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 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.

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.

Polychlorinated biphenyls (PCBs) are important class of persist organic pollutants that were used as a component of paints especially in printings, as plastificator of plastics and insulating materials in transformers and capacitors, heat transfer fluids, additives in hydraulic fluids in vacuum and turbine pumps. There is always a need to establish reliable procedures for predicting the bioconcentration potential of chemicals from the knowledge of their molecular structure, or from readily measurable properties of the substance. Hence, correlation and prediction of biococentration factors (BCFs) based on λ_max and vibration frequencies of various bonds viz υ(C-H) and υ(C=C) of biphenyl and its fifty-seven derivatives have been made. For the study, the molecular modeling and geometry optimization of the PCBs have been performed on workspace program of CAChe Pro 5.04 software of Fujitsu using DFT method. UV-visible spectra for each compound were created by electron transition between molecular orbitals as electromagnetic radiation in the visible and ultraviolet (UV-visible) region is absorbed by the molecule. The energies of excited electronic states were computed quantum mechanically. IR spectra of transitions for each compound were created by coordinated motions of the atoms as electromagnetic radiation in the infrared region is absorbed by the molecule. The force necessary to distort the molecule was computed quantum mechanically from its equilibrium geometry and thus frequency of vibrational transitions was predicted. Project Leader Program associated with CAChe has been used for multiple linear regression (MLR) analysis using above spectroscopic data as independent variables and BCFs of PCBs as dependent variables. The reliability of correlation and predicting ability of the MLR equations (models) are judged by R², R²_adj, se, q²_L10O and F values. This study reflected clearly that UV and IR spectroscopic data can be used to predict BCFs of a large number of related compounds within limited time without any difficulty.

Herein we introduce a reliable and effective method, allowing any musician, regardless of the theoretical background, to carry out a 4-way jazz harmonization of whatever melodic progression almost instantly, with few exceptions. Many jazz students experience a deep frustration in dealing with the harmonization of non-diatonic notes. Sometimes, moreover, a coherent harmonization of the aforementioned notes can turn out to be a very challenging task even for extremely skilled professionals. In this paper, the harmonization of the chromatic scale in different harmonic contexts is accurately discussed, by resorting to the well-known concepts of harmonic functions, tonicization, chromatic and diatonic parallelism, and auxiliary chords. All the chords are labelled so as to allow the reader to immediately understand their role in the particular harmonic context. Consequently, the procedure essentially translates into an optimization of the "harmonic flow".

Objective: 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. Methods: 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. Results: 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 Staphylococcus aureus, 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. Conclusion: 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.

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.

Pharmaceutical supply chains are critical to ensuring the availability of safe and effective medications, yet they face numerous challenges that can jeopardize public health. This article provides a comprehensive analysis of the key issues impacting pharmaceutical supply chains, including regulatory compliance, demand forecasting, supply chain visibility, quality assurance, and geopolitical risks. Regulatory compliance remains a significant concern due to the stringent guidelines imposed by authorities such as the FDA and EMA, which can lead to increased operational costs and time delays. Additionally, traditional demand forecasting methods often fail to accurately predict fluctuations in drug demand, resulting in stockouts or excess inventory. Limited supply chain visibility further complicates these challenges, hindering timely decision-making and operational efficiency. Quality assurance is paramount, as maintaining the integrity of pharmaceutical products throughout the supply chain is crucial to preventing costly recalls and ensuring patient safety. Moreover, the globalization of supply chains introduces vulnerabilities to geopolitical risks, trade disputes, and natural disasters. In response to these issues, this article outlines strategic recommendations for optimizing pharmaceutical supply chains. These include leveraging advanced analytics and IoT technologies to enhance demand forecasting and visibility, strengthening compliance through automated systems and training, fostering collaboration among stakeholders, implementing robust risk management frameworks, and investing in quality management systems. By adopting these strategies, pharmaceutical companies can enhance the efficiency and resilience of their supply chains, ultimately ensuring the continuous availability of essential medications for patients worldwide. This analysis serves as a critical resource for industry professionals seeking to navigate the complexities of pharmaceutical supply chains in an increasingly dynamic global environment.

Drug shortages represent a significant and growing challenge within the pharmaceutical supply chain, with profound implications for patient care, public health, and healthcare costs. This manuscript provides a comprehensive examination of the causes and impacts of drug shortages, highlighting the multifaceted nature of this issue. Key factors contributing to shortages include manufacturing complications, limited availability of active pharmaceutical ingredients (APIs), market dynamics that discourage the production of less profitable medications, and regulatory challenges that slow down the approval process for new manufacturing capacities. The consequences of these shortages are far-reaching. Patients often face treatment delays, which can lead to adverse health outcomes, increased hospitalization rates, and even mortality. Healthcare providers experience heightened operational costs as they seek alternative therapies and manage complications resulting from inadequate treatment. Furthermore, the frequent occurrence of drug shortages erodes public trust in both the healthcare system and the pharmaceutical industry, leading to decreased patient adherence to prescribed therapies. To mitigate the impacts of drug shortages, this manuscript proposes several strategic solutions, including enhanced communication among stakeholders, diversification of supply sources, increased regulatory flexibility, and collaborative approaches between public and private sectors. Additionally, raising awareness among healthcare providers and patients regarding the causes and potential alternatives can empower stakeholders to navigate shortages effectively. Ultimately, addressing drug shortages necessitates a proactive and coordinated effort from all participants in the pharmaceutical supply chain. By implementing these strategies, stakeholders can enhance the resilience of the supply chain, ensuring that essential medications remain accessible and that patient care is not compromised. The findings of this manuscript underscore the urgent need for ongoing vigilance and collaborative action to tackle the challenges posed by drug shortages, safeguarding public health and improving healthcare outcomes globally.

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.

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.

Data is the collection of facts and observations in terms of events, it is continuously growing, getting denser and more varied by the minute across different disciplines or fields. Hence, Big Data emerged and is evolving rapidly, the various types of data being processed are huge, but no one has ever thought of where this data resides, we therefore noticed this data resides in software’s and the codebases of the software’s are increasingly growing that is the size of the modules, functionalities, the size of the classes etc. Since data is growing so rapidly it also mean the codebases of software’s or code are also growing as well. Therefore, this paper seeks to discuss the 5V’s of big data in the context of software code and how to optimize or manage the big code. When we talk of "Big Code for Big Software's," we are referring to the specific challenges and considerations involved in developing, managing, and maintaining of code in large-scale software systems.

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_Sr well 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.

Carboxylates and their antimony(III) complexes experimentally scanned earlier for anti-leishmanial activity (IC₅₀) against five leishmanial strains viz., L. major, L. major (Pak), L. tropica, L. mex mex, and L. donovani. 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 & 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 (L. major); r² = 0.905, r²_CV = 0.507 (L. major (Pak)); r² = 0.980, r²_CV = 0.932 (L. tropica); r² = 0.781, r²_CV = 0.580 (L. mex mex) and r² = 0.634, r²_CV = 0.376 (L. donovani), and a comparison of the experimental values and the values obtained by theoretical calculations has been presented pictorially that shows close resemblance.

Magnetic iron nanoparticles (Fe₃O₄) were synthesized and characterized using Fourier Transformed Infrared ((FT-IR), UV-Visible spectrophotometer, Scanned Electron Microscopy (SEM) equipped with an Energy Dispersive X-ray spectrometer (EDX), and X-ray Diffraction (XRD). The synthesized nano catalyst was used in the transesterification of mahogany seed oil with methanol. The optimized reaction conditions gave a reaction yield of 88% at a catalyst concentration of 1.5% wt., a volume ratio of methanol to oil of 5:1, a reaction temperature of 60 °C, and a reaction time of 120 minutes. The Fe₃O₄ nanoparticles was regenerated from the mixture and reused for various circles by applying the optimum conditions obtained during the present study. The results showed that the biodiesel yield decreased by increasing the number of cycles when the regenerated catalyst was used. However, good conversion (81.9%) was obtained up to the 5th cycles. The elemental analysis of the synthesized magnetic iron nanoparticles Fe₃O₄) revealed the highest proportion of iron with 64.37 and 74.40% for atomic and weight concentration respectively, followed by oxygen with 34.27 and 24.50% for atomic and weight concentrations respectively. It could be concluded that the synthesized nano catalyst would serve as an excellent catalyst for the transesterification of vegetable oils.

The introduction of 4G LTE communication technology was basically designed to meet the increasing demand by users for high-quality multimedia services, data communication speed and improved quality of service (QOS). It is pertinent to note that, with an ever-increasing subscriber base, it is essential to assess and analyze the network performance. To perform this task, there is a need to use the key performance indicators (KPI). This research study evaluates KPI’s gathered from field measurements, using a statistical approach to establish the performance and determine the present condition of the quality of service offered by a 4G LTE network in Port Harcourt, Nigeria. In this study, a drive test approach was adopted to measure the KPI’s and analysis was achieved with the use of TEMs Discovery software adopting a statistical approach. The result showed the value range of the measured KPI’s were; RSSI (-90, -49.7dBm), RSRP (-117.7, -68.6 dBm), RSRQ (-14.2, -22.8dB) representing minimum and maximum values. The probability distribution of the various KPI’s showed that the best signal ranges were distributed as 38.21%, 69.63% and 65.63% for RSSI, RSRP and RSRQ respectively. The KPI parameters were within the acceptable range, though require optimization to provide better service for a greater population.