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Abstract This retrospective cohort study used Taussig Cancer Center’s Myeloma Patient Registry to identify adults with multiple myeloma diagnosed between January 2017-December 2021. Electronic health records data captured time from diagnosis to initial prescription fill for oral antimyeloma medications and initiation of facility administered or oral antimyeloma treatment. We identified 720 patients with a mean age at diagnosis of 67 years ±11; 55% were male, 77% White, 22% Black, 1% other races, covered by private insurance (36%), traditional Medicare (29%), Medicare Advantage (25%), and Medicaid (8.3%). Over a third of patients (37%) resided in an area in the most disadvantaged area deprivation index (ADI) quartile. The median available follow-up was 765 days. Seventy-five percent of the cohort filled an oral antimyeloma medication prescription (excluding corticosteroids), with a median time to fill of 28 days (IQR, 15–61). In the multivariable Cox regression model, Black race (vs. White, adjusted hazard ratio [aHR], 0.61, 95% CI, 0.42–0.87), older age at diagnosis (aHR per 1 year, 0.97, 95% CI, 0.95–0.98), diagnosis during an inpatient admission (aHR, 0.63, 95% CI, 0.43–0.92), and estimated glomerular filtration rate ≤29 ml/min/1.73 m2 (vs. ≥60, aHR, 0.46, 95% CI, 0.29–0.73) were negatively associated with prescription fill for oral antimyeloma medication at 30 days, while insurance type and ADI were not significant predictors.

Abstract Background Familial concordance of weight status is an emerging field of study that may guide the development of interventions that operate beyond the individual and within the family context. There is a dearth of published data for concordance of weight status within Pakistani households. Methods We assessed the associations between weight status of mothers and their children in a nationally representative sample of households in Pakistan using Demographic and Health Survey data from 2017–18. Our analysis included 3465 mother–child dyads, restricting to children under-five years of age with body mass index (BMI) information on their mothers. We used linear regression models to assess the associations between maternal BMI category (underweight, normal weight, overweight, obese) and child’s weight-for-height z-score (WHZ), accounting for socio-demographic characteristics of mothers and children. We assessed these relationships in all children under-five and also stratified by age of children (younger than 2 years and 2 to 5 years). Results In all children under-five and in children 2 to 5 years, maternal BMI was positively associated with child’s WHZ. For all children under-five, children of normal weight, overweight, and obese women had WHZ scores that were 0.21 [95% CI (confidence interval): 0.04, 0.37], 0.43 [95% CI: 0.25, 0.62], and 0.51 [95% CI: 0.30, 0.71] units higher than children of underweight women, respectively. For children ages 2 to 5, children of normal weight, overweight, and obese women had WHZ scores that were 0.26 [95% CI: 0.08, 0.44), 0.50 [95% CI: 0.30, 0.71), and 0.61 [95% CI: 0.37, 0.84] units higher than children of underweight women, respectively. There was no association between maternal BMI and child WHZ for children under-two. Conclusions The findings indicate that the weight status of mother’s is positively associated with that of their children, particularly after age 2. These associations further strengthen the call for research regarding interventions and policies aimed at healthy weight promotion among mothers and their children collectively, rather than focusing on individuals in isolation.

Objectives: Computer-aided detection (CAD) software packages quantify tuberculosis (TB)-compatible chest X-ray (CXR) abnormality as continuous scores. In practice, a threshold value is selected for binary CXR classification. We assessed the diagnostic accuracy of an alternative approach to applying CAD for TB triage: incorporating CAD scores in multivariable modeling. Methods: We pooled individual patient data from four studies. Separately, for two commercial CAD, we used logistic regression to model microbiologically confirmed TB. Models included CAD score, study site, age, sex, human immunodeficiency virus status, and prior TB. We compared specificity at target sensitivities ≥90% between the multivariable model and the current threshold-based approach for CAD use. Results: We included 4,733/5,640 (84%) participants with complete covariate data (median age 36 years; 45% female; 22% with prior TB; 22% people living with human immunodeficiency virus). A total of 805 (17%) had TB. Multivariable models demonstrated excellent performance (areas under the receiver operating characteristic curve [95% confidence interval]: software A, 0.91 [0.90-0.93]; software B, 0.92 [0.91-0.93]). Compared with threshold scores, multivariable models increased specificity (e.g., at 90% sensitivity, threshold vs model specificity [95% confidence interval]: software A, 71% [68-74%] vs 75% [74-77%]; software B, 69% [63-75%] vs 75% [74-77%]). Conclusion: Using CAD scores in multivariable models outperformed the current practice of CAD-threshold-based CXR classification for TB diagnosis.

E-government merupakan sebuah konsep pemerintahan yang menyelenggarakan layanan publik secara digital yang didukung oleh teknologi informasi, sehingga menjadi lebih prima. Salah satu bentuk layanan publik digital yang umum ada di berbagai sektor adalah helpdesk. Helpdesk memungkinkan pengguna dapat bertanya atau melaporkan sesuatu untuk kemudian dijawab oleh staf. Permasalahan yang ada adalah ketersediaan staf yang hanya dapat menjawab pada jam kerja, sedangkan pengguna bisa jadi menemukan permasalahan yang urgent di luar jam kerja. Oleh karena itu, solusi yang ditawarkan adalah penerapan chatbot, sehingga dapat melayani kapan pun, sekaligus meringankan kerja dari staf. Penelitian ini mencoba untuk merancang salah satu komponen pada chatbot, yaitu model klasifikasi intensi, dengan metode Long Short-Term Memory. Data yang digunakan merupakan 501 teks riwayat pertanyaan dari database Helpdesk TIK UB yang termasuk ke dalam 7 kelas intensi yang ditentukan. Data akan melalui beberapa tahap prapemrosesan sebelum kemudian dilakukan pemodelan dan beberapa pengujian. Tahap pengujian meliputi pemilihan embedding yang digunakan, pemilihan teknik augmentasi data, dan penyetelan hyperparameter. Hasil dari keseluruhan pengujian, didapatkan model terbaik yang mampu menghasilkan akurasi sempurna untuk data latih dan data uji, serta loss 0,004 untuk data latih dan 0,044 untuk data uji. Abstract E-government is a government concept that organizes digital public services supported by information technology, so that they become more excellent. One form of digital public service that is common in various sectors is the helpdesk. Helpdesk allows users to ask questions or report something to be answered by staff. The problem that exists is the availability of staff who can only answer during working hours, while users may find urgent problems outside of working hours. Therefore, the solution offered is the implementation of a chatbot, so that it can serve at any time, while easing the work of staff. This study attempts to design one of the components of the chatbot, namely the intention classification model, using the Long Short-Term Memory method. The data used are 501 question history texts from the Helpdesk TIK UB database which fall into the 7 specified intensity classes. The data will go through several pre-processing stages before then being modeled and tested. The testing phase includes selecting the embedding used, data augmentation techniques selection, and hyperparameter tuning. The best model is obtained which can produce perfect accuracy for training data and test data, as well as a loss of 0.004 for training data and 0.044 for test data.

Freedom of religion in Indonesia is an important and sensitive issue. Indonesia's religious diversity often leads to disputes and conflicts that disrupt harmony and unity. It is considered the emergence of diverse understandings and unfulfilled rights from religious differences. A theological approach can be an effective tool in facilitating the transformation of religious awakening and promoting religious freedom in Indonesia. This research aims to determine how theological perspectives influence the understanding and practice of religious freedom, which is communicated effectively. The research method used is descriptive, analysing the index of religious freedom in Indonesia with a theological analysis of religious freedom. The study's results highlight the importance of a theological approach in fostering a spiritual attitude and influencing expressions of religious freedom. The values of harmony, tolerance, and respect have encouraged efforts to build respect but still raise concerns about the emergence of conflict with the development of community interaction. Implementing effective strategies to place theology that encourages religious freedom in Indonesia is essential.

Collective intelligence systems like Chat Generative Pre-Trained Transformer (ChatGPT) have emerged. They have brought both promise and peril to cybersecurity and privacy protection. This study introduces novel approaches to harness the power of artificial intelligence (AI) and big data analytics to enhance security and privacy in this new era. Contributions could explore topics such as: leveraging natural language processing (NLP) in ChatGPT-like systems to strengthen information security; evaluating privacy-enhancing technologies to maximize data utility while minimizing personal data exposure; modeling human behavior and agency to build secure and ethical human-centric systems; applying machine learning to detect threats and vulnerabilities in a data-driven manner; using analytics to preserve privacy in large datasets while enabling value creation; crafting AI techniques that operate in a trustworthy and explainable manner. This article advances the state-of-the-art at the intersection of cybersecurity, privacy, human factors, ethics, and cutting-edge AI, providing impactful solutions to emerging challenges. Our research presents a revolutionary approach to malware detection that leverages deep learning (DL) based methodologies to automatically learn features from raw data. Our approach involves constructing a grayscale image from a malware file and extracting features to minimize its size. This process affords us the ability to discern patterns that might remain hidden from other techniques, enabling us to utilize convolutional neural networks (CNNs) to learn from these grayscale images and a stacking ensemble to classify malware. The goal is to model a highly complex nonlinear function with parameters that can be optimized to achieve superior performance. To test our approach, we ran it on over 6,414 malware variants and 2,050 benign files from the MalImg collection, resulting in an impressive 99.86 percent validation accuracy for malware detection. Furthermore, we conducted a classification experiment on 15 malware families and 13 tests with varying parameters to compare our model to other comparable research. Our model outperformed most of the similar research with detection accuracy ranging from 47.07% to 99.81% and a significant increase in detection performance. Our results demonstrate the efficacy of our approach, which unlocks the hidden patterns that underlie complex systems, advancing the frontiers of computational security.

IntroductionMusculoskeletal disorders (MSDs) are rapidly rising in Saudi Arabia, reaching levels similar to those in the Western world. Hence, we aimed to assess the prevalence of neck, shoulder, and lower back pains (musculoskeletal pain, MSP) among students at King Khalid University in Abha, Saudi Arabia.MethodsThis cross-sectional study was conducted at King Khalid University in Abha, Saudi Arabia, from March 2023 to August 2023. Inclusion criteria were: university students aged 18 years and older of both sexes who agreed to participate in the study. The modified Nordic questionnaire was used, which comprised three parts.ResultsOut of 536 respondents, 337 were women and 199 were men. The average body mass index (BMI) of the study population was 25.3 ± 4.01. In total, 223 (41.60%) had a history of MSDs. Only 232 (43.28%) of the population did regular exercise. According to multiple logistic regression analysis, factors associated with MSDs are mobile device use (with both hands) with a large neck tilt below the horizon line position (OR = 2.276, CI 1.178–4.397, p = 0.014), family history of trauma (OR = 5.450, 95% CI 3.371–8.811, p = 0.000), family history of MSDs (OR = 4.241, 95% CI 2.296–7.835, p = 0.000), coffee consumption (OR = 1.967, CI 1.281–3.020, p = 0.002), and time spent on electronic devices: 1–3 h (OR = 0.252, 95% CI 0.124–0.511, p = 0.0001), 4–6 h (OR = 0.455, 95% CI 0.237–0.873, p = 0.018), and 6–9 h (OR = 0.348, 95% CI 0.184–0.660, p = 0.001).ConclusionThe present study concludes that MSP among university students is high. A history of trauma, a family history of MSDs, the hand and neck position when using electronic devices, the amount of time spent using them, and regular exercise are risk factors that are strongly associated with MSP. There is strong evidence to suggest that increasing physical activity plays a significant role in enhancing the functionality of the musculoskeletal (MSK) system and alleviating pain. It is recommended that universities implement educational programs to raise awareness and health screenings about the impact of device usage on MSK health and the benefits of regular exercise.

This research aims to describe: 1) The validity, practicality and effectiveness of e-books using flip pdf pro through the Missouri Mathematics Project (MMP) learning model which was developed to improve metacognition abilities and mathematical self-efficacy of SMA Dharma Pancasila Medan students; 2) Increasing students metacognition abilities with e-books using flip pdf pro through the developed MMP learning model; 3) Increasing students mathematical self-efficacy with e-books using flip pdf pro through the developed MMP learning model. This research is development research using the ADDIE development model with a modification of Tessmer formative evaluation. This development model consists of 5 development stages, namely: Analysis, Design, Development, Implementation, and Evaluation. From the results of trial I and trial II, it was obtained: 1) E-book using flip pdf pro through the MMP learning model which was developed to improve students metacognition abilities and mathematical self-efficacy which was developed and met the criteria of being valid, practical and effective; 2) Increasing metacognition abilities with e-books using flip pdf pro through the MMP learning model that has been developed as seen from the N-gain value in trial I of 0.65, increasing to 0.74 in trial II, meaning it is in the category of " medium” to “high”; and 3) Increasing the mathematical self-efficacy of e-book students using flip pdf pro through the MMP learning model that has been developed as seen from the N-gain value in trial I of 0.36, increasing to 0.45 in trial II, meaning it is within "medium" category.

Abstract The presence of extramedullary disease (EMD) has been associated with poor outcomes in patients with relapsed-refractory multiple myeloma (RRMM). Herein, we report the outcomes of RRMM patients who were treated with standard-of-care (SOC) chimeric antigen receptor (CAR) T-cell therapy and had active extraosseous EMD before the infusion. Data were retrospectively collected from patients at three US institutions with the intent to receive SOC CAR T. Responses were assessed per the International Myeloma Working Group criteria. A total of 152 patients proceeded with infusion, of whom 47 (31%) had EMD (EMD group) and 105 (69%) did not (non-EMD group). Baseline patient characteristics were comparable between the two groups. The EMD group had a higher incidence of high-grade CRS, steroid and anakinra use, and thrombocytopenia on day +30 compared to the non-EMD group. In addition, the EMD group had an inferior overall response rate (58% vs 96%, p

The 3D printing process of Fused Deposition Modelling (FDM) can be used for fabricating metal parts without the need of mold. Metal Material Extrusion uses metal-polymer composite filament in the 3D printer to extrude the green part, then a de-binding process for removing the polymer binder, and eventually, the sintering process under high temperatures for making a metallic part. A comprehensive research is conducted in which sintering parameters including heating rate, dwell time, and sintering atmospheres are varied for metal parts mechanical characteristics including tensile strength, ductility, and density. Moreover, sintering atmospheres including vacuum and argon are taken into consideration in this research study. The metal part is known as silver part which has exhibited a high mechanical strength of 540 MPa, a considerable density of 7.57 g/cm3, and a high hardness of 220 HV under a vacuum sintering atmosphere. Whereas the metal parts produced under argon atmosphere have shown comparatively low mechanical strength 424 MPa, density 7.1 g/cm3 and 177 HV. The results are compared with an industrial benchmark process where catalytic de-binding and sintering are used to achieve 500 MPa tensile strength, density 7.83 g/cm3, and 175 HV. Scanning Electron Microscopy reveals that grains are coarsened in a vacuum, and grains appear peeled off along with small pores in the boundaries. The industrial benchmark part has uniform and finer grains.

Abstract The Plasmodium is responsible for malaria which poses a major health threat, globally. This study is based on the estimation of the relative abundance of mosquitoes, and finding out the correlations of meteorological parameters (temperature, humidity and rainfall) with the abundance of mosquitoes. In addition, this study also focused on the use of nested PCR (species-specific nucleotide sequences of 18S rRNA genes) to explore the Plasmodium spp. in female Anopheles. In the current study, the percentage relative abundance of Culex mosquitoes was 57.65% and Anopheles 42.34% among the study areas. In addition, the highest number of mosquitoes was found in March in district Mandi Bahauddin at 21 °C (Tmax = 27, Tmin = 15) average temperature, 69% average relative humidity and 131 mm rainfall, and these climatic factors were found to affect the abundance of the mosquitoes, directly or indirectly. Molecular analysis showed that overall, 41.3% of the female Anopheles pools were positive for genus Plasmodium. Among species, the prevalence of Plasmodium (P.) vivax (78.1%) was significantly higher than P. falciparum (21.9%). This study will be helpful in the estimation of future risk of mosquito-borne diseases along with population dynamic of mosquitoes to enhance the effectiveness of vector surveillance and control programs.

Abstract COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and based on these factors survey data were collected from COVID-recovered patients in Bangladesh. Employing diverse machine learning algorithms, we utilised the best prediction model for post-COVID-19 factors. Initial findings from statistical analysis were further validated using Chi-square to demonstrate significant relationships among these elements. Additionally, Pearson’s coefficient was utilized to indicate positive or negative associations among various physiological and neurological factors in the post-COVID-19 state. Finally, we determined the most effective machine learning model and identified key features using analytical methods such as the Gini Index, Feature Coefficients, Information Gain, and SHAP Value Assessment. And found that the Decision Tree model excelled in identifying crucial features while predicting the extent of post-COVID-19 impact.

Network security is a critical aspect of information technology, targeting to safeguard the confidentiality, integrity, and availability of data transmitted across computer networks. Intrusion Detection Systems (IDS) plays an essential role, serving as vigilant sentinels against illegal access, malicious actions, and potential threats. IDS operates on analysing the network or system activities, analyzing patterns, and detecting anomalies that may specify security breaches. The enhancement of network security via the integration of feature selection and machine learning, particularly in the context of IDS. Feature selection methods enable the identification and prioritization of key data attributes, optimizing the performance of machine learning algorithms by focusing on relevant information. Machine learning algorithms, such as decision trees, support vector machines, or neural networks, leverage the chosen features to dynamically adapt and learn from evolving cyber threats. Therefore, this study develops a new gravitational search algorithm-based feature selection with optimal quantum neural network (GSAFS-OQNN) model for intrusion detection and classification. The proposed GSAFS-OQNN approach lies in the effectual detection of intrusions. To accomplish this, the GSAFS-OQNN method exploits a Z-score normalization approach at the preprocessing step. Furthermore, GSAFS-OQNN technique designs the GSAFS model to derive an optimum subset of features. For intrusion detection, quantum neural network (QNN) is applied. Finally, the sandpiper optimization (SPO) technique is used to finetune the parameters of the QNN model. The experimental analysis of GSAFS-OQNN model is implemented on benchmark IDS datasets. The comprehensive results stated the betterment of GSAFS-OQNN method over recent approaches.

Research on Life Expectancy at Birth (LEB) and its relationship with several economic variables is interesting to be studied. LEB also describes the welfare condition of a country, where generally developed countries have a high level of LEB. This research will discuss the relationship between the variables of Life Expectancy at Birth (LEB), Corbon Dioxide (CO2) Emissions, Population Growth (PG), and Growth Gross Domestic Product (GDPG) in the case of Indonesia from 1950 to 2020. The aims of this study is to build a model of the relationship between variables LEB, CO2, PG and GDPG, and based on the best model obtained then will discuss the causal relationship between variables using Granger-Causality, Impulse Response Function (IRF), and forecasting and the proportion of error covariance decompositions. From the analysis results, the best model that describes the relationship between variables: LEB, CO2, PG, and GDPG is the Vector Error Correction Model (VECM). The results showed that there is a bidirectional relationship between the LEB variable and population growth. Partially, economic growth is proxied by GDPG affecting LEB and CO2 affecting PG. However, there was no effect of CO2 on LEB and GDPG in Indonesia. Based on the analysis results obtained, the chosen country is encouraged to consider the variables PG and GDPG in an effort to increase LEB in the future.

Minangkabau International Airport (BIM) located in West Sumatera is used as the main air transportation mode to travel outside the province. When waiting for flights, passengers will be in the airport lounge. However, the airport lounge still doesn’t have supporting facilities to maximize activities while in the lounge area. This research uses descriptive qualitative methods with direct observation, distributing questionnaires, and literature study as a sources. An environment can be said to be good if it can meet the needs of humans in it. Therefore, a supporting facility was created to maximize passengers' activities while in the airport lounge. This supporting facility also has a high level of mobility so that it can meet the needs of airport passengers who have high mobility in the form of a multifunctional partition. In addition, this facility uses cultural elements in the form of one typical Minangkabau motif, namely "Itiak Pulang Patang" which is transformed into a new design but still maintains the essence of the original form. This motif represents the accordance with the nature, behavior, and habits of the Minangkabau people who tend to migrate. The motif implements the purposes really well in designing supporting facility at BIM Keyword : behavioral approach; BIM Airport; design; minangkabau; supporting facilities.

Facial expression recognition (FER) technology is quite popular in the fields of computer vision, security monitoring, image classification, and many other related applications. Enhancing the computer's ability to read facial expressions is important for human-computer interaction, as it enables machines to understand and interact with human emotions. In this paper, a modified approach using neural networks (CNNs) is presented to accurately identify unique facial expressions. using the changes to a commonly used 12-layer CNN model to improve its performance in facial expression recognition (FER). The model is trained using dataset images, which allows it to better infer people's emotions from their facial features. To enhance the accuracy of the system, a preprocessing stage is incorporated. This stage involves several operations for data augmentation, including changing the color in images, such as HSV (Hue, Saturation, Value), YCbCr (Luma, Blue-difference Chroma, Red-difference Chroma), etc., to facilitate better interpretation of color information. Additionally, the preprocessing stage refines facial expression recognition by manipulating facial features and adding extra RGB details, improving the visual information provided by the images. The study specifically focuses on evaluating the effectiveness of this approach using the KDEF database. The KDEF database contains standardized images of facial expressions, making it suitable for assessing the performance of the proposed system. By combining the modified CNN model, training with images, and the preprocessing operations, the system's performance was significantly enhanced. As a result, it achieved recognition rates of up to 95%, indicating a notable improvement in accurately identifying unique facial expressions compared to previous approaches.

Carbon-based electrodes are widely used in photovoltaic, metal-ion batteries, supercapacitors, and sensors due to their low ohmic resistance and ability to integrate modifiers. Reduced graphene oxide (rGO) represents a promising alternative to graphite in carbon-based electrodes because of its high electrical conductivity, large surface area, and robust mechanical properties. This study aimed to optimize the fabrication parameters of rGO-MoS2 composite electrodes prepared via a one-pot hydrothermal method, focusing on maximizing their electrical conductivity for potential electronic applications. Taguchi analysis was employed to evaluate the impact of three key factors: rGO-MoS2 mixing ratio, annealing temperature, and annealing time. The novelty of this work lies in the systematic optimization approach using the Taguchi method, which allowed for the identification of the most significant factors and their optimal levels. The optimal combination was found to be a 1:1 ratio, 75 °C annealing temperature, and 15-min annealing time, resulting in an impressive electrical conductivity of 11 800 S m⁻1. This optimized rGO-MoS2 composite electrode exhibited an approximate 11 % reduction in sheet resistance compared to the unoptimized composite. Furthermore, all samples show an improvement in sheet resistance values compared to pure rGO and MoS2, which were 82.5 Ω/sq and 48.9 Ω/sq. Numerical simulations further revealed a 13.23 % improvement in device performance when the optimized rGO-MoS2 composite was implemented as the top electrode in an inverted perovskite solar cell, demonstrating the significant potential of this material for various electronic applications.

Abstract Requirements elicitation is one of the fundamental sub-processes of requirements engineering which is used to find the needs of stakeholders. There are several activities in this sub-process, i.e., identification of stakeholders and their requirements, software requirements prioritization, and analysis. Recommendation systems have been intertwined with the requirements elicitation process to predict the stakeholders’ requirements based on their preferences for functional and non-functional requirements. A number of systematic literature reviews (SLRs) have been carried out in the area of requirements elicitation. These SLRs do not support the applications of the recommendation systems during the requirements elicitation process. To deal with this issue, we present an SLR on recommendation systems-based software requirements elicitation processes, from 2009 to 2022, undertaking four research questions: (a) What are the different activities of the software requirements elicitation methods? (b) What are the applications of recommendation systems in the identification of the software requirements? (c) How the recommendation systems can facilitate the identification of stakeholders in the requirements elicitation process? (d) What are the ways to automate the selection of requirements elicitation techniques? The aim of this study is to identify the research gaps in the area of recommendation systems-based requirements elicitation processes and suggest future research directions.

This article investigates the profound implications of the application of pedagogical philosophy in mathematics teaching. Highlighting the key roles of constructivist philosophy, humanism, and ethics, the study discusses how these principles shape the way teachers approach mathematics teaching and influence student learning experiences. Through in-depth analysis, this article underlines the importance of philosophical harmony in creating an inspiring and effective learning environment. The integration of constructivist concepts emphasizes the active role of students in building their own knowledge, while the humanistic approach highlights aspects of creativity and personal development. In addition, this article explores how ethics can be integrated into mathematics learning, creating students who are not only technically skilled but also morally responsible. In conclusion, this article provides a deeper understanding of how pedagogical philosophy becomes a catalyst for positive transformation in mathematics teaching

This research is a literature study (library research) that uses a qualitative approach. The aim of this research is to examine the extent to which technology plays a role in mathematics learning from the perspective of the epistemology of mathematical philosophy. The study of the epistemology of the philosophy of mathematics that is being discussed is how does the development of the philosophy of mathematics influence technology? What is the relationship between the philosophy of mathematics and technology-based learning? As well as examples of technology-based mathematics learning models. The results and discussion of this article can be concluded: Philosophy is a science that has a close relationship in human life.The philosophy of mathematics influences the development of technology in general. Technological developments influence the development of learning. From a learning perspective, technology is not only something related to machines, but also related to people, ideas and activities in learning. The use of technology in the form of hardware and software can improve student learning outcomes. Currently, there are many software tools available for use in learning, such as GeoGebra. Choosing the right learning model and mathematics learning technology can make learning more effective

The aim of this research is to explore the implementation of mathematical philosophy in multicultural education. The research method used is the library research method, namely by conducting studies related to multicultural education, philosophy of mathematics, and the implementation of philosophy of mathematics in multicultural education. The research results showed that mathematical philosophy can be implemented in multicultural education, namely in the field of epistemological studies. In the field of epistemological studies, the philosophy of mathematics talks about the origins of the formation of mathematics and the study of knowledge about mathematics. The study of knowledge about mathematics cannot be separated from the characteristics of certainty, truth, objectivity, universalism and rationality. These characteristics can ultimately become the forerunner in the implementation of multicultural education. So as to create multicultural education that upholds justice, equality and unity.

The aim of this study is to study the correlation of mathematical philosophy with cultural-based learning models. The method of research used is the method of library research, i.e. conducting studies related to mathematical philosophy, philosophical mathematics learning, culture-based mathematic learning, the connection of mathematically philosopher with ethnomatematics, the relationship of mathematic philosophies with cultural-based learning models. The results of the research have been obtained that the philosofy of maths, the model of learning based on culture, and ethnomathematics have close connections in the context of understanding and teaching of Mathematics. Mathematical philosophy deals with the foundations of mathematical epistemology and ontology, while a culture-based learning model emphasizes the integration of values and cultural contexts in mathematics learning. Ethnomatematics refers to the study of mathematical ways of thinking in a particular culture. By understanding the cultures, traditions, and world views of a particular society, ethnomathematics can help develop more culturally relevant and meaningful learning models. Therefore, the interrelationship between mathematical philosophy, culture-based learning models, and ethnomathematics can create a more holistic and contextual approach to mathematics learning. Keywords: Mathematical Philosophy, Cultural Based Learning Model, Ethnomathematics

The effective usage of energy becomes crucial for the successful deployment and operation of unmanned aerial vehicles (UAVs) in different applications, such as surveillance, transportation, and communication networks. The increasing demand for UAVs in different industries such as agriculture, logistics, and emergency response has led to the development of more sophisticated and advanced UAVs. However, the limited onboard energy resource of UAVs poses a major problem for their long-term operation and endurance. In addition, artificial intelligence (AI) and machine learning (ML) could allow UAVs to make more informed and intelligent decisions regarding their operations, resulting in sustainable and more energy-efficient UAV deployment. This article designs a Hybrid Snake Optimizer-based Route Selection Approach for Unmanned Aerial Vehicles Communication (HSO-RSAUAVC) technique. The goal of the HSO-RSAUAVC technique is to explore and select optimal routes for UAV communication. In the presented HSO-RSAUAVC technique, the SO algorithm is integrated with Bernoulli Chaotic Mapping and Levy flight (LF) for enhanced performance. In addition, the HSO-RSAUAVC method derives a fitness function including residual energy (RE), distance, and UAV degree. By incorporating the HSO-RSAUAVC technique, we can dynamically adapt UAV paths to overcome obstacles, decrease communication interference, and optimize energy utilization. To validate the performance of the proposed model, a series of simulations were performed. The comparative result analysis illustrates the better performance of the HSO-RSAUAVC technique in improving the performance and reliability of UAV communication.

Malicious user recognition for spectrum sensing in Cognitive Radio Networks (CRNs) is a serious safety feature to safeguard effective and trustworthy process of these systems. Spectrum sensing permits CRNs to identify and employ accessible spectrum bands. As well as it is available to prospective interference and mischievous actions. To preserve network integrity, recognition of malicious consumers is vital. Deep learning (DL) based malicious consumer classification powers advanced neural network frameworks to recognize and flag possible threats inside a network. By examining numerous amounts of information, DL techniques can distinguish patterns as well as anomalies that are connected with malicious user performance plus system intrusions, scams or irregular action. This technique provides flexibility benefit that permits a network to learn and develop in evolving threats. It also offers an effectual revenue of improving network security in the difficult and active digital landscape. Therefore, this article develops an Optimal Deep Learning Empowered Malicious User Detection for Spectrum Sensing (ODL-MUDSS) in the CRN. The main intention of ODL-MUDSS model focused on automated identification and classification of MUs in CRN. To accomplish this, the ODL-MUDSS model primarily applies deep belief network (DBN) methodology for automated and accurate detection of MUs. In addition, recognition performance of DBN technique can be enhanced by use of sand cat swarm optimization (SCSO) algorithm and thereby improves the detection results. The performance validation of ODL-MUDSS technique is observed under different processes. The comprehensive outcomes stated enhanced performance of ODL-MUDSS model over other existing models with maximum accuracy of 97.75%, precision of 97.75%, recall of 97.75%, and F-score of 97.75%.

Content-based image retrieval (CBIR) in the healthcare field is an advanced technology that leverages the visual or content features of medical images to retrieve similar images from vast data. This technology has significant applications in healthcare, including medical diagnosis, research, and treatment planning. It is a diagnostic tool that enhances the explainability of computer-aided diagnoses (CAD) systems and provides decision-making support for healthcare professionals. A conventional way to CBIR is learning a distance metric by converting images into feature space where the distance between samples is a similarity measurement. CBIR systems can autonomously extract intricate features from medical images by leveraging convolutional neural networks (CNN) and other advanced deep learning techniques, enabling accurate and swift retrieval of relevant patient cases and diagnostic references. This manuscript designs an Archimedes Optimization Algorithm with Deep Learning Assisted Content-Based Image Retrieval in Healthcare Sector (AOADL-CBIRH) technique. The AOADL-CBIRH technique intends to retrieve similar images based on query images in the healthcare sector. In the presented AOADL-CBIRH technique, image pre-processing is initially performed using an adaptive bilateral filtering (ABF) approach to enhance the image quality. For deep feature extraction, the AOADL-CBIRH method applies the Efficient Channel Spatial Model (ECSM) with the ResNet50 model. To improve the retrieval performance, the AOADL-CBIRH technique employs AOA-based hyperparameter tuning for the ECSM-ResNet50 model. Lastly, the Manhattan distance metric determines the similarity between the images and retrieves them. The experimental evaluation of the AOADL-CBIRH algorithm is tested by using benchmark image dataset. The stimulation values signified the enhanced retrieval results of the AOADL-CBIRH technique over other models.

Biomedical image segmentation is a vital task in the analysis of medical imaging, including the detection and delineation of pathological regions or anatomical structures within medical images. It has played a pivotal role in a variety of medical applications, involving diagnoses, monitoring of diseases, and treatment planning. Conventionally, clinicians or expert radiologists have manually conducted biomedical image segmentation, which is prone to human error, subjective, and time-consuming. With the advancement in computer vision and deep learning (DL) algorithms, automated and semi-automated segmentation techniques have attracted much research interest. DL approaches, particularly convolutional neural networks (CNN), have revolutionized biomedical image segmentation. With this motivation, we developed a novel equilibrium optimization algorithm with a deep learning-based biomedical image segmentation (EOADL-BIS) technique. The purpose of the EOADL-BIS technique is to integrate EOA with the Faster RCNN model for an accurate and efficient biomedical image segmentation process. To accomplish this, the EOADL-BIS technique involves Faster R-CNN architecture with ResNeXt as a backbone network for image segmentation. The region proposal network (RPN) proficiently creates a collection of a set of region proposals, which are then fed into the ResNeXt for classification and precise localization. During the training process of the Faster RCNN algorithm, the EOA was utilized to optimize the hyperparameter of the ResNeXt model which increased the segmentation results and reduced the loss function. The experimental outcome of the EOADL-BIS algorithm was tested on distinct benchmark medical image databases. The experimental results stated the greater efficiency of the EOADL-BIS algorithm compared to other DL-based segmentation approaches.

Gastrointestinal (GI) cancer comprises esophageal, gastric, colon and rectal tumors. The diagnosis of GI cancer often relies on medical imaging modalities namely magnetic resonance imaging (MRI), histopathological slides, endoscopy, and computed tomography (CT) scans. This provides particular details about the size, location, and characteristics of tumors. The high death rate for GI cancer patients shows that it is possible to increase analysis for a more personalized therapy strategy which leads to improved prognosis and few side effects although many extrapolative and predictive biomarkers exist. Gastrointestinal cancer classification is a challenging but vital area of research and application within medical imaging and machine learning. Artificial intelligence (AI) based diagnostic support system, especially convolution neural network (CNN) based image examination tool, has enormous potential in medical computer vision. The study presents a GI Cancer Detection and Classification utilizing the African Vulture Optimization Algorithm with Transfer Learning (GICDC-AVOADL) methodology. The major aim of the GICDC-AVOADL model is to examine GI images for the identification of cancer. To achieve this, the GICDC-AVOADL method makes use of an improved EfficientNet-B5 method to learn features from input images. Furthermore, AVOA is exploited for optimum hyperparameter selection of the improved EfficientNet-B5 method. The GICDC-AVOADL technique applies dilated convolutional autoencoder (DCAE) For GI cancer detection and classification. A complete set of simulations was conducted to examine the enhanced GI cancer detection performance of the GICDC-AVOADL technique. The extensive results inferred superior performance of the GICDC-AVOADL algorithm over current models.

Load forecasting in Smart Grids (SG) is a major module of current energy management systems, that play a vital role in optimizing resource allocation, improving grid stability, and assisting the combination of renewable energy sources (RES). It contains the predictive of electricity consumption forms over certain time intervals. Load Forecasting remains a stimulating task as load data has exhibited changing patterns because of factors such as weather change and shifts in energy usage behaviour. The beginning of advanced data analytics and machine learning (ML) approaches; particularly deep learning (DL) has mostly enhanced load forecasting accuracy. Deep neural networks (DNNs) namely Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) have achieved popularity for their capability to capture difficult temporal dependencies in load data. This study designs a Short-Load Forecasting scheme using a Hybrid Deep Learning and Beluga Whale Optimization (LFS-HDLBWO) approach. The major intention of the LFS-HDLBWO technique is to predict the load in the SG environment. To accomplish this, the LFS-HDLBWO technique initially uses a Z-score normalization approach for scaling the input dataset. Besides, the LFS-HDLBWO technique makes use of convolutional bidirectional long short-term memory with an autoencoder (CBLSTM-AE) model for load prediction purposes. Finally, the BWO algorithm could be used for optimal hyperparameter selection of the CBLSTM-AE algorithm, which helps to enhance the overall prediction results. A wide-ranging experimental analysis was made to illustrate the better predictive results of the LFS-HDLBWO method. The obtained value demonstrates the outstanding performance of the LFS-HDLBWO system over other existing DL algorithms with a minimum average error rate of 3.43 and 2.26 under FE and Dayton grid datasets, respectively.

Abstract RNA-binding proteins (RBPs) modulate the expression level of several target RNAs (such as mRNAs) post-transcriptionally through interactions with unique binding sites in the 3′-untranslated region. There is mounting information that suggests RBP dysregulation plays a significant role in carcinogenesis. However, the function of FMR1 autosomal homolog 1(FXR1) in malignancies is just beginning to be unveiled. Due to the diversity of their RNA-binding domains and functional adaptability, FXR1 can regulate diverse transcript processing. Changes in FXR1 interaction with RNA networks have been linked to the emergence of cancer, although the theoretical framework defining these alterations in interaction is insufficient. Alteration in FXR1 expression or localization has been linked to the mRNAs of cancer suppressor genes, cancer-causing genes, and genes involved in genomic expression stability. In particular, FXR1-mediated gene regulation involves in several cellular phenomena related to cancer growth, metastasis, epithelial-mesenchymal transition, senescence, apoptosis, and angiogenesis. FXR1 dysregulation has been implicated in diverse cancer types, suggesting its diagnostic and therapeutic potential. However, the molecular mechanisms and biological effects of FXR1 regulation in cancer have yet to be understood. This review highlights the current knowledge of FXR1 expression and function in various cancer situations, emphasizing its functional variety and complexity. We further address the challenges and opportunities of targeting FXR1 for cancer diagnosis and treatment and propose future directions for FXR1 research in oncology. This work intends to provide an in-depth review of FXR1 as an emerging oncotarget with multiple roles and implications in cancer biology and therapy.

Lung and colon cancers are deadly diseases that can develop concurrently in organs and undesirably affect human life in some special cases. The detection of these cancers from histopathological images poses a complex challenge in medical diagnostics. Advanced image processing techniques, including deep learning algorithms, offer a solution by analyzing intricate patterns and structures in histopathological slides. The integration of artificial intelligence in histopathological analysis not only improves the proficiency of cancer detection but also holds the potential to increase prognostic assessments, eventually contributing to effective treatment strategies for patients with lung and colon cancers. This manuscript presents an Improved Water Strider Algorithm with Convolutional Autoencoder for Lung and Colon Cancer Detection (IWSACAE-LCCD) on HIs. The major aim of the IWSACAE-LCCD technique aims to detect lung and colon cancer. For noise removal process, median filtering (MF) approach can be used. Besides, deep convolutional neural network based MobileNetv2 model can be applied as a feature extractor with IWSA based hyperparameter optimizer. Finally, convolutional autoencoder (CAE) model can be applied to detect the presence of lung and colon cancer. To enhance the detection results of the IWSACAE-LCCD technique, a series of simulations were performed. The obtained results highlighted that the IWSACAE-LCCD technique outperforms other approaches in terms of different measures.

Network security is essential to our daily communications and networks. Cybersecurity researchers initiate the significance of emerging proficient network intrusion detection systems (IDS) for providing secure networks. While attackers endure to progress novel kinds of attacks and the network sizes endure to develop, the necessity for effectual IDS becomes significant. Additionally, IDS aims to offer confidentiality, integrity, and availability for the data communicated in networked computers by avoiding illegal access to the network. Several studies executed machine learning (ML) systems for emerging effectual IDS; then, with the advent of deep learning (DL) systems and artificial neural networks (ANNs) that create features automatically without human interference, researchers started to depend on DL approaches. This study introduces a new Golden Jackal Optimization Algorithm with Deep Learning Assisted Intrusion Detection System for Network Security (GJOADL-IDSNS) technique. The major intention of the GJOADL-IDSNS system lies in the effectual recognition and classification of the intrusions, to achieve network security. Primarily, data normalization is performed to scale the input data into a useful format. In the presented GJOADL-IDSNS technique, the GJOA-based feature selection (GJOA-FS) approach can be employed to elect an optimum subset of features. Next, the GJOADL-IDSNS methodology applies the attention-based bi-directional long short-term memory (A-BiLSTM) model. For hyperparameter tuning of the A-BiLSTM model, the GJOADL-IDSNS technique uses the salp swarm algorithm (SSA). The simulation value of the GJOADL-IDSNS technique has been tested utilizing benchmark datasets. The comparative results stated that the GJOADL-IDSNS technique achieves better performance than other models.

Objective: Our objective was to evaluate the safety and efficacy of selective renal artery embolization in the management of post-procedural acute renal bleeding. Materials and Methods: This was a prospective observational study that included all patients who presented to us with acute renal bleeding the following kidney procedures. Demographic, radiological, and invasive angiographic data of patients were recorded. Selective renal artery embolization was performed by an interventional radiologist, and patients were observed for any complications. Results: We received 20 patients with an average age of 46.2±13.18 years having bleeding following procedures done on the kidney. Fifteen (75%) patients had undergone procedure for renal stone disease, 3 (15%) had bleeding following nephron-sparing surgery (NSS), 1 (5%) patient had undergone percutaneous nephrostomy tube placement, while another patient had undergone biopsy of renal allograft. The average drop in hemoglobin recorded before the embolization was 2.45±0.69 mg/dL in percutaneous nephrolithotomy patients, 3.05±1.28 mg/dL in nephrolithotomy patients and 3.32±0.82 mg/dL following NSS. Renal pseudoaneurysm was the most common vascular lesion identified on angiography in 50% of patients, followed by arteriovenous fistula (AVF) in 30% of patients. A combination of pseudoaneurysm and AVF was seen in 10% of patients, and 10% of patients had active extravasation from injured vessels. One (5%) patient required emergency nephrectomy after two failed attempts of angioembolization. There were no major complications recorded except for urosepsis in 2 (10%) patients and acute kidney injury in 1 (5%) patient. Conclusion: Transarterial selective renal artery embolization is a safe and highly effective method to control postprocedural acute renal bleeding.

Cyberattacks on cyber-physical systems (CPS) have led to severe concerns, and then it is most significant to identify the attacks in the initial phase. But, there are major problems that are resolved in this area; it contains the capability of the security method for detecting earlier unknown attacks. These challenges are resolved with system behaviour analysis manners and semi-supervised or unsupervised machine learning (ML) approaches. The efficacy of the attack recognition method is strongly dependent upon the databases utilized for training the ML approaches. Anomaly detection is the procedure of recognizing anomalous procedures, which could not equal the predictable system behaviour. It permits the recognition of novel and secret attacks. Presently, anomaly detection systems are frequently executed utilizing ML like shallow (or traditional) learning and deep learning (DL). This article develops a Chaotic Tumbleweed Optimization Algorithm with Stacked Deep Learning based Cyberattack Detection (CTOASDL-CD) approach in the Industrial CPS platform. The CTOASDL-CD technique intends to exploit the FS with an optimal hyperparameter-tuned DL model for the detection of cyberattacks. To resolve the high dimensionality issue, the CTOASDL-CD technique uses CTOA for feature selection (FS) purposes. Besides, the stacked deep belief network (SDBN) model can be utilized for the recognition of cyberattacks. Finally, the tunicate swarm algorithm (TSA) has been deployed for the capable selection of hyperparameter values of the SDBN approach. To determine the improved performance of the CTOASDL-CD approach, a wide-ranging simulation value was performed. The experimental values demonstrated the capable outcome of the CTOASDL-CD methodology in accomplishing security in the industrial CPS environment.

Purpose: Knowledge of the length of the colon is relevant to understanding physiological and pathological function. It also has implications for diagnostic and clinical interventions, as well as for the design of delayed-release drug formulations and drug disposition modeling. Methods: Over the years, a range of different experimental methods have been employed to assess colon length. These methods vary from direct measurements on cadavers and during intraoperative procedures to measurements obtained from endoscopic and medical imaging techniques. However, no systematic review or meta-analysis of these findings has yet been carried out. In this systematic review, we identified 31 published experimental studies that measured the length of the human colon and/or its segments. Results: We synthesized the available data, comprising colon length measurements from 5741 adults and 337 children and young people, in a meta-analysis. The data contribute to our understanding of colon morphology and may have implications for clinical practice, particularly for colonoscopy and preoperative planning of surgical resections. Additionally, this review provides potential insights into anatomical correlates of functional diseases, such as constipation. Conclusions: This review highlights that non-invasive, non-destructive diagnostic imaging techniques, such as magnetic resonance imaging (MRI), can provide more physiologically relevant measurements of colon length. However, there is a need for more standardized measurement protocols and for additional pediatric data.

This article explores detecting and categorizing network traffic data using machine-learning (ML) methods, specifically focusing on the Domain Name Server (DNS) protocol. DNS has long been susceptible to various security flaws, frequently exploited over time, making DNS abuse a major concern in cybersecurity. Despite advanced attack, tactics employed by attackers to steal data in real-time, ensuring security and privacy for DNS queries and answers remains challenging. The evolving landscape of internet services has allowed attackers to launch cyber-attacks on computer networks. However, implementing Secure Socket Layer (SSL)-encrypted Hyper Text Transfer Protocol (HTTP) transmission, known as HTTPS, has significantly reduced DNS-based assaults. To further enhance security and mitigate threats like man-in-the-middle attacks, the security community has developed the concept of DNS over HTTPS (DoH). DoH aims to combat the eavesdropping and tampering of DNS data during communication. This study employs a ML-based classification approach on a dataset for traffic analysis. The AdaBoost model effectively classified Malicious and Non-DoH traffic, with accuracies of 75% and 73% for DoH traffic. The support vector classification model with a Radial Basis Function (SVC-RBF) achieved a 76% accuracy in classifying between malicious and non-DoH traffic. The quadratic discriminant analysis (QDA) model achieved 99% accuracy in classifying malicious traffic and 98% in classifying non-DoH traffic.

Computer-aided detection algorithms based on artificial intelligence are increasingly being tested and used as a means for detecting tuberculosis in countries where the epidemic is still present. Computer-aided detection tools are often presented as a global solution that can be deployed in all the geographical areas concerned by tuberculosis, but at the same time, they need to be adjusted and calibrated according to local populations’ characteristics. The aim of this article is to analyze the tensions between the standardization of computer-aided detection algorithms and their local adaptation and the political issues associated with these tensions. We undertook a qualitative analysis of practices associated with tuberculosis detection algorithms in different contexts, contrasting the perspectives of various stakeholders. Algorithms embed the promise of standardization through automation and the bypassing of variable human expertise such as that of radiologists, they are nonetheless objects of local practices that we have characterized as “tweaking.” This work of tweaking reveals how the technology is situated but also the many concerns of the users and workers (insertion in care, control over infrastructure, and political ownership). This should be better considered to truly make computer-aided detection innovative tools for tuberculosis management in global health.

Abstract The design and info‐architecture of the web content contribute significantly to achieving better usability and user experience as well as to adopting a website to its intended end‐users. Although several design techniques exist, choosing a suitable one for designing a particular type of website is difficult and needs further investigation. Thus, the objective of this research is to explore the impact of a design technique on web usability and to find out the best suitable design technique for a particular type of website. To achieve these objectives, two separate websites for providing electronic health services named Citizen Health Service portal were developed using the two mostly used design techniques, namely card sorting and interactive dialogue model (IDM). The usability of both websites was evaluated through a laboratory‐based usability testing approach with 20 participants. The study result showed that the IDM performs better in designing such an information‐intensive website.

Elections in Indonesia are closely linked to religion, with politicians using religion as a medium to appeal to voters. In addition, the increasingly widespread commodification of religious rituals in Indonesian politics confirms that religious symbols in campaigns are still part of democratisation. This research aims to explain how to understand the phenomenon of Islamic religious commodification in the context of democracy in Indonesia, starting from the use of religious symbols, forms of commodification, and their implications. This research uses a descriptive qualitative method to analyse the commodification of religion by three Indonesian politicians running for the Indonesian presidential election 2024. Data was collected from Instagram, with 1,154 posts indicating the commodification of religion. The data was analysed using document searches and experts' views and combined for descriptive analysis. The commodification of religious rituals in Presidential Elections in Indonesia has transformed spiritual values into political interests, using religion to gain support and establish a leadership identity. The three politicians have intertwined religious symbols and relationships with clerics, resulting in Islamic populist ideology and identity politics. This has led to the polarisation of society and the recruitment of politicians for electoral purposes.

A 316L stainless steel (SS) alloy was developed with 1, 3, and 5 vol% titanium (Ti) reinforcement using the powder injection molding route, representing a low-cost option for biomedical implants. The investigation encompassed 1300 °C, 1350 °C, and 1380 °C sintering temperatures to ascertain the optimal physical and mechanical properties. Both sintering temperature and Ti influenced sintered density, and Ti mitigated the deleterious effects of residual carbon. At higher sintering temperatures, carbon and silicon tended to migrate and accumulate at the brink of Ti, leading to the formation of intermetallic compounds and increased brittleness. Dispersed Ti particles within the 316L matrix acted as nucleation sites and enhanced solid solubility with improved density. An astounding 96.11 % sintered density was achieved at 3 vol% Ti sample sintered at 1380 °C. During the tensile test, 5 vol% Ti at 1380 °C exhibited a low modulus of 58.9 GPa, which is highly desirable for orthopedic implant application. The XRD, SEM, tensile test, and nano-indentation results collectively provide evidence of beta-titanium formation during the sintering process. Conversely, the sample incorporating 3 vol% titanium, sintered at 1380 °C, demonstrated a balanced performance, showcasing 432.94 ± 12.8 MPa ultimate tensile strength, 3.06 ± 0.17 % elongation, 74.2 GPa modulus, and 322 MPa and 423 MPa 0.2 % offset flexural and compressive yield strengths, respectively. Notably, an improvised wear resistance test underscored its aptitude for sliding wear resistance, solidifying its potential as a promising candidate for biomedical implants.

Abstract Brain metastasis in gastric cancer (GC) patients is a rare phenomenon that is associated with adverse clinical outcomes and poor survival rates. We conducted a retrospective cohort study to investigate the incidence, risk factors and prognostic factors of brain metastasis in GC patients. Data on sociodemographic and tumor characteristics of GC patients from 2010 to 2019 was extracted from the Surveillance, Epidemiology and End-Results (SEER) database. Descriptive statistics, multivariable logistic and Cox regression were applied on SPSS. Kaplan–Meier-Survival curves and ROC curves were constructed. A total of 59,231 GC patients, aged 66.65 ± 13.410 years were included. Brain metastasis was reported in 368 (0.62%) patients. On logistic regression, the risk of brain metastasis was significantly greater in males, patients aged

Benefits achieved by the biodegradable magnesium (Mg) and zinc (Zn) implants could be suppressed due to the invasion of infectious microbial, common bacteria, and fungi. Postoperative medications and the antibacterial properties of pure Mg and Zn are insufficient against biofilm and antibiotic-resistant bacteria, bringing osteomyelitis, necrosis, and even death. This study evaluates the antibacterial performance of biodegradable Mg and Zn alloys of different reinforcements, including silver (Ag), copper (Cu), lithium (Li), and gallium (Ga). Copper ions (Cu2+) can eradicate biofilms and antibiotic-resistant bacteria by extracting electrons from the cellular structure. Silver ion (Ag+) kills bacteria by creating bonds with the thiol group. Gallium ion (Ga3+) inhibits ferric ion (Fe3+) absorption, leading to nutrient deficiency and bacterial death. Nanoparticles and reactive oxygen species (ROS) can penetrate bacteria cell walls directly, develop bonds with receptors, and damage nucleotides. Antibacterial action depends on the alkali nature of metal ions and their degradation rate, which often causes cytotoxicity in living cells. Therefore, this review emphasizes the insight into degradation rate, antibacterial mechanism, and their consequent cytotoxicity and observes the correlation between antibacterial performance and oxidation number of metal ions.

Kota medan merupakan daerah dengan jumlah penduduk terbesar di Sumatera Utara. Pada tahun 2021-2022, Konsumsi buah pisang perkapita di kota medan diketahui bahwa jenis pisang kepok, pisang ambon dan pisang raja merupakan jenis pisang yang paling banyak di konsumsi. Tujuan dari penelitian ini adalah untuk mengetahui karakteristik konsumen buah pisang kepok dan menganalisis preferensi konsumen terhadap buah pisang kepok di Kota Medan. Penelitian menggunakan metode survei dengan jumlah sampel ditentukan secara quota sebanyak 42 responden konsumen pisang kepok di Kota Medan yang dipilih menggunakan metode pengambilan sampel purposive sampling. Analisis data yang digunakan dalam penelitian ini yakni analisis deskriptif dan Conjoint. Hasil penelitian menunjukkan bahwa karakteristik responden pisang kepok yakni Jenis kelamin Perempuan (83%), usia 41-50 (45%), Pendapatan Rp.2.500.000-Rp.5.000.000 (62%), tingkat Pendidikan S1 (48%), jumlah anggota keluarga 1-4 orang ( 81 %) dan pekerjaan ibu rumah tangga (48%). Selanjutnya hasil analisis menunjukkan bahwa preferensi konsumen terhadap buah pisang kepok adalah buah pisang kepok dengan harga Rp10.000, memiliki warna kulit kuning dengan bercak hitam, rasa yang manis, tidak berbiji dan memiliki jumlah buah tiap sisir sebanyak (18 buah).

Multiple myeloma (MM) is a hematologic malignancy caused by the clonal expansion of immunoglobulin-producing plasma cells in the bone marrow and/or extramedullary sites. Common manifestations of MM include anemia, renal dysfunction, infection, bone pain, hypercalcemia, and fatigue. Despite numerous recent advancements in the MM treatment paradigm, current therapies demonstrate limited long-term effectiveness and eventual disease relapse remains exceedingly common. Myeloma cells often develop drug resistance through clonal evolution and alterations of cellular signaling pathways. Therefore, continued research of new targets in MM is crucial to circumvent cumulative drug resistance, overcome treatment-limiting toxicities, and improve outcomes in this incurable disease. This article provides a comprehensive overview of the landscape of novel treatments and emerging therapies for MM grouped by molecular target. Molecular targets outlined include BCMA, GPRC5D, FcRH5, CD38, SLAMF7, BCL-2, kinesin spindle protein, protein disulfide isomerase 1, peptidylprolyl isomerase A, Sec61 translocon, and cyclin-dependent kinase 6. Immunomodulatory drugs, NK cell therapy, and proteolysis-targeting chimera are described as well.

The purpose of this study was to analyze the general description of the corn supply chain as a raw material for animal feed in the research area using the Food Supply Chain Networks (FSCN) model, to analyze the performance of the supply chain for corn as a raw material for animal feed in the study area using the Analytical Hierarchy Process (AHP) method. The samples in the research were farmers (raw material suppliers), animal feed entrepreneurs, animal feed traders and breeders consisting of 7 farmers (raw material suppliers), 5 animal feed entrepreneurs, 10 animal feed traders and 8 breeders with the research location is in the Simalungun Regency. Based on the results of the study it can be concluded that in general, the supply chain for corn as a raw material for animal feed in this study area is farmers (raw material suppliers) - animal feed entrepreneurs - animal feed traders - breeders (consumers). From a business point of view, the supply chain for corn as a raw material for animal feed has carried out two business processes, namely procurement and customer orders. Supply chain performance in the study area is quite consistent with a consistency ratio value of 0.015. The value points that there is good cooperation from partnerships such as raw material suppliers who are currently still able to meet demands from entrepreneurs, then the consistency obtained from this method is also related to a workforce that is skilled enough to help develop businesses and can support the development of product marketing.

Background: Peyronie’s disease (PD) is a localized fibrosis of tunica albuginea, which causes the anatomical and functional changes to the penis. Corporoplasty with grafting is indicated in severe (>60°) and complex curvature. Buccal mucosa is the most favored autologous graft material nowadays. The ventrolateral aspect of lingual mucosa has similar histological features to the rest of the oral cavity. Methods: This study aimed to test the efficacy, safety, durability, and reproducibility of corporoplasty with lingual mucosal graft (LMG) in the surgical treatment of PD in terms of surgical outcome, sexual function, and donor site complications. This prospective study included 19 patients of PD with severe and complex curvature, who underwent corporoplasty with LMG. Surgical and functional outcomes were assessed at follow-up planned at 2 weeks, 3 months, 6 months, 1 year, and 2 years. Results: The mean operative time was 126.31 ± 21.45 min. Additional Nesbit’s plication to correct the residual deformity was required in 26% (5 / 19) of patients. Straightening of the penis (curvature 1 cm) postoperatively was achieved in 63% (12 / 19) of patients and a shortening of penis occurred in 5% (1 / 19) of patients. Newer onset erectile dysfunction developed in 11% (2 / 19) of patients, and patient and partner satisfaction rates were 89% (17 / 19) and 84% (16 / 19), respectively, in a mean follow-up of 20.66 ± 5.37 months. Donor site complications were minimal and no patient had any salivary changes or speech disturbances. Conclusion: LMG provided excellent short-term results in terms of deformity correction, improved sexual function, and minimal donor site morbidity. The method is simple and reproducible, and multicenter studies with larger number of cases with longer follow-up are required to confirm these favorable results.

Multiple myeloma is the second-most common hematologic malignancy in adults worldwide. Despite ongoing advancement in therapeutic modalities, it remains an incurable disease with a 5-year survival rate of approximately 50%. The recent development and introduction of anti-BCMA immunotherapies into clinical practice, including chimeric antigen receptor T-cell (CAR-T) therapies and bispecific antibodies, has radically shifted the treatment paradigm. However, despite the promising potential of these therapies for broader application, frequent and significant adverse effects have been reported, both in short- and in long-term settings, requiring increasing awareness and vigilance in the treating team, close monitoring, and prompt interventions with a multidisciplinary approach. In this review, we will discuss the toxicities associated with CAR-T cell and bispecific antibody therapies, focusing on results from major clinical studies and real-world observations. In addition, we will emphasize on effective strategies for prevention, monitoring and management, and provide expert recommendations.

Background Pneumothorax is the most common complication of computed tomography (CT)-guided lung biopsy. The asymptomatic rate ranges from 17.5 to 72%. The symptomatic rate requiring chest tube insertion is 6 to 18%. Aims This article studies the role of management of postbiopsy pneumothoraces by needle aspiration and pigtail catheter insertion. Methods This was a prospective observational study conducted over 2 years. Postbiopsy and prior to withdrawing the coaxial cannula a CT data set was obtained to detect and quantify pneumothoraces as mild, moderate, and severe. In all asymptomatic cases of mild pneumothorax simple observation was done. In all asymptomatic cases of moderate pneumothorax, immediate needle aspiration was performed. In all symptomatic cases, cases with severe pneumothorax, and cases with progressively enlarging pneumothorax small caliber 6 to 8F pigtail catheters were inserted. Results Ninety-one cases had mild pneumothorax, 42 had moderate pneumothorax, and 18 had severe pneumothorax. In the 91 patients of mild pneumothorax only 1 (1%) patient showed increase in size of pneumothorax on follow-up requiring catheter insertion. In the 42 cases of moderate pneumothorax, which were managed by simple aspiration of pneumothorax, 4 (9.5%) cases showed increase in size of pneumothorax on follow-up. A total 23 cases required pigtail catheter insertion in our study. These constituted 15.2% of pneumothorax cases. The mean duration of catheterization in our study was 3.74 ± 1.09 days. Conclusion Majority of pneumothoraces are benign and do not require any intervention, just observation. Manual aspiration is an effective way of treating moderate pneumothoraces with success rate of 90%, thereby reducing the number of cases requiring catheter insertion; however, close observation is required as few cases may progress to severe pneumothorax and require pigtail insertion. Only a small percentage of biopsy cases (6.4%) require catheter insertion which is a safe and effective treatment.

Tujuan penelitian ini adalah untuk menunjukkan bagaimana impostor syndrome di kalangan santri/santriwati penghafal Al-Qur’an dan juga untuk mengetahui bagaimana kesiapan pesantren untuk menangani kasus impostor syndrome. Penelitian dilakukan di sebuah pesantren di Makassar (Pesantren x). Seringkali para penghafal Al-Qur’an dianggap sebagai orang-orang pilihan yang memutuskan untuk melestarikan ayat-ayat Tuhan sehingga mereka dianggap masyarakat sebagai orang paling baik diantara manusia. Namun untuk berada di posisi tersebut tidaklah mudah karena harus menghafal Al-Qur’an dengan kuantitas yang cukup banyak. Hal inilah yang mengharuskan penghafal memforsir dirinya untuk melakukan pengulangan secara terus-menerus.Kondisi tersebut, menghantarkan banyak siswa pada burnout phenomenon, yang membuat mereka lebih memilih bolos, berhenti atau bahkan stres.Sering kali kondisi yang memiliki hubungan dengan burnout phenomenon adalah impostor syndrome yang juga sekarang mendapat banyak ulasan dan menjadi perbincangan populer dibanyak penelitian. Penelitian ini menggunakan metode penelitian mix method research. Peneliti menggunakan metode kuantitatif untuk mengetahui impostor syndrome di kalangan santri/santriwati penghafal Al-Qur’an di pesantren x. Sementara itu untuk mengetahui kesiapan pesantren x untuk menangani kasus impostor syndrome, peneliti menggunakan metode kualitatif. Hasil penelitian ini menunjukkan angka fantastis mengenai para santri yang mengalami impostor syndrome, tidak satupun di antara subjek penelitian yang berjumlah 33 orang yang memiliki pengalaman imposter yang ringan. Sebagian besar berada pada kategori “sedang (15 Responden) dan sering (17 responden) bahkan ada satu orang di antara mereka teridentifikasi memiliki gejala impostor syndrome yang intens. Sementara itu dalam wawancara ditemukan pesantren x tidak cukup memiliki kesiapan yang baik dalam menanggulangi impostor syndromedi kalangan santri/santriwati.