Practical Guidelines For Implementation Research Articles

Building an AI Portfolio: A Technical Guide for Modern Logistics Specialists

This technical article provides a comprehensive framework for logistics specialists to build an effective AI portfolio in modern supply chain management. As the industry processes massive volumes of data annually, the integration of artificial intelligence has revolutionized logistics operations. The article addresses critical aspects, including demand forecasting, route optimization, inventory management, and security implementations. With AI implementations demonstrating significant performance improvements across cycle time reduction, transportation expense optimization, and inventory accuracy enhancement, the need for structured portfolio development has become increasingly important. The article outlines detailed technical requirements, implementation frameworks, and best practices for developing AI solutions in logistics, supported by real-world performance metrics and industry standards. Special attention is given to security considerations, scalability architecture, and professional development strategies, providing logistics professionals with a holistic approach to AI integration. The comprehensive coverage encompasses technical depth and practical implementation guidelines, making it a valuable resource for professionals seeking to advance their careers in AI-driven logistics operations.

In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays

Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments.

The Impact of Economic Policy Changes on International Trade and Relations

Economic policies continue to remain central in determining relations among nation states in the global economy that is progressively characterised by integrated globalisation. The newer changes in the course of economics; or due to the geopolitical conflicts, advancement in the field of technology, the impact of COVID-19, commerce worldwide has experience newer changes. A review of these policies is presented in this paper with a view of assessing their direct and indirect effect on international trade. Analyzing the experience of various countries, including both developed ones and the countries in the process of development, this paper aims to reveal how nations responded to these changes, as well as what strategies can work, and what difficulties can be encountered. Besides, the paper contains a prognosis of future developments of the international economy, having several scenarios and guidelines for practical implementation. These disconnects highlight the relevance of the critical philosophy in economic policy formulation since the globe economic policy should balance internal economic stable and international trade cooperation.

Blood Flow Restriction Exercise as a Novel Conservative Standard in Patients with Knee Osteoarthritis—A Narrative Review

Knee osteoarthritis is a prevalent joint disease affecting millions of individuals globally. While total knee arthroplasty is an effective treatment for advanced stages of KOA, it may not be suitable for earlier stages or younger patients. Supervised exercise therapy has proven to be the first-line treatment of preference in tackling pain and disability caused by KOA. However, the high intensities required to induce positive muscle adaptations are not indicated in this population, as this is often accompanied by pain, discomfort, and frustration, leaving low-load resistance training as the only feasible method of treatment. Recently, the use of blood flow restriction training has begun to emerge as a substitute for high-load resistance training. With BFRT, a cuff is applied around the proximal aspect of the affected limb, causing partial arterial and full venous occlusion, thereby inducing localized hypoxia and the accumulation of metabolites, mimicking the effects of high-load resistance training, albeit with low loads. Consequently, BFRT might offer a suitable and more effective alternative for KOA patients who are not (yet) eligible for TKA compared to traditional exercise therapy. This review aims to summarize the current evidence as regards the application of Blood Flow Restriction in exercise therapy for knee osteoarthritis patients, with particular consideration of the underlying mechanisms and its safety, as well as general guidelines for practical implementation in clinical practice. In doing so, this narrative review aims to create a framework for translating from theory into practice.

MODEL MONITORING WITH GRAFANA AND DYNATRACE: A COMPREHENSIVE FRAMEWORK FOR ENSURING ML MODEL PERFORMANCE

In the rapidly evolving landscape of Machine Learning (ML), ensuring the continued efficacy and reliability of deployed models is paramount. This paper introduces a robust framework for model monitoring, highlighting the capabilities of Grafana and Dynatrace. Grafana provides powerful visualization and alerting capabilities, while Dynatrace offers deep insights into system and application performance.By integrating these tools, organization can establish a holistic monitoring solution that tracks key performance indicators (KPIs), detects anomalies, and triggers alerts in real-time. This framework enables proactive management of model drift, data quality issues and resource utilization, thereby bolstering the trustworthiness of ML applications in production.Case studies and practical implementation guidelines illustrate the effectiveness of this approach across various industry domains. The proposed methodology represents a pivotal advancement in the field of ML model operations, facilitating enhanced decision-making, reduced downtime and heightened user satisfaction.

Security Considerations in iOS App Development: Encryption, Authentication, and Secure Data Storage

Abstract: Security is a paramount concern in iOS app development, as sensitive user data must be protected from unauthorized access and breaches. This scholarly article explores essential security measures, focusing on encryption protocols, authentication mechanisms, and secure data storage techniques specific to iOS platforms. By examining industry-standard encryption algorithms, secure authentication methods, and best practices for data storage, this study aims to provide developers with the knowledge and tools necessary to build robust and resilient iOS applications. Real-world case studies and practical implementation guidelines are presented to illustrate the effective application of these security measures. The findings emphasize the importance of adopting a comprehensive security approach to foster user trust and confidence in mobile applications

Integration of Acceptability Analyses into an Adaptive Landscape Co-Design and Management Approach—The Acceptability and Landscape Design Cycle (ALDC)

Acceptability analyses of place–based innovations provide crucial in-depth knowledge (e.g., perceptions and values on landscapes) for the social–ecological transformation of landscapes. However, previous acceptability analyses often neglected complex and ongoing processes. We argue that, for the design of a sustainability-oriented transformation and to address spatial and temporal dynamics in landscapes, an operational heuristic is needed; one that integrates acceptability analyses into an adaptive landscape co-design and management approach. Therefore, this conceptual–empirical paper introduces the concept of the ‘acceptability and landscape design cycle’ (ALDC), which is based on findings from various transdisciplinary innovation processes in the Spreewald region (Germany). It is composed of four iterative phases: (1) defining the preconditions for acceptability analysis, (2) conducting the acceptability analysis, (3) integrating the results into the landscape development strategy, and (4) re-designing and refining it. We illustrate the application of these phases using a case study of the cultural landscape in Spreewald. The paper provides practical implementation guidelines of the ALDC and contributes to a better understanding of the dynamics of acceptability decisions regarding the transformation processes of landscapes. Furthermore, it can advance the understanding of how co-evolution of socio-ecological systems occurs.

Parallel heterogeneous data‐fusion convolutional neural networks for improved rail bridge strike detection

AbstractLow clearance rail bridges provide vital crossings for freight and passenger trains and are susceptible to frequent strikes from overheight vehicles or equipment. Impact detection systems can help ensure the safety of railroad bridges and their users; such systems streamline monitoring efforts by providing near real‐time strike notifications to rail managers responsible for assessing a bridge after a strike. This paper develops parallel heterogeneous data‐fusion convolutional neural networks (PHD‐CNN) operating on data collected from in‐service rail bridges that improves detection and classification of from overheight vehicles. Convolutional neural networks (CNNs) automatically extract features from multiple data streams from different sensor modalities. The method provides a mechanism to homogenize and fuse disparate data streams for use as inputs to a classifier that distinguishes bridge strikes from passing trains. The study also provides practical implementation guidelines through a framework sensitivity characterization to examine the effects on performance of input data stream type, data set size, and CNN architecture complexity. Optimum networks detect, on average, 95% of bridge strikes with false positive rates less than 2%.

Review on Panic Buying Behavior during Pandemics: Influencing Factors, Stockpiling, and Intervention Strategies.

Panic buying poses significant challenges for individuals and societies. This paper provides a literature review on the process by which a pandemic crisis evolves into panic buying behavior. The review offers a comprehensive perspective on studies related to panic buying and mitigation efforts, categorizing them based on their contributions in three stages: factors influencing panic buying, the process of transforming panic into increased demand and stockpiling, and applicable intervention strategies to mitigate panic situations. The paper introduces the Socio-Economic Framework of Panic (SEFP) to illustrate the interaction between demand and supply during a panic. The review identifies a lack of quantitative models explicitly correlating influencing factors with panic and estimating panic demand. Additionally, it reveals that suggested intervention strategies often lack practical implementation guidelines. Using the SEFP, the importance of considering interventions at various stages is highlighted, ranging from controlling influencing factors and panic demands to overseeing stockpiling and supply-related activities. The paper also identifies research gaps in both qualitative and quantitative modeling, policymaking, and governance.

A Capture-Recapture-based Ascertainment Probability Weighting Method for Effect Estimation With Under-ascertained Outcomes.

Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a nonparametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment. Demonstrating its practical application, we apply the method to estimate the relationship between health care work and coronavirus disease 2019 testing in a Swedish region. We find that ascertainment probability weighting greatly influences the estimated association compared to conventional inverse probability weighting, underscoring the importance of accounting for under-ascertainment in studies with limited outcome data coverage. We conclude with practical guidelines for the method's implementation, discussing its strengths, limitations, and suitable scenarios for application.

Empowering complex-valued data classification with the variational quantum classifier

Empowering complex-valued data classification with the variational quantum classifier

Users' Emotional Responses and Behavioural Intentions to Augmented Reality Ad Content Designs

This article specifically examines the emotional implications of using augmented reality (AR) technology in advertising. It primarily focuses on how users' emotional reactions and subsequent actions are influenced by the design of AR advertisements. The text examines the multifaceted impact of emotion in commercial communication, encompassing diverse areas such as political advertising and online advertising that employs hilarious storytelling. The study subsequently examines the impact of emotional components of augmented reality (AR) advertisements on users, emphasizing the crucial roles played by immersive and interactive experiences, creative design, user engagement, and other aspects in guiding emotions and shaping behavioral intentions. Ultimately, the utilization of augmented reality (AR) technology is showcased in the domains of electronic commerce, internet-based food delivery, and educational gaming. In order to gain a comprehensive understanding of emotion in AR advertising, it is necessary to conduct thorough study on the particular application and implementation methods. Additionally, it is important to examine how technological, cultural, and societal factors impact user responses. This paper seeks to analyze and combine existing studies in order to uncover the potential of emotional applications in augmented reality (AR) marketing. Additionally, it attempts to offer guidelines for future research and practical implementation.

Green synthesis of CuO/Fe2O3/ZnO ternary composite photocatalyst using grape extract for enhanced photodegradation of environmental organic pollutant

This research delves into fabricating a CuO/Fe2O3/ZnO (CFZ) ternary composite photocatalyst, employing grape extract for its eco-conscious synthesis. The method intricately integrates copper acetate, ferric nitrate, and zinc acetate as precursor compounds, harmonizing them with grape extract serving as a green reducing agent. Meticulous microwave treatment and controlled calcination orchestrate the nuanced formation of the desired composite material. The extensive characterization, involving X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), energy-dispersive X-ray spectroscopy (EDXS), scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) surface area analysis, ultraviolet–visible diffuse reflectance spectroscopy (UV-DRS), and photoluminescence (PL) spectroscopy, unveils an array of favorable physical, chemical, and optical attributes conducive to proficient photocatalysis. Notably, CFZ–10mc showcases a narrower bandgap of 1.91 eV, which is pivotal for bolstering electron–hole separation, thereby enhancing its photocatalytic efficacy. Assessment of CFZ's performance in degrading Rhodamine B (RhB) under UV irradiation highlights an impressive 88.8% degradation efficiency within 120 min, accompanied by a kinetic rate constant of 1.81 × 10−2 min−1. Deliberation upon crucial parameters, including photocatalyst dosage, initial RhB pH, and reactor energy consumption, introduces the electrical energy per order (EEO) as a notable efficiency metric. CFZ manifests a substantial reduction in operational costs, estimated to be 18.10 times lower than conventional photolysis, signifying an EEO value of 509.17 kWh m−3 order−1. Optimal operational conditions propose a photocatalyst content of 1.5 g L−1 and an initial RhB pH of 7, fostering the prevalence of the primary active species, •OH. These findings illuminate CFZ's potential in mitigating organic pollutants, underlining its pivotal role in sustainable water remediation. Additionally, practical implementation guidelines for leveraging CFZ's capabilities in real-world applications are presented with care and consideration.

Exploring the Potential of AI and Machine Learning in Predictive Maintenance of Electrical Systems

This study investigates the transformative potential of integrating information systems in electrical technology, focusing on efficiency enhancement, improved monitoring, and heightened control. Through a mixed-methods approach encompassing surveys, interviews, observations, and case studies, the research reveals significant efficiency gains and real-time monitoring improvements. The study also addresses challenges such as data security and offers practical implementation guidelines, contributing valuable insights for stakeholders aiming to harness the benefits of integrated information systems in the realm of electrical technology

Ultrasonic array imaging of porosity defects with contrast enhancement based on dominant response subtraction

Porosity defects can be found in many engineering structures and their inspection remains a challenge in the field of ultrasonic non-destructive testing. In this paper, ultrasonic array imaging of porosity defects has been studied with the aim of improving the image quality in the “dead zone”, which is caused by the masking effects of the uppermost pores. The proposed approach first extracts contributions of the uppermost pores based on a single scattering model by adopting convolutional sparse coding. The extracted dominant contributions are then subtracted from the array data before forming an image, facilitating detection and localization of pores in the shadow zone. The performance of the proposed approach has been studied in simulation and experiments, and the mean localization errors of the pores are small (i.e., within 0.27 mm or 0.14λ). In addition, the effects of measurement noise and imaging parameters on robustness of the imaging result have been analyzed, providing guidelines for practical implementation of the proposed approach.

Corporate Social Responsibility in Sustainable Tourism Development towards Local Culture

As the global tourism industry expands, the delicate equilibrium between economic growth and the preservation of local cultures faces increasing challenges. This research explores the integration of Corporate Social Responsibility (CSR) principles into sustainable tourism development as a means to safeguard and promote local cultures. The study synthesizes concepts from cultural preservation, sustainable tourism, and CSR to construct a comprehensive framework. Findings highlight that CSR can catalyze cultural preservation by promoting authenticity, empowering local communities, and fostering responsible tourism practices. Challenges such as cultural commodification and the need for ongoing cultural sensitivity education are acknowledged. The research emphasizes the necessity of balancing economic objectives with cultural preservation and underscores the potential for transformative shifts in industry paradigms. Practical guidelines for implementation, the role of education, and collaboration among stakeholders are discussed. The study contributes to an enhanced understanding of how CSR can harmonize economic interests and cultural preservation, offering a blueprint for a more ethical, culturally respectful, and sustainable future for the tourism industry

The Utilization of the Ketogenic Diet for Adults with Status Epilepticus (P8-7.003)

<h3>Objective:</h3> To characterize the utilization of ketogenic diet (KD) among healthcare providers caring for adult patients with status epilepticus (SE). <h3>Background:</h3> KD is a high fat, low carbohydrate diet that has been shown to effectively treat refractory seizures, both in the outpatient and inpatient settings. The successful implementation of KD involves a multifaceted, interdisciplinary approach with many potential barriers. <h3>Design/Methods:</h3> 27-question web-based survey (Qualtrics®) targeting healthcare providers who manage adult SE patients in both academic and community-based settings distributed by various neurology and critical care specialties societies. <h3>Results:</h3> Of 156 respondents, 80% of physicians and 18% of non-physicians reported using KD for SE. Achieving ketosis (36.3%), lack of expertise (24.2%), and lack of resources (20.9%) were identified as some of the most important barriers limiting the utilization of KD. Absence of nutrition support (37.1%) and of pharmacy support (25.7%) were the most important missing resources. Reasons for stopping KD were identified as ineffectiveness (29.1%), difficulty achieving ketosis (24.6%) and side-effects (17.3%). The discrepancies are evident when comparing providers in the academic versus non-academic settings. The former has significantly more experience with the use of KD and EEG monitoring availability, and fewer barriers to its implementation. The need for more randomized clinical trials supporting efficacy (36.5%) and increased practical guidelines for implementation and maintenance of KD (29.6%) were mostly cited as factors to increase the utilization of KD for SE. <h3>Conclusions:</h3> We identified multiple target areas to improve the utilization of KD through future research. These include better interdisciplinary collaboration, studies exploring factors affecting the achievement of ketosis and the management of side effects, and large-scale clinical trials examining the efficacy of this treatment. KD represents an underexplored treatment modality which may prove crucial in improving management of SE. <b>Disclosure:</b> Dr. Jacinto Pereira Teixeira has nothing to disclose. Ms. Shannon has nothing to disclose. Dr. Robinson has nothing to disclose. Bakhtawar Ahmad has nothing to disclose. Mr. Katz has nothing to disclose. Guanhong Miao has nothing to disclose. Ms. Seckar has nothing to disclose. Dr. Bruzzone has nothing to disclose. Dr. Busl has received personal compensation in the range of $10,000-$49,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Society of Critical Care Medicine. The institution of Dr. Busl has received research support from University of Florida Self Insurance Program. The institution of Dr. Busl has received research support from National Institutes of Health. Dr. Busl has a non-compensated relationship as a Board Member with Art in Medicine that is relevant to AAN interests or activities. Dr. Busl has a non-compensated relationship as a Associate Editor with Critical Care Explorations that is relevant to AAN interests or activities. Dr. Busl has a non-compensated relationship as a Editorial Board Member with Neurocritical Care that is relevant to AAN interests or activities. Dr. Cervenka has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Nutricia. Dr. Cervenka has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Nestle Health Science/Vitaflo. The institution of Dr. Cervenka has received research support from Nestle Health Science/Vitaflo. Dr. Cervenka has received publishing royalties from a publication relating to health care. Dr. Maciel has received research support from American Heart Association. Dr. Maciel has received research support from National Institute of Health.

Establishing an effective partnership network in the sports paradigm

The paper offers a theoretical overview based on strategic approaches to the establishment of an effective partnership network to help the adaptability of sports clubs to modern competitive and everchanging conditions. Methodologically, the article is conceptual, based on contemporary data and multi-perspective development directions. Initially, the article indicates the importance of establishing affiliate networks in business. Subsequently, presents strategies for creating long-term relationships with diverse organizations and guidelines for practical implementation. Sports organizations should analyse their resources and potential future partners and choose the most appropriate practices to connect with them. Thus, they would strengthen their effectiveness of strategic club cooperation.

Design Space Exploration of Practical VVC Encoding for Emerging Media Applications

Versatile Video Coding (VVC/H.266) is the latest video coding standard designed for a broad range of next-generation media applications. This paper explores the design space of practical VVC encoding by profiling the Fraunhofer Versatile Video Encoder (VVenC). All experiments were conducted over five 2160p video sequences and their downsampled versions under the random access (RA) condition. The exploration was performed by analyzing the rate-distortion-complexity (RDC) of the VVC block structure and coding tools. First, VVenC was profiled to provide a breakdown of coding block distribution and coding tool utilization in it. Then, the usefulness of each VVC coding tool was analyzed for its individual impact on overall RDC performance. Finally, our findings were elevated to practical implementation guidelines: the highest coding gains come with the multi type tree (MTT) structure, adaptive loop filter (ALF), cross component linear model (CCLM), and bi-directional optical flow (BDOF) coding tools, whereas multi transform selection (MTS) and affine motion estimation are the primary candidates for complexity reduction. To the best of our knowledge, this is the first work to provide a comprehensive RDC analysis for practical VVC encoding. It can serve as a basis for practical VVC encoder implementation or optimization on various computing platforms.

Formulating IP strategies for service-intense business models: A roadmapping-based approach

The increasing adaptation of product-service systems (PSS) creates unmet challenges for intellectual property (IP) management and requires an approach to formulate specialised IP strategies for service-intense business models. Building on a roadmapping-based approach for IP strategy formulation we propose a specialised process and template that was developed and demonstrated in one in-depth qualitative case study with a UK-based high-tech scale-up SME. The study shows how the six-step process and template can be applied for the formulation of an IP strategy for a service-intense company. The research demonstrates that a distinct consideration of the PSS components, namely service, product and infrastructure is needed when formulating IP strategies as these different components are likely related to different types of IP rights. This increases the comprehensibility of the IP strategy and provides guidelines for practical implementations.