Background and aims: Hair analysis for drug detection is one of the widely accepted imperative techniques in the field of forensic toxicology. The current study was designed to investigate the efficacy of chromatography for detection of drugs of abuse in hair.Method: A comprehensive review of articles from last two decades on hair analyses via PubMed and similar resources was performed. Issues concerning collection, decontamination and analytical techniques are summarised. Physiochemical nature of hair, mechanism of drug incorporation and its stability in hair are briefly discussed. Furthermore, various factors affecting results and interpretation are elucidated.Result: A hair sample is chosen over traditional biological samples such blood, urine, saliva or tissues due to its inimitable ability to provide a longer time frame for drug detection. Its collection is almost non-invasive, less cumbersome and does not involve any specialised training/expertise. Recent advances in analytical technology have resulted in better sensitivity, reproducibility and accuracy, thus providing a new arena of scientific understanding and test interpretation.Conclusion: Though recent studies have yielded many insights into drug binding and drug incorporation in hair, the major challenge in hair analysis lies in the interpretation of results, which may be affected by external contamination and thus lead to false-positives. Therefore, there is a need for more sensitive and selective analysis methods to be developed in order to minimise factors that induce the effect of melanin, age and so on, and this would certainly provide a new dimension to hair analysis and its applications.

Quantum algorithms for predictive analytics is important across various sectors such as finance, healthcare, marketing and supply chain management where accurate predictions can be used to optimise operations and decision-making processes. This chapter addresses Specific quantum machine learning algorithms such as Quantum Support Vector Machines (QSVM), Quantum K-nearest Neighbors (QKNN) and Quantum Neural Networks (QNN). These algorithms enhance classification, regression and complex predictive tasks by accelerating training processes and managing large feature spaces effectively. And, to this, quantum optimization algorithms like Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) are mentioned for their role in optimising model parameters and improving prediction accuracy. In addition, it discusses quantum data encoding methods, such as Quantum Fourier Transform (QFT) and amplitude encoding. Along with this, it acknowledges significant challenges such as hardware limitations, algorithmic complexity and practical integration issues.

Firearms are extensively used in shooting incidents, functions or for recreational purposes around the world. However, illegal modifications to a firearm can pose a major challenge to the forensic community, as their examination and subsequent individualisation are not always easy when compared to standard firearms due to the non-availability of standard comparison material and the damage caused to the firearm during test firing. We present a case report of unusual firing by an improvised firearm adapted to produce a loud noise during a marriage party. This led to high pressure inside the barrel, damaging the extractor assembly of the rifle and caused serious injuries to bystanders. An expert must be aware of all usual and unusual results of firings. In this case, an unusual method of firing two bullets shows the generation of high pressure inside the barrel causing the barrel and extractor assembly to break and act as secondary projectile. These cases need thorough investigation and careful observation to achieve positive outcomes.