Major Technology Research Articles

Flushing Out Homeowner Perceptions to Improve Voluntary Septic to Sewer Conversion With Social Marketing

Background Septic systems are recognized as a contributor to water quality degradation. Septic to sewer conversion programs are being implemented to mitigate nutrient pollution but success of voluntary programs is dependent on homeowner participation. The purpose of this study was to identify elements of potential social marketing strategies that could advance the voluntary adoption of septic to sewer conversion programs. Focus of the Article This study used formative survey research to assess residents’ knowledge, attitudes, and perceived benefits of and barriers to septic systems conversion in Florida, USA to inform future interventions promoting this major, costly home infrastructure change that reduces wastewater nutrient contamination. Research Question This study attempts to answer the following questions: (1) Does septic system conversion status (mandatory vs. voluntary) influence homeowners’ knowledge, attitudes, and perceived benefits and barriers to septic to sewer conversion? (2) What social marketing variables are the best predictors of attitude about septic to sewer conversion? Importance to the Social Marketing Field In contrast to inexpensive, repeated habits which are the target of many social marketing campaigns, costly one-time behaviors have been minimally explored by social scientists and social marketers. This study demonstrates how social marketing research can be applied to advance the adoption of costly, one-time infrastructure improvements, with septic to sewer conversions serving as the study context. There are substantial opportunities for social marketers to support local government and other organizations in their promotion of major technologies, including, but not limited to septic system conversions. Methods 518 adult Florida residents who were also septic system owners completed an online survey instrument that assessed subjective and objective knowledge, attitudes about septic to sewer conversion, and motivators and barriers to conversion. Results Septic system homeowners are largely supportive of septic conversion programs; however, the greatest perceived benefits are personal and not associated with environmental improvements. Despite many septic to sewer conversion projects being initiated for environmental purposes, behavior change efforts should promote the benefits of conversion as perceived by the homeowner. The primary barrier is the upfront cost, and alternative funding structures should be investigated. Recommendations for Research or Practice This study indicates that target audience outreach is an essential component of the environmental planning process. Considering the costs and complexity of water quality infrastructure projects, environmental management professionals should consider a robust audience-specific social marketing program as part of the implementation and planning process. The findings presented here suggest opportunities for social marketers to partner with environmental management professionals tasked with encouraging septic to sewer conversion or other costly and significant one-time behaviors.

Not all sunshine and rainbows: exploring the dark side of AI in interactive marketing

PurposeThe surge of artificial intelligence (AI) applications and subsequent adoption by consumers and marketers has ignited substantial research exploring the benefits and opportunities of AI. Despite this, little attention has been given to its unintended negative consequences. In this paper, the authors examine both the practitioner and academic sides of ethical AI. In doing so, the authors conduct an extensive review of the AI literature to identify potential issues pertaining to three areas: individual consumers, societal and legal. The authors identify gaps and offer questions to drive future research.Design/methodology/approachThe authors review recent academic literature on AI in marketing journals, and top ethical principles from three top technology developers (Google, IBM and Meta) in conjunction with media reports of negative AI incents. They also identify gaps and opportunities for future research based on this review.FindingsThe bibliographic review reveals a small number of academic papers in marketing that focus on ethical considerations for AI adoption. The authors highlight concerns for academic researchers, marketing practitioners and AI developers across three main areas and highlight important issues relating to interactive marketing.Originality/valueThis paper highlights the under-researched negative outcomes of AI adoption. Through an extensive literature review, coupled with current responsible AI principles adopted by major technology companies, this research provides a framework for examining the dark side of AI.

Towards intelligent trust-based incident and evidence management models for Internet of Vehicles: A survey

Internet of Vehicles (IoV) is a rapidly evolving technology that attracts interest from active research communities such as Internet of things, data security and Artificial Intelligence. The proposed review critically and comprehensively analyses the current research in the Internet of Vehicles domain particularly focusing on its key components V2X scenario, evidence management, incident reporting with warning and Trust in IoV with emphasis on the convergence of the major technologies Blockchain, Artificial Intelligence and IoT. The proposed framework derives from the fact that the decisions in an IoV ecosystem are driven by analyzing the complex data collected from several Internet of Things (IoT) devices related to vehicles, infrastructure, and road users followed by applying ML algorithms for intelligent decision making and advanced algorithms for ensuring security and trust. The review will be an eyeopener for researchers in the connected vehicles domain, shedding light into the recent advancements in each of the major components along with the identification of critical challenges and future research recommendations.

An evaluation of current technology in supply chain management

This research presents a scoping review of the evaluation of current technology that is being used in SCM by analysis of existing studies and contemporary concepts on this topic. A methodology based on a scoping review approach in the subject discipline was organized to carry out this research. Two databases and one search engine were searched for journals from 2019 to 2023 using relevant keywords designed for the study. Information regarding research designs, year of publications, publication authors, and findings of each study were documented. Out of the obtainable preliminary 70 studies extracted from the selected databases, only 44 research papers were accepted to meet the exclusion and inclusion criteria delineated for this study, where 45 % of papers were published between 2020–2021. Five different technological research areas were addressed in this scoping review. The major technology areas were AI and the IoT, followed by Data analytics and Big data IoT, and last but not least, Blockchain technology. The main obstacles to applying technology to SCM include change management, human reception/acceptance of these technologies, technical knowledge of the workforce already in place, and the significant costs associated with implementing these solutions. Leveraging technology improves customer happiness, reduces errors, and increases organizational responsiveness. Less crucial but still important are the difficulties in locating qualified workers and the possibility that technology will surpass human performance. However, there is undeniable proof that technology has many benefits; when applied wisely, businesses can benefit greatly from this new technology.

Analysis of Principles and Tactics Behind Data-Tracking

As the applications and their scope developed by several major technology firms such as Alphabet and Meta advance, these companies are found to be facing compliance issues due to increasing user concerns about privacy. The research investigates the tactical strategy utilized by these companies in addressing the relevant problems and allegations and then offers recommendations for effective tactics. Based on the principles of novel and previous tracking tools, the research analyzes the disadvantages of current innovative tools and discovers a developing orientation according to the main dimensions of user psychological analysis, transparency issues, and problems linked to advertising arrangements. The implication highlights the possible factors that contribute to users' concerns and the essential aspects to enhance the safety measures of these extensively utilized platforms.

Calculation of the energy dependence of fission fragments yields and kinetic energy distributions for neutron-induced 235U fission* *Supported by the National Natural Science Foundation of China (12075105), the NSFC-Nuclear Technology Innovation Joint Fund (U2167203), the Major Science and Technology Projects of Gansu Province (22ZD6GB020), the Fundamental Research Funds for the Central Universities of China (lzujbky-2022-ey14, lzujbky-2022- kb07)

Fission fragments yields and average total kinetic energy are fundamental nuclear data for nuclear energy applications and the study of nuclear devices. Certain fission products, such as 95Zr, 99Mo, 140Ba, 144Ce, and 147Nd, serve as burnup monitors, assessing the number of fissions induced by neutrons on 235U. However, current experimental data for these fission products worldwide are inconsistent, introducing significant uncertainty into related scientific research. In this study, we employed the Potential-driving Model to calculate the independent yields of 235U and evaluate its advantages in such calculations. Additionally, we investigated the energy dependence of independent yields to select important products. Furthermore, we calculated the cumulative yields of 95Zr, 99Mo, 140Ba, 144Ce, and 147Nd, and compared them with existing literature data to explore the energy dependence of fission products for 235U. Given the lack of fission product yield data above 14.8 MeV, we extended our calculated incident neutron energy to 20 MeV, aiming to support future scientific research. The Geant4 physical model does not consider the influence of incident neutron energy on the average total kinetic energy of fission fragments; thus, we introduced the excitation function of the total kinetic energy of fission fragments recommended by Madland et al., which effectively describes the experimental data of the average total kinetic energy of fragments formed in 235U fission. In this paper, we comprehensively discuss the energy dependence of fission product yields and average total kinetic energy.

Niraparib maintenance therapy using an individualised starting dose in patients with platinum-sensitive recurrent ovarian cancer (NORA): final overall survival analysis of a phase 3 randomised, placebo-controlled trial

Niraparib significantly prolonged progression-free survival versus placebo in patients with platinum-sensitive, recurrent ovarian cancer (PSROC), regardless of germline BRCA mutation (gBRCAm) status, in NORA. This analysis reports final data on overall survival (OS). This randomised, double-blind, placebo-controlled, phase 3 trial enrolled patients across 30 centres in China between 26 September 2017 and 2 February 2019 (clinicaltrials.gov, NCT03705156). Eligible patients had histologically confirmed, recurrent, (predominantly) high-grade serous epithelial ovarian cancer, fallopian tube carcinoma, or primary peritoneal carcinoma (no histological restrictions for those with gBRCAm) and had received ≥2 prior lines of platinum-based chemotherapy. Patients were randomised (2:1) to receive niraparib or placebo, with stratification by gBRCAm status, time to recurrence following penultimate platinum-based chemotherapy, and response to last platinum-based chemotherapy. Following a protocol amendment, the starting dose was individualised: 200mg/day for patients with bodyweight <77kg and/or platelet count <150×103/μL at baseline and 300mg/day otherwise. OS was a secondary endpoint. Totally, 265 patients were randomised to receive niraparib (n=177) or placebo (n=88), and 249 (94.0%) received an individualised starting dose. As of 14 August 2023, median follow-up for OS was 57.9 months (IQR, 54.8-61.6). Median OS (95% CI) with niraparib versus placebo was 51.5 (41.4-58.9) versus 47.6 (33.3-not evaluable [NE]) months, with hazard ratio [HR] of 0.86 (95% CI, 0.60-1.23), in the overall population; 56.0 (36.1-NE) versus 47.6 (31.6-NE) months, with HR of 0.86 (95% CI, 0.46-1.58), in patients with gBRCAm; and 46.5 (41.0-NE) versus 46.9 (31.8-NE) months, with HR of 0.87 (95% CI, 0.56-1.35), in those without. No new safety signals were identified, and myelodysplastic syndromes/acute myeloid leukaemia occurred in three (1.7%) niraparib-treated patients. Niraparib maintenance therapy with an individualised starting dose demonstrated a favourable OS trend versus placebo in PSROC patients, regardless of gBRCAm status. Zai Lab (Shanghai) Co., Ltd; National Major Scientific and Technological Special Project for "Significant New Drugs Development" in 2018, China [grant number 2018ZX09736019].

Deep learning prediction of yields of fluid catalytic cracking via differential evolutionary dual-stage attention-based LSTM

Fluid catalytic cracking (FCC) was one of the major technologies for converting low-quality heavy oil into valuable light oil in the refining industry. The prediction of FCC yields was of great significance to the online optimization of yield distribution and quality control. In this work, the deep learning with differential evolutionary dual-stage attention-based long short-term memory (LSTM) method was proposed for FCC yields prediction. The first layer of attention mechanism was introduced to extract information from process parameters, feed properties, and clustering of catalyst data, and the second layer of attention mechanism adaptively evaluated the weights for process features of different time-step. The adaptive differential evolution algorithm based on phased mutation strategy was used to optimize the weights to avoid the algorithm falling into local optimum. The FCC yields were predicted by five LSTM-based models under scenarios of clustering and non-clustering of catalyst data. The results indicate that the root-mean-square errors (RMSEs) of FCC yields prediction via clustering method were reduced by 1.5–95.8% compared with non-clustering. The proposed method outperformed all other models, of which the RMSEs of dry gas, liquefied petroleum gas (LPG), gasoline, and diesel yields prediction in clustering scenarios were 0.10761, 0.00582, 0.40749, and 0.00670, respectively. Compared with the conventional machine learning, basic LSTM, and dual-stage attention-based LSTM models, the RMSEs of proposed model decreased by 11.3–94.6%, 1.9–94.6%, and 5.1%–96.6%, respectively.

Touch Pad Based Modern Restaurants Automation using Arduino and Zigbee Technology

Abstract: The main objective of this project is to develop an embedded system restaurants by using Zigbee technology and touchpad. The project designed here deals with wireless technology in the hotel management area. The present existing system working is very tedious process like ordering to one person, sending that with another person to kitchen and sending the same to the cashier. This total procedure will be wind up by using this project. By using Zigbee technology we can reduce the man power in the hotels and also it reduces the time wastage. The major technology which we are using in this project is Zigbee wireless communication with the interface of the touchpad to the Arduino ATMEGA328 controller. By the usage of touchpad with audio announcement may even reduce the difficulty to the blind and illiterates about the dish and the information can be sent to the kitchen in the restaurant by using Zigbee Technology where the details of the dish along with table number will be displayed in the LCD at the receiving section by activating an alarm as an acknowledgement.

Digital educational platforms in primary education: the case of Catalonia

ABSTRACT The use of digital educational platforms has increased in schools, especially during Covid-19, accelerating digital transformation and the expansion of data collection. However, the platformisation phenomenon in this context is still largely unexplored. The present study aims to identify the presence and uses of digital platforms in the educational context of Catalonia and significant changes post-Covid-19 through a validated questionnaire. Results from 491 primary school teachers show that almost 90% of the digital tools used are proprietary platforms belonging to the major technology companies. Many relationships between frequencies and types of tools have been identified as well as a significant evolution of their use in the wake of Covid-19. Some recommendations at different levels are drawn up.

Adaptive Mission Control Architecture with Flexible Levels of Autonomy

The future battlefield operation concept does not focus on advanced and complete weapon systems, but requires a new battlefield operation concept that can effectively demonstrate offensive power by combining a large number of low-cost, miniaturized weapons. Recently, research on the autonomous application of major technologies that make up the mission control system is actively underway. However, since the mission control system is still dependent on the operator's operating ability when operating multiple robots, there are limitations to simply applying the automation technology of the existing mission control system. Therefore, we understand how changes in operator capabilities affect multi-robot operation and propose an adaptive mission control architecture design method that supports multi-robot integrated operation by adjusting the level of autonomy of the mission control system according to changes in operator capability.

Prospects of emerging 3D bioprinting technologies: major technology components, technology developers, and end users—Part I

Abstract Bioprinting technology aims to create 3D structures with living cells to mimic real tissue and organ functions. The process involves various additive technologies, including 3D bioprinters and bioinks. Bioinks comprise hydrogels, scaffolds, additives, growth factors, and living cells. While much of the technology is still in the exploratory stages, it has successfully produced living tissue, blood vessels, and bones. Research suggests the potential for bioprinting whole organs to revolutionize medical procedures. Current major business components include various types of bioprinters and advanced bioinks. Advancements in technologies such as cellular reprogramming hold the potential to enhance the development of superior bioinks, thereby enabling the fabrication of 3D bioprinted tissues. 3D bioprinting technology offers significant benefits across research, personalized medicine, and other applications. This review provides a flavor of the potential benefits of using 3D bioprinting technology in various areas of usage, including Research and Development (R&amp;D), and its applications on a wide spectrum, including personalized medicine.

Technical Status and Development Trend of Medical Electron Linear Accelerators

More than 70% of tumor patients require radiotherapy. Medical electron linear accelerators are important high-end radiotherapy equipment for tumor radiotherapy. With the application of artificial intelligence technology in medical electron linear accelerator, radiotherapy has evolved from ordinary radiotherapy to today's intelligent radiotherapy. This study introduces the development history, working principles and system composition of medical electron linear accelerators. It outlines the key technologies for improving the performance of medical linear electron accelerators, including beam control, multi-leaf collimator, guiding technology and dose evaluation. It also looks forward to the development trend of major radiotherapy technologies, such as biological guided radiotherapy, FLASH radiotherapy and intelligent radiotherapy, which provides references for the development of medical electron linear accelerators.

Enabling one-step regeneration of LiBH4 with self-sustaining hydrogen in its spent fuel – One pathway to storing renewable hydrogen

LiBH4 (LB in short) with a hydrogen capacity as high as 18.5 wt% and a low molecular weight (21.78 g mol−1) is among the most promising candidates for the hydrogen-based economy. However, current major technologies in (re)generation of LB rely heavily on energy-intensive processes, which greatly prohibits practical scaling-up of applications. Here we report a sustainable and effective approach to (re)generate LB via converting renewable H+ in crystalline water into H-, achieving a desirable yield of ∼50%. This one-step synthesis method relies on the reaction between spent fuels, specifically LiBO2·xH2O, and Mg-based alloys to form LB under ambient atmosphere. Notably, our approach surpasses the efficiency of other conventional method, such as LiH-B-H2 and MgH2-LiBO2 systems, which not only bypasses the energy-intensive dehydration procedure of LiBO2·xH2O (∼470 ℃) but also eliminates the use of costly hydrides or high-pressure H2. Our findings indicate that Mg participated in the regeneration process prior to Al in Mg-Al alloys and [BH4]- is gradually evolved from other intermediate species [BHx(OH)4-x]- (x = 0, 1, 2, 3). By combining hydrogen release and efficient storage of hydrogen-rich substrate in a closed materials cycle, this study may shed light on a promising step toward application of renewable hydrogen in a fuel cell-based hydrogen economy.

Soil bacterial community in a photovoltaic system adopted different survival strategies to cope with small-scale light stress under different vegetation restoration modes.

Solar photovoltaic (PV) power generation is a major carbon reduction technology that is rapidly developing worldwide. However, the impact of PV plant construction on subsurface microecosystems is currently understudied. We conducted a systematic investigation into the effects of small-scale light stress caused by shading of PV panels and sampling depth on the composition, diversity, survival strategy, and key driving factors of soil bacterial communities (SBCs) under two vegetation restoration modes, i.e., Euryops pectinatus (EP) and Loropetalum chinense var. rubrum (LC). The study revealed that light stress had a greater impact on rare species with relative abundances below 0.01% than on high-abundance species, regardless of the vegetation restoration pattern. Additionally, PV shadowing increased SBCs' biomass by 20-30% but had varying negative effects on the numbers of Operational Taxonomic Unit (OTU), Shannon diversity, abundance-based coverage estimator (ACE), and Chao1 richness index. Co-occurrence and correlation network analysis revealed that symbiotic relationships dominated the key SBCs in the LC sample plots, with Chloroflexi and Actinobacteriota being the most ecologically important. In contrast, competitive relationships were significantly increased in the EP sample plots, with Actinobacteriota having the most ecological importance. In the EP sample plot, SBCs were found to be more tightly linked and had more stable ecological networks. This suggests that EP is more conducive to the stability and health of underground ecosystems in vulnerable areas when compared with LC. These findings offer new insights into the effects of small-scale light stress on subsurface microorganisms under different vegetation restoration patterns. Moreover, they may provide a reference for optimizing ecological restoration patterns in fragile areas.

Big AI: Cloud infrastructure dependence and the industrialisation of artificial intelligence

Critical scholars contend that ‘There is no AI without Big Tech’. This study delves into the substantial role played by major technology conglomerates, including Amazon, Microsoft, and Google (Alphabet), in the ‘industrialisation of artificial intelligence’. This concept encapsulates the shift of AI technologies from the research and development stage to practical, real-world applications across diverse industry sectors, resulting in new dependencies and associated investments. We employ the term ‘Big AI’ to encapsulate the structural convergence of AI and Big Tech, characterised by the profound interdependence of AI with the infrastructure, resources, and investments of these major technology companies. Using a ‘technographic’ approach, our study scrutinises the infrastructural support and investments of Big Tech in the AI sector, focussing on corporate partnerships, acquisitions, and financial investments. Additionally, we conduct a detailed examination of the complete spectrum of cloud platform products and services offered by Amazon, Microsoft, and Google. We demonstrate that AI is not merely an abstract idea but an actual technology stack encompassing infrastructure, models, applications, and an ecosystem of applications and companies relying on this stack. Significantly, these tech giants have seamlessly integrated all three components of the stack into their cloud offerings. Furthermore, they have developed industry-focussed solutions and marketplaces aimed at attracting third-party developers and businesses, fostering the growth of a broader AI ecosystem. This analysis underscores the intricate interdependence between AI and cloud infrastructure, emphasising the industry-specific aspects of cloud AI.

The Work of Art in the Age of Digital Commodification

Digitization is taking over every sphere of life—including the arts. Through the process of digital commodity fetishism, major technology companies threaten to efface the very qualities that make creative expression—particularly the performing arts—distinct and meaningful. To resist or even question these forces, we must excavate an invisible digital politics that can displace (and replace) traditional sources of authority in the performing arts. By examining the basic mechanisms of the “creator economy,” this politics can be found and confronted—in the arts and beyond.

Neural rehabilitation based on brain computer interface

BCI is a newly developed technology that can be used for neurological rehabilitation of brain injuries and paralysis. This article first reviews the history of BCI, and then introduces two major neurorehabilitation BCI technologies for neurorecording and neuromodulation, invasive and non-invasive. For each technology, we describe the challenges of each technology, analyzing the current state and future development trends. In terms of non-invasive technology, the most mature and extensive EEG brain signal analysis methods and two brain regulation technologies, transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES), as well as the application of brain signal analysis techniques such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy fNIRS) were also discussed. In terms of invasive technology, the implantable neural interface technology focuses on implantable neural interface technology, which collects cortical electrical brain signals (ECoG), which can obtain more original neural information and has high temporal and spatial resolution characteristics. In recent years, with the development of microelectronics technology and material technology, invasive BCI technology has made a lot of cutting-edge progress, which is an important future development direction of neurorehabilitation technology.

A 3D nanoscale optical disk memory with petabit capacity.

High-capacity storage technologies are needed to meet our ever-growing data demands1,2. However, data centres based on major storage technologies such as semiconductor flash devices and hard disk drives have high energy burdens, high operation costs and short lifespans2,3. Optical data storage (ODS) presents a promising solution for cost-effective long-term archival data storage. Nonetheless, ODS has been limited by its low capacity and the challenge of increasing its areal density4,5. Here, to address these issues, we increase the capacity of ODS to the petabit level by extending the planar recording architecture to three dimensions with hundreds of layers, meanwhile breaking the optical diffraction limit barrier of the recorded spots. We develop an optical recording medium based on a photoresist film doped with aggregation-induced emission dye, which can be optically stimulated by femtosecond laser beams. This film is highly transparent and uniform, and the aggregation-induced emission phenomenon provides the storage mechanism. It can also be inhibited by another deactivating beam, resulting in a recording spot with a super-resolution scale. This technology makes it possible to achieve exabit-level storage by stacking nanoscale disks into arrays, which is essential in big data centres with limited space.

Types of Batteries for New Energy Electric Vehicles

This article aims to study and explore the different types of batteries used in new energy electric vehicles, and classify them. As environmental preservation and sustainable development gain greater prominence, the adoption of new energy electric vehicles as a viable alternative to conventional fuel-based vehicles has surged. Concurrently, there have been remarkable advancements in battery technologies supporting these electric vehicles. Understanding the classification and characteristics of electric vehicle batteries is of great significance for promoting the development of the electric vehicle industry. This article will provide a detailed introduction to several major battery technologies, including lithium-ion batteries, sodium ion batteries, and solid-state-state batteries, and analyze their advantages and disadvantages; Application fields and future development trends.