Articles

The Heat-Affected Zone (HAZ) is one of the major parameters which determines the structural integrity in welded API 5L X52 pipelines, especially in a sour gas service containing Hydrogen Sulphide (H2S) [1]. This thorough review paper performed the analysis of different international standards including Det Norske Veritas Germanischer Lloyd DNVGL-ST-F101, American Petroleum Institute API 1104, National Association of Corrosion Engineers NACE MR0175 and American Society of Mechanical Engineers ASME B31.8. The researcher analysed the governing and regulation of Heat Affected Zone microstructure and associated properties including strength, hardness and toughness through the specifications like heat values, time between passes and Post Weld Heat Treatment (PWHT), welding specifications and qualification of welding procedures, welders and consumables [2]. By collecting and analysing the data from recent studies (2018-2025), the researchers found out that the standards focusing on prescribing strict recipes for welding and qualification procedures like DNVGL-ST-F101, including explicit heat input limits (typically 1.0-2.0 kJ/mm), obligatory post-weld heat treatment (PWHT), and rigorous consumable qualifications, perform well in Heat Affected Zone in sour gas service. The maximum hardness retirement i.e. 22 HRC (248 HV) obligated for sour media creates a critical limitation that forces optimization of welding procedures. The standards which focus on final quality or the end results rather than prescribing any specific method for producing the weld (Performance-based standards) like API 1104 remains less stringent which results in greater variation in properties around the weld i.e. the HAZ area impacting the service reliability in a worse manner during the sour media [3]. Contemporary world adopting new digital welding techniques for monitoring during weld production, prediction of the microstructural properties of HAZ based on machine learning and other advanced methods like hybrid welding processes which combines different precision welding techniques provide promising results for production of high quality and reliable welds which offer enhanced safety and reliability during sour service [4].

<b>Objective:</b> This systematic review aimed to identify, synthesize, and critically analyze the available evidence on clinical protocols used for the prevention and treatment of prosthetic joint infection (PJI) in total hip arthroplasty (THA), based on studies published between 2000 and 2025. <b>Methods:</b><b> </b>The review was conducted according to PRISMA guidelines. Electronic searches were performed in PubMed (MEDLINE), Scopus, Web of Science, and Embase between January and April 2025. Eligible studies included clinical trials, cohort studies, case-control studies, systematic reviews, and meta-analyses published in English that addressed either preventive or therapeutic strategies for PJI in THA. Study selection, data extraction, and quality assessment were carried out independently by two reviewers. Due to the heterogeneity of the included studies, a qualitative synthesis was performed. <b>Results:</b><b> </b>A total of 32 studies were included. Preventive measures identified in the literature comprised combined antibiotic prophylaxis (cefazolin and gentamicin), multimodal perioperative protocols such as ACERTO, nasal decolonization for <i>Staphylococcus aureus</i>, silver-impregnated dressings, and structured post-discharge surveillance. Treatment strategies included DAIR (Debridement, Antibiotics, and Implant Retention), the DAPRI technique, one-stage and two-stage revision surgeries, muscle flap reconstructions, and protocols without spacers. These interventions were associated with significantly reduced infection rates and improved clinical outcomes when applied appropriately and in accordance with patient-specific factors. <b>Conclusion:</b><b> </b>Effective prevention and treatment of PJI in total hip arthroplasty require a systematic and evidence-based approach. Integrated protocols—spanning preoperative optimization, meticulous intraoperative techniques, and rigorous postoperative monitoring—have proven effective in reducing infection incidence. In cases of established infection, surgical management must be tailored to the timing of infection, microbial profile, and host conditions. Two-stage revision remains the gold standard for complex infections, while one-stage revision and emerging techniques like DAPRI offer promising results in selected cases. This review contributes to the standardization of clinical practice and supports improved patient outcomes.

The pharmaceutical industry is undergoing a profound transformation driven by advancements in Information Technology (IT), with Enterprise Resource Planning (ERP) systems playing a pivotal role in reshaping operations. These systems offer integrated solutions that streamline key business processes, such as production, inventory management, supply chain optimization, regulatory compliance, and data integration, contributing significantly to operational efficiency and organizational agility. This paper explores the evolution and impact of ERP systems within the pharmaceutical sector, highlighting their contributions to overcoming the industry’s inherent challenges, including complex regulatory requirements, the need for accurate and real-time data, and the demand for supply chain resilience. The integration of cloud-based ERP solutions, the incorporation of emerging technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), and enhanced data analytics capabilities have revolutionized pharmaceutical IT. These advancements not only reduce operational costs, improve forecasting accuracy, and enhance collaboration but also ensure compliance with stringent global regulations, such as Good Manufacturing Practices (GMP) and FDA guidelines. Moreover, ERP systems have been instrumental in managing the pharmaceutical supply chain, ensuring product traceability, and improving inventory control and order fulfillment processes. This manuscript examines how ERP systems enable pharmaceutical companies to maintain high standards of product quality, improve decision-making, and ensure the safety and efficacy of drugs through robust tracking and auditing mechanisms. A case study of a pharmaceutical company that implemented an ERP system demonstrates the tangible benefits, including increased operational efficiency, improved compliance rates, and enhanced customer satisfaction. However, despite the clear advantages, challenges such as customization complexities, data integration issues, and resistance to change remain. As the pharmaceutical industry continues to evolve, ERP systems will remain a cornerstone of digital transformation, facilitating smarter decision-making, better resource management, and enhanced collaboration across global operations. This paper also identifies future trends, including the potential of AI and blockchain technologies in further strengthening ERP systems and transforming the pharmaceutical landscape.

Artificial Immune Systems (AIS) are bio-inspired computational frameworks that emulate the adaptive mechanisms of the human immune system, such as self/non-self discrimination, clonal selection, and immune memory. These systems have demonstrated significant potential in addressing complex challenges across optimization, anomaly detection, and adaptive system control. This paper provides a comprehensive exploration of AIS applications in domains such as cybersecurity, resource allocation, and autonomous systems, highlighting the growing importance of hybrid AIS models. Recent advancements, including integrations with machine learning, quantum computing, and bioinformatics, are discussed as solutions to scalability, high-dimensional data processing, and efficiency challenges. Core algorithms, such as the Negative Selection Algorithm (NSA) and Clonal Selection Algorithm (CSA), are examined, along with limitations in interpretability and compatibility with emerging AI paradigms. The paper concludes by proposing future research directions, emphasizing scalable hybrid frameworks, quantum-inspired approaches, and real-time adaptive systems, underscoring AIS's transformative potential across diverse computational fields.

The synthesis and optimization of Active Pharmaceutical Ingredients (APIs) is fundamental to pharmaceutical drug development, directly influencing drug efficacy, safety, and cost-effectiveness. Over recent years, significant advancements in synthetic methodologies and manufacturing technologies have transformed API production. This manuscript provides an overview of the latest innovations in API synthesis, focusing on key techniques such as green chemistry, continuous flow chemistry, biocatalysis, and automation. Green chemistry principles, including solvent substitution and catalytic reactions, have enhanced sustainability by reducing waste and energy consumption. Continuous flow chemistry offers improved reaction control, scalability, and safety, while biocatalysis provides an eco-friendly alternative for synthesizing complex and chiral APIs. Additionally, the integration of automation and advanced process control using machine learning and real-time monitoring has optimized production efficiency and consistency. The manuscript also discusses the challenges associated with regulatory compliance and quality assurance, highlighting the role of advanced analytical techniques such as HPLC, NMR, and mass spectrometry in ensuring API purity. Looking ahead, personalized medicine and smart manufacturing technologies, including blockchain for traceability, are expected to drive further innovation in API production. This review concludes by emphasizing the need for continued advancements in sustainability, efficiency, and scalability to meet the evolving demands of the pharmaceutical industry, ultimately enabling the development of safer, more effective, and environmentally responsible medicines.

High-Performance Computing (HPC) is indispensable for large-scale scientific simulations, but achieving optimal performance on modern supercomputers is increasingly challenging. As HPC systems scale toward exascale, they face escalating complexity in hardware, software, and workloads. Traditional optimization methods (manual tuning and heuristic algorithms) struggle to cope with dynamic workloads and intricate system behaviors. Artificial Intelligence (AI) techniques offer a promising approach to address these challenges. This article provides an overview of AI-powered optimization in HPC, focusing on how machine learning and related AI methods enhance performance, efficiency, and scalability of scientific simulations. We survey key AI techniques applied to HPC optimization including machine learning for performance modeling, deep reinforcement learning for resource management, and AI-driven surrogate models for accelerating simulations and illustrate their impact through case studies in domains such as job scheduling and fluid dynamics. We discuss the practical applications of these techniques, highlighting reported performance gains (e.g., substantial reductions in simulation run time and improved resource utilization). We also examine the challenges in integrating AI with HPC (such as training overhead, data movement, and reliability concerns) and outline future directions for research. The convergence of AI and HPC is poised to produce “smart” simulation workflows that intelligently adapt and optimize in real time, pushing the frontiers of scientific computing.

Groundwater is undeniably crucial to people's lives, particularly in coastal regions. Therefore, it is imperative to address this vital water source strategically and implement a management plan to maintain its optimal state. The salinization of groundwater poses a significant challenge for coastal communities, stemming from factors like excessive groundwater extraction from coastal aquifers, reduced recharge, rising sea levels, climate change, and other causes. Saltwater intrusion (SWI) is a prevalent issue that needs attention, as it significantly threatens groundwater quantity and quality. SWI happens when saline water infiltrates coastal aquifers, contaminating freshwater supplies. This review article aims to define SWI, explore its causes and influencing factors, and discuss various monitoring techniques. Additionally, it examines different modeling methods and management tools, including remote sensing, field surveys, modeling approaches, and optimization techniques. To mitigate the adverse effects of SWI, several control measures are outlined, along with their pros and cons. The final section reviews previous SWI studies and case studies from the Nile Delta, Sinai Peninsula, and North-West coast in Egypt. These studies offer suggestions, adaptations, and mitigation measures for future research.

Delirium is a major complication most commonly observed in patients with advanced cancer. However, despite its prevalence, the early diagnosis, management, and prevention of this condition have not seen significant progress. Aim of this research is to provide insights into the prevalence of delirium, the optimization of interventions for managing delirium symptoms, their effectiveness and the impact of underlying factors on the reversibility of delirium in advanced cancer patients receiving palliative care. The review involved systematic searches of relevant databases including MEDLINE, CINAHL, ProQuest Nursing and Allied Health, and PsychInfo using refined search terms. Eight publications out of 614 studies originally searched were selected and critically reviewed. Their quality was assessed using Joanna Briggs Institute's Critical Appraisal Tool for Case Series. Data abstraction and content analysis were performed to synthesize the findings. Delirium is prevalent among advanced cancer patients in palliative care, with rates ranging from 10.3% to 24.1%. Pharmacotherapy and non-pharmacological interventions showed effectiveness in reducing delirium symptoms. Delirium was found to be reversible through palliative care interventions, antipsychotic medications, and exercise therapy. Effective delirium management is crucial in improving the quality of life of cancer patients. This review emphasizes the importance of subtype-specific treatments, standardized guidelines, and long-term follow-up studies. Implementing evidence-based individualized approaches to delirium management can optimize treatment efficacy and clinical outcomes in patients as well as improve the quality of care. Tailored interventions, standardized protocols, and further research are hereby recommended.

Active Pharmaceutical Ingredients (APIs) form the backbone of pharmaceutical formulations, influencing their efficacy, safety, and stability. Essence control of APIs involves stringent regulation and optimization of their chemical, physical, and biological properties to ensure consistent quality and therapeutic outcomes. This manuscript explores the critical aspects of essence control in APIs, including synthesis, characterization, quality assessment, and regulatory considerations. The synthesis of Active Pharmaceutical Ingredients is a pivotal stage in pharmaceutical manufacturing, where precise control over chemical reactions and process conditions is paramount to achieving high-quality, safe, and effective medicines. Advances in synthetic methodologies, optimization strategies, sustainability practices, and the implementation of PAT technologies continue to drive innovation in API synthesis, supporting the development of novel therapeutic agents and enhancing pharmaceutical manufacturing efficiency.

The recent development of Mobile System-on-Chip (SoC) has completely changed the functionality of the contemporary smartphones, wearables, and IoT devices through enhanced computational strength but with stringent power and heat constraints. Mobile applications are becoming more complex (e.g., real-time computer vision, high-resolution imaging, on-device artificial intelligence, etc.) and SoCs have to provide high throughput, good work load distribution, and responsive multitasking without sacrificing battery life. To overcome these issues, modern SoCs are based on heterogeneous multicore designs, with big. LITTLE designs, special purpose accelerators, like NPUs and ISPs, and memory arranged hierarchies and backed with high bandwidth buses. Also, more sophisticated power-management approaches like Dynamic Voltage and Frequency Scaling (DVFS), clock gating, and power gating provide fine-tuning energy management in the active and idle conditions. Most recently, scheduling and thermal sensitivity resource allocation using AI has become a promising approach in increasing real-time refinement and flexibility. This paper gives a detailed discussion of performance-driven and power-efficient design methods in mobile SoCs, including architectural innovations, workload optimization, and built-in AI-assisted energy management. The discussion identifies major research trends, system-level developments, and new challenges defining next-generation mobile computing systems.

The swift changes in the complexity of the project, scarcity of resources, and pressure on the organization have demonstrated the necessity of more intelligent and data-driven planning and scheduling methods. Conventional methods tend to be less effective in the tasks of uncertainty management, dynamic environment, and dynamism of the project ecosystems of the modern world. The application of machine learning (ML) in the planning and scheduling of projects is thoroughly reviewed in this paper, and it is evident that ML may help to improve the accuracy of forecasts, optimize resource distribution, and make decisions. This paper offers an analysis of the basic concepts of project management, evidence-based practices, and how supervised and unsupervised learning, as well as reinforcement learning concepts, can be combined in the scheduling process. Furthermore, the new literatures are examined to bring out new techniques, including predictive modelling, optimization algorithms, simulation-based planning and adaptive scheduling models. The review determines the main strengths, weaknesses, and gaps in the research that impact the adoption of ML-driven approaches. All in all, the paper describes the ways in which machine learning technologies can change the traditional way of project management by creating efficient, resilient, and proactive planning systems.

The building construction industry holds a crucial role in the reduction of greenhouse gas emissions globally. The targets for greenhouse gas emissions may not be achieved without a defined strategic plan to meet up with the set targets from various sectors of the economy. Recognizing the enormous potential that the building industry holds in contributing to global greenhouse gas GHG emission reduction, this study describes a framework on how optimization techniques can be used as a guide for emission reduction targets for the housing sector using illustrations of the onsite and offsite building construction industry. Given that some of the GHG gases are also sources of air pollution, this study includes a discussion on how the effort to address air pollution can be used to find a consensus towards addressing the concern about GHG emissions. This study presents procedures for simplified methods of estimation of GHG emissions that various municipalities around the globe can use to estimate and report the emissions from the building construction industry. The study presents a unifying strategy for emission management. The study also demonstrates how programming methods can be applied to GHG emissions management. The approach used in this study is transferable to other industries. The study recommends a unifying strategy for the management and control of emissions in the building construction industry. The study also recommends a coordinated effort in sharing best practices for emission control and management from all jurisdictions globally. In the effort to reduce global emission targets, further studies like this and its expansion is recommended for all sectors of the global economy. It is recommended that these studies should be followed by a concrete effort to achieve good implementation of sustainable emission reduction targets globally.

The aim of this article is to examine the impacts of Coronavirus Disease -19 (Covid-19) vaccines on economic condition and sustainable development goals. In other words, we are going to study the economic condition during Covid19. We have studied the economic costs of pandemic, benefits in terms of gross domestic product (GDP), public finances and employment, investment on vaccines around the world, progress and totally the economic impacts of vaccines and the impacts of emerging markets (EM) on achieving sustainable development goals (SDGs), including no poverty, good health and well-being, zero hunger, reduced inequality etc. The importance of emerging economies in reducing the harmful effects of the Corona has also been noted. We have tried to do experimental results and forecast daily new death cases from Feb-2020 to Aug-2021 in Iran using Artificial Neural Network (ANN) and Beetle Antennae Search (BAS) algorithm as a case study with econometric models and regression analysis. The findings show that Covid19 has had devastating economic and health effects on the world, and the vaccine can be very helpful in eliminating these effects specially in long-term. We observed that there is inequality in the distribution of Corona vaccines in rich countries compared to poor which EM can decrease the gap between them. The results show that both models (i.e., Artificial intelligence (AI) and econometric models) almost have the same results but AI optimization models can robust the model and prediction. The main contribution of this article is that we have surveyed the impacts of vaccination from socio-economic viewpoint not just report some facts and truth. We have surveyed the impacts of vaccines on sustainable development goals and the role of EM in achieving SDGs. In addition to using the theoretical framework, we have also used quantitative and empirical results that have rarely been seen in other articles.

When a drug interacts with its receptor, the nonpolar substituent of drug and receptor proteins attract each other because they have opposite magnitude with respect to each other. X-rays structure studies reflected that the S2/S2’ pocket in HIV-1 protease enzyme are essentially hydrophobic. The residues that make up these pockets are Val-32, Ile-47, Ile-50, and Ile-84 in each monomeric polypeptidic unit of the protease enzyme. Δπ_dr and ΔSASA_dr have been used to measure the extent of hydrophobic interaction between peptidic protease inhibitors and receptor proteins (binding site: valine‒isoleucine; and catalytic site: glycine‒aspartic acid‒threonine) on the HIV-1 protease enzyme. For measurement of hydrophobic interaction, the molecular modeling and geometry optimization of all the inhibitors and the receptor amino acids have been carried out with CAChe Pro software by opting semiempirical PM3 methods. Log P was calculated using the atom-typing scheme of Ghose and Crippen, while solvent accessible surface area by conductor likes screening model. π_d, π_r, SASA_Sd and SASA_Srwell describe the hydrophobicities of the substituents and play the effective role for site selectivity for interaction of the drug with the receptor. Comparative study of values of Δπ_dr and ΔSASA_dr show the order of hydrophobic interaction with respect to amino acids: Asp > Thr > Val > Ile and Thr > Val > Asp > Ile, respectively. Further, comparative study of the values of (ΣΔπ_dr)_binding-site, (ΣΔπ_dr)_{catalytic-site}, (ΣΔSASA_dr)_binding-site, (ΣΔSASA_dr)_{catalytic-site} shows that peptidic HIV-1-PRIs interact with binding site rather than catalytic site as binding site have lower value of ΣΔπ_dr and ΣΔSASA_dr. Among the binding site, Val has maximum interaction than Ile, as it has lower vale of Δπ_dr and ΔSASA_dr.

In this research work, we have applied “Lipinski’s RO5” for pharmacokinetics (PK) study and to predict the activity of peptidic HIV-1 protease inhibitors. Peptidic HIV-1-PRIs have been taken from literature with their observed biological activities (OBAs) in term of IC50. The logarithms of the inverse of IC50 have been used as biological end point o(log1/C) in the study. For calculation of physicochemical parameters, the molecular modeling and geometry optimization of all the derivatives have been carried out with CAChe Pro software using semiempirical PM3 method. Prediction of the biological activity of the inhibitors has shown that the best QSAR model is constructed from pharmacokinetic properties, molecular weight and hydrogen bond acceptor. This also proved that these properties play important role to describe the PKs of the drugs. On the basis of the derived models one can build up a theoretical basis to access the biological activity of the compounds of the same series.

Carboxylates and their antimony(III) complexes experimentally scanned earlier for anti-leishmanial activity (IC₅₀) against five leishmanial strains viz., <i>L. major, L. major (Pak),</i><i> </i><i>L. tropica, L. mex mex, </i>and<i> L. donovani</i>. These activities have been theoretically predicted by DFT method along with quantitative structure-activity relationship (QSAR) study. Molecular modeling and geometry optimization of the all the eight compounds have been performed on workspace program of CAChe Pro software of Fujitsu by opting B88-PW91 (Becke '88; Perdew &#x26; Wang '91) GGA (generalized-gradient approximation) energy functional with DZVP (double-zeta valence polarized ) basis set in DFT (Density Functional Theory). For QSAR, multiple linear regression (MLR) analysis has been performed on Project Leader Program associated with CAChe. The reliability of correlation between experimental activities and predicted activities are r²= 0.826, r²_CV= 0.426 (<i>L. major</i>); r²= 0.905, r²_CV= 0.507 (<i>L. major (Pak)</i>); r²= 0.980, r²_CV= 0.932 (<i>L. tropica</i>); r²= 0.781, r²_CV= 0.580 (<i>L. mex mex</i>) and r²= 0.634, r²_CV= 0.376 (<i>L. donovani</i>), and a comparison of the experimental values and the values obtained by theoretical calculations has been presented pictorially that shows close resemblance.

To ensure maximum flexibility, service providers offer a variety of computing options with regard to CPU, memory capacity, and network bandwidth. At the same time, the efficient operation of current cloud applications requires an infrastructure that can adjust its configuration continuously across multiple dimensions, which are generally not statically predefined. Our research shows that these requirements are hardly met with today's typical public cloud and management approaches. To provide such a highly dynamic and flexible execution environment, we propose the application-driven autonomic management of data center resources as the core vision for the development of a future cloud infrastructure. As part of this vision and the required gradual progress toward it, we present the concept of composable infrastructure and its impact on resource allocation for multi-cloud environments. We introduce relevant techniques for optimizing resource allocation strategies and indicate future research opportunities [1]. Many cloud service providers offer computing instances that can be configured with arbitrary capacity, depending on the availability of certain hardware resources. This level of configurability provides customers with the desired flexibility for executing their applications. Because of the large number of such prerequisite instances with often varying characteristics, service consumers must invest considerable effort to set up or reconfigure elaborate resource provisioning systems. Most importantly, they must differentiate the loads to be distributed between jobs that need to be executed versus placeholder jobs, i.e., jobs that trigger the automatic elasticity functionality responsible for resource allocator reconfiguration. Operations research reveals that the optimization of resource allocator reconfiguration strategies is a fundamentally difficult problem due to its NP-hardness. Despite these challenges, dynamic resource allocation in multi-clouds is becoming increasingly important since modern Internet-based service settings are dispersed across multiple providers [2].

Factory owners and vehicle manufacturers increasingly opt for predictive analytics to inform their decisions. While predictive analytics have been proven to provide insights into the initiation of maintenance measures before a machine actually fails, the right models and features could have a significant impact on the budget spent and resources allocated. This means that financially oriented questions need to at least partially guide the decisions in the planning phase of data science projects. Data-driven approaches will play an increasingly important role, but only a few of the firms that were confident performed logistic regression models for predictive maintenance. Also, from the available knowledge, data-driven classification models connecting vehicle component failures and the occurrence of delays at the assembly line have not been published. This paper utilizes a real-world data-driven approach using classification models in predictive analytics by vehicle manufacturers and thereby links the financial implications of such data science projects to their results. We expand the existing literature on predictive maintenance and possess a unique dataset of newly launched series of vehicles, presented as-is. Our research context is of interest to researchers and practitioners in the automotive industry that manage and plan the final vehicle assembly with just-in-time principles, factoring the consequences of component failures on the assembly process. Key findings of this paper highlight that while minor tweaking of the models is possible, their potential input in decision-making processes for budget optimization is limited.

Managing supply chains efficiently has become a major concern for organizations. One of the important factors to optimize in supply chain management is logistics. The advent of technology and the increase in data availability allow for the enhancement of the efficiency of logistics in a supply chain. This discussion focuses on the blending of analytics with innovation in logistics to improve the operations of a supply chain. An approach is presented on how predictive analytics can be used to improve logistics operations. In order to analyze big data in logistics effectively, an artificial intelligence computational technique, specifically deep learning, is employed. Two case studies are illustrated to demonstrate the practical employability of the proposed technique. This reveals the power and potential of using predictive analytics in logistics to project various KPI values ahead in the future based on the contemporary data from the logistics operations; sheds light on the innovative technique of employing deep learning through deep learning-based predictive analytics in logistics; suggests incorporating innovative techniques like deep learning with predictive analytics to develop an accurate forecasting technique in logistics and optimize operations and prevent disruption in the supply chain. The network of supply chains has become more complex, necessitating the need for the latest technological advancements. The sectors that have gained a fair amount of attention for the application of technology to optimize their operations are manufacturing, healthcare, aerospace, and the automotive industry. A little attention has been diverted to the logistics sector; many describe how analytics and artificial intelligence can be used in the logistics sector to achieve higher optimization. Currently, significant research has been done in optimizing logistics operations. Nevertheless, with the explosive volume of historical data being produced by the logistics operations of an organization, there is a great opportunity to learn valuable insights from the data accumulated over time for more long-term strategic planning. To develop the logistics operations in an organization, the use of historical data is essential to understand the trends in the operations. For example, regular maintenance planning and resource allocation based on trends are long-term activities that will not affect logistics operations immediately but can affect the business’s strategic planning in the long run. A predictive analysis technique employed on historical data of logistics can narrow down conclusions based on the future trends of logistics operations. Thus, the technique can be used to prevent the disruption of the supply chain.

Traffic congestion across the globe is a multimodal problem, intertwining vehicular, pedestrian, and bicycle traffic. The relationship between the multimodal traffic flow is a key factor in understanding urban traffic dynamics. The impact of excessive congestion extends to the excessive cost spent on traffic maintenance, as well as the inherent transportation inefficiency and delayed travel times. From an urban transportation standpoint, an immediate consideration on one hand is monitoring traffic conditions and demand cycles, while on the other hand inducing flow modifications that benefit the traffic network and mitigate congestion. Embedded and centralized control systems that characterize modern traffic management systems extract traffic conditions specific to their regions but lack communication between networks. Moreover, innovative methods are required to provide more accurate up-to-date traffic forecasts that characterize real-world traffic dynamics and facilitate optimal traffic management decisions. In this chapter, we briefly outline the main difficulties and complexities in modeling, managing, and forecasting traffic dynamics. We also compare various conventional and modern Intelligent Transportation Strategies in terms of accuracy and applicability, their performance, and potential opportunities for optimization of multimodal traffic flow and congestion reduction. This chapter introduces various proposed data-driven models and tools employed for traffic flow prediction and management, investigating specific strategies' strengths, weaknesses, and benefits in addressing various real-world traffic management problems. We describe that the design phase of dependable Intelligent Transportation Systems bears unique requirements in terms of the robustness, safety, and response times of their components and the encompassing system model. Furthermore, this architectural blueprint shares similarities with distributed coordinate searching and collective adaptive systems. Town size-independent models induce systemic performance improvements through reconfigurable embedded functionality. These AI techniques feature elaborate anytime planner-engagers ensuring near-optimal performances in an unbiased behavior when the model complexity is varied. Sustainable models minimize congestion during peaks, flooding, and emergency occurrences as they adhere to area-specific regulations. Security-aware and fail-safe traffic management systems relinquish reasonable assurances of persistent operation under various environmental settings, to acknowledge metropolis and complex traffic junctions. The chapter concludes by outlining challenges, research questions, and future research paths in the field of transportation management.