted pulsed laser deposition (TAPLD). Pulsed Laser deposition (PLD) technique is one of the promising methods to synthesize nano-structures. It can provide the stable nucleation sites in orders of magnitudes higher than for MBE and sputtering deposition. The desired size of purely metallic Bi NP’s of can be easily controlled by adjusting the temperature of the substrate varying from 1000 C to 250 0C. When the temperatures of the substrate raised step wise the average size of Bi NP’s appeared to be increased by maintaining the uniform distribution of NP’s on the Si surfaces. The diameter range of NP’s is ~33-84 nm shows size distribution constrained in the limited range. The EDS results show that the 0D Bi NP’s synthesized at high temperature (250 0C) at a high vacuum still remained in a metallic phase. Moreover, XRD, TEM and SAED results showed that these Bi NP’s are hexagonal in crystalline in a space group R -3 m and no traces of bismuth oxide, confirming that Bi NP’s synthesized at wide range of temperatures persisted of the pure Bi-metallic phase.

Keywords: metal nano particles, bismuth, pulsed laser deposition (PLD), nano particles, temperature assisted growth

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Authors: Vabya Kumar Pandit, Mudit Mittal, N. Prahlad Rao, Ramnath Babu

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TeamIndus is the only Indian team competing for the Google Lunar XPRIZE(GLXP). The GLXP is a global competition to challenge the private entities to soft land a rover on the moon, travel minimum 500 meters and transmit high definition images and videos to Earth. Towards this goal, the TeamIndus strategy is to design and developed lunar lander that will deliver a rover onto the surface of the moon which will accomplish GLXP mission objectives. This paper showcases the various system level noise control techniques adopted by Electrical Distribution System (EDS), to achieve the required Electromagnetic Compatibility (EMC) of the spacecraft. The design guidelines followed to control Electromagnetic Interference by proper electronic package design, grounding, shielding, filtering, and cable routing within the stipulated mass budget, are explained. The paper also deals with the challenges of achieving Electromagnetic Cleanliness in presence of various Commercial Off-The-Shelf (COTS) and In-House developed components. The methods of minimizing Electrostatic Discharge (ESD) by identifying the potential noise sources, susceptible areas for charge accumulation and the methodology to prevent arcing inside spacecraft are explained. The paper then provides the EMC requirements matrix derived from the mission requirements to meet the overall Electromagnetic compatibility of the Spacecraft.

Keywords: electromagnetic compatibility, electrostatic discharge, electrical distribution systems, grounding schemes, light weight harnessing

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Authors: A. Elsayed, M. H. Dewaidar, M. Ghali

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We report on deposition of smooth, pinhole-free, low-surface roughness ( < 4nm) and high optical quality cadmium sulfide (CdS) thin films on glass substrates using our new method based on chemical surface deposition principle. In this method, cadmium acetate and thiourea are used as reactants under special growth conditions for deposition of CdS films. X-ray diffraction (XRD) measurements were used to examine the crystal structure properties of the deposited CdS films. In addition, UV-vis transmittance and low-temperature (4K) photoluminescence (PL) measurements were performed for quantifying optical properties of the deposited films. Interestingly, we found that XRD pattern of the deposited films has dramatically changed when the growth temperature was raised during the reaction. Namely, the XRD measurements reveal a structural change of CdS film from Cubic to Hexagonal phase upon increase in the growth temperature from 75 °C to 200 °C. Furthermore, the deposited films show high optical quality as confirmed from observation of both sharp edge in the transmittance spectra and strong PL intensity at room temperature. Also, we found a strong effect of the growth conditions on the optical band gap of the deposited films; where remarkable red-shift in the absorption edge with temperature is clearly seen in both transmission and PL spectra. Such tuning of both optical band gap and crystal structure of the deposited CdS films; can be utilized for tuning the electronic bands alignments between CdS and other light harvesting materials, like CuInGaSe or CdTe, for potential improvement in the efficiency of all-solution processed solar cells devices based on these heterostructures.

Keywords: thin film, CdS, new method, optical properties

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Authors: Kelvin Lo

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Nowadays, numerical modelling in geotechnical engineering is very common but sophisticated. Many advanced input settings and considerable computational efforts are required to optimize the design to reduce the construction cost. To optimize a design, it usually requires huge numerical models. If the optimization is conducted manually, there is a potentially dangerous consequence from human errors, and the time spent on the input and data extraction from output is significant. This paper presents an automation process introduced to numerical modelling (Plaxis 2D) of a trench excavation supported by a secant-pile retaining structure for a top-down tunnel project. Python code is adopted to control the process, and numerical modelling is conducted automatically in every 20m chainage along the 200m tunnel, with maximum retained height occurring in the middle chainage. Python code continuously changes the geological stratum and excavation depth under groundwater flow conditions in each 20m section. It automatically conducts trial and error to determine the required pile length and the use of props to achieve the required factor of safety and target displacement. Once the bending moment of the pile exceeds its capacity, it will increase in size. When the pile embedment reaches the default maximum length, it will turn on the prop system. Results showed that it saves time, increases efficiency, lowers design costs, and replaces human labor to minimize error.

Keywords: automation, numerical modelling, Python, retaining structures

Procedia PDF Downloads 51

Authors: Adriana C. Panayi, Riaz A. Agha, Brady A. Sieber, Dennis P. Orgill

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Background: Increased rates of both breast cancer and obesity have resulted in more women seeking breast reconstruction. These women may be at increased risk for perioperative complications. A systematic review was conducted to assess the outcomes in obese women who have undergone breast reconstruction following mastectomy. Methods: Cochrane, PUBMED and EMBASE electronic databases were screened and data was extracted from included studies. The clinical outcomes assessed were surgical complications, medical complications, length of postoperative hospital stay, reoperation rate and patient satisfaction. Results: 33 studies met the inclusion criteria for the review and 29 provided enough data to be included in the meta-analysis (71368 patients, 20061 of which were obese). Obese women were 2.3 times more likely to experience surgical complications (95 percent CI 2.19 to 2.39; P < 0.00001), 2.8 times more likely to have medical complications (95 percent CI 2.41 to 3.26; P < 0.00001) and had a 1.9 times higher risk of reoperation (95 percent CI 1.75 to 2.07; P < 0.00001). The most common complication, wound dehiscence, was 2.5 times more likely in obese women (95 percent CI 1.80 to 3.52; P < 0.00001). Sensitivity analysis confirmed that obese women were more likely to experience surgical complications (RR 2.36, 95% CI 2.22–2.52; P < 0.00001). Conclusions: This study provides evidence that obesity increases the risk of complications in both implant and autologous reconstruction. Additional prospective and observational studies are needed to determine if weight reduction prior to reconstruction reduces the perioperative risks associated with obesity.

Keywords: autologous reconstruction, breast cancer, breast reconstruction, literature review, obesity, oncology, prosthetic reconstruction

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Authors: Mehwish Asghar

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Breast Cancer (BC) is a common type of cancer among women. Its screening is usually performed using different imaging modalities such as magnetic resonance imaging, mammogram, X-ray, CT, etc. Among these modalities’ mammogram is considered a powerful tool for diagnosis and screening of breast cancer. Sophisticated machine learning approaches have shown promising results in complementing human diagnosis. Generally, machine learning methods can be divided into two major classes: one is Radiomics analysis (RA), where image features are extracted manually; and the other one is the concept of convolutional neural networks (CNN), in which the computer learns to recognize image features on its own. This research aims to improve the incidence of early detection, thus reducing the mortality rate caused by breast cancer through the latest advancements in computer science, in general, and machine learning, in particular. It has also been aimed to ease the burden of doctors by improving and automating the process of breast cancer detection. This research is related to a relative analysis of different techniques for the implementation of different models for detecting and classifying breast cancer. The main goal of this research is to provide a detailed view of results and performances between different techniques. The purpose of this paper is to explore the potential of a convolutional neural network (CNN) w.r.t feature extractor and as a classifier. Also, in this research, it has been aimed to add the module of Radiomics for comparison of its results with deep learning techniques.

Keywords: breast cancer (BC), machine learning (ML), convolutional neural network (CNN), radionics, magnetic resonance imaging, artificial intelligence

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Authors: Shitu Sani

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The paper is advanced on the historical background of the effects of excess work load on lecturers in higher institutions of learning which will assess the socio-economic and psychological disposition of lecturers in the realm of quality production. The paper further discusses the significant roles played by excess work load in general transformation of higher education, which will give the management and stake holders input for successful development of higher education. Even though all forms of work and organizational procedures are potential source of stress and stressors. In higher institution of leaning, lecturers perform many responsibilities such as lecturing, carrying out research and engaging in community services. If these multiple roles could not be handle property it would have result in stress which may have negative impact on job performance, and it’s relation with instructional technology. A sample 191 lecturers were randomly selected from the higher institutions in the northern west zone in Nigerian using two instruments i.e. work load stress management question and job performance Approval, data were collected on lecturers of socio-economic and physiological stress and job performances. Findings of the study shows that lecture experienced excess work load in academic activities. Lecturer’s job performance was negatively influences by socio-economic and psychological work stress. Among the recommendation made were the need for organizing regular induction courses for lecturers on stress, and enhance interpersonal relations among the lecturers as well as provision of electronic public address system to reduce the stress.

Keywords: effect, excess, lecturers, workload

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Authors: E. Bennion, A. Acharya, S. Barnes, D. Ferrell, S. Luckett-Cole, G. Mower, J. Nelson, Y. Nguyen

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Objective: This study aimed to evaluate the validity of a firearm injury query in the Early Notification of Community-based Epidemics syndromic surveillance system. Syndromic surveillance data are used at the Utah Department of Health for early detection of and rapid response to unusually high rates of violence and injury, among other health outcomes. The query of interest was defined by the Centers for Disease Control and Prevention and used chief complaint and discharge diagnosis codes to capture initial emergency department encounters for firearm injury of all intents. Design: Two epidemiologists manually reviewed electronic health records of emergency department visits captured by the query from April-May 2020, compared results, and sent conflicting determinations to two arbiters. Results: Of the 85 unique records captured, 67 were deemed probable, 19 were ruled out, and two were undetermined, resulting in a positive predictive value of 75.3%. Common reasons for false positives included non-initial encounters and misleading keywords. Conclusion: Improving the validity of syndromic surveillance data would better inform outbreak response decisions made by state and local health departments. The firearm injury definition could be refined to exclude non-initial encounters by negating words such as “last month,” “last week,” and “aftercare”; and to exclude non-firearm injury by negating words such as “pellet gun,” “air gun,” “nail gun,” “bullet bike,” and “exit wound” when a firearm is not mentioned.

Keywords: evaluation, health information system, firearm injury, syndromic surveillance

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Authors: Joseph Salim

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This paper proposes the development of a supervised machine learning system to predict the consequences of untreated bruxism (teeth grinding) on gingival (gum) recession and jaw pain (most often bilateral jaw pain with possible headaches and limited ability to open the mouth). As a general dentist in a multi-specialty practice, the author has encountered many patients suffering from these issues due to uncontrolled bruxism (teeth grinding) at night. The most effective treatment for managing this problem involves wearing a nightguard during sleep and receiving therapeutic Botox injections to relax the muscles (the masseter muscle) responsible for grinding. However, some patients choose to postpone these treatments, leading to potentially irreversible and costlier consequences in the future. The proposed machine learning model aims to track patients who forgo the recommended treatments and assess the percentage of individuals who will experience worsening jaw pain, gingival (gum) recession, or both within a 3-to-5-year timeframe. By accurately predicting these outcomes, the model seeks to motivate patients to address the root cause proactively, ultimately saving time and pain while improving quality of life and avoiding much costlier treatments such as full-mouth rehabilitation to help recover the loss of vertical dimension of occlusion due to shortened clinical crowns because of bruxism, gingival grafts, etc.

Keywords: artificial intelligence, machine learning, predictive insights, bruxism, teeth grinding, therapeutic botox, nightguard, gingival recession, gum recession, jaw pain

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Authors: Hang Ju, Changmin Zhu

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The development level of the BeiDou Navigation Satellite System (BDS) can strongly reflect national defense strength as an important spatial information infrastructure. BDS can be not only used for military purposes, such as intelligence gathering, nuclear explosion monitoring, emergency communications, but also for location services, transportation, mapping, precision agriculture. In order to ensure the national defense security and the wide application of BDS in civil and military areas, BDS must be autonomous and controllable. As a complex system of knowledge-intensive, knowledge transformation runs through the whole process of research and development, production, operation, and maintenance of BDS. Based on the perspective of knowledge transformation, this paper expounds on the meaning of socialization, externalization, combination, and internalization of knowledge transformation, and the coupling relationship of autonomy and control on the basis of analyzing the status quo and problems of the autonomy and control of BDS. The autonomous and controllable framework of BDS based on knowledge transformation is constructed from six dimensions of management capability, R&D capability, technical capability, manufacturing capability, service support capability, and application capability. It can provide support for the smooth implementation of information security policy, provide a reference for the autonomy and control of the upstream and downstream industrial chains in Beidou, and provide a reference for the autonomous and controllable research of aerospace components, military measurement test equipment, and other related industries.

Keywords: knowledge transformation, BeiDou Navigation Satellite System, autonomy and control, framework

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Authors: Kapil Khanna, Swee Kuik, Joowon Ban

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The current linear economic system is unsustainable due to its dependence on the uncontrolled exploitation of diminishing natural resources. The integration of business innovation and supply chain management has brought about the redesign of business processes through the implementation of a closed-loop approach. The circular economy (CE) offers a sustainable solution to improve business opportunities in the near future by following the principles of rejuvenation and reuse inspired by nature. Those business owners start to rethink and consider using waste as raw material to make new products for consumers. The implementation of CE helps organisations to incorporate new strategic plans for decreasing the use of virgin materials and nature resources. Supply chain partners that are geographically dispersed rely heavily on innovative approaches to support supply chain management. Presently, numerous studies have attempted to establish the concept of supply chain management (SCM) by integrating CE principles, which are commonly denoted as circular SCM. While many scholars have recognised the challenges of transitioning to CE, there is still a lack of consensus on business best practices that can facilitate companies in embracing CE across the supply chain. Hence, this paper strives to scrutinize the SCM practices utilised for CE, identify the obstacles, and recommend best practices that can enhance a company's ability to incorporate CE principles toward business innovation and supply chain performance. Further, the paper proposes future research in the field of using specific technologies such as artificial intelligence, Internet of Things, and blockchain as business innovation tools for supply chain management and CE adoption.

Keywords: business innovation, challenges, circular supply chain, supply chain management, technology

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Authors: Christopher G. Harris, Guy D. Eslick

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Purpose: Lobular carcinoma in situ (LCIS) is a known risk factor for breast cancer of unclear significance when detected in association with invasive carcinoma. This meta-analysis aims to determine the impact of LCIS on local recurrence risk for individuals with breast cancer treated with breast conservation therapy to help guide appropriate treatment strategies. Methods: We identified relevant studies from five electronic databases. Studies were deemed suitable for inclusion where they compared patients with invasive breast cancer and concurrent LCIS to those with breast cancer alone, all patients underwent breast conservation therapy (lumpectomy with adjuvant radiation therapy), and local recurrence was evaluated. Recurrence data were pooled by use of a random effects model. Results: From 1488 citations screened by our search, 8 studies were deemed suitable for inclusion. These studies comprised of 908 cases and 10638 controls. Median follow-up time was 90 months. There was a significantly increased overall risk of local breast cancer recurrence for individuals with LCIS in association with breast cancer following breast conservation therapy [pOR 1.87; 95% CI 1.14-3.04; p = 0.012]. The risk of local recurrence was non-significantly increased at 5 [pOR 1.09; 95% CI 0.48-2.48; p = 0.828] and 10 years [pOR 1.90; 95% CI 0.89-4.06; p = 0.096]. Conclusions: Individuals with LCIS in association with invasive breast cancer have an increased risk of local recurrence following breast conservation therapy. This supports consideration of aggressive local control of LCIS by way of completion mastectomy or re-excision for certain high-risk patients.

Keywords: breast cancer, breast conservation therapy, lobular carcinoma in situ, lobular neoplasia, local recurrence, meta-analysis

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Authors: G. M. Hamada, A. A. Al-Gathe, A. M. Al-Khudafi

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Determination of water saturation in sandstone is a vital question to determine the initial oil or gas in place in reservoir rocks. Water saturation determination using electrical measurements is mainly on Archie’s formula. Consequently accuracy of Archie’s formula parameters affects water saturation values rigorously. Determination of Archie’s parameters a, m, and n is proceeded by three conventional techniques, Core Archie-Parameter Estimation (CAPE) and 3-D. This work introduces the hybrid system of parallel self-organizing neural network (PSONN) targeting accepted values of Archie’s parameters and, consequently, reliable water saturation values. This work focuses on Archie’s parameters determination techniques; conventional technique, CAPE technique, and 3-D technique, and then the calculation of water saturation using current. Using the same data, a hybrid parallel self-organizing neural network (PSONN) algorithm is used to estimate Archie’s parameters and predict water saturation. Results have shown that estimated Arche’s parameters m, a, and n are highly accepted with statistical analysis, indicating that the PSONN model has a lower statistical error and higher correlation coefficient. This study was conducted using a high number of measurement points for 144 core plugs from a sandstone reservoir. PSONN algorithm can provide reliable water saturation values, and it can supplement or even replace the conventional techniques to determine Archie’s parameters and thereby calculate water saturation profiles.

Keywords: water saturation, Archie’s parameters, artificial intelligence, PSONN, sandstone reservoir

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Authors: Jasmin Cowin

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Augmented reality digital technologies, big data, and the need for a teacher workforce able to meet the demands of a knowledge-based society are poised to lead to major changes in the field of education. This paper explores applications and educational use cases of augmented reality digital technologies for educational organizations during the Fourth Industrial Revolution. The Fourth Industrial Revolution requires vision, flexibility, and innovative educational conduits by governments and educational institutions to remain competitive in a global economy. Educational organizations will need to focus on teaching in and for a digital age to continue offering academic knowledge relevant to 21st-century markets and changing labor force needs. Implementation of contemporary disciplines will need to be embodied through learners’ active knowledge-making experiences while embracing ubiquitous accessibility. The power of distributed ledger technology promises major streamlining for educational record-keeping, degree conferrals, and authenticity guarantees. Augmented reality digital technologies hold the potential to restructure educational philosophies and their underpinning pedagogies thereby transforming modes of delivery. Structural changes in education and governmental planning are already increasing through intelligent systems and big data. Reimagining and redesigning education on a broad scale is required to plan and implement governmental and institutional changes to harness innovative technologies while moving away from the big schooling machine.

Keywords: fourth industrial revolution, artificial intelligence, big data, education, augmented reality digital technologies, distributed ledger technology

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Authors: Luis N. Pereira, Marco P. Carrasco

Abstract:

Dynamic pricing is a revenue management strategy in which hotel suppliers define, over time, flexible and different prices for their services for different potential customers, considering the profile of e-consumers and the demand and market supply. This means that the fundamentals of dynamic pricing are based on economic theory (price elasticity of demand) and market segmentation. This study aims to define a dynamic pricing strategy and a contextualized offer to the e-consumers profile in order to improve the number of reservations of an online distribution channel. Segmentation methods (hierarchical and non-hierarchical) were used to identify and validate an optimal number of market segments. A profile of the market segments was studied, considering the characteristics of the e-consumers and the probability of reservation a room. In addition, the price elasticity of demand was estimated for each segment using econometric models. Finally, predictive models were used to define rules for classifying new e-consumers into pre-defined segments. The empirical study illustrates how it is possible to improve the intelligence of an online distribution channel system through an optimal dynamic pricing strategy and a contextualized offer to the profile of each new e-consumer. A database of 11 million e-consumers of an online distribution channel was used in this study. The results suggest that an appropriate policy of market segmentation in using of online reservation systems is benefit for the service suppliers because it brings high probability of reservation and generates more profit than fixed pricing.

Keywords: dynamic pricing, e-consumers segmentation, online reservation systems, predictive analytics

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Authors: Mahsa Kallhor Mohammadi, Hamideh Reshadatjoo

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This study verified the relationship between in-service training and employees’ feeling of psychological ownership. This research applied a descriptive survey that investigated a correlation between variables. The target population was 140 employees of a Drilling Fluid and Waste Management Service Company, and the sample was 123 employees who were selected randomly and encouraged to complete an electronic questionnaire which was designed based on standard questionnaires for research variables covering 62 questions. The face validity of the questionnaire was supported by an experimental test, and its content validity was approved by the thesis supervisor and consulting advisor. For the descriptive statistics frequency tables and diagrams, measures of central tendency such as mode, median, and mean and measures of variability such as variance, standards deviation, and quartile deviation were used. In the inferential statistics section, the Pearson correlation coefficient was used to verify the relationship between the variables of the research. According to the results, all of the research hypotheses were supported. According to hypothesis 1, there was a positive and significant relationship between training policy-making and employees’ psychological ownership (r=0/408, α=0/05). According to hypothesis 2, there was a positive and significant relationship between training planning and employees’ psychological ownership (r=0/446, α=0/05). According to hypothesis 3, there was a positive and significant relationship between providing the training and employees’ psychological ownership (r=0/512, α=0/05). According to hypothesis 4, there was a positive and significant relationship between training performance management and employees’ psychological ownership (r=0/462, α=0/05). According to hypothesis 5, there was a positive and significant relationship between employees’ motivation and psychological ownership (r=0/694, α=0/05). Therefore, through systematic in-service training, which is in the same line with the strategic goals of an organization and is based on scientific needs analysis, design, implementation, and evaluation, it is possible to improve employees’ sense of psychological ownership toward an organization.

Keywords: in-service training, motivation, organizational behavior, psychological ownership

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Authors: Haaris Khan, Farhad Udwadia

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South Asians are the fastest-growing immigrant population in Canada and are 3-4 times more likely to develop diabetes. In a primary care setting, language barriers continue to persist as a prominent obstacle when delivering crucial health information. Given the abundance of languages in the South Asian community and the varying levels of English fluency, there is compelling evidence that these language barriers can adversely impact health outcomes. The microvascular and macrovascular complications of poor diabetic management are well established and universally recognized. However, these are often difficult concepts to grasp for even individuals fluent in English. In order to lessen the burden of language barriers, we developed a comprehensive guide in various languages that discuss the complications and screening guidelines for diabetic and prediabetic patients. The guide is presented in the form of a pamphlet, with an electronic version being constructed as well, that provides basic information on diabetic retinopathy, neuropathy and nephropathy as well as the screening recommendations. We also conducted a review of the literature around the topic and incorporated our findings into our project. Our goal is for primary care physicians to have this resource and to be able to provide the link or pamphlet to patients in need. Our presentation also provides a comprehensive overview of some of the other barriers that individuals in the South Asian community face when seeking care. Given the staggering number of individuals in the South Asian community with diabetes and the morbidity and mortality associated with diabetes and its complications, effective community-specific strategies are needed to mitigate the potential consequences of poor diabetes management.

Keywords: diabetes, patient education, ophthalmology, vascular surgery

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Authors: Si Yin Tee

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Bimetallic nanostructures have received tremendous interests as a new class of nanomaterials which may have better technological usefulness with distinct properties from those of individual atoms and molecules or bulk matter. They excelled over the monometallic counterparts because of their improved electronic, optical and catalytic performances. The properties and the applicability of these bimetallic nanostructures not only depend on their size and shape, but also on the composition and their fine structure. These bimetallic nanostructures are potential candidates for bio-applications such as biosensing, bioimaging, biodiagnostics, drug delivery, targeted therapeutics, and tissue engineering. Herein, gold-incorporated copper (Cu/Au) nanostructures were synthesized through the controlled disproportionation of Cu⁺-oleylamine complex at 220 ºC to form copper nanowires and the subsequent reaction with Au³⁺ at different temperatures of 140, 220 and 300 ºC. This is to achieve their synergistic effect through the combined use of the merits of low-cost transition and high-stability noble metals. Of these Cu/Au nanostructures, Cu/Au nanotubes display the best performance towards electrochemical non-enzymatic glucose sensing, originating from the high conductivity of gold and the high aspect ratio copper nanotubes with high surface area so as to optimise the electroactive sites and facilitate mass transport. In addition to high sensitivity and fast response, the Cu/Au nanotubes possess high selectivity against interferences from other potential interfering species and excellent reproducibility with long-term stability. By introducing gold into copper nanostructures at a low level of 3, 1 and 0.1 mol% relative to initial copper precursor, a significant electrocatalytic enhancement of the resulting bimetallic Cu/Au nanostructures starts to occur at 1 mol%. Overall, the present fabrication of stable Cu/Au nanostructures offers a promising low-cost platform for sensitive, selective, reproducible and reusable electrochemical sensing of glucose.

Keywords: bimetallic, electrochemical sensing, glucose oxidation, gold-incorporated copper nanostructures

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Authors: Yulin Rao, Zhixuan Li, Burra Venkata Durga Kumar

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Intrusion detection refers to monitoring the actions of internal and external intruders on the system and detecting the behaviours that violate security policies in real-time. In intrusion detection, there has been much discussion about the application of neural network technology and artificial immune system (AIS). However, many solutions use static methods (signature-based and stateful protocol analysis) or centralized intrusion detection systems (CIDS), which are unsuitable for real-time intrusion detection systems that need to process large amounts of data and detect unknown intrusions. This article proposes a framework for a distributed intrusion detection system (DIDS) with multi-agents based on the concept of AIS and neural network technology to detect anomalies and intrusions. In this framework, multiple agents are assigned to each host and work together, improving the system's detection efficiency and robustness. The trainer agent in the central server of the framework uses the artificial neural network (ANN) rather than the negative selection algorithm of AIS to generate mature detectors. Mature detectors can distinguish between self-files and non-self-files after learning. Our analyzer agents use genetic algorithms to generate memory cell detectors. This kind of detector will effectively reduce false positive and false negative errors and act quickly on known intrusions.

Keywords: artificial immune system, distributed artificial intelligence, multi-agent, intrusion detection system, neural network

Procedia PDF Downloads 109

Authors: Nawal Yacoub Halim Abdelmasih

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The intersection between development and human rights has been the factor of scholarly debate for a long term. therefore, some of standards, which enlarge from the proper to development to the human rights-based totally method to development, had been adopted to apprehend the dynamics among the two standards. no matter these attempts, the exact relationship among improvement and human rights has not been completely determined but. however, the inevitable interdependence between the two notions and the idea that improvement efforts ought to be undertaken with the aid of giving due regard to human rights ensures has won momentum in recent years. then again, the emergence of sustainable development as a extensively common technique in development dreams and policies makes this unsettled convergence even extra complicated. The vicinity of sustainable improvement in human rights regulation discourse and the function of the latter in making sure the sustainability of development applications name for a scientific observe. as a result, this newsletter seeks to discover the relationship among development and human rights, particularly focusing at the location given to sustainable development principles in international human proper regulation. it'll similarly quest whether or not there is a proper to sustainable improvement diagnosed therein. as a result, the item asserts that the ideas of sustainable improvement are immediately or circuitously diagnosed in diverse human rights contraptions, which affords an affirmative response to the question raised hereinabove. This paintings, therefore, will make expeditions via international and regional human rights devices in addition to case legal guidelines and interpretative hints of human rights bodies to show this speculation.

Keywords: sustainable development, human rights, the right to development, the human rights-based approach to development, environmental rights, economic development, social sustainability human rights protection, human rights violations, workers’ rights, justice, security

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Authors: Shivaram P. Arunachalam, Mustafa Y. Sir, Andy Boggust, David M. Nestler, Thomas R. Hellmich, Kalyan S. Pasupathy

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Emergency departments (EDs) provide urgent care to patients throughout the day in a complex and chaotic environment. Real-time location systems (RTLS) are increasingly being utilized in healthcare settings, and have shown to improve safety, reduce cost, and increase patient satisfaction. Radio Frequency Identification Device (RFID) data in an ED has been shown to compute variables such as patient-provider contact time, which is associated with patient outcomes such as 30-day hospitalization. These variables can provide avenues for improving ED operational efficiency. A major challenge with ED financial operations is under-coding of critical care services due to physicians’ difficulty reporting accurate times for critical care provided under Current Procedural Terminology (CPT) codes 99291 and 99292. In this work, the authors propose a framework to optimize ED critical care billing using RFID data. RFID estimated physician-patient contact times could accurately quantify direct critical care services which will help model a data-driven approach for ED critical care billing. This paper will describe the framework and provide insights into opportunities to prevent under coding as well as over coding to avoid insurance audits. Future work will focus on data analytics to demonstrate the feasibility of the framework described.

Keywords: critical care billing, CPT codes, emergency department, RFID

Procedia PDF Downloads 131