Search results for: deep foundation pit

Authors: Anggun Yuliana Putri, Melisa Rahmadini

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The Ministry of National Development Planning in Indonesia has reported that more than eight million Indonesian children are still malnourished. Based on national statistics, and a recent ranking from NGO Save the Children, Indonesia is one of 15 countries making the fastest gains in cutting child malnutrition among 165 developing countries. According to a United Nations Children’s Fund, at least 7.6 million Indonesian children under the age of 5 or one out of every three suffer from stunted growth, a primary manifestation of malnutrition in early childhood, the report ranked Indonesia as having the fifth largest number of children under 5 suffering from stunted growth worldwide. Addressing the problem of malnutrition in Indonesia, Aksi Cepat Tanggap (ACT) Foundation, a humanitarian organization working with Carrefour, acts as donor and pursues several solutions to the problem, especially of malnourished children and infants in South Tangerang area, Indonesia. The objective of this study was to examine the community empowerment driven by ACT Foundation in order to maintain the good status continuity of child and toddler after the children malnutrition recovered. Research was conducted using qualitative approach through in-depth interview and observation to find out how the Bengkel Gizi Terpadu (Integrated Nutrion Workshop) programs make people empowered. Bengkel Gizi Terpadu (BGT) is divided into 3 sequences of activities, there were: integrated malnutrition rehabilitation; provision of health education to mothers of infants and young children; and family economic empowerment to head of household. Results showed that after empowerment process has been done through training and provision of knowledge to the mothers and families about the important of nutrition and health, there were 30 of 100 mothers who participated actively. Then, there were 45 of 100 heads of household who participated in business training were able to open a business on their own which provided and controlled by ACT as stakeholder in this program. The further findings revealed that BGT programs are able to form community health workers and provide employment opportunities to community. This study believes that integrated nutrition workshop program is the solution to maintain good nutrition among children in South Tangerang, through empowerment of parents and community members, via education and business training program. Both programs prepared parents with economic sustenance and necessary information, a pre-requisite to end malnutrition in children.

Keywords: community, empowerment, malnutrition, training

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Authors: Mohd Imtiaz S, Saurabh Jain, Pothiraj K.

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In today’s highly competitive passenger vehicle market, comfortable driving experience is one of the key parameters significantly weighed by the customer. Smooth ride and handling of the vehicle with exceptionally reliable wheel end solution is a paramount requirement in passenger Sports Utility Vehicle (SUV) vehicles subjected to challenging terrains and loads with rigid rear drive axle configuration. Traditional wheel-end bearing systems in passenger segment rigid rear drive axle utilizes the semi-floating layout, which imparts vertical bending loads and torsion to the axle shafts. The wheel-end bearing is usually a Single or Double Row Deep-Groove Ball Bearing (DRDGBB) or Double Row Angular Contact Ball Bearing (DRACBB). This solution is cost effective and simple in architecture. However, it lacks effectiveness against the heavy loads subjected to a SUV vehicle, especially the axial trust at high-speed cornering. This paper describes the solution of Double Row Taper Roller Bearing (DRTRB) wheel-end for a SUV vehicle in the rigid rear drive axle and improvement in terms of maximizing its load carrying capacity along with better reliability in terms of axial thrust in high-speed cornering. It describes the advantage of geometry of DRTRB over DRDGBB and DRACBB highlighting contact and load flow. The paper also highlights the vehicle level considerations affecting the B10 life of the bearing system for better selection of the DRTRB wheel-ends systems. This paper also describes real time vehicle level results along with theoretical improvements.

Keywords: axial thrust, b10 life, deep-groove ball bearing, taper roller bearing, semi-floating layout.

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Authors: Velimir "monty" Vesselinov, Trais Kliplhuis, Hope Jasperson

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Artificial intelligence (AI) is a powerful tool in the geothermal energy sector, aiding in both exploration and utilization. Identifying promising geothermal sites can be challenging due to limited surface indicators and the need for expensive drilling to confirm subsurface resources. Geothermal reservoirs can be located deep underground and exhibit complex geological structures, making traditional exploration methods time-consuming and imprecise. AI algorithms can analyze vast datasets of geological, geophysical, and remote sensing data, including satellite imagery, seismic surveys, geochemistry, geology, etc. Machine learning algorithms can identify subtle patterns and relationships within this data, potentially revealing hidden geothermal potential in areas previously overlooked. To address these challenges, a SIML (Science-Informed Machine Learning) technology has been developed. SIML methods are different from traditional ML techniques. In both cases, the ML models are trained to predict the spatial distribution of an output (e.g., pressure, temperature, heat flux) based on a series of inputs (e.g., permeability, porosity, etc.). The traditional ML (a) relies on deep and wide neural networks (NNs) based on simple algebraic mappings to represent complex processes. In contrast, the SIML neurons incorporate complex mappings (including constitutive relationships and physics/chemistry models). This results in ML models that have a physical meaning and satisfy physics laws and constraints. The prototype of the developed software, called GeoTGO, is accessible through the cloud. Our software prototype demonstrates how different data sources can be made available for processing, executed demonstrative SIML analyses, and presents the results in a table and graphic form.

Keywords: science-informed machine learning, artificial inteligence, exploration, utilization, hidden geothermal

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Authors: Shaibu Baanni Azumah, William Adzawla

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Rice is an important staple crop, with current demand higher than the domestic supply in Ghana. This has led to a high and unfavourable import bill. Therefore, recent policies and interventions in the agricultural sub-sector aim at promoting various improved agricultural technologies in order to improve domestic production and reduce the importation of rice. In this study, we examined the effect of the adoption of Urea Deep Placement (UDP) technology by rice farmers on the position of the production frontier. This involved 200 farmers selected through a multi stage sampling technique in the Northern region of Ghana. A Cobb-Douglas stochastic frontier model was fitted. The result showed that the adoption of UDP technology shifts the output frontier outward and also move the farmers closer to the frontier. Farmers were also operating under diminishing returns to scale which calls for redress. Other factors that significantly influenced rice production were farm size, labour, use of certified seeds and NPK fertilizer. Although there was an opportunity for improvement, the farmers were highly efficient (92%), compared to previous studies. Farmers’ efficiency was improved through increased education, household size, experience, access to credit, and lack of extension service provision by MoFA. The study recommends the revision of Ghana’s agricultural policy to include the UDP technology. Agricultural Extension officers of the Ministry of Food and Agriculture (MoFA) should be trained on the UDP technology to support IFDC’s drive to improve adoption by rice farmers. Rice farmers are also encouraged to expand their farm lands, improve plant population, and also increase the usage of fertilizer to improve yields. Mechanisms through which credit can be made easily accessible and effectively utilised should be identified and promoted.

Keywords: efficiency, rice farmers, stochastic frontier, UDP technology

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Authors: Mihaela Popescu, Alexandru Bitoleanu

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This paper is focused on the reference current calculation in the compensation mode of the active DC traction substations. The so-called p-q theory of the instantaneous reactive power is used as theoretical foundation. The compensation goal of total compensation is taken into consideration for the operation under both sinusoidal and nonsinusoidal voltage conditions, through the two objectives of unity power factor and perfect harmonic cancelation. Four blocks of reference current generation implement the conceived algorithms and they are included in a specific Simulink library, which is useful in a DSP dSPACE-based platform working under Matlab/Simulink. The simulation results validate the correctness of the implementation and fulfillment of the compensation tasks.

Keywords: active power filter, DC traction, p-q theory, Simulink library

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Authors: Ranjot Kaur, Om P. Katare, Anupama Sharma, Sarah R. Dennison, Kamalinder K. Singh, Bhupinder Singh

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Respiratory microbial infections being among the top leading cause of death worldwide are difficult to treat as the microbes reside deep inside the airways, where only a small fraction of drug can access after traditional oral or parenteral routes. As a result, high doses of drugs are required to maintain drug levels above minimum inhibitory concentrations (MIC) at the infection site, unfortunately leading to severe systemic side-effects. Therefore, delivering antimicrobials directly to the respiratory tract provides an attractive way out in such situations. In this context, current study embarks on the systematic development of lung lia pid-modified chitosan nanoparticles for inhalation of voriconazole. Following the principles of quality by design, the chitosan nanoparticles were prepared by ionic gelation method and further coated with major lung lipid by precipitation method. The factor screening studies were performed by fractional factorial design, followed by optimization of the nanoparticles by Box-Behnken Design. The optimized formulation has a particle size range of 170-180nm, PDI 0.3-0.4, zeta potential 14-17, entrapment efficiency 45-50% and drug loading of 3-5%. The presence of a lipid coating was confirmed by FESEM, FTIR, and X-RD. Furthermore, the nanoparticles were found to be safe upto 40µg/ml on A549 and Calu-3 cell lines. The quantitative and qualitative uptake studies also revealed the uptake of nanoparticles in lung epithelial cells. Moreover, the data from Spraytec and next-generation impactor studies confirmed the deposition of nanoparticles in lower airways. Also, the interaction of nanoparticles with DPPC monolayers signifies its biocompatibility with lungs. Overall, the study describes the methodology and potential of lipid-coated chitosan nanoparticles in futuristic inhalation nanomedicine for the management of pulmonary aspergillosis.

Keywords: dipalmitoylphosphatidylcholine, nebulization, DPPC monolayers, quality-by-design

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Authors: Danni Cong, Meiping Wu, Hua Mu, Xiaofeng He, Junxiang Lian, Juliang Cao, Shaokun Cai, Hao Qin

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Gravity field is of great significance in geoscience, national economy and national security, and gravitational gradient measurement has been extensively studied due to its higher accuracy than gravity measurement. Gravity gradient sensor, being one of core devices of the gravity gradient instrument, plays a key role in measuring accuracy. Therefore, this paper starts from analyzing the working principle of the gravity gradient sensor by Newton’s law, and then considers the relative motion between inertial and non-inertial systems to build a relatively adequate mathematical model, laying a foundation for the measurement error calibration, measurement accuracy improvement.

Keywords: gravity gradient, gravity gradient sensor, accelerometer, single-axis rotation modulation

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Authors: Ella Tyuryumina, Alexey Neznanov

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This study is an attempt to obtain reliable data on the natural history of breast cancer growth. We analyze the opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) to try to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. The research aim is to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoMPaS and corresponding software. We are interested in: 1) modelling the whole natural history of the primary tumor and the secondary distant metastases; 2) developing adequate and precise CoMPaS which reflects relations between the primary tumor and the secondary distant metastases; 3) analyzing the CoMPaS scope of application; 4) implementing the model as a software tool. The foundation of the CoMPaS is the exponential tumor growth model, which is described by determinate nonlinear and linear equations. The CoMPaS corresponds to TNM classification. It allows to calculate different growth periods of the primary tumor and the secondary distant metastases: 1) ‘non-visible period’ for the primary tumor; 2) ‘non-visible period’ for the secondary distant metastases; 3) ‘visible period’ for the secondary distant metastases. The CoMPaS is validated on clinical data of 10-years and 15-years survival depending on the tumor stage and diameter of the primary tumor. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer growth models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. The CoMPaS model and predictive software: a) fit to clinical trials data; b) detect different growth periods of the primary tumor and the secondary distant metastases; c) make forecast of the period of the secondary distant metastases appearance; d) have higher average prediction accuracy than the other tools; e) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoMPaS: the number of doublings for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases. The CoMPaS enables, for the first time, to predict ‘whole natural history’ of the primary tumor and the secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on the primary tumor sizes. Summarizing: a) CoMPaS describes correctly the primary tumor growth of IA, IIA, IIB, IIIB (T1-4N0M0) stages without metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and inception of the secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, metastases in lymph nodes, primary tumor, survival

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Authors: Paul Holmes, Mike N. G.

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Foundation-year doctors face increased stress, pressure and uncertainty when starting new rotations throughout their first years of work. This research questionnaire resulted in an induction cheat sheet and induction video that enhanced the Junior doctor's understanding of how to work effectively within the plastic surgery department at NHS Tayside. The objectives and goals were to improve the transition between cohorts of junior doctors in ward 26 at Ninewells Hospital. Before this quality improvement project, the induction pack was 74 pages long and over eight years old. With the support of consultant Mike Ng a new up-to-date induction was created. This involved a questionnaire and cheat sheet being developed. The questionnaire covered clerking, venipuncture, ward pharmacy, theatres, admissions, specialties on the ward, the cardiac arrest trolley, clinical emergencies, discharges and escalation. This audit has three completed cycles between August 2022 and August 2023. The cheat sheet developed a concise two-page A4 document designed for doctors to be able to reference easily and understand the essentials. The document format is a table containing ward layout; specialty; location; physician associate, shift patterns; ward rounds; handover location and time; hours coverage; senior escalation; nights; daytime duties, meetings/MDTs/board meetings, important bleeps and codes; department guidelines; boarders; referrals and patient stream; pharmacy; absences; rota coordinator; annual leave; top tips. The induction video is a 10-minute in-depth explanation of all aspects of the ward. The video explores in more depth the contents of the cheat sheet. This alternative visual format familiarizes the junior doctor with all aspects of the ward. These were provided to all foundation year 1 and 2 doctors on ward 26 at Ninewells Hospital at NHS Tayside Scotland. This work has since been adopted by the General Surgery Department, which extends to six further wards and has improved the effective handing over of the junior doctor’s role between cohorts. There is potential to further expand the cheat sheet to other departments as the concise document takes around 30 minutes to complete by a doctor who is currently on that ward. The time spent filling out the form provides vital information to the incoming junior doctors, which has a significant possibility to improve patient care.

Keywords: induction, junior doctor, handover, plastic surgery

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Authors: Mazouz Halima, Belghachi Abdrahmane

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Effects of electron irradiation-induced deep level defects have been studied on both n/p and p/n indium phosphide solar cells with very thin emitters. The simulation results show that n/p structure offers a somewhat better short circuit current but the p/n structure offers improved circuit voltage, not only before electron irradiation, but also after 1MeV electron irradiation with 5.1015 fluence. The simulation also shows that n/p solar cell structure is more resistant than that of p/n structure.

Keywords: InP solar cell, p/n and n/p structure, electron irradiation, output parameters

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Authors: Pichaya Poonlarp, Danai Boonyakiat, C. Chuamuangphan, M. Chanta

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Current consumers’ behavior trends have changed towards more health awareness, the well-being of society and interest of nature and environment. The Royal Project Foundation is, therefore, well aware of organic agriculture. The project only focused on using natural products and utilizing its highland biological merits to increase resistance to diseases and insects for the produce grown. The project also brought in basic knowledge from a variety of available research information, including, but not limited to, improvement of soil fertility and a control of plant insects with biological methods in order to lay a foundation in developing and promoting farmers to grow quality produce with a high health safety. This will finally lead to sustainability for future highland agriculture and a decrease of chemical use on the highland area which is a source of natural watershed. However, there are still shortcomings of the postharvest management in term of quality and losses, such as bruising, rottenness, wilting and yellowish leaves. These losses negatively affect the maintenance and a shelf life of organic vegetables. Therefore, it is important that a research study of the appropriate and effective postharvest management is conducted for an individual organic vegetable to minimize product loss and find root causes of postharvest losses which would contribute to future postharvest management best practices. This can be achieved through surveys and data collection from postharvest processes in order to conduct analysis for causes of postharvest losses of organic pak-choi, baby pak-choi, and choy sum. Consequently, postharvest losses reduction strategies of organic vegetables can be achieved. In this study, postharvest losses of organic pak choi, baby pak-choi, and choy sum were determined at each stage of the supply chain starting from the field after harvesting, at the Development Center packinghouse, at Chiang Mai packinghouse, at Bangkok packing house and at the Royal Project retail shop in Chiang Mai. The results showed that postharvest losses of organic pak-choi, baby pak-choi, and choy sum were 86.05, 89.05 and 59.03 percent, respectively. The main factors contributing to losses of organic vegetables were due to mechanical damage and underutilized parts and/or short of minimum quality standard. Good practices had been developed after causes of losses were identified. Appropriate postharvest handling and management, for example, temperature control, hygienic cleaning, and reducing the duration of the supply chain, postharvest losses of all organic vegetables should be able to remarkably reduced postharvest losses in the supply chain.

Keywords: postharvest losses, organic vegetables, handling improvement, shelf life, supply chain

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Authors: Igor Vladimirovich Kuzminov

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The proposed article is an alternative version of the Theory of Relativity. The version is based on the concepts of classical Newtonian physics and does not deny the existing calculation base. The proposed theory completely denies Einstein's existing Theory of Relativity. The only thing that connects these theories is that the proposed theory is also built on postulates. The proposed theory is intended to establish the foundation of classical Newtonian physics. The proposed theory is intended to establish continuity in the development of the fundamentals of physics and is intended to eliminate all kinds of speculation in explanations of physical phenomena. An example of such speculation is Einstein's Theory of Relativity (E).

Keywords: the theory of relativity, postulates of the theory of relativity, criticism of Einstein's theory, classical physics

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Authors: Heeho Kim, Byeong-Hae Sohn

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This study has examined the culture commonality of Northeast Asian countries based on Confucian values, and their relations to institutional economic integration. This study demonstrates that Confucian values inherent in the Northeast Asian countries have served as the cultural ethos for the rapid economic growth of this region since the 1960s and will be able to form the foundation of Northeast Asian values in the future. This paper re-appreciates these cultural values as a necessary condition for regional integration to catalyze the stagnated discussions about economic integration and extends its inter-weaving connection role for intra-regional transaction among China, Japan and Korea.

Keywords: Confucianism, Northeast Asia, economic integration, economic growth, regional conflicts

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Authors: Kevin Wang, Jian Ge, Yinan Zhao, Kevin Willis

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Kepler has discovered over 4000 exoplanets and candidates. However, current transit planet detection techniques based on the wavelet analysis and the Box Least Squares (BLS) algorithm have limited sensitivity in detecting minor planets with a low signal-to-noise ratio (SNR) and long periods with only 3-4 repeated signals over the mission lifetime of 4 years. This paper presents a novel precise-period transit signal detection methodology based on a new Graphics Processing Unit (GPU) Fast Folding algorithm in conjunction with a Convolutional Neural Network (CNN) to detect low SNR and/or long-period transit planet signals. A comparison with BLS is conducted on both simulated light curves and real data, demonstrating that the new method has higher speed, sensitivity, and reliability. For instance, the new system can detect transits with SNR as low as three while the performance of BLS drops off quickly around SNR of 7. Meanwhile, the GPU Fast Folding method folds light curves 25 times faster than BLS, a significant gain that allows exoplanet detection to occur at unprecedented period precision. This new method has been tested with all known transit signals with 100% confirmation. In addition, this new method has been successfully applied to the Kepler of Interest (KOI) data and identified a few new Earth-sized Ultra-short period (USP) exoplanet candidates and habitable planet candidates. The results highlight the promise for GPU Fast Folding as a replacement to the traditional BLS algorithm for finding small and/or long-period habitable and Earth-sized planet candidates in-transit data taken with Kepler and other space transit missions such as TESS(Transiting Exoplanet Survey Satellite) and PLATO(PLAnetary Transits and Oscillations of stars).

Keywords: algorithms, astronomy data analysis, deep learning, exoplanet detection methods, small planets, habitable planets, transit photometry

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Authors: A. V. Harutyunyan

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The investigation results of rocks at high pressures and temperatures have revealed the intervals of changes of seismic waves and density, as well as some processes taking place in rocks. In the serpentinized rocks, as a consequence of dehydration, abrupt changes in seismic waves and density have been recorded. Hydrogen-bearing components are released which combine with carbon-bearing components. As a result, hydrocarbons formed. The investigated samples are smelted. Then, geofluids and hydrocarbons migrate into the upper horizons of the Earth crust by the deep faults. Then their differentiation and accumulation in the jointed rocks of the faults and in the layers with collecting properties takes place. Under the majority of the hydrocarbon deposits, at a certain depth, magmatic centers and deep faults are recorded. The investigation results of the serpentinized rocks with numerous geological-geophysical factual data allow understanding that hydrocarbons are mainly formed in both the offshore part of the ocean and at different depths of the continental crust. Experiments have also shown that the dehydration of the serpentinized rocks is accompanied by an explosion with the instantaneous increase in pressure and temperature and smelting the studied rocks. According to numerous publications, hydrocarbons and diamonds are formed in the upper part of the mantle, at the depths of 200-400km, and as a consequence of geodynamic processes, they rise to the upper horizons of the Earth crust through narrow channels. However, the genesis of metamorphogenic diamonds and the diamonds found in the lava streams formed within the Earth crust, remains unclear. As at dehydration, super high pressures and temperatures arise. It is assumed that diamond crystals are formed from carbon containing components present in the dehydration zone. It can be assumed that besides the explosion at dehydration, secondary explosions of the released hydrogen take place. The process is naturally accompanied by seismic phenomena, causing earthquakes of different magnitudes on the surface. As for the diamondiferous kimberlites, it is well-known that the majority of them are located within the ancient shield and platforms not obligatorily connected with the deep faults. The kimberlites are formed at the shallow location of dehydrated masses in the Earth crust. Kimberlites are younger in respect of containing ancient rocks containing serpentinized bazites and ultrbazites of relicts of the paleooceanic crust. Sometimes, diamonds containing water and hydrocarbons showing their simultaneous genesis are found. So, the geofluids, hydrocarbons and diamonds, according to the new concept put forward, are formed simultaneously from serpentinized rocks as a consequence of their dehydration at different depths of the Earth crust. Based on the concept proposed by us, we suggest discussing the following: -Genesis of gigantic hydrocarbon deposits located in the offshore area of oceans (North American, Mexican Gulf, Cuanza-Kamerunian, East Brazilian etc.) as well as in the continental parts of different mainlands (Kanadian-Arctic Caspian, East Siberian etc.) - Genesis of metamorphogenic diamonds and diamonds in the lava streams (Guinea-Liberian, Kokchetav, Kanadian, Kamchatka-Tolbachinian, etc.).

Keywords: dehydration, diamonds, hydrocarbons, serpentinites

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Authors: Dorsa Bakhshandehgeyazdi

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This paper examines elective future of security in the Middle East trying to find a way that could take the district from a shaky past to a more secure future. Taking a gander at five situations about the eventual future of world legislative issues, in particular, globalization, fragmentation, conflict of civilizations, majority rule peace and the development of a security group, the paper contends that albeit every situation has its qualities (and in addition shortcomings), it is the situation that predicts the foundation of a security group that joins a more express thought for forming a more secure future for the Middle East.

Keywords: Middle East, Globalization, Fragmentation, Conflict of civilizations, Majority rule peace, Development of a security group

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Authors: Loulwah Bokhari, Jori Nazer, Hala Sultan

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Nowadays, artificial intelligence (AI) is used as a foundation for many activities in modern computing applications at home, in vehicles, and in businesses. Many modern machines are built to carry out a specific activity or purpose. This is where the Amazon Alexa application comes in, as it is used as a virtual assistant. The purpose of this paper is to explore the use of Amazon Alexa among people and how it has improved and made simple daily tasks easier for many people. We gave our participants several questions regarding Amazon Alexa and if they had recently used or heard of it, as well as the different tasks it provides and whether it successfully satisfied their needs. Overall, we found that participants who have recently used Alexa have found it to be helpful in their daily tasks.

Keywords: artificial intelligence, Echo system, machine learning, feature for feature match

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Tianji Jiang

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Habermas' definition of public sphere is a classical and well-regarded theory of the formation of public opinions, laying the foundation for many researches on public opinions and public media. In recent decades, public media have been changing rapidly as social media are gaining increasing importance. However, the occurrence of group polarization on social media, which is a hot issue today, is challenging Habermas' theory of the public sphere. This article reviews the public sphere theory and studies group polarization and social media. It proposes ideas on how to understand group polarization within the public sphere and comes up with some suggestions and ideas to reduce polarization on social media.

Keywords: public sphere, social media, group polarization, echo chamber, public opinion

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Authors: Umbreen Salim, Anila Noor

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This paper analyses the influences and faces of colonialism in women’s participation in economic activity in postcolonial Pakistan, through postcolonial feminist economic lens. It is an attempt to probe the shifts in gender inequalities that have existed in three stages; pre-colonial, colonial, and postcolonial times in the Indo-Pak subcontinent. It delves into an inquiry of pre-colonial as it is imperative to understand the situation and context before colonisation in order to assess the deviations associated with its onset. Hence, in order to trace gender inequalities this paper analyses from Mughal Era (1526-1757) that existed before British colonisation, then, the gender inequalities that existed during British colonisation (1857- 1947) and the associated dynamics and changes in women’s vulnerabilities to participate in the economy are examined. Followed by, the postcolonial (1947 onwards) scenario of discriminations and oppressions faced by women. As part of the research methodology, primary and secondary data analysis was done. Analysis of secondary data including literary works and photographs was carried out, followed by primary data collection using ethnographic approaches and participatory tools to understand the presence of coloniality and gender inequalities embedded in the social structure through participant’s real-life stories. The data is analysed using feminist postcolonial analysis. Intersectionality has been a key tool of analysis as the paper delved into the gender inequalities through the class and caste lens briefly touching at religion. It is imperative to mention the significance of the study and very importantly the practical challenges as historical analysis of 18th and 19th century is involved. Most of the available work on history is produced by a) men and b) foreigners and mostly white authors. Since the historical analysis is mostly by men the gender analysis presented misses on many aspects of women’s issues and since the authors have been mostly white European gives it as Mohanty says, ‘under western eyes’ perspective. Whereas the edge of this paper is the authors’ deep attachment, belongingness as lived reality and work with women in Pakistan as postcolonial subjects, a better position to relate with the social reality and understand the phenomenon. The study brought some key results as gender inequalities existed before colonisation when women were hidden wheel of stable economy which was completely invisible. During the British colonisation, the vulnerabilities of women only increased and as compared to men their inferiority status further strengthened. Today, the postcolonial woman lives in deep-rooted effects of coloniality where she is divided in class and position within the class, and she has to face gender inequalities within household and in the market for economic participation. Gender inequalities have existed in pre-colonial, during colonisation and postcolonial times in Pakistan with varying dynamics, degrees and intensities for women whereby social class, caste and religion have been key factors defining the extent of discrimination and oppression. Colonialism may have physically ended but the coloniality remains and has its deep, broad and wide effects in increasing gender inequalities in women’s participation in the economy in Pakistan.

Keywords: colonialism, economic participation, gender inequalities, women

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Authors: Davinder Singh, Jaimal Singh Khamba, Tarun Nanda

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Growth of the Indian economy has accelerated to 8% and efforts are on to further propel it to 10%. Undoubtedly, all the segments of the economy, viz. agriculture, industry and services have to improve their contribution to the economy. Growth of Micro-small and medium enterprises (MSMEs) is a sine qua non for the growth of industry, exports and other segments of the economy. Furthermore, promotion of entrepreneurship is also vital for sustenance and upward movement of the current growth trajectory of the economy. The MSME sector acts as a catalyst in upholding and encouraging the creation of the innovative spirit and entrepreneurship in the economy, thereby helping in laying the foundation for rapid industrial development. In this competitive world, they need to be able to confront the increasing competition from developed and emerging economies and to plug into the new market opportunities.

Keywords: characteristics, management, MSMEs, technology infrastructure

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Authors: Chithra A. V., Somoshree Datta, Sandeep Nithyanandan

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Sign Language Recognition (SLR) is the process of interpreting and translating sign language into spoken or written language using technological systems. It involves recognizing hand gestures, facial expressions, and body movements that makeup sign language communication. The primary goal of SLR is to facilitate communication between hearing- and speech-impaired communities and those who do not understand sign language. Due to the increased awareness and greater recognition of the rights and needs of the hearing- and speech-impaired community, sign language recognition has gained significant importance over the past 10 years. Technological advancements in the fields of Artificial Intelligence and Machine Learning have made it more practical and feasible to create accurate SLR systems. This paper presents a distinct approach to SLR by framing it as a video classification problem using Deep Learning (DL), whereby a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) has been used. This research targets the integration of sign language recognition into healthcare settings, aiming to improve communication between medical professionals and patients with hearing impairments. The spatial features from each video frame are extracted using a CNN, which captures essential elements such as hand shapes, movements, and facial expressions. These features are then fed into an RNN network that learns the temporal dependencies and patterns inherent in sign language sequences. The INCLUDE dataset has been enhanced with more videos from the healthcare domain and the model is evaluated on the same. Our model achieves 91% accuracy, representing state-of-the-art performance in this domain. The results highlight the effectiveness of treating SLR as a video classification task with the CNN-RNN architecture. This approach not only improves recognition accuracy but also offers a scalable solution for real-time SLR applications, significantly advancing the field of accessible communication technologies.

Keywords: sign language recognition, deep learning, convolution neural network, recurrent neural network

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Authors: Kamran Siddique, Yangwoo Kim, Zahid Akhtar

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In recent years, the technological advancements have led to a deluge of data from distinctive domains and the need for development of solutions based on parallel and distributed computing has still long way to go. That is why, the research and development of massive computing frameworks is continuously growing. At this particular stage, highlighting a potential research area along with key insights could be an asset for researchers in the field. Therefore, this paper explores one of the emerging distributed computing frameworks, Apache Hama. It is a Top Level Project under the Apache Software Foundation, based on Bulk Synchronous Processing (BSP). We present an unbiased and critical interrogation session about Apache Hama and conclude research directions in order to assist interested researchers.

Keywords: apache hama, bulk synchronous parallel, BSP, distributed computing

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Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

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Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

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Authors: R. Simões, M. Silveirinha

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Driven by the need to understand the results of previous research that clearly shows deep unbalances of the media discourses about women and men in spite of the growing numbers of female journalists, our paper aims to progress from the 'what' to the 'why' of these unbalanced representations. Furthermore, it does so at a time when journalism is undergoing a dramatic change in terms of professional practices and in how media organizations are organized and run, affecting women in particular. While some feminist research points to the fact that female and male journalists evaluate the role of the news and production methods in similar ways feminist theorizing also suggests that thought and knowledge are highly influenced by social identity, which is also inherently affected by the experiences of gender. This is particularly important at a time of deep societal and professional changes. While there are persuasive discussions of gender identities at work in newsrooms in various countries studies on the issue will benefit from cases that focus on the particularities of local contexts. In our paper, we present one such case: the case of Portugal, a country hit hard by austerity measures that have affected all cultural industries including journalism organizations, already feeling the broader impacts of the larger societal changes of the media landscape. Can we gender these changes? How are they felt and understood by female and male journalists? And how are these discourses framed by androcentric, feminist and post-feminist sensibilities? Foregrounding questions of gender, our paper seeks to explore some of the interactions of societal and professional forces, identifying their gendered character and outlining how they shape journalism work in general and the production of unbalanced gender representations in particular. We do so grounded in feminist studies of journalism as well as feminist organizational and work studies, looking at a corpus of 20 in-depth interviews of female and male Portuguese journalists. The research findings illustrate how gender in journalism practices interacts with broader experiences of the cultural and economic contexts and show the ambivalences of these interactions in news organizations.

Keywords: gender, journalism, newsroom culture, Portuguese journalists

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Authors: A. Perdomo, J. R. Bacca, Q. E. Jabid

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In recent years, technological and investigative progress has been achieved in the area of monitoring of equipment and installation as a result of a deeper understanding of physical phenomenon associated with the non-destructive tests (NDT). The modal analysis proposes an efficient solution to determine the dispersion curves of an arbitrary waveguide cross-sectional. Dispersion curves are essential in the discontinuity localization based on guided waves. In this work, an isotropic hollow cylinder is dynamically analyzed in ANSYS to obtain resonant frequencies and mode shapes all of them associated with the dispersion curves. The numerical results provide the relation between frequency and wavelength which is the foundation of the dispersion curves. Results of the simulation process are validated with the software GUIGW.

Keywords: ansys APDL, dispersion curves, guide waves, modal analysis

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Authors: N. S. Ajisha

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In today’s competitive world the quality of the service you deliver is one of the important factor that determine customer satisfaction. Service quality is considered to be one important determinant to evaluate customer satisfaction and the relationship between service quality and customer satisfaction is considered as the foundation in researches on customer satisfaction. This research examines to do a gap analysis between the perception and expectation of the services delivered and find relation between the service quality and customer satisfaction. Service quality is found out here using the SERVQUAL model. And it finds out the dimension of service quality which is more important to measure customer satisfaction. The dimensions which we measure using SERVQUAL include the tangibles, reliability, responsiveness, assurance, and empathy. This study involves primary data collection like market survey.

Keywords: customer satisfaction, service quality, retail service quality, Kochi

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Authors: S. Benmebarek, S. Benmoussa, N. Benmebarek

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Natural soils are often deposited in layers. The estimation of the bearing capacity of the soil using conventional bearing capacity theory based on the properties of the upper layer introduces significant inaccuracies if the thickness of the top layer is comparable to the width of the foundation placed on the soil surface. In this paper, numerical computations using the FLAC code are reported to evaluate the two clay layers effect on the bearing capacity beneath rigid circular rough footing subject to axial static load. The computation results of the parametric study are used to illustrate the sensibility of the bearing capacity, the shape factor and the failure mechanisms to the layered strength and layered thickness.

Keywords: numerical modeling, circular footings, layered clays, bearing capacity, failure

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Authors: Tianjie Zhang, Bingqian Cheng, Peng Zeng

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Engineering science and technology progress and innovation have become an important engine to promote social development. The reform exploration of "new engineering" in China has drawn extensive attention around the world, with its connotation as "to cultivate future diversified, innovative and outstanding engineering talents by taking ‘fostering character and civic virtue’ as the guide, responding to changes and shaping the future as the construction concept, and inheritance and innovation, crossover and fusion, coordination and sharing as the principal approach". In this context, Tianjin University, as a traditional Chinese university with advantages in engineering, further launched the CCII (Coherent-Collaborative-Interdisciplinary-Innovation) program, raising the cultivation idea of integrating new liberal arts education, multidisciplinary engineering education and personalized professional education. As urban planning practice in China has undergone the evolution of "physical planning -- comprehensive strategic planning -- resource management-oriented planning", planning education has also experienced the transmutation process of "building foundation -- urban scientific foundation -- multi-disciplinary integration". As a characteristic and advantageous discipline of Tianjin University, the major of Urban and Rural Planning, in accordance with the "CCII Program of Tianjin University", aims to build China's top and world-class major, and implements the following educational reform measures: 1. Adding corresponding English courses, such as advanced course on GIS Analysis, courses on comparative studies in international planning involving ecological resources and the sociology of the humanities, etc. 2. Holding "Academician Forum", inviting international academicians to give lectures or seminars to track international frontier scientific research issues. 3. Organizing "International Joint Workshop" to provide students with international exchange and design practice platform. 4. Setting up a business practice base, so that students can find problems from practice and solve them in an innovative way. Through these measures, the Urban and Rural Planning major of Tianjin University has formed a talent training system with multi-disciplinary cross integration and orienting to the future science and technology.

Keywords: China, higher education reform, innovation, new engineering education, rural and urban planning, Tianjin University

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