Abstract: Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide and remains therapeutically challenging owing to its marked inter- and intratumoral heterogeneity, diverse etiologies, and high rates of drug resistance. This review aims to summarize the current knowledge on the complexity of HCC and to evaluate emerging therapeutic strategies, with a particular focus on targeting the RNA-binding protein HuR as a novel approach to overcome treatment limitations. Methods: A narrative review was conducted of peer-reviewed publications focusing on HCC pathogenesis, tumor heterogeneity, resistance mechanisms, and therapeutic developments. Emphasis was placed on studies investigating the molecular drivers of HCC, tumor microenvironment interactions, and novel treatment strategies. Results: HCC progression is driven by complex interactions between genetic, epigenetic, and environmental factors, resulting in significant variability in treatment response. Tumor heterogeneity, cancer stem cell populations, and an immunosuppressive tumor microenvironment contribute to resistance to conventional therapies, including multikinase inhibitors and immune checkpoint inhibitors. Emerging strategies targeting these mechanisms, such as combination immunotherapies, metabolic targeting, and epigenetic modulation, show promise, but remain limited by incomplete efficacy. HuR is a central post-transcriptional regulator that stabilizes mRNAs encoding oncogenic and pro-survival factors. Preclinical studies have demonstrated that the pharmacological inhibition of HuR disrupts tumor-promoting pathways and enhances therapeutic sensitivity. Conclusions: The complexity of HCC necessitates multifaceted precision-based therapeutic approaches. Targeting HuR is a promising strategy for addressing tumor heterogeneity and drug resistance. Continued integration of molecular profiling, advanced technologies, and rational combination therapies is critical for translating these advances into improved clinical outcomes.

Abstract: This study assessed the current status of irrigation systems and water management practices in Rwanda’s irrigated agricultural zones focusing on Nasho Government Funded Irrigation (GFI) scheme in Kirehe District and Kagitumba Irrigation Scheme in Nyagatare District. A mixed descriptive approach was applied combining field observation with structured questionnaires administered through Kobo Toolbox to 224 respondents in Nasho and 188 respondents in Kagitumba. Field observations were used to evaluate the physical condition and functionality of irrigation infrastructure while questionnaires captured stakeholder perceptions, water management practices, institutional arrangements and operational challenges. Results show that both irrigation schemes are operational but function below optimal efficiency due to multiple constraints. In Nasho, irrigation performance is mainly affected by sedimentation in canals and reservoirs, pump inefficiencies and inadequate maintenance practices leading to unreliable water delivery. In Kagitumba, despite the use of modern center pivot systems performance is constrained by pipeline corrosion, pressure losses, sediment-laden water and uneven water distribution. Across both schemes, more than 80% of respondents reported frequent system failures while over 95% indicated the absence of formal irrigation scheduling practices. Water management remains largely reactive with limited preventive maintenance and weak technical capacity among users and institutions. The study concludes that improving irrigation efficiency in Rwanda requires integrated interventions combining infrastructure rehabilitation, strengthened maintenance systems, improved water governance and farmer capacity development to enhance sustainable water use and agricultural productivity.

Abstract: Enzyme replacement therapy (ERT) for central nervous system symptoms and newborn screening (NBS) are available in Japan for patients with mucopolysaccharidosis type II (MPS II). The participants were individuals referred to our facility through NBS who were suspected of having neuronopathic MPS II. We reviewed the clinical course of patients who received intracerebroventricular (ICV)-ERT, idursulfase beta (Hunterase®), followed by hematopoietic stem cell transplantation (HSCT) using umbilical cord blood. Longitudinal measurements of heparan sulfate (HS) in the cerebrospinal fluid (CSF) were performed as a therapeutic biomarker, and developmental age was evaluated. Three patients diagnosed and treated with ICV-ERT received cord blood transplantation (CBT). All patients achieved successful engraftment with no severe complications except for one patient with sinusoidal obstruction syndrome. The HS in the CSF showed a temporary increase during the ERT discontinuation period owing to CBT and a subsequent reduction after the resumption of ICV-ERT. The patients exhibited age-appropriate development. The pattern of change in HS suggests the importance of continuing ICV-ERT even after HSCT. The combination of ICV-ERT and CBT may yield promising outcomes in patients with neuronopathic MPS II and underscores the importance of early intervention through NBS.

Abstract: This paper presents a novel optimal control-based beamforming framework for phased antenna arrays, targeting advanced wireless communication and radar applications, including 5G systems. Unlike conventional beamforming techniques such as Fourier-based methods and adaptive algorithms (e.g., LMS and RLS), the proposed approach formulates the beam synthesis problem as a discrete-time optimal control problem. The antenna array is modeled using a state-space representation, and a quadratic cost function is introduced to jointly minimize the deviation from a desired radiation pattern and the excitation power. The optimal excitation weights are derived using the Linear Quadratic Regulator (LQR) framework by solving the discrete-time algebraic Riccati equation. This formulation enables an effective trade-off between sidelobe suppression, main lobe accuracy, and power efficiency. Simulation results demonstrate that the proposed method achieves a well-focused main beam, significantly reduced sidelobe levels, and improved directivity compared to conventional approaches. Furthermore, the framework offers robustness and computational efficiency, making it suitable for real-time implementation, particularly on embedded platforms such as FPGA-based systems. Overall, the proposed optimal control-based beamforming approach provides a powerful and flexible solution for next-generation antenna systems in 5G and radar applications.

Abstract: The impact of jawbone diseases extends far beyond the mouth. Worldwide, cancer patients completing chemotherapy often have routine dental checkups, with about 70% of patients with breast and prostate cancer and 30–40% of those with lung and other solid tumours being diagnosed with bone metastases. Oncologists treating these patients usually prescribe bisphosphonates to protect their bones, a common and necessary treatment. Yet determining whether the jawbone is starting to deteriorate is something neither the patient nor the dentist can easily detect. Studies show that medication-related osteonecrosis of the jaw (MRONJ) affects a growing share of the millions of patients on antiresorptive therapies across Egypt, the Gulf, and North Africa (up to 15%, compared to just 0.01% in the general population). Nevertheless, by the time it becomes clinically visible, the bone damage is often already irrecoverable. Patients recovering from head and neck radiotherapy, elderly patients with chronic bone loss, and those living with metabolic bone disorders face the same invisible progression. Moreover, all experience the same diagnostic gap, as the primary imaging tool, CBCT, has major drawbacks: its interpretation relies heavily on visual inspection, making conclusions highly subjective, along with the shortage of specialists in many areas, causing patients’ conditions to deteriorate between visits. Globally, more than 12.5 million Cone Beam Computed Tomography (CBCT) scans are performed annually, with total imaging volume increasing by over 50% in the last five years. Additionally, in the Middle East and Africa alone, the CBCT imaging market is projected to grow at a compound annual rate of 12.91% through 2032. This growth creates an expanding diagnostic workload that current practices are unable to meet, highlighting the need for new automated and reliable imaging models. In this work, we propose AutoCBCT, an automated CBCT model that combines Attention U-Net segmentation — which learns to focus on anatomically relevant structures while ignoring noise — with Euclidean Distance Transform-based thickness mapping to produce a spatial heatmap of the entire jaw. Indicators for MRONJ, osteoradionecrosis, fibrous dysplasia, and resorption are based on established clinical criteria. The proposed framework serves as a support tool for clinical decision-making through resorption grading (based on the Cawood and Howell classification) and automated detection of abnormal bone density patterns. The proposed approach is evaluated using 443 CBCT scans from the ToothFairy international dataset, obtained from three commercial scanner platforms with voxel spacings of 0.160–0.300 mm. Bone segmentation achieved a mean Dice coefficient of 0.884 ± 0.020 (range 0.798–0.938), with all 443 cases exceeding the clinical acceptance threshold of 0.7. The thickness estimation of the bone showed a mean absolute error of 0.209 ± 0.094 mm, with 99% of patients below 0.5 mm and every patient below 1.0 mm. The total mean thickness of the bone was 2.797 ±0.410 mm. The clinical data showed that 73.6% of the patients required augmentation or reconstruction. There was no abnormal bone in this dataset.

Abstract: Indoor robots continuously capture and process camera images, heavily taxing battery life and bandwidth. While approximate analog computing offers massive power savings by deliberately degrading sensor precision, its impact on closed-loop robotic autonomy remains largely unexplored. In this work, we introduce the first system-level evaluation framework explicitly linking analog circuit-level imperfections—including low-bit quantization, read noise, and dynamic-range clipping—with downstream 3D navigation. Through 10,500 end-to-end planning evaluations combined with 1,996 geometric mapping evaluations using diverse perception and mapping algorithms, we uncover a severe non-linear "error cascade" across the software stack. Crucially, we identify a fundamental perception paradigm shift: while semantic free-space segmentation exhibits extreme fault tolerance down to 4-bit precision, geometric perception tasks have task-specific minimum precision requirements: visual odometry remains viable at 6-bit, while monocular depth estimation acts as the system’s tightest constraint, demanding a full 8-bit baseline. Furthermore, our energy-quality Pareto analysis reveals a counter-intuitive anomaly: deliberately applying an aggressive 0.8V dynamic range clipping acts as an analog-domain noise filter, which, when paired with TSDF mapping and Theta* planning, simultaneously reduces front-end energy and increases the overall navigation success rate from 61% to 64%. Ultimately, this work provides actionable quantitative guidelines for interdisciplinary hardware-algorithm co-design in next-generation edge robotics.

Abstract: Production of a biodegradable, environmentally friendly polymer film material, composed of potato starch (PS), xanthan gum (XG), and plasticizers: glycerin, sorbitol, and citric acid, was carried out. The effect of these components on the structural and biopolymer composite mechanical properties, including elasticity and tensile strength, was investigated. The addition of XG significantly reduces the hardness for the film forming materials, thereby lowering the difficulty of gelatinization. It was demonstrated that increasing the plasticizers mass during composite blend preparation improved elasticity but reduced the mechanical strength of the films. It is assumed that these additives in the biopolymer disrupted hydrogen bonds and other intermolecular contacts between starch and gum macro chains. Glycerol influences the elasticity of the bioplastic, while sorbitol influences its strength. Taking various factors into account, the optimal combined concentration of glycerol, sorbitol and citric acid was determined in composite during film preparation. Based on the results of the new polymeric films’ flexibility study, it was concluded that they could be used as a replacement for traditional, non-biodegradable polymeric materials. At the optimal concentration of components, the strength of polymer films is 1.6 MPa, and the relative elongation is 45%.

Abstract: Anaerobic bacteria are the dominant group in the animal intestinal microbiota, and most strains cannot grow or proliferate normally upon exposure to air. Blautia sp. AUH-JLD56 (KF374935) is a strictly anaerobic strain previously isolated by our research group from human feces. Under anaerobic conditions, this strain converts arctigenin to 3′-demethylarctigenin (3′-DMAG), reaching a maximum conversion concentration of 3.6 mM. To improve the oxygen tolerance of this wild-type strain, we performed long-term oxygen tolerance domestication and successfully obtained an oxygen-tolerant mutant. Phenotypic analysis showed that the growth of the oxygen-tolerant mutant under aerobic conditions (OD_{600 nm} = 2.37) was slightly lower than that of the wild-type under strictly anaerobic conditions (OD_{600 nm} = 2.82). Compared with the wild-type, the mutant exhibited an accelerated aerobic growth rate and enabled stable conversion of arctigenin. Notably, under aerobic conditions, the mutant achieved a maximum conversion concentration of 8.2 mM, which is significantly higher than the 3.6 mM obtained with the wild-type under anaerobic conditions. This study realizes, for the first time, efficient aerobic bioconversion of arctigenin to 3′-DMAG using an oxygen-tolerant derivative of a strict anaerobe, thereby overcoming the oxygen-dependent limitation of such strains. Our approach provides a new strategy and technical reference for the oxygen tolerance domestication and industrial application of other intestinal strict anaerobes with specific enzymatic functions.

Abstract:

Formaldehyde (HCHO), a prevalent indoor air pollutant released from furniture and building materials, poses significant health risks due to its carcinogenic nature. In this study, a binary cuprous oxide–titanium dioxide (Cu₂O–TiO₂) composite photocatalyst was synthesized via a hydrothermal method to enable efficient visible-light-driven degradation of gaseous formaldehyde at ambient temperature. The structural, mor-phological, and optical properties of the as-prepared catalysts were characterized us-ing XRD, SEM, TEM, EDX, and UV-Vis spectroscopy. While pristine Cu₂O exhibited a formaldehyde degradation efficiency of approximately 68% under white light illumi-nation, the incorporation of TiO₂ markedly enhanced the photocatalytic performance. Among the different mass ratios tested, the Cu₂O–TiO₂ (1:1) composite demonstrated the highest activity, achieving 83% degradation of formaldehyde within 240 minutes under white light. Enhanced performance is attributed to the formation of a hetero-junction that reduces the effective bandgap, promotes charge separation, and sup-presses electron–hole recombination. Additionally, the generation of carbon dioxide and water as end products confirmed complete mineralization. The catalyst also showed good reusability, retaining over 81% efficiency after five cycles. This work presents a cost-effective, stable, and visible-light-active Cu₂O–TiO₂ heterojunction photocatalyst with strong potential for indoor air purification applications.

Abstract:

Disordered eating in young adults is shaped by sociocultural pressures and may be modulated by genetic variation. We examined sex differences in eating-pathology, psychosocial correlates, at two candidate loci Hypocretin and Neuropeptide S (HCRTR1 rs10914456; NPSR1 rs324981). A total of 550 individuals visiting various nutrition clinics were initially approached for participation in the study. Of these, 460 consented to take part ,after exclusions, 360 completed SCOFF; 200 scoring >2 proceeded to EAT-26 and comprised the analytic sample (100 males, 100 females). Psychosocial factors (media influence, academic pressure, peer pressure, isolation/loneliness, and K-pop self-comparison) were assessed by a structured questionnaire. EAT-26 total and subscales were compared by sex (t-tests). Genotypes were contrasted by sex using χ² tests; allele frequencies were derived from genotype counts and ORs with CI were computed. Females showed higher EAT-26 total scores than males (29.7±1.9 vs 23.2±1.3; t(198)=2.82, p<0.005); 68% of females and 76% of males scored ≥20. Anorexia subscale scores were greater in females (t(198)=3.713, p<0.0003), as well as binge-eating scores (t(198)=1.722, p<0.05); bulimia indices did not differ by sex (p>0.05). Body dissatisfaction was common (87%) without sex difference (p>0.05).Significant sex associations were observed for media influence (χ²=67.94, p<0.05), academic pressure (χ²=45.6, p<0.0001), K-pop self-comparison (χ²=112.12, p<0.0001), peer pressure (χ²=46.37, p<0.05),and isolation/loneliness (χ²=28.72, p<0.0001).Genotyping data revealed marked sex-dependent associations at both loci. For HCRTR1 rs10914456, female cases showed a significantly higher frequency of the risk (TT) genotype, conferring 4.86-fold greater odds of carrying T-allele relative to males (OR = 4.86, 95% CI: 1.46–16.17, p = 0.001). In contrast, for NPSR1 rs324981, males exhibited a pronounced T-allele–driven risk pattern, being T-carriers (AT+TT) relative to females (OR = 4.11, 95% CI 1.23–13.68, p = 0.022).Within females specifically, the AA genotype was significantly overrepresented compared with T-carrying genotypes (AA vs AT+TT: OR = 3.25, 95% CI: 1.59–6.66, p = 0.0013).Collectively, these results highlight a female-specific recessive risk pattern at HCRTR1 and a male-specific dominant T-allele effect at NPSR1, underscoring robust sex-differentiated genetic susceptibility to disordered eating. Overall females exhibited severe eating-pathology and heightened psychosocial sensitivity than males, while genetic risk showed locus-specific sex patterns. Integrating psychosocial screening with genetic profiling may lead to early intervention.

Abstract: Background/Objectives: Celiac disease is an immune-mediated enteropathy with heterogeneous gastrointestinal and extraintestinal manifestations. Psychiatric symptoms, arthralgia, thyroid comorbidity, anemia, and abnormal liver tests can obscure recognition in primary care. Methods: We report a de-identified reflective case from routine family medicine practice, structured in accordance with CARE case-report principles. Results: A woman in her early sixties with hypothyroidism and glaucoma presented with new low mood, anhedonia, somnolence, generalized anxiety, increased alcohol intake, poor appetite, weight loss, abdominal bloating, diarrhea, flatulence, and polyarthralgia. Investigations showed mild anemia, markedly elevated ferritin and liver enzymes, uncontrolled hypothyroidism, and strongly positive tissue transglutaminase IgA (>250 kIU/L; reference 0.0-14.9). Radiographs showed mild osteoarthritis and osteopenia without erosive arthropathy. CT abdomen/pelvis excluded malignancy but showed severe diffuse hepatic steatosis and mild pancreatic atrophy. The patient declined gastroenterology referral and confirmatory endoscopy with duodenal biopsy. A working diagnosis of probable celiac disease was made; she commenced a gluten-free diet, received alcohol-cessation advice, had levothyroxine adjusted, and was followed in the community. Most symptoms improved within six months. Conclusions: Celiac serology should be considered in adults with anxiety or depressive symptoms accompanied by gastrointestinal symptoms, weight loss, arthralgia, autoimmune thyroid disease, or unexplained liver-test abnormalities. The case also highlights diagnostic uncertainty, and follow-up needs when biopsy is declined.

Abstract: This study employed the 5-meter Accuracy Digital Elevation Model (DEM) developed by the Geospatial Information Authority of Japan to analyze the spatial distribution of Yayoi-period archaeological sites using a Geographic Information System (GIS)–based approach. Unlike conventional prefecture-level classifications, this method enables higher spatial precision and more intuitive visual interpretation. The analysis provides new insights into the long-standing debate over the location of Yamatai (Yamataikoku) approximately 1,800 years ago and significantly increases the likelihood that it was located in northern Kyushu. The results also reveal clear regional specialization within northern Kyushu. The areas around present-day Asakura City and Ogori City appear to have functioned primarily as military centers, whereas the Yoshinogari site—one of the largest Yayoi settlements in Japan—shows strong specialization in agriculture, especially large-scale wet-rice cultivation. The area corresponding to present-day Fukuoka City likely served as a major urban center combining both military and agricultural functions. In addition, the study suggests that “Jimmu’s Eastern Expedition” may preserve certain historical elements rather than being entirely mythical. By introducing GIS-based methods and the supplemental use of generative AI, this study represents both a pilot project and an attempt to advance the digital transformation (DX) of ancient historical studies in Japan.

Abstract: Chronic migraine affects 1–2% of the global population and is the leading cause of neurological disability among women under 50 years of age. The advent of calcitonin gene–related peptide (CGRP)-targeting monoclonal antibodies and small-molecule receptor antagonists has constituted the first disease-specific preventive paradigm; nonetheless, real-world registries demonstrate that 30–50% of treated patients fail to revert to an episodic phenotype, with medication-overuse headache further complicating clinical management. The therapeutic ceiling observed with single-target CGRP pharmacology implies that chronification is governed by mechanisms operating upstream of, in parallel with, and beyond the trigeminovascular neuropeptide loop. The present narrative review synthesises converging evidence from 2020 to 2026 and advances a multi-stratum model in which chronic migraine is conceptualised as an emergent systems failure. Within the trigeminocervical complex, the alarmin high-mobility group box 1 (HMGB1) is proposed to function as an upstream catalyst of the Toll-like receptor 4 (TLR4)–NF-κB–CGRP signalling axis; murine nitroglycerin models indicate that HMGB1 silencing attenuates neuroinflammation and central sensitisation. Clinical data obtained from patients with medication-overuse headache reveal elevated circulating concentrations of lipopolysaccharide, HMGB1, and hypoxia-inducible factor 1-alpha, consistent with intestinal-barrier compromise driving sustained systemic neuroinflammation. Preclinical findings from 2026 document sex-specific perturbations of the gut microbiota and faecal metabolome, together with augmented allodynia in female chronic-migraine models; complementary work demonstrates that sleep restriction and caffeine synergistically reduce the trigeminovascular activation threshold in a sex-dependent manner. Functional neuroimaging implicates sustained decoupling of the salience, default-mode, and central-executive networks as the putative neural substrate of interictal cognitive morbidity. A complementary computational account, grounded in the Free Energy Principle, conceptualises chronification as the consolidation of pathologically rigid prior beliefs—a hypothesis amenable to falsification via task-based contingent-negative-variation, mismatch-negativity, and Hierarchical Gaussian Filter modelling of probabilistic-learning paradigms. It is concluded that progress in chronic-migraine research requires a transition from single-target optimisation toward multi-stratum intervention, anchored in a longitudinal transitional cohort with integrated neuroimaging, electrophysiological, microbial, and ecological-momentary endpoints.

Abstract: Continual learning (CL), also referred to as lifelong learning, aims to develop intelligent systems capable of learning continuously from sequential data while retaining previously acquired knowledge. As AI systems are increasingly deployed in dynamic real-world environments, CL has become essential for enabling long-term adaptation without catastrophic forgetting. This review provides a structured overview of major CL paradigms, including task-incremental, domain-incremental, class-incremental, online, multimodal, and federated CL. We examine the theoretical foundations of CL, particularly the stability-plasticity dilemma, catastrophic forgetting, transfer dynamics, and representation learning. In addition, we analyze major methodological categories, including regularization-based, replay-based, architecture-based, optimization-based, representation-learning, and parameter-efficient approaches. Recent developments involving transformers, prompt learning, foundation models, and multimodal adaptation are also discussed as emerging directions in modern CL research. Furthermore, this review highlights important issues related to benchmark fragmentation, evaluation inconsistency, memory constraints, computational efficiency, scalability, and privacy-aware learning. We also summarize key application domains, including computer vision, natural language processing, robotics, healthcare, and medical imaging. Finally, we identify open research challenges and future directions toward scalable, reliable, and deployment-oriented lifelong learning systems capable of operating effectively in continuously evolving environments.

Abstract:

A hypothesis was proposed that continuous dual-circuit mixing can be achieved by equipping a feed mixer-distributor with two leveling–mixing finger shafts, which, after lifting the feed mass to a certain height, collect it in the central part of the hopper and divide it into two flows directed toward the end walls of the hopper. In this case, continuous dual-circuit mixing is performed during each rotation of the leveling–mixing shaft. A structural and technological scheme, engineering documentation, and an experimental prototype of the feed mixer-distributor were developed. The machine consists of a 3.0 m³ hopper, two horizontal augers, two leveling–mixing finger shafts, a loading conveyor, and a drive mechanism. Theoretical investigations were conducted, and analytical expressions were obtained to determine the circumferential velocity of the fingers of the leveling–mixing device, which should ensure the movement of the feed mixture without scattering and provide the release of the feed mass from the finger surface at a finger rotation angle of 30°. Calculations based on the obtained analytical expressions showed that the critical circumferential velocity of the fingers was 0.8 m/s, while the rotational speed of the finger shaft was 19 min^-1. An analytical expression was also obtained to determine the velocity of feed mixture movement along the finger surface. Based on the calculations, the optimal value of this velocity was found to be 0.7 m/s. This value corresponds to the rational velocity of feed mixture transportation toward the end walls of the hopper. Laboratory experiments were carried out using the feed mixer-distributor at a leveling–mixing finger shaft rotational speed of n = 20 min^-1. The optimal mixing time required to achieve the target mixture uniformity was 5.5 min, which is 15.4% lower than that of existing machines. Comparative experiments also showed that incorporating the leveling–mixing device into the feed mixer-distributor reduced the power consumption of the mixing process by 34%.

Abstract: Microbiology laboratories generate extensive experimental outputs that are often in-sufficiently translated into applied innovation and technology development. This study presents a Routine-to-Research-to-Innovation (R2R) framework integrating routine labor-atory workflows with bioactivity validation, formulation development, and intellectual property (IP) mapping. Lactic acid bacteria isolated from Thai fermented foods demon-strated strong bacteriocin activity and storage stability, while secondary metabolites de-rived from Streptomyces and Brevibacillus exhibited antibacterial, antioxidant, and an-ti-inflammatory activities with prototype formulation potential. Red palm oil-based sys-tems enriched with microbial bioactives also showed favorable physicochemical stability under accelerated conditions. Patent landscape analysis (Thailand, 2020–2025) demon-strated translational alignment between laboratory outputs and existing innovation do-mains, supporting the potential application of the R2R framework in translational micro-biology, technology transfer, and early-stage innovation development.

Abstract: Introduction- Undergoing drastic metabolic and behavioral transformations, carcinogenesis is a cumulative process which leads to excessive proliferation in an unusual manner, camouflage to escape surveillance by the immune system. Aim-The aim of this review is to provide an in-depth exploration of immunology of cancer, highlighting the mechanisms and various aspects immune system, how it interacts with cancer cells and the challenges coming in its way due to tumor cell immune evasion. Method-A comprehensive literature survey and search was made across major electronic databases, which include PubMed, Scopus, and Web of Science, covering publications up to June 2025. The search strategy employed combinations of keywords and medical terms relevant to tumors. Conclusion-Representing one of the most significant advances in the field of oncology the evolving field of cancer immunotherapy offers promising treatment options thereby harnessing the body’s immune system to target cancer cells. Justification-it is not our intention to revisit many of the issues relating to tumor immunology, which have already been covered in detail previously in the literature. Rather this article focuses on the aspects that by compiling desperate foundational knowledge and parallel newer advances in this rapidly evolving field, the review offers a holistic framework of worth to our researchers, clinicians, and students working in the area of cancer immunology and oncology.

Abstract: While aspect-based sentiment analysis (ABSA) has gained significant progress in the identification of explicit opinion targets, the more challenging case, implicit aspects, has not been sufficiently studied. Implicit aspect extraction is particularly challenging as it relies on contextual and semantic cues and requires systems to infer what reviewers mean rather than just say. In this paper, we propose a four-component hybrid solution for explicit and implicit aspect extraction that formulates aspect extraction as a controlled text generation task. The solution combines: (i) a fine-tuned decoder-only large language model as a generative baseline, (ii) an iterative residual generation strategy that recovers multiple aspects through successive regeneration passes, (iii) paraphrase-based input transformation to broaden the contextual signal, and (iv) domain-specific knowledge graphs activated by linguistic signals to infer implicit aspects. The novelty is not in the individual components themselves, but in the principled orchestration of these components and the gating logic for when each stage is activated. Extensive experiments are conducted on eight benchmark ABSA datasets in both English and Arabic including SemEval 2014, 2015, 2016, ACOS and M-ABSA for English and SemEval 2016, HAAD, and M-ABSA for Arabic. The proposed solution consistently outperforms strong baseline methods and recent state-of-the-art models on English datasets with F1-scores of 0.8533, 0.713, 0.7859, 0.793 and 0.664 respectively, and F1-scores of 0.7336, 0.4765 and 0.7601 on Arabic datasets respectively. These results demonstrate the effectiveness of generative modeling, iterative generation, paraphrasing and structured knowledge for aspect extraction, and the potential of the proposed approach for implicit aspect identification in particular for morphologically rich and low-resource languages such as Arabic.

Abstract: It is shown that the existence of a maximal proper acceleration implies a bound for the acceleration in FICS.

Abstract: The use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, especially for creating Intrusion Detection Systems (IDS), has become increasingly important. These systems are essential for detecting malicious behaviour, identifying network issues, and stopping cyberattacks in real time. Although extensive research has been conducted on various ML and Deep Learning (DL) models for IDS, the current literature remains incomplete. It has many different datasets, methods, and evaluation standards. As cyber threats become more advanced, it is crucial to conduct a thorough analysis of ML techniques for intrusion detection. The goal of this Systematic Literature Review (SLR) is to give a full picture of the most recent academic articles on ML-based IDS. The study addresses important research questions about the most widely used algorithms, the types of attacks and network environments covered, the methodological problems that remain unsolved, and the new trends that should shape future research. Following the PRISMA framework, we conducted a systematic review of peer-reviewed articles published between January 2022 and May 2025. We searched IEEE Xplore, ACM Digital Library, and SpringerLink, yielding 22,558 initial records. After carefully applying strict inclusion criteria, 125 papers were selected for the final analysis. We created a standardised data extraction form (i.e., using MS Excel) to gather bibliographic details, research emphasis, methodological strategies, datasets, evaluation criteria, and recognised constraints. We employed thematic analysis to develop a clear taxonomy. We identified five main research themes in our analysis: (1) ensemble and hybrid learning pipelines focused on performance optimisation (30 papers), (2) context-specific IDS designs for Internet of Things (IoT), cloud, and Software-Defined Networking (SDN) environments (34 papers), (3) data-centric engineering that deals with class imbalance and feature selection (20 papers), (4) deep neural architectures for representation learning (31 papers), and (5) trustworthiness concerns like adversarial robustness, zero-day detection, and Explainable AI (XAI) (10 papers). Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Random Forests are the most commonly used algorithms, often combined. Nonetheless, significant deficiencies remain: about 2% of papers incorporate XAI, only 4% focus on adversarial robustness, and none validate their models in real-world production settings. Denial of Service (DoS) and Distributed DoS (DDoS) are the most common types of attacks in the literature, while Web attacks, ransomware, and advanced persistent threats remain poorly studied. The number of publications grows at an average of 30.2% annually, but the field still relies on legacy benchmark datasets rather than operational validation.