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In this research article, we essentially aim to examine the stability properties of a certain type of Cohen-Grossberg neural network. The analysed neural network involves multiple delay parameters. These delay parameters complicate the dynamical behaviour of the system, thereby increasing the risk of oscillations and chaotic behaviour, which adversely affect system stability. However, under specific system parameter constraints, the stability of the system can be ensured. In our study, we developed new adequate stability conditions that guarantee global asymptotic stability for neutral-type Cohen-Grossberg artificial neural networks with multiple delays. These conditions, which can serve as an alternative to the results in the literature, are derived by utilizing suitable Lyapunov functionals and the Lyapunov theorem. The proposed stability conditions are formulated as algebraic equations. Within this context, our proposed stability conditions can be easily examined by using some mathematical methods and software tools. By carrying out a detailed analysis of an instructive numerical example, the results obtained in this article are also shown to establish alternative stability criteria to the corresponding stability conditions given in the past literature.

Research summary: Market conditions are known to matter for firm performance and growth. This study explores how changing levels of uncertainty and competition affect interfirm ties of entrepreneurial firms as markets transition from nascent to growth stage. Tracing six entrepreneurial game publishers during the growth stage of the U.S. wireless gaming market, the findings reveal that in a growth stage market, as uncertainty decreases, certain ties of entrepreneurial firms are terminated. First, existing partners may cut ties and become competitors after entering the market directly. This is a \"winner's curse\" as more successful firms are more likely to entice their partners to enter the market directly. Second, ties may be terminated as prominent firms that are \"overwhelmed\" with too many partners cut ties with low to mediocre performance, while their remaining partners enter a positive spiral of tie strength and performance. Finally, as uncertainty decreases, new firms may enter the market as competitors to prominent firms. While entrepreneurial firms with high- and low-performing ties to prominent partners may find ties with these new entrants attractive, those with mediocre ties to few prominent partners find this move too risky and wait for a first mover to legitimate it. Overall, the findings show that changing levels of uncertainty and competition in growth stage markets can have different consequences for firms due to heterogeneity in their ties and power relative to partners. The findings provide several contributions to literature regarding the relationship among interfirm ties, firm performance, and market evolution. Managerial summary: Based on interviews at six entrepreneurial game publishers in the United States and their partners, this study shows how changing levels of uncertainty and competition in growing markets can have different consequences for firms based on the different types of alliances in their portfolio and their power relative to partners. The findings highlight the importance of managing partners differently based on alliance type and goal of the partner. They advocate remaining flexible in alliance management as information asymmetries, intentions and bargaining power of partners can change and lead to abrupt alliance dissolution. They show that alliance portfolio management goes beyond a firm's capability of managing individual alliances, and provide a tool for managers to evaluate their alliance portfolios and take the necessary precautions.

This study proposes an innovative product design to enhance the safety of indoor furniture. Currently, furniture presents safety concerns due to various factors such as improper placement, excessive loads, and environmental influences. The most prominent of these safety issues is furniture tip-over. Therefore, product designs aimed at preventing tip-over have been developed to ensure safety in indoor environments and avert potential accidents. In the initial phase of the study, the factors contributing to tip-over in a standard cabinet were investigated: the height of force application, leg diameter, leg length, leg’s position on the bottom panel, and the furniture’s load status. In the second phase, designs to prevent tip-over were developed. Following prototype production stages, the developed product was mounted on the cabinet, and experiments incorporating product variables and other variations were conducted. The results indicate that the effectiveness of the developed tip-over prevention product varies depending on usage, with its impact on preventing tip-over ranging between 17.5% and 54.3%. These findings suggest that the designed product can significantly enhance indoor safety and offers a potential alternative solution for preventing furniture tip-over.

Owing to its potential advantages such as scalability, low latency and power efficiency, optical computing has seen rapid advances over the last decades. Here, we present the design and analysis of cascadable all-optical NAND gates using diffractive neural networks. We encoded the logical values at the input and output planes of a diffractive NAND gate using the relative optical power of two spatially-separated apertures. Based on this architecture, we numerically optimized the design of a diffractive neural network composed of 4 passive layers to all-optically perform NAND operation using diffraction of light, and cascaded these diffractive NAND gates to perform complex logical functions by successively feeding the output of one diffractive NAND gate into another. We numerically demonstrated the cascadability of our diffractive NAND gates by using identical diffractive designs to all-optically perform AND and OR operations, which can be formulated as AND I 1 , I 2 = NAND NAND I 1 , I 2 , NAND I 1 , I 2 and OR I 1 , I 2 = NAND NAND I 1 , I 1 , NAND I 2 , I 2 , respectively. We also designed an all-optical half-adder that takes two logical values as input and returns their sum and the carry using cascaded diffractive NAND gates. Cascadable all-optical NAND gates composed of spatially-engineered passive diffractive layers can serve optical computing platforms.

This study aims to explore the transformative potential of Artificial Intelligence (AI) in international trade, focusing on its key roles in optimizing trade operations, enhancing trade finance, and expanding market access. In trade optimization, AI leverages advanced machine learning and predictive analytics to enhance demand forecasting, route optimization, and customs procedures, leading to more efficient logistics and inventory management. In trade finance, AI can automate document processing and risk assessment, increasing access to finance and enhancing transactional transparency, particularly through integration with blockchain technology. In terms of market access, AI-driven analytics can identify consumer trends and competitive dynamics, enabling personalized marketing and overcoming linguistic and cultural barriers. Due to the lack of quantitative data, this study employed qualitative research methods, specifically a multiple-case-study approach. The case studies of leading companies such as Alibaba, DHL, and Maersk showcase how they leverage AI to optimize their trade operations, improve customer service, and achieve greater efficiency. These real-world examples demonstrate AI's practical applications and significant benefits in the global trade landscape. However, the adoption of AI in international trade is not without challenges. These include issues around data quality, ethical concerns, technological complexity, and public perception. Policy recommendations highlight the need for a robust data infrastructure, establishing ethical AI guidelines, and fostering international cooperation to align data protection regulations.

PurposeWei et al. evaluated the global research in spine using scientometric methods based on a sample of 13,115 papers published in 5 spine journals from 2004 to 2013. This study builds on this pioneering study and provides up-to-date and thorough information on spine based on a sample of 166,962 papers for the stakeholders.Method‘Articles’ and ‘reviews’ published in ‘English’ in the journals indexed by the ‘Web of Science’ primary databases between 1980 and 2017 were retrieved through the use of an optimal keyword set for titles of both papers and ten spine journals. The information on document types and number of papers, authors, countries, funding bodies, institutions, publication years, journals, ‘Web of Science’ subject categories, and ten top citation classics were analyzed.ResultsA large sample of 166,962 papers were retrieved. The ‘reviews’ and ‘proceedings papers’ formed 5.8 and 2.8% of the sample, respectively. ‘Fehlings’, ‘Vaccaro’, ‘Takahashi’, ‘Lenke’, and ‘Gokaslan’ were the most-prolific authors. Nearly 0.7% of the papers had group authors besides single authors. The US was the most prolific country publishing 37.3% of the sample whilst Europe contributed to more than 39.8% of the sample. Only, 26.6% of the papers disclosed research funding. Among 40,897 institutions, ‘Harvard University’ was the most-prolific institution whilst the US institutions dominated the top-institution list. The research output steadily rose from 1375 papers in 1980 to 9357 papers in 2016 whilst 69.2% of the papers were published after 2000. Ten spine journals published only 23.4% of the sample. ‘Clinical Neurology’, ‘Orthopedics’, ‘Neurosciences’, and ‘Surgery’ was the most prolific subject categories. The top citation classic was a paper by van der Linden et al. on ankylosing spondylitis.ConclusionsThe optimal design of research sample made it possible to obtain nearly 13 times the size of the sample in Wei et al. as a true representation of the research in spine through the use of an optimal keyword set for the titles of both papers and 10 spine journals. However, despite the inefficient design of the incentive structures for the relevant stakeholders, the research in spine had expanded 6.8 times since 1980.

The emergence of Maritime Autonomous Surface Ships (MASS) marks a transformative shift in the maritime sector, driven by increasing digitalisation and technological innovation. However, this shift introduces complex cyber-physical risks that are not adequately addressed by traditional marine insurance frameworks. Cyber incidents, such as targeted attacks on navigation systems, can lead to physical consequences like collisions or groundings, while also causing digital losses through data corruption. These intertwined risks blur the boundaries between conventional and cyber threats, revealing significant gaps in current insurance coverage. Traditional marine policies, including Institute Time Clauses – Hulls and War and Strike Clauses, primarily focus on physical assets such as hull, machinery, and cargo, with limited or no provisions for cyber-related incidents. As MASS rely heavily on interconnected digital systems, the lack of tailored insurance coverage exposes them to substantial operational and financial vulnerabilities. This paper examines how well current policies respond to the unique challenges of autonomous vessels, especially cyber-related incidents that lead to losses such as fire. The findings highlight an urgent need to reform existing insurance strategies. As a potential solution, the paper proposes hybrid policies that adapt conventional marine and war risk coverage to explicitly include cyber risks.

Background Gastric cancer is one of the deadliest cancers, currently available therapies have limited success. Cancer-associated fibroblasts (CAFs) are pivotal cells in the stroma of gastric tumors posing a great risk for progression and chemoresistance. The poor prognostic signature for CAFs is not clear in gastric cancer, and drugs that target CAFs are lacking in the clinic. In this study, we aim to identify a poor prognostic gene signature for CAFs, targeting which may increase the therapeutic success in gastric cancer. Methods We analyzed four GEO datasets with a network-based approach and validated key CAF markers in The Cancer Genome Atlas (TCGA) and The Asian Cancer Research Group (ACRG) cohorts. We implemented stepwise multivariate Cox regression guided by a pan-cancer analysis in TCGA to identify a poor prognostic gene signature for CAF infiltration in gastric cancer. Lastly, we conducted a database search for drugs targeting the signature genes. Results Our study revealed the COL1A1, COL1A2, COL3A1, COL5A1, FN1 , and SPARC as the key CAF markers in gastric cancer. Analysis of the TCGA and ACRG cohorts validated their upregulation and poor prognostic significance. The stepwise multivariate Cox regression elucidated COL1A1 and COL5A1 , together with ITGA4, Emilin1 , and TSPAN9 as poor prognostic signature genes for CAF infiltration. The search on drug databases revealed collagenase clostridium histolyticum , ocriplasmin, halofuginone, natalizumab, firategrast, and BIO-1211 as the potential drugs for further investigation. Conclusions Our study demonstrated the central role of extracellular matrix components secreted and remodeled by CAFs in gastric cancer. The gene signature we identified in this study carries high potential as a predictive tool for poor prognosis in gastric cancer patients. Elucidating the mechanisms by which the signature genes contribute to poor patient outcomes can lead to the discovery of more potent molecular-targeted agents and increase the therapeutic success in gastric cancer.

Deep learning uses multilayered artificial neural networks to learn digitally from large datasets. It then performs advanced identification and classification tasks. To date, these multilayered neural networks have been implemented on a computer. Lin et al. demonstrate all-optical machine learning that uses passive optical components that can be patterned and fabricated with 3D-printing. Their hardware approach comprises stacked layers of diffractive optical elements analogous to an artificial neural network that can be trained to execute complex functions at the speed of light. Science , this issue p. 1004 All-optical deep learning can be implemented with 3D-printed passive optical components. Deep learning has been transforming our ability to execute advanced inference tasks using computers. Here we introduce a physical mechanism to perform machine learning by demonstrating an all-optical diffractive deep neural network (D 2 NN) architecture that can implement various functions following the deep learning–based design of passive diffractive layers that work collectively. We created 3D-printed D 2 NNs that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can execute; will find applications in all-optical image analysis, feature detection, and object classification; and will also enable new camera designs and optical components that perform distinctive tasks using D 2 NNs.

Hypoxia-inducible factor-1α (HIF-1α) is a crucial mediator of intra-tumoral heterogeneity, tumor progression, and unresponsiveness to therapy in tumors with hypoxia. Gastric tumors, one of the most aggressive tumors in the clinic, are highly enriched in hypoxic niches, and the degree of hypoxia is strongly correlated with poor survival in gastric cancer patients. Stemness and chemoresistance in gastric cancer are the two root causes of poor patient outcomes. Based on the pivotal role of HIF-1α in stemness and chemoresistance in gastric cancer, the interest in identifying critical molecular targets and strategies for surpassing the action of HIF-1α is expanding. Despite that, the understanding of HIF-1α induced signaling in gastric cancer is far from complete, and the development of efficacious HIF-1α inhibitors bears various challenges. Hence, here we review the molecular mechanisms by which HIF-1α signaling stimulates stemness and chemoresistance in gastric cancer, with the clinical efforts and challenges to translate anti-HIF-1α strategies into the clinic.