Numerous industrial, medical, and scientific applications currently depend on robotic manipulators, which are essential to the expanding robotics sector. These manipulators cannot operate well without advanced control algorithms that consider precision, adaptability, and flexibility. This work is focused on listing many types, applications, and functions of control strategies for robotic manipulators. The review commences with a broad overview of manipulators, including subjects such as cable-driven, parallel, and serial models. It subsequently contextualizes the unique mechanical and functional characteristics of each type. The next sections encompass a range of control procedures, from conventional techniques such as PID and linear control to complex systems that integrate hybrid control frameworks like machine learning, and artificial intelligence. This comprehensive analysis addresses some enduring issues like task flexibility, computational efficiency, and environmental uncertainty, together with the advantages and disadvantages of each approach. This study also emphasizes emergent domains, including federated learning, blockchain integration, and quantum computing, while also identifying prospective future research topics. By combining current information and outlining potential developments, the study provides researchers and engineers with a reference for improving the control of robotic manipulators, hence improving performance and reliability in challenging working environments. The findings of this work demonstrate the efficacy of advanced control methodologies and pave the way for advances that could transform robotic manipulation in the future.

The paper studies the capability of revised intensity and impulsive strength of geomagnetic storms and activities to identify severe and minor-system-damage space weather (SvSW and MSW) events from normal space weather (NSW) events occurred in 1981-2024, including the 10-11 May 2024 major event. SvSW caused power outage, MSW caused capacitor tripping and high induced voltage in power transformers, etc., and NSW did not cause such damaging effects. Using interplanetary coronal mass ejection (ICME) and solar energetic particle (SEP) data, the paper also studies the primary causes of the events. Revised impulsive strength of low latitude storms (IpsSYM-Hr) identifies all 3 SvSW and all 9 MSW events from over 1300 NSW events with large margins of 52 nT and 25 nT, respectively, though it does not distinguish between SvSW and MSW events. The conventionally used storm intensity even after revision (SYM-HMinr) and geomagnetic activity parameters (AEmax and IpsAE) in high latitudes where damages usually happen identify only 1 SvSW event. The passage of ICMEs with sharply increased large positive IEFy at ICME front and beyond through background positive IEFy seems leading to severe damages. On the other hand, ICMEs with negative or small positive IEFy at ICME front but becoming highly positive in magnetic cloud in general seems leading to minor damages. The major event on 10-11 May 2024 did not cause severe damages probably because IEFy at the ICME front was small and fluctuating. SEP flux (>30 MeV) seems indicating severe damages but not minor damages.

This study proposes a novel workflow for estimating groundwater recharge, using the Densu Basin in Ghana as a case study. The method combines the water table fluctuation method (WTFM) and the soil water balance (SWB) approach to address data limitations and enhance estimation accuracy. Recharge estimates from both methods revealed significant spatial and seasonal variability, with higher rates in the northern sectors characterized by favorable soil types (sandy clay loam and sandy clay) and extensive forest cover. The WTFM, applied from 2004 to 2009, yielded an average monthly recharge of 43.9 mm, while the SWB approach, covering 1960 to 2015, estimated a lower average of 33.7 mm. The JJA season (June-July-August) exhibited the highest recharge contribution. The study employed WTFM estimates to bias-correct SWB estimates, with linear scaling emerging as the most effective method. This correction resulted in mean monthly recharge rates ranging from 27 to 39 mm/month, increasing from the southern to the northern parts of the basin. The SCS method estimated an annual runoff of 439 mm (26\% of annual rainfall). The research highlights the significant impact of soil properties, land cover, and rainfall patterns on recharge dynamics and recommends the need for adaptive, localized water management strategies to ensure the long-term sustainability of groundwater resources in the Densu Basin.

Purpose—This study investigates the low adoption rate of green agricultural technologies among farmers, focusing on the impact of natural disasters and farmers’ risk aversion variability. This research also aims to explore potential response strategies for these challenges. Design/methodology/approach—This study employs a dynamic perspective to assess the influence of natural disasters on farmers’ adoption of green agricultural technology. It uses a series of robustness checks, including substitution of the explained variable, to ensure the reliability of the findings. The underlying mechanism is explored through the lens of risk aversion, and a heterogeneity analysis is conducted to understand the differential impacts across various farmer types. This study also evaluates the mitigating effects of agricultural subsidies and insurance on the negative impacts of natural disasters. Findings—This study revealed the following: 1. Natural disasters significantly inhibit the likelihood and degree of farmers’ adoption of green agricultural technologies, with the conclusion remaining robust after the robustness checks. 2. The frequency of natural disasters increases farmers’ risk aversion, which in turn affects their decisions to adopt green agricultural technologies, with higher risk aversion correlating with lower adoption and engagement rates. 3. The heterogeneity analysis shows that natural disasters suppress the adoption behaviors of small-scale farmers, those with lower farming income proportions, and those with fewer types of agricultural machinery. 4. Agricultural subsidies and participation in agricultural insurance can reduce the negative effects of natural disasters on farmers’ adoption of green technology. Originality/value—This study provides original insights into the overlooked consequences of natural disasters on the adoption of green agricultural technologies and the role of risk aversion in farmers’ decision-making. It offers valuable policy recommendations for enhancing natural disaster warning and response mechanisms, intensifying technical training, and improving agricultural insurance systems to foster green and sustainable development of agriculture in China. This study contributes to the literature by highlighting the need for targeted support mechanisms to address the challenges faced by different farmer demographics during natural disasters.

Cultivated land quality (CLQ) is the inherent capacity of cultivated land to function in productive activities and depends on soil properties, management practices and landscape patterns. The aim of this study was to evaluate the current status of CLQ and landscape patterns in the Qushui County and assess the influence of landscape patterns on CLQ. Consequently, complementary policies are recommended to improve CLQ on the Tibetan Plateau. The results indicated that the soil in Qushui County was classified as sandy loam, with a bulk density, effective soil thickness, pH, cation exchangeable capacity, organic matter, total nitrogen, available phosphorus and available potassium of 1.41 g cm -3, 63.03 cm, 7.58, 8.82 cmol kg -1, 18.27 g kg -1, 1.14 g kg -1, 15.82 mg kg -1 and 85.53 mg kg -1, respectively. Elevation and soil type were key factors influencing CLQ. Consequently, the optimum quality of cultivated land was observed at low elevations, with fluvo-aquic soils and cold brown calcic soils. These effects were driven mainly by human activities, including irrigation, drainage measures and protected forests on farmland. Landscape patterns, which are products of human activity, constitute another important factor affecting CLQ. The results confirmed that CLQ had significant positive spatial relationships with the largest patch index (LPI), mean patch area (AREA_MN) and aggregation index (AI), whereas it had significant negative spatial relationships with the mean shape index (SHAPE_MN) and fractal dimension index (FRAC_MN) (P≤0.01). Additionally, the influences of the area–edge, shape and aggregation indices on the CLQ were either independent or interactive, with interactions being dominant. These results confirm that CLQ depends on its area and regular shape. The government should guide farmers to optimize cultivated landscapes through systematic management, leading to an improvement in CLQ.

A document by Adem Pinar. Click on the document to view its contents.

Governance is not abstract—it is an operating system. The United States was not just founded; it was coded. The Constitution is not a symbolic document—it is an executable source code that defines system architecture, execution logic, permission structures, and update mechanisms. This paper shows that governance, like software, is a rule-based system designed for stability, modularity, and controlled iteration. Legislators function as core developers, maintaining and refining the legal codebase. Citizens act as processors, executing the nation’s laws in real time. Amendments serve as structured software updates, modifying execution logic while preserving system integrity. The Supreme Court operates as a debugging mechanism, ensuring coherence and preventing system-breaking contradictions. Democracy is an open-source governance system. Unlike monarchies or dictatorships, which function as closed-source systems with centralized control, democracy allows for decentralized participation in shaping its future. This presents both infinite potential and infinite risk. When well-maintained, an open-source system fosters innovation, adaptability, and resilience. When neglected or corrupted, it leads to fragmentation, inefficiency, and collapse.The United States is the most successful open-source governance experiment in history. However, legal technical debt—outdated laws, bureaucratic inefficiencies, and systemic friction—has accumulated, slowing the system’s ability to adapt. Understanding governance as a codebase allows for intentional, structured improvements while ensuring stability.The fundamental question is not whether the system can change—it is always evolving—but rather: who will shape it, and for what purpose?

In this article, performance analysis of gate-all-around negative capacitance vertical tunnel FET (GAA-NC-VTFET) is proposed. The device creates a heterojunction design with negative capacitance HfO 2 (NC-HfO 2), and the presented structure is analyzed for the reduce of various short channel effect for minimal power applications. Because of the negative capacitance effect, the presented device shows steeper characteristics and is designed to lower the leakage current while maintaining a high I ON/I OFF ratio. The polarization effect of negative capacitance has provided better outcomes in terms of lower point subthreshold slope (SS) of 18 mV/dec and leakage current of the order of 10 -14 A/µm. An enhanced I ON / I OFF ratio of around 10 10 has been attained having a length of channel of 18 nm. Since I OFF value of the gadget is low, its power consumption is about µW, making it acceptable for low power applications.

This study aims to comprehensively understand the functional characteristics and mutational effects of the SARS-CoV-2 spike protein through biological experiments and bioinformatics methods. Using deep mutational scanning (DMS) technology, high-throughput sequencing, and protein reconstruction techniques, we systematically analyzed the impact of S protein mutations on its binding affinity to the ACE2 receptor. The results showed that key mutation sites, such as D614G and N501Y, significantly enhanced the binding affinity of the S protein to ACE2, thereby increasing the virus’s transmissibility and immune evasion capabilities. By utilizing AlphaFold for three-dimensional structure prediction and protein docking simulations, we constructed an adaptive landscape model of the S protein, revealing the adaptive changes of different genotypes. Combining experimental data and computational simulations, this study not only validated the accuracy of model predictions but also provided scientific evidence for monitoring viral mutations and designing future vaccines.

The Western honey bee, Apis mellifera, plays a vital role in ecology, agriculture, and the economy, making the global decline in honey bee populations and colony losses a critical concern. Here we present a novel metagenomic approach for studying American Foulbrood (AFB), a severe bacterial disease caused by Paenibacillus larvae, by directly sequencing DNA from infected bee brood samples. Using this method, we achieved up to 89.5% P. larvae reads from infected samples, allowing successful assembly of 31 high-quality P. larvae draft genomes from Danish apiaries without the need for traditional time-consuming bacterial cultivation. Our approach revealed previously undetected multi-strain infections, and identified distinct lineages of ERIC types I and II circulating in Denmark, with ERIC-II being predominant (61.3%) during 2019-2020. Through pangenomic analysis incorporating 500 previously published high-quality P. larvae genomes, we identified significant genomic differences between ERIC types, including a 12.4% larger genome size in ERIC-I strains. Furthermore, we successfully recovered and authenticated P. larvae DNA from a 19th-century museum specimen, demonstrating the feasibility of studying historical AFB outbreaks. This study establishes direct metagenomic sequencing as a rapid and sensitive method for AFB surveillance while providing new insights into P. larvae diversity and evolution.

Doxorubicin (DOX), an anthracycline antibiotic, is widely used to treat a range of solid tumors and hematological malignancies. However, its clinical application in breast cancer is hindered by toxic side effects and the development of multidrug resistance (MDR). Enhancing the selective targeting of DOX and overcoming MDR are critical to improving treatment efficacy. Here, we present a DNA nanoflower (DNF)-based delivery system, designed via rolling circle amplification (RCA) and multi-primer amplification (MCA), which co-delivers antisense oligonucleotides (ASO) and DOX to human breast cancer cells (MCF-7). This system, named DNF-ASO@DOX, effectively promotes gene silencing, enhances drug accumulation, and significantly inhibits cell proliferation. Furthermore, in vivo studies using mouse models of breast cancer demonstrated potent therapeutic effects, highlighting DNF-ASO@DOX as a promising strategy for enhanced anti-tumor therapy.

Controlled human infection model (CHIM) studies involve the deliberate exposure of healthy volunteers to pathogens under controlled conditions and can offer valuable insights for vaccine development and infectious disease research. While these studies have a strong safety record and are increasingly deployed worldwide, their associated biosecurity risks remain underexplored. This paper examines three key CHIM study biosecurity risk areas: onward transmission of pathogens, information hazards, and challenge agent manufacturing risks. The risk of pathogen transmission from study participants or staff to the broader community, particularly in outpatient settings or if participants withdraw from a study, requires careful management through isolation protocols and other mitigation measures. Information hazards arise from the potential dual-use nature of study findings and protocols, necessitating a balance between open scientific communication and risk mitigation policies, although CHIM studies are generally low risk for information misuse. Challenge agent manufacturing presents risks including laboratory-acquired infections, adventitious agent contamination, and mutations affecting transmissibility or susceptibility to treatment. Specific biosecurity risks related to CHIMs may be especially salient in low and middle-income countries (LMICs), in particular the effects of resource constraints or potential for pathogen transmission in endemic settings. We propose risk mitigation strategies to enhance biosecurity without compromising the role of these studies in advancing infectious disease research and vaccine development.

Autism spectrum disorder is a complex neurological and developmental disorder that is characterized by altered brain structures and interconnectivity, which results in a vast array of psychosocial and physiological irregularities. This is due to the complex genetic topography of autism and to the intersectionality of genetic and environmental factors that contribute to the development of this disorder. To better understand the genetic factors that cause autism, the gene linc00896, which encodes a long non-coding intergenic RNA, was biochemically and biologically analyzed primarily through Circular Dichroism, Liquid Chromatography and Mass Spectrometry. From this analysis, it was determined that LINC00896 RNA has a vast interactome and that through this interactome LINC00896 RNA influences numerous cellular processes that contribute to the symptoms of autistic patients. Additionally, the structural analysis of LINC00896 RNA indicated stable but flexible secondary and tertiary structures that support the numerous binding interactions identified in the interactome. Through these empirical findings, the linc00896 gene was identified as being an important genetic factor that contributes to the development of autism.

Increasing drought frequency and severity are driving global forest dieback. Growth recorded in tree rings may predict drought ‘winners’ and ‘losers,’ but past studies of growth in drought-killed trees have produced conflicting support for different theories about drought mortality. We found that clusters of growth behaviors computed from the rings of 2,934 drought-killed and drought-surviving trees from seven species were not consistent with any single theory. Drought-killed subalpine fir and Engelmann spruce trees exhibited “flashy” growthhighly variable climate-growth responses over timecompared to survivors. Drought-killed Scots pine and Norway ospruce trees showed stable, climate-insensitive growth compared to survivors, suggesting “decoupling” from climate. Finally, in red oak and subalpine fir, disturbances like fire, logging, and biotic agents possibly influenced declines in climate sensitivity in both drought-killed and surviving trees. Our consolidated conceptual framework may be useful for predicting future tree mortality, while providing enhanced ecological and physiological understanding.

Adaptation to abiotic stresses generally relies on traits that are not independent from those affecting species interactions. Still, the impact of such evolutionary processes on coexistence remains elusive. Here, we studied two spider mite species evolving separately on tomato plants that either hyper-accumulated cadmium, a stressful environment for the mites, or on control plants without cadmium. Through combining experimental evolution and structural stability theory, we found that adaptation to cadmium of both species shifted predictions from exclusion to coexistence. This shift occured due to a simultaneous increase in intra and a decrease in interspecific competition, but only in cadmium environments. These predictions were further confirmed with complementary experiments of population dynamics, underscoring that evolution of single species in a new environment, even in absence of interspecific competitors, shapes species coexistence. Hence, population shifts to novel environments may have unforeseen evolutionary consequences for community composition and the maintenance of species diversity.

Host heterogeneity in disease transmission is commonly seen across host-pathogen systems and identifying individuals who contribute disproportionately to pathogen transmission (i.e. superspreaders) is key to understanding disease dynamics and managing outbreaks. It is often assumed that shedding intensity is directly proportional to infection intensity. However, theory predicts that co-infection might modulate the relationship between infection intensity and shedding, promoting increased onward transmission. Here we quantify the relative importance of infection intensity and co-infection on shedding in Heterakis gallinarum, a gastrointestinal helminth of gamebirds. We found that infection intensity was a poor predictor of shedding intensity. Instead, increased shedding was linked to co-infections with other endoparasites. Our results show that shedding intensity is not simply explained by infection intensity, but rather is the result of complex host-parasite and parasite-parasite interactions. This highlights the importance of considering such interactions in understanding disease emergence and persistence in natural populations.

Whether climate variations affect income distribution has received widespread scientific interest. This paper focuses on the impacts of climate change-induced aridity on income inequality using two measures of drought, the Aridity Index (AI) and the Standardized Potential Evapo-Transpiration Index (SPEI) and country-level panel data from 165 countries for the period 1999-2019. The analysis reveals that increased drought reduces the income share of the poorest while increasing that of the wealthiest 10% and 1%. We identify agricultural production and migration flows as the most significant drivers behind this phenomenon. To ensure accuracy, we employed a dynamic panel model and spatial econometric techniques to account for the interdependence between income inequality and both time and location.

Background: Until now, no epigenome-wide association studies (EWAS) has investigated the impact of allergen immunotherapy (AIT) on DNA methylation in a longitudinal set-up. Herein, we investigated whether differences in DNA methylation occur in birch pollen allergic patients undergoing six months of birch pollen AIT, assessed alterations in methylation-based blood cell type composition, and correlated DNA methylation to serological AIT biomarkers. Methods: We performed genome-wide DNA-methylation analysis on bisulfite-converted DNA derived from whole blood samples of 16 birch pollen-allergic patients (pre-/post-birch pollen AIT) and 15 placebo (pre-/post-placebo treatment). Results: Our analysis identified cg22187251, located within a regulatory region upstream of the glucosaminyl (N-acetyl) transferase 2 ( GCNT2) gene and cg22336863 upstream of the transcription start site of actin binding rho activating protein ( ABRA), as differentially methylated. DNA methylation levels of cg22187251 in post-AIT-treated patients approximated those observed in a non-allergic reference cohort. Functional assays revealed that this region exhibits methylation-dependent promoter and enhancer activity. We identified differentially methylated sites within the HLA gene complex, and an AIT-specific increase of CD8+ T cell populations accompanied by a decrease in NK cell proportion. Moderate to strong correlations with clinical biomarkers (such as specific IgG 4) were observed for 46% of the top 100 differentially methylated sites. Conclusions: GCNT2 and ABRA are implicated in Rho-signaling, a pathway involved in Th2 differentiation. GCNT2 modulates the SMAD-dependent TGF-β pathway, indicating a role in mediating AIT-induced immunotolerance. We provide evidence of DNA methylation within a regulatory region of a relevant gene, potentially restoring methylation levels to a non-allergic state.

The combined effect of the plurality of jurisdiction, contexts, and languages has necessitated advanced legal document processing tools to efficiently capture nuances and semantic knowledge (concepts & relations inclusive). This project introduces a novel deep-learning framework designed to address the complexities of legal document translation and summarization, focusing on the Indian legal system and its linguistic diversity. To define a tangible scope, we focus on knowledge representation & corpus curation across all high court judgements in India while targeting abstractive summarization powered by LLMs & translation of English legal documents to Tamil. Our methodology integrates three key innovations: a multi-level Named Entity Recognition (NER) system & knowledge graph generation system for capturing the ontology of legal court documents in the Indian subcontext, a recursive context-aware prompting approach using state-of-the-art LLM for abstractive summarization, and a custom-built Neural Machine Translation (NMT) architecture tailored for English-to-Tamil translation. The NER system employs a hierarchical approach, utilizing Spacy for initial entity detection, a custom Skip-gram model for nuanced term extraction, and advanced LLM-based methods for complex legal relationships. The summarization module leverages state-of-the-art LLMs, optimized through recursive context-aware prompting, to produce contextually rich and condensed summaries.

Identifying the main taxonomic, phylogenetic and trait dimensions of beta diversity, and evaluating their prospective drivers, advances our understanding of patterns and processes involved in the evolution of biological assemblages. Using comprehensive databases on the distribution, phylogeny, and morphological traits (later referred as functional traits) of Amazonian freshwater fishes, we analyzed beta diversity patterns of these three dimensions to evaluate prospective historical and contemporary drivers. We mostly focused on the pure turnover components of these three beta diversity dimensions (Taxoβsim, Phyloβsim, Traitβsim) and related them to Amazon Basin-wide predictors using multiple regression on distance matrices. We found mean taxonomic beta diversity about two times higher than mean phylogenetic and six times higher than species traits beta diversity, and coincident spatial patterns in Taxoβsim and Phyloβsim dimensions, whereas Traitβsim seemed more diffuse and heterogeneous across space. Our models revealed the prominent influence of sub-basins geographic distances, habitat harshness and water color types on the taxonomic and phylogenetic dimensions of beta diversity, together with smaller individual effects of current temperature and habitat types, historical sub-basins connections and marine incursions, and sampling effort. By contrast, Traitβsim was weakly explained only by sampling effort and current sub-basins hydro-morphological conditions. These results point to leading effects of dispersal limitation, environmental filtering and historical contingencies in explaining Amazonian fish assemblages taxonomic and phylogenetic beta diversity patterns, but not functional traits turnover.

Sparse numerical datasets are common in applied mathematics, astronomy, finance, and healthcare, posing challenges due to their high dimensionality and sparsity. Most values are zero, complicating optimal feature selection. To address this, the Hybrid Stacked Sparse Autoencoder (HSSAE) integrates L1 and L2 regularization with binary cross-entropy loss to enhance feature selection efficiency. L1 regularization penalizes large weights, simplifying data representations, while L2 regularization prevents overfitting by limiting the total weight size. Additionally, the dropout technique improves model performance by randomly deactivating neurons during training, ensuring the model relies on the active neurons. Batch normalization stabilizes weight distributions, reducing computational time and accelerating convergence. The proposed HSSAE model was compared with traditional classifiers such as Decision Tree, Random Forest, K-Nearest Neighbors, Naïve Bayes and a deep learning algorithm Stacked Sparse Autoencoder model (SSAE) using a sparse diabetic dataset. Quantitively, the proposed HSSAE produced the highest accuracy (88.73%) compared to the traditional classifiers and SSAE Model. The ability of the proposed HSSAE model to generate good feature selection makes the model robust and suitable for any kind of sparse data applications, especially for sensitive applications such as the healthcare diabetes dataset which requires high accuracy in the prediction.

Background and Purpose: Diabetic wound affects the health and safety of the people with diabetes. FGF21 has been shown to promote diabetic wound healing, but its low half-life and transdermal rate limit its use. This study was used to investigate the role of TDP1-FGF21 in promoting the diabetic wound healing. Experimental Approach: To increase the transdermal rate of FGF21, a cell-penetrating peptide, TDP1 was N-terminally fused to FGF21 to produce a fusion protein, TDP1-FGF21. TDP1-FGF21 was expressed in a prokaryotic expression system and purified by chromatography. To evaluate whether TDP1-FGF21 could regulate the macrophage polarization to promote diabetic wound healing, the levels of histopathological changes, macrophages polarization, and protein expression were assessed after the application of TDP1-FGF21-containg hydrogel to the wound models of diabetic mice. To confirm whether TDP1-FGF21 could regulate the macrophage polarization in vitro, the levels of macrophage polarization, macrophages metabolic reprogramming, mitochondria and the protein expression were assessed after the application of TDP1-FGF21. Key Results: TDP1-FGF21 was purified by chromatography. TDP1 effectively promoted the transition of diabetic wounds from the inflammatory stage to the proliferative stage by enhancing the transdermal efficiency of FGF21. Both FGF21 and TDP1-FGF21 had the same effect on promoting macrophage polarization. Further mechanistic investigation showed that both TDP1-FGF21 and FGF21 could increase the expression of p-AMPK, SIRT1, and PGC-1a compared with untreated diabetic wounds. Conclusion and Implications: The findings suggested that TDP1 could enhance the transdermal efficiency of FGF21. TDP1-FGF21 could improve the diabetic wound healing through regulating macrophage polarization by targeting the AMPK/SIRT1/PGC-1a pathway.

The effective treatment strategies for glioblastoma (GBM) are still limited at present. Identifying therapeutic targets in GBM and developing corresponding drugs are unmet needs. Here, we find that VCP is highly expressed in GBM cells and correlates with glioma malignancy. V8 is derived from Wogonin analogues, which bind to VCP to inhibit GBM growth. V8 inactivates its ATPase activity, and induces protein aggregates in cytoplasm and mitochondria. Abnormally accumulated VCP on mitochondria induced by VCP inhibitor further recruits PRKN, leading to the co-localization of mitophagy receptors with mitochondria to initiate mitophagy. However, inhibiting VCP also disturbs lysosomal pH, preventing the degradation of abnormal mitochondria. As a consequence, mitochondria with protein aggregates accumulate and release excessive mt-ROS which lead to the demise of GBM cells. In conclusion, VCP not only maintains mitochondrial proteostasis, but also keeps the integrity of lysosome to clear damaged mitochondria by mitophagy. Targeting VCP with its inhibitors, such as V8, causes mitochondria dysfunction, effectively suppresses the viability of GBM and may represent a potential strategy for GBM treatment.

In this article, we investigate the impact of dependent channel coefficients on two-user Multiple-Access Relay Channels (MARC) with Non-Orthogonal Multiple Access (NOMA) in the presence and absence of eavesdroppers. We derive closed-form expressions for the Ergodic Sum Rate (ESR), Outage Probability (OP), and Strictly Positive Secrecy Capacity (SPSC). Using the copula method to obtain the joint probability distribution of dependent channel coefficients, our results show that negative correlation enhances ESR at low Signal-to-Noise Ratio (SNR) levels, while positive correlation significantly improves ESR at high SNR levels due to better signal alignment. The OP analysis shows that negative correlation leads to lower outage probability. Finally, SPSC results indicate that positive dependency improves performance by aligning the capacities of both users. Additionally, we derived optimal power allocation factors for each SNR and dependency condition, showing that negative dependency does not show a stable behavior in power allocation factors and occurs under unstable channel conditions. This information helps in developing better strategies for NOMA systems in urban environments. We validate our numerical analysis through simulations, confirming the trends and effectiveness of the proposed schemes in optimizing channel conditions and SNR at the base station.