Limitations Of Technology Research Articles

Multilingualism, Technology, and Religious Moderation in Indonesian Islamic Boarding Schools

Communities that can speak more than one language as a daily habit are not only happening in the present era but have long existed in the pesantren environment. However, multilingualism in pesantren is often only seen as a capacity enhancement of the santri's knowledge. On the other hand, the multilingual ability of the Santri is an asset in forming an attitude of religious moderation. This article then presents the results of qualitative research using the concept of additive multilingualism which focuses on pesantren students in Indonesia. Through observations, interviews, and literature review, this research can describe the reality of multilingualism in pesantren and its impact on the religious attitudes of its santri. It turns out that the religious moderation attitude of the students is closely related to the multilingual learning process in pesantren that takes place with technological restrictions. The application of additive multilingualism is aligned with the pillars of religious moderation in four ways. First, respect for locality is reflected in the Santri's habit of using local languages. Second, multilingualism is an expression of anti-violence, this is reflected in Santri's acceptance of linguistic and ethnic diversity as a brotherhood base. This is what manifests in the Third pillar by making santri a tolerant community as a result of learning from the reality of ethnic and linguistic diversity in their environment. Fourth, the introduction of santri to foreign languages is directed to reaffirm their national commitment through the use of foreign languages in flag ceremonies. The limitation of technology in multilingual learning is also intended to keep students from the bad influence of technology and to maintain the discipline of students in maintaining a pesantren culture that is friendly to locality and sensitive to the times

Advancing Patient Safety: The Future of Artificial Intelligence in Mitigating Healthcare-Associated Infections: A Systematic Review.

Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention. We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs. We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital's HAI incidence from 1.31% to 0.58%. AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.

Development Hybrid High-Speed Electro Pneumatic Spindles Drive Unit

Abstract The essence of the research is the search for geometric, material and technological limits of the new concept of spinning spindles. Considering the trend of introducing new generation electric drives into the segment of high-speed spinning spindles (over 120,000 rpm), it is necessary to map and describe the basic physical connections, to find the necessary dependencies and correlations of individual design parameters and their limits for the design of a new generation of spindles. Due to the design limitations of the original and years-used spindle drive concept with a belt and the requirement for the production and development of a new generation of spun fibers, it is necessary to expand the basic knowledge for the design of spindles of the new concept, the design of which will be based on the knowledge and dependencies obtained by measuring and examining the basic principles and determining parameters for the design of spindle structures of a new progressive concept.

Management Challenges in Implementing the Merdeka Curriculum

This study focuses on implementing the Merdeka curriculum from the perspective of its management challenges, exploring the experiences and perceptions of stakeholders related to implementing the Merdeka Curriculum at MAN Jombang and using a qualitative study, interviews, observations, and documentation as data collection techniques. Data analysis used data reduction, coding, and theme identification stages. To ensure data validity, the researcher applied source triangulation. The study findings indicate that implementing the Merdeka Curriculum faces challenges related to technology access and teacher readiness. Technology limitations, including unequal access to devices, hinder effective project-based learning, and administrative staff acknowledge the need for infrastructure improvements. Students appreciate the curriculum's focus on personal interests but need more guidance to navigate independent learning. Teachers have received training in project-based learning, but many teachers need help implementing new methods effectively. Although progress has been made, continued support, increased resources, and collaborative efforts among stakeholders are critical to successfully implementing the Merdeka Curriculum and improving educational outcomes. Limitations of the study include limited observation time and potential bias in participant responses. This study has yet to explore the long-term impacts of implementing the Merdeka Curriculum. Stakeholder collaboration research can provide deeper insights into practical strategies to address challenges and improve educational outcomes in Islamic education.

Optimizing and investigating the charging time of phase change materials in a compact-latent heat storage using pareto front analysis, artificial neural networks, and numerical simulations

There's a growing emphasis on adopting eco-friendly energy sources to mitigate greenhouse gas emissions and foster sustainability. While renewable energy sources like solar power offer numerous benefits, they have some limitations. Additionally, storing electricity generated from solar panels can be costly and challenging due to limitations in battery technology and storage capacity. Therefore, phase change material-based thermal energy storage systems offer a promising solution to these challenges. These systems also play a crucial role in enhancing buildings' energy efficiency and sustainability. The dimensionality of these systems affects their performance. Traditional systems often rely on fixed dimensions, leading to non-uniform thermal conditions within the storage medium. To address these challenges, adopting a compact-latent heat storage (C-LHS) mechanism was recommended herein. Besides, some fins were inserted into the C-LHS system to alter the heat transfer dynamics within the device. Three of the critical parameters of the fins were changed to examine their impact on the charging time. Artificial neural networks and genetic algorithms were employed to determine the optimal positioning of fins to minimize the duration of material to get both part (melt fraction of 0.8) and full (melt fraction of 1) melting capacities. Here, the primary aim was to present a predictive model capable of accurately forecasting the charging time of the material. In all the presented finned samples, the entire PCM melted in less than 15,110 s (4 h and 12 min) and was able to fully utilize the energy absorption capacity in latent form, whereas in the non-finned system, only 68.6 % of the material managed to melt within the entire duration of the investigation (5 h). After analyzing the data, two optimal configurations of OS1 and OS2 with the minimum time for melt fractions of 0.8 and 1 were introduced. In the OS2 configuration, it took 16,200 s (4 h and 30 min) to absorb 4929 kJ of energy. The non-finned sample absorbed only 3250 kJ of energy during the same period. In General, OS2 achieved total energy absorption 51.6 % faster than the non-finned sample. Therefore, the introduced system has the potential to increase absorbed energy in the rest of the daylight hours to further amounts by increasing the number of units and employing a larger volume of material.

P-functionalization of Ni Fe − Electrocatalysts from Prussian blue analogue for enhanced anode in anion exchange membrane water electrolysers

Efficient hydrogen generation from water-splitting is widely acknowledged as a priority route to promote the hydrogen economy. Anion exchange membrane water electrolyzers (AEMWE) offer multiple advantages in improving performance and minimizing the cost limitations of current electrolysis technologies. However, the persistence of issues related to the limited electrocatalytic activity of such materials and their poor stability under operating conditions makes developing highly active, stable, platinum-group-metal-free electrocatalysts for oxygen evolution reaction (OER) necessary.We report the development of Prussian blue analogues (PBA)-derived NiFe-based electrocatalysts through a mild aqueous phase precipitation method, followed by thermal stabilization and phosphorus doping. The formation of the NiFe-PBA-precursor with a framework nanocubic Ni(II)[Fe(III)(CN)6]2/3 structure was confirmed by X-ray diffraction, scanning electron microscopy, and inductively coupled plasma analysis. The NiFe-PBA-precursor was subjected to thermal stabilization and phosphorus doping to provide the material with enhanced OER catalytic activity and stability. The existence of OER active sites based on NiFe and NiFeP has been revealed by transmission electron microscopy, X-ray photoelectron spectroscopy, and electrochemical characterization in a three-electrode cell configuration in a 1 M KOH electrolyte. NiFe-PBA and NiFeP-PBA were assembled at the anode side of an AEMWE, resulting in an excellent electrochemical performance both in terms of current density at 2.0 V using 1 M KOH (1.21 A cm−2) and durability, outperforming the benchmark catalyst.

Adaptive tracking control method for shearer cutting trajectory based on the combined strategy

Adaptive tracking control method for shearer remains challenging caused by the contradiction between coal recovery rate and equipment safety under current technological limitations, has drawn more and more attention over the past decades due to its important role in reducing personnel, increasing safety, and improving efficiency for underground mining. Therefore, a novel adaptive tracking control method for shearer cutting trajectory based on the combined strategy is proposed, whose core is the switching method of the sub-strategies based on the linear quadratic regulator (LQR) and error band control (EBC) with switching threshold determined by the Multi-strategy Marine Predator Algorithm. The performance of the proposed method is demonstrated by comparing with the existing methods reported. The experimental results reveal that the proposed method maintains a high recovery rate while improving coal mining efficiency.

Issues of the Russian liquefied natural gas market in the context of sanctions pressure and ways to solve them

Liquefied natural gas export is currently a rather promising sector of the Russian fuel and energy complex. Gas transportation by special gas carriers makes it possible to supply the resource independently of the pipeline system. In recent years, the liquefied natural gas market role has been significant for the Russian economy. This industry has not been subject to significant export restrictions by other countries, unlike pipeline gas exports. However, under the conditions of sanctions imposed by “unfriendly” countries on the supply of technologies and equipment required for liquefied natural gas production and transportation, not only the industry development, but also implementation of the Energy Strategy of the Russian Federation for the period up to 2035 is threatened, especially major projects implementation on the Arctic coast of the country. All of the above speaks for the relevance of the study, the objectives of which are to assess the Russian market of liquefied natural gas in the context of the introduced technological restrictions, to identify the key problems arising as a result of this unfavorable situation and to determine the directions for the industry further development.

Challenges Encountered by Police Officers During Crime Investigations

This study highlights the vital function of police officers involved in criminal investigations in maintaining the integrity of Indian society through effective law enforcement and fair justice. However, it identifies several obstacles that impede their efficiency, including heavy workloads, lack of adequate resources, shortage of officers, external pressures, and technological limitations, particularly when addressing serious crimes, cybercrimes, and offences involving cryptocurrency. These issues contribute to decreased morale among officers who investigate crimes and increase public distrust, especially amidst growing media scrutiny. Furthermore, the psychological strain of handling complex cases worsens these challenges. The study calls for comprehensive reforms to alleviate workloads, enhance the number of investigative officers, increase the sanctioned strength of police officers, improve resource allocation, integrate technology into investigations, and create a more efficient and culturally sensitive police system. Implementing these reforms is essential for strengthening investigative capabilities, adapting to the changing criminal landscape of the 21st century, and ensuring law enforcement's dedication to safeguarding the integrity of Indian society.

The dual effect of COVID-19: Diverse digitalization approaches in micro- and small businesses

ABSTRACT This study empirically examines the controversial impact of the pandemic on the digitalization of micro- and small enterprises (MSEs) in the Canton of Fribourg, Switzerland. Previous research suggests that the COVID-19 pandemic has accelerated digital transformation while also amplifying preexisting vulnerabilities among MSEs. Several MSEs benefited greatly from digitalization to survive; others faced serious obstacles because of preexisting financial and technological limitations. Our study reveals that, on the one hand, micro-enterprises exhibited greater levels of digitalization compared to their larger counterparts, mitigating the “liability of smallness” through greater flexibility and adaptability. On the other hand, financial and organizational structure constraints limited the broader digital adoption among small enterprises that prioritized short-term survival over strategic transformation. These insights underscore the need for targeted policies to address the digitalization challenges faced by MSEs, focusing on improving access to digital infrastructure and enhancing their antifragility in the face of future crises.

MVSF-AB: Accurate antibody-antigen binding affinity prediction via multi-view sequence feature learning.

Predicting the binding affinity between antigens and antibodies accurately is crucial for assessing therapeutic antibody effectiveness and enhancing antibody engineering and vaccine design. Traditional machine learning methods have been widely used for this purpose, relying on interfacial amino acids' structural information. Nevertheless, due to technological limitations and high costs of acquiring structural data, the structures of most antigens and antibodies are unknown, and sequence-based methods have gained attention. Existing sequence-based approaches designed for protein-protein affinity prediction exhibit a significant drop in performance when applied directly to antibody-antigen affinity prediction due to imbalanced training data and lacking design in the model framework specifically for antibody-antigen, hindering the learning of key features of antibodies and antigens. Therefore, we propose MVSF-AB, a Multi-View Sequence Feature learning for accurate Antibody-antigen Binding affinity prediction. MVSF-AB designs a multi-view method that fuses semantic features and residue features to fully utilize the sequence information of antibody-antigen and predicts the binding affinity. Experimental results demonstrate that MVSF-AB outperforms existing approaches in predicting unobserved natural antibody-antigen affinity and maintains its effectiveness when faced with mutant strains of antibodies. Datasets we used and source code are available on our public GitHub repository https://github.com/TAI-Medical-Lab/MVSF-AB.

Life cycle assessment and cost analysis of innovative agar extraction technologies from red seaweeds

Developing efficient and sustainable extraction technologies for valuable biocompounds from seaweed is crucial to overcome the limitations of conventional technologies. This study aims to compare three innovative technologies for agar extraction from two red seaweed species, G. sesquipedale and G. vermiculophylla: subcritical water extraction (performed at 125 °C, 2.5 atm, 1 min, and at 140 °C, 3.8 atm, 1 s), moderate electric fields (applied at 85 °C for 120 min and 95 °C for 180 min), and a combination of both methods. The comparison used life cycle assessment and life cycle costing methodologies, considering a gate-to-gate approach. The combined technology demonstrated the lowest energy consumption, with 67 MJ/kgagar for G. vermiculophylla and 100 MJ/kgagar for G. sesquipedale. A carbon footprint reduction of up to 94 % was obtained when compared to the control, with 15.9 kgCO2 eq. /kgagar for G. vermiculophylla and 20.4 kgCO2 eq. /kgagar for G. sesquipedale. Using photovoltaic panels as alternative energy further cut carbon emissions by 50 %. The cost analysis showed that the combined technology was the most cost-effective extraction method.

CAR T-cell therapy in acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aggressive leukemic malignancy that affects myeloid lineage progenitors. Relapsed or refractory AML patients continue to have poor prognoses, necessitating the development of novel therapy alternatives. Adoptive T-cell therapy with chimeric antigen receptors (CARs) is an intriguing possibility in the field of leukemia treatment. Chimeric antigen receptor T-cell therapy is now being tested in clinical trials (mostly in phase I and phase II) using AML targets including CD33, CD123, and CLL-1. Preliminary data showed promising results. However, due to the cellular and molecular heterogeneity of AML and the co-expression of some AML targets on hematopoietic stem cells, these clinical investigations have shown substantial "on-target off-tumor" toxicities, indicating that more research is required. In this review, the latest significant breakthroughs in AML CAR T cell therapy are presented. Furthermore, the limitations of CAR T-cell technology and future directions to overcome these challenges are discussed.

Reducing tree volume overestimation in quantitative structure models using modeled branch topology and direct twig measurements

Abstract Quantitative Structure Models (QSMs) are fit to tree point clouds to represent the topology of trees as a network of cylinders. QSMs allow for the calculation of metrics difficult to measure without destructive sampling, including total tree volume. Current limitations in terrestrial laser scanning technology make small branches difficult to accurately resolve, causing overestimation of small branch volume in QSMs, which can translate into overestimating tree biomass. We present a new method called Real Twig to correct overestimated small branch and twig cylinders in QSMs. Real Twig differs from current methods by using twig diameters measured directly from corresponding tree species to model a unique taper for every path in the QSM, using the QSM’s inherent branching topology, but without relying on predefined mathematical or allometric relationships. To test Real Twig, we generated QSMs for different sets of trees that had detailed dry mass and density measurements obtained via felling after scanning. QSM-based biomass estimates were obtained by multiplying the tree’s QSM-based volume estimate by the tree’s specific basic density value. We trained our method with high-quality data consisting of five northern red oak (Quercus rubra L.) and five red maple (Acer rubrum L.) trees, using two different versions of TreeQSM, a widely used algorithm for generating QSMs. We further tested our method on three publicly available datasets, including managed forests and large tropical trees, collected with both phase-shift or time-of-flight sensors. QSMs corrected with our Real Twig method showed a very large improvement in tree biomass estimation, with a relative mean error of −1.2%, a relative root mean square error of 10.5%, and a concordance correlation coefficient of 0.999, compared to a relative mean error 76.8%, a relative root mean square error of 48.7%, and a concordance correlation coefficient of 0.982, when using the standard outputs of TreeQSM.

Multi-round decentralized dataset distillation with federated learning for Low Earth Orbit satellite communication

Satellite communication and Low Earth Orbit (LEO) satellites are important components of the 6G network, widely used for Earth observation tasks due to their low cost and short return period, making them a key technology for 6G network connectivity. Due to limitations in satellite system technology and downlink bandwidth, it is not feasible to download all high-resolution image information to ground stations. Even in existing federated learning (FL) methods, sharing well-trained parts of the model can still bottleneck with increasing model size. To address these challenges, we propose a new federated learning framework (FL-M3D) for LEO satellite communication that employs multi-round decentralized dataset distillation techniques. It allows satellites to independently extract local datasets and transmit them to ground stations instead of exchanging model parameters. Communication costs depend only on the size of the synthesized dataset and do not increase with larger models. However, the heterogeneity of satellite datasets can lead to sample ambiguity and decreased model convergence speed. Therefore, we propose distilling the datasets to mitigate the negative effects of data heterogeneity. Through experiments using real-world image datasets, FL-M3D reduces communication volume in simulated satellite networks by approximately 49.84% and achieves improved model performance.

Transposable elements as drivers of dedifferentiation: Connections between enhancers in embryonic stem cells, placenta, and cancer.

Transposable elements (TEs) have emerged as important factors in establishing the cell type-specific gene regulatory networks and evolutionary novelty of embryonic and placental development. Recently, studies on the role of TEs and their dysregulation in cancers have shed light on the transcriptional, transpositional, and regulatory activity of TEs, revealing that the activation of developmental transcriptional programs by TEs may have a role in the dedifferentiation of cancer cells to the progenitor-like cell states. This essay reviews the recent evidence of the cis-regulatory TEs (henceforth crTE) in normal development and malignancy as well as the key transcription factors and regulatory pathways that are implicated in both cell states, and presents existing gaps remaining to be studied, limitations of current technologies, and therapeutic possibilities.

A deep learning algorithm for the detection of aortic dissection on non-contrast-enhanced computed tomography via the identification and segmentation of the true and false lumens of the aorta.

Aortic dissection is a life-threatening clinical emergency, but it is often missed and misdiagnosed due to the limitations of diagnostic technology. In this study, we developed a deep learning-based algorithm for identifying the true and false lumens in the aorta on non-contrast-enhanced computed tomography (NCE-CT) scans and to ascertain the presence of aortic dissection. Additionally, we compared the diagnostic performance of this algorithm with that of radiologists in detecting aortic dissection. We included 320 patients with suspected acute aortic syndrome from three centers (Beijing Anzhen Hospital Affiliated to Capital Medical University, Fujian Provincial Hospital, and Xiangya Hospital of Central South University) between May 2020 and May 2022 in this retrospective study. All patients underwent simultaneous NCE-CT and contrast-enhanced CT (CE-CT). The cohort comprised 160 patients with aortic dissection and 160 without aortic dissection. A deep learning algorithm, three-dimensional (3D) full-resolution U-Net, was continuously trained and refined to segment the true and false lumens of the aorta to determine the presence of aortic dissection. The algorithm's efficacy in detecting dissections was evaluated using the receiver operating characteristic (ROC) curve, including the area under the curve (AUC), sensitivity, and specificity. Furthermore, a comparative analysis of the diagnostic capabilities between our algorithm and three radiologists was conducted. In diagnosing aortic dissection using NCE-CT images, the developed algorithm demonstrated an accuracy of 93.8% [95% confidence interval (CI): 89.8-98.3%], a sensitivity of 91.6% (95% CI: 86.7-95.8%), and a specificity of 95.6% (95% CI: 91.2-99.3%). In contrast, the radiologists achieved an accuracy of 88.8% (95% CI: 83.5-94.1%), a sensitivity of 90.6% (95% CI: 83.5-94.1%), and a specificity of 94.1% (95% CI: 72.9-97.6%). There was no significant difference between the algorithm's performance and radiologists' mean performance in accuracy, sensitivity, or specificity (P>0.05). The algorithm proficiently segments the true and false lumens in aortic NCE-CT images, exhibiting diagnostic capabilities comparable to those of radiologists in detecting aortic dissection. This suggests that the algorithm could reduce misdiagnoses in clinical practice, thereby enhancing patient care.

Increased frequency of repeat expansion mutations across different populations.

Repeat expansion disorders (REDs) are a devastating group of predominantly neurological diseases. Together they are common, affecting 1 in 3,000 people worldwide with population-specific differences. However, prevalence estimates of REDs are hampered by heterogeneous clinical presentation, variable geographic distributions and technological limitations leading to underascertainment. Here, leveraging whole-genome sequencing data from 82,176 individuals from different populations, we found an overall disease allele frequency of REDs of 1 in 283 individuals. Modeling disease prevalence using genetic data, age at onset and survival, we show that the expected number of people with REDs would be two to three times higher than currently reported figures, indicating underdiagnosis and/or incomplete penetrance. While some REDs are population specific, for example, Huntington disease-like 2 in Africans, most REDs are represented in all broad genetic ancestries (that is, Europeans, Africans, Americans, East Asians and South Asians), challenging the notion that some REDs are found only in specific populations. These results have worldwide implications for local and global health communities in the diagnosis and counseling of REDs.

Towards a Conversational Ethics of Large Language Models

Abstract Large Language Models are one of the most prominent examples of current uses of AI, and one of the most urgently pursued normative tasks is to make them and their interactive user-interfaces—open-domain chatbots—safe. However, in this paper, we elaborate first on why such a limited view on the permissibility and desirability of their utterances falls conceptually flat, is philosophically insufficient, and leads to severe technological limits. We then propose a positive normative concept, appropriateness, that can provide the required orientation for determining what chatbots may or may not say, and distinguish between technical-discursive, social, and moral appropriateness. Lastly, we explore the requirements for social and conversational contexts to fulfill appropriateness-requirements, and find that positionality is a key element for their position in discourse.

Technology availability, sector policies and behavioral change are complementary strategies for achieving net-zero emissions

In this study, we analyze the effects of technology availability, political coordination, and behavioral change on transformation pathways toward net-zero greenhouse gas emissions in the European Union by 2050. We implemented an iterative stakeholder dialogue to co-design the scenarios that were calculated using a global multi-regional energy-economy-land-climate model. We find that in scenarios without behavioral change and with restriction of technologies, the target of greenhouse gas neutrality in the European Union cannot be reached. Already a target of 200 Mt CO2eq/yr requires CO2 prices above 100 €/tCO2 in 2030 across all sectors in all scenarios. The required CO2 price can increase to up to 450 €/tCO2 by 2030 if technologies are constrained, if no complementary regulatory measures are implemented, and if changes in consumer behavior towards a more sustainable lifestyle do not materialize.