The Complementary Effect of Manufacturing Process Modularity and IS Flexibility on Agility in Manufacturing

In the situation of shortened product life cycles, modular product design enables organizations to adapt to unanticipated changes in their environments. This study extends modular systems theory to manufacturing process design and posits that: (i) firms can design their manufacturing processes for the same product into either an integrated or modularized structure, thereby being agile in dynamic environments, and (ii) the effect of manufacturing process modularity on agility is complemented by information systems (IS) flexibility. Conceptually, this study explains how important the congruence between the IS and manufacturing processes is to achieving agility in manufacturing and seeks to demonstrate how an IS adapts to shape agility. For practice, this study suggests that firms should focus their efforts on both IS flexibility and manufacturing process modularity, as well as their harmonization, in addition to modular product design

THE EFFECT OF IT CAPABILITIES ON CROSSFUNCTIONAL CAPABILITIES

Research in both the strategy and information systems (IS) areas has identified capabilities as key to competing effectively in dynamic and turbulent environments. Firms realize benefits by adopting complementary combinations of capabilities. We draw on Grant’s hierarchy of capabilities to develop a model that explains how a firm’s information technology (IT) capabilities can enhance the intangible value of the firm through their effect on a set of cross-functional capabilities. Using a sample of 394 firms drawn from industry ranking surveys, our preliminary results suggest that some cross-functional capabilities have a positive impact on the long term, intangible performance of a firm as measured by Tobin’s q

Wearable grain silo working environment sensing and safety alarm system

A set of wearable granary working environment sensing and security alarm system is developed to ensure the safety of granary staff. The gas sensor, piezoelectric ceramic chip, infrared transmitting and receiving tube and photosensitive resistance are used as the core components of each circuit module to collect the gas concentration signal, human respiration intensity signal, pulse intensity signal and light signal in the granary. AD0809 module chip is used to convert analog data into digital data and send it to MCU for information processing to make alarm judgment. At the same time, PTR2000 module is used to transmit the sensor data to the upper computer. The upper computer determines whether to alarm through data comparison, and transmits the alarm signal to the mobile phone through Wi-Fi in real time. Each module cooperates with each other, information real-time transmission to complete the detection of granary environment and danger alarm. The test results show that the system can meet the safety operation requirements of large and medium-sized state-owned grain depot

Modeling Multi-wavelength Pulse Profiles of Millisecond Pulsar PSR B1821-24

PSR B1821$-$24 is a solitary millisecond pulsar (MSP) which radiates multi-wavelength pulsed photons. It has complex radio, X-ray and $\gamma$-ray pulse profiles with distinct peak phase-separations that challenge the traditional caustic emission models. Using the single-pole annular gap model with suitable magnetic inclination angle ($\alpha=40^\circ$) and viewing angle ($\zeta=75^\circ$), we managed to reproduce its pulse profiles of three wavebands. It is found that the middle radio peak is originated from the core gap region at high altitudes, and the other two radio peaks are originated from the annular gap region at relatively low altitudes. Two peaks of both X-ray and $\gamma$-ray wavebands are fundamentally originated from annular gap region, while the $\gamma$-ray emission generated from the core gap region contributes somewhat to the first $\gamma$-ray peak. Precisely reproducing the multi-wavelength pulse profiles of PSR B1821$-$24 enables us to understand emission regions of distinct wavebands and justify pulsar emission models.Comment: Accepted for publication in Ap

Biomedical Image Splicing Detection using Uncertainty-Guided Refinement

Recently, a surge in biomedical academic publications suspected of image manipulation has led to numerous retractions, turning biomedical image forensics into a research hotspot. While manipulation detectors are concerning, the specific detection of splicing traces in biomedical images remains underexplored. The disruptive factors within biomedical images, such as artifacts, abnormal patterns, and noises, show misleading features like the splicing traces, greatly increasing the challenge for this task. Moreover, the scarcity of high-quality spliced biomedical images also limits potential advancements in this field. In this work, we propose an Uncertainty-guided Refinement Network (URN) to mitigate the effects of these disruptive factors. Our URN can explicitly suppress the propagation of unreliable information flow caused by disruptive factors among regions, thereby obtaining robust features. Moreover, URN enables a concentration on the refinement of uncertainly predicted regions during the decoding phase. Besides, we construct a dataset for Biomedical image Splicing (BioSp) detection, which consists of 1,290 spliced images. Compared with existing datasets, BioSp comprises the largest number of spliced images and the most diverse sources. Comprehensive experiments on three benchmark datasets demonstrate the superiority of the proposed method. Meanwhile, we verify the generalizability of URN when against cross-dataset domain shifts and its robustness to resist post-processing approaches. Our BioSp dataset will be released upon acceptance