Computational Modeling Techniques Research Articles

Optimizing catalytic surface coatings in FDM-Printed sustainable materials: Innovations in chemical engineering

This research brings in the advancement of sustainable, high-performance engineering solutions where catalytic surface coatings are pursued to integrate with fused deposition modeling printed sustainable materials. The work is centered on optimization of catalytic coatings for higher efficiency and durability, which is innovatively linked with the advance chemical engineering. In probing the influence of different catalytic materials and deposition methods on FDM-printed substrates, we applied advanced surface functionalization, nano-engineering, and computational modeling techniques. Among other elements, this research approach utilized ANN with PSO algorithms in optimizing the parametric setting that best yielded high catalytic performance. The results obtained show considerable improvements in catalytic activity and the coating's lifetime, promising such applications in energy, environmental, and chemical industries. This study not only draws attention to the potential of FDM-printed sustainable materials but also demonstrates the potential of chemical engineering innovations for optimizing catalytic surface coatings toward the development of high-performance, sustainable technologies.

Unveiling the Anti-convulsant Potential of Novel Series of 1,2,4-Triazine- 6H-Indolo[2,3-b]quinoline Derivatives: In Silico Molecular Docking, ADMET, DFT, and Molecular Dynamics Exploration.

Epilepsy is a chronic neurological disorder caused by irregular electrical activity in the brain. To manage this disorder effectively, it is imperative to identify potential pharmacological targets and to understand the pathophysiology of epilepsy in depth. This research aimed to identify promising leads from a library of 1,2,4-triazine-6Hindolo[ 2,3-b]quinoline derivatives and optimize them using in silico and dynamic processes. We used computational studies to examine 1,2,4-Triazine-6H-indolo[2,3-b]quinoline derivatives. Some methods were used to strengthen the stability of binding sites, including Docking, ADMET, IFD, MMGBSA, Density Functional Theory (DFT), and Molecular Dynamics. HRSN24 and HRSN34 exhibited promising pharmacokinetic and pharmacodynamic characteristics compared to standard drugs (Carbamazepine and Phenytoin) and a co-crystal ligand (Diazepam). Both HRSN24 and HRSN34 presented notable Glide Xp docking scores (-4.528 and -4.633 Kcal/mol), IFD scores (-702.22 and -700.3 Kcal/mol), and MMGBSA scores (-45.71 and -14.46 Kcal/mol). HRSN24 was selected for molecular dynamics and DFT analysis. During MD, HRSN24 identified LYS21, GLY22, ASP24, ARG26, VAL53, MET55, and SER308 as the most important amino acid residues for hydrophobic interactions. A DFT computation was performed to determine the physicochemical properties of HRSN24, revealing a total energy of -1362.28 atomic units, a HOMO value of -0.20186, and a LUMO value of -0.01915. Based on computational modelling techniques, an array of 1,2,4-triazine-6H-indolo [2,3-b]quinoline derivatives were evaluated for their anti-convulsant properties. A stable compound within the GABAA receptor was identified by HRSN24, suggesting its affinity as an anti-convulsant.

RESPONSE SURFACE METHODOLOGY (RSM) AS A TOOL IN PHARMACEUTICAL FORMULATION DEVELOPMENT

Response surface methodology (RSM) serves as a valuable tool in pharmaceutical formulation development, facilitating the optimization of drug formulations by systematically exploring the effects of multiple variables on desired responses. This methodology involves the design of experiments to generate mathematical models that predict the relationship between formulation parameters and critical quality attributes. By utilizing statistical techniques such as factorial design, central composite design, and Box-Behnken design, RSM enables the identification of optimal formulation conditions while minimizing the number of experimental trials. Across iterative experimentation and model refinement, RSM assists in understanding the complex interactions between formulation components, process variables, and product characteristics. In this review, we discuss the application of RSM in pharmaceutical formulation studies, highlighting its efficacy in optimizing drug delivery systems, enhancing product stability, and ensuring quality control. In addition, we explore recent advancements in RSM-driven approaches, including its integration with computational modeling and artificial intelligence techniques for enhanced formulation design and process optimization. Overall, RSM offers a systematic and efficient approach for developing robust pharmaceutical formulations, thereby accelerating the drug development process and improving therapeutic outcomes.

Computational modeling of weld-line impacts on mechanical behavior of fiber-reinforced thermoplastics

The areas where the weld line is located are weak areas in terms of impact strength and tensile strength, which negatively affects the overall strength of the final product. It can also cause visual defects on the surface and create an aesthetically undesirable situation. The injection molding process introduces anisotropic behaviors in materials, particularly in weld-line areas, which are proned to mechanical weaknesses. This study aims to enhance the predictability of the mechanical performance of injection-molded fiber-reinforced thermoplastic composites (FRPs) through a comprehensive computational modeling technique. By using software such as MOLDEX3D and DIGIMAT RP, this research integrates real-time data on fiber orientation and weld-line effects into the finite element analysis (FEA) models. Simulations of 40% glass fiber reinforced polyamide (PA6) revealed the impact of different gate numbers on mechanical strength, highlighting the influence of weld-line regions. The findings suggest that incorporating fiber orientation and weld-line data significantly improves the accuracy of FEA models, leading to better predictions in the performance of the parts.

Gas Tunnel Engineering of Prolyl Hydroxylase Reprograms Hypoxia Signaling in Cells

AbstractCells have evolved intricate mechanisms for recognizing and responding to changes in oxygen (O2) concentrations. Here, we have reprogrammed cellular hypoxia (low O2) signaling via gas tunnel engineering of prolyl hydroxylase 2 (PHD2), a non‐heme iron dependent O2 sensor. Using computational modeling and protein engineering techniques, we identify a gas tunnel and critical residues therein that limit the flow of O2 to PHD2’s catalytic core. We show that systematic modification of these residues can open the constriction topology of PHD2’s gas tunnel. Using kinetic stopped‐flow measurements with NO as a surrogate diatomic gas, we demonstrate up to 3.5‐fold enhancement in its association rate to the iron center of tunnel‐engineered mutants. Our most effectively designed mutant displays 9‐fold enhanced catalytic efficiency (kcat/KM=830±40 M−1 s−1) in hydroxylating a peptide mimic of hypoxia inducible transcription factor HIF‐1α, as compared to WT PHD2 (kcat/KM=90±9 M−1 s−1). Furthermore, transfection of plasmids that express designed PHD2 mutants in HEK‐293T mammalian cells reveal significant reduction of HIF‐1α and downstream hypoxia response transcripts under hypoxic conditions of 1 % O2. Overall, these studies highlight activation of PHD2 as a new pathway to reprogram hypoxia responses and HIF signaling in cells.

Gas Tunnel Engineering of Prolyl Hydroxylase Reprograms Hypoxia Signaling in Cells.

Cells have evolved intricate mechanisms for recognizing and responding to changes in oxygen (O2) concentrations. Here, we have reprogrammed cellular hypoxia (low O2) signaling via gas tunnel engineering of prolyl hydroxylase 2 (PHD2), a non-heme iron dependent O2 sensor. Using computational modeling and protein engineering techniques, we identify a gas tunnel and critical residues therein that limit the flow of O2 to PHD2's catalytic core. We show that systematic modification of these residues can open the constriction topology of PHD2's gas tunnel. Using kinetic stopped-flow measurements with NO as a surrogate diatomic gas, we demonstrate up to 3.5-fold enhancement in its association rate to the iron center of tunnel-engineered mutants. Our most effectively designed mutant displays 9-fold enhanced catalytic efficiency (kcat/KM=830±40 M-1 s-1) in hydroxylating a peptide mimic of hypoxia inducible transcription factor HIF-1α, as compared to WT PHD2 (kcat/KM=90±9 M-1 s-1). Furthermore, transfection of plasmids that express designed PHD2 mutants in HEK-293T mammalian cells reveal significant reduction of HIF-1α and downstream hypoxia response transcripts under hypoxic conditions of 1 % O2. Overall, these studies highlight activation of PHD2 as a new pathway to reprogram hypoxia responses and HIF signaling in cells.

Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia.

Behavioral speech tasks have been widely used to understand the mechanisms of speech motor control in typical speakers as well as in various clinical populations. However, determining which neural functions differ between typical speakers and clinical populations based on behavioral data alone is difficult because multiple mechanisms may lead to the same behavioral differences. For example, individuals with cerebellar ataxia (CA) produce atypically large compensatory responses to pitch perturbations in their auditory feedback, compared to typical speakers, but this pattern could have many explanations. Here, computational modeling techniques were used to address this challenge. Bayesian inference was used to fit a state feedback control (SFC) model of voice fundamental frequency (fo) control to the behavioral pitch perturbation responses of speakers with CA and typical speakers. This fitting process resulted in estimates of posterior likelihood distributions for five model parameters (sensory feedback delays, absolute and relative levels of auditory and somatosensory feedback noise, and controller gain), which were compared between the two groups. Results suggest that the speakers with CA may proportionally weight auditory and somatosensory feedback differently from typical speakers. Specifically, the CA group showed a greater relative sensitivity to auditory feedback than the control group. There were also large group differences in the controller gain parameter, suggesting increased motor output responses to target errors in the CA group. These modeling results generate hypotheses about how CA may affect the speech motor system, which could help guide future empirical investigations in CA. This study also demonstrates the overall proof-of-principle of using this Bayesian inference approach to understand behavioral speech data in terms of interpretable parameters of speech motor control models.

Neutrophils under the microscope: neutrophil dynamics in infection, inflammation, and cancer revealed using intravital imaging.

Neutrophils rapidly respond to inflammation resulting from infection, injury, and cancer. Intravital microscopy (IVM) has significantly advanced our understanding of neutrophil behavior, enabling real-time visualization of their migration, interactions with pathogens, and coordination of immune responses. This review delves into the insights provided by IVM studies on neutrophil dynamics in various inflammatory contexts. We also examine the dual role of neutrophils in tumor microenvironments, where they can either facilitate or hinder cancer progression. Finally, we highlight how computational modeling techniques, especially agent-based modeling, complement experimental data by elucidating neutrophil kinetics at the level of individual cells as well as their collective behavior. Understanding the role of neutrophils in health and disease is essential for developing new strategies for combating infection, inflammation and cancer.

Enhancing Energy Harvesting Efficiency from Ambient Sources through Advanced Materials and Nanotechnology

The utilization of ambient energy sources for powering small-scale electronic devices and sensors has garnered significant attention due to its potential for sustainable and autonomous operation. This research focuses on enhancing the efficiency of energy harvesting from ambient sources, such as vibrations, heat differentials, and electromagnetic fields, through the integration of advanced materials and nanotechnology into energy harvesting systems. The study begins with a comprehensive review of existing energy harvesting technologies, highlighting their limitations in terms of efficiency, scalability, and adaptability to various ambient energy sources. It identifies the key challenges faced in achieving high energy conversion rates and explores the potential of advanced materials and nanoscale structures to address these challenges. One aspect of the research involves the development and characterization of novel materials with superior energy conversion properties. This includes the synthesis of piezoelectric materials for vibration energy harvesting, thermoelectric materials for heat-to-electricity conversion, and nanomaterials for enhancing electromagnetic energy harvesting efficiency. Advanced characterization techniques, such as scanning electron microscopy (SEM) and X-ray diffraction (XRD), are employed to analyze the structural and electrical properties of these materials. Furthermore, the study investigates the design and fabrication of nanostructured energy harvesting devices optimized for specific ambient energy sources. This involves the integration of nanoscale components, such as nanostructured electrodes, into energy harvesting systems to improve energy capture and conversion rates. Finite element analysis (FEA) simulations and experimental testing are conducted to evaluate the performance and efficiency of these nano-enhanced energy harvesting devices under real-world conditions. The research also addresses the optimization of energy harvesting system parameters, including resonance tuning, impedance matching, and energy management circuits, to maximize energy extraction and utilization. Computational modeling techniques, such as multiscale modeling and finite element simulations, are utilized to optimize system configurations and improve overall energy harvesting efficiency. Overall, this research contributes to the advancement of energy harvesting technologies by leveraging advanced materials and nanotechnology, paving the way for sustainable and autonomous power generation from ambient energy sources for a wide range of applications.

Cryo-EM Structure of raiA ncRNA From Clostridium Reveals a New RNA 3D Fold

Advancements in genome-wide sequence analysis have led to the discovery of numerous novel bacterial non-coding RNAs (ncRNAs). These ncRNAs have been categorized into various RNA families and classes based on their size, structure, function, and evolutionary relationships. One such ncRNA family, raiA, is notably abundant in the bacterial phyla Firmicutes and Actinobacteria and is remarkably well-conserved across many Gram-positive bacteria. In this study, we integrated cryo-electron microscopy single-particle analysis with computational modeling and biochemical techniques to elucidate the structural characteristics of raiA from Clostridium sp. CAG 138. Our findings reveal the globular 3D fold of raiA, providing valuable structural insights. This analysis paves the way for future investigations into the functional properties of raiA, potentially uncovering new regulatory mechanisms in bacterial ncRNAs.

Аналіз даних теплового режиму комутаційного обладнання комп’ютерних мереж на основі відновлення сигналів температурних датчиків

The article considers the task of analyzing distributed data of temperature modes of chips of switching equipment of computer networks. For this, a temperature measurement system using a temperature sensor is used. In the case when the temperature sensor is inside the chip, the speed of the measurement system's response to temperature changes is satisfactory. However, when the sensor is outside the chip, due to the inertness of the thermal contact, the response speed of the measuring system is low. In this situation, the effectiveness of the control system becomes unsatisfactory. To overcome the inertness of temperature sensors, it is proposed to analyze the distributed data that is received in digital form on the data processing server by restoring the distorted signals of nonlinear measuring subsystems «chip – temperature sensor» based on the use of their mathematical models in the form of a partial sum of the Volterra integro-power series. The identification of the mathematical model of the measurement subsystem is carried out on a finite interval of time by conducting a series of experiments using test signals. The method of reducing the number of test signals based on taking into account the specificity of the impact of nonlinearity on the results of experiments is considered. The obtained model is the basis for solving the inverse problem of restoring the signal of temperature influence at the sensor input. Since this problem is incorrect, it is suggested to supplement the model with a regularization parameter and reduce the problem to a correct one. To use the model over an infinite period of time, a computer modeling technique is proposed using restarts of computing processes, which are carried out in several streams with a shift in time. The result of calculations is formed by combining fragments from different streams. To check the reliability of the results obtained by applying the developed method, the solutions of the model problems are given.

Basalt fibers corrosion in concrete: New perspectives originating from computational modelling techniques employment

Most of engineering disciplines or research areas have recently witnessed strategic changes leading to formulation of new challenges and revision of existing activities. Promoting the environmental aspects to the highest priority level was mostly in common. Seeking for energy efficient solutions, concrete society has mentioned basalt as a promising candidate having a potential to replace steel in the future. The chemical disharmony between basalt and alkaline environment of concrete, however, represents the most challenging tasks that obstructs the replacement procedure due to durability issues. Since only 3.3 % of the research priorities in this area deals with the corrosion and degradation problems, while the mainstream (57.0 %) prefers determination of various material properties, this problem seems to be undervalued. Additionally, computational modelling approaches are surprisingly not applied as frequently (6.1 %) as in other disciplines, although they could provide the essential long-term research. Simplified methods therefore still dominate these estimations approaches. This paper therefore brings an overview on basalt fiber corrosion in concrete, while summarizing the most topical gaps slowing down the basalt application progress. Since these gaps originate mostly from the simplifications adopted, specific solutions are proposed respectively, exploiting the unique advantages and possibilities of computational modelling approaches that are omitted. The topicality as well as the potential of the advanced approach is demonstrated on a critical example dealing with corrosion analysis evaluation in a specific wall segment. Highlighting the differences between simplified- and advanced techniques outputs, the main findings are concluded, and the most topical gaps are identified.

Experimental Success in Marburg Virus Vaccination

The Marburg virus (MRV), classified within the Filoviridae family, was initially identified in 1967, precipitating Marburg virus disease (MARV), a severe and often fatal hemorrhagic fever. With its pronounced infectiousness, high mortality rate, and proclivity for epidemic outbreaks, MARV stands as a formidable public health menace. Despite extensive sporadic outbreaks and its designation as a priority disease, the quest for efficacious drugs or vaccines against MRV remains an ongoing challenge. Nonetheless, the relentless pursuit of scientific inquiry, augmented by the innovative application of immunoinformatics, is propelling forward vaccine development endeavors. Current vaccine candidates, spanning from VSV-based formulations to virus-like particle vaccines, exhibit encouraging outcomes in preclinical and clinical evaluations, boasting notable efficacy and safety profiles. Furthermore, the exploration of multivalent vaccine strategies, designed to target a spectrum of hemorrhagic fever viruses, holds promise in fortifying pandemic preparedness efforts. Immunoinformatics assumes a pivotal role in this context, offering predictive insights into vaccine candidate selection and optimization, thereby facilitating expedited development tailored to diverse demographic cohorts. The integration of computational modeling techniques into vaccine development paradigms represents a transformative avenue for effectively controlling MRV outbreaks on a global scale. Sustained collaboration and continued research endeavors are imperative to fully harness the potential of these advancements in confronting the formidable challenge posed by the lethal Marburg virus and safeguarding global public health.

Multiscale neural dynamics in sleep transition volatility across age scales: a multimodal EEG-EMG-EOG analysis of temazepam effects.

Recent advances in computational modeling techniques have facilitated a more nuanced understanding of sleep neural dynamics across the lifespan. In this study, we tensorize multiscale multimodal electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals and apply Generalized Autoregressive Conditional Heteroskedasticity (GARCH) modeling to quantify interactions between age scales and the use of pharmacological sleep aids on sleep stage transitions. Our cohort consists of 22 subjects in a crossover design study, where each subject received both a sleep aid and a placebo in different sessions. To understand these effects across the lifespan, three evenly distributed age groups were formed: 18-29, 30-49, and 50-66years. The methodological framework implemented here employs tensor-based machine learning techniques to compute continuous wavelet transform time-frequency features and utilizes a GARCH model to quantify sleep signal volatility across age scales. Support Vector Machines are used for feature ranking, and our analysis captures interactions between signal entropy, age, and sleep aid status across frequency bands, sleep transitions, and sleep stages. GARCH model results reveal statistically significant volatility clustering in EEG, EMG, and EOG signals, particularly during transitions between REM and non-REM sleep. Notably, volatility was higher in the 50-66 age group compared to the 18-29 age group, with marked fluctuations during transitions from deep sleep to REM sleep (standard deviation of 0.35 in the older group vs. 0.30 in the 18-29 age group, p < 0.05). Statistical comparisons of volatility across frequency bands, age scales, and sleep stages highlight significant differences attributable to sleep aid use. Mean conditional volatility parameterization of the GARCH model reveals directional influences, with a causality index of 0.75 from frontal to occipital regions during REM sleep transition periods. Our methodological framework identifies distinct neural behavior patterns across age groups associated with each sleep stage and transition, offering insights into the development of targeted interventions for sleep regularity across the lifespan.

Peptides as modulators of FPPS enzyme: A multifaceted evaluation from the design to the mechanism of action

Bone diseases are medical conditions caused by the loss of bone homeostasis consecutive to increased osteoclast activity and diminished osteoblast activity. The mevalonate pathway (MVA) is crucial for maintaining this balance since it drives the post-translational prenylation of small guanosine triphosphatases (GTPases) proteins. Farnesyl pyrophosphate synthase (FPPS) plays a crucial role in the MVA pathway. Consequently, in the treatment of bone-related diseases, FPPS is the target of FDA-approved nitrogen-containing bisphosphonates (N-BPs), which have tropism mainly for bone tissue due to their poor penetration in soft tissues. The development of inhibitors targeting the FPPS enzyme has garnered significant interest in recent decades due to FPPS's role in the biosynthesis of cholesterol and other isoprenoids, which are implicated in cancer, bone diseases, and other conditions. In this study, we describe a multidisciplinary approach to designing novel FPPS inhibitors, combining computational modeling, biochemical assays, and biophysical techniques. A series of peptides and phosphopeptides were designed, synthesized, and evaluated for their ability to inhibit FPPS activity. Molecular docking was employed to predict the binding modes of these compounds to FPPS, while Surface Plasmon Resonance (SPR) and Nuclear Magnetic Resonance (NMR) spectroscopy experiments – based on Saturation Transfer Difference (STD) and an enzymatic NMR assay – were used to measure their binding affinities and kinetics. The biological activity of the most promising compounds was further assessed in cellular assays using murine colorectal cancer (CRC) cells. Additionally, genomics and metabolomics profiling allowed to unravel the possible mechanisms underlying the activity of the peptides, confirming their involvement in the modulation of the MVA pathway. Our findings demonstrate that the designed peptides and phosphopeptides exhibit significant inhibitory activity against FPPS and possess antiproliferative effects on CRC cells, suggesting their potential as therapeutic agents for cancer.

Overcoming Ion Trapping in Chevrel Phase Compounds via Tailored Anion Substitution: An Integrated Study of Theory, Synthesis, and In Operando Techniques for Reversible Aqueous Zn-Ion Batteries.

Sustainable batteries are key for powering electronic devices of the future, with aqueous zinc-ion batteries (AZIBs) standing out for their use of abundant, readily available elements, and safer production processes. Among the various electrode materials studied for AZIBs, the Chevrel Phase, Mo6S8 has shown promise due to its open framework, but issues with zinc ion trapping have limited its practical application. In this work, we employed computational methods to investigate the insertion-deinsertion mechanism in a series of isostructural Mo6S8-xSex (x = 0-8) solid solutions as materials that could balance the gravimetric capacity and reversible cycling for AZIBs. Density functional theory (DFT) calculations revealed that increasing the Se content would reduce the binding energy of Zn within the structures, enabling Zn deinsertion compared to the Mo6S8 structure. Experiments confirmed the formation of Mo6S8-xSex (x = 0-8) solid solutions, and electrochemical testing showed improved reversibility of Zn insertion/deinsertion as the amount of Se increased, consistent with the computational predictions. Furthermore, combined in operando X-ray diffraction and electrochemical studies revealed a continuous, gradual Zn-insertion process into Mo6S4Se4, in stark contrast to the abrupt phase changes observed upon Zn insertion in Mo6S8 and Mo6Se8. DFT calculations attributed the stabilization of Zn0.5Mo6S4Se4 as a prime reason for preventing phase separation, making Se-substituted compounds promising materials for high-performance AZIBs. Overall, this interdisciplinary approach, integrating computational modeling, materials synthesis, and advanced characterization techniques, offers a pathway for fine-tuning anion chemistry that can help create high-performance electrode materials for sustainable energy storage technologies.

Unveiling the impact of SUMOylation at K298 site of heat shock factor 1 on glioblastoma malignant progression

BackgroundGlioblastoma (GBM) poses a significant medical challenge due to its aggressive nature and poor prognosis. Mitochondrial unfolded protein response (UPRmt) and the heat shock factor 1 (HSF1) pathway play crucial roles in GBM pathogenesis. Post-translational modifications, such as SUMOylation, regulate the mechanism of action of HSF1 and may influence the progression of GBM. Understanding the interplay between SUMOylation-modified HSF1 and GBM pathophysiology is essential for developing targeted therapies. MethodsWe conducted a comprehensive investigation using cellular, molecular, and in vivo techniques. Cell culture experiments involved establishing stable cell lines, protein extraction, Western blotting, co-immunoprecipitation, and immunofluorescence analysis. Mass spectrometry was utilized for protein interaction studies. Computational modeling techniques were employed for protein structure analysis. Plasmid construction and lentiviral transfection facilitated the manipulation of HSF1 SUMOylation. In vivo studies employed xenograft models for tumor growth assessment. ResultsOur research findings indicate that HSF1 primarily undergoes SUMOylation at the lysine residue K298, enhancing its nuclear translocation, stability, and downstream heat shock protein expression, while having no effect on its trimer conformation. SUMOylated HSF1 promoted the UPRmt pathway, leading to increased GBM cell proliferation, migration, invasion, and reduced apoptosis. In vivo studies have confirmed that SUMOylation of HSF1 enhances its oncogenic effect in promoting tumor growth in GBM xenograft models. ConclusionThis study elucidates the significance of SUMOylation modification of HSF1 in driving GBM progression. Targeting SUMOylated HSF1 may offer a novel therapeutic approach for GBM treatment. Further investigation into the specific molecular mechanisms influenced by SUMOylated HSF1 is warranted for the development of effective targeted therapies to improve outcomes for GBM patients.

Design Optimization of a Run-Of-River Small Hydropower Plant: A Case Study of Mabula Kapi Site

Prior to construction, small or large hydropower plants undergo feasibility studies, which involve technical, economic, financial, social, and environmental aspects. Technical studies, particularly, include various engineering disciplines. This study sought to conduct a component of technical feasibility studies, focused on design optimization of the turbine-plant capacity size for a run-of-river case study called Mabula Kapi, on Kaombe River, Serenje, Zambia. The study method used computational modelling, simulation, and optimization techniques, and used secondary data from previous site. A prefeasibility study conducted earlier had underestimated the turbine-plant capacity owing to underestimated discharge data, owing to lack of site gauging station. A less accurate discharge estimation method involving transposition from a gauged similar catchment had been used. A later feasibility-level site hydrological study found improved discharge data. Riding on the improved discharge data, this study found the following optimised plant design parameters based on plant design discharge of 6.20 m3/s: Installed capacity of 10.20 MW; Annual energy production of 42 GWh; Capacity factor of 47.1%; 3 x Pelton wheel of 272 rpm speed, 2.1 m diameter, 2-nozzles of diameter 0.16 m each, 21 buckets of 0.58m width each; and 3 x 4 MVA generators of 50Hz, 22-poles, and 11kV.

Dynamic Load Balancing and Service Prioritization Algorithm for Cloud Computing Environments

Bridge infrastructure plays a critical role in ensuring the safe and efficient transportation of goods and people. This research paper investigates the strength characteristics of bridges with a focus on understanding the factors influencing their structural integrity and resilience. The study employs a comprehensive methodology that includes field inspections, laboratory testing, and advanced computational modeling techniques to assess the performance of various bridge types under different loading conditions. Findings reveal the complex interplay between design parameters, material properties, and environmental factors in determining bridge strength and durability. Moreover, the research highlights the importance of proactive maintenance strategies and innovative engineering solutions to enhance the resilience of bridge infrastructure against aging, deterioration, and extreme events. The insights gained from this study have significant implications for bridge design, maintenance practices, and public safety, ultimately contributing to the sustainability and reliability of transportation networks.

Application of surface geophysical investigations and pumping test data analysis for better characterization of aquifer hydraulic parameters, Upper Awash Sub-Basin, Central Ethiopia

Study regionUpper Awash River Sub-basin in central Ethiopia Study focusThe research aimed to estimate aquifer parameters and conduct resistivity modeling in the study area. Data were collected from 890 Schlumberger Vertical Electrical Soundings (VES) points strategically distributed across six selected zones. The VES data were analyzed using computer modeling and curve-matching techniques to interpret the resistivity responses of the geological formations. This analysis provided insights into the subsurface characteristics and identified potential aquifer zones. New insights for the regionThe interpreted curves from the VES data and Root Mean Square (RMS%) values ranging from 0.36 % to 1.67 %, indicating a good fit between observed and modeled data. Resistivity modeling and geoelectrical sections were produced along specific lines, visualizing subsurface structures and confirming the resistivity responses of the geological formations. To determine aquifer parameters, the Dar-Zarrouk parameters were calculated from the interpreted curves, yielding crucial information about hydraulic properties such as hydraulic conductivity (K) and transmissivity (T). Hydraulic conductivity values ranged from 1.1 m/day to 21.7 m/day, while transmissivity values ranged from 144.3 m²/day to 2763.3 m²/day. Validation against borehole pumping test data showed strong correlations: R² = 0.9873 for transmissivity and transverse resistance, and R² = 0.9352 for hydraulic conductivity and resistivity. These findings enhance the understanding of the complex volcanic aquifer characteristics in the Upper Awash River Sub-basin, aiding sustainable groundwater management and development in the area.