Alternative Method Research Articles

Readiness of Indonesian pre-service science teachers for society 5.0

This research aimed to assess the readiness of Indonesian pre-service science teachers by examining their perceptions of impending challenges, desired competencies, and anticipated instructional methodologies in preparing for the challenges of Society 5.0. Using a quantitative survey research methodology, 884 pre-service science teachers from 16 universities were surveyed. The research instrument's validity and reliability were assessed utilizing the Rasch model. Inferential statistics were employed to assess the readiness of pre-service science teachers for future challenges, taking into account their backgrounds. The test outcomes showed the Cronbach's Alpha coefficient reached 0.92, is deemed "excellent", affirming the instrument's high reliability. The findings of the study revealed that the majority of teachers share a consistent perception of future challenges and recognize the importance of mastering 21st-century skills, particularly critical thinking, creative thinking, communication, and collaboration. However, respondents believe that the current learning process does not adequately train these skills. They expressed a desire for an alternative learning method that can enhance their skills while accommodating their diverse backgrounds, including place of residence, economic background, and technological proficiency. Further research is needed to identify alternative teaching methods that can effectively foster these abilities in diverse backgrounds.

Effect of novel composite materials on human injury and rehabilitation training in athletic long jump sports

Articular cartilage injuries are prevalent in track and field long jump and can affect an athlete’s health. However, current therapeutic options cannot effectively repair the damaged cartilage tissue. It is important to find alternative treatment methods that can provide better results for injured athletes. In this study, carbon nanofibers (CNFs) were employed to enhance the properties of hyaluronic acid (HA) and develop nanocomposite hydrogel scaffolds. Bone marrow mesenchymal stem cells extracted from the joints of experimental mice were subsequently loaded onto the CNFs-HA scaffolds and evaluated for bioactivity and repair capacity. The TEM photographs of CNFs-MA displayed an orderly arrangement of fibers. Observing the FT-IR spectral characteristics of CNFs-MA revealed a telescopic vibration peak at 1700 cm−1. CNFs-HA demonstrated a rapid increase in cartilage damage repair score after 15 days, and at 1 month, the joint damage repair score of CNFs-HA was significantly different from that of pure HA and the untreated group. Based on the foregoing results, it can be inferred that CNFs-HA, as prepared, is biocompatible and efficacious in treating articular cartilage tissue injury. Furthermore, this study this study provides a reliable solution for cartilage injury treatment and establishes an experimental basis for its clinical management.

АНАЛІЗ АЛГОРИТМІВ ВИЯВЛЕННЯ ЗІТКНЕНЬ У ТРИВИМІРНИХ ВІРТУАЛЬНИХ СЕРЕДОВИЩАХ

The problem addressed in this paper concerns the real-time detection of collisions in virtual environments, which can consume a significant portion of computational re-sources, particularly in complex simulations with numerous objects. Ensuring a minimum frame rate of 30 FPS is crucial for user-friendly simulations. The focus is on the narrow phase of collision detection, where objects in close proximity are examined for potential collisions. This paper proposes a collision detection algorithm leveraging Directed Bounding Parallels (AABB) R-tree structure and Oriented Bounding Parallelepiped Inter-section (OAABB) method to address this phase efficiently. Experimental results demon-strate the effectiveness of the algorithm in detecting intersections with high interaction rates. Various publicly available toolkits exist for narrow phase collision detection, em-ploying different bounding volume hierarchies and constraint volumes. Comparing these approaches is challenging due to performance variations influenced by factors such as object shapes, contact types, and model sizes. Nonetheless, these algorithms are crucial for accurately detecting collisions in virtual environments and gaming applications. Bounding Volume Hierarchies (BVH), commonly used for collision detection, organ-ize object geometry and improve performance by reducing test pair numbers through vol-ume constraints. The proposed algorithm utilizes R-tree hierarchies of directed bounding parallelepipeds and OAABB to enhance collision detection efficiency by minimizing up-date and intersect operations. The choice of constraint volume type impacts collision de-tection performance, with oriented parallelepipeds offering faster intersections. To expedite collision detection, each feature is represented by an R-tree data struc-ture in its local coordinate system. Hierarchical trees are constructed by grouping adja-cent surfaces, with leaves indicating surface geometry. Additionally, R-trees are em-ployed to spatially organize triangles within surfaces to quickly discard non-intersecting triangles. The algorithm leverages OAABB, a concept involving the common volume between two oriented bounding parallelepipeds, to filter out primitives that cannot intersect. Tra-versal algorithms are utilized to reduce the number of node visits, addressing inefficien-cies observed in traditional schemes. OAABB contributes significantly to reducing volume refresh operations. Experimental evaluations demonstrate the proposed algorithm's efficacy in real-world industrial applications, achieving interactive performance. Comparisons with al-ternative methods highlight the algorithm's effectiveness in collision detection. Overall, the proposed approach offers a robust solution to the narrow phase collision detection problem in virtual environments.

A comparison of methods for estimating parameters of the stochastic Lomax process: through simulation study

In this paper, we study the problem of parameter estimation of the stochastic Lomax diffusion process, this process was introduced in [A. Nafidi, I. Makroz, and R. Gutiérrez Sánchez, A stochastic lomax diffusion process: Statistical inference and application, Mathematics, 2021][14], and the authors suggested the method of simulated annealing to find the maximum likelihood estimators of this process. In this work, we propose alternative methods for finding the maximum likelihood estimators, namely Genetic algorithm and Nelder-Mead, we also investigate the use of Markov Chain Monte Carlo method to determine the model parameters. Finally, an example of application through the simulation of paths for the process is suggested. Then, a comparison is made between the application of three algorithms based on their accuracy and time of execution.

Hybrid classification of XGBoost-based ADAM optimization for coronary artery disease diagnosis

A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy.

Implementation of Wood-Framed Buildings in the Nordic Region: A MADAMOS (an Integrated Multi-Criteria Decision-Making Approach for Profitable Realization Alternatives) Method

This study optimizes the sustainable implementation of light wood-framed buildings in the Nordic region using multi-criteria decision-making. Integrating prefabrication, transportation logistics, and multi-criteria decision-making (MCDM) methods enhances cost efficiency, time savings, and quality assurance. Significant international and local impact promotes sustainable construction practices. Strong promotion fosters industry-wide adoption. The presented framework enables stakeholders to make informed decisions, enhancing the efficiency and effectiveness of building implementation processes and fostering sustainable development in the construction industry.

Microscale Structure Additive Manufacturing Control Method Using Localized Electrochemical Deposition

Micro‐nano scale 3D printing is attracting attention as an alternative manufacturing method for a variety of applications in electronic information, biomedical, and sensing devices. Localized electrochemical deposition (LECD) based on nanopipettes is naturally superior and economical for the manufacturing of high‐freedom, impurity‐free metal conductors. However, flexible printing sacrifices roughness and straightness. Therefore, the present study initially establishes a simulation model for LECD and subsequently analyzes the key factors that influence the quality and stability of the deposited structure. Furthermore, an effective solution is proposed to enhance the surface quality. Second, a constant deposition current control method based on fuzzy proportional–intergral–derivaite (PID) is proposed to effectively suppress the fluctuation and improve the stability and quality of the deposited structure. Finally, compared with the open‐loop control method, the proposed constant current control method can reduce the radial fluctuation of the copper pillar structure by 87%, and effectively improve the surface quality of the structure. The proposed method provides a new idea to carry out LCED‐based 3D printing with high resolution at the micro‐nano scale.

Proposal and practicality of an alternative blue-light hazard risk assessment method for high-intensity white-light sources at workplaces

Objectives. It is prescribed to determine blue-light hazard (BLH) weighted radiances, L B , for an assessment of spotlights with an angular subtense α ≥ 11 mrad . The BLH weighted irradiance, E B , can be used alternatively for smaller sources. Appropriate instruments are not common among persons commissioned with risk assessment (RA), and especially L B measurements may be challenging. Therefore, a practical BLH RA approach is proposed that is based on illuminance, E v , pre-calculated blackbody BLH efficacies of luminous radiation, K B,v Planck , and solid angle considerations. Methods. The practicality of this method was examined and compared against other RA approaches. Results. To ensure comparability of the applied instruments, measurements were performed close to a radiance standard, showing deviations within the lamp’s expanded uncertainties ( < 4 % ), whereas the deviations were ± 15 % for longer distances. Focusing on a complex light-emitting diode (LED) spotlight, all detected values could be converted to L B by means of the RA methods within ± 20 % . Two field tests with several spotlights yielded maximum permissible exposure durations (MPED) obtained from the different RA approaches that agreed among each other within uncertainties largely below ± 30 % . Conclusion. The general practicality of the proposed E v method can be concluded for a workplace BLH RA of white-light sources.

A Survey on Unbalanced Classification: How Can Evolutionary Computation Help?

Unbalanced classification is an essential machine learning task, which has attracted widespread attention from both the academic and industrial communities due mainly to its broad applications. Evolutionary computation (EC) has contributed greatly to unbalanced classification. However, to the best of our knowledge, there have not been any comprehensive investigations on the strengths and weaknesses of alternative EC methods in addressing various challenging problems in unbalanced classification. This paper reviews the literature which utilize EC techniques for unbalanced classification, with the aim of revealing the contributions of EC to unbalanced classification, providing an overview of recent advances, and identifying limitations of existing works. In addition, we present a series of real-world applications, and identify open challenges as well as possible research directions for the future.

A High-Throughput Microdroplet-Based Single Cell Transfection Method for Gene Knockout Based on the CRISPR/Cas9 System.

The efficient application of the newly developed gene-editing method CRISPR/Cas9 requires more accurate intracellular gene delivery. Traditional delivery approaches, such as lipotransfection and non-viral delivery methods, must contend with major problems to overcome the drawbacks of low efficiency, high toxicity, and cell-type dependency. The high-throughput microdroplet-based single-cell transfection method presented herein provides an alternative method for delivering genome-editing reagents into single living cells. By accurately controlling the number of exogenous plasmids in microdroplets, this method can achieve high-efficiency delivery of nucleic acids to different types of single cells. This paper presents a high-throughput quantitative DNA transfection method for single cells and explores the optimal DNA transfection conditions for specific cell lines. The transfection efficiency of cells at different concentrations of DNA in microdroplets is measured. Under the optimized transfection conditions, the method is used to construct gene-knockout cancer cell lines to determine specific gene functions through the CRISPR/Cas9 knockout system. In a case study, the migration ability of TRIM72 knockout cancer cells is inhibited, and the tumorigenicity of cells in a zebrafish tumor model is reduced. A single-cell microfluidic chip is designed to achieve CRISPR/Cas9 DNA transfection, dramatically improving the transfection efficiency of difficult-to-transfect cells. This research demonstrates that the microdroplet method developed herein has a unique advantage in CRISPR/Cas9 gene-editing applications.

Multiple Authentication Schemes for Sign-In Process

Abstract: In the ever-evolving landscape of digital applications, user authentication plays a vital role in ensuring data security and privacy. Traditional username-password authentication methods may not always suffice, leading to the need for "Alternate Sign-In Methods." This project aims to develop a robust and user-friendly authentication system using advanced Machine Learning (ML) techniques. The system's primary objective is to offer users multiple authentication options beyond the conventional methods, such as Image Authentication, Pattern-based Authentication, Username-Password Authentication, and Fingerprint-based Authentication. Content-based filtering and collaborative filtering approaches will be employed to ensure efficient and relevant authentication options for each user. The project focuses on enhancing user convenience, security, and overall experience in digital platforms by tailoring sign-in methods to individual preferences.

Design and Fabrication of Solar Dryer

bstract: In a present modern busy life leading by the people, they are showing interest in eating readymade food items which can be instantly cooked and also save their time .In these readymade food items dried ingredients (fruits and vegetables) are used for long term preservative purpose .The drying of food products are done by traditional method which is called as "Direct sun drying". Which takes more time to dry with less efficiency and also due to open sun drying process hygiene is less due to dust particles present in the atmosphere. To overcome these circumstances solar dryer is one of the alternative method used for drying food products. A detailed study is done on the present Solar dryer on it’s performance &amp; efficiency and also some developments of new technologies are done. So, that the factors like drying effect, performance, efficiency &amp; temperature storing capacity can be increased and also innovative IOT temperature sensor are used to track the temperature readings according to the weather conditions.

Investigation of Fourier Transform Infrared (FT-IR) Spectroscopy and Chemometric Analysis Method as an Alternative Method in the Diagnosis of Prostate Cancer

Prostate cancer is one of the most common types of cancer in men. It usually grows slowly and may not show obvious symptoms at first. Prostate cancer can be diagnosed by symptoms or by a doctor performing certain tests during routine health checkups. These tests include physical examination, PSA (Prostate Specific Antigen) Test, biopsy, imaging techniques, and Gleason score. In addition, Fourier transform infrared spectroscopy (FT-IR) is an analysis method used for prostate cancer diagnosis. This study aims to demonstrate FT-IR spectroscopy as an alternative method to other diagnostic methods in the diagnosis of prostate cancer. The FT-IR spectroscopy method is used to examine the molecular structure of samples. For prostate cancer diagnosis, FT-IR spectroscopy can be used to identify molecular changes in prostate tissue and identify characteristics by which cancerous cells differ from healthy cells. FT-IR spectroscopy is based on spectral data obtained by exposing samples to infrared radiation. These spectral data are based on properties associated with the movements of the molecules contained in the samples, such as vibration, rotation and bending. Molecular changes caused by diseases such as prostate cancer may be evident in these spectral data. These changes can provide information about the presence or stage of cancerous cells. Data obtained using FT-IR spectroscopy is processed with statistical analysis methods. These analyses are used to identify molecular differences between cancerous and healthy prostate tissues. In this way, FTIR spectroscopy can help obtain sensitive and accurate results in the diagnosis of prostate cancer.

Multi-Instance Learning with One Side Label Noise

Multi-instance Learning (MIL) is a popular learning paradigm arising from many real applications. It assigns a label to a set of instances, which is called a bag, and the bag’s label is determined by the instances within it. A bag is positive if and only if it has at least one positive instance. Since labeling bags is more complicated than labeling each instance, we will often face the mislabeling problem in MIL. Furthermore, it is more common that a negative bag has been mislabeled to a positive one, since one mislabeled instance will lead to the change of the whole bag label. This is an important problem that originated from real applications, e.g., web mining and image classification, but little research has concentrated on it as far as we know. In this article, we focus on this MIL problem with one side label noise that the negative bags are mislabeled as positive ones. To address this challenging problem, we propose, to the best our our knowledge, a novel multi-instance learning method with one side label noise. We design a new double weighting approach under traditional framework to characterize the “faithfulness” of each instance and each bag in learning the classifier. Briefly, on the instance level, we employ a sparse weighting method to select the key instances, and the MIL problem with one size label noise is converted to a mislabeled supervised learning scenario. On the bag level, the weights of bags, together with the selected key instances, will be utilized to identify the real positive bags. In addition, we have solved our proposed model by an alternative iteration method with proved convergence behavior. Empirical studies on various datasets have validated the effectiveness of our method.

Phage cocktail superimposed disinfection: A ecological strategy for preventing pathogenic bacterial infections in dairy farms

Bovine mastitis (BM) is mainly caused by bacterial infection that has a highly impact on dairy production, affecting both economic viability and animal well-being. A cross-sectional study was conducted in dairy farms to investigate the prevalence and antimicrobial resistance patterns of bacterial pathogens associated with BM. The analysis revealed that Staphylococcus (49%), Escherichia (16%), Pseudomonas (11%), and Klebsiella (6%) were the primary bacterial pathogens associated with mastitis. A significant proportion of Staphylococcus strains displayed multiple drug resistance. The use of disinfectants is an important conventional measure to control the pathogenic bacteria in the environment. Bacteriophages (Phages), possessing antibacterial properties, are natural green and effective disinfectants. Moreover, they mitigate the risk of generating harmful disinfection byproducts, which are commonly associated with traditional disinfection methods. Based on the primary bacterial pathogens associated with mastitis in the investigation area, a phage cocktail, named SPBC-SJ, containing seven phages capable of lysing S. aureus, E. coli, and P. aeruginosa was formulated. SPBC-SJ exhibited superior bactericidal activity and catharsis effect on pollutants (glass surface) compared to chemical disinfectants. Clinical trials confirmed that the SPBC-SJ-based superimposed disinfection group (phage combined with chemical disinfectants) not only cut down the dosage of disinfectants used, but significantly reduced total bacterial counts on the ground and in the feeding trough of dairy farms. Furthermore, SPBC-SJ significantly reduced the abundance of Staphylococcus and Pseudomonas in the environment of the dairy farm. These findings suggest that phage-based superimposed disinfection is a promising alternative method to combat mastitis pathogens in dairy farms due to its highly efficient and environmentally-friendly properties.

Efficacy of Intra-Articular Corticosteroid Injection for Nonsurgical Management of Trapeziometacarpal Osteoarthritis: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

As osteoarthritis (OA) of the trapeziometacarpal (TMC) joint leads to a high degree of disease burden with compromises in rudimentary and fine movements of the hand, intra-articular injections may be a desirable treatment option. However, because there are no evidence-based guidelines, the choice of intra-articular injection type is left to the discretion of the individual surgeon in collaboration with the patient. The purpose of our study was to perform a systematic review and meta-analysis using level I studies to compare outcomes following corticosteroid and alternative methods of intra-articular injections for the management of TMC OA. Our hypothesis was that intra-articular corticosteroid injections were no more effective than other methods of intra-articular injections for the management of TMC OA. A systematic literature search was performed. Eligible for inclusion were randomized control trials reporting on intra-articular corticosteroid injection for the management of TMC OA. Clinical outcomes were recorded. The 10 included studies comprised 673 patients. The mean age was 57.8 ± 8.3 years, with a mean follow-up of 6.4 ± 2.7 months. There was no significant difference in visual analog scale scores, grip strength and tip pinch strength between corticosteroids and hyaluronic acid at short- and medium-term follow-up. Further, there was no difference in visual analog scale pain scores at rest at medium-term follow-up between corticosteroids and platelet-rich plasma. Despite short-term improvement with intra-articular corticosteroid injections, there was no significant difference in pain and functional outcomes following intra-articular corticosteroid injections compared to hyaluronic acid or platelet-rich plasma administration. Given the affordability, ease of administration, and efficacy associated with corticosteroids, they are a favorable option when considering the choice of intra-articular injection for the management of TMC OA. Therapeutic II.

Amalgamating Multi-Task Models with Heterogeneous Architectures

Multi-task learning (MTL) is essential for real-world applications that handle multiple tasks simultaneously, such as selfdriving cars. MTL methods improve the performance of all tasks by utilizing information across tasks to learn a robust shared representation. However, acquiring sufficient labeled data tends to be extremely expensive, especially when having to support many tasks. Recently, Knowledge Amalgamation (KA) has emerged as an effective strategy for addressing the lack of labels by instead learning directly from pretrained models (teachers). KA learns one unified multi-task student that masters all tasks across all teachers. Existing KA for MTL works are limited to teachers with identical architectures, and thus propose layer-to-layer based approaches. Unfortunately, in practice, teachers may have heterogeneous architectures; their layers may not be aligned and their dimensionalities or scales may be incompatible. Amalgamating multi-task teachers with heterogeneous architectures remains an open problem. For this, we design Versatile Common Feature Consolidator (VENUS), the first solution to this problem. VENUS fuses knowledge from the shared representations of each teacher into one unified generalized representation for all tasks. Specifically, we design the Feature Consolidator network that leverages an array of teacher-specific trainable adaptors. These adaptors enable the student to learn from multiple teachers, even if they have incompatible learned representations. We demonstrate that VENUS outperforms five alternative methods on numerous benchmark datasets across a broad spectrum of experiments.

Mining Gaze for Contrastive Learning toward Computer-Assisted Diagnosis

Obtaining large-scale radiology reports can be difficult for medical images due to ethical concerns, limiting the effectiveness of contrastive pre-training in the medical image domain and underscoring the need for alternative methods. In this paper, we propose eye-tracking as an alternative to text reports, as it allows for the passive collection of gaze signals without ethical issues. By tracking the gaze of radiologists as they read and diagnose medical images, we can understand their visual attention and clinical reasoning. When a radiologist has similar gazes for two medical images, it may indicate semantic similarity for diagnosis, and these images should be treated as positive pairs when pre-training a computer-assisted diagnosis (CAD) network through contrastive learning. Accordingly, we introduce the Medical contrastive Gaze Image Pre-training (McGIP) as a plug-and-play module for contrastive learning frameworks. McGIP uses radiologist gaze to guide contrastive pre-training. We evaluate our method using two representative types of medical images and two common types of gaze data. The experimental results demonstrate the practicality of McGIP, indicating its high potential for various clinical scenarios and applications.

Alternative and Classical Processes for Disinfection of Water Polluted by Fungi: A Systematic Review

The introduction of the first list of priority pathogenic fungi by the World Health Organization stresses the need to research and develop public health actions to mitigate infections caused by fungi. One of those actions involves the water disinfection systems, which comprise classical and alternative methods that have been developed in the last decades. Thereby, this work reviews the disinfection of fungi by classical methods such as chlorination, ozonation, and ultraviolet (UV) treatments and alternative advanced oxidation processes (AOPs) such as photo-Fenton, photocatalysis, or couplings of UV with peroxides. The matrices of aquatic systems (sewage, groundwater, drinking water, among others) were considered. A bibliometric analysis is performed initially, and then some aspects of the resistance to antifungals are presented, and the efficiency of the diverse processes in the reduction in fungal loading is also revised. Herein, it is shown the role of the disinfecting agents (e.g., chlorine, hydroxyl radical, or light) and their effects on fungi structures (e.g., direct DNA damage, or indirect damage due to the action of radicals). Moreover, gaps, such as the treatment of antifungal-resistant fungi and limited information about combinations among AOPs, related to the disinfection of water polluted by fungi, were identified.

Silver Nanoparticles as Antiviral and Antibacterial Agents: A Comprehensive Review of Synthesis Methods and Therapeutic Application

AbstractSilver nanoparticles have shown inhibitory capabilities and its use in battling viral infections are strengthened by its potent antibacterial activity. Notably, since it directly affects antiviral efficacy, the proper choice of synthesis process for making silver nanoparticles is a vital element to be taken into consideration. The synthesis of silver nanoparticles for the treatment of viral and bacterial infections involves creating silver particles at the nanoscale with unique antimicrobial properties. Various methods exist for synthesizing silver nanoparticles, including chemical reduction, green synthesis, and physical methods. Chemical reduction involves using reducing agents to convert silver ions into silver nanoparticles. Green synthesis employs reducing agents in an environmentally friendly manner. Physical methods rely on techniques such as laser ablation or thermal evaporation to produce silver nanoparticles. In order to provide technological guidance for choosing antiviral silver nanoparticle manufacturing methods, this paper introduces and explores alternative synthesis methods for creating silver nanoparticles.