Search results for: electronic intelligence

Authors: Aziz Rezapour, Abdosaleh Jafari, Marziye Hadian, Elaheh Mazaheri

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Introduction: Cardiac rehabilitation is an accurate educational and sporting program designed to help heart patients to increase their physical activities and reduce the risk factors that make their health worse and help to a healthier lifestyle so that they can return to their families and society with a better spirit. The aim of this study was to examine the cost-effectiveness and cost-utility of cardiac rehabilitation programs for patients with cardiovascular diseases. Methods: In the present review study, published articles related to cost-effectiveness and cost-utility of cardiac rehabilitation programs for patients with cardiovascular diseases within the time interval between 2004 and 2019 were searched using electronic databases. The methodological quality of the structure of articles was examined by Drummond’s standard checklist. Results: The results of reviewing studies showed that most of the studies related to the economic evaluation of cardiac rehabilitation programs in patients with cardiovascular disease were flawed in Drummond’s criteria, and only one study adhered to Drummond’s criteria. The results of the present study indicated use of cardiac rehabilitation programs in patients with cardiovascular disease was cost-effective. Conclusion: The results of this review study showed that although the results of the studies were different in terms of a number of aspects, such as the study perspective, the time horizons, and the costs of rehabilitation programs, they achieved a similar conclusion, they concluded that the use of cardiac rehabilitation programs in patients with cardiovascular diseases, leading to higher quality-adjusted life years (QALYs) and lower costs.

Keywords: economic evaluation, systematic review, cardiac rehabilitation, Drummond’s checklist

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Authors: Taurai T. Chikotie, Bruce W. Watson

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The manifestation of ‘new’ infectious diseases and the re-emergence of ‘old’ infectious diseases now present global problems and Southern Africa has not been spared from such calamity. Although having an organized public health system, countries in this region have failed to leverage on the proliferation in use of Information and Communication Technologies to promote effective disease surveillance. Objective: The objective of this study was to critically review and analyse the crucial variables to consider in the development and implementation of electronic disease surveillance systems in public health within the context of Southern Africa. Methodology: A critical review of literature published in English using, Google Scholar, EBSCOHOST, Science Direct, databases from the Centre for Disease Control (CDC and articles from the World Health Organisation (WHO) was undertaken. Manual reference and grey literature searches were also conducted. Results: Little has been done towards harnessing the potential of information technologies towards disease surveillance and this has been due to several challenges that include, lack of funding, lack of health informatics experts, poor supporting infrastructure, an unstable socio-political and socio-economic ecosystem in the region and archaic policies towards integration of information technologies in public health governance. Conclusion: The Southern African region stands to achieve better health outcomes if they adopt the use of e-disease surveillance systems in public health. However, the dynamics and complexities of the socio-economic, socio-political and technical variables would need addressing to ensure the successful development and implementation of e-disease surveillance systems in the region.

Keywords: critical review, disease surveillance, public health informatics, Southern Africa

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Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

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Authors: Zsanett Bahor, Cristina Nunes-Fonseca, Gillian L. Currie, Emily S. Sena, Lindsay D.G. Thomson, Malcolm R. Macleod

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Psychosis is ranked as the third most disabling medical condition in the world by the World Health Organization. Despite a substantial amount of research in recent years, available treatments are not universally effective and have a wide range of adverse side effects. Since many clinical drug candidates are identified through in vivo modelling, a deeper understanding of these models, and their strengths and limitations, might help us understand reasons for difficulties in psychosis drug development. To provide an unbiased summary of the preclinical psychosis literature we performed a systematic electronic search of PubMed for publications modelling a psychotic disorder in vivo, identifying 14,721 relevant studies. Double screening of 11,000 publications from this dataset so far established 2403 animal studies of psychosis, with the most common model being schizophrenia (95%). 61% of these models are induced using pharmacological agents. For all the models only 56% of publications test a therapeutic treatment. We propose a systematic review of these studies to assess the prevalence of reporting of measures to reduce risk of bias, and a meta-analysis to assess the internal and external validity of these animal models. Our findings are likely to be relevant to future preclinical studies of psychosis as this generation of strong empirical evidence has the potential to identify weaknesses, areas for improvement and make suggestions on refinement of experimental design. Such a detailed understanding of the data which inform what we think we know will help improve the current attrition rate between bench and bedside in psychosis research.

Keywords: animal models, psychosis, systematic review, schizophrenia

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Authors: Prakash Singh, Piet Maphodisa Kgohlo

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Knowledge-worker productivity is one of the biggest leadership challenges facing all organizations in the twenty-first century. It cannot be denied that knowledge-worker productivity affects all organizations. The work and the workforce are both undergoing greater changes currently than at any time, since the beginning of the industrial revolution two centuries ago. Employees welcome collegial teaming (CT) as an innovative way to develop their work-integrated learning competencies. Human resource development policies must evoke the symbiotic relationship between CT and work-integrated learning, seeing that employees need to be endowed with the competence to move from one skill to another, as each one becomes obsolete, and to simultaneously develop their cognitive and emotional intelligence. The outcome of this relationship must culminate in the development of highly productive knowledge-workers. While this study focuses on teachers, the conceptual framework and the findings of this research can be beneficial for any organization, public or private sector, business or non-business. Therefore, in this quantitative study, the benefits of CT are considered in developing human resources to sustain knowledge-worker productivity. The ANOVA p-values reveal that the majority of teachers agree that CT can empower them to overcome the challenges of managing curriculum change. CT can equip them with continuous and sustained learning, growth and improvement, necessary for knowledge-worker productivity. This study, therefore, confirms that CT benefits all workers, immaterial of their age, gender or experience. Hence, this exploratory research provides a new perspective of CT in addressing knowledge-worker productivity when organizational change alters the vision of the organization.

Keywords: collegial teaming, human resource development, knowledge-worker productivity, work-integrated learning

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Authors: Farbod Behnaminia, Saeed Samet

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The COVID-19 pandemic has paralyzed many lives until a vaccine was available, which caused the so-called “new normal.” According to the World Health Organization (WHO), COVID-19 is an infectious disease. It can cause significant illness or death in anyone. Governments and health officials tried to impose rules and regulations to avoid and slow down transmission. Therefore, software engineers worldwide developed applications to trace and track patients’ movements and notify others, mainly using Bluetooth. In this way, everyone could be informed whether they come in close contact with someone who has COVID-19 and takes proper safety precautions. Because most of the applications use technologies that can potentially reveal the user’s identity and location, researchers have debated privacy preservation and how to improve user privacy during such pandemics. Thanks to Distributed Ledger Technology (DLT), there have been some proposed methods to develop privacy-preserving Patient Tracking Systems in the last two years. As an instance of the DLT, Blockchain is like a decentralized peer-to-peer database that maintains a record of transactions. Transactions are immutable, transparent, and anonymous in this system. We conducted a comprehensive evaluation of the literature by looking for papers in the relevant field and dividing them into pre- and post-pandemic systems. Additionally, we discussed the many uses of blockchain technology in pandemic control. We found that two major obstacles facing blockchain implementation across many healthcare systems are scalability and privacy. The Polkadot platform is presented, along with a review of its efficacy in tackling current concerns. A more scalable healthcare system is achievable in the near future using Polkadot as well as a much more privacy-preserving environment.

Keywords: blockchain, electronic record management, EHR, privacy-preserving, patient tracking, COVID-19, trust and confidence, Polkadot

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Authors: Ali Riza Perçin, Eser Bingül

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Internet technologies have created opportunities with which people share their ideologies, thoughts and products. This virtual world, named social media has given the chance of gathering individual users and people from the world's remote locations and establishing an interaction between them. However, to an increasingly higher degree terrorist organizations today use the internet and most notably social-network media to create the effects they desire through a series of on-line activities. These activities, designed to support their activities, include information collection (intelligence), target selection, propaganda, fundraising and recruitment to name a few. Meanwhile, these have been used as the most important tool for recruitment especially from the different region of the world, especially disenfranchised youth, in the West in order to mobilize support and recruit “foreign fighters.” The recruits have obtained the statue, which is not accessible in their society and have preferred the style of life that is offered by the terrorist organizations instead of their current life. Like other terrorist groups, for a while now the terrorist organization Islamic State (IS) in Iraq and Syria has employed a social-media strategy in order to advance their strategic objectives. At the moment, however, IS seems to be more successful in their on-line activities than other similar organizations. IS uses social media strategically as part of its armed activities and for the sustainability of their military presence in Syria and Iraq. In this context, “Generation Y”, which could exist at the critical position and undertake active role, has been examined. Additionally, the explained characteristics of “Generation Y” have been put forward and the duties of families and society have been stated as well.

Keywords: social media, "generation Y", terrorist organization, islamic state IS

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Authors: Essam Mansour

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The purpose of this study is to explore the potential role of South Valley University (SVU) libraries’ manpower, collections and services in the fight against terrorism in the Upper Egypt. A quantitative research methodology was used in the form of a survey sent to 127 library staff at the SVU. The survey was undertaken from June to July 2015 with a response rate 73.2%. Printed materials were the most adequate collections in the SVU libraries. Other materials, such as CDs/DVDs, audiovisual materials, microfilm and microfiche, online resources and electronic materials respectively were inadequate at SVU libraries. Few of the services provided by SVU libraries were characterized as adequate services, some are inadequate and other services do not exist. The average of the facilities provided by SVU libraries was somewhat adequate. Activities, such as holding social field trips, holding training workshops and holding academic field trip were, at least, somewhat adequate to SVU libraries. SVU libraries had no a significant role in fighting terrorism in the Upper Egypt. There is no a relationship between the SVU library staff’s professional characteristics and the potential role that their libraries may play in the fight against this phenomenon. As a result of the lack of SVU libraries’ collections, services, facilities and activities, this study concluded that that such role could not be achieved. Almost all the library staff admitted that this severe lack has affected the provision of library patrons and members of the library community to these collections and services, which help in countering the threat of terrorism. Despite the significance of all these problems faced by SVU libraries in the fight against terrorism, it was found that the inadequacy of the library opening hours is significantly correlated with the professional characteristics of the library staff, particularly their job title and work experience.

Keywords: terrorism, national security, university libraries, south valley university, Egypt, survey

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Authors: Deepak Deshpande, Vikas Minchekar

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Day by day in modern India communal forces became stronger and stronger. Each and every caste group trying to show their strength through massive marches. Such kind of marches or ralliesruinous national integrity in India. To test this assumption present investigation has been carried out. This research was undertaken by using survey techniques. The study has been carried out in two phases. In the first stage, the students’ attitudes were collected while in the second phase the views of the members of the historical association were collected. The social dominance orientation scale and sources of social dominance inventory have been administered on 200 college students belonging to Maratha caste. Analyzed data revealed ahigh level of social dominance in Maratha caste students. Approximately, 80 percent students have reported that they have learned such dominance from the medieval history. The other sources disappear very less prominent. These results and present Indian social situation have been communicated with the members of the historical association of India. The majority members of this association agreed with this reality. The consensus also received on that Maratha caste person experiencing dominance due to the misinterpretation of the King Shivaji Empire; synchronize by politicians. The survey monkey app was used through electronic mail to collect the views on ‘The attitude towards the modification of curricula questionnaire’. The maximum number of members of the historical association agreed to employ to teach the medieval Indian history accordingly the secular perspectives.

Keywords: social dominance orientation, secular perceptive, national integrity, Maratha caste and medieval Indian history

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: Richard Molyneux, Christopher Parrott, Kirill Horoshenkov

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This paper aims to demonstrate how various algorithms can be implemented within swarms of autonomous robots to provide continuous inspection within underground pipeline networks. Current methods of fault detection within pipes are costly, time consuming and inefficient. As such, solutions tend toward a more reactive approach, repairing faults, as opposed to proactively seeking leaks and blockages. The paper presents an efficient inspection method, showing that autonomous swarm robotics is a viable way of monitoring underground infrastructure. Tailored adaptations of various Vehicle Routing Problems (VRP) and path-planning algorithms provide a customised inspection procedure for complicated networks of underground pipes. The performance of multiple algorithms is compared to determine their effectiveness and feasibility. Notable inspirations come from ant colonies and stigmergy, graph theory, the k-Chinese Postman Problem ( -CPP) and traffic theory. Unlike most swarm behaviours which rely on fast communication between agents, underground pipe networks are a highly challenging communication environment with extremely limited communication ranges. This is due to the extreme variability in the pipe conditions and relatively high attenuation of acoustic and radio waves with which robots would usually communicate. This paper illustrates how to optimise the inspection process and how to increase the frequency with which the robots pass each other, without compromising the routes they are able to take to cover the whole network.

Keywords: autonomous inspection, buried pipes, stigmergy, swarm intelligence, vehicle routing problem

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Authors: Selam Mulugeta, Barkot Milkias, Mesfin Araya, Abel Worku, Eyasu Mulugeta

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In Ethiopia, there is inadequate information on non-adherence to antidepressant treatment in patients with depressive disorders. Having awareness of the pattern of adherence is important in future prognosis, quality of life, and functionality in these patients. This hospital-based cross-sectional quantitative study was done on a sample of 216 consecutive outpatients with depressive disorders. Data were collected using questionnaires through in-person and phone call interviews. The 8-item Morisky scale was used to assess the pattern of medication adherence. Other specially developed tools were used to obtain sociodemographic and clinical information from electronic medical records and patient interviews. Data were analyzed using the Statistical Package for the Social Sciences Version - 25. Univariate and multivariable analyses were carried out to assess factors associated with non-adherence. 90% of the participants had a primary diagnosis of major depressive disorder. Based on the 8-item Morisky Medication Adherence Scale, the prevalence of non-adherence was found to be 84.7%. Living distance between 11 to 50 km from the hospital (AOR= 11, 95% CI (29,46.6)), post-secondary level of education (AOR= 8.3, 95% CI (1, 64.4)) and taking multiple medications (AOR= 6.1, 95% CI (1, 34.9)) were found to have significantly increased odds of non-adherence. Non-adherence was significantly associated with factors such as increased living distance from the hospital, relatively higher educational level, and polypharmacy. Proper and patient-centered psychoeducation, addressing the communication gap between patients and doctors, adherence to prescribing guidelines, avoiding polypharmacy unless indicated & working on accessibility of treatment is essential to decrease non-adherence.

Keywords: depressive disorders, Ethiopia, medication adherence, Addis Ababa

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Authors: Sanaz Mosadeghsedghi, Mathew Hudder, Mohammad Ali Baghbanzadeh, Charbel Atallah, Seyedeh Laleh Dashtban Kenari, Konstantin Volchek

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Lithium has recently been extensively used in lithium-ion batteries (LIBs) for electric vehicles and portable electronic devices. The conventional evaporative approach to recover and concentrate lithium is extremely slow and may take 10-24 months to concentrate lithium from dilute sources, such as geothermal brines. To response to the increasing industrial lithium demand, alternative extraction and concentration technologies should be developed to recover lithium from brines with low concentrations. In this study, a combination of electrocoagulation (EC) and electrodialysis (ED) was evaluated for the recovery of lithium from geothermal brines. The brine samples in this study, collected in Western Canada, had lithium concentrations of 50-75 mg/L on a background of much higher (over 10,000 times) concentrations of sodium. This very high sodium-to-lithium ratio poses challenges to the conventional direct-lithium extraction processes which employ lithium-selective adsorbents. EC was used to co-precipitate lithium using a sacrificial aluminium electrode. The precipitate was then dissolved, and the leachate was treated using ED to separate and concentrate lithium from other ions. The focus of this paper is on the study of ED, including a two-step ED process that included a mono-valent selective stage to separate lithium from multi-valent cations followed by a bipolar ED stage to convert lithium chloride (LiCl) to LiOH product. Eventually, the ED cell was reconfigured using mono-valent cation exchange with the bipolar membranes to combine the two ED steps in one. Using this process at optimum conditions, over 95% of the co-existing cations were removed and the purity of lithium increased to over 90% in the final product.

Keywords: electrochemical separation, electrocoagulation, electrodialysis, lithium extraction

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Authors: Hatem Diaf, Patrice Melinon, Antonio Pereira, Bernard Moine, Nicholas Blanchard, Florent Bourquard, Florence Garrelie, Christophe Donnet

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Glassy carbon exhibits excellent biological compatibility with live tissues meaning it has high potential for applications in life science. Moreover, glassy carbon has interesting properties including 'high temperature resistance', hardness, low density, low electrical resistance, low friction, and low thermal resistance. The structure of glassy carbon has long been a subject of debate. It is now admitted that glassy carbon is 100% sp2. This term is a little bit confusing as long sp2 hybridization defined from quantum chemistry is related to both properties: threefold configuration and pi bonding (parallel pz orbitals). Using plasma laser deposition of carbon clusters combined with pulsed nano/femto laser annealing, we are able to synthesize thin films of glassy carbon of good quality (probed by G band/ D disorder band ratio in Raman spectroscopy) without thermal post annealing. A careful inspecting of Raman signal, plasmon losses and structure performed by HRTEM (High Resolution Transmission Electron Microscopy) reveals that both properties (threefold and pi orbitals) cannot coexist together. The structure of the films is compared to models including schwarzites based from negatively curved surfaces at the opposite of onions or fullerene-like structures with positively curved surfaces. This study shows that a huge collection of porous carbon named vitreous carbon with different structures can coexist.

Keywords: glassy carbon, cluster deposition, coating, electronic structure

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Authors: Qingyu Zhang, Helin Ma, Lili Weng, Mei Cao

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Green supply chain management has been an important organizational strategy to reduce environmental risks and improve financial performance. Firms have to adopt green supply chain practices to meet the official regulations and reduce peer pressure in China. This paper exhibits an empirical study of the drivers of green supply chain management practices and the environmental and economic performance of green supply chain management implementation in Chinese firms. While China is the fastest-growing emerging economy, it has paid a high ecological price. It is reported that China hosts 7 of the world’s 10 most polluted cities. The continued environmental deterioration and the resultant heightened regulatory control and public scrutiny have posed new operating challenges to firms conducting business in China. These challenges make the country an ideal setting to conduct the present study. A research questionnaire was developed to gather data in China. The questionnaire targeted managers and employees in Chinese companies. The data were collected in the last quarter of 2015, involving industries such as electronic & communicational equipment, textile & clothing, pharmaceutical & healthcare, and so on. This study confirms and validates that (1) both internal and external drivers play a significant role in the implementation of green supply chain management practices; (2) green purchase and investment recovery have a significant impact on firms’ environmental and economic performance; (3) with the improvement of the firms’ environmental performance, their economic performance will improve.

Keywords: economic performance, environmental performance, external driver, green supply chain management

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Authors: Bilge B. Calik, Ummuhan B. Aslan, Suat Erel, Sehmus Aslan

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Objective: Intellectual disability (ID) is characterized by limitations in both intellectual functioning and adaptive behavior, which covers many everyday social and practical skills. The aim of this study was to evaluate the balance and coordination performance determined between mild and moderate ID adolescents who regularly play sport. Methods: The study comprised a total of 179 participants, of which 135 were male adolescents with mild and moderate-level ID who regularly play sports (16.52 ± 2.17 years) and 44 age-matched male adolescents with typical development without ID who do not do any sports (16.52 ± 0.99 years). The participants with ID were students of Special Education Schools for the mentally disabled and had been diagnosed with ID at a Ministry of Health Hospital. The adolescents with mild and moderate ID had been playing football in their school teams at least 2 days a week, for at least one year. Balance and coordination of adolescents were assessed by Bilateral coordination and balance subtests of Short Form Bruininks-Oseretsky Test of Motor Proficiency (BOT-2 SF). Results: As a result of the evaluations comparing coordination and balance scores significant differences were determined between all three groups in favor of the peers without ID (p<0.05). Conclusions: It was observed that balance and coordination levels of adolescents with mild ID were better than those of adolescents with moderate-level ID but lower than those of peers without ID. These results indicate a relationship between IQ level and motor performance. Further comparative studies are needed on individuals with ID who play and do not play sports in order to examine the impact of participation in sports on the motor skills of individuals with ID.

Keywords: balance, coordination, intellectual disability, motor skills, sport

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Authors: Boshra Mozaffar, Iskandar Idris

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Taste change is a common complication after Bariatric surgery (BS). However, the cause of this is still not clear. Since zinc is important fortaste perception, zinc deficiency, which is common after BS, may play an important role for taste change after BS. In this review, we aimto collate evidence relating to taste change and zinc deficiencyin relation to BS; effects of zinc replacement on taste perception in general and thereafter discuss the possible role of zinc deficiency to induce taste change after BS. A literature search was conducted, using four electronic bibliographical databases—EMBASE, PubMed, AMED and MEDLINE. We identified all available and relevant articles published before 30th February 2021.In total, 33 studies were included. The total number of participants analysed was N= 3264. We showed that taste change is a frequent complication after BS, especially after Roux-en-Y gastric bypass RYGBP comparing to other types of procedures. Patients' taste sensitivity differs among studies, but the most important decline in taste preference was observed for sweet food. Twelve studies investigating zinc deficiency following BS showed a significant decrease in zinc levels at six months after surgery. Supplementation with 45–50 mg of zinc sulphate was effective in improving taste, except in cancer patients, who showed no improvement in taste following zinc supplementation. Zinc deficiency appears to be associated with taste change after BS. Supplementation with much higher levels of zinc, at 45–50 mg, was effective in taste change treatment for many cases of taste disorder. The currently recommended levels of zinc replacements currently prescribed to patients following BS were not effective for avoiding zinc deficiency after BS—and thus not effective for averting taste change. It is therefore suggested that taste change following BS is closely related to zinc deficiency induced by the surgery.

Keywords: taste change, taste disorder, bariatric surgery, zinc, zinc sulphate or Zn, deficiency, supplementation, and micro-nutrient deficiencies

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Authors: J. Iqbal, Z. Zara

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Five new Acceptor-Donor-Acceptor (A-D-A) type small donor molecules (M1-M5) namely; dimethyl cyanoacetate terthiophene di(methylthiophene) dibenzosilole (DMCAO3TBS) (M1), dimelononitrile terthiophene di(methylthiophene) dibenzosilole (DMCNTBS) (M2), dimethyl rhodanine terthiophene di(methylthiophene) dibenzosilole (DMRTBS) (M3), dimelanonitrile terthiophene di(methylthiophene) methyl fluorene (DMCNTF) (M4) and dimethyl rhodanine terthiophene di(methylthiophene) methyl fluorine (DMRTF) (M5) were designed and theoretically explored their electronic, photophysical and geometrical properties via DFT best functional MPW1PW91/6-311G (d,p) level of theory with respect to reference molecules dioctyl cyanoacetate terthiophene di(octylthiophene) dioctylfluorene (DCAO3TF) (Ra) and dioctyl cyanoacetate terthiophene di(octylthiophene) octylcarbazole (DCAO3TCz) (Rb). Among the designed donor molecules (M1-M5), M2 and M4 represented lowest band gap value (2.480 eV and 2.47 eV) with distinctive broad absorption peak at 598 and 601 nm in chloroform due to the presence of stronger electron withdrawing acceptor molecule which pulls the λmax value towards red shift. Theoretically estimated reorganization energies of these molecules recommended excellent property of charge mobility. The designed donor molecules M1-M5, demonstrated lower λe value with reference to their λh, showing that these molecules could be ideal candidates for the transfer of electron with and M2, M4 are best among these as champion molecules with having lowest λe (0.006 D and 0.005 D respectively). Additionally, the Voc of M2 and M4 are 2.01 eV and 1.85 eV respectively with reference respect to PCBM. Thus, our present investigation suggested that our designed donor molecules (M1-M5) are suitable candidates for the solar cell and proposed for high and better performance for the small molecule based solar cell devices.

Keywords: dibenzisilol, donor materials, hole mobility, organic solar cells

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Authors: Ahmed A. Abd El Rahim, Mohamed M. M. Essa, Magdy M. A. Shabana, Said A. Mohamed, Mohamed Ibrahim Mabrouk

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Background: Sacroiliac joint (SIJ) dysfunction is considered the main cause of pregnancy-related back pain, which may continue to persist postnatally. Myofascial release technique (MFR) is an application of low-intensity, prolonged stretch to myofascial structures to improve function by increasing the sliding properties of restricted myofascial tissues. Purpose: This study was designed to investigate the effect of MFR on postnatal SIJ hypo-mobility. Materials and Methods: Fifty postnatal women complaining of SIJ hypo-mobility participated in this study. Their ages ranged from 26 to 35 yrs., and their body mass index (BMI) didn`t exceed 30 kg/m2. They were randomly assigned to two equal groups, group A (Gr. A) and group B (Gr. B). Both groups received three sessions per week for eight successive weeks. Gr. A received a traditional physical therapy program, while Gr. B received a traditional physical therapy program in addition to MFR. Doppler imaging of vibration was utilized to measure SIJ mobility pre- and post-intervention, and an electronic digital goniometer was used to measure back flexion and extension Range of motion. Results: Findings revealed a statistical improvement in post-intervention values of SIJ mobility in addition to trunk flexion and extension ROM in Gr. B compared to Gr. A (P<0.001). Conclusion: Adding MFR to traditional physical therapy programs is highly recommended in the treatment of SIJ hypo-mobility in postnatal women.

Keywords: sacroiliac hypo-mobility, sacroiliac dysfunction, myofascial release technique, traditional physical therapy, postnatal

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Christodoulou Christos, Politis Anastasios

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The utilization of Wireless Body Area Network (WBAN) is experiencing a notable surge in popularity as a result of its widespread implementation in the field of smart health. WBANs utilize small sensors implanted within the human body to monitor and record physiological indicators. These sensors transmit the collected data to hospitals and healthcare facilities through designated access points. Bio-sensors exhibit a diverse array of shapes and sizes, and their deployment can be tailored to the condition of the individual. Multiple sensors may be strategically placed within, on, or around the human body to effectively observe, record, and transmit essential physiological indicators. These measurements serve as a basis for subsequent analysis, evaluation, and therapeutic interventions. In conjunction with physical health concerns, numerous smartwatches are engineered to employ artificial intelligence techniques for the purpose of detecting mental health conditions such as depression and anxiety. The utilization of smartwatches serves as a secure and cost-effective solution for monitoring mental health. Physiological signals are widely regarded as a highly dependable method for the recognition of emotions due to the inherent inability of individuals to deliberately influence them over extended periods of time. The techniques that WBANs employ to recognize emotions are thoroughly examined in this article.

Keywords: emotion recognition, wireless body area network, WBAN, ERC, wearable devices, psychological signals, emotion, smart-watch, prediction

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Authors: Palash Kumar Mollick, Leire Olazar, Laura Santamaria, Pablo Comendador, Manomita Mollick, Gartzen Lopez, Martin Olazar

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The entire world is skeptical towards a silver line technology of converting plastic waste into valuable synthetic gas. At this junction, besides an adequately studied conventional catalytic process for steam reforming, a non-thermal plasma is being introduced. Organic volatiles are produced in the first step, pyrolysing the plastic materials. Resultant lightweight olefins and carbon monoxide are the major components that undergo a steam reforming process to achieve syngas. A non-thermal plasma consists of ionized gases and free electrons with an electronic temperature as high as 10³ K. Organic volatiles are, in general, endorganics inactive and thus demand huge bond-breaking energy. Conventional catalyst is incapable of providing the required activation energy, leading to poor thermodynamic equilibrium, whereas a non-thermal plasma can actively collide with reactants to produce a rich mix of reactive species, including vibrationally or electronically excited molecules, radicals, atoms, and ions. In addition, non-thermal plasma provides nonequilibrium conditions leading to electric discharge only in certain degrees of freedom without affecting the intrinsic chemical conditions of the participating reactants and products. In this work, we report thermodynamic and kinetic aspects of the conversion of organic volatiles into syngas using a non-thermal plasma. Detailed characteristics of plasma and its effect on the overall yield of the process will be presented.

Keywords: non thermal plasma, plasma catalysis, steam reforming, syngas, plastic waste, green energy

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Authors: R. S. Quintal, J. L. Tesch Santos, M. D. Davis, E. C. de Santana, M. de F. Bandeira dos Santos

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The objective is to identify the contributions from the introduction of the computerized system deal within the Accounting Department of Brazilian Navy Financial Directorship and its possible effects on the budgetary and financial harvest of Brazilian Navy. The relevance lies in the fact that the management process is responsible for the continuous improvement of organizational performance through higher levels of quality in their activities. Improvements in organizational processes have direct effects on crops cost, quality, reliability, flexibility and speed. The method of study of this research is the case study. The choice of case study attended, among other demands, a need for greater flexibility to study processes related to a computerized system. The sources of evidence were used literature, documentary and direct observation. Direct observation was made by monitoring the implementation of the computerized system in the Division of Management Analysis. The main findings of the study point to the fact that the computerized system may contribute significantly to the standardization of information. There was improvement of internal processes in the division of management analysis, made possible the consolidation of a standard management and performance analysis that contribute to global homogeneity in the treatment of information essential to the process of decision making. This study has limitations related to the fact the search result be subject exclusively to the case studied, and it is impossible to generalize to other organs of government.

Keywords: process management, management control, business intelligence, Brazilian Navy

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Authors: Rong Sun, Laihong Shen

Abstract:

La-based perovskite oxygen carriers, especially the doped-La(M)FeO₃, showed excellent performances during chemical looping processes. However, the mechanisms of the undoped and doped La(M)FeO₃ are not clear at present, making the mechanisms clear may help the development of chemical looping technologies. In this paper, the method based on the density function theory (DFT) was used to analysis the influence of Ca, Sr, and Ba doping of La on the electronic structure, while the CO oxidation mechanisms on the surface of LaFeO₃ and Ca-doped LaFeO₃ oxygen carriers were also analyzed. The results showed that the band gap was decreased by the doping of low valence. While the doping of low valence element Ca, Sr, and Ba at La site simultaneously resulted to the moving of the valence band toward high energy and made the valence band cross the Fermi energy level. This was resulted from the holes generated by divalent ion substitution. The holes can change the total magnetization from antiferromagnet to weakly ferromagnetism. The calculation results about the formation of oxygen vacancy showed that substitutions of Ca, Sr, and Ba caused a large drop in oxygen vacancy formation energy, indicating that the bulk oxygen transport was improved. Based on the optimized bulk of the undoped and Ca-doped LaFeO₃(010) surface, the CO adsorption was analyzed. The results indicated that the adsorption energy increased by divalent ion substitution, meaning that the adsorption stability decreased. The results can provide a certain theoretical basis for the development of perovskite oxides in chemical looping technologies.

Keywords: chemical looping technologies, lanthanum ferrate (LaFeO₃), divalent ion substitution, CO oxidation

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Authors: Raymond Xu, Ashley Hua, Andrew Wang, Yuru Lin

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During COVID-19, the depression rate has increased dramatically. Young adults are most vulnerable to the mental health effects of the pandemic. Lower-income families have a higher ratio to be diagnosed with depression than the general population, but less access to clinics. This research aims to achieve early depression detection at low cost, large scale, and high accuracy with an interdisciplinary approach by incorporating clinical practices defined by American Psychiatric Association (APA) as well as multimodal AI framework. The proposed approach detected the nine depression symptoms with Natural Language Processing sentiment analysis and a symptom-based Lexicon uniquely designed for young adults. The experiments were conducted on the multimedia survey results from adolescents and young adults and unbiased Twitter communications. The result was further aggregated with the facial emotional cues analyzed by the Convolutional Neural Network on the multimedia survey videos. Five experiments each conducted on 10k data entries reached consistent results with an average accuracy of 88.31%, higher than the existing natural language analysis models. This approach can reach 300+ million daily active Twitter users and is highly accessible by low-income populations to promote early depression detection to raise awareness in adolescents and young adults and reveal complementary cues to assist clinical depression diagnosis.

Keywords: artificial intelligence, COVID-19, depression detection, psychiatric disorder

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Authors: Anagha Pavithran Vattamparambil, Niranjana Regimon

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Dalit and tribal communities in India have the largest suicide rate; however, the current literature does not reflect this reality. While existing research acknowledges socio-cultural risk factors, it fails to discuss structural issues pertaining to marginalized communities in India. Furthermore, the language is framed in an individualistic manner which denies room for recognizing systemic violence and injustice among causative agents of suicide. We aim to examine the representation of Dalit and tribal identities and their experiences of marginalisation as a contributive factor of suicide, as well as discuss the epistemic injustice involved in its exclusion. Electronic searches of PubMed, PsychInfo, and Web of Science databases will be carried out from inception till January 2023 to conduct a systematic scoping review of peer-reviewed articles; it will include all studies involving psychological autopsy in India. A narrative synthesis will be performed to gain insight into the inclusion of the experiences of Dalits and Tribals, the absence of which indicates a lacking understanding of suicide in India. It is also expected to highlight the alienation of lived experiences and narratives of marginalisation from mainstream discourse on suicide that constitutes epistemic injustice. There is a complex interplay of psychological, socio-cultural, economic, and political factors for suicide in the Indian setting. But, political and systemic issues are often downplayed in suicide etiology, including casteist assault, rape, violence, public humiliation, and discrimination which deserves more research attention.

Keywords: dalits, marginalisation, psychological autopsy, suicide, tribals

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Authors: Cristian Pauna

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Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: Nathaniel Carpenter

Abstract:

Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

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Authors: Francisco Pedroza-Montero, Jesús Naín Pedroza-Montero, Diego Soto-Puebla, Osiris Alvarez-Bajo, Beatriz Castaneda, Sofía Navarro-Espinoza, Martín Pedroza-Montero

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Nanodiamonds have been widely studied for their physical properties, including chemical inertness, biocompatibility, optical transparency from the ultraviolet to the infrared region, high thermal conductivity, and mechanical strength. In this work, we studied how the fluorescence spectrum of nanodiamonds quenches concerning the concentration in aqueous solutions systematically ranging from 0.1 to 10 mg/mL. Our results demonstrated a non-linear fluorescence quenching as the concentration increases for both of the NV zero-phonon lines; the 5 mg/mL concentration shows the maximum fluorescence emission. Furthermore, this behaviour is theoretically explained as an electronic recombination process that modulates the intensity in the NV centres. Finally, to gain more insight, the FRET methodology is used to determine the fluorescence efficiency in terms of the fluorophores' separation distance. Thus, the concentration level is simulated as follows, a small distance between nanodiamonds would be considered a highly concentrated system, whereas a large distance would mean a low concentrated one. Although the 5 mg/mL concentration shows the maximum intensity, our main interest is focused on the concentration of 0.5 mg/mL, which our studies demonstrate the optimal human cell viability (99%). In this respect, this concentration has the feature of being as biocompatible as water giving the possibility to internalize it in cells without harming the living media. To this end, not only can we track nanodiamonds on the surface or inside the cell with excellent precision due to their fluorescent intensity, but also, we can perform thermometry tests transforming a fluorescence contrast image into a temperature contrast image.

Keywords: nanodiamonds, fluorescence spectroscopy, concentration, bioimaging, thermometry

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Authors: Saeid Mehvari, Yolanda Sanchez-Vicente, Sergio González Sánchez, Khalid Lafdi

Abstract:

Abstract- Materials with a combination of transparency, electrical conductivity, and flexibility are required in the ‎growing electronic sector. In this research, electrically conductive and flexible films have been prepared. These ‎composite films consist of dispersing micro-copper particles into polyurethane (PU) matrix. Two sets of samples were ‎made using both spin coating technique (sample thickness lower than 30 μm) and materials casting (sample thickness ‎lower than 100 μm). Copper concentrations in the PU matrix varied from 0.5 to 20% by volume. The dispersion of ‎micro-copper particles into polyurethane (PU) matrix were characterised using optical microscope and scanning electron ‎microscope. The electrical conductivity measurement was carried out using home-made multimeter set up under ‎pressures from 1 to 20 kPa through thickness and in plane direction. It seems that samples made by casting were not ‎conductive. However, the sample made by spin coating shows through-thickness conductivity when they are under ‎pressure. The results showed that spin-coated films with higher concentration of 2 vol. % of copper displayed a ‎significant increase in the conductivity value, known as percolation threshold. The maximum conductivity of 7.2 × 10-1 ‎S∙m-1 was reached at concentrations of filler with 20 vol. % at 20kPa. A semi-empirical model with adjustable ‎coefficients was used to fit and predict the electrical behaviour of composites. For the first time, the finite element ‎method based on the representative volume element (FE-RVE) was successfully used to predict their electrical ‎behaviour under applied pressures. ‎

Keywords: electrical conductivity, micro copper, numerical simulation, percolation threshold, polyurethane, RVE model

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