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OBJECTIVES: There is no sufficient data to provide a clear picture of out-of-hospital cardiac arrest (OHCA) across Türkiye. This study is the first to present the prognostic outcomes of OHCA cases and the factors associated with these outcomes. MATERIALS AND METHODS: The study was conducted in a prospective, observational, multicenter design under the leadership of the Emergency Medicine Association of Turkey Resuscitation Study Group. OHCA cases aged 18 years and over who were admitted to 28 centers from Türkiye were included in the study. Survived event, return of spontaneous circulation (ROSC), survival to hospital discharge, and neurological outcome at discharge were investigated as primary outcomes. RESULTS: One thousand and three patients were included in the final analysis. 61.1% of the patients were male, and the average age was 67.0 ± 15.2. Cardiopulmonary resuscitation (CPR) was performed on 86.5% of the patients in the prehospital period by emergency medical service, and bystander CPR was performed on only 2.9% by nonhealth-care providers. As a result, the survived event rate was found to be 6.9%. The survival rate upon hospital discharge was 4.4%, with 2.7% of patients achieving a good neurological outcome upon discharge. In addition, the overall ROSC and sustained ROSC rates were 45.2% and 33.4%, respectively. In the multiple logistic regression analysis, male gender, initial shockable rhythm, a shorter prehospital duration of CPR, and the lack of CPR requirement in the emergency department were determined to be independent predictors for the survival to hospital discharge. CONCLUSION: Compared to global data, survival to hospital discharge and good neurological outcome rates appear to be lower in our study. We conclude that this result is related to low bystander CPR rates. Although not the focus of this study, inadequate postresuscitative care and intensive care support should also be discussed in this regard. It is obvious that this issue should be carefully addressed through political moves in the health and social fields.

Abstract Advancements in cloud computing, flying ad-hoc networks, wireless sensor networks, artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development of smart cities. Owing to their massive interconnectedness, high volumes of data are collected and exchanged over the public internet. Therefore, the exchanged messages are susceptible to numerous security and privacy threats across these open public channels. Although many security techniques have been designed to address this issue, most of them are still vulnerable to attacks while some deploy computationally extensive cryptographic operations such as bilinear pairings and blockchain. In this paper, we leverage on biometrics, error correction codes and fuzzy commitment schemes to develop a secure and energy efficient authentication scheme for the smart cities. This is informed by the fact that biometric data is cumbersome to reproduce and hence attacks such as side-channeling are thwarted. We formally analyze the security of our protocol using the Burrows–Abadi–Needham logic logic, which shows that our scheme achieves strong mutual authentication among the communicating entities. The semantic analysis of our protocol shows that it mitigates attacks such as de-synchronization, eavesdropping, session hijacking, forgery and side-channeling. In addition, its formal security analysis demonstrates that it is secure under the Canetti and Krawczyk attack model. In terms of performance, our scheme is shown to reduce the computation overheads by 20.7% and hence is the most efficient among the state-of-the-art protocols.

BackgroundFor the provision of optimal care in a suicide prevention helpline, it is important to know what contributes to positive or negative effects on help seekers. Helplines can often be contacted through text-based chat services, which produce large amounts of text data for use in large-scale analysis. ObjectiveWe trained a machine learning classification model to predict chat outcomes based on the content of the chat conversations in suicide helplines and identified the counsellor utterances that had the most impact on its outputs. MethodsFrom August 2021 until January 2023, help seekers (N=6903) scored themselves on factors known to be associated with suicidality (eg, hopelessness, feeling entrapped, will to live) before and after a chat conversation with the suicide prevention helpline in the Netherlands (113 Suicide Prevention). Machine learning text analysis was used to predict help seeker scores on these factors. Using 2 approaches for interpreting machine learning models, we identified text messages from helpers in a chat that contributed the most to the prediction of the model. ResultsAccording to the machine learning model, helpers’ positive affirmations and expressing involvement contributed to improved scores of the help seekers. Use of macros and ending the chat prematurely due to the help seeker being in an unsafe situation had negative effects on help seekers. ConclusionsThis study reveals insights for improving helpline chats, emphasizing the value of an evocative style with questions, positive affirmations, and practical advice. It also underscores the potential of machine learning in helpline chat analysis.

BackgroundWith the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. ObjectiveThis study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explore the feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinical practice. MethodsWe used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982 messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machine learning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. ResultsThe deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior (accuracy=0.72, area under the curve [AUC]=0.95, Cohen κ=0.69). It differentiated MI congruent and incongruent counselor behavior (AUC=0.92, κ=0.65) and evocative and nonevocative language (AUC=0.92, κ=0.66). For client behavior, the model achieved an accuracy of 0.70 (AUC=0.89, κ=0.55). The model’s interpretable predictions discerned client change talk and sustain talk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. ConclusionsThe results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set size is sufficiently large with enough training samples for each behavioral code, these methods can be trained and applied to other domains and languages, offering a scalable and cost-effective way to evaluate MI adherence, accelerate behavioral coding, and provide therapists with personalized, quick, and objective feedback.

Penelitian ini bertujuan untuk mendeskripsikan bentuk-bentuk kesalahan berbahasa bidang morfologi pada karangan eksposisi siswa Kelas VI SD Negeri 1 Blangkajeren, Kabupaten Gayo Lues. Data penelitian ini berupa karangan eksposisi siswa sebanyak 30 karangan. Metode yang digunakan dalam penelitian ini adalah metode deskriptif kualitatif. Setelah data terkumpul dilakukan analisis kemudian dideskripsikan dalam bentuk tulisan. Teknik analisis data yang digunakan adalah teknik tes, yakni siswa diminta untuk menulis karangan eksposisi. Hasil penelitian ini menunjukkan bahwa dari 30 karangan eksposisi terdapat sejumlah kesalahan morfologi dalam karangan eksposisi siswa yakni kesalahan dalam afiksasi, kesalahan kompositum, kesalahan reduplikasi, kesalahan penggunaan kata baku/ tidak baku, dan kesalahan karena adanya interfrensi bahasa Gayo terhadap bahasa Indonesia dalam karangan eksposisi siswa.

Conversations with topics that are locally contextual often produces incoherent topic modeling results using standard methods. Splitting a conversation into its individual utterances makes it possible to avoid this problem. However, with increased data sparsity, different methods need to be considered. Baseline bag-of-word topic modeling methods for regular and short-text, as well as topic modeling methods using transformer-based sentence embeddings were implemented. These models were evaluated on topic coherence and word embedding similarity. Each method was trained using single utterances, segments of the conversation, and on the full conversation. The results showed that utterance-level and segment-level data combined with sentence embedding methods performs better compared to other non-sentence embedding methods or conversation-level data. Among the sentence embedding methods, clustering using HDBScan showed the best performance. We suspect that ignoring noisy utterances is the reason for better topic coherence and a relatively large improvement in topic word similarity.

Background: The success of battle against COVID 19 depends on public adherence towards infection control measures, which is greatly affected by their knowledge, attitude, and practices towards the COVID 19 pandemic. Objectives: Among the residents in the rural field practice area of a teaching hospital in Mangaluru:- 1) To assess knowledge, attitude, practice regarding preventive measures of COVID 19. 2) To assess stigma and risk perception of COVID 19. Methodology: A community based cross sectional study, by house-to-house visit was conducted among 323 participants residing in the rural field practice area of A.J. Institute of Medical Sciences & Research Centre, Mangaluru. Multistage sampling was adopted. Details regarding socio-demographic characteristics; knowledge, attitude, practice regarding preventive measures of COVID 19; risk perception and stigmatizing behavior was collected. Analysis was done using SPSS trial version 28. Results: 61% of participants were male; 27% had primary education and 33.1% were unemployed. Mass media was the most common source of COVID 19 related information. The knowledge of preventive measures of COVID 19 was good in 97.5% of the participants. 98.1% had positive attitude towards the preventive measures of COVID 19. The practice of preventive measures of COVID 19 good in 63.5%. 87.6% had taken two dose of COVID 19 vaccine. Low risk perception was seen in 96% and 83.6% had low stigmatizing behavior towards COVID 19. Gender was significant predictor of attitude scores. Age, gender, educational status, occupation status, type of family was the significant predictor of practices scores. Conclusion: Majority of the participants had good knowledge, positive attitude and good practice of preventive measures of COVID 19. As a result of effective IEC effort by different authorities is reaching rural part of India, people are aware of the preventive measures, has positive attitude about it and are practicing them.

In the last two decades, foreign capital investments in the financial sector of emerging markets have increased tremendously. Foreign direct investment is now important for every country. With the development of globalization, foreign capital investments have started to take place more in the financial sector. Turkey, which is among the developing countries, is among the preferred countries for foreign direct investments. The regulations that took place after the crises in the banking sector in 2000 and 2001 and the growth trend in the world economies made the banking sector attractive. It cannot be denied that foreign investments have positive contributions to the current account deficit, employment, gross domestic product, and raising quality standards with competition rules. In addition, the indirect effect of foreign capital is a larger increase in the deposit and loan volume of the banking system due to the increase in production, and an increase in trade volume and profit. The secondary effects of foreign capital investments on the sector are that it creates a multiplier effect on service standards, product variety and profit margin together with developing market conditions. This study describes the tests and analyzes on the effect of loan interest net profit and volume of the Turkish banking sector on the net profit of public and private banks. The main purpose of this study is to evaluate the effect of non-performing loans on banks' profits. Since profitability is a very broad concept, models covering the return on assets ratio and return on equity ratio, which are widely used in studies in the literature, have been established. Unlike other studies, the effect of non-performing loans on the net profit margin is also examined. The study is carried out on banks with at least 100 branches operating in Turkey. In practice, the relationship between the net profit margin of non-performing loans, return on assets and return on equity is analyzed using the data for the period 2012-2021.

Lung adenocarcinoma usually presents with cough, hemoptysis, and weight loss with radiologic findings of suspicious nodules and masses. It rarely mimics other diseases such as pneumonia. We here present a case of a female patient who presented with acute hypoxemic respiratory failure and radiologic findings of multi-lobar consolidation and was found to have lung adenocarcinoma.

Outsourcing data to remote cloud providers is becoming increasingly popular amongst organizations and individuals. A semi-trusted server uses Searchable Symmetric Encryption (SSE) to keep the search information under acceptable leakage levels whilst searching an encrypted database. A dynamic SSE (DSSE) scheme enables the adding and removing of documents by performing update queries, where some information is leaked to the server each time a record is added or removed. The complexity of structures and cryptographic primitives in most existing DSSE schemes makes them inefficient, in terms of storage, and query requests generate overhead costs on the Smart Device Client (SDC) side. Achieving constant storage cost for SDCs enhances the viability, efficiency, and easy user experience of smart devices, promoting their widespread adoption in various applications while upholding robust privacy and security standards. DSSE schemes must address two important privacy requirements: forward and backward privacy. Due to the increasing number of keywords, the cost of storage on the client side is also increasing at a linear rate. This article introduces an innovative, secure, and lightweight Dynamic Searchable Symmetric Encryption (DSSE) scheme, ensuring Type-II backward and forward privacy without incurring ongoing storage costs and high-cost query generation for the SDC. The proposed scheme, based on an inverted index structure, merges the hash table with linked nodes, linking encrypted keywords in all hash tables. Achieving a one-time O(1) storage cost without keyword counters on the SDC side, the scheme enhances security by generating a fresh key for each update. Experimental results show low-cost query generation on the SDC side (6,460 nanoseconds), making it compatible with resource-limited devices. The scheme outperforms existing ones, reducing server-side search costs significantly.

AbstractMulti-drug resistant (MDR) bactearial strains have posed serious health issues, thus leading to a significant increase in mortality, morbidity, and the expensive treatment of infections. Metal-organic frameworks (MOFs), comprising metal ions and a variety of organic ligands, have been employed as an effective drug deliveryy vehicle due to their low toxicity, biodegradability, higher structural integrity and diverse surface functionalities. Polydopamine (PDA) is a versatile biocompatible polymer with several interesting properties, including the ability to adhere to biological surfaces. As a result, modifying drug delivery vehicles with PDA has the potential to improve their antimicrobial properties. This work describes the preparation of PDA-coated Zn-MOFs for improving curcumin’s antibacterial properties against S. aureus and E. coli. Powder X-ray diffraction (P-XRD), FT-IR, scanning electron microscopy (SEM), and DLS were utilized to characterize PDA-coated Zn-MOFs. The curcumin loading and in vitro release of the prepared MOFs were also examined. Finally, the MOFs were tested for bactericidal ability against E. coli and S. aureus using an anti-bacterial assay and surface morphological analysis. Smaller size MOFs were capable of loading and releasing curcumin. The findings showed that as curcumin was encapsulated into PDA-coated MOFs, its bactericidal potential was significantly enhanced, and the findings were further supported by SEM which indicated the complete morphological distortion of the bacteria after treatment with PDA-Cur-Zn-MOFs. These studies clearly indicate that the PDA-Cur-Zn-MOFs developed in this study are extremely promising for long-term release of drugs to treat a wide range of microbial infections.

Multigeneration systems have gained popularity and approval from scientific minds due to the enhancements these systems bring to total energy utilization efficiency. The use of waste heat from different thermodynamic cycles ensures deceleration of environment pollution. In this paper, a newly conceptualized poly-generation system combining power, cooling and desalination is proposed. Despite the high efficiency generated by the next generation supercritical CO2 (sCO2) Brayton cycles, the pre-coolers (cooling components) of the power block experience a substantial amount of low-grade energy loss. Utilizing this energy loss to increase thermal efficiency further has been the driving force behind this research. The proposed system has three subsystems which are sCO2 Partial cooling cycle with reheat for power generation, followed by a ME-TVC (multi-effect desalination-thermal vapor compression) cycle for producing fresh water and finally an absorption refrigeration cycle (ARC) to provide refrigeration effect. The waste heat from precoolers of sCO2 partial cooling cycle are being utilized by the desalination plant and refrigeration cycle for enhanced system performance. A comprehensive mathematical model is developed and a parametric assessment is performed. The parametric investigation focuses on the primary performance parameters, namely net power output, freshwater yield, energy utilization efficiency, coefficient of performance, and gain output ratio, to assess the multi-generation system's performance across various operational scenarios. The variables that are optimized are turbine inlet temperature of the sCO2 cycle, temperature difference of the generator and the evaporating temperature of the absorption cycle, the number and temperature of the effects of the desalination system. The results show that the greatest energy efficiency of the developed system is up to 70%. The 1st law energy efficiency is termed as Energy Utilization Factor (EUF) in the present work. This term adheres to the logic of the first law of thermodynamics by relating the total energy outputs to the system (work plus refrigeration capacity and heat supplied to the desalination plant) to the total energy input. Using the default parameters, the COP of the ARC is 0.667 and the EUF of the combined system is 59%. Both of these values are higher when compared to previous combined cycles found in literature.

Abstract Wireless sensor networks (WSNs) are increasingly being used for data monitoring and collection purposes. Typically, they consist of a large number of sensor nodes that are used remotely to collect data about the activities and conditions of a particular area, for example, temperature, pressure, motion. Each sensor node is usually small, inexpensive, and relatively easy to deploy compared to other sensing methods. For this reason, WSNs are used in a wide range of applications and industries. However, WSNs are vulnerable to different kinds of security threats and attacks. This is primarily because they are very limited in resources like power, storage, bandwidth, and processing power that could have been used in developing their defense. To ensure their security, an effective Intrusion detection system (IDS) need to be in place to detect these attacks even under these constraints. Today, traditional IDS are less effective as these malicious attacks are becoming more intelligent, frequent, and complex. Denial of service (DOS) attack is one of the main types of attacks that threaten WSNs. For this reason, we review related works that focus on detecting DoS attacks in WSN. In addition, we developed and implemented several Deep learning (DL) based IDS. These systems were trained on a specialized dataset for WSNs called WSN-DS in detecting four types of DoS attacks that affects WSNs. They include the Blackhole, Grayhole, Flooding, and Scheduling attacks. Finally, we evaluated and compared the results and we discuss possible future works.

This study examines the use of social media by individuals during protests in China (Hong Kong), Iraq, Iran, and Lebanon.

Due to subjectivity in oral assessment, much concentration has been put on obtaining a satisfactory measure of consistency among raters. However, obtaining consistency might not result in valid decisions. One matter that is at the core of both reliability and validity in oral performance is rater training. Recently, Multifaceted Rasch Measurement (MFRM) has been adopted to address the problem of rater bias and inconsistency; however, no research has incorporated the facets of test takers’ ability, raters’ severity, task difficulty, group expertise, scale criterion category, and test version together in a piece of research along with their two-sided impacts. Moreover, little research has investigated how long rater training effects last. Consequently, this study explored the influence of the training program and feedback by having 20 raters score the oral production, as measured by the CEP (Community English Program) test, produced by 300 test takers in three phases, i.e., before, immediately after and long after the training program. The results indicated that training can lead to higher degrees of interrater reliability and diminished measures of severity/leniency, and biasedness. However, it won't lead the raters into total unanimity, except for making them more self-consistent. Although rater training might result in higher internal consistency among raters, it cannot eradicate individual differences. That is, experienced raters, due to their idiosyncratic characteristics, did not benefit as much as inexperienced ones. This study also showed that the outcome of training might not endure in long run after training; thus, it requires ongoing training letting raters regain consistency.

Pantoea stewartii subsp. stewartii is a pathogen that causes Stewart wilt on maize. This disease is important in maize. Infection early in growth can cause yield losses of up to 100%. This review examines the history, recent developments, economic impacts and developments of detection technology of Stewart wilt diseases. This paper was created by reviewing several articles relevant to the purpose of the topic. The article's results revealed that the pathogen underwent many changes, including its name, taxonomy, physiological abilities, status as an important pathogen in many countries and the development of detection technology. Currently, P. stewartii subsp. stewartii belongs to the Erwiniaceae family and has physiological abilities that can be distinguished from bacteria of the same genus and species. This pathogen has been reported to spread to more than 82 countries, including Indonesia, with 18 host plants. Some areas have reported pathogens in Indonesia, but no vector has been written. The biggest economic threat caused by this disease is the industrial production of corn seeds which require strict phytosanitary requirements and are free from pathogens. To anticipate its spread, there are four methods of detection of pathogenic bacteria that are commonly used, but serological and molecular detection technologies are the main recommendations.

Abstract Due to subjectivity in oral assessment, much concentration has been put on obtaining a satisfactory measure of consistency among raters. However, the process for obtaining more consistency might not result in valid decisions. One matter that is at the core of both reliability and validity in oral assessment is rater training. Recently, multifaceted Rasch measurement (MFRM) has been adopted to address the problem of rater bias and inconsistency in scoring; however, no research has incorporated the facets of test takers’ ability, raters’ severity, task difficulty, group expertise, scale criterion category, and test version together in a piece of research along with their two-sided impacts. Moreover, little research has investigated how long rater training effects last. Consequently, this study explored the influence of the training program and feedback by having 20 raters score the oral production produced by 300 test-takers in three phases. The results indicated that training can lead to more degrees of interrater reliability and diminished measures of severity/leniency, and biasedness. However, it will not lead the raters into total unanimity, except for making them more self-consistent. Even though rater training might result in higher internal consistency among raters, it cannot simply eradicate individual differences related to their characteristics. That is, experienced raters, due to their idiosyncratic characteristics, did not benefit as much as inexperienced ones. This study also showed that the outcome of training might not endure in long term after training; thus, it requires ongoing training throughout the rating period letting raters regain consistency.

Interactive digital modules have now become a new commodity in the world of learning that must be a major concern in order to facilitate an efficient and flexible learning process. The purpose of this development is to create augmented reality to be used as a learning resource by all academics. This study uses the Lee Owens development model which consists of 4 stages, namely assessment/analysis, design, development and implementation, and evaluation. This development activity involved a learning media expert, a material expert, and 8 small group pilots to assess the feasibility of the digital module being developed. Based on the presentation of the validator's assessment of the augmented reality, it can be concluded that augmented reality is very valid criteria which means it is very good for use in learning with a percentage of 90.25% of media experts, 88.75% from material experts, and 86.5% from small group trials. Researchers recommend using this material as a learning resource for Information Technology courses that can be accessed on the Halu Oleo University website. Researchers also suggest making augmented reality as part of the learning materials developed.

Purpose: The aim of this study is to investigate the power of disease severity scores to predict the development of Severe Acute Pancreatitis (SAP) and mortality in the early period over 65 years old diagnosed with acute pancreatitis in the emergency department. Materials and Methods: We calculated RANSON (on admission) and Computed Tomography Severity Index (CTSI) in addition to Bedside Index for Severity in Acute Pancreatitis (BISAP) score on admission to the emergency department. Results: One hundred and sixty patients (46.9% over 80 years of age) were included in the study. We observed statistically higher length of hospitalization, longer duration of stay in the intensive care unit, SAP and higher mortality in patients over 80 years of age. When we examined the ROC curve, we determined that the AUC values of the BISAP score were highest in both SAP and mortality estimation (AUC: 0.911, 95% CI 0.861-0.962; AUC: 0.918, 95% CI 0.864-0.9722, respectively). Binary logistic analysis indicated a 4.7-fold increased risk for SAP and a 12.3-fold increased mortality for each unit increase in BISAP score value. Conclusion: BISAP may be a good predictor for SAP and mortality estimation on admission to the emergency department in patients over 65 years of age with acute pancreatitis.

Abstract Student-centered learning assessment (SCLA) constitutes a major component of current educational initiatives at the University of Technology and Applied Sciences (UTAS). However, little research has been conducted on English teachers’ understanding and practices of SCL assessment. Therefore, this study seeks to explore English teachers’ understanding and practices of SCL assessment at UTAS in Oman. The findings could provide information regarding teachers’ understanding and practices of SCLA. The findings may contribute to how English teachers define SCLA, what SCL-related activities they conduct, and how often these are conducted. Sixty-one teachers participated in the study with an average of 24 years of experience. A series of interviews and questions were used to elicit data from the participants. A questionnaire was used to explore teachers’ understanding of SCLA. Interviews were used in conjunction with the questionnaires to obtain more detailed information from the participants. The findings of this study showed that each of the English teachers has their definitions and understandings of SCLA; however, it was difficult to understand teachers’ definitions of SCLA due to the lack of a common definition for this term in the literature. Teachers should be encouraged to empower students by working in mixed groups on the basis that the advanced students each head up separate groups. The implication is to allow less able students to mimic and imitate their peers and improve their comprehension, pronunciation, and vocabulary in and out of the classroom. Future research could be enhanced by other stakeholders, such as students and administrators, involvement.

Objective To construct a machine-learning (ML) model for health systems with organised falls prevention programmes to identify older adults at risk for fall-related admissions.Design This prognostic study used population-level administrative health data to develop an ML prediction model.Setting This study took place in Alberta, Canada during 2018–2019.Participants Albertans aged 65 and older with at least one prior admission. Those with palliative conditions or emigrated out of Alberta were excluded.Exposure Unit of analysis was the individual person.Main outcomes/measures We identified fall-related admissions. A CatBoost model was developed on 2018 data to predict risk of fall-related emergency department visits or hospitalisations. Temporal validation was done using 2019 data to evaluate model performance. We reported discrimination, calibration and other relevant metrics measured at the end of 2019 on both ranked predictions and predicted probability thresholds. A cost-savings simulation was performed using 2019 data.Results Final number of study participants was 224 445. The validation set had 203 584 participants with 19 389 fall-related events (9.5% pretest probability) and an ML model c-statistic of 0.70. The highest ranked predictions had post-test probabilities ranging from 40% to 50%. Net benefit analysis presented mixed results with some net benefit using the ML model in the 6%–30% range. The top 50 percentile of predicted risks represented nearly $C60 million in health system costs related to falls. Intervening on the top 25 or 50 percentiles of predicted risk could realise substantial (up to $C16 million) savings.Conclusion ML prediction models based on population-level administrative data can assist health systems with fall prevention programmes identify older adults at risk of fall-related admissions and reduce costs. ML predictions based on ranked predictions or probability thresholds could guide subsequent interventions to mitigate fall risks. Increased access to diverse forms of data could improve ML performance and further reduce costs.

Untrusted servers are servers or storage entities lacking complete trust from the data owner or users. This characterization implies that the server hosting encrypted data may not enjoy full trust from data owners or users, stemming from apprehensions related to potential security breaches, unauthorized access, or other security risks. The security of searchable encryption has been put into question by several recent attacks. Currently, users can search for encrypted documents on untrusted cloud servers using searchable symmetric encryption (SSE). This study delves deeply into two pivotal concepts of privacy within dynamic searchable symmetric encryption (DSSE) schemes: forward privacy and backward privacy. The former serves as a safeguard against the linkage of recently added documents to previously conducted search queries, whereas the latter guarantees the irretrievability of deleted documents in subsequent search inquiries. However, the provision of fine-grained access control is complex in existing multi-user SSE schemes. SSE schemes may also incur high computation costs due to the need for fine-grained access control, and it is essential to support document updates and forward privacy. In response to these issues, this paper suggests a searchable encryption scheme that uses simple primitive tools. We present a multi-user SSE scheme that efficiently controls access to dynamically encrypted documents to resolve these issues, using an innovative approach that readily enhances previous findings. Rather than employing asymmetric encryption as in comparable systems, we harness low-complexity primitive encryption tools and inverted index-based DSSE to handle retrieving encrypted files, resulting in a notably faster system. Furthermore, we ensure heightened security by refreshing the encryption key after each search, meaning that users are unable to conduct subsequent searches with the same key and must obtain a fresh key from the data owner. An experimental evaluation shows that our scheme achieves forward and Type II backward privacy and has much faster search performance than other schemes. Our scheme can be considered secure, as proven in a random oracle model.

In this work, we established, validated, and optimized a novel computational framework for tracing arbitrarily oriented actin filaments in cryo-electron tomography maps. Our approach was designed for highly complex intracellular architectures in which a long-range cytoskeleton network extends throughout the cell bodies and protrusions. The irregular organization of the actin network, as well as cryo-electron-tomography-specific noise, missing wedge artifacts, and map dimensions call for a specialized implementation that is both robust and efficient. Our proposed solution, Struwwel Tracer, accumulates densities along paths of a specific length in various directions, starting from locally determined seed points. The highest-density paths originating from the seed points form short linear candidate filament segments, which are further scrutinized and classified by users via inspection of a novel pruning map, which visualizes the likelihood of being a part of longer filaments. The pruned linear candidate filament segments are then iteratively fused into continuous, longer, and curved filaments based on their relative orientations, gap spacings, and extendibility. When applied to the simulated phantom tomograms of a Dictyostelium discoideum filopodium under experimental conditions, Struwwel Tracer demonstrated high efficacy, with F1-scores ranging from 0.85 to 0.90, depending on the noise level. Furthermore, when applied to a previously untraced experimental tomogram of mouse fibroblast lamellipodia, the filaments predicted by Struwwel Tracer exhibited a good visual agreement with the experimental map. The Struwwel Tracer framework is highly time efficient and can complete the tracing process in just a few minutes. The source code is publicly available with version 3.2 of the free and open-source Situs software package.

This research focuses on implementing PAI Learning with a Scientific Learning Approach in Increasing the Effectiveness of PAI Learning with the 2013 Curriculum at SMA Negeri 3 Tebing Tinggi City, North Sumatra. The purpose of this research was to find out the planning, implementation, and obstacles to learning PAI with a scientific learning approach in increasing the effectiveness of learning PAI. This research method is qualitative research; the sources of this research are primary and secondary, and data collection techniques are observation, interviews, and documentation studies. The data analysis technique of this research is data reduction, data presentation, and conclusion—guarantees for data validity, namely credibility, transferability, dependability, certainty, and triangulation. The results of this study are PAI Learning Planning using the Scientific Learning Approach in the 2013 Curriculum at SMA Negeri 3 Tebing Tinggi City, North Sumatra. It has been made well and implemented optimally, and in the future, it will be even better with more mature preparation; it has been implemented to the fullest by applying the 5M scientific method in learning, namely: Seeing, Asking, Exploring, Associating, and Communicating, Obstacles to Implementation of PAI Learning using the Scientific Learning Approach in the 2013 Curriculum at SMA Negeri 3 Tebing Tinggi City, North Sumatra So far it can be overcome with solutions and cohesiveness.

This study proposes and explores a unique integrated cooling and power system in which waste heat from a gas turbine (GT) cycle is collected and used to generate electricity via a supercritical CO2 (sCO2) recompression Brayton cycle (sCO2RBC) and an organic Rankine cycle (ORC). An absorption refrigeration cycle (ARC) recovers waste heat from the sCO2RBC in order to provide a cooling impact. An energy analysis is carried out where mathematical modelling has been performed by a validated one-dimensional Python code. For working fluids e.g., air, combustion/exhaust gas, and NH3–H2O solution, property equations are used for obtaining the properties of respective state points. Impacts of different parameters are investigated on the total power output, net cooling effect and net energy utilization. The work emphasizes on modeling a novel integrated cooling and heating power system that utilizes the next generation sCO2 cycle, as well as other conceivable combinations, to effectively and reliably recover the waste heat. The analysis reveals that except for difference in the temperature in the hot side of the generator, all of the parameters increase resulting in an increase in net work done. The increase in temperature at inlet of GT, pressure ratio of the RBC, the decrease in GT pressure ratio, isentropic efficiency of GT, and lastly the temperature variation in the hot side of absorption refrigeration cycle generator affects the co-efficient of performance (COP) & energy utilization factor (EUF) of combined system. The EUF achieves a highest value of 49.09% and COP of 0.72.

Abstract Background First Nations (FN) people of Canada experience health, social, and systemic inequities due to colonization. Consequently, COVID-19 has placed further mental health stress on people related to personal finances, employment security and worry over infection, resulting in exacerbated effects of unresolved past medical and physical traumas. This study aims to understand the experiences related to mental health in an Alberta FN community during the early stages of the pandemic. Methods In partnership with FN leadership, the study implemented an online cross-sectional survey. Adults from a large FN community in Alberta, Canada, were asked to complete a survey, including two mental health-related screening questionnaires: (1) Generalized Anxiety Disorder-2 item; and (2) Patient Health Questionnaire-2 item. In addition, respondents could provide responses to open-ended questions about their experiences. Results Among 106 respondents, 95 (89.6%) finished the survey; 18% of adults screened positive for depressive symptoms (score of 3 or greater) and reported difficulty following public health advice for using hand sanitizer, maintaining social distancing, or self-isolating. 21% of adults screened positive for symptoms of anxiety (score of 3 or greater) and reported difficulty maintaining social distance, self-isolating, obtaining food and clothing, or meeting other basic living requirements. Conclusions FN communities may be disproportionately affected by COVID-19, and may experience exacerbated symptoms of anxiety, depression and overall poor mental health and well-being. Additional supports and services, including for mental health, should be considered for FN in the context of COVID-19 public health measures. Highlights The COVID-19 pandemic has brought upon increased stress and accompanying symptoms of anxiety and depression for a First Nations community in Alberta. Studies, such as this one, that characterize the influence of the COVID-19 pandemic on mental health among First Nations people, are urgently needed because of increasing demands on healthcare systems due to the pandemic and potential delays in the care of patients living with pre-existing mental health conditions. There is an opportunity to capitalize on First Nations people’s experiences of post-traumatic growth proactively supporting/maintaining their well-being and possibly the development of community-based mental health interventions and supports.

Abstract Background Preventatives measures to combat the spread of COVID− 19 have introduced social isolation, loneliness and financial stress. This study aims to identify whether the COVID-19 pandemic is related to changes in suicide-related problems for help seekers on a suicide prevention helpline. Methods A retrospective cohort study was conducted using chat data from a suicide prevention helpline in the Netherlands. The natural language processing method BERTopic was used to detect common topics in messages from December 1, 2019 until June 1, 2020 (N = 8589). Relative topic occurrence was compared before and during the lock down starting on March 23, 2020. The observed changes in topic usage were likewise analyzed for male and female, younger and older help seekers and help seekers living alone. Results The topic of the COVID-19 pandemic saw an 808% increase in relative occurrence after the lockdown. Furthermore, the results show that help seeker increased mention of thanking the counsellor (+ 15%), and male and young help seekers were grateful for the conversation (+ 45% and + 32% respectively). Coping methods such as watching TV (− 21%) or listening to music (− 15%) saw a decreased mention. Plans for suicide (− 9%) and plans for suicide at a specific location (− 15%) also saw a decreased mention. However, plans for suicide were mentioned more frequently by help seekers over 30 years old (+ 11%) or who live alone and (+ 52%). Furthermore, male help seekers talked about contact with emergency care (+ 43%) and panic and anxiety (+ 24%) more often. Negative emotions (+ 22%) and lack of self-confidence (+ 15%) were mentioned more often by help seekers under 30, and help seekers over 30 saw an increased mention of substance abuse (+ 9%). Conclusion While mentions of distraction, social interaction and plans for suicide decreased, expressions of gratefulness for the helpline increased, highlighting the importance of contact to help seekers during the lockdown. Help seekers under 30, male or who live alone, showed changes that negatively related to suicidality and should be monitored closely.

Aim:As far as we have investigated, although there are researches on QT and QTc interval, there are no studies evaluating T wave peak-to-end distance (Tp-e interval), the ratio of Tp-e to QT and QTc used in the evaluation of cardiac arrhythmia risk and ventricular repolarization changes in patients with hypoxia due to hypertensive pulmonary edema. Therefore, in this study was aimed to study whether there is a change in Tp-e interval, the ratio of Tp-e to QTc in hypoxia due to hypertensive pulmonary edema.Materials and Methods:Forty patients diagnosed with hypertensive pulmonary edema in the emergency room were included in the study retrospectively. Forty patients with similar age and gender distribution were included in the study as a control group. All patients underwent 12-lead electrocardiography (ECG). In addition to the routine measurements, Tp-e interval, the ratio of Tp-e to QTc were measured in ECG. Study data were grouped as patients with and without hypoxia.Results:Mean age for patients was 68.60±15.25. QTc interval, Tp-e interval and Tpe/QTc values were found to be significantly higher in hypoxia caused by hypertensive pulmonary edema (p

Multimedia in learning could make the learning atmosphere more attractive for student attention and student interactions. The objectives of this research were: (1) describe the use of multimedia learning by elementary school teachers in Kendari City; (2) analyze the comparison of the use of multimedia learning in terms of school accreditation by the teacher at elementary school in Kendari City; (3) analyze aspects of obstacles in the use of multimedia learning for elementary school teachers in Kendari City. This research used mixed methods with a sequential exploratory strategy held at the Kendari City Elementary School. The school sample was determined using the random cluster sampling technique selecting 3 schools from 11 sub-districts. Each sub-district took 2 accredited A and non-A public schools and 1 accredited private school so that the total respondents were 66 people. Research data was collected through observation forms and interviews. The results of this research showed that: (1) multimedia learning use by primary school teachers in Kendari City was still good; (2) there was a meaningful comparison of the use of multimedia learning by elementary school teachers in Kendari City in terms of school accreditation; (3) aspects of barriers to the use of multimedia learning by primary school teachers in Kendari City include: (a) teachers 'lack competence in IT, (b) teachers' misconceptions about learning multimedia, (c) the lack of support structures and infrastructures, (d)) the lack of training on multimedia learning means, (e) the lack of involvement of the educational software used by teachers to support learning activities.

Purpose: The aim of this retrospective observational study is to compare C-reactive protein to albumin ratio and CURB-65 score in the emergency department in terms of predicting mortality in patients over the age of 18 who were hospitalized for COVID-19 pneumonia. Materials and Methods: The study includes 613 patients hospitalized between March 15 and April 30, 2020 due to COVID-19 pneumonia detected on thorax computed tomography at the emergency department pandemic area. Hospitalized patients were divided into groups according to positive and negative real-time polymerase chain reaction results. Results: While 73.1% (n: 448) of 613 patients included in the study were hospitalized in the ward, 26.9% (n: 165) were hospitalized in intensive care. 8.6% (n: 53) of the total patients died. In non-survivors patients the mean CURB 65 score was 4±1 (and C-Reactive Protein to Albumin Ratio was 5.6±4.2 Multivariate logistic regression analysis showed that CURB 65 and high C-Reactive Protein to Albumin Ratio are independent risk factors for COVID-19 pneumonia. Conclusion: The C-reactive protein to albumin ratio is as sensitive as CURB 65 and can guide the clinician in the early detection of patients with poor prognosis COVID-19 pneumonia.

The purpose of this mixed methods research is to describe the implementation of the 2013 curriculum learning tools by elementary school teachers, the differences of the curriculum implementation, and to describe what teachers face in implementing it. The results of data analysis indicate that participants implemented the 2013 curriculum well in all school accreditation level. The problems faced by the elementary school teachers are relative to learning resources, limited facilities and infrastructure, and limited opportunities tocontinuing professional development. Keywords: Curriculum implementation; learning tools; elementary school teachers

This study aims to determine the influence of the application of Information Search learning strategies and learning motivation on PAI learning outcomes of SMA Negeri 1 Merbau students as well as the interaction between learning strategies and learning motivation on PAI learning outcomes. This research is a quantitative study with a population of as many as all class XI students at SMA Negeri 1 Merbau consisting of 6 study groups. Based on the Cluster Random Sampling technique, one is an Information Search learning class and one is an Expository learning class. The instruments of this study are tests and questionnaires. The test of normality of the learning outcomes data is the Liliefors test, and the homogeneity test is the Fisher test and the Bartlett Test. The data analysis technique is a two-path Anava at a significant α = 0.05 followed by the Schiche test. The results showed that there was a shift in the Information Search learning strategy on the learning outcomes of PAI students of SMA Negeri 1 Merbau with high (X̅= 28.6) and Low (X̅=19.6) learning motivations. In this case, the PAI learning outcomes of students with high motivation are higher than those of pai students with low learning motivation.

Learners’ perception of culture on receptive skills, more specifically on reading comprehension, has been an area facing a big challenge contemporarily. The current research aimed to investigate the cultural perception of Iranian EFL learners on reading comprehension ability. To conduct the study, an exploratory quantitative ex-post facto design (Dornyei, 2007) consisting of a questionnaire and interview study was used. Participants, 50 intermediate EFL learners (25 males, 25 females), were selected using the Oxford Placement Test. The participants were provided with two passages with different cultural contents (one containing Iranian culture and another containing culture of English-speaking countries). Then participants were required to fill in a Likert-scale questionnaire which was distributed through Google Form online platform. Also, an interview session was conducted to gather more detailed data. Interviews were recorded, transcribed, and coded to find common themes. Analysis of interview and questionnaire data showed that learners hold similar cultural perceptions on reading comprehension ability, yet gender and social class have no significant impact on learners’ perceptions. The results of the study specified that Iranian EFL learners held a positive attitude towards the integration of cultural materials into reading passages to better comprehend the target language. Moreover, it was seen that gender and social class did not impact significantly learners’ cultural perceptions of English reading comprehension. The findings imply that ESL students with various reading levels or capabilities can learn from materials that are culturally diverse, specifically the ones at higher reading proficiency levels.

This study investigates how user demographics influence the linguistic characteristics and readability of the reviews and the performance of sentiment classification methods. Two restaurant review datasets representing consumers of distinct demographics, such as English language fluency levels, socio-culture, and geographic locations, are compared. It is observed that demographic dissimilarities affect various linguistic attributes, such as sentence structure and vocabulary usage of reviews. The Mann–Whitney U test indicates significant differences in these two sets of reviews on various linguistic aspects. Regarding the readability of the two corpora, no noticeable difference is perceived by Flesch Reading Ease (FRE) test. The discrepancy in the results of lexicon-based and machine learning-based sentiment classification methods in two corpora suggests that demography has an influential role in the performance of sentiment classifiers.

Background: Perfusion index (PI) has use to monitor sympathetic response changes to pain. In this study, we aimed to evaluate the utility of using perfusion index as an objective marker of pain relief and of the need for rescue analgesia in ED patients with documented renal colic. Methods: We conducted a prospective observational study between January 2020 and December 2020. The demographic characteristics of the patients, their complaints, nephrolithiasis histories, vital signs, PI, and VAS scores (on admission and after treatment) were recorded. Results: A total of 144 patients were included. All patients were administered 20 mg of Tenoxicam on admission. There was a statistically significant difference between the PI (

The paper is devoted to the study of steady-state transmission problem between two Herschel-Bulkley fluids in a three dimensional thin layer.

The objective of this article is to examine the contribution of the Business Centers of Private Banks in the financing of SMEs in the wilaya of Bejaia, through a qualitative and quantitative exploratory study based on a field survey. The results show that banking regulations towards SMEs can be described as rigid and that the financing constraint lies in bureaucratic practices, the slowness of administrative procedures, as well as the high level of guarantees required.

Making stock investment decisions is a complex challenge that investors continuously face. When it comes to an uncertain future, making the wrong decision can result in massive losses. The paper aims to develop an artificial neural networks-based model predicting the closing price of top-six traded industrial ISX-listed stocks, which can guide investment decisions. The sample consisted of daily indexes ISX-released from (3/3/2019) to (31/3/2019). Matlab 2014b was used to run artificial neural networks using nntool software. Model's performance was evaluated using Mean squared error (MSE), Root mean squared error (RMSE), and R squared. Empirical results demonstrated the ability and efficiency of artificial neural networks to predict closing prices with high accuracy. As a result, we recommended employing Artificial Neural Networks model to predict stock prices as well as relying on to make decisions.

The aim of the research is to reveal the reality of teacher performance evaluation in the Sultanate of Oman in light of some global models. The study followed a qualitative descriptive research design. Seven forms of teacher formative and summative assessments were analyzed. Besides, an analytical template was developed, consisting of six areas related to the teaching performance of teachers. These included: lesson planning and preparation, learning environment, education, professional development, student academic, and community and parental partnership. The study reached a number of results; the most notable is the lack of change of forms for more than a decade despite the rapid development of the educational system in the sultanate in areas close to the teacher. It has also found that the formative evaluation focuses only on his/her rile in the classroom, such as lesson planning, preparation and learning environment. Further, it does not focus on his/her role outside the classroom, such as professional development, and community and parental partnership. Additionally, this evaluation is not based on any professional standards. Therefore, its tools, standards, and assessment need to be improved. The study has also shown that the final evaluation needs do not have sufficient standards. Additionally, both types of assessments do not relate to each other. Based on these results, the study has recommended to improve teacher performance evaluation in the Sultanate of Oman.

BackgroundCOVID-19 disrupted health care, causing a decline in the health of patients with chronic diseases and a need to reimagine diabetes care. With the advances in telehealth programs, there is a need to effectively implement programs that meet the needs of patients quickly. ObjectiveThe aim of this paper was to create a virtual boot camp program for patients with diabetes, in 3 months, from project conception to the enrollment of our first patients. Our goal is to provide practical strategies for rapidly launching an effective virtual program to improve diabetes care. MethodsA multidisciplinary team of physicians, dieticians, and educators, with support from the telehealth team, created a virtual program for patients with diabetes. The program combined online diabetes data tracking with weekly telehealth visits over a 12-week period. ResultsOver 100 patients have been enrolled in the virtual diabetes boot camp. Preliminary data show an improvement of diabetes in 75% (n=75) of the patients who completed the program. Four principles were identified and developed to reflect the quick design and launch. ConclusionsThe rapid launch of a virtual diabetes program is feasible. A coordinated, team-based, systematic approach will facilitate implementation and sustained adoption across a large multispecialty ambulatory health care organization.

Introduction: Benign tracheal stenosis is a common problem encountered after tracheal intubation or tracheostomy. It can be managed by surgical or nonsurgical techniques. This case series describes the outcome of 11 cases of endobronchial treatment for complex tracheal stenoses. Methods: A retrospective study was carried out in two hospitals in Lebanon. Patients were contacted on a regular basis for 6 months and asked about the presence of dyspnea and its characteristics. Results: The most common presenting symptom was inspiratory stridor. Five patients (45.45%) were not satisfied after the bronchoscopic intervention. Six patients (54.55%) were satisfied with the outcome. All were initially treated with argon plasma coagulation and dilation. If any persistent symptoms were present, stenting was done. Three patients had a stent placement. Failure of stenting occurred with two patients. None of the satisfied patients had any early symptoms. Conclusion: Bronchoscopic interventions yielded acceptable results when treating complex stenoses. More data is still needed to guide physicians for better approaches. When confronting complex tracheal stenosis, a multidisciplinary approach between surgical and nonsurgical doctors is preferred to choose the best medical care.

IntroductionResearch on simplified antibiotic regimens for outpatient treatment of 'Possible Serious Bacterial Infection' (PSBI) and the subsequent World Health Organization (WHO) guidelines provide an opportunity to increase treatment coverage. This multi-country implementation research initiative aimed to learn how to implement the WHO guideline in diverse contexts. These experiences have been individually published; this overview paper provides a summary of results and lessons learned across sites.Methods summaryA common mixed qualitative and quantitative methods protocol for implementation research was used in eleven sites in the Democratic Republic of Congo (Equateur province), Ethiopia (Tigray and Oromia regions), India (Haryana, Himachal Pradesh, Maharashtra, and Uttar Pradesh states), Malawi (Central Region), Nigeria (Kaduna and Oyo states), and Pakistan (Sindh province). Key steps in implementation research were: i) policy dialogue with the national government and key stakeholders, ii) the establishment of a 'Technical Support Unit' with the research team and district level managers, and iii) development of an implementation strategy and its refinement using an iterative process of implementation, programme learning and evaluation.Results summaryAll sites successfully developed and evaluated an implementation strategy to increase coverage of PSBI treatment. During the study period, a total of 6677 young infants from the study catchment area were identified and treated at health facilities in the study area as inpatients or outpatients among 88179 live births identified. The estimated coverage of PSBI treatment was 75.7% (95% CI 74.8% to 78.6%), assuming a 10% incidence of PSBI among all live births. The treatment coverage was variable, ranging from 53.3% in Lucknow, India to 97.3% in Ibadan, Nigeria. The coverage of inpatient treatment ranged from 1.9% in Zaria, Nigeria, to 33.9% in Tigray, Ethiopia. The outpatient treatment coverage ranged from 30.6% in Pune, India, to 93.6% in Zaria, Nigeria. Overall, the case fatality rate (CFR) was 14.6% (95% CI 11.5% to 18.2%) for 0-59-day old infants with critical illness, 1.9% (95% CI 1.5% to 2.4%) for 0-59-day old infants with clinical severe infection and 0.1% for fast breathing in 7-59 days old. Among infants treated as outpatients, CFR was 13.7% (95% CI 8.7% to 20.2%) for 0-59-day old infants with critical illness, 0.9% (95% CI 0.6% to 1.2%) for 0-59-day old infants with clinical severe infection, and 0.1% for infants 7-59 days old with fast breathing.ConclusionImportant lessons on how to conduct each step of implementation research, and the challenges and facilitators for implementation of PSBI management guideline in routine health systems are summarised and discussed. These lessons will be used to introduce and scale-up implementation in relevant Low- and middle-income countries.

Although cryo-electron microscopy (cryo-EM) has been successfully used to derive atomic structures for many proteins, it is still challenging to derive atomic structures when the resolution of cryo-EM density maps is in the medium resolution range, such as 5–10 Å. Detection of protein secondary structures, such as helices and β-sheets, from cryo-EM density maps provides constraints for deriving atomic structures from such maps. As more deep learning methodologies are being developed for solving various molecular problems, effective tools are needed for users to access them. We have developed an effective software bundle, DeepSSETracer, for the detection of protein secondary structure from cryo-EM component maps in medium resolution. The bundle contains the network architecture and a U-Net model trained with a curriculum and gradient of episodic memory (GEM). The bundle integrates the deep neural network with the visualization capacity provided in ChimeraX. Using a Linux server that is remotely accessed by Windows users, it takes about 6 s on one CPU and one GPU for the trained deep neural network to detect secondary structures in a cryo-EM component map containing 446 amino acids. A test using 28 chain components of cryo-EM maps shows overall residue-level F1 scores of 0.72 and 0.65 to detect helices and β-sheets, respectively. Although deep learning applications are built on software frameworks, such as PyTorch and Tensorflow, our pioneer work here shows that integration of deep learning applications with ChimeraX is a promising and effective approach. Our experiments show that the F1 score measured at the residue level is an effective evaluation of secondary structure detection for individual classes. The test using 28 cryo-EM component maps shows that DeepSSETracer detects β-sheets more accurately than Emap2sec+, with a weighted average residue-level F1 score of 0.65 and 0.42, respectively. It also shows that Emap2sec+ detects helices more accurately than DeepSSETracer with a weighted average residue-level F1 score of 0.77 and 0.72 respectively.

Bengali is a low-resource language that lacks tools and resources for various natural language processing (NLP) tasks, such as sentiment analysis or profanity identification. In Bengali, only the translated versions of English sentiment lexicons are available. Moreover, no dictionary exists for detecting profanity in Bengali social media text. This study introduces a Bengali sentiment lexicon, BengSentiLex, and a Bengali swear lexicon, BengSwearLex. For creating BengSentiLex, a cross-lingual methodology is proposed that utilizes a machine translation system, a review corpus, two English sentiment lexicons, pointwise mutual information (PMI), and supervised machine learning (ML) classifiers in various stages. A semi-automatic methodology is presented to develop BengSwearLex that leverages an obscene corpus, word embedding, and part-of-speech (POS) taggers. The performance of BengSentiLex compared with the translated English lexicons in three evaluation datasets. BengSentiLex achieves 5%–50% improvement over the translated lexicons. For identifying profanity, BengSwearLex achieves documentlevel coverage of around 85% in an document-level in the evaluation dataset. The experimental results imply that BengSentiLex and BengSwearLex are effective resources for classifying sentiment and identifying profanity in Bengali social media content, respectively.

Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between—or specific shape on—individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, Spaghetti Tracer, to characterizing the structural arrangement of filaments in the challenging 3D maps of subcellular components. Assuming that the tomogram can be rotated such that the filaments are oriented in a mean direction, the proposed framework first identifies local seed points for candidate filament segments, which are then grown from the seeds using a dynamic programming algorithm. We validate various algorithmic variations of our framework on simulated tomograms that closely mimic the noise and appearance of experimental maps. As we know the ground truth in the simulated tomograms, the statistical analysis consisting of precision, recall, and F1 scores allows us to optimize the performance of this new approach. We find that a bipyramidal accumulation scheme for path density is superior to straight-line accumulation. In addition, the multiplication of forward and backward path densities provides for an efficient filter that lifts the filament density above the noise level. Resulting from our tests is a robust method that can be expected to perform well (F1 scores 0.86–0.95) under experimental noise conditions.

The auto-combustion method synthesized CuO NPs and Ag/CuO NPs. The Ag/CuO NPs were analyzed using Fourier-transform infrared, X-ray diffraction, scanning electron microscope, and Energy-dispersive X-ray spectroscopy instrumental analyses. The energy band gap, as determined by DRS properties, decreases from 3.82 to 3.50 eV for pure CuO and 10% Ag/CuO NPs, respectively. The photodegradation efficiency of Rhodamine-B & Carmine by 10% Ag/CuO NPs was nearly 98.9 and 97.8%, respectively. Antimicrobial trials revealed that the antimicrobial efficacy of Ag/CuO NPs at several dosages (20, 40, 60, 80, 100, and 120 µg/mL) against human pathogens was initially assessed using the agar well-diffusion method, and then the broth dilution method. Noticeably, the minimum inhibitory concentration of Ag/CuO NPs for all pathogens ranged from 100 to 120 µg/ml, was determined. Generally, the observed minimum microbicide concentration has a wide range of Ag/CuO NPs doses, ranging from 150 to 300 µg/ml, which helps kill (99.99%) all tested pathogenic cells. The largest relative inhibitory activities (%) were recorded against Escherichia coli (81.45 ± 1.39) at 120 g/mL of Ag/CuO NPs and 100 μg/mL (80.43 ± 0.59), followed by 80 µg/mL (72.33 ± 0.82). Additionally, the lowest relative inhibitory activities (%) were monitored versus fungal cells and Gram-positive bacteria at 120 µg/mL of Ag/CuO NPs as 52.17 ± 1.49 and 53.42 ± 1.71; respectively., (© 2024. The Author(s).)

The self-gelation utilizes natural vanillin as a primary component of vanilla bean extract, and as a crosslinking agent for entangling chitosan-PVA hydrogels. This involves a Schiff-base reaction, where amino group of chitosan (CH) interacts with aldehyde group of vanillin (Van). The optimized formula of formed hydrogels is chosen based on achieving a well-balanced combination of self-healing capability, mechanical strength, sustained release profile, and hydrophilic tendency. The prepared hydrogel is thoroughly characterized using SEM and FTIR analyses, swelling ratio, hydrolytic rate assessment, and in vitro drug release profiling. CH-PVA-Van hydrogels demonstrate controlled drug release that is sustained for over 7 days. Furthermore, antimicrobial tests indicate strong activity of CH-PVA-Van-l-arginine against Gram-positive bacteria, compared to tested yeast or Gram-negative bacteria using multiple human pathogens. Subsequently, in vitro biological assays are conducted to confirm the effectiveness of the prepared hydrogel in promoting wound healing and bone regeneration through cytotoxicity assay and wound scratch assay. The composite hydrogels achieved 95% wound healing after 24 hours, attributed to the release of NO from the loaded l-Arg and its essential role in the wound healing process. Consequently, CH-PVA-Van hydrogels emerge as a promising system for loading l-arginine and exhibiting potential for biomedical applications with antibacterial efficacy., Competing Interests: The authors declare that they have no competing interests., (This journal is © The Royal Society of Chemistry.)

Competing Interests: Disclosure The authors report no conflicts of interest in this work.

Rice is one of the crops cultivated in Malaysia, and it is the main diet for most of the population. In this study, dispersive liquid-liquid micro-extraction (DLLME) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were used to develop, optimise and validate a reliable, easy-to-use and quick approach to detect aflatoxin B1 (AFB1), aflatoxin B2 (AFB2), aflatoxin G1 (AFG1) and aflatoxin G2 (AFG2). The extraction recoveries in DLLME were enhanced by the addition of 5% salt, utilising chloroform as the extraction solvent and acetonitrile as the dispersive solvent. The DLLME parameters - the extraction solvent volume, salt concentration and dispersive solvent volume were optimised with Box-Behnken design (BBD) and response surface methodology (RSM). Under optimised experimental conditions, excellent linearity was obtained with a limit of detection (LOD) ranging from 0.125 to 0.25 ng g -1 , a limit of quantitation (LOQ) ranging from 0.25 to 0.3 ng g -1 and a correlation value ( R 2 ) of 0.990. The matrix effects were between -11.1% and 19.9%, and recoveries ranged from 87.4% to 117.3%. The optimised and validated method was used effectively to assess aflatoxins contamination in 20 commercial rice samples collected from local supermarkets in Penang, Malaysia. AFB1 was detected at 0.41-0.43 ng g -1 in two rice samples, below the regulatory limit.