Nearly all industries exhibit geographic concentration. Most theories of the location of industry explain the persistence of these production centers as the result of economic efficiency. This article argues instead that heterogeneity in entrepreneurial opportunities, rather than differential performance, maintains geographic concentration. Entrepreneurs need exposure to existing organizations in the industry to acquire tacit knowledge, obtain important social ties, and build self‐confidence. Thus, the current geographic distribution of production places important constraints on entrepreneurial activity. Due to these constraints, new foundings tend to reify the existing geographic distribution of production. Empirical evidence from the shoe industry supports this thesis.
Purpose – The purpose of this paper is to establish the progress of information and communication technology (ICT) based on a review of papers published in tourism and hospitality journals between 2009 and 2013. Design/methodology/approach – Based on three major databases, 107 journal papers were retrieved and reviewed. The papers were grouped into two major categories, consumer and supplier, which generally comprise the key players in the industries. Findings – A content analysis showed that hospitality and tourism industries use ICT in different functional units and for different applications. This, in turn, indicates their wide adoption in the industry. Industrial implications are discussed. Practical implications – On the basis of the content analysis, industry practitioners can learn about up-to-date practices and decide how to take advantage of recent technological developments. Originality/value – A major contribution of this paper is the comprehensive review of recently published papers in tourism and hospitality journals from the perspectives of consumer and supplier.
Enabling AI literacy in the general population at scale is a complex challenge requiring multiple stakeholders and institutions collaborating together. Industry and technology companies are important actors with respect to AI, and as a field, we have the opportunity to consider how researchers and companies might be partners toward shared goals. In this symposium, we focus on a collection of partnership projects that all involve Google and all address AI literacy as a comparative set of examples. Through a combination of presentations, commentary, and moderated group discussion, the session, we will identify (1) at what points in the life cycle do research, practice, and industry partnerships clearly intersect; (2) what factors and histories shape the directional focus of the partnerships; and (3) where there may be future opportunities for new configurations of partnership that are jointly beneficial to all parties.
Md Istiauk Hossain Rifat, Moin Khan, Mohammad Zunaed
The textile industry in Bangladesh is one of the most energy-intensive sectors, yet its monitoring practices remain largely outdated, resulting in inefficient power usage and high operational costs. To address this, we propose a real-time Non-Intrusive Load Monitoring (NILM)-based framework tailored for industrial applications, with a focus on identical motor-driven loads representing textile cutting machines. A hardware setup comprising voltage and current sensors, Arduino Mega and ESP8266 was developed to capture aggregate and individual load data, which was stored and processed on cloud platforms. A new dataset was created from three identical induction motors and auxiliary loads, totaling over 180,000 samples, to evaluate the state-of-the-art MATNILM model under challenging industrial conditions. Results indicate that while aggregate energy estimation was reasonably accurate, per-appliance disaggregation faced difficulties, particularly when multiple identical machines operated simultaneously. Despite these challenges, the integrated system demonstrated practical real-time monitoring with remote accessibility through the Blynk application. This work highlights both the potential and limitations of NILM in industrial contexts, offering insights into future improvements such as higher-frequency data collection, larger-scale datasets and advanced deep learning approaches for handling identical loads.
Abstract The ternary Cu/ZnO/Al2O3 catalyst is widely used in the industry for renewable methanol synthesis. The tenuous trade‐off between the strong metal–support interaction (SMSI)‐induced Cu–ZnOx interface and the accessible Cu surface strongly affects the activity of the final catalyst. Successes in the control of oxide migration on adsorbate‐induced SMSI catalysts have motivated this to develop a supercritical CO2 activation strategy to synchronously perfect the Cu0–O–Znδ+ interface and Cu0–Cu+ surface sites through the manipulation of the adsorbate diffusion kinetics, which involves *OC2H5 and “side‐on” fixed CO2 species. This findings illustrate that the adsorbate on ZnOx can facilitate its secondary uniform nucleation and induce a ZnxAl2Oy spinel phase and that CO2 adsorption on metallic Cu0 produces an activated CuxO amorphous shell. Such a structural evolution unlocks a dual‐response pathway in methanol synthesis, thus enabling Cu/ZnO/Al2O3 with a twofold increase in catalytic activity. This atomic‐level design of active sites and understanding of supercritical CO2‐induced structural evolution will guide the future development of high‐performance supported metal catalysts.
Milk and dairy products serve as crucial sources of nutrition for humans, and their composition is extensively researched. Milk exosomes are nanoscale phospholipid bilayer vesicles released from the milk source and present in the extracellular environment, which contain protein, lipid, and genetic material, among other cell-specific substances. Milk exosomes distinguish themselves from other types of exosomes in that they are rich in sources and readily producible on a large scale. They can be selectively absorbed by target cells to exert physiological effects and thereby influence various physiological and pathological processes. Milk exosomes directly participate in and regulate diverse physiological processes by carrying their endogenous bioactive molecules including proteins, microRNAs, and lipids, manifesting significant biological activity, likewise act as ideal carriers of active substances due to their outstanding biocompatibility and high loading capacity, rendering them suitable for drug delivery and transportation. Hence, this article summarizes the effects of endogenous active substances and the role of milk exosomes as signal transduction carriers, and reviews the latest research progress in regulating intestinal health, so as to facilitate in-depth research and extensive application of milk exosomes in domains such as food science, nutrition, and medicine.
Bastien Muraccioli, Mathieu Celerier, Mehdi Benallegue
et al.
Physical Human-Robot Interaction (pHRI) is critical for implementing Industry 5.0, which focuses on human-centric approaches. However, few studies explore the practical alignment of pHRI to industrial-grade performance. This paper introduces a versatile control framework designed to bridge this gap by incorporating the torque-based control modes: compliance control, null-space compliance, and dual compliance, all in static and dynamic scenarios. Thanks to our second-order Quadratic Programming (QP) formulation, strict kinematic and collision constraints are integrated into the system as safety features, and a weighted hierarchy guarantees singularity-robust task tracking performance. The framework is implemented on a Kinova Gen3 collaborative robot (cobot) equipped with a Bota force/torque sensor. A DualShock 4 game controller is attached to the robot's end-effector to demonstrate the framework's capabilities. This setup enables seamless dynamic switching between the modes, and real-time adjustments of parameters, such as transitioning between position and torque control or selecting a more robust custom-developed low-level torque controller over the default one. Built on the open-source robotic control software mc_rtc, our framework ensures reproducibility for both research and industrial deployment, this framework demonstrates a step toward industrial-grade performance and repeatability, showcasing its potential as a robust pHRI control system for industrial environments.
T. Q. D. Pham, K. D. Tran, Khanh T. P. Nguyen
et al.
As global industries transition towards Industry 5.0 predictive maintenance PM remains crucial for cost effective operations resilience and minimizing downtime in increasingly smart manufacturing environments In this chapter we explore how the integration of Federated Learning FL and blockchain BC technologies enhances the prediction of machinerys Remaining Useful Life RUL within decentralized and human centric industrial ecosystems Traditional centralized data approaches raise concerns over privacy security and scalability especially as Artificial intelligence AI driven smart manufacturing becomes more prevalent This chapter leverages FL to enable localized model training across multiple sites while utilizing BC to ensure trust transparency and data integrity across the network This BC integrated FL framework optimizes RUL predictions enhances data privacy and security establishes transparency and promotes collaboration in decentralized manufacturing It addresses key challenges such as maintaining privacy and security ensuring transparency and fairness and incentivizing participation in decentralized networks Experimental validation using the NASA CMAPSS dataset demonstrates the model effectiveness in real world scenarios and we extend our findings to the broader research community through open source code on GitHub inviting collaborative development to drive innovation in Industry 5.0
Designing effective debt collection systems is crucial for improving operational efficiency and reducing costs in the financial industry. However, the challenges of maintaining script diversity, contextual relevance, and coherence make this task particularly difficult. This paper presents a debt collection system based on real debtor-collector data from a major commercial bank. We construct a script library from real-world debt collection conversations, and propose a two-stage retrieval based response system for contextual relevance. Experimental results show that our system improves script diversity, enhances response relevance, and achieves practical deployment efficiency through knowledge distillation. This work offers a scalable and automated solution, providing valuable insights for advancing debt collection practices in real-world applications.
The effective separation of acetylene (C2H2) from carbon dioxide (CO2) and ethylene (C2H4) presents considerable challenges in the petrochemical industry. In this work, we report a novel sulfate-pillared (SO42–) ultra-microporous material, denoted as SOFOUR-DPDS-Ni (SOFOUR = SO42–, 4-DPDS = 4,4′-dipyridyldisulfide), for efficient C2H2 capture from both CO2 and C2H4. The sulfate pillars play a crucial role in inducing robust negative electrostatic potentials within the intralayer cavities and interlayer channels, thereby facilitating the selective recognition of C2H2. As a result, SOFOUR-DPDS-Ni demonstrates a remarkable C2H2 adsorption capacity of 1.60 mmol g–1 at 0.01 bar, an exceptional selectivity of 174 for the 50/50 C2H2/CO2 mixture, and a high selectivity of 65 for the 1/99 C2H2/C2H4 mixture. These impressive metrics position SOFOUR-DPDS-Ni as a promising adsorbent for benchmark C2H2 separations. Dynamic breakthrough experiments validate its outstanding performance in separating C2H2 from both the CO2 and C2H4 mixtures. Computational simulations reveal the strong interactions between C2H2 and sulfate pillars, shedding light on the underlying mechanisms driving the adsorption process.
Abstract Background Granulosa cell (GC) apoptosis, ferroptosis, and other programmed cell death processes are markers of follicular aging. Quercetin has been shown to reduce ferroptosis, however, its effects on ferroptosis in poultry remains unexplored. Our preliminary study identified ferroptosis in aging ovaries. Therefore, in the present study, 540-day-old Mountain Plum-blossom chickens were fed with quercetin supplementation at varying doses (0.2, 0.4, and 0.6 g/kg), and examined its molecular effects on GC ferroptosis using an in vitro Erastin-induced model. Results The results showed that quercetin supplementation significantly increased egg production, which confirmed its potential to alleviate ferroptosis in chicken ovarian tissue. The in vitro experiment revealed that quercetin and Fer-1 (positive control) mitigated Erastin-induced ferroptosis in GCs. Further, transcriptome analysis revealed that quercetin modulated key genes such as acyl-CoA synthetase long-chain family member 4 (ACSL4), solute carrier family 7 member 11 (SLC7A11), and transferrin receptor (TFRC), involved in ferroptosis regulation. The results further showed that quercetin also reduced Erastin-induced apoptosis and inflammation by modulating the expression of genes and proteins related to apoptosis and inflammatory factors (NF-κB, TNF-α, IL-6, and IL-10). Conclusion Taken together, the results showed that quercetin improves egg production performance in chickens and mitigates ovarian ferroptosis in aging hens, and inhibits Erastin-induced ferroptosis, inflammation, and apoptosis in GCs. These findings revealed the protective role of quercetin in poultry ovarian tissue and its cellular mechanisms against detrimental factors in poultry production. Graphical Abstract
Siddarth Malreddy, Matthew Lawhon, Usha Amrutha Nookala
et al.
Adopting advances in recommendation systems is often challenging in industrial settings due to unique constraints. This paper aims to highlight these constraints through the lens of feature interactions. Feature interactions are critical for accurately predicting user behavior in recommendation systems and online advertising. Despite numerous novel techniques showing superior performance on benchmark datasets like Criteo, their direct application in industrial settings is hindered by constraints such as model latency, GPU memory limitations and model reproducibility. In this paper, we share our learnings from improving feature interactions in Pinterest's Homefeed ranking model under such constraints. We provide details about the specific challenges encountered, the strategies employed to address them, and the trade-offs made to balance performance with practical limitations. Additionally, we present a set of learning experiments that help guide the feature interaction architecture selection. We believe these insights will be useful for engineers who are interested in improving their model through better feature interaction learning.
This paper investigates the transformative potential of Generative AI (Gen-AI) technologies, particularly large language models, within the building industry. By leveraging these advanced AI tools, the study explores their application across key areas such as automated compliance checking and building design assistance. The research highlights how Gen-AI can automate labor-intensive processes, significantly improving efficiency and reducing costs in building practices. The paper first discusses the two widely applied fundamental models-Transformer and Diffusion model-and summarizes current pathways for accessing Gen-AI models and the most common techniques for customizing them. It then explores applications for text generation, such as compliance checking, control support, data mining, and building simulation input file editing. Additionally, it examines image generation, including direct generation through diffusion models and indirect generation through language model-supported template creation based on existing Computer-Aided Design or other design tools with rendering. The paper concludes with a comprehensive analysis of the current capabilities of Gen-AI in the building industry, outlining future directions for research and development, with the goal of paving the way for smarter, more effective, and responsive design, construction, and operational practices.