International Journal of Progressive Sciences and Technologies
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Development Of A New Sedimentation Potential Measuring Device For Silica Suspensions In Various Dispersion Media
Full text linkThis study presents a new device designed to measure the sedimentation potential in silica suspensions. The device represents a significant advance in sedimentation analysis techniques [1]. This device is being used for the first time, which will significantly improve the understanding of sedimentation dynamics and measurement accuracy in suspensions of hydroxides, acids, and salts
Functional Qualification Comparison Studies between the Preferentially Selected OPC and PPC Cements Produced in Nepal
Full text linkOne of the most eminent building composites used ubiquitously in diverse engineering arenas is concrete; a reinforced admixture comprising with the definite amount cements, aggregates, water, minerals, and many other supplementary cementitious materials. Among these ingredients, the quite indispensable one that vows to dispense exceptional hardening strengths and conformational stabilities with notable weather resiliencies is cement. Owing to its versatile clinker phases, severe kiln processes, and unequal temporal hydration/rehydration affinities leading to the production of discretely functioning chemical derivatives, the entire commercial markets across the globe consume its brands primarily as OPC and PPC. The exclusive reasons behind their variable hydration, plasticity, gluing, agglomerating, setting, hardening, and enduring potencies that are disclosed comprehensively via the spectroscopic microstructural assessment procedures still become a great dearth of evidences. This contribution utmost aims to address all these functional inequalities and dissimilar workability aptitudes of the OPC & PPC cement grains by accessing to their generic crystallite phases and microstructural coherent domains through XRD and FTIR spectroscopic means. The careful probing of the X-ray diffraction angles, scattering bands, basal spacing (d-space), band intensity & interferences superposition regimes, symmetry & spatial distribution range, net integral area & FWHM, crystallites sizes ( & growth propensities, and number density of the closely stacked layers & edge plane thickness exemplify their disproportionate founding phases featuring the different packing ratio & interatomic layers crosslinking force, crystal defects & disorderliness, compressive & tensile strains, conglomeration cum grain size evolutions rates, adsorbing sites & exposable surface areas, etc. And, the rigorous judgements of the IR vibration bands and fingerprint regions deduce all those characteristic phases and their chemical moieties explicitly. These spectral interpretations not only declare their unalike functionalizing qualifications and cementitious features but also assure unidentical dissolution rates, hydration propensities, plasticity properties, and all the aftermath stiffening chemistries. We believe this insightful comparative analyses would be the leading industrial guidelines & consumer doctrines mainly for upgrading the specific cement brands & accreditations commercially
Machine Learning And Public Health Policies For Climate Migrants: Africa And Eu Perspectives
Full text linkEnvironmental stressors, including floods, droughts, and rising sea levels, are progressively influencing global migration trends, with a notable impact in Sub-Saharan Africa. Health systems, particularly in Africa and the European Union (EU), are insufficiently equipped to meet the complex healthcare needs of climate migrants. Existing policies often lack integration of advanced technological solutions, such as machine learning (ML), which could offer transformative potential for improving healthcare access and outcomes for climate migrants. The primary problem addressed in this study is the gap in utilizing ML to enhance public health policies for climate migrants, focusing on data collection, real-time decision-making, and service delivery adaptation. This study employs a qualitative approach, conducting a comprehensive review of existing literature and case studies across Africa and the EU. The research explores how ML can address gaps in public health systems, particularly in managing migration flows and health risks, by analysing current policy frameworks and identifying opportunities for integrating ML-based solutions. The findings reveal that ML can significantly enhance healthcare systems by improving data analysis, facilitating resource allocation, and predicting health risks associated with climate migration. Moreover, there is a notable opportunity for cross-regional collaboration between Africa and the EU in leveraging ML for more responsive and adaptive health systems. The study recommends the integration of ML into public health policies, advocating for the development of data-driven, real-time decision-making frameworks. It calls for fostering international cooperation and knowledge-sharing to maximize ML's potential in addressing shared migration challenges. The study concludes that, integrating ML into public health frameworks is essential for effectively addressing the growing challenges posed by climate migration, ensuring that health systems are both responsive and adaptive to the evolving needs of climate migrants.
Effect of Supply Chain Quality Management Practices on the Performance of Africa Improved Food ( AIF) 2020-2023
No full textThis study investigated how supply chain quality management practices affect the performance of Africa Improved Food Rwanda (AIF) from 2020 to 2023. Despite AIF’s efforts in quality planning, assurance, control, and continuous improvement, its overall performance has remained suboptimal, likely due to weaknesses in these management practices. The research focused on four main objectives: evaluating the effects of quality planning, assurance, control, and continuous process improvement on AIF’s performance.The study used theories such as resource-based theory, Deming’s quality improvement theory, and reliability theory. Data were collected from 170 AIF employees using questionnaires, interviews, and documentation, and analyzed both quantitatively and qualitatively. Findings from the first objective indicated that item 3 indicates that “Organisation has requirements and recommendations that specify how management operations are to be conducted at a company to ensure that quality is the end result (SOP)” responded at very highest mean and the responses were homogeneous (mean= 5.00, SD=0.000), findings from the second objective indicated that “Actions and Measures are taken after a report of Monitoring and Evaluation in terms of ensuring quality” responded at very highest mean and the responses were homogeneous (mean= 4.88, SD=0.422), and item 2 indicates that “The Organisation machines and equipment’s are being checked regularly by RSB” responded at very highest mean and the responses were homogeneous (mean= 5.00, SD=0.000), findings from the third objective indicated that being practiced” responded at very highest mean and the responses were homogeneous (mean= 4.76, SD=0.437), finally from the fourth objective indicated that item 1 indicates that “The Organisation has training policies for employee” responded at very highest mean and the responses were homogeneous (mean= 5.00, SD=0.000).. The study concludes that AIF should continually enhance its safety standards and minimize risks associated with substandard products to improve overall performance
Beyond Life and Death: Understanding Neural Correlates of Near-Death Experiences Through EEG and fMRI
Full text linkNear-death experiences (NDEs) are a phenomenon that often changes people’s lives drastically. It occurs when a person goes through extreme psychological or physical trauma and survives by narrowly escaping death. This includes out-of-body feelings, intense life reviews, feelings of peace, and meeting deceased loved ones. NDEs are challenging to explain because they are fundamentally subjective, and the neural basis is even more difficult to explain. However, newer and more progressive techniques of neuroimaging such as EEG and fMRI machines have provided more evidence towards the correlates of these experiences. This review focuses on the neurochemical and physiological changes accompanying NDEs with emphasis on the cortical and subcortical regions of the brain. It also analyzes the EEG results regarding oscillation of the brain and fMRI result that mark particular brain parts that make neural networks responsible for NDEs. The clinical and philosophical perspectives of these findings along with the methodological approaches to study such phenomena are also provided. The point of this review is to interpret how profound the results are and attempt to outline what future research needs to be aimed at attempts to unmask the neurosciences of NDEs
Unmasking The Blisters: A Case Of Pediatric Linear IGA Bullous Dermatosis Mimicking Bullous Impetigo
Full text linkIntroduction – Linear IgA bullous dermatosis (LABD) is one of many diseases that fall under the umbrella of bullous diseases. It is a rare and chronic autoimmune condition affecting both adult and pediatric populations. In children, it is also known as chronic bullous disease of childhood and presents with a typical cluster of jewel-like appearance. Case presentation – We report a case of a 6-year-old boy who presented with vesicles and bullae over a course of 10 days. The patient was initially diagnosed with bullous impetigo and started on treatment with topical mupirocin and oral amoxicillin-clavulanate. Upon seeing no improvement, the patient returned and was diagnosed with linear IgA bullous dermatosis based on direct immunofluorescence testing (DIF). He was initiated on dapsone and topical corticosteroids, which resulted in gradual remission of the disease. Conclusion – Given that LABD is a rare disease, it is often misdiagnosed. Its clinical features, although typical, are not specific to the disease. Diagnostic investigations include histopathological studies and direct immunofluorescence testing. Although the diagnosis of LABD is challenging, its treatment is relatively simple with dapsone.
La Concurrence Déloyale Entre Les Motocyclistes, Les Taxis-Bus Et Les Taxis-Voitures Dans La Ville De Kinshasa
Full text linkL’objectif de cet article est d'analyser les formes de concurrence déloyale entre les motocyclistes, les taxis-bus et les taxis-voitures à Kinshasa, et d’évaluer ses impacts sur le secteur du transport urbain. L’étude, menée à travers des entretiens, des questionnaires et des observations de terrain, met en évidence une concurrence exacerbée par l’absence de régulation et l’informalité du secteur. Les résultats montrent que cette concurrence a des effets négatifs sur la sécurité des usagers, la rentabilité des opérateurs et la qualité des services offerts. L’article propose des recommandations pour améliorer la régulation, la professionnalisation des transporteurs et la sécurité dans le secteur du transport urbain à Kinshasa
Optimization of Injection Molding Parameters for Enhanced Mechanical Performance of Plastic Latch Mechanisms A Moldflow Simulation Study
Full text linkAbstract: This study investigates the optimization of injection parameters (temperature, pressure, and flow rate) for improving the quality, safety, and environmental efficiency of plastic latching mechanisms using Moldflow Plastic Insight (MPI). A latching mechanism CAD model designed in Pro/ENGINEER was tested in 100 simulation scenarios. Results showed that higher injection temperatures significantly reduced fill time by 40%, while increased pressures extended freezing time by 15%. Flow rate had a strong influence on bulk temperature and molecular orientation. Optimal gate positioning minimized air traps and thermal hotspots. The integration of health, safety, and environmental (HSE) principles in process design demonstrates the potential to enhance product quality, worker safety, and reduce environmental footprint.Keywords: Plastic injection molding, Moldflow simulation, injection parameters, latching mechanism, thermal distribution, health and safety, environmental performanc
New Generation Light Ammunition 6.8mm Caliber For NGSW (Next Generation Squad Weapon)
Full text linkTo be able to increase the fighting power of the United States Infantry troops, the US Army developed lightweight ammunition, namely 6.8 mm caliber. The development of 6.8 mm ammunition is due to the development of body armor (individual troop personal protective equipment) which increasingly uses materials that are difficult to penetrate by old ammunition such as 5.56 mm caliber and 7.62mm caliber at relevant distances. With this 6.8 mm caliber, it is expected that even body armor with strong materials can be penetrated even with a long shooting distance of about 600 m, with such a distance it is penetrated, especially with a close distance it will certainly facilitate the penetrating power of the 6.8 mm caliber against body armor with strong materials. In this paper, a literature study was conducted on the latest 6.8 mm ammunition for NGSW (Next Generation Squad Weapon) which began to be developed in the United States. There are three Defense Industries in the United States that are trusted by the US Army to be able to conduct research and development related to 6.8 mm ammunition as NGSW (Next Generation Squad Weapon), namely Sig Sauer, General Dynamics, and Textron Systems. Each industry produces 6.8 mm lightweight ammunition with different ammunition materials and also makes prototypes of weapons with different types. Textron System produces 6.8 mm ammunition with CT (cased telescoped) ammunition models, General Dynamics produces 6.8 mm TVCM (True Velocity Cartridge) ammunition with SAAMI standards, and for Sig Sauer produces 6.8 mm Hybrid Cartridge ammunition made from steel. As for the prototype weapons, namely the Textron NGSW-R 6.8 mm made by Textron Systems, the RM 277 Assault Rifle with the “BullPup” system made by General Dynamics, and for Sig Sauer's 6.8 mm MCX Spear. Of the three American Defense Industries, Sig Sauer is considered to have the best design and development, both for ammunition design and the design of the Assault Rifle prototype that will use the 6.8 mm NGSW ammunition
A Deep Learning Approach to Multi-class and Multi-label Cassava Leaf Disease Detection Using MobileNetV2 and Image Augmentation
Full text linkThis paper presents the development of a deep learning model using pre-trained models to detect multiple cassava leaf diseases using simultaneous multi-class and multi-label classification. Cassava diseases often occur as single or mixed infections, complicating visual diagnosis critical to food security in tropical regions.Existing models, designed primarily for single-label classification, struggle with overlapping symptoms. To address this, a dataset of 10,000 expert- annotated cassava leaf images was compiled from PlantVillage. The MobileNetV2 model, optimized for mobile deployment, was trained using binary cross-entropy loss with Sigmoid activation for Multi-label classification and Categorical cross-entropy loss with Softmax activation. The model achieved 95% accuracy, outperforming NasNet, demonstrating effective real-time, in-field disease diagnosis. This research advances agricultural AI applications by enabling scalable, mobile-compatible cassava disease detection