Commercial Context Research Articles

Artificial Intelligence applications for Information management in sustainable Supply Chain Management: A Systematic Review and future research agenda

In a Sustainable Supply Chain (SSC) context, information management offers a unique perspective on the digital economy and information management. Artificial intelligence (AI) is developing into a more robust digital field to facilitate quick information access and intelligent decisions in expanding commercial contexts. These days, Supply Chains (SC) would crumble without robust information systems. Applying AI and information management is crucial in determining the direction of sustainable supply chain management (SSCM). A systematic literature review (SLR) of the use of AI in SSCM is conducted in this research. The authors can identify crucial factors of the present literature using bibliometric and network analysis. AI is essential to the SSC to address sustainability challenges and manage the large volumes of data produced by numerous industrial processes. In the corpus of research that is already accessible, there is currently no comprehensive and bibliometric analysis of the potential for AI techniques for information management in SSC. Scientific publications were analysed from an objective point of view. Based on our results, we have drafted a proposal for an AI supply chain framework. Researchers, policymakers, and SCM practitioners may all benefit from the approach. This study is the first to analyse AI applications for information management in SSCM. In consideration of this, organizations are now exploring AI capabilities to improve operational efficiency and innovate their processes. This will assist industry people in understanding how AI methods support SC processes in their optimization to attain sustainability in SC practices.

Pendaftaran Merek yang Memiliki Persamaan pada Pokoknya pada Kelas Berbeda dalam Perspektif Peraturan Perundang-Undangan

Simply said, a brand is any kind of distinctive symbol or name used in commercial contexts. There is still some gray area in Indonesia's trademark recording rules when it comes to the idea of basically identical trademarks and how to register ones that are quite similar to others in various classifications. From a legislative standpoint, this study intends to explore the notion of fundamental similarities in brands and its restrictions. It will also evaluate the recording of substantially similar trademarks in various classes in Indonesia. Using the framework of UU No 20 of 2016 concerning Marks and Geographical Indications, the study approach employed is normative legal research, which looks at the recording of trademarks in various classes that are basically comparable. Because the idea of similarity in essence is subjective and open to different interpretations by different people, the study's findings reveal that trademark recording requirements are vague on the issue. Nevertheless, while looking at statutory requirements, it is possible to register marks that are essentially similar to existing marks under UU No. 20 of 2016, provided that the marks are in a separate class.

The protective effect of Amitriptyline on experimental colitis through inhibiting TLR4/MD2 signaling pathway.

Amitriptyline, a pleiotropic tricyclic antidepressant, possesses anti-oxidant and anti-inflammatory properties. Despite its diverse benefits, the specific effects of amitriptyline on IBD are not yet well defined. To explore this, we utilized a DSS-induced colitis model to examine the anti-inflammatory effects of amitriptyline and the underlying mechanisms by which it operates. Our research revealed that amitriptyline is effective in alleviating several pathological manifestations associated with colitis. This includes improvements in body weight retention, reductions in DAI, lessening of colon length shortening, and repair of colonic mucosal damage. Treatment with amitriptyline significantly protected mucosal injury by preserving the population of goblet cells and increasing the expression of tight junction proteins. Furthermore, we observed that amitriptyline effectively countered immune cell infiltration, specifically neutrophils and macrophages, while simultaneously lowering the levels of inflammatory cytokines such as TNF-α, IL-1β, and IL-6. Additionally, RNA sequencing analysis pointed to the potential involvement of the TLR pathway in the anti-colitic effects induced by amitriptyline. Subsequent Western blot analysis indicated that amitriptyline significantly inhibited the TLR4-mediated NF-κB signaling pathway. To bolster our findings, in vitro studies demonstrated that amitriptyline down-regulated the TLR4/NF-κB/MAPK signaling cascades in mouse macrophages stimulated with LPS. Further molecular investigations revealed that amitriptyline was able to suppress the elevated expression of MD-2 that LPS stimulation typically induces. In summary, our findings suggest that amitriptyline effectively mitigates DSS-induced colitis in mice through the inhibition of TLR4/MD-2 pathway signaling, indicating its potential repurposing for IBD treatment. Significance Statement The potential of utilizing amitriptyline in treating IBD appears promising, leveraging its established safety and dosing profile as an antidepressant. Our observations show that amitriptyline can alleviate pathological symptoms, inflammation, and intestinal mucosal damage in mice with colitis induced by DSS. The protective effect observed appear to be linked to the inhibition of the TLR4/MD2 signaling pathway. By exploring novel applications for existing medications, we can optimize amitriptyline's efficacy and broaden its impact in both medical and commercial contexts.

Digital Dreams and Dubious Dollars: The Challenge of Immersive Van Gogh

The purpose of this study is to discuss the popularity, questions, and controversies surrounding art-themed digital immersive exhibitions based on the life and paintings of Dutch painter Vincent Van Gogh (1853-1890). Since 2019, art-themed immersive exhibits have become extremely popular, with attendance that outstrips many museums showing the same artists, spawning controversy and challenging the fine art world. This study examines the rise of immersive art experiences, arguments for and against immersive spectacles as a way of experiencing artwork, and theoretical perspectives. Methodologies include research into historical, cultural, and commercial contexts including reviews, analyses, critiques, and participant observation. The study discusses components of the exhibition and presents specific arguments for and against them by producers, art critics, critical theorists, and spectators. Questions considered include: Are these exhibitions offering authentic art experiences, or are they exploiting great art for profit? Are they making art more accessible, or are they leading people away from seeing original paintings? Is the way people relate to art fundamentally changing in the age of social media, and if so, how will museums adjust? This study is novel in that it presents and evaluates diverging viewpoints on these controversies and proposes a more flexible approach that centers participant experiences over marketing claims and critical objections.

Hydrogel‐Based Droplet Electricity Generators: Intrinsically Stretchable and Transparent for Seamless Integration in Diverse Environments

AbstractDroplet‐based electricity generators (DEGs) have emerged as innovative devices capable of transducing the kinetic energy of falling water droplets into electrical power, thereby presenting a promising solution for ambient energy harvesting. For effective application, DEGs must exhibit transparency and flexibility to facilitate aesthetic integration and optical/mechanical adaptability for utilization on natural objects and wearable devices. In this study, a hydrogel‐based DEG (Hy‐DEG) designed is proposed to be seamlessly integrated into everyday environments without compromising design or functionality. With the intrinsic characteristics of hydrogel, the Hy‐DEG exhibits superior optical transmittance (≈99%) and stretchability (≈70% of its original length), generating the electrical output performance of ≈45 V of voltage, 15 µA of current, and 42 µW of maximum peak power, with single droplet impact. This capability of the Hy‐DEG can operate electronic devices for environmental monitoring while maintaining seamless integration with commonly encountered surfaces. In this context, this study elucidates the characteristics of hydrogel bestowing novel functionalities upon the DEG, expanding the potential for energy harvesting in residential and commercial contexts. The implications of this research extend beyond conventional energy generation, fostering the development of self‐powered, transparent, and flexible devices.

The ethical edge: Unveiling the power of virtuousness in retailing from employees’ perspectives

Virtue ethics is a central pillar for business success, yet remains unexplored in retailing as research targets primarily unethical or negative deviant customer behaviour. By revealing forms of virtuous, positively deviating customer behaviours and their consequences, we aim to uncover new possibilities for motivating employees to remain in their jobs, which holds special relevance in times of labour scarcity. Our findings highlight the importance of virtuous customer behaviour in strengthening intrinsic motivation, self-efficacy, wellbeing and retention among employees.By integrating virtue ethics into a commercial context, we contribute to theoretical and practical research as we offer managerial recommendations for cultivating virtuous customer behaviour and for promoting employees' commitment to their firm.

The Role Of Hedonic Motivation In Social Commerce Toward Consumer Purchase Intention

This study investigates the influence of Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) elements on customer purchase intention within the context of social commerce, with a focus on the moderating role of hedonic motivation (HM). TPB factors, including attitude (ATD), subjective norm (SN), and perceived behavioral control (PBC), along with TAM factors - perceived ease of use (PEOU) and perceived usefulness (PU) - were examined for their connection to consumer purchase intention (CPI). Hedonic motivation's role as a moderator between TPB, TAM, and purchase intention was explored due to its impact on sensory, creative, and emotional responses of hedonic consumers towards products. The study, involving 500 online survey participants, employed SPSS 26.0 for initial analysis and Smart PLS 3.0 for structural equation modeling. Results supported seven out of ten hypotheses, offering valuable insights into hedonic motivation's regulatory effect on customer purchase intention in the realm of social commerce, contributing to a deeper understanding of this evolving market.

Shaping Trust through Argument Quality: Exploring its Impact on Purchase Intentions in Social Commerce

The research aimed to investigate the influence of argument quality, namely perceived informativeness and persuasiveness, on trust and purchase intention within a social commerce context, focusing on TikTok. A quantitative method was applied, including 150 respondents. Participants were selected based on specific criteria of being active social commerce users. Additionally, the participants’ age was restricted to between 18 and 23 years old since data indicated that Generation Z with a strong tendency to shop online, used TikTok more than other social media platforms. Data collection included distributing questionnaires through Google Forms, using a 5-point Likert scale. Data analysis was conducted using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The results show that perceived informativeness and persuasiveness affect trust towards both members of TikTok and the platform. It has a positive impact on purchase intention due to higher levels of trust in social commerce members and the platform. Meanwhile, trust towards members significantly affects the TikTok platform, and the relationship is non-significant. A potential differentiation is reported concerning the formation of trust. In this context, trust in individual users does not necessarily influence the platform within the context of TikTok social commerce. These insights are valuable for marketers and platform developers seeking to improve user engagement and transaction rates on social commerce platforms. The implications suggest that enhancing the informativeness and persuasiveness of content can be a strategic method for enhancing trust and promoting purchase intention.

Zoroastrians in the Economy of Yazd During the Qajar and Pahlavi I Dynasties

ABSTRACT In the Qajar period, Zoroastrian merchants of Iran were able to engage in commercial activities with fewer restrictions than in the past. In this article, the economic performance of the Zoroastrians in this period was examined based on the institutional approach as well as the reflection of this trend on the historical and commercial context of Yazd (as one of the most important centres of Zoroastrian society) by studying the historical texts and field studies in the historical context of this city. Based on the results, it was found that the existence of formal and informal institutions had a direct impact on the prosperity or stagnation of the commercial activities of Zoroastrians in both Qajar and first Pahlavi periods. Following the emergence of supporting institutions, the architectural spaces related to their commercial activity relatively increased, but the resistance of informal institutions prevented their widespread presence in the city’s main market.

Effect of solvothermal-treated polyethylene on the mechanical properties of sandcrete blocks and concrete

In building construction, seepage and dampness in walls present serious problems since they can cause structural failures and damage in both residential and commercial contexts. Finding more affordable options is necessary as mitigating these problems frequently requires expensive fixes. This study aims to evaluate the effects of solvothermal-treated polyethylene added at concentrations ranging from 0 to 1.0% on the mechanical properties of concrete and sandcrete blocks. In addition to assessing the workability, split tensile strength, and compressive strength of concrete samples at different levels of the polyethylene (PE) inclusion, the study looked into the water absorption capacity, density, and compressive strength of sandcrete blocks. The blocks’ compressive strength and water absorption capacity reduced as the amount of polyethylene additives rose, although there was a slight increase in the density. These changes remain well within the Nigerian Industrial Standards’ specified limits. Hardened concrete shows a drop in density, compressive strength, and split tensile strength with increasing polyethylene content, while fresh concrete's workability decreases as the percentage increases. 0.4% incorporation of the treated polyethylene achieved the target strength of 20 N/mm2 while Sandcrete blocks with up to 1% of the solvothermal treated polyethylene had compressive strengths more than the 2.5 N/mm2 minimum required for non-load-bearing walls. About 61% reduction in water absorption was achieved in 48 h by the blocks, presenting a promising and cost-effective solution for seepage-related issues in building construction.

A shift from contractarianism to ‘characterisation’: Assessing the ideal Australian approach for determining fiduciary duties in a commercial context

In the wake of excessive judicial reliance on a contractarian approach to the assessment of fiduciary duties in commercial contexts, both judicial and academic concern has been voiced as to whether such an approach is the most appropriate to ensure fair dealing. What's more, the emergence of novel commercial relationships facilitated by changing values in business and exponential leaps in the capabilities and use of technology warrant a reassessment of whether contractarianism is fit for the purpose of ushering in appropriate legal analysis so that such new developments can be appropriately facilitated. This article argues that contractarianism is not fit for that purpose and that the ‘characterisation’ of the relationship between parties is a superior and more equitable approach to determining the extent of fiduciary duties in commercial contexts.

Web Conferencing

Society, people, and institutions have reaped the benefits of technologies that allow online meetings and collaboration in real-time ¬¬¬¬- often known as web conferencing technologies. Most of the benefits of these technologies were observed during the COVID-19 pandemic, where efforts to prevent the spread of the virus introduced mass social distancing. In higher education, web conferencing benefits a growing number of students across the world. It enables synchronous online learning, where learning takes place in real time, with communication technologies that permit live audio, video, and text transmission. Extant research suggests that the interplay of technical and social challenges within synchronous online learning, before the COVID-19 pandemic and beyond, has not been comprehensively investigated. Additionally, there is a need for thorough studies on the effects of prolonged virtual interactions on individuals, teams, and organizations, stemming from the features of web conferencing technologies. This paper reports ongoing research aimed at exploring the technical and social challenges that affect the success of web conferencing in synchronous online learning. The research investigates the experiences of students and teachers who use these technologies for learning and teaching, and the challenges they face. The work uses theories of technology acceptance and the psychology of user behaviours. The research aims to advance knowledge and offer valuable information to educators and learners. It seeks to enhance the effectiveness of teaching and learning processes using web conferencing technologies, ensuring that they successfully achieve their intended outcomes. The results of this study, in the long-term, will also inform the development of better and improved interventions, practices and strategies for web conferencing, that can be applied in educational and even commercial contexts. The paper also discusses an exploratory study that was conducted to explore the effects of webcam usage on learner engagement in training and tutorial sessions among higher education students. The exploratory study showed that the webcam, even though rarely used in most sessions, had an impact on learner engagement, and can be explored further. Additionally, the technical and social aspects in these online sessions are complex and need more examination.

Adapting Businesses to the 6G Security Paradigm: Insights from Commerce 5.0

Within the context of Commerce 5.0, this study examines organizations' potential and difficulties when adjusting to the 6G security paradigm. The primary goals are to look into how 6G technology is developing, assess cybersecurity opportunities and problems, pinpoint strategic imperatives for business adaptation, investigate using Commerce 5.0 to gain a competitive edge, and look at upcoming trends and things business leaders should know. The approach used is a thorough analysis of the knowledge and insights from Commerce 5.0. Key findings point to the revolutionary potential of 6G technology for enterprises, new cybersecurity requirements, and strategic imperatives. The report emphasizes the significance of valuing ethics and cybersecurity readiness and embracing hyperconnectivity, customization, and innovation. The consequences of policy point to the need for infrastructure spending, cybersecurity best practices promotion, encouragement of innovation and teamwork, and the creation of regulatory frameworks for new technology. This study offers insightful information to industry stakeholders, legislators, and corporate executives trying to make sense of the intricacies of the 6G security paradigm while promoting innovation and expansion in the digital economy.

Integrated Electro‐Optic Frequency Combs: Theory and Current Progress

AbstractOptical frequency combs (OFCs) have evolved into one of the most active areas of photonics, underpinning advancements in both fundamental science and commercial contexts. Electro‐optic modulation for OFC generation offers excellent versatility, stability, and phase coherence. With the rapid progress in micro‐nano fabrication techniques, electro‐optic frequency combs (EOFCs) have been realized on many integrated platforms, including silicon on insulator (SOI), indium phosphide (InP), and lithium niobate on insulator (LNOI). These compact, low‐cost, and energy‐efficient EOFCs support a wide range of applications. In this study, we review the theory for the generation of integrated EOFCs, summarize the demonstrations on the main integrated platforms, present applications, and envision the development directions of integrated EOFCs in view of materials, devices, and applications.

Evaluation of NASEM 2021 on predictions of milk protein yield on Quebec commercial dairy farms

A recent study assessed the ability of 4 feed evaluation models to predict milk protein yield (MPY) under commercial context, with data from of 541 cows from 23 dairy herds in the province of Québec, Canada. However, the recently published Nutrient Requirements of Dairy Cattle from the National Academies of Sciences, Engineering, and Medicine (NASEM, 2021) was not released at that time. Thus, the current study evaluated NASEM using the same data set. To be consistent with the previous study, predicted DMI was used. Therefore, MPY was predicted using the 2 estimations of DMI proposed by NASEM: one based on animal characteristics only (DMIAo) and one also including ration characteristics (DMIA&R). For each type of DMI estimates, 2 MPY predictions were made, using: 1) the multivariate equation directly published in NASEM and 2) a variable efficiency of utilization of MP predicted using inputs and outputs from NASEM, published a posteriori. With the 2 approaches, multivariate and variable efficiency, the DMIA&R yielded the best MPY predictions. The multivariate equation showed a regression bias between observed and predicted MPY with both DMI estimations. The estimated variable efficiency allowed for MPY predictions without mean and regression biases. With DMIA&R, concordance correlation coefficients (CCC) were 0.72 and 0.78 for MPY predicted using the multivariate and variable efficiency equations, respectively. In comparison, DMIAo CCC were 0.60 and 0.71, respectively. In conclusion, on commercial farms, where dairy rations are usually optimized for a group of cows, estimates of DMI based on animal and rations characteristics yielded the best MPY predictions. The multivariate equation from NASEM predicted MPY with a regression bias whereas the variable efficiency of utilization of MP based on MP and energy supplies resulted in no bias in MPY predictions.

The Licensee Called - He Wants His Peace Back!

English jurisprudence has long established that only those with a legal interest in land may bring an action for nuisance. Although thought to have been challenged by the Court of Appeal in Khorasandjian v Bush, the House of Lords in Hunter v Canary Wharf Ltd affirmed the original position, noting the importance of nuisance as a tort against land. With the highest Court in the nation in Fearn v Tate Gallery reframing nuisance as a tort against land, it appears that Courts have conclusively closed the prospects of relief under nuisance for those whose rights fall short of a legal interest in land such a licensees. Yet the policy imperatives advocating for the converse are not insignificant. Licences are important not only in both the domestic and commercial context. This paper seeks to examine the principles on standing to sue for nuisance to consider whether there is anything to gain from extending its protection to licensees. It argues that there is sufficient reason to afford some protection for licensees, albeit it might best be done not by expanding the tort of nuisance, but through the development of a new tort following Manchester v Dutton as identified by Professor Adam Baker.

Realizing Efficient On-Device Language-based Image Retrieval

Advances in deep learning have enabled accurate language-based search and retrieval, e.g., over user photos, in the cloud. Many users prefer to store their photos in the home due to privacy concerns. As such, a need arises for models that can perform cross-modal search on resource-limited devices. State-of-the-art cross-modal retrieval models achieve high accuracy through learning entangled representations that enable fine-grained similarity calculation between a language query and an image, but at the expense of having a prohibitively high retrieval latency. Alternatively, there is a new class of methods that exhibits good performance with low latency, but requires a lot more computational resources, and an order of magnitude more training data (i.e. large web-scraped datasets consisting of millions of image-caption pairs) making them infeasible to use in a commercial context. From a pragmatic perspective, none of the existing methods are suitable for developing commercial applications for low-latency cross-modal retrieval on low-resource devices. We propose CrispSearch, a cascaded approach that greatly reduces the retrieval latency with minimal loss in ranking accuracy for on-device language-based image retrieval. The idea behind our approach is to combine a light-weight and runtime-efficient coarse model with a fine re-ranking stage. Given a language query, the coarse model effectively filters out many of the irrelevant image candidates. After this filtering, only a handful of strong candidates will be selected and sent to a fine model for re-ranking. Extensive experimental results with two SOTA models for the fine re-ranking stage, on standard benchmark datasets show that CrispSearch results in a speedup of up to 38 times over the SOTA fine methods with negligible performance degradation. Moreover, our method does not require millions of training instances, making it a pragmatic solution to on-device search and retrieval.

Young people's alcohol use in and around water: A scoping review of the literature.

The surrounding social and commercial context, including alcohol advertising, heavily influences alcohol consumption. Alcohol use is a major risk factor for both fatal and non-fatal drowning, particularly for young people. We conducted a scoping review to explore the peer-reviewed literature on the use of alcohol by young people (aged 15-34 years) in the context of aquatic environments. Five electronic academic databases were searched for English-language studies conducted in high-income countries and published in the last 15 years (since 2008). The MetaQAT framework was used to assess methodological quality of included studies. The review included a total of 24 studies, including those addressing the prevalence of and/or risk factors for alcohol use in aquatic environments among young people (n = 13); the epidemiology of alcohol-related unintentional drowning in young people (n = 9); and interventions to reduce alcohol-related harm around water (n = 3). Findings suggest that young people commonly consume alcohol around water, particularly young men. We found multiple influences on this behaviour, including the perception of risk, location of aquatic activity and presence of others, particularly peers. Understanding the literature addressing alcohol use around water among young people will assist in identifying and setting priorities for drowning prevention, including the need to mitigate the effects of alcohol advertising which promotes drinking in and around water. There is a clear imperative to address the use of alcohol by young people in aquatic environments. These findings have key implications for public health policy, advocacy and practice.

Forensic Analysis of Drones Attacker Detection Using Deep Learning

Purpose: This research proposes deep learning techniques to assist forensic analysis in drone accident cases. This process is focused on detecting attacking drones. In this research, we also compare several deep learning and make some comparisons of the best methods for detecting drone attackers.Methods: The methods applied in this research are YOLO, SSD, and Fast R-CNN. Additionally, to validate the effectiveness of the results, extensive experiments were conducted on the dataset. The dataset we use contains videos taken from drones, especially drone collisions. Evaluation metrics such as Precision, Recall, F1-Score, and mAP are used to assess the system's performance in detecting and classifying drone attackers.Results: This research show performance results in detecting and attributing drone-based threats accurately. In this experiment, it was found that YOLOV5 had superior results compared to YOLOV3 YOLOV4, SSD300, and Fast R-CNN. In this experiment we also detected ten types of objects with an average accuracy value of more than 0.5.Novelty: The proposed system contributes to improving security measures against drone-related incidents, serving as a valuable tool for law enforcement agencies, critical infrastructure protection and public safety. Furthermore, this underscores the growing importance of deep learning in addressing security challenges arising from the widespread use of drones in both civil and commercial contexts.

The COVID-19 Tweets Classification Based on Recurrent Neural Network

Due to its extensive use in both public and commercial contexts, sentiment analysis on Twitter has recently received much attention, particularly concerning tweets about COVID-19. Information about COVID-19 has been widely spread over social media, resulting in various views, opinions, and emotions about this pandemic, significantly impacting people's health. It is exceedingly challenging for the authorities to find these rumors on these public platforms manually. This paper proposes a framework for text classification using the RNN model and its updates, such as LSTM, BiLSTM, and GRU. This study aims to determine the best recurrent network model for handling cases of Twitter data classification. We utilized Twitter data relevant to COVID-19 and the lockdown with four classification classes (sad, joy, fear, and anger). In addition, this study aims to prove whether GloVe pre-trained word embedding can increase the accuracy of model predictions. The training and testing datasets were split into 80% and 20%, respectively. Therefore, in this experiment an early stopping technique was used with a limit of 15 epochs and a minimum delta of 0.01, meaning that training will be stopped if there is no improvement of 0.1% accuracy after 15 epochs. We used the f1-score average to measure the accuracy of the classification task results. The test results show that the BiLSTM model with GloVe word embedding yields the best f1-score compared to other models. Moreover, in all model testing, the f1-score value of the 'fear' class displays the highest accuracy compared to other classes.