Programmers Research Articles

Development of a pharmacy technician training program and advanced pharmacy technician roles: A health system's journey amidst an evolving national practice landscape.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Small-scale fisherfolk organizations: Do current conditions facilitate their effective contribution to the value chain?

Small-scale fishing organizations in Colombia face significant barriers in accessing fair markets for their fish products. This reality has led to the development of various support and promotion programs, which have not prospered as sustainable strategies. The aim of this work was to analyze the predominant strengths and weaknesses within fisherfolk organizations, assessing their capacity to be part of a more equitable value chain. The Diagnosis of the Small-scale Fisherfolk Organizations tool (DOPA, for its Spanish acronym) was developed, integrating five components and including 22 indicators. This tool assesses organizations considering five components: i) organizational capacity, ii) social capital and welfare, iii) risks, iv) fishing activity information, and v) commercialization. The obstacles identified within the context of small-scale fishing in a value chain are directly linked with their social fabric, the policy and legislation of development programs, and the lack of understanding of fishing activity as an economic sector. These barriers have impacts on the well-being of fisherfolk as well as on the management of fishing resources.

Fuzzy Logic Concepts, Developments and Implementation

Over the past few decades, the field of fuzzy logic has evolved significantly, leading to the development of diverse techniques and applications. Fuzzy logic has been successfully combined with other artificial intelligence techniques such as artificial neural networks, deep learning, robotics, and genetic algorithms, creating powerful tools for complex problem-solving applications. This article provides an informative description of some of the main concepts in the field of fuzzy logic. These include the types and roles of membership functions, fuzzy inference system (FIS), adaptive neuro-fuzzy inference system and fuzzy c-means clustering. The processes of fuzzification, defuzzification, implication, and determining fuzzy rules’ firing strengths are described. The article outlines some recent developments in the field of fuzzy logic, including its applications for decision support, industrial processes and control, data and telecommunication, and image and signal processing. Approaches to implementing fuzzy logic models are explained and, as an illustration, Matlab (version R2024b) is used to demonstrate implementation of a FIS. The prospects for future fuzzy logic developments are explored and example applications of hybrid fuzzy logic systems are provided. There remain extensive opportunities in further developing fuzzy logic-based techniques, including their further integration with various machine learning algorithms, and their adaptation into consumer products and industrial processes.

A Computer Vision Model for Seaweed Foreign Object Detection Using Deep Learning

Seaweed foreign object detection has become crucial for food consumption and industrial use. This process not only can prevent potential health issues, but also maintain the overall marketability of seaweed production in the food industry. Traditional methods of inspecting seaweed foreign objects heavily rely on human judgment, which deals with large volumes with diverse impurities and can be inconsistent and inefficient. An automation system for real-time seaweed foreign object detection in the inspection process should be adopted. However, automated seaweed foreign object detection has several challenges due to its dependency on visual input inspection, such as an uneven surface and undistinguishable impurities. In fact, limited access to advanced technologies and high-cost equipment would also influence visual input acquisition, thereby hindering the advancement of seaweed foreign object detection in this field. Therefore, we introduce a computer vision model utilizing a deep learning-based algorithm to detect seaweed impurities and classify the samples into ‘clean’ and ‘unclean’ categories. In this study, we managed to identify six types of seaweed impurities including sand sticks, shells, discolored seaweed, grass, worm shells, and mixed impurities. We collected 1204 images and our model’s performance was thoroughly evaluated based on comparisons with three pre-trained models, i.e., Yolov8, ResNet, and MobileNet. Our experiment shows that Yolov8 outperforms the other two models with an accuracy of 98.86%. This study also included the development of an Android application to validate the deep learning engine to ensure its optimal performance. Based on our experiments, the mobile application managed to classify 50 pieces of seaweed samples within 0.2 s each, showcasing its potential use in large-scale production lines and factories. This research demonstrates the impact of Artificial Intelligence on food safety by offering a scalable and efficient solution that can be deployed in other food production processes facing similar challenges. Our approach paves the way for broader industry adoption and advancements in automated foreign object detection systems by optimizing detection accuracy and speed.

Authentic Leadership-A Concept Analysis.

To report an analysis of the concept of authentic leadership. Concept analysis. Data sources included core databases: CINAHL, PubMed, Embase, ProQuest, APA PsycINFO, ERIC, ABI/Inform and Tomlinson's Nursing Core Collection (2010). Walker and Avant's method was used to identify descriptions, antecedents, consequences and empirical referents of the concept. Model, related and contrary cases were developed. Defining attributes were identified as self-awareness, transparency, balanced processing, internalised moral perspective, caring, shared decision making and moral/ethical courage. Antecedents were authenticity, positive psychological capacities, life experiences, leadership development programme, supportive organisational climate and an ethical climate. Consequences are employee well-being and satisfaction, positive work environment and enhanced performance and safety. While authentic leadership is an emerging and popular leadership approach, there are gaps in relation to agreed definitions. Moreover, core textbooks within the subject area also fail to provide these definitions. This concept analysis represents the first comprehensive and robust analysis of the concept of authentic leadership that will serve to reliably inform research, education and practice. This analysis reveals that authentic leadership can have a positive effect on staff well-being and satisfaction, their work environment, performance and ultimately safety. This has potential for a positive impact on patient care and patient outcomes by fostering an ethical and supportive work environment. This study revealed a new comprehensive understanding of authentic leadership that provides conceptual clarity that will guide research and practice in the field, which refines the existing understanding, highlighting essential attributes and their relevance in nursing practice. No patient or public contribution.

Modern Classification of Respiratory Diseases: Innovations in the International Classification of Diseases of the 11th Revision

A comprehensive analysis has been performed on the innovations in the classification of respiratory diseases introduced in ICD-11, comparing it with the previous ICD-10 version. This analysis underscores the critical importance of implementing a more detailed and systematic approach to categorizing diseases of the respiratory system. The enhancements found in ICD-11 have been proven to open up new opportunities for the timely and high-quality provision of medical and pharmaceutical care. This is particularly significant for patients suffering from various injuries, including those resulting from traffic accidents, participation in combat operations, and thoracic injuries. The updated classification system in ICD-11 allows for more precise diagnosis and treatment plans, which is essential for improving patient outcomes. By providing a more granular level of detail in disease categorization, healthcare professionals can better understand the nuances of respiratory conditions. This leads to more effective communication among doctors, pharmacists, and patients, thereby improving the legal and professional relationships within the healthcare system. Integrating the ICD-11 classification into educational and training programs is essential for advancing the qualifications of specialists. By incorporating it into curricula and professional development programs, medical practitioners can stay abreast of the latest advancements in disease classification and patient care. This integration is also vital for eliminating medical errors, as a more detailed classification system reduces the likelihood of misdiagnosis and improper treatment. Furthermore, adopting ICD-11 supports the ongoing development of the healthcare sector in Ukraine. It aligns the country's medical practices with international standards, facilitating better collaboration with global health organizations. This alignment is crucial for enhancing the overall quality of healthcare services provided to the population. In conclusion, the transition to ICD-11 represents a significant advancement in the classification of respiratory diseases. Its implementation is imperative for improving medical and pharmaceutical care, enhancing specialist training programs, and strengthening the doctor-pharmacist-patient relationship. Embracing this updated classification system will not only reduce medical errors but also contribute to the further development of Ukraine's healthcare sector.

A Novel Data Analytics Methodology for Discovering Behavioral Risk Profiles: The Case of Diners During a Pandemic

Understanding tourist profiles and behaviors during health pandemics is key to better preparedness for unforeseen future outbreaks, particularly for tourism and hospitality businesses. This study develops and applies a novel data analytics methodology to gain insights into the health risk reduction behavior of restaurant diners/patrons during their dining out experiences in a pandemic. The methodology builds on data relating to four constructs (question categories) and measurements (questions and attributes), with the constructs being worry, health risk prevention behavior, health risk reduction behavior, and demographic characteristics. As a unique contribution, the methodology generates a behavioral typology by identifying risk profiles, which are expressed as one- and two-level decision rules. For example, the results highlighted the significance of restaurants’ adherence to cautionary measures and diners’ perception of seclusion. These and other factors enable a multifaceted analysis, typology, and understanding of diners’ risk profiles, offering valuable guidance for developing managerial strategies and skill development programs to promote safer dining experiences during pandemics. Besides yielding novel types of insights through rules, another practical contribution of the research is the development of a public web-based analytics dashboard for interactive insight discovery and decision support.

Case Studies on the use of Microsampling for Nonclinical Studies in Pharmaceutical Drug Discovery and Development.

The implementation of microsampling approaches for use in nonclinical discovery and development pharmaceutical studies can have a major impact on improving animal ethics through the use of fewer animals and less invasive procedures for the collection of toxicokinetic and pharmacokinetic samples. In addition, the approach offers the opportunity for obtaining improved quality of data for these studies. This can include the determination of additional timepoints and endpoints, and the ability to obtain exposure data from the same animals utilized to measure other study endpoints. This manuscript presents a number of cases where a variety of microsampling approaches have been successfully implemented by a number of organizations and serves as a guide for those considering the use of microsampling approaches as part of their drug discovery and development programs, as the adoption of these approaches are not yet universal.

The Impact of Ecological Footprint, Urbanization, Education, Health Expenditure, and Industrialization on Child Mortality: Insights for Environment and Public Health in Eastern Europe.

The purpose of this study is to examine the connection between child mortality in Eastern Europe and ecological footprint, urbanization, education, health expenditure, and industrialization. The study acknowledges the significance of understanding how these factors influence the infant mortality rates in this region from 1993 to 2022. The Grossman Health Outcome (H-O) model investigates the theoretical framework. For the existence of the cross-sectional dependency, mixed-order unit root, and cointegration problem, the famous Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) approach is applied. The research also used the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) to check robustness. The findings illustrated that health expenditure and education lessen the infant mortality rate in Eastern European countries. But ecological footprint, industrialization and unemployment raise the infant mortality rate. According to the CS-ARDL findings, expenditure on healthcare significantly reduces child mortality. Still, the ecological footprint significantly impacts increasing child mortality. However, the AMG and CCEMG models demonstrate that investing in education is the most effective strategy for reducing child mortality. Therefore, the government of Eastern European countries should provide more priorities in the sustainable urbanization, health expenditure, and education sectors. The robustness of the AMG and CCEMG also demonstrated the strength of the CS-ARDL findings. This research paper contributes to SDG 3 by examining the environmental and health factors that influence child mortality in Eastern Europe. Policymakers, public health professionals, and other stakeholders can use the findings to inform the development and implementation of programs that specifically target the identified causes of child mortality.

Improvements in the randomness and security of digital currency using the photon sponge hash function through Maiorana–McFarland S-box replacement

Abstract There is a demand for a digital currency that facilitates remote trading over the Internet and strives to reduce external control, ensuring that transactions are conducted only between authorized persons or parties involved in the transfer. The financial sector has been significantly impacted by cryptocurrency, or digital currency, which brings new potential and challenges. The concept of digital currency is thoroughly examined, as are its complicated implications on various aspects of the economy. Trading digital currencies via the Internet may be vulnerable to theft and forgery due to the development of hacker programs. Therefore, we proposed to design a new digital currency and then built a 16 × 16 structure and filled the matrix with random numbers within GF(251). We used the random algorithm (Chun–Hui He’s iteration), then generated four directions, and used polynomial equations for the purpose of distributing powers between the parties in our future complete system. The original matrix was encoded with the photon sponge hash function after updating the S box by integrating the algorithms (Chun–Hui He’s iteration, Mariorana–McFarland method), and the results were good security measures (correlation coefficient, bijective property, balanced criteria, completeness criteria, and strict avalanche criteria) as well as the encryption and decryption time became faster (0.0005202). The major objective is to design a new digital currency system toward achieving security, scalability, and comprehensive adoption, looking into how it might improve security, promote financial inclusion, and change the present payment systems.

Substance use recovery: Global health perspectives and experiences

Addictive behavior treatment networks comprise harm-reduction services and recovery programs. Recovery is an approach toward interventions for addictive behavior that focuses on the individual’s empowerment, abilities, and life skills. Rehabilitation-oriented programs have evolved from traditional therapeutic communities to comprehensive services that are integrated into social and health networks and delivered by multidisciplinary professionals. This paper aims to analyze the development of recovery programs, drawing from quantitative and qualitative data from an action research approach. It also presents theoretical models and best practices for recovery. In summary, recovery-oriented programs must be integrated with harm-reduction networks, social and employment services, and health systems. Furthermore, as recovery is based on empowerment and peer social support, there is an urgent need to develop structured programs to address these issues. Moreover, there is a need to design and implement specific interventions for a broad range of populations and develop evaluation systems to assess the effectiveness and relevance of recovery-oriented programs.

Ramachandran-like Conformational Space for DNA.

DNA's ability to exist in a wide variety of structural forms, subforms, and secondary motifs is fundamental to numerous biological processes and has driven the development of biotechnological applications. Major determinants of DNA flexibility are the multiple torsional degrees of freedom around the phosphodiester backbone. This high complexity can be rationalized by using two pseudotorsional angles linking atoms P and C4', from which Ramachandran-like plots can be built. In this contribution, we explore the distribution of η (eta: C4'i-1-Pi-C4'i-Pi+1) and θ (theta: Pi-C4'i-Pi+1-C4'i+1) angles in known experimental structures retrieved from the Protein Data Bank (PDB), subdividing the conformational space into different datasets. After the removal of the canonical/helical conformations typical of the B-form, we find the existence of a conformational map with clearly permitted and forbidden regions. Some of these regions are populated with specific DNA forms, like Z- or A-DNA, or by specific secondary motifs, like G-quadruplexes and junctions. We evaluated the sequence dependency and energy relationship among the high-density regions identified in the η-θ space. Furthermore, we analyzed the effect produced by proteins and cations when bound to DNA, finding that specific proteins produce some nonhelical conformations, while other regions appear to be stabilized by divalent cations.

Influence of Carbon-Nitride Dot-Emitting Species and Evolution on Fluorescence-Based Sensing and Differentiation.

Carbon dots have attracted widespread interest for sensing applications based on their low cost, ease of synthesis, and robust optical properties. We investigate structure-function evolution on multiemitter fluorescence patterns for model carbon-nitride dots (CNDs) and their implications on trace-level sensing. Hydrothermally synthesized CNDs with different reaction times were used to determine how specific functionalities and their corresponding fluorescence signatures respond upon the addition of trace-level analytes. Archetype explosives molecules were chosen as a testbed due to similarities in substituent groups or inductive properties (i.e., electron withdrawing), and solution-based assays were performed using ratiometric fluorescence excitation-emission mapping (EEM). Analyte-specific quenching and enhancement responses were observed in EEM landscapes that varied with the CND reaction time. We then used self-organizing map models to examine EEM feature clustering with specific analytes. The results reveal that interactions between carbon-nitride frameworks and molecular-like species dictate response characteristics that may be harnessed to tailor sensor development for specific applications.

Exploring essential features for developing a human resource management mobile application: Insights from the middle east

This study aims to investigate the key features necessary for the development of a mobile application for human resource management (HRM). It addresses the shortage of empirical studies on digital HRM in the Middle East and emphasizes the increasing importance of HRM mobile applications in the region. The research utilizes qualitative research methods, including the Delphi method, in-depth interviews, literature review, historical analysis, and critical discussion.A qualitative research strategy is employed, analyzing data from 43 HR experts and mobile app development team members from international and local businesses in the UAE and the Kingdom of Bahrain. NVivo software is used for data analysis, encompassing over 120 transcriptions, notes, and academic papers. The study identifies and explores the logical and practical features required for an HRM mobile app. It uncovers challenges related to the Delphi survey experiences, such as managing heterogeneous information and ensuring compatibility with iOS and Android platforms. This research fills a gap in the literature by investigating the digital HRM landscape and HRM mobile applications in the Middle East. It provides valuable insights from HR and technology experts, contributing to the advancement of digital HRM, mobile applications, and the application of the Delphi method methodology. The study highlights implications for industries to enhance HR operations and productivity through the adoption of mobile HR applications. Theoretical contributions include new insights into the digital HRM literature, specifically regarding the identification and discussion of essential features in HRM mobile applications. Practical implications involve the benefits of transitioning from traditional HRM to digitalized HRM in the professional era, as identified through the analyzed features of the HR app. The study offers practical insights for the development and implementation of mobile HR apps. Organizations can leverage these insights to create effective mobile HR apps that enhance HR operations and employee productivity.

Tailored controllable bifunctional fluorescent-magnetic nanobeads for new generation fast-response immunosensor with dual-signal amplification

Immunosensors based on the fluorescence-linked immunosorbent assay (FLISA) platform are now widely used; however, the long-term response and lower sensitivity limited their development in clinical applications. In this study, we first tailored bifunctional magnetic nanoparticle (MNP)-quantum dot (QD) nanobeads (MQBs) with rapid magnetic response and strong fluorescence intensity for the FLISA platform to construct a fast-response immunosensor. Under optimal conditions, we constructed a dual-signal amplification immunosensor based on an MQB-driven FLISA platform for a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antigen. We achieved a 29-fold increase in fluorescence intensity over the QD nanobeads (SiO2/QD/SiO2, QBs). Compared with the conventional FLISA platform-based QBs, the sensitivity of the developed immunosensor increased 63-fold to 7.50 pg/mL in half the detection time of common FLISA (only 30 min). More important, the test results of this dual-signal amplification immunosensor showed good specificity and accuracy. This novel MQB magnetic-driven FLISA platform with high throughput, as a proof of concept, effectively improved sensitivity and shortened reaction time. As a result, it is expected to have significant potential in the field of point-of-care testing.

Viability Study of SYCL as a Unified Programming Model for Heterogeneous Systems Based on GPUs in Bioinformatics

Bioinformatics and Computational Biology are disciplines that have used GPUs for over two decades to accelerate data processing in computational applications, where CUDA has been the most utilized programming language. However, the exclusive use of CUDA presents a portability issue, as it is only compatible with NVIDIA GPUs and not with other heterogeneous architectures, such as AMD or Intel GPUs, or any other type of accelerator. To address this portability challenge, the Khronos Group recently introduced the SYCL standard, a multi-platform programming model that offers a high-level programming interface. This standard facilitates the development of portable applications that can efficiently leverage the capabilities of different hardware devices, such as NVIDIA, Intel, and AMD GPUs, without needing significant modifications to the source code. Therefore, this thesis set out as its general objective to evaluate the feasibility of SYCL as a unified, portable, and efficient heterogeneous programming model for the design and development of computationally demanding applications on heterogeneous GPU-based systems, specifically in the field of bioinformatics. Initially, a detailed investigation was conducted about heterogeneous programming models, performance metrics, and bioinformatics concepts, in order to establish the theoretical foundations of this thesis. Then, the SW# suite was chosen as the case study, as it represents a clear example of a CUDA-based bioinformatics application for biological sequence alignment. Using the SYCLomatic tool, a complete migration of the CUDA code to SYCL was carried out, which involved modifying the generated code and solving runtime errors. Additionally, the functionality was verified, optimizations were applied, and the resulting SYCL code was standardized to be compatible with other SYCL implementations. Subsequently, multiple experiments were conducted to evaluate the functionality and performance portability of the software migrated to SYCL. These experiments involved running the application on a wide variety of HPC platforms, including different CPUs and GPUs from various manufacturers. The results obtained demonstrated performance comparable to CUDA in most configurations, confirming the effectiveness of SYCL. Moreover, good performance portability across platforms was observed due to SYCL's ability to run on various hardware combinations. Furthermore, performance remained consistent when switching SYCL implementations. In conclusion, this study demonstrates that SYCL is a viable alternative as a unified, portable, and efficient programming model in the context of heterogeneous computing with GPUs for bioinformatics applications. The findings of this study lay the groundwork for the transition of legacy applications and the development of new solutions leveraging the capabilities of SYCL.

Ethical Concerns Upon Artificial Intelligence Empowered Human Resource Management: A Qualitative Study among Middle-level Managers from Beijing Technology Companies

The evolution of artificial intelligence (AI) in organizational management has significantly enhanced operational efficiency. However, it has introduced ethical challenges in human resource management. Between January to March 2024, this study conducted 21 semi-structured interviews with middle-level managers from high-tech companies in Beijing. Through word frequency analysis, the study found that key topics among the managers were “company,” “data,” “system,” and “problem,” with “AI” frequently recurring in the discussions. Sentiment analysis revealed generally favorable attitudes toward AI in human resource management (AI-HRM), along with nuanced emotional expressions such as inquiry, introspection, recommendation, challenge, adaptation, resistance, and indifference. The sentiment distribution of keywords aligned with topic trends. Thematic analysis identified key ethical concerns in AI-HRM, including issues related to data collection and utilization, human versus machine decision-making, quantitative versus qualitative assessment methodologies, the balance between fairness and efficiency, the need for trustworthy, explainable, and transparent AI, and the oversight of AI-HRM. This study contributes to the ethical investigation of AI-HRM from the perspective of middle-level managers, highlighting themes that are critical for understanding the theory, application, and future development of AI-HRM.

Fusing remote and social sensing data for flood impact mapping

AbstractThe absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end‐to‐end system, that ingests data from multiple nontraditional data sources such as remote sensing, social sensing, and geospatial data. We employ state‐of‐the‐art natural language processing and computer vision models to identify flood exposure, ground‐level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real‐world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through various statistical analyses using official ground‐truth data, showcasing its strong performance and explanatory power of integrating multiple data sources. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard‐hit districts and enhancing disaster response.

Early career academics’ professional learning in a PBL-focused pedagogical development program

ABSTRACT This study explores early career academics’ (ECAs) professional learning in a Danish university’s Professional Learning (PL) program, based on problem- and project-based learning (PBL) principles. Using a complexity theory lens, professional learning is viewed as a dynamic process involving intrapersonal, behavioral, relational, and contextual sources. Data from 38 written reflections via a questionnaire and six in-depth interviews were analyzed to assess ECAs’ learning outcomes and challenges during and after the PL program. The findings indicate that the program advanced participants’ PBL and pedagogical knowledge, changed their pedagogical beliefs and teaching practices, and improved their collaborative skills and interactions with students and colleagues. Several challenges ECAs confronted are reported at individual and organizational levels, namely, time management, the learning process, the actual teaching and supervision process, institutional culture, and resource allocation. The study suggests ways for ECAs, program designers and organizers, and university management in promoting more effective professional development processes.

Leadership and psychological capital in enhancing the performance of child-friendly schools for chil-dren’s physical activities

Leadership styles and psychological capital play crucial roles in enhancing school performance. Integrating student leadership opportunities and physical activities into child-friendly school programs may improve overall school performance through various psychological factors. The research aims to know the effect of leadership and psychological capital on enhancing the performance of child-friendly schools for children’s physical activities. This research is descriptive quantitative, with the number of participants being 175 teachers selected using the purposive sampling technique. Data collection techniques use survey methods, while data collection instruments use questionnaires developed from several previous studies that are considered to have been tested for validity. Data analysis uses SEM-PLS technique with the SmartPLS 4 analysis tool to test the predictive relationship between each construct. The results showed that transformational leadership significantly influences the performance of Child-Friendly Schools, with a p-value of 0.006 <0.05. Psychological capital significantly influences performance with a p-value of 0.039<0.05. Student leadership does not moderate the relationship between psychological capital on performance with a value of 0.199>0.05, transformational leadership on performance with a value of 0.385>0.05, and workplace spirituality on performance with a value of 0.848>0.05. The results of this study provide a deeper understanding of the factors that influence the performance of Child-Friendly Schools, as well as practical implications for leadership development, human resource management, and educational staff development programs within Child-Friendly Schools.