Search results for: psychosocial model social integration and recovery

Authors: Olupemi E. Oludare

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This paper investigates the creative practice engaged in Juju music, a Nigerian Neo-traditional genre of the Yoruba, and its influence on the consciousness of societal praxis. It takes a musical and cultural perspective, as representational indices of how the people’s religious, social, educational, and political consciousness is expressed in their music. The study adopts the historical cum descriptive design in its methodology, tracing the historical development of Juju music, the appropriation of musical and cultural materials in its creative process, and a descriptive analysis of its musical practice, in order to substantiate the role and function of Juju music and its musicians in the political, philosophical, and social consciousness of Nigeria’s pre- and post-independence epoch. Data were collected through oral interviews of selected Juju practitioners, stakeholders, and enthusiasts. It also employed the use of discography of Juju musicians. This paper discusses musical factors such as form, melodic and rhythmic patterns, and thematic materials, while highlighting cultural factors such as linguistic elements, with textual analysis, as a conscious avenue of expression. The study revealed that Juju musicians composed their music by engaging both indigenous and foreign musical materials, as a means of creative practice for musical entertainment, while expressing the people’s consciousness of their beliefs, values, and socio-political issues, hence the music functioning as a vehicle for social commentaries. The popularization and commercialization of Juju music brought the musicians national and international accolades, subsequently attracting contributions from contemporary musicians, which led to innovations of new brands, such as ‘Afro-Juju’, ‘Gospel-Juju’, ‘Hip-Hop-Juju’, etc., albeit retaining the basic musical elements of its progenitor, as a conscious music for socio-cultural functions. This study concludes that Juju music and its musicians remain germane in the musical scene of the nation’s social, educational, and political terrain, especially in the current Nigerian democratic climate. This paper recommends the promotion and patronage of the Juju music in its original form, to prevent its decline in current times, since it serves as an enrichment of national identity both in Nigeria, and Internationally.

Keywords: appropriation, consciousness, creative practice, national identity, neo-traditional

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Authors: Pouya Kaafi, Gholamreza Ghodrati Amiri

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Abnormal loads due to natural events, implementation errors and some other issues can lead to occurrence of progressive collapse in structures. Most of the past researches consist of 2- Dimensional (2D) models of steel frames without consideration of the floor system effects, which reduces the accuracy of the modeling. While employing a 3-Dimensional (3D) model and modeling the concrete slab system for the floors have a crucial role in the progressive collapse evaluation. In this research, a 3D finite element model of a 5-story steel building is modeled by the ABAQUS software once with modeling the slabs, and the next time without considering them. Then, the progressive collapse potential is evaluated. The results of the analyses indicate that the lack of the consideration of the slabs during the analyses, can lead to inaccuracy in assessing the progressive failure potential of the structure.

Keywords: abnormal loads, composite floor system, intermediate steel moment resisting frame system, progressive collapse

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Authors: Sebastian Kölbl, Thomas Reitmaier, Mathias Hartmann

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Trial and error approaches to adapt wearable support structures to human physiology are time consuming and elaborate. However, during preliminary design, the focus lies on understanding the interaction between exoskeleton and the human body in terms of forces and moments, namely body mechanics. For the study at hand, a multi-body simulation approach has been enhanced to evaluate actual forces and moments in a human dummy model with and without a digital mock-up of an active exoskeleton. Therefore, different motion data have been gathered and processed to perform a musculosceletal analysis. The motion data are ground reaction forces, electromyography data (EMG) and human motion data recorded with a marker-based motion capture system. Based on the experimental data, the response of the human dummy model has been calibrated. Subsequently, the scalable human dummy model, in conjunction with the motion data, is connected with the exoskeleton structure. The results of the human-machine interaction (HMI) simulation platform are in particular resulting contact forces and human joint forces to compare with admissible values with regard to the human physiology. Furthermore, it provides feedback for the sizing of the exoskeleton structure in terms of resulting interface forces (stress justification) and the effect of its compliance. A stepwise approach for the setup and validation of the modeling strategy is presented and the potential for a more time and cost-effective development of wearable support structures is outlined.

Keywords: assistive devices, ergonomic design, inverse dynamics, inverse kinematics, multibody simulation

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Authors: J. Banski

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Regardless of residence place, the type of business and the social system, an individual or groups of people use the accumulated knowledge and continuously deepen and expand its scope. Knowledge is needed by human beings to carry out certain tasks, achieve desired goals or make decisions. Knowledge is an attribute of the people of a region and is identified with the total experience and information that its residents and institutions possess, including the ability to use it. It is subject to constant development, which is the result of both the deepening and exchange of knowledge among the residents of a particular area, as well as the influx of knowledge with newly arriving residents. A good example of the aforementioned processes is in rural areas, where we are dealing with two basic groups of people between whom knowledge transfer takes place. The first group is made up of people who have lived in the village for a long time, while the second group is made up of people who migrate temporarily or permanently to the countryside. The English-language literature uses the terms oldtimers and newcomers for these groups, respectively. Newcomers, usually possessing different life experiences, cultural patterns and competencies, can be rich sources of knowledge for villagers. At the same time, the latter, with different knowledge and experience, along with knowledge of local conditions and customs, can also be an important source of knowledge for incomers to the countryside. The countryside is a particularly interesting environment for studying social interactions and the accompanying transfer of knowledge. This is because it is characterized by a high intensity of neighborly contact and a high level of trust in the private sphere. As a result of the migratory influx of new residents, the social and cultural image of the countryside is changing due to the interpenetration of urban and rural life patterns. Research on rural-urban knowledge transfer is both an opportunity to halt negative trends in the social and economic development of rural areas and support the establishment of a basis for rural renewal. This paper discusses the results of research on urban-rural knowledge transfer based on case studies carried out in a dozen villages from different regions of Poland. Their purpose was to answer three basic research questions: 1) what types of knowledge are transferred between urban and rural residents? 2) what are the main directions and intensity in knowledge transfer? And 3) what are the consequences of knowledge transfer between urban and rural residents?

Keywords: rural areas, villages, newcomers, knowledge transfer, Poland

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Authors: Afef Dridi, Salah Mezlini

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Since experimental devices cannot calculate stress and deformation of complex structures. The Finite Element Method FEM has been widely used in several fields of research. One of these fields is orthodontics. The advantage of using such a method is the use of an accurate and non invasive method that allows us to have a sufficient data about the physiological reactions can happening in soft tissues. Most of researches done in this field were interested in the study of stresses and deformations induced by orthodontic apparatus in soft tissues (alveolar tissues). Only few studies were interested in the distribution of stress and strain in the orthodontic brackets. These studies, although they tried to be as close as possible to real conditions, their models did not reproduce the clinical cases. For this reason, the model generated by our research is the closest one to reality. In this study, a numerical model was developed to explore the stress and strain distribution under the application of real conditions. A comparison between different material properties was also done.

Keywords: visco-hyperelasticity, FEM, orthodontic treatment, inverse method

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Authors: Ivan Balachin, Geanette Polanco, Jiang Xingliang, Hu Qin

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The problem of global warming raised up interest towards renewable energy sources. To reduce cost of wind energy is a challenge. Before building of wind park conditions such as: average wind speed, direction, time for each wind, probability of icing, must be considered in the design phase. Operation values used on the setting of control systems also will depend on mentioned variables. Here it is proposed a procedure to be include in the evaluation of the performance of a wind turbine, based on the amplitude of wind changes, the number of changes and their duration. A generic study case based on actual data is presented. Data analysing techniques were applied to model the power required for yaw system based on amplitude and data amount of wind changes. A theoretical model between time, amplitude of wind changes and angular speed of nacelle rotation was identified.

Keywords: field data processing, regression determination, wind turbine performance, wind turbine placing, yaw system losses

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Authors: Garima Joshi, Pratap Sharan, V. Sreenivas, Nand Kumar, Kameshwar Prasad, Ashima N. Wadhawan

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Schizophrenia is a debilitating disorder and is marked by cognitive impairment, which deleteriously impacts the social and professional functioning along with the quality of life of the patients and the caregivers. Often the cognitive symptoms are in their prodromal state and worsen as the illness progresses; they have proven to have a good predictive value for the prognosis of the illness. It has been shown that intensive cognitive rehabilitation (CR) leads to improvements in the healthy as well as cognitively-impaired subjects. As the majority of population in India falls in the lower to middle socio-economic status and have low education levels, using the existing packages, a majority of which are developed in the West, for cognitive rehabilitation becomes difficult. The use of technology is also restricted due to the high costs involved and the limited availability and familiarity with computers and other devices, which pose as an impedance for continued therapy. Cognitive rehabilitation in India uses a plethora of retraining methods for the patients with schizophrenia targeting the functions of attention, information processing, executive functions, learning and memory, and comprehension along with Social Cognition. Psychologists often have to follow an integrative therapy approach involving social skills training, family therapy and psychoeducation in order to maintain the gains from the cognitive rehabilitation in the long run. This paper reviews the methodologies and cognitive retaining programs used in India. It attempts to elucidate the evolution and development of methodologies used, from traditional paper-pencil based retraining to more sophisticated neuroscience-informed techniques in cognitive rehabilitation of deficits in schizophrenia as home-based or supervised and guided programs for cognitive rehabilitation.

Keywords: schizophrenia, cognitive rehabilitation, neuropsychological interventions, integrated approached to rehabilitation

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Authors: Xincheng Jiang, Yanyan An, Yaoyao Huang, Wei Ding, Manli Sun, Hong Li, Huaili Zheng

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The residual antibiotics in the environment will pose a threat to the environment and human health. Thus, efficient removal and rapid detection of norfloxacin (NOR) in wastewater is very important. The main sources of NOR pollution are the agricultural, pharmaceutical industry and hospital wastewater. The total consumption of NOR in China can reach 5440 tons per year. It is found that neither animals nor humans can totally absorb and metabolize NOR, resulting in the excretion of NOR into the environment. Therefore, residual NOR has been detected in water bodies. The hazards of NOR in wastewater lie in three aspects: (1) the removal capacity of the wastewater treatment plant for NOR is limited (it is reported that the average removal efficiency of NOR in the wastewater treatment plant is only 68%); (2) NOR entering the environment will lead to the emergence of drug-resistant strains; (3) NOR is toxic to many aquatic species. At present, the removal and detection technologies of NOR are applied separately, which leads to a cumbersome operation process. The development of simultaneous adsorption-flocculation removal and FTIR detection of pollutants has three advantages: (1) Adsorption-flocculation technology promotes the detection technology (the enrichment effect on the material surface improves the detection ability); (2) The integration of adsorption-flocculation technology and detection technology reduces the material cost and makes the operation easier; (3) FTIR detection technology endows the water treatment agent with the ability of molecular recognition and semi-quantitative detection for pollutants. Thus, it is of great significance to develop a smart water treatment material with high removal capacity and detection ability for pollutants. This study explored the feasibility of combining NOR removal method with the semi-quantitative detection method. A magnetic Cu-Ni-S/Ni foam was synthesized by in-situ loading Cu-Ni-S nanostructures on the surface of Ni foam. The novelty of this material is the combination of adsorption-flocculation technology and semi-quantitative detection technology. Batch experiments showed that Cu-Ni-S/Ni foam has a high removal rate of NOR (96.92%), wide pH adaptability (pH=4.0-10.0) and strong ion interference resistance (0.1-100 mmol/L). According to the Langmuir fitting model, the removal capacity can reach 417.4 mg/g at 25 °C, which is much higher than that of other water treatment agents reported in most studies. Characterization analysis indicated that the main removal mechanisms are surface complexation, cation bridging, electrostatic attraction, precipitation and flocculation. Transmission FTIR detection experiments showed that NOR on Cu-Ni-S/Ni foam has easily recognizable FTIR fingerprints; the intensity of characteristic peaks roughly reflects the concentration information to some extent. This semi-quantitative detection method has a wide linear range (5-100 mg/L) and a low limit of detection (4.6 mg/L). These results show that Cu-Ni-S/Ni foam has excellent removal performance and semi-quantitative detection ability of NOR molecules. This paper provides a new idea for designing and preparing multi-functional water treatment materials to achieve simultaneous removal and semi-quantitative detection of organic pollutants in water.

Keywords: adsorption-flocculation, antibiotics detection, Cu-Ni-S/Ni foam, norfloxacin

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Authors: M. Najafi, F. Rahimi Dehgolan

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In this paper, the dynamic modeling of a single-link flexible beam with a tip mass is given by using Hamilton's principle. The link has been rotational and translational motion and it was assumed that the beam is moving with a harmonic velocity about a constant mean velocity. Non-linearity has been introduced by including the non-linear strain to the analysis. Dynamic model is obtained by Euler-Bernoulli beam assumption and modal expansion method. Also, the effects of rotary inertia, axial force, and associated boundary conditions of the dynamic model were analyzed. Since the complex boundary value problem cannot be solved analytically, the multiple scale method is utilized to obtain an approximate solution. Finally, the effects of several conditions on the differences among the behavior of the non-linear term, mean velocity on natural frequencies and the system stability are discussed.

Keywords: non-linear vibration, stability, axially moving beam, bifurcation, multiple scales method

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Authors: Kunle Oparinde

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The Covid-19 pandemic has seen several rumors and conspiracy theories overtake the truth in many online platforms across several African countries. Just as the coronavirus has travelled widely, misinformation has equally spread. Thus, it is important to launch investigations into these conspiracy theories in order to detect them early and as a result, implore health practitioners and agencies to be more proactive in repelling misinformation while at the same time provide the general populace with purely undiluted information regarding the virus. Through social media posts on platforms such as Twitter, Facebook, and WhatsApp, as well as online platforms such as Google, this study intends to draw as many instances as possible of infodemic and misinformation by reviewing and analyzing these texts and the resulting implication if the misinformation continues to gain popularity. The study discovers the use of conspiracy theories, rumors, hyperbolism, and unverified claims as elements of infodemic used during the coronavirus pandemic. Importantly, the findings of the study will assist the public to be cautious and vigilant against false information that are being peddled as original.

Keywords: infodemic, miscommunication, accuracy, social media, rumors, conspiracy

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Authors: Hui-Chieh Li

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Circular economy promotes greater resource productivity and avoids pollution through greater recycling and re-use which bring benefits for both the environment and the economy. The concept is contrast to a linear economy which is ‘take, make, dispose’ model of production. A well-design reverse logistics service strategy could enhance the willingness of recycling of the users and reduce the related logistics cost as well as carbon emissions. Moreover, the recycle brings the manufacturers most advantages as it targets components for closed-loop reuse, essentially converting materials and components from worn-out product into inputs for new ones at right time and right place. This study considers demand-supply interaction, time-dependent recycle demand, time-dependent surplus value of recycled product and constructs models on recycle service strategy for the recyclable waste collector. A crucial factor in optimizing a recycle service strategy is consumer demand. The study considers the relationships between consumer demand towards recycle and product characteristics, surplus value and user behavior. The study proposes a recycle service strategy which differs significantly from the conventional and typical uniform service strategy. Periods with considerable demand and large surplus product value suggest frequent and short service cycle. The study explores how to determine a recycle service strategy for recyclable waste collector in terms of service cycle frequency and duration and vehicle type for all service cycles by considering surplus value of recycled product, time-dependent demand, transportation economies and demand-supply interaction. The recyclable waste collector is responsible for the collection of waste product for the manufacturer. The study also examines the impacts of utilization rate on the cost and profit in the context of different sizes of vehicles. The model applies mathematical programming methods and attempts to maximize the total profit of the distributor during the study period. This study applies the binary logit model, analytical model and mathematical programming methods to the problem. The model specifically explores how to determine a recycle service strategy for the recycler by considering product surplus value, time-dependent recycle demand, transportation economies and demand-supply interaction. The model applies mathematical programming methods and attempts to minimize the total logistics cost of the recycler and maximize the recycle benefits of the manufacturer during the study period. The study relaxes the constant demand assumption and examines how service strategy affects consumer demand towards waste recycling. Results of the study not only help understanding how the user demand for recycle service and product surplus value affects the logistics cost and manufacturer’s benefits, but also provide guidance such as award bonus and carbon emission regulations for the government.

Keywords: circular economy, consumer demand, product surplus value, recycle service strategy

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Authors: Ram Prasad Baral

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A large number of herb materials contain Essential Oils with extensive bioactivities. Acknowledging the importance of plants and its medicinal value, extraction of Essential Oil had been done using Steam Distillation method. In this project, Steam Distillation was used to extract oil from different plant materials like Chamomilla recutita (L.) Rauschert, Artemisia Vulgaris L, Rhododendron anthopogon D. Don, Cymbopogon nardus L, Andropogon nardus, Cinnamomum tamala, Juniperus spp, Cymbopohonflexuosus flexuous, Mantha Arvensia, Nardostachys Jatamansi, Wintergreen Essential Oil, and Valeriana Officinalis. Research has confirmed centuries of practical use of essential oils, and we now know that the 'fragrant pharmacy' contains compounds with an extremely broad range of biochemical effects. Essential oils are so termed as they are believed to represent the very essence of odor and flavor. The recovery of Essential Oil from the raw botanical starting material is very important since the quality of the oil is greatly influenced during this step. There is a variety of methods for obtaining volatile oils from plants. Steam distillation method was found to be one of the promising techniques for the extraction of essential oil from plants as reputable distiller will preserve the original qualities of the plant. The distillation was conducted in Clevenger apparatus in which boiling, condensing, and decantation was done. Analysis of essential oil was done using Gas Chromatography-Mass Spectrometer apparatus, which gives evaluates essential oil qualitatively and quantitatively. The volume of essential oil obtained was changing with respect to temperature and time of heating.

Keywords: Chamomilla recutita (L.) Rauschert, Artemisia Vulgaris L, Rhododendron anthopogon D. Don, Cymbopogon nardus L, Andropogon nardus, Cinnamomum tamala, Juniperus spp, Cymbopohonflexuosus flexuous, Mantha

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Authors: Samar Rabah

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Background: Incense woods are special kind of trees called Agarwood, which characterized by good smelling odors and many medical benefits. Incense smoke is heavily used in Saudi Arabia although comprehensive studies of its effects on health are limited. The present study demonstrated lung ultrastructure changes of mice after exposure and cessation to Incense smoke. Eighty mice are divided equally into four groups, three groups are exposed to different concentrations of Incense smoke (2, 4 and 6 gm) for three months, while the fourth group is control one. At the end of each month, lungs of five animals from each group are gathered, while the last five animals from each group are kept for another 60 days without exposure to the Incense smoke to allow for recovery. Results: Transmission electron microscope investigations of all exposed groups showed hypertrophy and hyperplasia in Clara Cells and some an enlargement of the macrophage to the point that it fills a large part of the alveolar lumen. Scanning electron microscope marks presence of mucus materials attached to the epithelial bronchioles. After prevention of exposure to the Incense smoke for 60 days, necrosis and degeneration in some cells of epithelial bronchioles, fibrosis of peribronchial, thickening in alveolar walls and aggregation of lymphoid cells were demonstrated. Conclusion: Based on the above findings and other related studies (not published), we conclude that exposure to Incense smoke causes harmful effects due to sever changes in pulmonary ultrastructure, such effects do not disappear even when Incense smoke inhalation was stopped. Therefore, we recommend that Incense smoke should use only in open places to reduce its harms.

Keywords: Incense smoke, lungs, ultrastructure of lungs, Agarwood

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Authors: Noga Bregman

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Accurate and efficient earthquake detection and phase picking are crucial for seismic hazard assessment and emergency response. This study introduces EQMamba, a deep-learning method that combines the strengths of the Earthquake Transformer and the Mamba model for simultaneous earthquake detection and phase picking. EQMamba leverages the computational efficiency of Mamba layers to process longer seismic sequences while maintaining a manageable model size. The proposed architecture integrates convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and Mamba blocks. The model employs an encoder composed of convolutional layers and max pooling operations, followed by residual CNN blocks for feature extraction. Mamba blocks are applied to the outputs of BiLSTM blocks, efficiently capturing long-range dependencies in seismic data. Separate decoders are used for earthquake detection, P-wave picking, and S-wave picking. We trained and evaluated EQMamba using a subset of the STEAD dataset, a comprehensive collection of labeled seismic waveforms. The model was trained using a weighted combination of binary cross-entropy loss functions for each task, with the Adam optimizer and a scheduled learning rate. Data augmentation techniques were employed to enhance the model's robustness. Performance comparisons were conducted between EQMamba and the EQTransformer over 20 epochs on this modest-sized STEAD subset. Results demonstrate that EQMamba achieves superior performance, with higher F1 scores and faster convergence compared to EQTransformer. EQMamba reached F1 scores of 0.8 by epoch 5 and maintained higher scores throughout training. The model also exhibited more stable validation performance, indicating good generalization capabilities. While both models showed lower accuracy in phase-picking tasks compared to detection, EQMamba's overall performance suggests significant potential for improving seismic data analysis. The rapid convergence and superior F1 scores of EQMamba, even on a modest-sized dataset, indicate promising scalability for larger datasets. This study contributes to the field of earthquake engineering by presenting a computationally efficient and accurate method for simultaneous earthquake detection and phase picking. Future work will focus on incorporating Mamba layers into the P and S pickers and further optimizing the architecture for seismic data specifics. The EQMamba method holds the potential for enhancing real-time earthquake monitoring systems and improving our understanding of seismic events.

Keywords: earthquake, detection, phase picking, s waves, p waves, transformer, deep learning, seismic waves

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Authors: Hyun Seop Uhm, Yong Ho Choi, Ji Eun Kim, Young Hoon Lee

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Weapon-target assignment (WTA) is a problem that assigns available launchers to appropriate targets in order to defend assets. Various algorithms for WTA have been developed over past years for both in the static and dynamic environment (denoted by SWTA and DWTA respectively). Due to the problem requirement to be solved in a relevant computational time, WTA has suffered from the solution efficiency. As a result, SWTA and DWTA problems have been solved in the limited situation of the battlefield. In this paper, the general situation under continuous time is considered by Time based Weapon Target Assignment (TWTA) problem. TWTA are studied using the mixed integer programming model, and three heuristic algorithms; decomposed opt-opt, decomposed opt-greedy, and greedy algorithms are suggested. Although the TWTA optimization model works inefficiently when it is characterized by a large size, the decomposed opt-opt algorithm based on the linearization and decomposition method extracted efficient solutions in a reasonable computation time. Because the computation time of the scheduling part is too long to solve by the optimization model, several algorithms based on greedy is proposed. The models show lower performance value than that of the decomposed opt-opt algorithm, but very short time is needed to compute. Hence, this paper proposes an improved method by applying decomposition to TWTA, and more practical and effectual methods can be developed for using TWTA on the battlefield.

Keywords: air and missile defense, weapon target assignment, mixed integer programming, piecewise linearization, decomposition algorithm, military operations research

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Authors: Armin Rahmatfam, Mohammad Zehsaz, Farid Vakili Tahami, Nasser Ghassembaglou

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In this paper the thermal ratcheting, kinematic hardening parameters C, γ, isotropic hardening parameters and also k, b, Q combined isotropic/kinematic hardening parameters have been obtained experimentally from the monotonic, strain controlled cyclic tests at room and elevated temperatures of 20°C, 100°C, and 400°C. These parameters are used in nonlinear combined isotropic/kinematic hardening model to predict better description of the loading and reloading cycles in the cyclic indentation as well as thermal ratcheting. For this purpose, three groups of specimens made of Aluminum 7075-T6 have been investigated. After each test and using stable hysteretic cycles, material parameters have been obtained for using in combined nonlinear isotropic/kinematic hardening models. Also the methodology of obtaining the correct kinematic/isotropic hardening parameters is presented.

Keywords: combined hardening model, kinematic hardening, isotropic hardening, cyclic tests

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Authors: Manoel Pereira, Alvaro Veiga, Camila Epprecht, Renato Costa

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This paper introduces an empirical version of the Esscher transform for risk-neutral option pricing. Traditional parametric methods require the formulation of an explicit risk-neutral model and are operational only for a few probability distributions for the returns of the underlying. In our proposal, we make only mild assumptions on the pricing kernel and there is no need for the formulation of the risk-neutral model for the returns. First, we simulate sample paths for the returns under the physical distribution. Then, based on the empirical Esscher transform, the sample is reweighted, giving rise to a risk-neutralized sample from which derivative prices can be obtained by a weighted sum of the options pay-offs in each path. We compare our proposal with some traditional parametric pricing methods in four experiments with artificial and real data.

Keywords: esscher transform, generalized autoregressive Conditional Heteroscedastic (GARCH), nonparametric option pricing

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Authors: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Helena Baffoe

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Context: In the United States, there have been significant advancements in programs aimed at improving the lives of individuals with mental health disorders and substance abuse problems. However, public attitudes and beliefs regarding these issues have not improved correspondingly. This study aims to explore the perceptions and beliefs surrounding mental health disorders and substance abuse in the context of data analytics in the field of social work. Research Aim: The aim of this research is to provide empirical evidence on the beliefs and perceptions regarding mental health disorders and substance abuse. Specifically, the study seeks to answer the question of whether being diagnosed with a mental disorder implies a diagnosis of substance abuse. Additionally, the research aims to analyze the specific roles that social workers can play in addressing individuals with mental disorders. Methodology: This research adopts a data-driven methodology, acquiring comprehensive data from the Substance Abuse and Mental Health Services Administration (SAMHSA). A noteworthy causal connection between mental disorders and substance abuse exists, a relationship that current literature tends to overlook critically. To address this gap, we applied logistic regression with an Instrumental Variable approach, effectively mitigating potential endogeneity issues in the analysis in order to ensure robust and unbiased results. This methodology allows for a rigorous examination of the relationship between mental disorders and substance abuse. Empirical Findings: The analysis of the data reveals that depressive, anxiety, and trauma/stressor mental disorders are the most common in the United States. However, the study does not find statistically significant evidence to support the notion that being diagnosed with these mental disorders necessarily implies a diagnosis of substance abuse. This suggests that there is a misconception among the public regarding the relationship between mental health disorders and substance abuse. Theoretical Importance: The research contributes to the existing body of literature by providing empirical evidence to challenge prevailing beliefs and perceptions regarding mental health disorders and substance abuse. By using a novel methodological approach and analyzing new US data, the study sheds light on the cultural and social factors that influence these attitudes.

Keywords: mental health disorder, substance abuse, empirical evidence, logistic regression with IV

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Authors: Mai M. Farid, Ximeng Yang, Tomoharu Kuboyama, Chihiro Tohda

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Trigonelline is a major alkaloid component derived from Trigonella foenum-graecum L. (fenugreek) and has been reported before as a potential neuroprotective agent, especially in Alzheimer’s disease (AD). However, the previous data were unclear and used model mice were not well established. In the present study, the effect of trigonelline on memory function was investigated in Alzheimer’s disease transgenic model mouse, 5XFAD which overexpresses the mutated APP and PS1 genes. Oral administration of trigonelline for 14 days significantly enhanced object recognition and object location memories. Plasma and cerebral cortex were isolated at 30 min, 1h, 3h, and 6 h after oral administration of trigonelline. LC-MS/MS analysis indicated that trigonelline was detected in both plasma and cortex from 30 min after, suggesting good penetration of trigonelline into the brain. In addition, trigonelline significantly ameliorated axonal and dendrite atrophy in Amyloid β-treated cortical neurons. These results suggest that trigonelline could be a promising therapeutic candidate for AD.

Keywords: alzheimer’s disease, cortical neurons, LC-MS/MS analysis, trigonelline

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Authors: Tatiana Ponomarenko, Oksana Marinina, Marina Nevskaya

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Modern corporate social responsibility (CSR) is a sphere of multilevel responsibility of a company toward society represented by various stakeholders. The relevance of CSR management grows due to the active development of socially responsible investing (principles for responsible investment) taking into account factors of environmental, social and corporate governance (ESG), growing attention of the investment community in general to the long-term stability of companies and the quality of control of nonfinancial risks. The modern approach to CSR strategic management is aimed at the creation of trustful relationships with stakeholders, on the basis of which a contribution to the sustainable development of companies, regions, and national economics is insured. However, the practical concepts of social responsibility in mining companies are different, which leads to various degrees of application of CSR. A number of companies implement CSR using a traditional (limited) understanding of responsibility toward employees and counteragents, the others understand CSR much wider and try to use leverages of efficient cooperation. As in large mining companies the scope of CSR measures is diverse and characterized by different indices, the study was aimed at evaluating CSR efficiency on the basis of a proprietary methodology and determining the level of development of CSR management in terms of anti-crisis, reactive and proactive development. The methodology of the research includes analysis of integrated global reporting initiative (GRI) reports of large mining companies; choice of most representative sectoral agents by a criterion of the regularity of issuance and publication of reports; calculation of indices of evaluation of CSR level of the selected companies in dynamics. The methodology of evaluation of CSR level is based on a rating score of changes in standard indices of GRI reports by economic, environmental, and social directions. Result. By the results of the analysis, companies of fuel and energy and metallurgic complexes, in overwhelming majority, reflecting three indices out of a wide range of possible indicators of SDGs (Sustainable Development Goals), were selected for the study. The evaluation of the scopes of CSR of the companies Gazprom, LUKOIL, Metalloinvest, Nornikel, Rosneft, Severstal, SIBUR, SUEK corresponds to the reactive type of development according to a scale of CSR strategic management, which is the average value out of the possible values. The chief drawback is that companies, in the process of analyzing global goals, often choose the goals which relate to their own activities, paying insufficient attention to the interests of the stakeholders inside the country. This fact evidences the necessity of searching for more effective mechanisms of CSR control. Acknowledgment: This article is prepared within grant support of the RFBR, project 19-510-44013 'Development of the concept of mineral resources value formation in the context of sustainable development in resource-oriented economies'.

Keywords: sustainable development, corporate social responsibility, development strategies, efficiency assessment

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Authors: Ahmed Alaoui

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This paper contributes to the ongoing debate as to the relevance of translation studies to professional practitioners. It exposes the various misconceptions permeating the links between theory and practice in the translation landscape in the Arab World. It is a thesis of this paper that specialization in translation should be redefined; taking account of the fact, that specialized knowledge alone is neither crucial nor sufficient in technical translation. It should be tested against the readability of the translated text, the appropriateness of its style and the usability of its content by end-users to carry out their intended tasks. The paper also proposes a preliminary model to establish a working link between theory and practice from the perspective of professional trainers and practitioners, calling for the latter to participate in the production of knowledge in a systematic fashion. While this proposal is driven by a rather intuitive conviction, a research line is needed to specify the methodological moves to establish the mediation strategies that would relate the components in the model of knowledge transfer proposed in this paper.

Keywords: knowledge transfer, misconceptions, specialized texts, translation theory, translation practice

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Authors: Jixiang Zhang, Yanhua Wang

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A dynamic of Bertrand duopoly game is analyzed, where players use different production methods and choose their prices with bounded rationality. The equilibriums of the corresponding discrete dynamical systems are investigated. The stability conditions of Nash equilibrium under a local adjustment process are studied. The stability conditions of Nash equilibrium under a local adjustment process are studied. The stability of Nash equilibrium, as some parameters of the model are varied, gives rise to complex dynamics such as cycles of higher order and chaos. On this basis, we discover that an increase of adjustment speed of bounded rational player can make Bertrand market sink into the chaotic state. Finally, the complex dynamics, bifurcations and chaos are displayed by numerical simulation.

Keywords: Bertrand duopoly model, discrete dynamical system, heterogeneous expectations, nash equilibrium

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Authors: Kuplevatsky V., Kuplevatskay, Cherkashin M., Berezina N.

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After 6 months, a violation of the differentiation of the structure of the gland due to edema in 100%. 20% retained signs of a tumor according to DWI/ADC data. By 12 months, the reduction in the size of the gland is 100%. In all cases, no diffusion restriction was observed. The study after 18 months showed no significant changes in all (100%) patients. In the study, 24 months after treatment, the size of the gland was stable in all cases (+/- up to 5%). Diffuse decrease in T2VI signals from peripheral zones, without signs of diffusion restriction in 100%. After 30 months, signs of recovery of adenomatous changes in the transient zone were revealed in 85%. After 36 and 42 months, the restoration of organ differentiation was observed in 93% of patients. In 4 patients, by the 48th month, signs of biochemical relapse were clinically noted. According to the MRI data, signs of a local relapse were revealed. After 48 months, there were signs of restoration of organ differentiation, which allowed the use of PI-RADS criteria. The study after 54 months showed no changes compared to the control. 60 months after treatment, 97% of patients showed a restoration of differentiation of the gland structure, which allows evaluating the organ according to PI-RADS criteria Conclusions: The beginning of restoration of the structure of the prostate gland began 24 months after proton radiation therapy, the PI-RADS criteria can be fully applied after 48 months of treatment. Control studies every 6 months without clinical signs of relapse are not advisable. Local control of the prostate tumor after proton radiation therapy was achieved in 95% of patients during the entire follow-up period ( 60 months).

Keywords: proton therapy, prostate cancer, MRI imaging, PI-RADS

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Authors: Marcho Setiawan Handok, Samuel S. Lemma, Abdoulaye Fofana, Naseef Mansoor

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This paper is proposing a method to predict the outcome of the National Football League match with data from 2019 to 2022 and compare it with other popular models. The model uses open-source statistical data of each team, such as passing yards, rushing yards, fumbles lost, and scoring. Each statistical data has offensive and defensive. For instance, a data set of anticipated values for a specific matchup is created by comparing the offensive passing yards obtained by one team to the defensive passing yards given by the opposition. We evaluated the model’s performance by contrasting its result with those of established prediction algorithms. This research is using a neural network to predict the score of a National Football League match and then predict the winner of the game.

Keywords: game prediction, NFL, football, artificial neural network

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Authors: P. S. Jain, K. D. Bobade, S. J. Surana

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A simple, selective, precise and Stability-indicating High-performance thin-layer chromatographic method for analysis of Naftopidil both in a bulk and in pharmaceutical formulation has been developed and validated. The method employed, HPTLC aluminium plates precoated with silica gel as the stationary phase. The solvent system consisted of hexane: ethyl acetate: glacial acetic acid (4:4:2 v/v). The system was found to give compact spot for Naftopidil (Rf value of 0.43±0.02). Densitometric analysis of Naftopidil was carried out in the absorbance mode at 253 nm. The linear regression analysis data for the calibration plots showed good linear relationship with r2=0.999±0.0001 with respect to peak area in the concentration range 200-1200 ng per spot. The method was validated for precision, recovery and robustness. The limits of detection and quantification were 20.35 and 61.68 ng per spot, respectively. Naftopidil was subjected to acid and alkali hydrolysis, oxidation and thermal degradation. The drug undergoes degradation under acidic, basic, oxidation and thermal conditions. This indicates that the drug is susceptible to acid, base, oxidation and thermal conditions. The degraded product was well resolved from the pure drug with significantly different Rf value. Statistical analysis proves that the method is repeatable, selective and accurate for the estimation of investigated drug. The proposed developed HPTLC method can be applied for identification and quantitative determination of Naftopidil in bulk drug and pharmaceutical formulation.

Keywords: naftopidil, HPTLC, validation, stability, degradation

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Authors: Rabbia Idrees, Ananda Maiti, Saurabh Garg, Muhammad Bilal Amin

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IoT devices are the basic building blocks of IoT network that generate enormous volume of real-time and high-speed data to help organizations and companies to take intelligent decisions. To integrate this enormous data from multisource and transfer it to the appropriate client is the fundamental of IoT development. The handling of this huge quantity of devices along with the huge volume of data is very challenging. The IoT devices are battery-powered and resource-constrained and to provide energy efficient communication, these IoT devices go sleep or online/wakeup periodically and a-periodically depending on the traffic loads to reduce energy consumption. Sometime these devices get disconnected due to device battery depletion. If the node is not available in the network, then the IoT network provides incomplete, missing, and inaccurate data. Moreover, many IoT applications, like vehicle tracking and patient tracking require the IoT devices to be mobile. Due to this mobility, If the distance of the device from the sink node become greater than required, the connection is lost. Due to this disconnection other devices join the network for replacing the broken-down and left devices. This make IoT devices dynamic in nature which brings uncertainty and unreliability in the IoT network and hence produce bad quality of data. Due to this dynamic nature of IoT devices we do not know the actual reason of abnormal data. If data are of poor-quality decisions are likely to be unsound. It is highly important to process data and estimate data quality before bringing it to use in IoT applications. In the past many researchers tried to estimate data quality and provided several Machine Learning (ML), stochastic and statistical methods to perform analysis on stored data in the data processing layer, without focusing the challenges and issues arises from the dynamic nature of IoT devices and how it is impacting data quality. A comprehensive review on determining the impact of dynamic nature of IoT devices on data quality is done in this research and presented a data quality model that can deal with this challenge and produce good quality of data. This research presents the data quality model for the sensors monitoring water quality. DBSCAN clustering and weather sensors are used in this research to make data quality model for the sensors monitoring water quality. An extensive study has been done in this research on finding the relationship between the data of weather sensors and sensors monitoring water quality of the lakes and beaches. The detailed theoretical analysis has been presented in this research mentioning correlation between independent data streams of the two sets of sensors. With the help of the analysis and DBSCAN, a data quality model is prepared. This model encompasses five dimensions of data quality: outliers’ detection and removal, completeness, patterns of missing values and checks the accuracy of the data with the help of cluster’s position. At the end, the statistical analysis has been done on the clusters formed as the result of DBSCAN, and consistency is evaluated through Coefficient of Variation (CoV).

Keywords: clustering, data quality, DBSCAN, and Internet of things (IoT)

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Authors: Shanna Swart, Caryn Fenner, Athanasios Kotsiopoulos, Susan Harrison

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Linear alkanes are produced as by-products from the increasing use of gas-to-liquid fuel technologies for synthetic fuel production and offer great potential for value addition. Their current use as low-value fuels and solvents do not maximize this potential. Therefore, attention has been drawn towards direct activation of these aliphatic alkanes to more useful products such as alcohols, aldehydes, carboxylic acids and derivatives. Cytochrome P450 monooxygenases (P450s) can be used for activation of these aliphatic alkanes using whole-cells or cell-free systems. Some limitations of whole-cell systems include reduced mass transfer, stability and possible side reactions. Since the P450 systems are little studied as cell-free systems, they form the focus of this study. Challenges of a cell-free system include co-factor regeneration, substrate availability and enzyme stability. Enzyme immobilization offers a positive outlook on this dilemma, as it may enhance stability of the enzyme. In the present study, 2 different P450s (CYP153A6 and CYP102A1) as well as the relevant accessory enzymes required for electron transfer (ferredoxin and ferredoxin reductase) and co-factor regeneration (glucose dehydrogenase) have been expressed in E. coli and purified by metal affinity chromatography. Glucose dehydrogenase (GDH), was used as a model enzyme to assess the potential of various enzyme immobilization strategies including; surface attachment on MagReSyn® microspheres with various functionalities and on electrospun nanofibers, using self-assembly based methods forming Cross Linked Enzymes (CLE), Cross Linked Enzyme Aggregates (CLEAs) and spherezymes as well as in a sol gel. The nanofibers were synthesized by electrospinning, which required the building of an electrospinning machine. The nanofiber morphology has been analyzed by SEM and binding will be further verified by FT-IR. Covalent attachment based methods showed limitations where only ferredoxin reductase and GDH retained activity after immobilization which were largely attributed to insufficient electron transfer and inactivation caused by the crosslinkers (60% and 90% relative activity loss for the free enzyme when using 0.5% glutaraldehyde and glutaraldehyde/ethylenediamine (1:1 v/v), respectively). So far, initial experiments with GDH have shown the most potential when immobilized via their His-tag onto the surface of MagReSyn® microspheres functionalized with Ni-NTA. It was found that Crude GDH could be simultaneously purified and immobilized with sufficient activity retention. Immobilized pure and crude GDH could be recycled 9 and 10 times, respectively, with approximately 10% activity remaining. The immobilized GDH was also more stable than the free enzyme after storage for 14 days at 4˚C. This immobilization strategy will also be applied to the P450s and optimized with regards to enzyme loading and immobilization time, as well as characterized and compared with the free enzymes. It is anticipated that the proposed immobilization set-up will offer enhanced enzyme stability (as well as reusability and easy recovery), minimal mass transfer limitation, with continuous co-factor regeneration and minimal enzyme leaching. All of which provide a positive outlook on this robust multi-enzyme system for efficient activation of linear alkanes as well as the potential for immobilization of various multiple enzymes, including multimeric enzymes for different bio-catalytic applications beyond alkane activation.

Keywords: alkane activation, cytochrome P450 monooxygenase, enzyme catalysis, enzyme immobilization

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Authors: Amanda Silva Parra, Dayra Yisel García Ramirez

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In developing countries, agricultural "modernization" has led to a loss of biodiversity and inefficiency of agricultural systems, manifested in increases in Greenhouse Gas Emissions (GHG) and the C footprint, generating the susceptibility of systems agriculture to environmental problems, loss of biodiversity, depletion of natural resources, soil degradation and loss of nutrients, and a decrease in the supply of products that affect food security for peoples and nations. Each year agriculture emits 10 to 12% (5.1 to 6.1 Gt CO2eq per year) of the total estimated GHG emissions (51 Gt CO2 eq per year). The FAO recommends that countries that have not yet done so consider declaring sustainable agriculture as an essential or strategic activity of public interest within the framework of green economies to better face global climate change. The objective of this research was to estimate the balance of GHG in agricultural systems of Cumaral, Meta (Colombia), to contribute to the recovery and sustainable operation of agricultural systems that guarantee food security and face changes generated by the climate in a more intelligent way. To determine the GHG balances, the IPCC methodologies were applied with a Tier 1 and 2 level of use. It was estimated that all the silvopastoral systems evaluated play an important role in this reconversion compared to conventional systems such as improved pastures. and degraded pastures due to their ability to capture C both in soil and in biomass, generating positive GHG balances, guaranteeing greater sustainability of soil and air resources.

Keywords: climate change, carbon capture, environmental sustainability, GHG mitigation, silvopastoral systems

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Authors: Ahmet Diken, Tahsin Karabulut

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The factor which developing countries are in need is capital. Such countries make an effort to increase their income in order to meet their expenses for employment, infrastructure, superstructure investments, education, health and defense. The sole income of the countries is taxes collected from businesses. The businesses should drive profit and return in order to be able to toll. In a world where competition exists, different strategies may be followed by business in developing countries and they must specify their target markets. İn order to minimize cost and maximize profit, SMEs have to concentrate on target markets and select cost oriented strategy. In this study, a theoretical model is suggested that SME firms have to act as cluster between each other, and also must be optimal provider for large scale firms. SMEs’ policy must be supported by public. This relationship can benefit large scale firms to have brand over the world, and this organization increases value added for developing countries.

Keywords: competitiveness, countries, SMEs developing, sustainability

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