هدف: در سالهای اخیر، ارتباطات مستقیم ماهواره با تجهیزات هوشمند زمینی بهعنوان یکی از فناوریهای نوین در حوزة ارتباطات ماهوارهای، موردتوجه صنعت فضایی قرارگرفته است. تحلیل و بررسی این حوزه با بهرهگیری از رویکرد علمسنجی، یکی از روشهای مؤثر برای ارزیابی ابعاد گوناگون این فناوری به شمار میآید. هدف این پژوهش، تحلیل علمی و ترسیم حوزههای پیشرو و فعال در این فناوری بر پایه اطلاعات نمایه شده در پایگاه استنادی اسکوپوس طی سالهای 2011 تا 2024 است.روششناسی: این پژوهش ازنظر هدف، کاربردی است و با بهرهگیری از روشهای رایج در مطالعات علمسنجی شامل شبکة همواژگانی، شبکة همنویسندگی و شبکة همکشوری انجامشده است. جامعة آماری پژوهش، شامل تمامی مدارک علمی مرتبط با «فناوری ارتباطات مستقیم ماهواره با تجهیزات هوشمند زمینی» است که در فاصلة سالهای 2011 تا 2024 در پایگاه استنادی اسکوپوس نمایه شدهاند. درمجموع، 168 مدرک بازیابی شد که با استفاده از نرمافزارهایی مانند ووس ویوور و گفی مورد تحلیلهای آماری قرار گرفتند و نقشههای مربوط به این حوزه ترسیم و مصورسازی شدند.یافتهها: نتایج نشان میدهد که روند تولیدات علمی در این حوزه از سال 2021 تا 2024 صعودی بوده و رشد چشمگیری داشته است. در این میان، کشور آلمان با انتشار 42 مدرک پژوهشی، بهعنوان کشور برتر و دانشگاه بوندسوهر مونیخ هم بهعنوان نهاد پیشرو شناسایی شدهاند. همچنین، آندریاس کنوپ با انتشار 14 مقاله علمی، فعالترین پژوهشگر این حوزه محسوب میشود.نتیجهگیری: یافتههای پژوهش نشاندهندة اهمیت سایر فناوریهای نوین مانند نسل پنجم شبکة ارتباطی، اینترنت اشیا و هوش مصنوعی در توسعة زیرساختهای ارتباطات ماهوارهای هستند. علاوه بر این، در مسیر توسعه این فناوری، توجه به محدودیتهای طیف فرکانسی موردنظر و مدیریت تداخل، امری ضروری و اجتنابناپذیر است.
Science (General), Information resources (General)
Arroussi, Abdelaziz, Laksaci, Hamza, Djaafri, Mohammed
et al.
The pollution of water resources by hazardous contaminants is a major obstacle to the provision of safe and potable water worldwide. Cost-effective, innovative, renewable, and environmentally friendly technologies are essential for wastewater treatment. In the current work, the potential of using raw date palm petiole (DPP) for the removal of Bezaktiv Marine S-BL (SBL) dye is investigated under a wide range of experimental conditions in batch mode. Experimental results show that the removal of SBL dye by DPP is highly dependent on contact time, initial pH, initial dye concentration, and temperature. The removal of SBL dye is relatively rapid where the equilibrium state is reached within a contact time of 120 min. Moreover, the biosorbent exhibits high stability in SBL adsorption ability over a wide pH range (4–12). Furthermore, the SBL dye adsorption increases with increase in initial concentration. The maximum adsorption capacity of the SBL dye on DPP is evaluated to be 110 mg/g, which is much higher than numerous other materials. In addition, a thermodynamic study indicates that the adsorption of SBL dye by DPP is a feasible exothermic and spontaneous process.The findings of the present study indicate that untreated DPP wastes can be considered effective materials for the treatment of textile dyes in wastewater in general and SBL in particular.
Prottay Kumar Adhikary, Isha Motiyani, Gayatri Oke
et al.
Abstract
BackgroundThe quality and accessibility of menstrual health education (MHE) in low- and middle-income countries, including India, remain inadequate due to persistent challenges (eg, poverty, social stigma, and gender inequality). While community-driven initiatives have sought to raise awareness, artificial intelligence offers a scalable and efficient solution for disseminating accurate information. However, existing general-purpose large language models (LLMs) are often ill-suited for this task, tending to exhibit low accuracy, cultural insensitivity, and overly complex responses. To address these limitations, we developed MenstLLaMA—a specialized LLM tailored to the Indian context and designed to deliver MHE empathetically, supportively, and accessibly.
ObjectiveWe aimed to develop and evaluate MenstLLaMA—a specialized LLM tailored to deliver accurate, culturally sensitive MHE—and assess its effectiveness in comparison to existing general-purpose models.
MethodsWe curated MENST—a novel, domain-specific dataset comprising 23,820 question-answer pairs aggregated from medical websites, government portals, and health education resources. This dataset was systematically annotated with metadata capturing age groups, regions, topics, and sociocultural contexts. MenstLLaMA was developed by fine-tuning Meta-LLaMA-3-8B-Instruct, using parameter-efficient fine-tuning with low-rank adaptation to achieve domain alignment while minimizing computational overhead. We benchmarked MenstLLaMA against 9 state-of-the-art general-purpose LLMs, including GPT-4o, Claude-3, Gemini 1.5 Pro, and Mistral. The evaluation followed a multilayered framework: (1) automatic evaluation using standard natural language processing metrics (BLEU [Bilingual Evaluation Understudy], METEOR [Metric for Evaluation of Translation with Explicit Ordering], ROUGE-L [Recall-Oriented Understudy for Gisting Evaluation-Longest Common Subsequence], and BERTScore [Bidirectional Encoder Representations from Transformers Score]); (2) evaluation by clinical experts (N=18), who rated 200 expert-curated queries for accuracy and appropriateness; (3) medical practitioner interaction through the ISHA (Intelligent System for Menstrual Health Assistance) interactive chatbot, assessing qualitative dimensions (eg, relevance, understandability, preciseness, correctness,context sensitivity
ResultsMenstLLaMA achieved the highest scores in BLEU (0.059) and BERTScore (0.911), outperforming GPT-4o (BLEU: 0.052, BERTScore: 0.896) and Claude-3 (BERTScore: 0.888). Clinical experts preferred MenstLLaMA’s responses over gold-standard answers in several culturally sensitive cases. In medical practitioners’ evaluations using the ISHA—the chat interface powered by MenstLLaMA—the model scored 3.5 in relevanceunderstandabilityprecisenesscorrectnesscontext sensitivityunderstandabilityrelevanceprecisenesscorrectnesstoneflowcontext sensitivity
ConclusionsMenstLLaMA demonstrates exceptional accuracy, empathy, and user satisfaction within the domain of MHE, bridging critical gaps left by general-purpose LLMs. Its potential for integration into broader health education platforms positions it as a transformative tool for menstrual well-being. Future research could explore its long-term impact on public perception and menstrual hygiene practices, while expanding demographic representation, enhancing context sensitivity, and integrating multimodal and voice-based interactions to improve accessibility across diverse user groups.
Computer applications to medicine. Medical informatics, Public aspects of medicine
Anand D. Sarwate, Flavio P. Calmon, Oliver Kosut
et al.
Since being proposed in 2006, differential privacy has become a standard method for quantifying certain risks in publishing or sharing analyses of sensitive data. At its heart, differential privacy measures risk in terms of the differences between probability distributions, which is a central topic in information theory. A differentially private algorithm is a channel between the underlying data and the output of the analysis. Seen in this way, the guarantees made by differential privacy can be understood in terms of properties of this channel. In this article we examine a few of the key connections between information theory and the formulation/application of differential privacy, giving an ``operational significance'' for relevant information measures.
The medium and long-term demand prediction for water resources is critical to the country’s macro-allocation of water resources and the formulation of a long-term regional economic development strategy. However, the official prediction report of China’s water resources was created ten years ago, and its results are far from accurate in practice making it difficult to direct China’s water resource planning in the coming ten years. By introducing the demand forecasting method for the first time, this study built a mathematical prediction model that integrates “data constraint + mathematical prediction + actuarial prediction”. The conclusions are as follows: (1) The new method developed in this research is more accurate than previous prediction methods and has a higher degree of matching with actual data. Despite the problem of trend extrapolation, this model can accurately understand the long-term trend. However, the amount of data must be calculated. (2) China’s consumption of total water, agricultural water, industrial water, domestic water, and ecological water will predict to be 552.9 billion m3, 319.1 billion m3, 95.7 billion m3, 95.5 billion m3 and 42.6 billion m3 in 2030, respectively, as well as 504.7 billion m3, 281.1 billion m3, 61.4 billion m3, 101.4 billion m3, and 60.8 billion m3 in 2050, respectively, indicating obvious structural changes. (3) China’s industrial structure evolves frequently; therefore, water resource forecasting must account for changes in industrial structure as well as geographical variances. In North China, agricultural water demand will fall, but ecological water investment will rise. The planning and distribution of the nation’s water resources will significantly be influenced theoretically and practically by the development of this methodology study. This method is universal and can be used for long-term prediction of future water demand in other countries.
Mehdi Rouissat, Mohammed Belkheir, Ibrahim S. Alsukayti
et al.
Internet of Things (IoT) networks are being widely deployed for a broad range of critical applications. Without effective security support, such a trend would open the doors to notable security challenges. Due to their inherent constrained characteristics, IoT networks are highly vulnerable to the adverse impacts of a wide scope of IoT attacks. Among these, flooding attacks would cause great damage given the limited computational and energy capacity of IoT devices. However, IETF-standardized IoT routing protocols, such as the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL), have no relevant security-provision mechanism. Different variants of the flooding attack can be easily initiated in RPL networks to exhaust network resources and degrade overall network performance. In this paper, a novel variant referred to as the Destination Information Object Flooding (DIOF) attack is introduced. The DIOF attack involves an internal malicious node disseminating falsified information to instigate excessive transmissions of DIO control messages. The results of the experimental evaluation demonstrated the significant adverse impact of DIOF attacks on control overhead and energy consumption, which increased by more than 500% and 210%, respectively. A reduction of more than 32% in Packet Delivery Ratio (PDR) and an increase of more than 192% in latency were also experienced. These were more evident in cases in which the malicious node was in close proximity to the sink node. To effectively address the DIOF attack, we propose a new lightweight approach based on a collaborative and distributed security scheme referred to as DIOF-Secure RPL (DSRPL). It provides an effective solution, enhancing RPL network resilience against DIOF attacks with only simple in-protocol modifications. As the experimental results indicated, DSRPL guaranteed responsive detection and mitigation of the DIOF attacks in a matter of a few seconds. Compared to RPL attack scenarios, it also succeeded in reducing network overhead and energy consumption by more than 80% while maintaining QoS performance at satisfactory levels.
M. Nikhil Krishnan, Myna Vajha, Vinayak Ramkumar
et al.
For a convolutional code in the presence of a symbol erasure channel, the information debt $I(t)$ at time $t$ provides a measure of the number of additional code symbols required to recover all message symbols up to time $t$. Information-debt-optimal streaming ($i$DOS) codes are convolutional codes which allow for the recovery of all message symbols up to $t$ whenever $I(t)$ turns zero under the following conditions; (i) information debt can be non-zero for at most $τ$ consecutive time slots and (ii) information debt never increases beyond a particular threshold. The existence of periodically-time-varying $i$DOS codes are known for all parameters. In this paper, we address the problem of constructing explicit, time-invariant $i$DOS codes. We present an explicit time-invariant construction of $i$DOS codes for the unit memory ($m=1$) case. It is also shown that a construction method for convolutional codes due to Almeida et al. leads to explicit time-invariant $i$DOS codes for all parameters. However, this general construction requires a larger field size than the first construction for the $m=1$ case.
Marco Antonio Peña-Chavez, Jorge Antonio Zacatecas Ibañez, Jorge Saenz-Mata
et al.
The main objective of this study was to evaluate the antioxidant capacity and possible toxicological potential of the infusions prepared from Ibervillea sonorae. For this, infusions were prepared from the root of I. sonorae, using the skin or the pulp. The antioxidant capacity of the infusions was evaluated through the ABTS and DPPH tests. While, the Artemia salina model, the MTT test using Vero and MCF-7 cells, and the Ames test were used to evaluate the possible toxicological potential. The results showed that I. sonorae peels were more lethal (60-80%) than pulp (<50%). Interestingly, I. sonorae pulp showed greater toxicity on Vero cells (IC50 = 222 µg / mL) than infusions made using the skins (IC50 = 379 µg / mL). Infusions of I. sonorae, either in the form of peel or pulp, showed high mutagenicity at 1000 and 500 µg/mL. The ingestion of infusions prepared from the I. sonorae plant could cause serious damage to health.
DOI: https://doi.org/10.54167/tecnociencia.v16i2.903
With the onset of the COVID-19 pandemic and the succession of its waves, the transmission of this disease and the number of deaths caused by it have been increasing. Despite the various vaccines, the COVID-19 virus is still contagious and dangerous for affected people. One of the remedies to this is precaution, and particularly social distancing. In the same vein, this paper proposes a remote voting system, which has to be secure, anonymous, irreversible, accessible, and simple to use. It therefore allows voters to have the possibility to vote for their candidate without having to perform the operation on site. This system will be used for university elections and particularly for student elections. We propose a platform based on a decentralized system. This system will use two blockchains communicating with each other: the public Ethereum blockchain and the private Quorum blockchain. The private blockchain will be institution-specific. All these blockchains send the necessary data to the public blockchain which manages different data related to the universities and the ministry. This system enables using encrypted data with the SHA-256 algorithm to have both security and information security. Motivated by the high energy consumption of blockchain and by the performance improvements in low-power, a test is performed on a low-power embedded platform Raspberry PI4 showing the possibility to use the Blockchain with limited resources.
Consider $L$ users, who each hold private data, and one fusion center who must compute a function of the private data of the $L$ users. To accomplish this task, each user may utilize a public and noiseless broadcast channel in a non-interactive manner. In this setting, and in the absence of any additional resources such as secure links, we study the optimal communication rates and minimum information leakages on the private user data that are achievable. Specifically, we study the information leakage of the user data at the fusion center (beyond the knowledge of the function output), as well as at predefined groups of colluding users who eavesdrop one another. We derive the capacity region when the user data is independent, and inner and outer regions for the capacity region when the user data is correlated.
This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service. An adaptive optimal service resource management strategy was developed considering a value co-creation model in health information service with a focus on collaborative and interactive with users. The deep reinforcement learning algorithm was embedded in the Internet of Things (IoT)-based health information service system (I-HISS) to allocate service resources by controlling service provision and service adaptation based on user engagement behaviour. The simulation experiments were conducted to evaluate the significance of the proposed algorithm under different user reactions to the health information service.
Fisheries researchers have focused on the value of information (VOI) in fisheries management and trade-offs since scientists and managers realized that information from different resources has different contribution in the management process. We picked seven indicators, which are log-normal annual catch observation error (Cobs), annual catch observation bias (Cbias), log-normal annual index observation error (Iobs), maximum length observation bias (Linfbias), observed natural mortality rate bias (Mbias), observed von Bertalanffy growth parameter K bias (Kbias), and catch-at-age sample size (CAA_nsamp), and built operating models (OMs) to simulate fisheries dynamics, and then applied management strategy evaluation (MSE). Relative yield is chosen as the result to evaluate the contribution of the seven indicators. Within the parameter range, there was not much information value reflected from fisheries-dependent parameters including Cobs, Cbias, and Iobs. On the other hand, for fisheries-independent parameters such as Kbias, Mbias, and Linfbias, similar tendency of the information value was showed in the results, in which the relative yield goes down from the upper bound to the lower bound of the interval. CAA_nsamp had no impact on the yield after over 134 individuals. The VOI analysis contributes to the trade-offs in the decision-making process. Information with more value is more worthy to collect in case of waste of time and money so that we could make the best use of scientific effort. But we still need to improve the simulation process such as enhancing the diversity and predictability in an OM. More parameters are on the way to be tested in order to collect optimum information for management and decision-making.
Science, General. Including nature conservation, geographical distribution
Materials in nature have fascinating properties that serve as a continuous source of inspiration for materials scientists. Accordingly, bio-mimetic and bio-inspired approaches have yielded remarkable structural and functional materials for a plethora of applications. Despite these advances, many properties of natural materials remain challenging or yet impossible to incorporate into synthetic materials. Natural materials are produced by living cells, which sense and process environmental cues and conditions by means of signaling and genetic programs, thereby controlling the biosynthesis, remodeling, functionalization, or degradation of the natural material. In this context, synthetic biology offers unique opportunities in materials sciences by providing direct access to the rational engineering of how a cell senses and processes environmental information and translates them into the properties and functions of materials. Here, we identify and review two main directions by which synthetic biology can be harnessed to provide new impulses for the biologization of the materials sciences: first, the engineering of cells to produce precursors for the subsequent synthesis of materials. This includes materials that are otherwise produced from petrochemical resources, but also materials where the bio-produced substances contribute unique properties and functions not existing in traditional materials. Second, engineered living materials that are formed or assembled by cells or in which cells contribute specific functions while remaining an integral part of the living composite material. We finally provide a perspective of future scientific directions of this promising area of research and discuss science policy that would be required to support research and development in this field.
We present a versatile automated theorem proving framework capable of automated discovery, simplification and proofs of inner and outer bounds in network information theory, deduction of properties of information-theoretic quantities (e.g. Wyner and Gács-Körner common information), and discovery of non-Shannon-type inequalities, under a unified framework. Our implementation successfully generated proofs for 32 out of 56 theorems in Chapters 1-14 of the book Network Information Theory by El Gamal and Kim. Our framework is based on the concept of existential information inequalities, which provides an axiomatic framework for a wide range of problems in information theory.
As conversational search becomes more pervasive, it becomes increasingly important to understand the user's underlying information needs when they converse with such systems in diverse domains. We conduct an in-situ study to understand information needs arising in a home cooking context as well as how they are verbally communicated to an assistant. A human experimenter plays this role in our study. Based on the transcriptions of utterances, we derive a detailed hierarchical taxonomy of diverse information needs occurring in this context, which require different levels of assistance to be solved. The taxonomy shows that needs can be communicated through different linguistic means and require different amounts of context to be understood. In a second contribution we perform classification experiments to determine the feasibility of predicting the type of information need a user has during a dialogue using the turn provided. For this multi-label classification problem, we achieve average F1 measures of 40% using BERT-based models. We demonstrate with examples, which types of need are difficult to predict and show why, concluding that models need to include more context information in order to improve both information need classification and assistance to make such systems usable.
We study two dual settings of information processing. Let $ \mathsf{Y} \rightarrow \mathsf{X} \rightarrow \mathsf{W} $ be a Markov chain with fixed joint probability mass function $ \mathsf{P}_{\mathsf{X}\mathsf{Y}} $ and a mutual information constraint on the pair $ (\mathsf{W},\mathsf{X}) $. For the first problem, known as Information Bottleneck, we aim to maximize the mutual information between the random variables $ \mathsf{Y} $ and $ \mathsf{W} $, while for the second problem, termed as Privacy Funnel, our goal is to minimize it. In particular, we analyze the scenario for which $ \mathsf{X} $ is the input, and $ \mathsf{Y} $ is the output of modulo-additive noise channel. We provide analytical characterization of the optimal information rates and the achieving distributions.
The older persons in our society are a special group of people in need of additional measures of care and protection. They have medical, financial, emotional and social needs. The novel Coronavirus disease 2019 (COVID-19) only exacerbates those needs. COVID-19 is a new disease, and there is limited information regarding the disease. Based on currently available information, older persons and people of any age who have serious underlying medical conditions may be at higher risk of severe illness from COVID-19. Family physicians provide care for individuals across their lifespan. Because geriatricians are internists or family physicians with post-residency training in geriatric medicine, they are major stakeholders in geriatric care. The authors are concerned about the absence of a COVID-19 response guideline/special advisory targeting the vulnerable population of older adults. The management and response to COVID-19 will be implemented in part based on the local context of available resources. Nigeria has been described as a resource-constrained nation. Infection prevention in older persons in Nigeria will far outweigh the possibilities of treatment given limited resources. The aim was to recommend actionable strategies to prevent COVID-19-related morbidity or mortality among older persons in Nigeria and to promote their overall well-being during and after the pandemic. These recommendations cut across the geriatric medicine domains of physical health, mental health, functioning ability and socio-environmental situation.
Sérgio José de Sousa, Thiago Magela Rodrigues Dias, Adilson Luiz Pinto
Com o crescente volume de dados e usuários de sistemas de currículos, a dificuldade de encontrar especialistas é cada vez maior. Neste trabalho é proposto metodologia de extração de dados abertos dos currículos da Plataforma Lattes, um tratamento para esses dados e investiga uma abordagem de Agente de Recomendação baseado em redes neurais profundas com autoencoder.
Bibliography. Library science. Information resources, Information resources (General)
Mohammad Fathian, Hossein Sharifi, Faranaksadat Solat
This study examines how gamification mechanics could be used in online retailers' loyalty programs. In other words, this article attempts to create a conceptual model for the relationship between gamification mechanics and customer loyalty elements. We used a field study to conduct our research. In order to validate the survey, 450 customers from one of the greatest online stores in Iran were questioned. The results of this survey were used to validate our 11 phrases on the relationship between gamification mechanics and customer loyalty. The results were analyzed using confirmatory factor analysis, path analysis, and model fitness tests in structural equations modeled in the Lisrel software. According to the research findings, the relationship between variables and the proposed conceptual model was confirmed. Based on the performed analysis, all 11 phrases were verified<strong>.</strong>
The digital divide is deeply felt by undergraduate students in resource-restricted universities, but creative, if also labor-intensive, solutions exist for instructors negotiating paywalls and other institutional impediments. In this essay, I argue that teaching early modern drama outside the restraints of the Shakespearean archive and through a host of digital archives, databases, and tools not only engages students in inquiry-based, active learning but also cultivates a critical sense of how digital tools obviate and exacerbate questions of access. To make my case, I describe how I designed and taught a course on non-Shakespearean drama for English majors at Shippensburg University, one of Pennyslvania’s state-funded universities. After describing the mechanics of the course, I further theorize and examine the ways centering digital archives, databases, and tools as course texts enables students to think critically about the content available through these resources as well as the information hierarchies and receptions histories they promulgate.
History of scholarship and learning. The humanities