Search results for: lid driven cavity

Authors: Abid Ali Abid

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One-dimensional (1-D) particle-in-cell (PIC) electrostatic simulations are carried out to investigate the electrostatic waves, whose constituents are hot, cold and beam electrons in the background of motionless positive ions. In fact, the electrostatic modes excited are electron acoustic waves, beam driven waves as well as Langmuir waves. It is assessed that the relevant plasma parameters, for example, hot electron temperature, beam electron drift speed, and the electron beam density significantly modify the electrostatics wave's profiles. In the nonlinear stage, the wave-particle interaction becomes more evident and the waves have obtained its saturation level. Consequently, electrons become trapped in the waves and trapping vortices are clearly formed. Because of this trapping vortices and mixing of the electrons in phase space, finally, lead to electrons thermalization. It is observed that for the high-density value of the beam-electron, the solitary waves having a bipolar form of the electric field. These solitons are the nonlinear Brenstein-Greene and Kruskal wave mode that attributes the trapping of electrons potential well of phase-space hole. These examinations revealed that electrostatic waves have been exited in beam-plasma model and producing waves having broad-frequency ranges, which may clarify the broadband electrostatic noise (BEN) spectrum studied in the auroral zone.

Keywords: electron acoustic waves, trapping of cold electron, Langmuir waves, particle-in cell simulation

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Authors: Nayer Mofidtabatabaei

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Advancements in artificial intelligence (AI) have transformed various fields, including psychology and behavioral sciences. This paper explores the diverse ways in which AI is applied to enhance research, diagnosis, therapy, and understanding of human behavior and mental health. We discuss the potential benefits and challenges associated with AI in these fields, emphasizing the ethical considerations and the need for collaboration between AI researchers and psychological and behavioral science experts. Artificial Intelligence (AI) has gained prominence in recent years, revolutionizing multiple industries, including healthcare, finance, and entertainment. One area where AI holds significant promise is the field of psychology and behavioral sciences. AI applications in this domain range from improving the accuracy of diagnosis and treatment to understanding complex human behavior patterns. This paper aims to provide an overview of the various AI applications in psychological and behavioral sciences, highlighting their potential impact, challenges, and ethical considerations. Mental Health Diagnosis AI-driven tools, such as natural language processing and sentiment analysis, can analyze large datasets of text and speech to detect signs of mental health issues. For example, chatbots and virtual therapists can provide initial assessments and support to individuals suffering from anxiety or depression. Autism Spectrum Disorder (ASD) Diagnosis AI algorithms can assist in early ASD diagnosis by analyzing video and audio recordings of children's behavior. These tools help identify subtle behavioral markers, enabling earlier intervention and treatment. Personalized Therapy AI-based therapy platforms use personalized algorithms to adapt therapeutic interventions based on an individual's progress and needs. These platforms can provide continuous support and resources for patients, making therapy more accessible and effective. Virtual Reality Therapy Virtual reality (VR) combined with AI can create immersive therapeutic environments for treating phobias, PTSD, and social anxiety. AI algorithms can adapt VR scenarios in real-time to suit the patient's progress and comfort level. Data Analysis AI aids researchers in processing vast amounts of data, including survey responses, brain imaging, and genetic information. Privacy Concerns Collecting and analyzing personal data for AI applications in psychology and behavioral sciences raise significant privacy concerns. Researchers must ensure the ethical use and protection of sensitive information. Bias and Fairness AI algorithms can inherit biases present in training data, potentially leading to biased assessments or recommendations. Efforts to mitigate bias and ensure fairness in AI applications are crucial. Transparency and Accountability AI-driven decisions in psychology and behavioral sciences should be transparent and subject to accountability. Patients and practitioners should understand how AI algorithms operate and make decisions. AI applications in psychological and behavioral sciences have the potential to transform the field by enhancing diagnosis, therapy, and research. However, these advancements come with ethical challenges that require careful consideration. Collaboration between AI researchers and psychological and behavioral science experts is essential to harness AI's full potential while upholding ethical standards and privacy protections. The future of AI in psychology and behavioral sciences holds great promise, but it must be navigated with caution and responsibility.

Keywords: artificial intelligence, psychological sciences, behavioral sciences, diagnosis and therapy, ethical considerations

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Authors: Pablo Raul Yanyachi, Alfredo Mamani Saico, Jorch Mendoza, Wang Xinsheng

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The Attitude Determination and Control System (ADCS) plays a pivotal role in the operation of nanosatellites such as Cubesats, managing orientation and stability during space missions. Within the ADCS, Reaction Wheels (RW) are electromechanical devices responsible for adjusting and maintaining satellite orientation through the application of kinetic moments. This study focuses on the characterization and analysis of a specific Reaction Wheel integrated into an ADCS prototype developed at the National University of San Agust´ın, Arequipa (UNSA). To achieve this, a single-axis Test Bench was constructed, where the reaction wheel consists of a brushless motor and an inertia flywheel driven by an Electronic Speed Controller (ESC). The research encompasses RW characterization, energy consumption evaluation, dynamic modeling, and control. The results have allowed us to ensure the maneuverability of ADCS prototypes while maintaining energy consumption within acceptable limits. The characterization and linearity analysis provides valuable insights for sizing and optimizing future reaction wheel prototypes for nanosatellites. This contributes to the ongoing development of aerospace technology within the scientific community at UNSA.

Keywords: test bench, nanosatellite, control, reaction wheel

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Authors: K. Raghu, M. N. Vathhsala, Naveen Aradya, Sharth

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River sand is the most preferred fine aggregate for mortar and concrete. River sand is a product of natural weathering of rocks over a period of millions of years and is mined from river beds. Sand mining has disastrous environmental consequences. The excessive mining of river bed is creating an ecological imbalance. This has lead to have restrictions imposed by ministry of environment on sand mining. Driven by the acute need for sand, stone dust or manufactured sand prepared from the crushing and screening of coarse aggregate is being used as sand in the recent past. However manufactured sand is also a natural material and has quarrying and quality issues. To reduce the burden on the environment, alternative materials to be used as fine aggregates are being extensively investigated all over the world. Looking to the quantum of requirements, quality and properties there has been a global consensus on a material – Granulated slags. Granulated slag has been proven as a suitable material for replacing natural sand / crushed fine aggregates. In developed countries, the use of granulated slag as fine aggregate to replace natural sand is well established and is in regular practice. In the present paper Granulated slag has been experimented for usage in mortar. Slags are the main by-products generated during iron and steel production in the steel industry. Over the past decades, the steel production has increased and, consequently, the higher volumes of by-products and residues generated which have driven to the reuse of these materials in an increasingly efficient way. In recent years new technologies have been developed to improve the recovery rates of slags. Increase of slags recovery and use in different fields of applications like cement making, construction and fertilizers help in preserving natural resources. In addition to the environment protection, these practices produced economic benefits, by providing sustainable solutions that can allow the steel industry to achieve its ambitious targets of “zero waste” in coming years. Slags are generated at two different stages of steel production, iron making and steel making known as BF(Blast Furnace) slag and steel slag respectively. The slagging agent or fluxes, such as lime stone, dolomite and quartzite added into BF or steel making furnaces in order to remove impurities from ore, scrap and other ferrous charges during smelting. The slag formation is the result of a complex series of physical and chemical reactions between the non-metallic charge(lime stone, dolomite, fluxes), the energy sources(coal, coke, oxygen, etc.) and refractory materials. Because of the high temperatures (about 15000 C) during their generation, slags do not contain any organic substances. Due to the fact that slags are lighter than the liquid metal, they float and get easily removed. The slags protect the metal bath from atmosphere and maintain temperature through a kind of liquid formation. These slags are in liquid state and solidified in air after dumping in the pit or granulated by impinging water systems. Generally, BF slags are granulated and used in cement making due to its high cementious properties, and steel slags are mostly dumped due to unfavourable physio-chemical conditions. The increasing dump of steel slag not only occupies a plenty of land but also wastes resources and can potentially have an impact on the environment due to water pollution. Since BF slag contains little Fe and can be used directly. BF slag has found a wide application, such as cement production, road construction, Civil Engineering work, fertilizer production, landfill daily cover, soil reclamation, prior to its application outside the iron and steel making process.

Keywords: steel slag, river sand, granulated slag, environmental

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Authors: Tomilola J. Ajayi

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A category of nanovalves system containing the α-cyclodextrin (α-CD) ring on a stalk tethered to the pores of mesoporous silica nanoparticles (MSN) is theoretically and computationally modelled. This functions to control opening and blocking of the MSN pores for efficient targeted drug release system. Modeling of the nanovalves is based on the interaction between α-CD and the stalk (p-anisidine) in relation to pH variation. Conformational analysis was carried out prior to the formation of the inclusion complex, to find the global minimum of both neutral and protonated stalk. B3LYP/6-311G**(d, p) basis set was employed to attain all theoretically possible conformers of the stalk. Six conformers were taken into considerations, and the dihedral angle (θ) around the reference atom (N17) of the p-anisidine stalk was scanned from 0° to 360° at 5° intervals. The most stable conformer was obtained at a dihedral angle of 85.3° and was fully optimized at B3LYP/6-311G**(d, p) level of theory. The most stable conformer obtained from conformational analysis was used as the starting structure to create the inclusion complexes. 9 complexes were formed by moving the neutral guest into the α-CD cavity along the Z-axis in 1 Å stepwise while keeping the distance between dummy atom and OMe oxygen atom on the stalk restricted. The dummy atom and the carbon atoms on α-CD structure were equally restricted for orientation A (see Scheme 1). The generated structures at each step were optimized with B3LYP/6-311G**(d, p) methods to determine their energy minima. Protonation of the nitrogen atom on the stalk occurs at acidic pH, leading to unsatisfactory host-guest interaction in the nanogate; hence there is dethreading. High required interaction energy and conformational change are theoretically established to drive the release of α-CD at a certain pH. The release was found to occur between pH 5-7 which agreed with reported experimental results. In this study, we applied the theoretical model for the prediction of the experimentally observed pH-responsive nanovalves which enables blocking, and opening of mesoporous silica nanoparticles pores for targeted drug release system. Our results show that two major factors are responsible for the cargo release at acidic pH. The higher interaction energy needed for the complex/nanovalve formation to exist after protonation as well as conformational change upon protonation are driving the release due to slight pH change from 5 to 7.

Keywords: nanovalves, nanogate, mesoporous silica nanoparticles, cargo

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Authors: Yaxian Chen, Yeonhee Park

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Driven by evolving FDA recommendations, modern clinical trials demand innovative designs that strike a balance between statistical rigor and ethical considerations. Covariate-adjusted response-adaptive (CARA) designs bridge this gap by utilizing patient attributes and responses to skew treatment allocation in favor of the treatment that is best for an individual patient’s profile. However, existing CARA designs for survival outcomes often hinge on specific parametric models, constraining their applicability in clinical practice. In this article, we address this limitation by introducing a CARA design for survival outcomes (CARAS) based on the Cox model and a variance estimator. This method addresses issues of model misspecification and enhances the flexibility of the design. We also propose a group sequential overlapweighted log-rank test to preserve type I error rate in the context of group sequential trials using extensive simulation studies to demonstrate the clinical benefit, statistical efficiency, and robustness to model misspecification of the proposed method compared to traditional randomized controlled trial designs and response-adaptive randomization designs.

Keywords: cox model, log-rank test, optimal allocation ratio, overlap weight, survival outcome

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Authors: Olha Yarova

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This study is a collaborative project between the American University of Central Asia and parent association of children with Down syndrome ‘Sunterra’ that took place in Bishkek, Kyrgyzstan. The purpose of the study was to explore whether principles and techniques of applied behavior analysis (ABA) could be used to teach children with Down syndrome socially significant behaviors. ABA is considered to be one of the most effective treatment for children with autism, but little research is done on the particularity of using ABA to children with Down syndrome. The data for the study was received during clinical observations; work with children with Down syndrome and interviews with their mothers. The results show that many ABA principles make the work with children with Down syndrome more effective. Although such children very rarely demonstrate aggressive behavior, they show a lot of escape-driven and attention seeking behaviors that are reinforced by their parents and educators. Thus functional assessment can be done to assess the function of problem behavior and to determine appropriate treatment. Prompting and prompting fading should be used to develop receptive and expressive language skills, and enhance motor development. Even though many children with Down syndrome work for praise, it is still relevant to use tangible reinforcement and to know how to remove them. Based on the results of the study, the training for parents of children with Down syndrome will be developed in Kyrgyzstan, country, where children with Down syndrome are not accepted to regular kindergartens and where doctors in maternity hospitals tell parents that their child will never talk, walk and recognize them

Keywords: down syndrome, applied behavior analysis, functional assessment, problem behavior, reinforcement

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Authors: Florian Kiebert, Hagen Schmidt

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Standing surface acoustic waves (sSAWs) have already been used to manipulate particles in a microfluidic channel made of polydimethylsiloxan (PDMS). Usually two identical facing interdigital transducers (IDTs) are exploited to form an sSAW. Further, it has been reported that an sSAW can be generated by a single IDT using a superstrate resonating cavity or a PDMS post. Nevertheless, both setups utilising a traveling surface acoustic wave (tSAW) to create an sSAW for particle manipulation are costly. We present a simplified setup with a tSAW and a PDMS channel to form an sSAW. The incident tSAW is reflected at the rear PDMS channel wall and superimposed with the reflected tSAW. This superpositioned waves generates an sSAW but only at regions where the distance to the rear channel wall is smaller as the attenuation length of the tSAW minus the channel width. Therefore in a channel of 500µm width a tSAW with a wavelength λ = 120 µm causes a sSAW over the whole channel, whereas a tSAW with λ = 60 µm only forms an sSAW next to the rear wall of the channel, taken into account the attenuation length of a tSAW in water. Hence, it is possible to concentrate and trap particles in a defined region of the channel by adjusting the relation between the channel width and tSAW wavelength. Moreover, it is possible to generate a particle gradient over the channel width by picking the right ratio between channel wall and wavelength. The particles are moved towards the rear wall by the acoustic streaming force (ASF) and the acoustic radiation force (ARF) caused by the tSAW generated bulk acoustic wave (BAW). At regions in the channel were the sSAW is dominating the ARF focuses the particles in the pressure nodes formed by the sSAW caused BAW. On the one side the ARF generated by the sSAW traps the particle at the center of the tSAW beam, i. e. of the IDT aperture. On the other side, the ASF leads to two vortices, one on the left and on the right side of the focus region, deflecting the particles out of it. Through variation of the applied power it is possible to vary the number of particles trapped in the focus points, because near to the rear wall the amplitude of the reflected tSAW is higher and, therefore, the ARF of the sSAW is stronger. So in the vicinity of the rear wall the concentration of particles is higher but decreases with increasing distance to the wall, forming a gradient of particles. The particle gradient depends on the applied power as well as on the flow rate. Thus by variation of these two parameters it is possible to change the particle gradient. Furthermore, we show that the particle gradient can be modified by changing the relation between the channel width and tSAW wavelength. Concluding a single IDT generates an sSAW in a PDMS microchannel enables particle gradient generation in a well-defined microfluidic flow system utilising the ARF and ASF of a tSAW and an sSAW.

Keywords: ARF, ASF, particle manipulation, sSAW, tSAW

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Authors: Giovanni Cicceri, Roberta Maisano, Nathalie Morey, Salvatore Distefano

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The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes.

Keywords: environmental internet of things, EIoT, machine learning, anomaly detection, environment monitoring

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Authors: H. Hammoudi, S. Bendenia, K. Marouf-Khelifa, R. Marouf, J. Schott, A. Khelifa

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The ionic exchange of the NaX zeolite by Cu2+ and/or Zn2+ cations is progressively driven while following the development of some of its characteristic: crystallinity by XR diffraction, profile of isotherms, RI criterion, isosteric adsorption heat and microporous volume using both the Dubinin–Radushkevich (DR) equation and the t-plot through the Lippens–de Boer method which also makes it possible to determine the external surface area. Results show that the cationic exchange process, in the case of Cu2+ introduced at higher degree, is accompanied by crystalline degradation for Cu(x)X, in contrast to Zn2+-exchanged zeolite X. This degradation occurs without significant presence of mesopores, because the RI criterion values were found to be much lower than 2.2. A comparison between the binary and ternary exchanges shows that the curves of CuZn(x)X are clearly below those of Zn(x)X and Cu(x)X, whatever the examined parameter. On the other hand, the curves relating to CuZn(x)X tend towards those of Cu(x)X. This would again confirm the sensitivity of the crystalline structure of CuZn(x)X with respect to the introduction of Cu2+ cations. An original result is the distortion of the zeolitic framework of X zeolites at middle exchange degree, when Cu2+ competes with another divalent cation, such as Zn2+, for the occupancy of sites distributed within zeolitic cavities. In other words, the ternary exchange accentuates the crystalline degradation of X zeolites. An unexpected result also is the no correlation between crystal damage and the external surface area.

Keywords: adsorption, crystallinity, ion exchange, zeolite

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Authors: Cheng-yu Hsieh, Bo Zhang, Ahmed Hambaba

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Stock market prediction has long been an area of interest for both expert analysts and investors, driven by its complexity and the noisy, volatile conditions it operates under. This research examines the efficacy of combining the Patch Time Series Transformer (PatchTST) with wavelet transforms, specifically focusing on Haar and Daubechies wavelets, in forecasting the adjusted closing price of the S&P 500 index for the following day. By comparing the performance of the augmented PatchTST models with traditional predictive models such as Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformers, this study highlights significant enhancements in prediction accuracy. The integration of the Daubechies wavelet with PatchTST notably excels, surpassing other configurations and conventional models in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE). The success of the PatchTST model paired with Daubechies wavelet is attributed to its superior capability in extracting detailed signal information and eliminating irrelevant noise, thus proving to be an effective approach for financial time series forecasting.

Keywords: deep learning, financial forecasting, stock market prediction, patch time series transformer, wavelet transform

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Authors: Eman M. Eissa, Hany M. Hasanin, Mahmoud Abd-Elhamid, S. M. Muyeen, T. Fernando, H. H. C. Iu

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Among all available wind energy conversion systems (WECS), the direct driven permanent magnet synchronous generator integrated with power electronic interfaces is becoming popular due to its capability of extracting optimal energy capture, reduced mechanical stresses, no need to external excitation current, meaning less losses, and more compact size. Simple structure, low maintenance cost; and its decoupling control performance is much less sensitive to the parameter variations of the generator. This paper attempts to present a review of the control and optimization strategies of WECS based on permanent magnet synchronous generator (PMSG) and overview the most recent research trends in this field. The main aims of this review include; the generalized overall WECS starting from turbines, generators, and control strategies including converters, maximum power point tracking (MPPT), ending with DC-link control. The optimization methods of the controller parameters necessary to guarantee the operation of the system efficiently and safely, especially when connected to the power grid are also presented.

Keywords: control and optimization techniques, permanent magnet synchronous generator, variable speed wind turbines, wind energy conversion system

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Authors: Pejman Hosseinioun, Hasan Shakeri, Ghasem Ghorbanirostam

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In recent years, we have seen an increasing importance of research and study on knowledge source, decision support systems, data mining and procedure of knowledge discovery in data bases and it is considered that each of these aspects affects the others. In this article, we have merged information source and knowledge source to suggest a knowledge based system within limits of management based on storing and restoring of knowledge to manage information and improve decision making and resources. In this article, we have used method of data mining and Apriori algorithm in procedure of knowledge discovery one of the problems of Apriori algorithm is that, a user should specify the minimum threshold for supporting the regularity. Imagine that a user wants to apply Apriori algorithm for a database with millions of transactions. Definitely, the user does not have necessary knowledge of all existing transactions in that database, and therefore cannot specify a suitable threshold. Our purpose in this article is to improve Apriori algorithm. To achieve our goal, we tried using fuzzy logic to put data in different clusters before applying the Apriori algorithm for existing data in the database and we also try to suggest the most suitable threshold to the user automatically.

Keywords: decision support system, data mining, knowledge discovery, data discovery, fuzzy logic

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Authors: Mejbri Sami, Zhani Khalifa, Zarzoum Kamel, Ben Bacha Habib, Koschikowski Joachim, Pfeifle Daniel

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Many places worldwide, especially arid and semi-arid remote regions, are suffering from the lack of drinkable water and the situation will be aggravated in the near future. Furthermore, remote areas are characterised by lack of conventional energy sources, skilled personnel and maintenance facilities. Therefore, the development of small to medium size, stand-alone and robust solar desalination systems is needed to provide independent fresh water supply in remote areas. This paper is focused on experimental studies on compact membrane distillation (MD) solar desalination prototype located at the Mechanical Engineering Department site, Kairouan University, Kairouan, Tunisia. The pilot system is designed and manufactured as a part of a research and development project funded by the MESRS/BMBF. The pilot system is totally autonomous. The electrical energy required to operate the unit is generated through a field of 4 m² of photovoltaic panels, and the heating of feed water is provided by a field of 6 m² of solar collectors. The Kairouan plant performance of the first few months of operation is presented. The highest freshwater production of 150 L/d is obtained on a sunny day in July of 633 W/m²d.

Keywords: experimental, membrane distillation, solar desalination, Permeat gap

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Authors: Agnieszka Krzemińska, Katarzyna Świderek, Vicente Molinier, Piotr Paneth

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In order to understand in details the interactions between ligands and the enzyme isotope effects were studied between clinically used drugs that bind in the active site of Human Immunodeficiency Virus Reverse Transcriptase, HIV-1 RT, as well as triazole-based inhibitor that binds in the allosteric pocket of this enzyme. The magnitudes and origins of the resulting binding isotope effects were analyzed. Subsequently, binding isotope effect of the same triazole-based inhibitor bound in the active site were analyzed and compared. Together, these results show differences in binding origins in two sites of the enzyme and allow to analyze binding mode and place of newly synthesized inhibitors. Typical protocol is described below on the example of triazole ligand in the allosteric pocket. Triazole was docked into allosteric cavity of HIV-1 RT with Glide using extra-precision mode as implemented in Schroedinger software. The structure of HIV-1 RT was obtained from Protein Data Bank as structure of PDB ID 2RKI. The pKa for titratable amino acids was calculated using PROPKA software, and in order to neutralize the system 15 Cl- were added using tLEaP package implemented in AMBERTools ver.1.5. Also N-terminals and C-terminals were build using tLEaP. The system was placed in 144x160x144Å3 orthorhombic box of water molecules using NAMD program. Missing parameters for triazole were obtained at the AM1 level using Antechamber software implemented in AMBERTools. The energy minimizations were carried out by means of a conjugate gradient algorithm using NAMD. Then system was heated from 0 to 300 K with temperature increment 0.001 K. Subsequently 2 ns Langevin−Verlet (NVT) MM MD simulation with AMBER force field implemented in NAMD was carried out. Periodic Boundary Conditions and cut-offs for the nonbonding interactions, range radius from 14.5 to 16 Å, are used. After 2 ns relaxation 200 ps of QM/MM MD at 300 K were simulated. The triazole was treated quantum mechanically at the AM1 level, protein was described using AMBER and water molecules were described using TIP3P, as implemented in fDynamo library. Molecules 20 Å apart from the triazole were kept frozen, with cut-offs established on range radius from 14.5 to 16 Å. In order to describe interactions between triazole and RT free energy of binding using Free Energy Perturbation method was done. The change in frequencies from ligand in solution to ligand bounded in enzyme was used to calculate binding isotope effects.

Keywords: binding isotope effects, molecular dynamics, HIV, reverse transcriptase

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Authors: Luxy Grebers Swend Sinaga, Akhmaloka

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The global energy crisis, triggered by declining fossil The global energy crisis, triggered by declining fossil fuel reserves and exacerbated by population growth and increasing energy demand, was driven the development of renewable energy sources. One of the green energy alternatives being developed is biodiesel. Transesterification is at the core of biodiesel production, where fatty acids in oil are converted into methyl esters with the aid of a catalyst. Lipases exhibit high activity and stability during catalysis, especially under harsh conditions. Lipase (Lk1) isolated from organic waste compost at the Bandung Institute of Technology, Bandung, West Java, shows promising potential in this field. The thermostable lipase was purified using Ni-NTA affinity chromatography, followed by SDS-PAGE analysis for purity confirmation. Characterizing the transesterification activity of Lk1 is essential for assessing its effectiveness in converting oil into biodiesel, including methyl esters. The results of this study showed that Lk1 exhibited the highest activity on a methyl palmitate substrate, with an optimum temperature of 60°C, very stable activity in the non-polar solvent n-hexane, and was able to maintain its optimum activity for up to 1 hour. These characters make Lk1 highly suitable for biodiesel production, as it meets the main criteria for the transesterification process in producing renewable energy.

Keywords: biodiesel, lipase Lk1, transesterification, renewable energy, thermostability

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Authors: Florian Wohlfeil, Orestis Terzidis, Louisa Hellmann

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Radical technological innovations enable companies to reach strong market positions and are thus desirable. On the other hand, the innovation process is related to significant costs and risks. Hence, the knowledge of the factors that influence success is crucial for technology driven companies. In a previous study, we have developed a conceptual framework of 25 Critical Success Factors for radical technological innovations and mapped them to four main categories: Technology, Organization, Market, and Process. We refer to it as the Technology-Organization-Market-Process (TOMP) framework. Taking the TOMP framework as a reference model, we conducted a structured and focused literature review of eleven standard books on the topic of radical technological innovation. With this approach, we aim to evaluate, expand, and clarify the set of Critical Success Factors detailed in the TOMP framework. Overall, the set of factors and their allocation to the main categories of the TOMP framework could be confirmed. However, the factor organizational home is not emphasized and discussed in most of the reviewed literature. On the other hand, an additional factor that has not been part of the TOMP framework is described to be important – strategy fit. Furthermore, the factors strategic alliances and platform strategy appear in the literature but in a different context compared to the reference model.

Keywords: Critical Success Factors, radical technological innovation, TOMP framework, innovation process

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Authors: Basel Halak, Christian Hall, Syed Abdul Father, Nelson Chow Wai Kit, Ruwaydah Widaad Raymode

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The attack surface on computing devices is becoming very sophisticated, driven by the sheer increase of interconnected devices, reaching 50B in 2025, which makes it easier for adversaries to have direct access and perform well-known physical attacks. The impact of increased security vulnerability of electronic systems is exacerbated for devices that are part of the critical infrastructure or those used in military applications, where the likelihood of being targeted is very high. This continuously evolving landscape of security threats calls for a new generation of defense methods that are equally effective and adaptive. This paper proposes an intelligent defense mechanism to protect from physical tampering, it consists of a tamper detection system enhanced with machine learning capabilities, which allows it to recognize normal operating conditions, classify known physical attacks and identify new types of malicious behaviors. A prototype of the proposed system has been implemented, and its functionality has been successfully verified for two types of normal operating conditions and further four forms of physical attacks. In addition, a systematic threat modeling analysis and security validation was carried out, which indicated the proposed solution provides better protection against including information leakage, loss of data, and disruption of operation.

Keywords: anti-tamper, hardware, machine learning, physical security, embedded devices, ioT

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Authors: Shital Patil, Surendra Bhosale

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Cardiac diseases are the leading causes of mortality and morbidity in the world, from recent few decades accounting for a large number of deaths have emerged as the most life-threatening disorder globally. Machine learning and Artificial intelligence have been playing key role in predicting the heart diseases. A relevant set of feature can be very helpful in predicting the disease accurately. In this study, we proposed a comparative analysis of 4 different features selection methods and evaluated their performance with both raw (Unbalanced dataset) and sampled (Balanced) dataset. The publicly available Z-Alizadeh Sani dataset have been used for this study. Four feature selection methods: Data Analysis, minimum Redundancy maximum Relevance (mRMR), Recursive Feature Elimination (RFE), Chi-squared are used in this study. These methods are tested with 8 different classification models to get the best accuracy possible. Using balanced and unbalanced dataset, the study shows promising results in terms of various performance metrics in accurately predicting heart disease. Experimental results obtained by the proposed method with the raw data obtains maximum AUC of 100%, maximum F1 score of 94%, maximum Recall of 98%, maximum Precision of 93%. While with the balanced dataset obtained results are, maximum AUC of 100%, F1-score 95%, maximum Recall of 95%, maximum Precision of 97%.

Keywords: cardio vascular diseases, machine learning, feature selection, SMOTE

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Authors: Maryam Aloshan, Imam Mohammad Ibn Saud, Ahmad Khudair

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This study assesses the data governance maturity of nonprofit organizations in Riyadh, Saudi Arabia, using the National Data Maturity Index (NDI) framework developed by the Saudi Data and Artificial Intelligence Authority (SDAIA). Employing a survey designed around the NDI model, data maturity levels were evaluated across 14 dimensions using a 5-point Likert scale. The results reveal a spectrum of maturity levels among the organizations surveyed: while some medium-sized associations reached the ‘Defined’ stage, others, including large associations, fell within the ‘Absence of Capabilities’ or ‘Building’ phases, with no organizations achieving the advanced ‘Established’ or ‘Pioneering’ levels. This variation suggests an emerging recognition of data governance but underscores the need for targeted interventions to bridge the maturity gap. The findings point to a significant opportunity to elevate data governance capabilities in Saudi nonprofits through customized capacity-building initiatives, including training, mentorship, and best practice sharing. This study contributes valuable insights into the digital transformation journey of the Saudi nonprofit sector, aligning with national goals for data-driven governance and organizational efficiency.

Keywords: nonprofit organizations-national data maturity index (NDI), Saudi Arabia- SDAIA, data governance, data maturity

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Authors: Sarah Werner, Michael J. Pritchard

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From pilots to engineers, the skills development within the aerospace industry is exceptionally broad. Employers often struggle with finding the right mixture of qualified skills to fill their organizational demands. This effort to find qualified talent is further complicated by the industrial delineation between two key areas: aviation and aerospace. In a broad sense, the aerospace industry overlaps with the aviation industry. In turn, the aviation industry is a smaller sector segment within the context of the broader definition of the aerospace industry. Furthermore, it could be conceptually argued that -in practice- there is little distinction between these two sectors (i.e., aviation and aerospace). However, through our unstructured text analysis of over 6,000 job listings captured, our team found a clear delineation between aviation-related jobs and aerospace-related jobs. Using techniques in natural language processing, our research identifies an integrated workforce skill pattern that clearly breaks between these two sectors. While the aviation sector has largely maintained its need for pilots, mechanics, and associated support personnel, the staffing needs of the aerospace industry are being progressively driven by integrative engineering needs. Increasingly, this is leading many aerospace-based organizations towards the acquisition of 'system level' staffing requirements. This research helps to better align higher educational institutions with the current industrial staffing complexities within the broader aerospace sector.

Keywords: aerospace industry, job demand, text mining, workforce development

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Authors: Odewumi Olatunde, Adegun Sunday

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Linguistic diversity is seen to complicate attempts to build a stable and cohesive political community. Hence, the challenge of integration is enormous in a multi-ethno-lingual country like Nigeria. In the same vein, justification for minority language rights claims in relation to broader political theories of justice, freedom and democracy cannot be ignored. It is in the light of the fore-going that this paper explores Nigeria’s experiments at language policy and planning(LPP) and the long drawn agitations for self-determination and linguistic freedom by the minority ethnic groups in the polity which has been exacerbated by the National Policy on Education language provisions. The paper succinctly reviews Nigeria’s LPP efforts and its attendant theater of conflicts; explores international attempts at evolving normative principles of freedom and equality for language policy and finally evaluates the position of the Nigerian LPP in the light of evolving international conventions. On this premise, it is concluded that giving a conscientious and honest implementation of the Nigerian language provisions as assessed from their face validity, the nation’s efforts could be exonerated from running afoul of any known civilized values and best practices. It is, therefore, recommended that an effectual and consistent commitment to implementation driven by a renewed political will is what is required for the nation to succeed in this direction.

Keywords: integration, rights, challenge, conventions, policy

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Authors: Waleed Afandi

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Artificial intelligence (AI) has rapidly transformed various sectors, including education. This paper explores the integration of AI in education, emphasizing its potential to revolutionize learning processes, enhance teaching methodologies, and personalize education. We examine the historical context of AI in education, current applications, and the potential challenges and ethical considerations associated with its implementation. By reviewing a wide range of literature, this study aims to provide a comprehensive understanding of how AI can be leveraged to improve educational outcomes and the future directions of AI-driven educational innovations. Additionally, the paper discusses the impact of AI on student engagement, teacher support, and administrative efficiency. Case studies highlighting successful AI applications in diverse educational settings are presented, showcasing the practical benefits and real-world implications. The analysis also addresses potential disparities in access to AI technologies and suggests strategies to ensure equitable implementation. Through a balanced examination of the promises and pitfalls of AI in education, this study seeks to inform educators, policymakers, and technologists about the optimal pathways for integrating AI to foster an inclusive, effective, and innovative educational environment.

Keywords: artificial intelligence, education, personalized learning, teaching methodologies, educational outcomes, AI applications, student engagement, teacher support, administrative efficiency, equity in education

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Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics-informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on a strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary conditions to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of the Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful in studying various optical phenomena.

Keywords: deep learning, optical soliton, physics informed neural network, partial differential equation

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Authors: Prof. Hao Mao, Phd Qia Wei, Dr.Siwei Cao

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The administrative system is the organisational carrier for managing the operation of the market and the basic guarantee for achieving innovation incentives. This paper takes the reform of provincial administrative institutions in the process of Chinese national intellectual property administrative system reform in 2018 as a quasi-natural experiment to assess the impact of IP administrative system reform on enterprise innovation. The study finds that reducing the independence of some provincial administrative institutions will lead to a reduction in the number of local enterprises' innovations and a decrease in the quality of innovations, which is mainly triggered by a decrease in R&D investment due to a decrease in the strength of subsidy policies. The new round of intellectual property administrative system reform in 2023 elevated the administrative status of China National Intellectual Property Administration (CNIPA), and re-strengthened the top-level design and centralization of IP administration. This paper clarifies the role of the 2018 IP administrative system reform on China's market innovation, provides empirical evidence for the properly handling government market relations and property rights incentives and other institutional designs, and also provides empirical references for further promoting the improvement of national and local IP institutional mechanisms and the implementation of the innovation-driven development strategy in the new round of reform.

Keywords: intellectual property, administrative systems, reform, high-quality innovation

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Authors: Justyna Rybicka, Ashutosh Tiwari, Shane Enticott

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In the age of automation and computation aiding manufacturing, it is clear that manufacturing systems have become more complex than ever before. Although technological advances provide the capability to gain more value with fewer resources, sometimes utilisation of the manufacturing capabilities available to organisations is difficult to achieve. Flexible manufacturing systems (FMS) provide a unique capability to manufacturing organisations where there is a need for product range diversification by providing line efficiency through production flexibility. This is very valuable in trend driven production set-ups or niche volume production requirements. Although FMS provides flexible and efficient facilities, its optimal set-up is key in achieving production performance. As many variables are interlinked due to the flexibility provided by the FMS, analytical calculations are not always sufficient to predict the FMS’ performance. Simulation modelling is capable of capturing the complexity and constraints associated with FMS. This paper demonstrates how discrete event simulation (DES) can address complexity in an FMS to optimise the production line performance. A case study of an automotive FMS is presented. The DES model demonstrates different configuration options depending on prioritising objectives: utilisation and throughput. Additionally, this paper provides insight into understanding the impact of system set-up constraints on the FMS performance and demonstrates the exploration into the optimal production set-up.

Keywords: discrete event simulation, flexible manufacturing system, capacity performance, automotive

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Authors: Albert T. Akume, Yahya M. Abdullahi

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The impact of poverty on individual and society is grave, hence the efforts by the government to eradicate or alleviate. In Nigeria the various efforts to reduce rural poverty by empowering them and making the process of their development self-sustaining have ended dismally. That notwithstanding, government determination to conquer poverty has not diminish as in the early 1990s the government with financial collaboration from the World Bank and African Development Bank introduced the fadama project. It is against this backdrop that this paper uses the documentary and analytical research methods to examine the implication the fadama development project has for community capacity development and human security in Nigeria. From the analysis it was discovered the fadama project improved household income of fadama farmers, community empowerment, participatory development planning and support for demand driven productive investment in farm and non-farm activities including community infrastructures. Despite this impressive result the fadama project is challenged by conflict especially in northern Nigeria and late delivery of necessary farm consumables that aid improved productivity. It was therefore recommended that the government should strengthen her various state security institutions to proactively mitigate conflicts and to ensure that farm consumables and other support services reach farmers timely.

Keywords: capacity development, empowerment, fadama, human security, poverty reduction, theory of change, sustainable development

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Xinyi Le

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In this paper, 3D printing orientations were determined through our perceptual model. Some FDM (Fused Deposition Modeling) 3D printers, which are widely used in universities and industries, often require support structures during the additive manufacturing. After removing the residual material, some surface artifacts remain at the contact points. These artifacts will damage the function and visual effect of the model. To prevent the impact of these artifacts, we present a fuzzy extreme learning machine approach to find printing directions that avoid placing supports in perceptually significant regions. The proposed approach is able to solve the evaluation problem by combing both the subjective knowledge and objective information. Our method combines the advantages of fuzzy theory, auto-encoders, and extreme learning machine. Fuzzy set theory is applied for dealing with subjective preference information, and auto-encoder step is used to extract good features without supervised labels before extreme learning machine. An extreme learning machine method is then developed successfully for training and learning perceptual models. The performance of this perceptual model will be demonstrated on both natural and man-made objects. It is a good human-computer interaction practice which draws from supporting knowledge on both the machine side and the human side.

Keywords: 3d printing, perceptual model, fuzzy evaluation, data-driven approach

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Authors: Yelda Yenel, Orkut Acele

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This research aims to examine the role of celebrities in the securitization and desecuritization of Syrian migrants in Türkiye on social media platforms. Traditionally, the securitization process has been driven by political actors and mainstream media. However, with the rise of social media, celebrities have emerged as influential actors, contributing to these processes. The topic of Syrian migrants, particularly those arriving in Türkiye after 2011, has sparked national debates, framing them both as a security threat and as a humanitarian issue, thereby dividing public opinion.The primary objective of this study is to analyze celebrities’ discourses about migrants on social media and to explore how these narratives contribute to the processes of securitization (presenting migrants as a threat) and desecuritization (framing migrants within a humanitarian context). This research will focus on social media platforms such as Twitter and Instagram, examining celebrities' posts and analyzing the narratives produced through content and discourse analysis techniques.By investigating how celebrities frame the migrant issue and how these frames resonate with the public, this study seeks to explore the impact of celebrity discourse on the securitization and desecuritization processes. Additionally, it will examine the influence of celebrities on social media users, offering a new perspective on how securitization theory is shaped by the role of celebrities in the digital age.

Keywords: securitization, desecuritization, celebrities, Syrian migrants, social media discourse

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