Microplastics (MPs) are synthetic polymer particles that are generally less than 5 mm in size and have attracted heightened scrutiny due to their pervasive presence in the environment, along with their toxicological significance. Several research investigations documented its presence in humans as a profound finding in biological tissues and fluids crossing barriers, leading to oxidative and inflammatory pathways alterations associated with blood, placenta, cardiovascular, pulmonary, nephrotic, other systems, and their disorders. Given the ubiquitous utilization of microplastics across diverse sectors, it is imperative to systematically investigate and elucidate their potential toxicological effects on biological systems through rigorous and mechanistically informed research. This review will also provide the synthesis of recent mechanistic data on the toxicity that can be caused by MPs and will determine key gaps that impede efficient human health risk evaluation. A structured literature search was conducted via PubMed, Web of Science, and Scopus databases, mostly from the studies published between 2010 and 2026. The studies of exposure characteristics and biological effects were analyzed in vitro, in vivo, and in human biomonitoring, and the primary focus of the interventions includes oxidative stress, inflammation, apoptosis, hepatotoxicity, and metabolic malfunction. MPs possess various physicochemical properties, such as a low particle size, various shapes, surface area, polymer composition, and the presence of sorbed or intrinsic additives. When MPs are taken up by cells, they can induce oxidative stress via increasing ROS, eventually leading to high lipid peroxidation, mitochondrial malfunction, DNA fragmentation, and eventually cell death. MPs also cause pro-inflammatory cytokine responses, including TNF-α, IL-1β, and IL-6, altering the immune system and cell profile, leading to systemic inflammation. In aquatic and terrestrial organisms, these microplastics have a harmful impact on growth, reproduction, and behavior in a time- and dose-dependent manner. Under conditions of controlled exposure, the organ-specific toxicities that have been reported include hepatic, renal, neurological, reproductive, and cardiovascular systems. Although the fields of mechanistic knowledge are growing, there is still a substantial amount of uncertainty; there is a lack of characterization of the long-term effects of low-dose chronic exposure, the kinetics of bioaccumulation, biodegradation potential, and transgenerational effects. In addition, there are no standardized procedures for the characterization of MPs, nor the reporting of the distribution of size or exposure measurements, which limits the comparability of cross-studies and makes it difficult to assess risks quantitatively. The dynamics of interactions of MPs between co-adsorbed contaminants like heavy metals, polycyclic aromatic hydrocarbons, and endocrine-disrupting chemicals are also yet to be explored. Although all evidence available to date does indicate biologically plausible mechanisms of MP-induced toxicity, integrated research employing standardized analytical protocols, an environmentally relevant exposure model, and human epidemiological data is required to ensure that laboratory results are translated into evidence-based public health and regulatory actions. This review offers an in-depth analysis of the existing molecular understanding of MP-induced toxicity, demonstrates organism-level impacts throughout species, and establishes vital fields for future studies. In order to develop competent guidelines to minimize MP exposure and its adverse health effects, it is crucial to cover these gaps via research that incorporates toxicology and environmental science.

A hybrid optical amplifier (HOA) is designed and optimized for the transmission of 40 dense wavelength division multiplexed system (DWDM) channels modulated at 10 Gbps having 25 GHz spacing at the edge of the L and U wavelength bands over more than 250 km. Multi-parameter optimization process is used to achieve the highest gain and best gain flatness. Different power combinations distributed among four lasers of a total forward pumping power (1270 mW) and total backward pumping power of either 730 mW or 850 mW are investigated for their effect on the hybrid amplifier gain and flatness. The best power distribution among the pumps provides about 31 dB overall gain with a flatness about 0.8 dB and noise figure ∼5.7 dB. It is found that the red-shifted pumps’ wavelengths should be used in the forward direction, while the blue-shifted pumps’ wavelengths should be used in the backward direction.

The deployment of techniques such as spectrum sharing, dynamic spectrum access, signal intelligence extraction, and optimal routing is expected to be driven by the internet of things (IoT), which is expected to require extremely efficient wireless communication. In this envisioned scenario, IoT devices should be able to independently obtain spectrum knowledge from the network in real time and apply it to dynamically modify wireless characteristics, such as frequency band, symbol modulation, coding rate, and route selection, for maximum performance. Due to issues like the lack of precise environmental models and excessive processing needs, traditional methods based on a priori network optimization may not be adequate given the predominance of small, mobile, and energy-constrained IoT devices. Many studies have concentrated on using machine learning approaches in IoT wireless communications to overcome these obstacles. This approach provides a flexible framework to handle challenging issues where the phenomenon being studied is too complicated or dynamic for conventional mathematical modeling. This study thoroughly looks at the state of the art when it comes to using machine learning to address important issues in IoT wireless communications, especially in the area of ad hoc networking. To assure practical practicality, the study begins with fundamental machine learning ideas and moves through existing research at the physical, data-link, and network layers of the protocol stack. It also looks into efforts toward hardware implementation. It also skims over machine learning’s more general IoT applications beyond wireless communication, and all throughout these talks, it closely examines the related unresolved issues and difficulties. <!----> <!----> <!----> <!---->

This paper investigates optimization and power/wavelength allocation of a RAMAN amplifier designed for Dense Wavelength Division Multiplexed (DWDM) Systems consisting of forty channels with frequency spacing 25 GHz at the edge of L and U wavelength bands (185 THz – 185.975 THz). Four pumps used in the forward direction with wavelengths at 1500, 1525, 1565, 1574 nm and four pumps in the backward direction with wavelengths at 1435, 1460, 1474, 1489 nm are used. The forward pumps power distribution and backward pump power distribution combinations are investigated for the Raman amplifier best performance. The performance of the system includes overall gain and flatness. The location of the wavelength in the forward or backward direction and the power distribution among each wavelength in an ascending or descending order is also investigated. It is found that the red shifted pump wavelengths should be used in the forward direction and the blue shifted pump wavelengths should be used in the backward direction for best performance.

Vehicular Ad Hoc Networks (VANETs) are a major enabling technology for Intelligent Transportation Systems (ITS), facilitating safety-critical, traffic-efficient vehicle-to-vehicle (V2V) communication. Nevertheless, due to the highly dynamic nature of the vehicular environment, heterogeneous mobility patterns that ensure stable clustering and consistent cluster head (CH) selection pose a challenge. Existing embedding-driven and graph-based vehicle clustering methods rely on localized, one-hop neighbourhood aggregation and assume homogeneous neighbour influence. To overcome these challenges, the paper proposes an attention-guided deep embedding framework that jointly models long-range structural interactions and heterogeneous vehicular influence, rather than restricting learning to immediate neighbours. The proposed algorithm trains latent vehicular representations that understand long-range structural interactions that go beyond immediate one-hop neighbourhoods using Deep Nonnegative Matrix Factorisation (DANMF). To capture the heterogeneous vehicle influence (varying mobility, distance over time of vehicles), the dominant connectivity patterns are highlighted by using an ensemble attention mechanism. This incorporates interaction-factor attention to highlight the high-weighted interaction patterns of latent representation, whereas, through vehicle-level proximity attention, the stable and behaviourally related neighbours are selectively highlighted. Clustering is done according to the learned embeddings, and cluster heads are selected based on the strongest affinity of vehicles to cluster centres, to guarantee that CHs represent varying influence of mobility and structural significance. Extensive simulations reveal that the proposed model outperforms baselines, with a maximum throughput of 7.1 \(\:Mbps\), CH stability of 90%, and a minimum packet delay of 4.5 \(\:ms\), which translates to 36% higher throughput, 32% more stable CHs, and 40% reduced packet delay than state-of-the-art techniques. Thus, this way, the proposed model provides a robust solution for stable V2V communication.