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This article provides entrepreneurs with information about library services and investigates the function of an Enhanced Business Value Calculator in identifying patrons who may be interested in participating in co-creation-driven experimental technology adoptions in libraries. By identifying the most productive users, the librarian could invite them to participate in the co-creation process, which results in more collaborative and knowledge-based technology experimentations. Patrons (high-performing users and their peers) actively participate in the design, execution, and post-experimentation phases of the experimentation process continuously through co-creation. Ongoing participation increases patron engagement, transforming patrons into library evangelists who support cooperative trials that result in novel services. The article culminates in a case study that shows how the public libraries in collaboration with the university library strategically experimented with ChatGPT to see if it might be used for its systematic literature review services for the entrepreneurs. [ABSTRACT FROM AUTHOR]

In the medical sector, electroencephalogram (EEG) signal analysis is a fascinating and indispensable diagnostic tool for brain diseases such as epilepsy. Neurobiological diseases are difficult and time-consuming to diagnose due to the excessive associated noise in the EEG signal, which can significantly diminish and impede accurate predictions of such diseases. Existing techniques rely on complex processing blocks, such as Granger causality, chaos analysis, mutual information estimation, etc., in order to function satisfactorily. This paper proposes the development of a novel EEG signal analysis system employing Deep Belief Signal Specification (FbDBSS) for the diagnosis of Schizophrenia (SZ). This signal is used to initially train the system alongside conventional EEG, followed by preprocessing. The resultant signal is sent to the classifier, which extracts its characteristics for use in the final analysis. MATLAB is used to implement the proposed design for determining the robustness score in terms of accuracy, precision, F-measure, recall (sensitivity), execution time, and power consumption. Achieving an accuracy of 97%, a precision of 96.8%, an F-measurement of 96%, a recall rate (i.e. sensitivity) of 97%, an execution time of 19 s, and a power consumption of 5dBW demonstrates the effectiveness of the proposed method for EEG signal analysis in health care monitoring. [ABSTRACT FROM AUTHOR]

The objective of the paper is to count the clear beats (R-peaks or QRS complexes) in the Electrocardiogram (ECG) signal after the pre-processing step. Among the three selected techniques, Independent Principal Component Analysis (IPCA), Fractional Fourier Transform (FrFT) and Fractional Wavelet Transform (FrWT) have Sensitivity (Se) of 99.92%, 99.95% and 99.98%, Accuracy (Acc) of 99.88%, 99.93% and 99.96%, Detection Error Rate (DER) of 0.110%, 0.066% and 0.036% along with the disturbance of 2.3%, 2% and 1.7% respectively. The lowest DER in FrWT indicates that it outperforms the IPCA and FrFT. The False Negative plus False Positive (FN + FP) by using the three techniques was 32, 25 and 13 while the output Signal-to-Noise Ratio (SNR) was found to be 24.42, 26.19 and 34.37 dB respectively. These results were figured out on Real Time Database (RT DB). The values of output SNR, Se and Acc were recorded to be 34.37 dB, 99.98% and 99.96% while using FrWT based pre-processing on the data from RT DB. The same technique when used on the data from MIT-BIH arrhythmia Database (MB DB) gave the values of 24.81 dB, 99.96% and 99.93% respectively. It has been observed that all the attributes of the components of ECG signals were preserved when filtered with FrFT. On the other hand, the FrWT segregates the motion artefacts and ECG signal in a better way than FrFT. This is due to the fact that in FrWT decomposition takes place on the signal rotated at a selected angle resulting in less computationally complex decompositions at various levels. It reveals that FrWT retains important biology-related information as the originally recorded ECG signal has. [ABSTRACT FROM AUTHOR]

This article presents an extensive Generative AI Technology Adoption Model intended to elucidate the complex process that entrepreneurs and other innovation ecosystem actors, for instance, libraries, go through for its adoption. The model suggests that the adoption process happens in three stages: PrePerception & Perception, Assessment, and Outcome. During the Pre-Perception & Perception Phase, entrepreneurs initiate their technology exploration by navigating social factors, domain experience, technological familiarity, system quality, training and support, interaction convenience, and anthropomorphism; with utilitarian value and hedonic values playing an important role. As they transition to the Assessment Stage, perceived usefulness, ease of use, and a novel addition, perceived enjoyment, shape their evaluations, leading to generations of emotions toward it, with utilitarian value overweighting hedonic values. The model finishes with the Outcome Stage, where emotions developed in the Assessment Stage become tangible intentions to switch (use technology or switch to human services). The adoption model highlights the adoption factors (also called latent variables) and their relationships grounded on researcher’s professional experiences and need to be further empirically validated. Entrepreneurial implications highlight the strategic insights of the model, providing a decision-making roadmap and highlighting the interaction between utilitarian and hedonistic values. Entrepreneurs can create well-informed technological integrations that are in line with business objectives by using the incremental decision-making process. The model’s focus on comparative evaluations gives entrepreneurs the ability to strategically map the usability of technology for the best possible commercial results. The Generative AI Technology Adoption Model offers a nuanced understanding of entrepreneurs’ technology adoption processes, which is also applicable to other actors in the innovation ecosystem. [ABSTRACT FROM AUTHOR]

Automated recognition of patterns in an ECG signal quintessentially requires removal of noises, such as Baseline wander (BLW), breathing activity, poor quality of electrodes and current flowing in the cables of the acquisition system, during its pre-processing for improving the signal quality to enable the identification of various physiological and pathological phenomena from it. In the literature, it has been well established that statistical domain technique viz. ICA (independent component analysis) surpasses the performance of even higher order filters in removing interferences by calculating independent components with much less computational/mathematical complexity and loss of information. Thus, it will result in higher signal-to-noise-ratios (SNRs) mitigating masking effects of various interferences. On the other hand, LDA (linear discriminant analysis) minimizes the variance and maximizes the distance between any two data-classes while detecting/classifying them resulting in very less false detections. Therefore in this paper, ICA and LDA are proposed to be used combinedly for pre-processing and classification of an ECG signal, respectively. Hence, important latent attributes of the ECG signal are retained with maximally statistically independent criteria using ICA and effective classification/detection is accomplished by cutting down the dimensional costs using LDA. The performance of ICA in ECG signal pre-processing is further compared with that obtained using ANF (adaptive notch filter) to further demonstrate its superiority. The proposed technique is able to achieve sensitivity (Se), detection error rate (DER) and output SNR of 99.92%, 0.122% and 37.77dB, respectively on Massachusetts Institute of Technology- Beth Israel Hospital Arrhythmia database (MB ARR DB). [ABSTRACT FROM AUTHOR]

The article offers a thorough analysis of the Enhanced Business Value Calculator and how it could revolutionize library services designed for patrons, especially entrepreneurs. By letting them choose which services they have used and how often, the calculator calculates the economic value of services. This facilitates the observation of consumer usage trends by libraries, enabling them to make strategic decisions regarding service advancements. It gives libraries the capacity to close talent shortages, improve services, strategically adopt new technologies, spot opportunities for open innovation partnerships, encourage patron co-creation, and gain a better awareness of user demands, which allows them to better tailor their offerings. These breakthroughs subsequently become business prospects. The calculator serves as a driving force behind ongoing advancements in services. [ABSTRACT FROM AUTHOR]

This column provides a case study of the University of Toronto's (UOT) Gerstein Science Information Center's Mobile Application Development Lab (MADLab). It examines the strategic positioning and services provided by MADLab within one of Canada's major academic libraries for science and health sciences, and shares author's own experiences in this research domain. The facility's emphasis on developing mobile apps, its partnership with UOT libraries to support their operations, and its potential commitment to establishing an experimental culture to drive technology adoptions, such as AI, are highlighted in the column. It also examines the cooperative partnership between MADLab and UOT libraries, illustrative of a mutually beneficial partnership that fosters entrepreneurship and creativity within the UOT community. As AI and technology continually evolve, the MADLab case study offers valuable insights into the transformative power of strategic positioning, experiential learning, and collaborative partnerships in the pursuit of knowledge dissemination and cutting-edge technological advancements in the time to come. [ABSTRACT FROM AUTHOR]

This column aims to explore the frameworks to help libraries foster digital innovation by leveraging AI technologies through continuous experimentation to innovate their services for their patrons. Additionally, the column seeks to highlight the benefits and interplay between the frameworks, providing insights for librarians interested in implementing AI solutions and driving technological advancements in library settings. The column reports two frameworks - The Need-Based Experimentation (NBE) Framework and the Curiosity-Based Experimentation (CBE) Framework based on the author's professional experiences and empirical observations of 10 university libraries' experimentation-driven AI technology adoption practices. The NBE framework focuses on experimenting with AI technologies that have the functional capability to address the library's current business needs. In contrast, the CBE framework explores AI technologies out of curiosity, aiming to gain practical experiences and uncover potential future applications, aligned with the librarian's interests. These frameworks guide librarians to effectively experiment with AI technology based on their motivations and goals. To the best of the authors' knowledge, there is no experimentation-driven framework for adopting AI technologies to assist libraries do so strategically. The adoption of AI should be influenced by carefully planned, ongoing experiments, the results of which should be deployed in real to inform adoption decisions. [ABSTRACT FROM AUTHOR]

Fourier analysis is well known to provide complete information of the frequencies present in a signal. But in the process, time information is lost. Therefore, its time-frequency representation is required for depicting both time and frequency information simultaneously. Therefore in this paper, fractional wavelet transform (FrWT) is proposed to be used for the first time for extracting the features of various datasets in a standard ECG database by combining the advantages of both fractional domain techniques and wavelets as case-II. Afterwards, Probabilistic Principal Component Analysis (PPCA) is used for detecting R-peaks for diagnosing heart abnormalities in various morphologies of the ECG signal. The proposed technique has been evaluated on the basis of sensitivity (SEN), detection error rate (DER), and positive predictivity (PPR) (of the detected ECG beats) for MIT-BIH Arrhythmia database (M/B Ar DB). Even though both FrFT and FrWT techniques exhibit a high degree of robustness, but SEN of 99.99%, DER of 0.026%, & PPR of 99.99% obtained by latter in case-II are better than SEN of 99.97%, DER of 0.053%, & PPR of 99.98% obtained by the former in case-I for M/B Ar DB. In this paper, average time error (ATE) is also obtained for the considered datasets establishing the effectiveness of the proposed technique further. These encouraging results suggest that the proposed methodology will go a long way in assisting the cardiologists to detect temporal patterns in a wide variety of electrophysiological cases, which is important for improved management of healthcare system. [ABSTRACT FROM AUTHOR]

This article presents a practical case study centered around the adoption of AI technology within a library setting through the lens of the Curiosity-Based Experimentation (CBE) Framework. It offers insights into the successful implementation of CBE by exploring a librarian's experimentation with a text-to-video generator AI tool. Motivated by the aim to enhance social media interactions and personal branding, the librarian creatively utilized the technology to craft novel videos. These videos, featuring university staff, students, and visitors, yielded engaging content for the library's online platforms. The article underscores the application of AI in libraries, illustrating how a CBE-driven approach can foster innovative solutions and amplify academic community engagement. Finally, some practical implications are provided to libraries which include resource allocation for experimentation, learning from failures, open communication, documentation, and interdisciplinary collaboration to foster innovation and enhancing library value. [ABSTRACT FROM AUTHOR]

Giant retailers sell their store brands and (re)sell substitute national brands for manufacturers (suppliers) simultaneously, which becomes an important sourcing and selling supply chain strategy in the sharing economy era. In this paper, we develop a game-theoretic model to examine the impact of risk-averse suppliers on dual sourcing decisions under supply disruption when two substitute products are sold in a common market. We examine the impact of risk-aversion, demand volatility, and supply disruption on the supply chain profitability and pricing policies. We hypothesise and then analytically derive several propositions on the dependencies of the profitability and risk-aversion, demand volatility and pricing, and risk-aversion and pricing. We also conduct numerical analysis to explore the impact of disruption. We offer insights for managers and researchers alike by uncovering the role of supplier risk-aversion for decision-making settings with substitute products and dual sourcing when product supply may be disrupted. [ABSTRACT FROM AUTHOR]

In recorded Electrocardiogram (ECG) signal, clinical information is masked by several noises and distortion resulting in low signal-to-noise-ratio (SNR). In this situation, an efficient pre-processing technique is required to improve SNR for efficient analysis of ECG signals. In this research article, performance of five pre-processing techniques viz. digital bandpass filter (DBPF), wavelet transform (WT), independent principal component analysis (IPCA), savitzky golay digital FIR filter (SGDFF) and fractional wavelet transform (FrWT) have been evaluated and compared for their effects on the efficiency of R-peak detection. FrWT has been utilized for pre-processing of ECG signal for the first time in this paper. A FrWT-based technique is also proposed using Yule Walker autoregressive modeling (YWARM) and Principal Component Analysis (PCA) for feature extraction and R-peak detection, respectively. YWARM is selected due to its more stable output for long time recorded ECG signal than existing techniques, whereas PCA is selected to get optimal dimensional feature vectors out of higher dimensional feature vectors. The proposed technique has been evaluated and compared with others on the basis of various performance parameters; SNR, mean squared error (MSE), sensitivity (SE), accuracy (Acc), and positive predictive value (PPV). The proposed technique yielded interesting results among all the methods; 34.37 dB of output SNR, 0.026% of MSE, 99.98% of SE, 99.97% of Acc and 99.99% of PPV on real time ECG database (RT DB) and 24.81 dB of output SNR, 0.099% of MSE, 99.96% of SE, 99.93% of Acc, and 99.97% of PPV on MIT-BIH Arrhythmia database (MIT-BIH Arr DB). [ABSTRACT FROM AUTHOR]

Plasmonic nanofluids have emerged as strong candidates for future working fluids in direct absorption solar collectors (DASC). However, in novel DASC systems, a significant temperature gradient exists over working fluid depth increasing emissive losses and hence, results in low thermal conversion efficiency. Considering these issues, a laboratory scale prototype of reverse-irradiated DASC system (RR–DASC) has been tested under controlled laboratory conditions and compared with a direct irradiated DASC system (DR–DASC) using silver nanofluids at three different mass concentrations i.e., 0.1 mg/L, 0.2 mg/L, and 0.4 mg/L. Ag nanoparticles were having monodisperse spherical shape with surface plasmon resonance (SPR) peak occurring around 395–400 nm. The experimental results showed that RR–DASC maintains uniform temperature over the fluid depth, while about 3–4°C temperature gradient was noticed in direct irradiated DASC system under similar operating conditions. A maximum photo-thermal conversion efficiency of about 80% was observed using 0.4 mg/L Ag nanofluids in RR–DASC system. The presented work can pave the ways for future development of highly efficient DASC systems for low to high temperature solar applications. [ABSTRACT FROM AUTHOR]

Proper diagnosis of clinical Electrocardiogram (ECG) is still a challenge. The minor variations in the attributes of ECG signal cannot be examined properly by simple visualization, rather an efficient technique is required to increase the chances of early prediction of the diseases. R-peak detection is one such important attribute. It plays an important role in the detection of Arrhythmias (heart diseases). Proper detection of Arrhythmias using R-peak requires two things: long time recording of the ECG and noise reduction. But, long time recording of ECG requires proper modeling for extracting features from long data records. In this paper, noise reduction is accomplished using a digital bandpass filter (DBPF), since its filtering characteristics are invariant with drift and temperature, and the features are extracted using Yule–Walker (YW) autoregressive modeling technique which is most appropriate for modeling non-stationary signals recorded for long times. So, databases of AHA (American Heart Association), Ventricular Tachyarrhythmia and MIT-BIH Arrhythmia have been investigated. A total of 18 ECG records were made for implementing the proposed methodology using MATLAB R2008b. The feature extraction step is performed by finding AR coefficients on the basis of the selected model order. YW method is applied for finding AR coefficients and Principal Component Analysis (PCA) is used for R-peak detection. In this paper, normal and abnormal signals have been considered during the detection process. The PCA without YW yields a sensitivity of 99.73%, a specificity of 99.80%, a detection rate (DR) of 99.73%, and an accuracy (ACC) of 99.66%, whereas the proposed PCA with YW (PCA + YW) yields a sensitivity (SE) of 99.88%, a specificity (SP) of 99.92%, a detection rate (DR) of 99.90%, and an accuracy (ACC) of 99.81%. Suitable comparisons of the results obtained using the proposed method have been carried out with those obtained using existing methods for illustration. [ABSTRACT FROM AUTHOR]

With the increasing awareness of the serious consequences of supply disruption risk, firms adopt various kinds of strategies to mitigate it. We consider a supply chain in which two suppliers sell components to two competing manufacturers producing and selling substitutable products. Supplier U is unreliable and cheap, while Supplier R is reliable and expensive. Firm C uses a contingent dual-sourcing strategy and Firm S uses a single-sourcing strategy. We study the implications of the contingent sourcing strategy under competition and in the presence of a possible supply disruption. The time of the occurrence of the supply disruption is uncertain and exogenous, but the procurement time of components is in the control of the firms. We show that supply disruption and procurement times jointly impact the firms’ buying decisions. We characterise the firms’ optimal order quantities and their expected profits under different cases. Subsequently, through numerical computations, we obtain additional managerial insights. Finally, as extensions, we study the impact endogenizing equilibrium sourcing strategies of asymmetric and symmetric firms, and of capacity reservation by Firm C with Supplier R to mitigate disruption. [ABSTRACT FROM PUBLISHER]

—Theft detection in the power sector is a significant challenge for power distribution companies worldwide. The power losses are mainly due to dissipation from wires or theft done by manipulating energy meters or tapping cables at the consumer end. With power theft becoming a global issue, automatic detection of robbery is the need of the hour. This paper presents a deep learning-based solution for automated detection of power theft using consumers' consumption data. In this work, fully connected neural networks are trained on daily consumption data, and customized convolutional neural networks (CNN) and residual networks are trained on weekly consumption data. The models are evaluated using the area under the receiver operating characteristics curve (AUC) metric, which measures the degree of separation between the predicted classes. The results obtained on the real dataset indicate that residual networks provide better results than other methods, and ResNet34 outperforms the existing methods in the literature. The proposed system has a high potential to detect power theft in households, which can help the authorities cut down non-technical losses occurring in the power sector. [ABSTRACT FROM AUTHOR]

ICTs for refugees and displaced persons: Edited by Carleen F. Maitland. Digital refugees concerns the use of ICTs by refugees, humanitarian organizations, and countries offering refugee protection. GIS is then situated in the refugee relief domain using critical cartography and refugee situation awareness theoretical frameworks. [Extracted from the article]

The intent of the present research work is to explore the prospect of replacing natural soil used in brick making by some extent with industrial waste, that is, lime mud, as obtained from paper mill waste. Extensive adoption of this mill waste as a brick making material will deter the environmental effects of this waste disposal. Moreover, its adoption will not only solve the problem of stocked waste piles but also scale down the usage of natural soil as a brick making material. In consideration of the central intend of this study to utilize industrial waste as an alternate raw material, yet another industrial waste—fly ash, has also been blended to the mix to manufacture unburnt bricks, such that, it may produce cementitious compounds with lime mud. So, the materials used for brick making in this study are: lime mud, collected from the dumping site of M/s Hindustan Paper Corporation Limited, Jagiroad, Assam, India; soil, collected from a brick manufacturing unit also near Jagiroad; and fly ash, collected from National Thermal Power Station, Badarpur, Delhi, India. The materials have been characterized with respect to their chemical and geotechnical properties. Then, experiments have been conducted on hand-moulded bricks of aforementioned mix in distinct proportions. In this study, all the bricks have been dried in the natural condition and then tested for compressive strength conforming to International standards. It has been duly noted that none of the treated unburnt bricks satisfies the requirements of standard codes. Nonetheless, in case of burnt bricks, the compressive strength satisfies the requirements of International standard codes wherein lime mud has been added up to a certain percentage (=20%) in soil-lime mud mix. [ABSTRACT FROM AUTHOR]