Hasil untuk "Industries. Land use. Labor"

Yassine Bougacha, Geoffrey Delhomme, Mélanie Ducoffe et al.

This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain (ODD), addressing issues like unrealistic landing scenarios and expanding coverage to multi-runway airports; (3) Benchmarking ML models for autonomous landing systems, introducing a framework for evaluating object detection subtask in a complex multi-instances setting, and providing associated open-source models as a baseline for AI models' performance.

Alexandra POPESCU-ZORICA

This paper explores the necessity for large enterprises to develop robust internal innovation capabilities as a strategic imperative for sustaining long-term competitiveness. While 83% of global companies prioritize innovation, only 3% demonstrate high innovation readiness, highlighting a gap between ambition and execution. The study employs a literature review and case study analysis to examine the structural and cultural barriers that hinder corporate innovation, including risk aversion, hierarchical decision-making, and a focus on operational efficiency over exploration. The research further evaluates leading innovation governance models, including Deschamps & Nelson’s governance structures and Brouwer’s hierarchical, market-driven, and hybrid models, as well as practical innovation frameworks such as the Ambidextrous Organization Model and Doblin’s Ten Types of Innovation. The findings suggest that companies must move beyond incremental improvements and acquisitions by implementing adaptive governance structures, cross-functional collaboration, and long-term strategic foresight. The paper identifies a set of best practices for developing innovation capacity, including the integration of intrapreneurship programs, AI-driven innovation, open innovation partnerships, and agile methodologies. The research concludes that organizations achieving a balance between governance structure, innovation frameworks, and strategic adaptability outperform competitors in growth and resilience. Recommendations include establishing leadership commitment, aligning innovation efforts with corporate strategy, and fostering a culture of experimentation to drive long-term business success.

Ashik E Rasul, Humaira Tasnim, Ji Yu Kim et al.

QuadPlanes combine the range efficiency of fixed-wing aircraft with the maneuverability of multi-rotor platforms for long-range autonomous missions. In GPS-denied or cluttered urban environments, perception-based landing is vital for reliable operation. Unlike structured landing zones, real-world sites are unstructured and highly variable, requiring strong generalization capabilities from the perception system. Deep neural networks (DNNs) provide a scalable solution for learning landing site features across diverse visual and environmental conditions. While perception-driven landing has been shown in simulation, real-world deployment introduces significant challenges. Payload and volume constraints limit high-performance edge AI devices like the NVIDIA Jetson Orin Nano, which are crucial for real-time detection and control. Accurate pose estimation during descent is necessary, especially in the absence of GPS, and relies on dependable visual-inertial odometry. Achieving this with limited edge AI resources requires careful optimization of the entire deployment framework. The flight characteristics of large QuadPlanes further complicate the problem. These aircraft exhibit high inertia, reduced thrust vectoring, and slow response times further complicate stable landing maneuvers. This work presents a lightweight QuadPlane system for efficient vision-based autonomous landing and visual-inertial odometry, specifically developed for long-range QuadPlane operations such as aerial monitoring. It describes the hardware platform, sensor configuration, and embedded computing architecture designed to meet demanding real-time, physical constraints. This establishes a foundation for deploying autonomous landing in dynamic, unstructured, GPS-denied environments.

Yohny Anwar, Indra Muis

The relationship among knowledge management, organizational ambidexterity, innovation, and marketing performance is crucial in driving organizational success and competitive advantage in today's rapidly changing business environment. The aim of the study is to look at relationship knowledge management, organizational ambidexterity, innovation, and marketing performance. Besides, it examines the effects of knowledge management and organizational ambidexterity on marketing performance as intervened by innovation as a mediator. The unit analysis is the owners of SMEs in Medan Municipality, North Sumatra Province, Indonesia. This research applies quantitative methods. The research population was 478 SMEs in technology and internet businesses registered in the Government Office of Cooperatives and Micro, Small, and Medium Enterprises (MSME) of Medan, North Sumatra, Indonesia. The respondents were 217 owners of SMEs. The sampling technique is simple random sampling. The data analysis uses the Partial Least Square technique. The research finds that both knowledge management and organizational ambidexterity have positive impacts on innovation. Innovation mediates the knowledge management-marketing performance relationship and organizational ambidexterity-marketing performance relationship. It is advisable for Small Medium Business owners to implement knowledge management, organizational ambidexterity, and innovation to increase their marketing performance.

Rimsha Jalil, Lal gul Khan, Muhammad Faheem Afzal

Background: Lateral epicondylitis, commonly known as tennis elbow, is characterized by pain and functional limitations at the elbow due to overuse. This study focuses on evaluating the effects of Tyler twist wrist extensor-strengthening exercises, aiming to provide insights into an effective intervention for this condition. Objective: This study aimed to determine the effects of Tyler twist wrist extensor strengthening exercises on pain, disability, and grip strength in patients with lateral epicondylitis. Methodology: A randomized controlled trial was conducted at Cina Medical Center Rawalpindi from February 2022 to January 2023. Fifty-two participants meeting the inclusion criteria were assigned to either Group A or Group B. Group A received eccentric Tyler twist exercises in addition to conventional physiotherapy, while Group B received conventional physiotherapy alone. Pain, functional disability, and grip strength were assessed at baseline, the second week, and the fourth week. Data was analyzed with SPSS version 25. Demographics and descriptive data is presented in form of percentages, frequencies and mean + SD. Normality of the data is determined via Shapiro Wilk Test. We applied mixed-way ANOVA to find the interaction between two groups. Results: Within-group analysis demonstrated a significant improvement in pain, functional disability, and grip strength for both groups (p-value < 0.01). Between-group analysis revealed a significant difference in pain, functional disability, and grip strength. Conclusion: The addition of eccentric Tyler twist exercises to conventional therapy showed a statistically significant difference in terms of pain, functional disability, and grip strength in patients with lateral epicondylitis. Keywords: Extensor Carpi Radialis Brevis, Lateral Epicondylitis.

Hafiz Mughees Ahmad, Afshin Rahimi

Workplace accidents continue to pose significant risks for human safety, particularly in industries such as construction and manufacturing, and the necessity for effective Personal Protective Equipment (PPE) compliance has become increasingly paramount. Our research focuses on the development of non-invasive techniques based on the Object Detection (OD) and Convolutional Neural Network (CNN) to detect and verify the proper use of various types of PPE such as helmets, safety glasses, masks, and protective clothing. This study proposes the SH17 Dataset, consisting of 8,099 annotated images containing 75,994 instances of 17 classes collected from diverse industrial environments, to train and validate the OD models. We have trained state-of-the-art OD models for benchmarking, and initial results demonstrate promising accuracy levels with You Only Look Once (YOLO)v9-e model variant exceeding 70.9% in PPE detection. The performance of the model validation on cross-domain datasets suggests that integrating these technologies can significantly improve safety management systems, providing a scalable and efficient solution for industries striving to meet human safety regulations and protect their workforce. The dataset is available at https://github.com/ahmadmughees/sh17dataset.

Debabrata Pal, Anvita Singh, Saumya Saumya et al.

The intrinsic capability of the Human Vision System (HVS) to perceive depth of field and failure of Instrument Landing Systems (ILS) stimulates a pilot to perform a vision-based manual landing over an autoland approach. However, harsh weather creates challenges, and a pilot must have a clear view of runway elements before the minimum decision altitude. To aid in manual landing, a vision-based system trained to clear weather-induced visual degradations requires a robust landing dataset under various climatic conditions. Nevertheless, to acquire a dataset, flying an aircraft in dangerous weather impacts safety. Also, this system fails to generate reliable warnings, as localization of runway elements suffers from projective distortion while landing at crosswind. To combat, we propose to synthesize harsh weather landing images by training a prompt-based climatic diffusion network. Also, we optimize a weather distillation model using a novel diffusion-distillation loss to learn to clear these visual degradations. Precisely, the distillation model learns an inverse relationship with the diffusion network. Inference time, pre-trained distillation network directly clears weather-impacted onboard camera images, which can be further projected to display devices for improved visibility.Then, to tackle crosswind landing, a novel Regularized Spatial Transformer Networks (RuSTaN) module accurately warps landing images. It minimizes the localization error of runway object detector and helps generate reliable internal software warnings. Finally, we curated an aircraft landing dataset (AIRLAD) by simulating a landing scenario under various weather degradations and experimentally validated our contributions.

Yi Li, Plamen Angelov, Zhengxin Yu et al.

As the development of deep learning techniques in autonomous landing systems continues to grow, one of the major challenges is trust and security in the face of possible adversarial attacks. In this paper, we propose a federated adversarial learning-based framework to detect landing runways using paired data comprising of clean local data and its adversarial version. Firstly, the local model is pre-trained on a large-scale lane detection dataset. Then, instead of exploiting large instance-adaptive models, we resort to a parameter-efficient fine-tuning method known as scale and shift deep features (SSF), upon the pre-trained model. Secondly, in each SSF layer, distributions of clean local data and its adversarial version are disentangled for accurate statistics estimation. To the best of our knowledge, this marks the first instance of federated learning work that address the adversarial sample problem in landing runway detection. Our experimental evaluations over both synthesis and real images of Landing Approach Runway Detection (LARD) dataset consistently demonstrate good performance of the proposed federated adversarial learning and robust to adversarial attacks.

Anton Gaek, Artem Sukhov

The problem of creating a three-sided dice with the probability of it landing on each of its sides being equal to 1/3 has been around for many years. Various approaches have been attempted, but as different authors achieved at different results, no uniform answer has been found. In this paper, the probability of a cylinder-shaped coin landing on its side is investigated, aiming to predict the final position of the coin, based on the starting physical and geometrical parameters of the coin. Pure physical methods and computer modeling have been used to find the parameters of a "fair" coin and to determine which parameters of the system affect the probability of the coin landing on an edge, and in what way. Statistical analysis of the situation has been made, to ensure that the probabilities we are trying to find, correspond to the appropriate physical events. Overall, two main possibilities for modeling the situation are considered: single-axis rotation (flat rotation) and the superposition of rotation against multiple axes (volumetric rotation). For each, a model was built and it was compared with computer modeling and real-life experiments. Dynamical and energetic approaches have been considered, and a model of the coin during its impact with the ground has been created. A robot that is capable of launching cylindrical coins, picking them up, and recording the result of the experiment, controlling starting parameters precisely, and distributing them to our needs, and automatically record the results of the experiment, was created. In conclusion, the parameters of a "fair" coin are provided, which have even probabilities landing on all its sides, as well as the dependencies of the probability of a coin landing on its edge, on different parameters.

Aleksandr Sidorenko, Mahdi Rezapour, Achim Wagner et al.

The Industry 4.0 paradigm manifests the shift towards mass customization and cyber-physical production systems (CPPS) and sets new requirements for industrial automation software in terms of modularity, flexibility, and short development cycles of control programs. Though programmable logical controllers (PLCs) have been evolving into versatile and powerful edge devices, there is a lack of PLC software flexibility and integration between low-level programs and high-level task-oriented control frameworks. Behavior trees (BTs) is a novel framework, which enables rapid design of modular hierarchical control structures. It combines improved modularity with a simple and intuitive design of control logic. This paper proposes an approach for improving the industrial control software design by integrating BTs into PLC programs and separating hardware related functionalities from the coordination logic. Several strategies for integration of BTs into PLCs are shown. The first two integrate BTs with the IEC 61131 based PLCs and are based on the use of the PLCopen Common Behavior Model. The last one utilized event-based BTs and shows the integration with the IEC 61499 based controllers. An application example demonstrates the approach. The paper contributes in the following ways. First, we propose a new PLC software design, which improves modularity, supports better separation of concerns, and enables rapid development and reconfiguration of the control software. Second, we show and evaluate the integration of the BT framework into both IEC 61131 and IEC 61499 based PLCs, as well as the integration of the PLCopen function blocks with the external BT library. This leads to better integration of the low-level PLC code and the AI-based task-oriented frameworks. It also improves the skill-based programming approach for PLCs by using BTs for skills composition.

Ragmoun Wided, Abdulaziz Abdulmohsen Alfalih

Can artificial intelligence networks promote extremism awareness through social intelligence and emotional intelligence? This research contributes to this question in the context of Saudi Arabia. This study defines a model of a cooperative process through an artificial intelligence network, based on knowledge exchange, to generate a high level of extremism awareness and social intelligence. Four main variables were adopted, developed, defined, and measured: artificial intelligence networks, social intelligence, emotional intelligence, and extremism awareness. We fixed attributes for contextualized interactions through a network platform, between professionals and non-professionals, against extremism. The application of artificial intelligence in such platforms lets members share reliable information to combat extremism more effectively. The findings demonstrate that network centrality, network scale, relationship strengths, relationship stability, and reciprocity developed through artificial intelligence networks stimulate extremism awareness by developing social awareness. Emotional intelligence also seems to be important. It moderates the link between platform users and extremism awareness. It facilitates situational and contextual awareness to define appropriate behavior.

Shahintaj Karimi, Ayatollah Karami, Fatemeh Alipanahiyan

Social capital is one of the influential components in the performance and success of cooperatives, including rural cooperatives, which is considered by experts. The purpose of this study is to investigate the role of social capital in the success of the rural cooperative in Dana women. The method of this research is descriptive-analytic and a questionnaire technique is used to collect information. The statistical population is 600 members of the rural women's rural cooperative in Dena County, according to Bartlett's table, 100 were identified. The questionnaire was the most important tool for collecting data. The results of the questionnaire were analyzed using Spss software. To test the hypothesis, t-test, Pearson correlation coefficient was used. In order to investigate the role of social capital in the success of rural women's cooperatives in Dena, indicators such as social capital, social trust, and social participation were measured. Based on the results obtained from the indicators of social capital research, social trust index the impact on the success of the DENA Women's Co-operative.

Lochana Telugu Rajesh, Tapadhir Das, Raj Mani Shukla et al.

The rapid growth in Internet of Things (IoT) technology has become an integral part of today's industries forming the Industrial IoT (IIoT) initiative, where industries are leveraging IoT to improve communication and connectivity via emerging solutions like data analytics and cloud computing. Unfortunately, the rapid use of IoT has made it an attractive target for cybercriminals. Therefore, protecting these systems is of utmost importance. In this paper, we propose a federated transfer learning (FTL) approach to perform IIoT network intrusion detection. As part of the research, we also propose a combinational neural network as the centerpiece for performing FTL. The proposed technique splits IoT data between the client and server devices to generate corresponding models, and the weights of the client models are combined to update the server model. Results showcase high performance for the FTL setup between iterations on both the IIoT clients and the server. Additionally, the proposed FTL setup achieves better overall performance than contemporary machine learning algorithms at performing network intrusion detection.

Qichao Wang, Huan Ma, Wentao Wei et al.

The rapid development of digital economy has led to the emergence of various black and shadow internet industries, which pose potential risks that can be identified and managed through digital risk management (DRM) that uses different techniques such as machine learning and deep learning. The evolution of DRM architecture has been driven by changes in data forms. However, the development of AI-generated content (AIGC) technology, such as ChatGPT and Stable Diffusion, has given black and shadow industries powerful tools to personalize data and generate realistic images and conversations for fraudulent activities. This poses a challenge for DRM systems to control risks from the source of data generation and to respond quickly to the fast-changing risk environment. This paper aims to provide a technical analysis of the challenges and opportunities of AIGC from upstream, midstream, and downstream paths of black/shadow industries and suggest future directions for improving existing risk control systems. The paper will explore the new black and shadow techniques triggered by generative AI technology and provide insights for building the next-generation DRM system.

Ante la puesta en marcha de la “Taxonomía europea de inversiones sostenibles” en la Unión Europea, en este artículo se analiza en qué medida las entidades del sector bancario informan a inversores sobre la economía circular para contribuir a los objetivos de sostenibilidad. A tal fin se analizan las memorias de sostenibilidad de una muestra de bancos españoles y se estudia el uso de instrumentos como el renting para modelos circulares a través de una entidad financiera como caso de estudio.

Gaël Kermarrec, Philipp Morgenstern

In this contribution, we introduce a multilevel approximation method with T-splines for fitting scattered point clouds iteratively, with an application to land remote sensing. This new procedure provides a local surface approximation by an explicit computation of the control points and is called a multilevel T-splines approximation (MTA). It is computationally efficient compared with the traditional global least-squares (LS) approach, which may fail when there is an unfavourable point density from a given refinement level. We validate our approach within a simulated framework and apply it to two real datasets: (i) a surface with holes scanned with a terrestrial laser scanner, and (ii) a patch on a sand-dune in the Netherlands. Both examples highlight the potential of the MTA for rapidly fitting large and noisy point clouds with variable point density and with similar results compared to the global LS approximation.

Philip T. Metzger, Greg W. Autry

This study estimates the cost of building lunar landing pads and examines whether any construction methods are economically superior to others. Some proposed methods require large amounts of mass transported from the Earth, others require high energy consumption on the lunar surface, and others have a long construction time. Each of these factors contributes direct and indirect costs to lunar activities. The most important economic variables turn out to be the transportation cost to the lunar surface and the magnitude of the program delay cost imposed by a construction method. The cost of a landing pad depends sensitively on the optimization of the mass and speed of the construction equipment, so a minimum-cost set of equipment exists for each construction method within a specified economic scenario. Several scenarios have been analyzed across a range of transportation costs with both high and low program delay costs. It is found that microwave sintering is currently the most favorable method to build the inner, high temperature zone of a lunar landing pad, although other methods are within the range of uncertainty. The most favorable method to build the outer, low temperature zone of the landing pad is also sintering when transportation costs are high, but it switches to polymer infusion when transportation costs drop below about \$110K/kg to the lunar surface. It is estimated that the Artemis Basecamp could build a landing pad with a budgeted line-item cost of \$229M assuming that transportation costs will be reduced modestly from the current rate \$1M/kg to the lunar surface to \$300K/kg. A landing pad drops to \$130M when the transportation cost drops further to \$100K/kg, or to \$47M if transportation costs fall below \$10K/kg. Ultimately, landing pads can be built around the Moon at very low cost, due to economies of scale.

Benjamin Niswonger

This paper highlights the potential for negative dynamic consequences of recent trends towards the formation of "skill-hubs". I first show evidence that skill acquisition is biased towards skills which are in demand in local labor markets. This fact along with large heterogeneity in outcomes by major and recent reductions in migration rates implies a significant potential for inefficient skill upgrading over time. To evaluate the impact of local bias in education in the context of standard models which focus on agglomeration effects, I develop a structural spatial model which includes educational investment. The model focuses on two sources of externalities: productivity through agglomeration and signaling. Both of these affect educational decisions tilting the balance of aggregate skill composition. Signaling externalities can provide a substantial wedge in the response to changes in skill demand and skill concentration with the potential for substantial welfare gains from a more equal distribution of skills.

Hamid Rahimian, Abbas Abbaspour, Hamidreza Zarrin

In this study, the effect of Customer Knowledge Management (CKM) on the "Quality of Services" (QS) and "Customer Satisfaction" (CS) has been investigated in the Mellat Bank. This is an applied-research running in a survey method. The sample consists of 107 employees and 372 customers of the Mellat Bank branches in the Kermanshah province. A questionnaire was prepared to measure the CKM level in terms of the bank employees and another one was made to determine the QS and CS in terms of the bank customers. In order to analyze the collected data, a one-sample t-test was used to identify the status of CKM. Then, using "Confirmatory Factor Analysis" (Path Analysis method), the relationships were tested. Finally, with the help of structural equation modeling, the path model was presented. The results showed that customer knowledge management in the Mellat Bank was above average and the appropriateness of the fit of the structural model and its compliance with the conceptual model of the research was observed. The standard estimate of the general model showed that customer knowledge management had a positive and significant effect on service quality and customer satisfaction, and the main hypotheses of the research were confirmed. The partial model standard estimate indicated that the dimensions of customer knowledge management were not prominent for bank branch employees.

Thomas M. Conte, Ian T. Foster, William Gropp et al.

While past information technology (IT) advances have transformed society, future advances hold even greater promise. For example, we have only just begun to reap the changes from artificial intelligence (AI), especially machine learning (ML). Underlying IT's impact are the dramatic improvements in computer hardware, which deliver performance that unlock new capabilities. For example, recent successes in AI/ML required the synergy of improved algorithms and hardware architectures (e.g., general-purpose graphics processing units). However, unlike in the 20th Century and early 2000s, tomorrow's performance aspirations must be achieved without continued semiconductor scaling formerly provided by Moore's Law and Dennard Scaling. How will one deliver the next 100x improvement in capability at similar or less cost to enable great value? Can we make the next AI leap without 100x better hardware? This whitepaper argues for a multipronged effort to develop new computing approaches beyond Moore's Law to advance the foundation that computing provides to US industry, education, medicine, science, and government. This impact extends far beyond the IT industry itself, as IT is now central for providing value across society, for example in semi-autonomous vehicles, tele-education, health wearables, viral analysis, and efficient administration. Herein we draw upon considerable visioning work by CRA's Computing Community Consortium (CCC) and the IEEE Rebooting Computing Initiative (IEEE RCI), enabled by thought leader input from industry, academia, and the US government.