This paper describes a system that is used to gather data from Smart Home sensors measuring energy consumption, analyze and predict future consumption by using machine learning algorithm for prediction. It focuses on the architecture of the system as well as the need for such system to exist in practice – why the consumption should be measured at all and how that measurement can be put to good use for achieving energy efficiency.

The understanding of various health-oriented vital sign data generated from body sensor networks (BSN) and discovery of the association between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where the occupants’ health status is continuously monitored remotely, it is essential to provide required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach to mine the incomplete (partial) periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce the productive-associated partial periodic frequent patterns as the set of correlated partial periodical frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients for quality of diagnosis, and also for better treatment and smart care, especially for the elderly people at smart home. We developed an efficient algorithm named PPFP-Growth (Productive Periodic Frequent Pattern growth) to discover all productive associated partial periodic patterns using these measures. PPFP-Growth is efficient, and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-Growth algorithm, and can filter a huge number of partial periodic patterns to reveal only the correlated ones.

Global coverage and scale up of interventions to reduce newborn mortality remains low, though progress has been achieved in improving newborn survival in many low-income settings. An important factor in the success of newborn health interventions, and moving to scale, is appropriate design of community-based programs and strategies for local implementation. We report the results of formative research undertaken to inform the design of a newborn health intervention in Cambodia. Information was gathered on newborn care practices over a period of three months using multiple qualitative methods of data collection in the primary health facility and home setting. Analysis of the data indicated important gaps, both at home and facility level, between recommended newborn care practices and those typical in the study area. The results of this formative research have informed strategies for behavior change and improving referral of sick infants in the subsequent implementation study. Collection and dissemination of data on newborn care practices from settings such as these can contribute to efforts to advance survival, growth and development of newborns for intervention research, and for future newborn health programming.

This article describes the use of the PI ProcessBook software tool for visualization and indirect monitoring of occupancy of SHC rooms from the measured operational and technical quantities for monitoring of daily living activities for support of independent life of elderly persons. The proposed method for data processing (predicting the CO₂ course using neural networks from the measured temperature indoor T_i (°C), temperature outdoor T_o (°C) and the relative humidity indoor rHi (%)) was implemented, verified and compared in MATLAB SW tool and IBM SPSS SW tool with IoT platform connectivity. Within the proposed method, the Stationary Wavelet Transform de noising algorithm was used to remove the noise of the resulting predicted course. In order to verify the method, two long-term experiments were performed, (specifically from February 8 to February 15, 2015, from June 8 to June 15, 2015) and two short-term experiments (from February 8, 2015 and from June 8, 2015). For the best results of the trained ANN BRM within the prediction of CO₂, the correlation coefficient R for the proposed method was up to 90%. The verification of the proposed method confirmed the possibility to use the presence of persons of the monitored SHC premises for rooms ADL monitoring.

The state detection of power cable is very important to ensure the reliability of power supply. Traditional sensors are mostly based on electric field detection, the operation is complex, and its efficiency needs to be improved. This paper optimizes the design and development of the magnetic field detection sensor for AC power cables. Firstly, through the establishment of the magnetic field sensor model, it is determined that permalloy is the material of the magnetic core, the optimal aspect ratio of the magnetic core is 20, and the ratio of coil length to core length is 0.3. Secondly, the coil simulation model is established, and it is determined that the optimal number of turns of the coil is 11000 turns, the diameter of the enamelled copper wire is 0.08mm, and the equivalent magnetic field noise of the sensor is 0.06pT. Finally, the amplifying circuit based on negative magnetic flux feedback is designed and the sensor is assembled, and the experimental circuit is built for sensitivity test. the results show that the sensitivity of the magnetic field sensor is 327.6mV/μT. The sensor designed in this paper has the advantages of small size, high sensitivity, easy to carry and high reliability.

In Taiwan, remote healthcare was initially limited to telemedicine services, and the systematic development of remote care was relatively delayed. It was not until 2007 that the Department of Health of the Executive Yuan first initiated the 'Remote Care Pilot Program,' followed by the 'Remote Care Service Improvement and Quality Enhancement Plan' in the subsequent year. In 2010, Taiwan officially launched the 'Remote Health Care Services Development Plan' and established a dedicated project office, marking the comprehensive start-up of remote care services. This stage saw significant contributions from artificial intelligence aids and ICT remote transmission technologies, greatly promoting the application of smart healthcare in home care. This study aims to explore the strategic gaps in the application of smart healthcare in home remote care. By analyzing gaps in 'legal regulations,' 'economic factors,' 'user behavior habits,' 'policy and culture,' and 'environment and technology,' this study employs a mixed-methods approach. Particularly, it uses the Analytic Hierarchy Process (AHP) to construct a weighted ranking of these strategic gaps. Through in-depth interviews with doctors and smart healthcare experts and the collection of 16 valid AHP questionnaire samples, which cover both industry and academia with a balanced distribution, the survey results are ensured to be representative and reliable. The findings indicate that 'legal regulations' are considered the most critical strategic gap that needs to be addressed first, followed by 'user behavior habits.' This suggests that when designing smart healthcare applications, greater consideration needs to be given to user acceptance and habits. By systematically analyzing and addressing these strategic gaps, this study not only provides solutions to issues in the application of smart healthcare in home remote care but also offers valuable references for future research and practice, aiming to make home remote care services more comprehensive and efficient.

The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an on-going theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT), Cognitive Internet of Things, Internet of Medical Things, and Cloud Computing are expanding smart home concepts and solutions, and their applications. The primary objective of the present research work was to design and develop IoT and cloud computing based smart home solutions. In addition, we also propose a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision facility. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time.

Mediterranean Diet (MD), rich in minimally processed plant foods and in monounsaturated fats, but low in saturated fats, meat and dairy products, represents one of the most studied diets for cardiovascular health. It has been shown, from both observational and randomized controlled trials, that MD reduces body weight, improves cardiovascular disease surrogates such as waist-to-hip ratio, lipids, and inflammation markers, and even prevents the development of fatal and non-fatal cardiovascular disease, diabetes, obesity, and other diseases. However, it is unclear whether it offers cardiovascular benefits from its individual components or as a whole. Furthermore, limitations in the methodology of studies and meta-analyses have raised some concerns on its potential cardiovascular benefits. MD is also associated with characteristic changes in the intestinal microbiota, mediated through its constituents. These include increased growth of species producing short-chain fatty acids, such as Clostridium leptum and Eubacterium rectale, increased growth of Bifidobacteria, Bacteroides and Faecalibacterium prausnitzii species, and reduced growth of Firmicutes and Blautia species. Such changes are known to be favorably associated with inflammation, oxidative status, and overall metabolic health. This review will focus on the effects of MD on cardiovascular health through its action on gut microbioma.

The Inefficient water use, varying and low productivity in Kenya public irrigation schemes is a major concern. It is therefore necessary to periodically monitor and evaluate the performance of public irrigation schemes. The performance of public irrigation in western Kenya was assessed by combining benchmarking methodology and principal component analysis. The aim was to quantify and rank the performance of pumped public irrigation schemes in Kenya. Eleven benchmarking indicators were computed for the period from 2012 to 2016 and compared to global benchmark values. The indicators used fall under agricultural productivity, water supply and financial performance categories. The computed agricultural productivity was 36%–51% in Ahero, 23%–42% in West Kano and 26%–50% Bunyala irrigation scheme. Water supply performance in Ahero, West Kano and Bunyala irrigation schemes varied from 24% to 58%, 3% to 49% and 19% to 43% respectively. Financial performance varied from 46% to 54% in Ahero, 25% to 32% in West Kano and 54%–56% in Bunyala irrigation scheme. An average overall performance efficiency of 46%, 39% and 31% was obtained in Ahero, Bunyala and West Kano irrigation schemes respectively. The performance of the irrigation schemes is very poor and measures on improving performance are needed.

Introduction: Atrial fibrillation (AF) is a common cardiac arrhythmia whose prevalence rises with age and is linked to increased morbidity and mortality due to the risk of thromboembolism. Management typically involves anticoagulation and rhythm control, but the treatment of elderly patients with multiple comorbidities requires careful consideration of drug side effects and interactions. Cibenzoline, a Vaughan-Williams class IA antiarrhythmic, is effective against AF but has risks of toxicity, especially in older people and those with renal impairment, due to its renal excretion and prolonged half-life in these populations. Discussion: This paper presents a case of an 80-year-old woman with paroxysmal AF who suffered cardiac arrest due to cibenzoline toxicity despite receiving standard adjusted dosages. The patient's cibenzoline blood concentration was significantly higher than the therapeutic range, leading to cardiotoxicity, which was exacerbated by drug interactions with bisoprolol and edoxaban, both metabolized by the CYP3A4 enzyme. Venoarterial extracorporeal membrane oxygenation (V-A ECMO) and intra-aortic balloon pump (IABP) successfully managed the cardiac arrest. This case emphasizes the unpredictable pharmacokinetics of cibenzoline in elderly patients with renal impairment and the need for vigilant therapeutic drug monitoring to avoid adverse events. Conclusions: Cibenzoline can pose a significant risk of severe toxicity in elderly patients with renal impairment, and drug interactions may potentiate this risk. The paper underscores the importance of careful dose adjustment and monitoring of drug levels in this population. In cases of cibenzoline-induced cardiac arrest, V-A ECMO and IABP are effective treatment modalities. The case study serves as a critical reminder for clinicians to remain vigilant for drug-induced complications and to apply aggressive supportive treatments when necessary.

IoT system becomes hot topic nowadays for smart home, IoT helps devices to communicate together without human intervention inside home, so it is offering many challenges. A new smart home IoT platform powered using electromagnetic energy harvesting is proposed in this paper. It contains a high gain transmitted antenna array and efficient circular polarized array rectenna system to harvest enough power from any direction to increase life time of the batteries used in IoT system. Optimized energy consumption, the software with adopting the Zigbee protocol of the sensor node and low power microcontroller are used to operate in lower power modes. The proposed system has an 84.6 days lifetime which is approximately 10 times the lifetime for similar system. On the other hand, the proposed power management circuit operated at 0.3 V DC to boost the voltage to ~3.7V from radio frequency energy harvesting and manage battery level to increase the batteries lifetime. A predictive indoor environment monitoring system is designed based on a novel hybrid system to provide a non-static plan, approve energy consumption and avoiding failure of sensor nodes in smart home.

The complexity and interconnection of smart cities provide huge political, technical, and socioeconomic challenges for the designers, integrators, and organisations who are responsible for the administration of these new entities. A growing number of studies are concentrating their attention on the safety, privacy, and potential dangers that exist within smart cities. These studies are drawing attention to the dangers that are associated with information security as well as the challenges that smart city infrastructure faces in the management and processing of personal data. This state of the art review of the literature analyses a number of issues pertaining to smart homes, offers a helpful synthesis of the important information found in the primary research, and creates a model for the interaction between smart cities. An overview of smart home research incorporating machine learning, including everything from definition to current research state. The state of art begins with a smart home definition, followed by descriptions of smart home elements, typical research projects, smart home network research status, smart home appliances, and difficulties. The term "smart home" refers to a subcategory of "everyday computing" that includes "smart technology" in order to improve a person's level of convenience, health, safety, and security while also reducing their When controlled by artificial intelligence, the applications for the smart home provide users with context-aware settings, services, and remote control, which significantly increases the level of user satisfaction.

A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalling and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and ongoing threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g. the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT devices,,Wi-Fi and Ethernet connections .

Home Security Systems are very important features of modern residential and office setups. This system is used to keep home safe from harmful gases, fire and intruders. It must be affordable, reliable and effective. In this paper, a GSM based home security system has been designed and implemented. The system consists of a GSM Module, MQ-2 smoke detector, Ultrasonic sonar sensor, LM35 temperature sensor, Arduino Uno and relay module. The MQ-2 smoke detector is used to detect harmful gases i.e. LPG, CH₄, CO, Alcohol, Smoke or Propane. Any of the harmful gases stated above is detected it will warn the owner by activating the buzzer and displaying a warning message on the LCD display. It will also send a SMS to the owner. Our system can also measure temperate and if the temperature is increased above a certain range it will compensate it by activating Air Conditioner through the relay module. The system will also send a SMS to the owner. It can also detect intruders using Ultrasonic sensor and warn us through buzzer, SMS and displaying message on the LCD. Our system is reliable as it has very fast response and cost effective and it has been developed using our own technologies.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

Background: Nursing home residents (NHRs) have special needs and may experience reduced functionality, higher sedentary behavior time (SBT) and decreased physical activity (PA). This study aimed to characterize NHRs based on SBT, PA, and functional performance (FP), and to identify associations between these factors. Methods: Cross-sectional study with 65 NHRs aged ≥60, evaluated by accelerometer (SBT, PA) and the following tests: Timed Up and Go (TUG), Short Physical Performance Battery (SPPB), Barthel Index (BI), and Mini-Mental State Examina-tion (MMSE). Health-related variables and anthropometry were assessed. The FP (TUG, SPPB, BI), SBT, PA intensities were used for K-means clustering analysis. Results: The cluster analysis identified: C1 – better FP, shorter SBT, and longer time in PA intensities; C2 - lower FP, longer SBT, and shorter time in PA. In addition, C2 were older, with lower MMSE, lower body mass index, and walking aids use. The SBT showed a negative weak correlation with BI. The moderate PA (MPA) was moderately correlated with the SPPB and strongly correlated with the TUG and BI. Conclusions: MPA is strongly associated with better FP and independence. NHRs have dif-ferent physical and cognitive profiles, which indicates the need for personalized strategies to prevent disability and increase PA.

Fever control is essential in patients with severe traumatic brain injury (TBI). The efficacy of therapeutic hypothermia (TH) in severe TBI has been investigated over the last few decades; however, in contrast to experimental studies showing benefits, no evidence of efficacy has been demonstrated in clinical practice. In this review, the mechanisms and history of hypothermia were briefly outlined, while the results of major randomized controlled trials (RCTs) and meta-analyses investigating TH for adult TBI are introduced and discussed. The retrieved meta-analyses showed conflicting results, with a limited number of studies indicating benefits of TH. Some studies have shown benefits of long-term TH compared with short-term TH. Although TH is effective at lowering elevated intracranial pressure (ICP), reduced ICP does not lead to favorable outcomes. Low-quality RCTs overestimated the benefits of TH, while high-quality RCTs showed no difference or worse outcomes with TH. RCTs assessing standardized TH quality demonstrated the benefits of TH. As TBI has heterogeneous and complicated pathologies, applying a uniform treatment may not be ideal. A meta-analysis of young patients who underwent early cooling and hematoma removal showed better TH results. TH should not be abandoned, and its optimal should be advocated on an individual basis.

(1) Background: There is not much research about obesity in nursing homes although knowledge will help us to develop customized treatment plans and prevention strategies, which may help to decrease the burden for all persons involved. The objective of conducting this study was to describe the prevalence of obesity and the association between obesity and care problems in nursing home patients. (2) Methods: This study is a secondary data analysis of data collected in an annually performed cross-sectional study called “Nursing Quality Measurement 2.0”. A standardized and tested questionnaire was used to collect data. (3) Results: 1236 nursing home patients took part and 16.7% of them were obese. The multivariate logistic regression analysis results show that urinary incontinence is significantly associated with the presence of obesity (OR 2.111). The other care problems of pressure injuries, fecal and double incontinence, physical restraints, falls and pain were not associated with obesity. (4) Conclusion: The results indicate that, in the nursing home setting, health care staff should pay special attention to the patients’ nutritional and continence status and help patients maintain a normal, healthy weight. Conducting more studies with larger sample sizes is recommended, as this will allow differentiation among different obesity classes.

(1) Background: Cardiorespiratory fitness (CRF) is a prognostic factor regarding long time morbidity and mortality. To assess CRF the well-known gold standard is cardiopulmonary exercise testing (CPET), which is a time consuming and expensive test. Therefore, alternative methods for routine evaluation of CRF are needed for the general population as well as for specific patient groups such as children and young adults with congenital heart disease. The Stair Climbing Test (SCT) is a simple and resource saving test reported in the current literature. This test is used in various settings to assess different health associated risk factors including cardiorespiratory fitness, muscular resilience, therapy effectiveness or pre- and postoperative morbidity and mortality.

Age-in-place can reduce the progress of dementia syndrome and improve the quality of life of the sufferers and their families. This work proposes an innovative information home support system to be established at the houses of people suffering from dementia. The main innovation of the system are its transparent character that overcomes the need for training of the sufferer, as well as the exploitation of a well-known technique for dementia, namely music therapy. It addresses the need of people suffering from dementia as well as their familiars to be recognized by them and have better interaction and collaboration. The system offers a ubiquitous recognition system, just by using smart devices like smart-phones or smart-wristbands. When a familiar person is detected in the house, then a sound file is reproduced on smart speakers placed in the house, in order to trigger the memory, based on the principles of music therapy. The system reproduced the appropriate time at 100% of the cases and the benefits of music therapy are evident daily. To the best of the authors' knowledge, this is the first system of its kind ever reported in the literature.

Hereditary Hemorrhagic Telangiectasia (HHT), also known as Osler-Weber-Rendu syndrome, is a rare and inherited vascular disorder, characterized by the development of arteriovenous malformations (AVMs) in various organs and telangiectasia (small AVM) in the mucocutaneous. The majority of HHT patients have haploinsufficiency of genes involved in the transforming growth factor-beta (TGFβ) signaling pathway, including endoglin (ENG), activin receptor-like kinase 1 (ALK1, also known as ACVRL1), or SMAD4. Active angiogenesis is also required for telangiectasia and AVM development. Anti-angiogenic strategies have been tested in patients and animal models extensively. However, the exact mechanisms for telangiectasia and AVM development remain unclear. In this review, we discuss recent advances in identifying disease mechanisms, and potential therapeutic targets.

Goals: To use visual logistics for interpreting COVID-19 molecular and rapid antigen test (RAgT) performance, determine prevalence boundaries where risk exceeds expectations, and evaluate benefits of recursive testing along home, community, and emergency spatial care paths. Methods: Mathematica/open access software helped graph relationships, compare performance patterns, and perform recursive computations. Results: Tiered sensitivity/specificity comprise: T1) 90%/95%; T2) 95%/97.5%; and T3) 100%/≥99%, respectively. In emergency medicine, median RAgT performance peaks at 13.2% prevalence, then falls below T1, generating risky prevalence boundaries. RAgTs in pediatric ERs/EDs parallel this pattern with asymptomatic worse than symptomatic performance. In communities, RAgTs display large uncertainty with median prevalence boundary of 14.8% for 1/20 missed diagnoses, and at prevalence >33.3-36.9% risk 10% false omissions for symptomatic subjects. Recursive testing improves home RAgT performance. Home molecular tests elevate performance above T1, but lack adequate validation. Conclusions: Widespread RAgT availability encourages self-testing. Asymptomatic RAgT and PCR-based saliva testing present the highest chance of missed diagnoses. Home testing twice, once just before mingling, and molecular-based self-testing help avoid false omissions. Community and ER/ED RAgTs can identify contagiousness in low prevalence (<22%). Real-world trials of performance, cost-effectiveness, and public health impact could identify home molecular diagnostics as the optimal diagnostic portal.

Mechanical ventilation (MV), used in patients with acute lung injury (ALI), induces diaphragmatic myofiber atrophy and contractile inactivity, termed ventilator-induced diaphragm dysfunction. Phosphoinositide 3-kinase- (PI3K-) is crucial in modulating fibrogenesis during the reparative phase of ALI; however, the mechanisms regulating the interactions among MV, myofiber fibrosis, and PI3K- remain unclear. We hypothesized MV with or without bleomycin treatment would increase diaphragm muscle fibrosis through the PI3K- pathway. Five days after receiving a single bolus of 0.075 units bleomycin intratracheally, C57BL/6 mice were exposed to 6 or 10 mL/kg MV for 8 h after receiving 5 mg/kg of AS605240 intraperitoneally. In wild-type mice, bleomycin exposure followed by MV 10 mL/kg prompted significant increases in disruptions of diaphragmatic myofibrillar organization, transforming growth factor-1, oxidative loads, Masson’s trichrome staining, extracellular collagen levels, positive staining of -smooth muscle actin, PI3K- expression, and myonuclear apoptosis (P &lt; 0.05). Decreased diaphragm contractility and peroxisome proliferator activated receptor- coactivator-1 levels were also observed (P &lt; 0.05). MV-augmented bleomycin-induced diaphragm fibrosis and myonuclear apoptosis were attenuated in PI3K-γ-deficient mice and through AS605240-induced inhibition of PI3K- activity (P &lt; 0.05). MV-augmented diaphragm fibrosis after bleomycin-induced ALI is partially mediated through PI3K-γ. Therapy targeting PI3K-γ may ameliorate MV-associated diaphragm fibrosis.

The increase in population and the challenges caused by it are always among the concerns of decision-makers and policymakers in large countries. One of the most serious concerns is in the field of energy consumption. Optimizing energy use is one of the most effective ways to manage limited resources and energy. Energy consumption in residential houses constitutes a significant percentage of the total energy consumption worldwide; therefore, current research focusing on mathematical models aims to provide a predictive multi-objective linear mathematical model for residential house consumption. For this purpose, the consumption of water, electricity, and gas for 80 residential houses, including 10 household appliances and equipment on a weekly basis, has been considered. The results showed that the mixed-integer multi-objective predictor model is able to optimize household consumption plans compared to the actual state of household energy consumption. Since the goals of household satisfaction and energy consumption costs in the model conflict with each other, the influence coefficient was calculated to demonstrate the simultaneous achievement of these goals. The optimal value achievable with the default model for the model's purposes is 69%.

It is important to increase the quality of health and medicine. A wearable system for continuous monitoring of the patient is important to overcome this issue. Thus a patient with Arrhythmia due to its low cost and success in saving the life of the patient was the right option for the care partner. In addition, the device will provide a consumer with a smart smartphone application with accurate pulse beat and body temperature data in real time. MAX 30100 and LM35 are primarily used for the detection of human heart and temperature. The performance of these sensors is generated by an arrhythmia algorithm in the esp32 segment.

The concept of vulnerable carotid plaques is pivotal in understanding the pathophysiology of is-chemic stroke secondary to large-artery atherosclerosis. At the macroscopic evaluation, vulnera-ble plaques are characterized by one or more of the following features: microcalcification, neo-vascularization, lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), thin fibrous caps, plaque surface ulceration, huge dimension determining stenosis, and plaque rupture. Recognizing these macroscopic characteristics is crucial to estimating the risk of cerebrovascular events, also in case of non-significant (less than 50%) stenosis. Inflammatory biomarkers, such as cytokines and adhesion molecules, lipid-related markers like oxidized low-density lipoprotein (LDL), and proteolytic enzymes capable of degrading extracellular matrix components are among the key molecules that will be scrutinized for their associative roles in plaque instability. Through their quantification and evaluation, these biomarkers reveal intricate molecular cross-talking govern-ing plaque inflammation, rupture potential, and thrombogenicity. Current evidence demonstrates that plaque vulnerability phenotypes are multiple and heterogeneous and are associated with as many highly complex molecular pathways that determine the activation of an immune-mediated cascade that culminates in thromboinflammation. This narrative review provides a comprehen-sive analysis of current knowledge on molecular biomarkers expressed by symptomatic carotid plaques. It explores the association of these biomarkers with the structural and compositional at-tributes that characterize vulnerable plaques

Home Security Systems are very important features of modern residential and office setups. This system is used to keep home safe from harmful gases, fire and intruders. It must be affordable, reliable and effective. In this paper, a GSM based home security system has been designed and implemented. The system consists of a GSM Module, MQ-2 smoke detector, Ultrasonic sonar sensor, LM35 temperature sensor, Arduino Uno and relay module. The MQ-2 smoke detector is used to detect harmful gases i.e. LPG, CH4, CO, Alcohol, Smoke or Propane. Any of the harmful gases stated above is detected it will warn the owner by activating the buzzer and displaying a warning message on the LCD display. It will also send a SMS to the owner. Our system can also measure temperate and if the temperature is increased above a certain range it will compensate it by activating Air Conditioner through the relay module. The system will also send a SMS to the owner. It can also detect intruders using Ultrasonic sensor and warn us through buzzer, SMS and displaying message on the LCD. Our system is reliable as it has very fast response and cost effective and it has been developed using our own technologies.

Merkel cell carcinoma (MCC) is a rare, highly aggressive skin cancer characterized by two etiologies: the first one, observed in most cases, is attributed to Merkel cell polyomavirus (MCPyV), a DNA tumor virus, while the second one is due to ultraviolet (UV) radiation exposure. Currently, there are limited diagnostic, prognostic, and therapeutic biomarkers available for MCC. MicroRNAs (miRNAs) are small RNA molecules that do not code for proteins but regulate various cellular processes, including cell cycle progression, proliferation, differentiation, and apoptosis. The dysregulation of miRNAs has been associated with a wide range of cancers since they can act as either tumor suppressors or oncogenes. This paper aims to collect, summarize and re-organize the recent discoveries concerning miRNAs, whose dysregulation in the pathogenesis and progression of MCC is slowly emerging. Furthermore, the potential clinical applications of miRNAs as diagnostic biomarkers, possible differentiation between MCPyV-related and unrelated MCC, predictors of response to therapy and prognostics are also discussed. From this review, it emerges that miRNAs may have, in the near future, the potential to become clinically significant in improving the diagnostic accuracy, prognostic evaluation, and therapeutic options of MCCs. Finally, the new perspectives on the poorly explored field of artificial intelligence in MCC are provided.

Abstract: Background: Bangladesh has achieved remarkable progress in reducing maternal mortality, yet postpartum deaths remain a significant issue. Emphasis on quality postnatal care (qPNC) is crucial, as increased coverage alone has not sufficiently reduced maternal morbidity and mortality. Methods: This study included data from the Bangladesh Maternal Mortality Survey of 32,106 mothers who delivered within three years prior to the survey. Descriptive statistics were used to report coverage and components of postnatal care stratified by covariates. Log-linear regression models were used to assess the determinants of quality postnatal care among facility and home births. Results: From 2010 to 2016, postnatal care coverage within 48 hours of delivery by a qualified provider rose from 23% to 47%. 94% of facility births received timely PNC, contrasted with only 6% for home births. Despite the increased coverage, quality of care remained as low as 1% for home births and 13% for facility births. Key factors affecting qPNC utilization included socio-demographic factors, pregnancy complications, delivery method, financial readiness, and mobile ownership. Conclusion: Importantly, deliveries assisted by skilled birth attendants correlated with higher quality postnatal care. The study reveals a significant gap between the coverage and quality of postnatal care in rural Bangladesh, especially for home births. It underscores the need for targeted interventions to enhance qPNC.

In recent years, the management of health systems is a main concern of governments and decision makers. Home health care is one of the newest methods of providing services to patients in developed societies that can respond to the individual lifestyle of modern age and the increase of life expectancy. The home health care routing and scheduling problem is a generalized version of the vehicle routing problem, which is extended to a complex problem by adding special features and constraints of health care problems. In this problem, there are multiple stakeholders such as nurses for which an increase of their satisfaction level is very important. In this study, a mathematical model is developed to expand traditional home health care routing and scheduling models to downgrading cost aspects by adding the objective of minimizing the difference between the actual and potential skills of the nurses. Downgrading can lead to a dissatisfaction of the nurses. In addition, skillful nurses have higher salaries and high-level services increase equipment costs and need more expensive trainings and nursing certificates. Therefore, downgrading can enforce hidden huge costs to the managers of a company. To solve the bi-objective model, an -constraint based approach is suggested and the model applicability and its ability to solve the problem in various sizes are discussed. A sensitivity analysis on the Epsilon parameter is conducted to analyze the effect of this parameter on the problem. Finally, some managerial insights are presented to help the managers in this field, and some directions for future studies are mentioned as well.

In this article, we analyse the ESG determinants of the “Elderly People Treated in Integrated Home Care”-EPIHC in the Italian regions between 2004 and 2022. We used data from the ISTAT-BES database. We used different econometric techniques i.e.: Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS and Weighted Least Squares-WLS. The results show that the EPIHC is positively associated with “Nurses, midwives, and Soil sealing by artificial cover" and negatively associated with "Museum heritage density and relevance" and "Trust in law enforcement agencies and firefighters fire". Furthermore, we have applied a k-Means algorithm with the Silhouette Coefficient and we find the presence of two clusters. Finally, we propose a confrontation among eight different machine-learning algorithms and we find that Linear Regression is the best predictive algorithm.

The quadruple burden of diseases, early discharge from hospital and hospital at home have resulted in home-based care services becoming a requirement in South Africa. The home-based care services generate a significant amount of health care risk waste that is mismanaged. However, more attention is given to the health care risk waste generated in hospitals and clinics than to health care risk waste generated by home-based caregivers. Therefore, this study investigates the health care risk waste management practices by home-based caregivers. The study adopted a mixed research approach, qualitative and quantitative methods, using a literature review, interviews, and questionnaires as means of data collection. Results show that there are different types of health care risk waste generated as a result of different activities performed by home-based caregivers, but that the waste was found to be managed in an unsafe manner. The majority of households receiving home-based care did not have basic sanitation facilities such as toilets, running water and waste removal services, aggravating the issue of health care risk waste mismanagement. The study recommends a new policy framework that will lead to safe management practices of generated health care risk waste to be adopted by home-based caregivers.

The rapid growth of the global wearable technologies market is projected to reach $61.30 billion by 2030 which reflects a shift towards personalised health monitoring embedded in everyday activities. This paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. Experimental results demonstrate the sensor’s capability to detect water droplets and glucose concentrations, showcasing promising applications for health monitoring. The embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day, due to official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price of around 17\% lower than the constant, but charges twice its value at peak hours. In addition to cost reduction, we propose a methodology to reduce user discomfort due to time shifting of controllable devices, presenting a balanced solution through the analytical analysis of a new method here called tariff space, derived from white tariff posts. To achieve this goal, we explore the geometric properties of the movement of devices through the tariff space (geometric locus of the load), over which we can define a limited region in which the cost of a load under the white tariff will be equal to or less than the constant tariff. As a trial for the efficiency of this new methodology, we collected some benchmarks (such as execution time and memory usage) against a classic multi-objective algorithm (Hierarchical) available in the language portfolio in which the project has been executed (Julia language). As a result, both methodologies achieve similar results, but the one presented in this article shows a significant reduction in processing time and memory usage, which could lead to the future implementation of the solution in a simple, low-cost embedded system like an ARM cortex M.

The home is becoming a key location for healthcare delivery, including the use of technology driven by autonomous systems (AS) to monitor and support healthcare plans. Using the example of a smart mirror, this paper describes the outcomes of focus groups with people with multiple sclerosis (MS; n=6) and people who have had a stroke (n=15) to understand their attitudes towards the use of AS for healthcare in the home. We thematic analysis to analyse the data. The results indicate that the use of such technology depends on the level of adaptability and responsiveness to the users’ specific circumstances, including their relationships with the healthcare system. A smart mirror would need to support manual entry, responsive goal setting, effective aggregation of data sources and integration with other technology, have a range of input methods, be supportive rather than prescriptive in messaging, and give the user full control of their data. Barriers to adoption include a perceived lack of portability and practicality, lack of accessibility and inclusivity, a sense of redundancy, being overwhelmed by multiple technological devices, and a lack of trust in data sharing. These results inform the development and deployment of future health technologies based on the lived experiences of people with health conditions who require ongoing care.

Keywords: Oral health, diabetes, periodontal disease, caries, periodontitis.

Home care centers face both an increase in demand and many variations during the execution of routes, compromising the routes initially planned: robust solutions are not effective enough, it is necessary to move on to resilient approaches. We create a close to reality use case supported by interviews of staff at home health care centers, where caregivers are faced with unexpected events that compromise their initial route. We model, analyze and compare three resilient approaches to deal with these disruptions: a baseline approach without any collaboration, a distributed collaborative approach, and a centralized collaborative approach, where we propose a centralization and sharing of information to improve local decision-making. The latter reduces the number of late arrivals by 11% and the total time of late arrival by 21%, and also halves the number of routes exceeding the end of work time (contrary to the distributed collaborative approach, due to the time wasted reaching colleagues). The use of a device, such as a smartphone application, to centralize and share information thus allows better mutual assistance between caregivers. Moreover, we highlight several possible openings like the coupling of simulation and optimization to propose a more resilient approach.

Racial and ethnic disparities exist in diabetes prevalence, health services utilization, and out-comes including disabling and life-threatening complications. Home health care may especially benefit older adults with diabetes through individualized education, advocacy, care coordina-tion, and psychosocial support for patients and their caregivers. This study examined factors as-sociated with hospital discharge to home health care and subsequent utilization of home health care among a cohort of Medicare beneficiaries with diabetes, age 50 and older, living in the United States. The cohort (n=786,758) was followed for 14 days after a diabetes-related index hospitalization, using linked Medicare administrative, claims, and assessment data (2014-2016). Multivariate logistic regression models included patient demographics, comorbidities, hospital length of stay, geographic region, neighborhood area deprivation, and rural/urban setting. In ful-ly adjusted models, hospital discharge to home health care was significantly less likely among Hispanic (OR 0.8, 95% CI 0.8-0.8) and American Indian (OR 0.8, CI 0.8-0.8) compared to white patients. Among those discharged to home health care, all racial/ethnic minority patients were less likely to receive services within 14-days. Further work should focus on eliminating systemic racism in home health care referral and systemic barriers to receiving home health care services.

This paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The sensor uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations are transmitted instead of videos. The sensor is highly affordable, accessible, and versatile. It was a CES 2021 Innovation Award Honoree, and has been selected by Amazon as one of only three fall detection devices integrated into its Alexa Together urgent response service, and has received very positive reviews from Amazon customers. It has also been used in different senior care settings in about ten different countries. The paper presents the main features of the system, the evidences and lessons learned from its practical applications, and future directions.

Recently, the Internet of Things (IoT) has attracted wide attention from many fields, especially healthcare, because of its large capacities of information perception and collection. In this paper, we present an IoT-based home telehealth system for providing smart healthcare management for individuals, especially older people. Each client node of the system is mainly composed of an electronic water meter that records the user's daily water usage and an unobtrusive sleep sensor that monitors the user's physiological parameters during sleep such as heart rate (HR), respiratory rate (RR), body movement (BM), and states on the bed or outside the bed. The sleep sensor senses the biosignals and states by a sensor matrix composed of 12 piezoelectric sensors. The collected data can be transmitted to a remote centralized cloud service by a wireless home gateway for analyzing the living pattern and rhythm of users. Furthermore, the periodic feedback of results can be provided to users themselves as well as their family and health advisers. In the present study, data collections from a total of 18 older subjects were made for one year to evaluate the effectiveness of the proposed system. By analyzing living pattern and rhythm, preliminary results indicate the effectiveness of the telehealth system and suggest the potential of the system regarding improvement of quality of life (QoL) of older people and promotion of their health.

This work is part of an ongoing research project to develop an unmanned flying social robot to monitor dependants at home in order to detect the person’s state and bring the necessary assistance. In this sense, this paper focuses on the description of a virtual reality (VR) simulation platform for the monitoring process of an avatar in a virtual home by a rotatory-wing autonomous unmanned aerial vehicle (UAV). This platform is based on a distributed architecture composed of three modules communicated through the Message Queue Telemetry Transport (MQTT) protocol: the UAV Simulator implemented in MATLAB/Simulink, the VR Visualiser developed in Unity, and the new emotion recognition (ER) System developed in Python. Using a face detection algorithm and a convolutional neural network (CNN), the ER System is able to detect the person’s face in the image captured by the UAV’s on-board camera and classify the emotion among seven possible ones (surprise, fear, happiness, sadness, disgust, anger or neutral expression). The experimental results demonstrate the correct integration of this new computer vision module within the VR platform, as well as the good performance of the designed CNN, with around 85% in the F1-score, a mean of the precision and recall of the model. The developed emotion detection system can be used in the future implementation of the assistance UAV that monitors dependent people in a real environment, since the methodology used is valid for images of real people.

This article presents a new input device for spastic patients and others with similar symptoms. The sensor consists of a disc used to determine positions and can replace standard joysticks in medical devices such as electric-powered wheelchairs. Using a standard joystick while operating a powered wheelchair can result in dangerous situations when spastic movements occur and extremities cramp making them uncontrollable. To avoid this, a disc was developed that can be controlled with any body part. By shifting weight (x- and y-axis) the disc can tilt in any direction creating a proportional output signal and can also be pushed down in the center (z-axis) to open a menu on a screen for example. When spasms occur it is impossible for users to get stuck on the input device because the disc is flat and can be mounted within a control panel. Body parts coming into contact with the disc would merely slide across the disc without triggering unintentional actions. The sensor presented here is also adaptive and can be adjusted to fit a user’s strength and range of motion. This proposal aims to develop an input device that enables spastic patients to operate sensitive systems safely.

Blockchain and Machine Learning gives the best solutions together in performing various tasks in the Smart Health care system. With these two new emerging technologies, that have materialized in the last decade. In this paper, we proposed secure, transparent and intelligent methods in the Internate of medical things industry using Machine learning models and blockchain technology to enhance security level and train our models to improve diagnostic, prevention, treatment of the patient, patient rights, patient autonomy and equality in the health care system.

Cardiovascular disease (CVD) is the leading cause of death worldwide. Cardiac rehabilitation (CR) has proven to be effective in reducing the rate of recurrence and disease as secondary prevention (evidence-based intervention) in patients who have suffered from myocardial infarction (MI). CR is a multidisciplinary path in which the patient is followed pharmacologically, from a psychological, nutritional, nursing and physiotherapy point of view. Post-MI patients present a reduction in the performance of the diaphragm muscle, the main inspiratory muscle, and this condition can become a risk factor for further relapses or for the onset of heart failure. Despite the solidity of the international guidelines for CR, the latter are lacking in specifically indicating an evaluation and training path regarding the inspiratory muscles in post-MI patients who have not undergone cardiac surgery. The article reviews the information on the adaptation of the diaphragm post-MI and highlights the need for clearer indications for a rehabilitation process that gives importance to the diaphragm.

The magnetic properties of 0.10 mm ultra-thin grain-oriented (UTGO) silicon steel and Fe-based amorphous (FBA) alloy under sinusoidal excitation were measured experimentally, and the magnetic field strength and iron loss of the two materials under different frequencies and magnetic densities were obtained. Based on the measured data, the magnetization and loss characteristics of the two materials were analyzed and compared. Furthermore, two Epstein square ring models of the same specification and different materials were designed, and the reliability of the models was verified. Then, the electromagnetic characteristics of the two Epstein square ring models at higher and lower frequencies were calculated by using the finite element method, and the iron loss was obtained and compared. The results show that the FBA alloy has good application characteristics at low frequency and low power, and the 0.10 mm UTGO silicon steel has good application characteristics at high frequency and high power. This research can provide important data basis to promote the application of these two materials in new energy equipment.

Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a Semantic Framework for detecting problems in ADLs execution, monitored through Smart Home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed Framework combines multiple Semantic Web technologies (i.e. ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations forming a Knowledge Graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviours in ADLs execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people in the spectrum of dementia, by offering a comprehensive way for clinicians to describe problematic behaviors in the every-day life of an individual.

Older adults with diabetes are at elevated risk of complications following hospitalization. Home health care services mitigate the risk of adverse events and facilitate a safe transition home. In the United States, when home health care services are prescribed, federal guidelines require they begin within two days of hospital discharge. This study examined the association between timing of home health care initiation and 30-day rehospitalization outcomes in a cohort of 786,734 Medicare beneficiaries following a diabetes-related index hospitalization. Of these patients, 26.6% were discharged to home health care. To evaluate the association between timing of home health care initiation and 30-day rehospitalizations, multivariate logistic regression models including patient demographics, clinical, geographic variables, and neighborhood socio-economic variables were used. Inverse probability-weighted propensity scores were incorporated into the analysis to account for potential confounding between the timing of home health care initiation and the outcome in the cohort. Compared to patients who received home health care within the recommended first two days, patients who received delayed services (3-7 days after discharge) had higher odds of rehospitalization (OR 1.28, 95% CI 1.25-1.32). Among patients who received late services (8-14 days after discharge), the odds of rehospitalization were four times greater than for patients receiving services within 2 days (OR 4.12, 95% CI 3.97-4.28). Timely initiation of home health care following diabetes-related hospitalizations is one strategy to improve outcomes.

Objectives: Female genital mutilation (FGM) is a significant public health problem. It is estimated around 14,700 women affected by FGM live in Switzerland, primarily among women with a history of migration. Our qualitative research investigated the sexual health of immigrant women living with FGM in Switzerland, describing their own perception of health, reproductive life and sexuality. Methods: We conducted semi-structured, in-depth interviews with a group of eight immigrant women of African origin living in Switzerland with Type III FGM (infibulation). Results: Seven of the women were from Somalia and one from Ivory Coast. All of the Somali women were mothers and married (two separated), and the Ivorian woman was a single mother. The women in our study reported a low level of sexual satisfaction and reproductive health. They affirmed their desire to improve, or at least change, their condition. Although they rarely talk with their husbands about sexual subject matter, they would like to include them more and improve dialogue. Conclusions: Specific socio-sexual management is recommended when caring for immigrant women living with FGM in order to respond to their specific health care needs. Multidisciplinary approaches may be able to offer more comprehensive health care, including facilitated communication to improve dialogue between women and health care professionals, and eventually between women and their husbands in discussing sexual matter.

The Smart Angel connected medical device allows ambulatory surgery patients to monitor their health by taking their own blood pressure and oxygen levels and by answering a health questionnaire from home. This preventive device must necessarily be "usable" by patients with different profiles. The objective of this article is, therefore, to better understand the links between certain characteristics of potential patients and usability. We conducted an experimental study involving thirty-six participants, investigating the effects of four patient characteristics (i.e. age, education, technophilia and health literacy) on usability measured in terms of effectiveness, efficiency, and satisfaction. The results show a moderate correlation between age, health literacy and usability. However, there is a weak correlation between technophilia and usability and no relationship between the level of education and usability. This study provides theoretical insights into the effects of user characteristics by means of personas in usability (ISO 9241-11).

This article introduces a new way of using a Fibre Bragg Grating (FBG) sensor for detecting the presence and number of occupants in the monitored space in a Smart Home (SH). CO₂ sensors are used to determine the CO₂ concentration of the monitored rooms in an SH. CO₂ sensors can also be used for occupancy recognition of the monitored spaces in SH. To determine the presence of occupants in the monitored rooms of the SH, the newly devised method of CO₂ prediction, by means of an Artificial Neural Network (ANN) with a Scaled Conjugate Gradient (SCG) algorithm using measurements of typical operational technical quantities (indoor temperature, relative humidity indoor and CO₂ concentration in the SH) is used. The goal of the experiments is to verify the possibility of using the FBG sensor in order to unambiguously detect the number of occupants in the selected room (R104) and, at the same time, to harness the newly proposed method of CO₂ prediction with ANN SCG for recognition of the SH occupancy status and the SH spatial location (rooms R104, R203, and R204) of an occupant. The designed experiments will verify the possibility of using a minimum number of sensors for measuring the non-electric quantities of indoor temperature and indoor relative humidity and the possibility of monitoring the presence of occupants in the SH using CO₂ prediction by means of the ANN SCG method with ANN learning for the data obtained from only one room (R203). The prediction accuracy exceeded 90% in certain experiments. The uniqueness and innovativeness of the described solution lie in the integrated multidisciplinary application of technological procedures (the BACnet technology control SH, FBG sensors) and mathematical methods (ANN prediction with SCG algorithm, the Adaptive Filtration with of LMS algorithm) employed for the recognition of number persons and occupancy recognition of selected monitored rooms of SH.

Background: The aim of this experimental work was to confirm the individual uniqueness of lip prints, to verify the presence of recurrent patterns according to the Suzuki and Tsuchihashi classification and to search for characteristics that can be used in establishing the sex of a subject. Methods: Lip prints were taken from a sample of 172 volunteers using liquid lipstick and transparent adhesive tape and were digitally analyzed. Results: 59 prints (35.3%) were not considered for Suzuki and Tsuchihashi classification due to quality errors and the impossibility of visualizing lip wrinkles in one or more sextants, all the digitized prints revealed themselves to be unique after having been analyzed using superimposition. Qualitatively valid prints indicated a prevalence of Type II in both male and female and the presence of sex-linked features. A major problem was that the analysis of the same print by different operators often resulted in a different attribution of a Type. Conclusions: The potential of cheiloscopy lies mainly in the matching of prints, given their uniqueness, and can be a valid tool in gender determination but the absence of a database greatly limits its use. It is necessary to establish an acquisition protocol for taking and analyzing prints that could reduce the errors committed by the operator.

Agriculture contributes towards South African economy, but this sector is highly vulnerable to risks imposed by climate change. Smallholder maize farmers are specifically susceptible to climate change impacts. Maize crop plays a crucial role in the country's food security as it is considered a stable food for households and source of feed for livestock. The study aimed to analyze the socio-economic factors influencing smallholder maize farmers’ willingness to adopt climate-smart agriculture. It was conducted in three different areas due to their specific agro-ecological zones. Multipurpose research design was used to gather data, and multistage random sampling to choose the study areas. Subsequently, 209 purposively selected farmers were interviewed face-to-face using structured questionnaires and focus discussion groups. Descriptive statistics and Double-hurdle regression model were used to analyze the data. Descriptive results indicated that 81%, 67%, and 63% farmers were willing to adopt CSA in Ga-Makanye, Gabaza, and Giyani, respectively. Empirical results from the first and second hurdles of the regression analysis showed that farmers’ education, crop diversification, information about CSA positively influenced the decision to adopt CSA while household size and agricultural experience negatively influenced the decision to adopt CSA. It is recommended that the Department of Agriculture, Land Reform, and Rural Development should provide CSA workshops and educational programs to farmers to enhance their knowledge and decision-making processes regarding adaptation strategies.

A previous report showed that the urine output of HPLBII-P in patients with diabetes mellitus and SARS-Cov-2 infection was increased as a sign of glomerular dysfunction. . The aim of this report was to investigate the relation of urine output of HPLBII-P to diabetes mellitus in two large community-based elderly populations i.e. the ULSAM and PIVUS cohorts. Methods HPLBII-P was measured by ELISA in the urine of a community-based cohort of 839 men (ULSAM) collected at 77 years of age and in urine of a community-based cohort of 75-year-old men, n=387, and women, n=401 (PIVUS). KIM-1, NGAL and Albumin were measured in urine and Cathepsin S and Cystatin C in serum. Results HPLBII-P was significantly raised among males with diabetes in the ULSAM (p

: Suicide is a serious public health problem, with a global mortality rate of 1.4% of all deaths worldwide, and the leading cause of unnatural death in Spain. The use of clinical simulation has proven to be a beneficial tool in the training of nursing students. Such experiences allow them to develop cognitive and affective skills that are fundamental for the detection of warning signs and use of interventions in cases of people who want to take their own lives. Working in a mental health environment can be difficult for nursing students; therefore, the purpose of this study was to explore the perceptions of nursing students on the approach, management and intervention of suicidal crisis through clinical mental health simulation. Methods: qualitative descriptive phenomenological study through focus groups and reflective narratives in a sample of 45 students. A thematic analysis was performed using ATLAS-ti. Results: After the analysis, three themes were obtained: (a) management and handling of emotions (b) identification of suicide motives (c) intervention in suicidal crisis. Discussion: Clinical simulation in mental health allows students to exercise clinical judgment reasoning, detect warning signs for a better treatment approach, and provide tools for effective intervention and management of patient care. The results of this study indicate that nursing students face challenges in approaching mental health clinical simulation due to lack of prior exposure.

Abstract: Laryngoscopy for tracheal intubation has been developed for many decades. Among various conventional laryngoscopes, videolaryngoscopes (VL) have been applied in different patient populations, including difficult airways. The safety and effectiveness of VL have been repeatedly studied in both normal and difficult airways. The superiority of VL then has been observed and is advocated as the standard of care. In contrast to laryngoscopy, the development of video-assisted intubating stylet (VS, also named as styletubation) has been noticed two dec-ades ago. Since then, sporadic clinical experiences of use have appeared in literature. In this re-view article, we presented our vast use experiences of the styletubation (more than 55,000 pa-tients since 2016). We found this technique is swift (the time to intubate: from 3 s to 10 s), smooth (first-attempt success rate: 100%), safe (no airway complications), and easy (high subjective satis-faction and fast learning curve for the novice trainees) in both normal and difficult airway sce-narios. We therefore propose styletubation technique can be feasibly applied as universal rou-tine use for tracheal intubation.

Acute and chronic respiratory diseases impose a significant economic and health burden. Distal small airway assessment requires invasive procedures such as a bronchoscopy and bronchoalveolar lavage. The Particles in Exhaled Air (PExA) device provides a non-invasive means of assessing small airways; it captures distal airway particles sized around 0.5-7m and contains particles of respiratory tract lining fluid (RTLF) that originate during airway closure and opening. The PExA device can measure particle count and mass according to particle size. Subsequent capture of particles in a thin membrane enables application of various analytical methods to assess a broad range of biomolecules including phospholipids, proteins, inflammatory profile, and other metabolites to characterise inflammation and diseases processes such as asthma, chronic obstructive pulmonary disease, acute respiratory distress syndrome, lung cancer, and more recently acute infections such as COVID-19. PExA analysis provides a promising alternative to traditional investigative methods in the assessment, diagnosis, and management of respiratory conditions. By evaluating specific biomarkers in the RTLF associated with airway inflammation, PExA may offer a more accurate and comprehensive understanding of the molecular-level changes in the small airways of patients with acute and chronic lung diseases.

Phyllanthus emblica (P. emblica) has been described in many studies to have multiple therapeutic activities and is considered an important part of Ayurvedic and Unani medicinal systems. This study successfully synthesized P. emblica loaded polyvinylalcohol/gelatin based cryogels (PVA/Gel/P.emblica) to examine their potential in wound dressing applications. Swelling tests, Brunauer-Emmett-Teller (BET) and morphology analysis were conducted within the scope of characterization studies. In vitro cell culture studies were performed using HaCaT cells to illustrate the suitability of the synthesized cryogels as wound dressing material. The swelling tests and BET analysis results showed that swelling and surface area properties of cryogels increased with increasing P. emblica amounts. Morphological results displayed that the cryogels had a dense, interconnected pore morphology and a macroporous structure. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), trypan blue exclusion and live-dead assay results revealed that adding of P. emblica into cryogels led to enhanced cell proliferation, increased cell number, and improved cell viability. Based on the phase contrast microscope, scanning electron microscope (SEM), immunofluorescence and giemsa staining images, it was observed that P. emblica promoted cell attachment, proliferation and penetration. In conclusion, it has been shown that PVA/Gel/P.emblica cryogels are suitable for use as wound dressing materials and can be developed with further studies.

Monitoring of clients living with HIV encompasses a specific array of activities that routinely check on various aspects of ART following its initiation depending on the availability of resources in that particular state, among other factors as per guidelines from the World Health Organisation. This is a significant practice as it promotes positive health outcomes for patients and efficient utilisation of ART regimens to reduce resistance to the medication – hence it is a measure to guard avoidable costs. It also forms a basis on which decisions are made from. It can be done clinically by assessing for signs e.g., skin rash and symptoms e.g., nausea and vomiting, checking vital organ function and measuring weights across all populations, or can be also be done by assessing specimens for their respective viral loads, white blood cell count, pregnancy status or other infections such as hepatitis B. There have not been any special indications required for one to be eligible for monitoring during ART in developing countries; all qualify for routine monitoring even though some may be more frequent than others in cases of comorbidities such as renal failure where medications can be attributed to exacerbating the condition.

Smart manufacturing encompasses a category of manufacturing that employs computer-integrated capabilities and technologies to enhance supply chain optimization, production efficiency, and recyclability. Yet limited studies have been conducted to optimize these prospects from the perspective of low resource countries where such innovative studies have not been adequately explored. This paper aims to examine the implications of smart manufacturing practices (Smart Procurement, Smart Supply Chain, Smart Production Planning and Control, Automation and Industrial Robot, and Supply Chain Configuration) on pro-environmental behaviour and develop a baseline moderated mediation model to explain the relationship between smart manufacturing and pro-environmental behaviour with a role of green dynamic capability, and environmental orientation. This study is anchored on quantitative research approach, deductive reasoning, and explanatory design. Cross – sectional survey data have been obtained from 382 manufacturing companies in Ghana. Our hypotheses have been tested using Structural Equation Modelling and SMART-PLS version 3.8.9. The study has revealed that smart manufacturing practices significantly and positively relate to pro-environmental behaviour. Moreover, green dynamic capability and environmental orientation moderate the relationship between smart manufacturing and pro-environmental behaviour. The implications of the study include the emergence of a baseline model to guide policymakers, practitioners, and academicians to explain the relationship between smart manufacturing and sustainable manufacturing in the context of developing countries. Again, the social implication of the study includes the realisation of decent job and economic growth, responsible consumption and production as well as actions taken to combat climate change.

This article summarizes the results of a research conducted to create an intelligent platform supporting small farmers growing vegetables. In Bulgaria, many farmers use relatively small areas for their production. Considering this situation, the platform is designed for regional use, trying to make maximum use of the specific climatic features of the monitored microdistrict. Consequently, special attention is paid to the possibilities of adapting the platform to the specific environment in which the observed plants grow. Thus, the platform is implemented as an agent-oriented system providing needed flexibility. The core of the platform is a personal assistant, which is responsible for tracking the vegetation cycle of the monitored crop and, upon detection of anomalies, preparing warnings to the farmer. The general architecture of the platform is also presented. In a distributed knowledge base, agriculture-specific knowledge are stored. Furthermore, a standardized interface for receiving data from the sensor networks located in the production areas is briefly described. Currently, the platform is being tested for monitoring four varieties of tomatoes grown on the territory of the national research center for potato breeding. Special attention is paid to the measurements carried out. Conclusions have been drawn that will help improve the platform.

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

This literature review paper delves into the synergies between Climate-Smart Agriculture (CSA) and the effective management of food losses. With the escalating challenges posed by climate change and the pressing need to address food security concerns, the intersection of these two critical domains becomes paramount for sustainable agricultural practices. The review explores existing research on the implementation of climate-smart agricultural techniques and their impact on mitigating food losses throughout the agricultural value chain. The paper synthesizes findings related to climate-resilient crop varieties, precision farming, water-use efficiency, and sustainable soil management, highlighting their contributions to reducing post-harvest losses and enhancing overall food security. In addition to summarizing current knowledge, this paper identifies gaps in the literature and proposes avenues for future research. This review serves as a foundation for researchers, policymakers, and practitioners seeking to contribute to the advancement of climate-smart agriculture and the reduction of food losses, paving the way for a more sustainable and resilient global food system.

In recent years, integration of electric vehicles (EVs) has increased dramatically due to their lower carbon emissions and reduced fossil fuel dependency. However, charging EVs could have significant impacts on the electrical grid. One promising method for mitigating these impacts is the use of renewable energy systems. Renewable energy systems can also be useful for charging EVs where there is no local grid. This paper proposes a new strategy for designing a renewable energy charging station consisting of wind turbines, a photovoltaic system, and an energy storage system to avoid the use of diesel generators in remote communities. The objective function is considered to be the minimization of the total net present cost, including energy production, components setup, and financial viability. The proposed approach, using stochastic modeling, can also guarantee profitable operation of EVs and reasonable effects on renewable energy sizing, narrowing the gap between real-life daily operation patterns and the design stage. The proposed strategy should enhance the efficiency of conventional EV charging stations. The key point of this study is the efficient use of excess electricity. The infrastructure of the charging station is optimized and modeled.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

The importance of cognitive health for academic success is well-recognized. However, longitudinal data regarding the associations of body fatness, physical fitness and cognition are relatively scarce and mixed. The purpose of this study was to investigate whether body fatness, cardiorespiratory fitness (CRF) and muscular fitness (MF) in preschool are associated with cognitive skills in first grade at school. 133 South Estonian children aged 6-7 years were recruited from 13 kindergartens and again at 7-8 years when they had entered school. Body fat percentage (BF%), CRF, MF as the mean of z-scores of relative handgrip strength and standing long jump tests, and cognitive skills (verbal, conceptual, perceptual) were studied. There were no associations between BF% and CRF in pre-school with perceptual, conceptual or verbal skills in school in boys and girls. In boys, higher MF in preschool predicted higher verbal skills (β = 0.293, p = 0.021) in school after adjustment for con-founders. Cognitive skills at baseline seemed predominantly predict later cognitive performance in school. In conclusion, higher muscular fitness in preschool predicted better verbal skills in first grade at school in boys but not in girls. Body fatness and cardiorespiratory fitness in preschool were not associated with cognitive skills in school.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) persists throughout the world with over 65 million registered cases of survivors with post-COVID-19 sequelae, also known as LongCOVID-19 (LongC). LongC survivors exhibit various symptoms that span multiple organ systems, including the nervous system. To search for neurological biomarkers of LongC, we investigated the soluble proteins present in the plasma and the proteins associated with plasma neuronal-enriched extracellular vesicles (nEVs) in 33 LongC patients with neurological impairment (nLongC), 12 COVID-19 survivors without any LongC symptoms (Cov), and 28 pre-COVID-19 healthy controls (HC). COVID-19 positive participants were infected from 2020-2022, not hospitalized, and were vaccinated or unvaccinated before infection. IL-1b was significantly increased in both nLongC and Cov and IL-8 elevated in only nLongC. Both brain-derived neurotropic factor and cortisol were significantly elevated in nLongC and Cov compared to HC. nEVs from people with nLongC had significantly elevated protein markers of neuronal dysfunction, including amyloid beta 42, pTau181 and TDP-43. This study shows chronic peripheral inflammation with increased stress after COVID-19 infection. Additionally, differentially expressed nEV neurodegenerative proteins were identified in people recovering from COVID-19 regardless of persistent symptoms.

Due to its devastating consequences, late life depression is an important public health problem. The aim of the study was the analysis of variables which may potentially influence risk of depression (GDS-SF). Furthermore, the aim was to study possible mediating effect of given variables on the relationship between the total protein concentration and risk of depression in older-adults with chronic diseases, and physical function impairment. The research sample included a total of 132 older adults with chronic conditions and physical function impairments, remaining under a long-term care in residential environment. Negative linear correlation was observed between patients’ physical functionality, total protein concentration, concentration of HDL cholesterol, arm circumference, and the risk of depression. Considerably stronger relationship was observed between total protein concentration, and GDS-SF, in elderly suffering from sensory dysfunction (b = −6.42, 95% CI = −11.27; −1.58). The effect of the mediation between depression risk is correlated to total protein concentration in blood serum, and the mediators are probably low function impairment and low levels of 25 (OH)D vitamin. Cohort control research is suggested to confirm the hypothesis.

Multicomponent training is recommended for people with dementia living in long-term care homes. Nevertheless, evidence is limited and people with severe dementia are often excluded from trials. Hence, the aim of this study was to investigate (1) the feasibility and (2) the requirements regarding a multicomponent training for people with moderate to severe dementia. The study was conducted as an uncontrolled single arm pilot study with a mixed methods approach. 15 nursing home residents with a mean age of 82 years (range: 75-90 years; female: 64%) with moderate to severe dementia received 16 weeks of multicomponent training. Feasibility and requirements of the training were assessed by a standardized observation protocol. Eleven participants regularly attended the intervention. The highest active participation was observed during gait exercises (64%), the lowest during strength exercises (33%). It was supportive if exercises were task-specific or related to everyday life. This study confirms that a multicomponent training for the target group is (1) feasible and well accepted. To enhance active participation (2) individual instructions and the implementation of exercises related to everyday life is required. The effectiveness of the adapted training should be tested in future randomized controlled trials.

In California, water is a perennial concern. As competition for water resources increases due to growth in population, California’s tree nut farmers are committed to improving the efficiency of water used for food production. There is an imminent need to have reliable methods that provide information about the temporal and spatial variability of crop water requirements, which allow farmers to make irrigation decisions at field scale. This study focuses on estimating the actual evapotranspiration and crop coefficients of an almond and pistachio orchard located in Central Valley (California) during an entire growing season by combining a simple crop evapotranspiration model with remote sensing data. A dataset of the vegetation index NDVI derived from Landsat-8 was used to facilitate the estimation of the basal crop coefficient (Kcb), or potential crop water use. The soil water evaporation coefficient (Ke) was measured from microlysimeters. The water stress coefficient (Ks) was derived from airborne remotely sensed canopy thermal-based methods, using seasonal regressions between the crop water stress index (CWSI) and stem water potential (Ystem). These regressions were statistically-significant for both crops, indicating clear seasonal differences in pistachios, but not in almonds. In almonds, the estimated maximum Kcb values ranged between 1.05 to 0.90, while for pistachios, it ranged between 0.89 to 0.80. The model indicated a difference of 97 mm in transpiration over the season between both crops. Soil evaporation accounted for an average of 16% and 13% of the total actual evapotranspiration for almonds and pistachios, respectively. Verification of the model-based daily crop evapotranspiration estimates was done using eddy-covariance and surface renewal data collected in the same orchards, yielding an r² >= 0.7 and average root mean square errors (RMSE) of 0.74 and 0.91 mm day^-1 for almond and pistachio, respectively. It is concluded that the combination of crop evapotranspiration models with remotely-sensed data is helpful for upscaling irrigation information from plant to field scale and thus may be used by farmers for making day-to-day irrigation management decisions.

(1) Background: The prediction of cervical lesions evolution is a challenge for clinicians. The prospective study aimed to determine and compare the predictive accuracy of cytology, HPV genotyping, and p16/Ki67 dual staining alone or in combination with personal risk factors for the prediction of progression, regression or persistence of cervical lesions in human papillomavirus (HPV)-infected patients; (2) Methods: This prospective study included HPV-positive patients with or without cervical lesions that underwent follow-up. Descriptive statistics and sensitivity analysis for individual or combined index tests were used for data analysis; (3) Results: The highest predictive performance for the progression of cervical lesions was achieved by a model that comprised a Pap smear suggestive of high-grade squamous intraepithelial lesion (HSIL), the presence of 16/18 HPV strains, a positive p16/Ki67 dual staining result along with the presence of at least 3 clinical risk factors, which had a sensitivity (Se) of 74.42%, a specificity of 97.92%, an area under the receiver operating curve (AUC) of 0.961, and an accuracy of 90.65%. The prediction of cervical lesions regression or persistence was modest; (4) Conclusions: Multiple testing or new biomarkers should be used to improve HPV-positive patient surveillance, especially for cervical lesion regression or persistence prediction.

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

Background: Diabetes mellitus (DM) and periodontitis are two chronic diseases that are interconnected. The coexistence of these conditions leads to increased severity of periodontitis and challenges in controlling blood glucose levels in diabetic patients. Objectives: The aim of this study was to evaluate both the influence of periodontal disease and its treatment in patients with DM in terms of a reduction in glycosylated hemoglobin levels at 3 and 6 months after treatment. Methods: A sequential, nonprobabilistic, controlled, prospective and longitudinal case−control study was carried out at the University Dental Clinic of the Morales Meseguer Hospital in Murcia, Spain. Thirty diabetic patients were divided into two groups: patients with periodontitis (test group) and patients without periodontitis (control group). The periodontal and endocrine-metabolic variables were measured at baseline and at 3 and 6 months after periodontal treatment for both groups. Results: Statistically significant differences were found at 3 and 6 months after periodontal treatment with respect to HbA1c levels in the group of periodontal patients. However, these differences were not observed in the control group. Conclusion: The presence of periodontal disease increased HbA1c levels in patients with DM. Nonsurgical periodontal treatment significantly decreased the degree of periodontal inflammation and the HbA1c level at 3 and 6 months after treatment.

Driving factors of smart city construction are exploratively studied by grounded theory method based on text sources from journal papers in SCI and CSSCI databases. Initial scale of driving factors about smart city construction is obtained on the basis of above analyses. This paper modifies measuring items of the initial scale with a small sample pretest and reliability test, then forms final items by exploratory factor analysis. According to the above scale, questionnaires are designed to obtain empirical data, and confirmatory factor analysis is used to verify further validity and reliability of the scale. The results show that driving factors of smart city construction include three main dimensions: problem-oriented factors, business- or technology-driven factors and endogenous development requirements.

Research focused on understanding wider systemic factors driving behavioral change is limited with a dominant focus on the role of individual farmer and psychosocial factors for farming practice change, including reducing fertilizer application in agriculture. Adopting a wider systems perspective, the current study examines change and the role that supporting services have on fertilizer application rate change. A total of 238 sugarcane growers completed surveys reporting on changes in fertilizer application along with factors that may explain behavior change. Logistic regressions and negative binomial count-data regressions were used to examine whether farmers had changed fertilizer application rates and if they had, how long ago they made the change, and to explore the impact of individual and system factors in influencing change. Approximately one in three sugarcane growers surveyed (37%) had changed the method they used to calculate fertilizer application rates for the cane land they owned/managed at some point. Logistic regression results indicated growers were less likely to change the basis for their fertilizer calculation if they regarded maintaining good relationships with other local growers as being extremely important, they had another source of off-farm income, and if they had not attended a government-funded fertilizer management workshop in the five years preceding the survey. Similar drivers promoted early adoption of fertilizer practice change; namely, regarding family traditions and heritage as being unimportant, having sole decision-making authority on farming activities and having attended up to 5 workshops in the five years prior to completing the survey. Results demonstrated the influence of government-funded services to support practice change.

This paper formulates the performance evaluation of cloud services as a multicriteria group decision making problem, and presents a fuzzy multicriteria group decision making method for evaluating the performance of cloud services. Interval-valued intuitionistic fuzzy numbers are used to model the inherent subjectiveness and imprecision of the performance evaluation process. An effective algorithm is developed based on the technique for order preference by similarity to ideal solution method and the Choquet integral operator for adequately solving the performance evaluation problem. An example is presented to demonstrate the applicability of the proposed fuzzy multicriteria group decision making method for solving the multicriteria group decision making problem in real world situations.

Smart City has emerged as the mainstream paradigm for urban governance innovation, sustainable development, and strategy upgrades, which is drawing attention from scholars worldwide. However, current frameworks for Smart City assessment remain incomplete and simplistic. In is paper, 30 national or provincial capitals in China were selected and we designed a tri-dimensional SPI model—Social, Physical, and Information Space—for smart city spatial development assessment. Utilizing methods such as entropy weighting, coupled coordination degree models, and the Dagum Gini coefficient, this study assesses the spatial development and coupled coordination of 30 cities from 2011 to 2019. Finally, by means of BP neural networks, the study examines the contribution of each indicator to the spatial coupled coordination. The results indicated that with a narrowing disparity in development speeds among different regions, the spatial coupled coordination development level of smart capitals in China has steadily increased, presenting a pattern of staggered distribution. Moreover, the IS subsystem plays the most significant role in coupled coordination. The significance of this research lies in its tri-dimensional spatial perspective of the spatial development and coupled coordination differences of the Smart City, providing evidence-based support for the regional layout and optimization in China.

This paper presents a new optical, multi-functional, high-resolution 3-axis sensor which serves to navigate and can, for example, replace standard joysticks in medical devices such as electric wheelchairs, surgical robots or medical diagnosis devices. A light source, e.g. a laser diode is affixed to a movable axis and projects a random geometric shape on an image sensor (CMOS or CCD). The software in the downstream microcontroller identifies the geometric shape’s center, distortion and size, then calculates X, Y, and Z coordinates. These coordinates can then be processed in attached devices. The 3-axis sensor is characterized by its very high resolution, precise reproducibility and plausibility of the coordinates produced. In addition, optical processing of the signal provides a high level of safety against electromagnetic and radio frequency interference. The sensor presented here is adaptive and can be adjusted to fit a user’s range of motion (stroke and force). This recommendation aims to optimize sensor systems such as joysticks in medical devices in terms of safety, ease of use, and adaptability.

This study aimed to determine shifts in microbial populations regarding richness and diversity from daily use of a popular over-the-counter nasal spray. In addition, the finding of nasal commensal bacterial species that overlap with the oral microbiome may prove to be potential probiotics for the “gateway microbiomes.” Nasal swab samples were obtained before and after using the most popular over-the-counter (OTC) nasal spray in 10 participants aged 18–48 years. All participants were healthy volunteers with no significant medical histories. The participants were randomly assigned a number by randomizing software and consisted of five men and five women. The sampling consisted of placing a nasal swab atraumatically into the nasal cavity, which is similar to the sampling performed for COVID-19 testing, with only very slight discomfort, if any. The samples were preserved and sent to Northwestern University Sequencing Center for whole-genome deep sequencing. After 21 days of OTC nasal spray use twice daily, the participants returned for further nasal microbiome sampling. The microbial analysis included all bacteria, archaea, viruses, molds, and yeasts via deep sequencing for species analysis. The Northwestern University Sequencing Center utilized artificial intelligence analysis to determine shifts in species and strains following nasal spray use that resulted in changes in diversity and richness.

In this work, an innovative numerical approach for polylinear approximation (polylinearization) of non-self-intersecting compact sensor characteristics (transfer functions) specified either pointwise or analytically is introduced. The goal is to optimally partition the sensor characteristic, i.e. to select the vertices of the approximating polyline (approximant) along with their positions on, the sensor characteristics so that the distance (i.e. the separation) between the approximant and the characteristic is rendered below a certain problem-specific tolerance. To achieve this goal, two alternative non-linear optimization problems are solved, whose essential difference is in the adopted quantitative measure of the separation between the transfer function and the approximant. In the first problem, which relates to absolutely integrable sensor characteristics (their energy is not necessarily finite, but they can be represented in terms of convergent Fourier series), the polylinearization is constructed by numerical minimization of the L^1--metric (a distance-based separation measure), concerning the number of polyline vertices and their locations. In the second problem which covers the quadratically integrable sensor characteristics (whose energy is finite, but they do not necessarily admit a representation in terms of convergent Fourier series), the polylinearization is constructed by minimizing numerically the L^2-metric (area-, or energy-based separation measure) for the same set of optimization variables –the locations, and the number of polyline vertices.

Climate change is a major development challenge to Ethiopia. Unless adaptation measures are widely implemented, climate change can set back development efforts and achievements by years. Recognizing this, the Government of Ethiopia (GoE) and civil society organizations have since recently been making considerable efforts to tackle the climate change problem. This paper documents the experience of CARE International in Ethiopia in facilitating bottom-up approaches to promote community-led disaster risk management and climate change adaptation planning through a participator scenario planning (PSP) methodology. PSP is a coordinated approach which leverages a variety of stakeholders' skills and mandates to explore potential climate change risks and their impacts, and then develop locally relevant and shared adaptation action plans that support livelihoods, social capital and ecosystem resilience. The approach has facilitated timely access to and communication of seasonal climate advisories which in turn is empowering communities to take advantage of opportunities that climate presents, which is a key part of adapting to climate change. The institutionalization of the approach by district and regional disaster prevention and food security coordination offices in the southern pastoral regions of Ethiopia encouraged integration of PSP into community livelihood adaptation and local government development planning, hence continuity of the process. Sustainability is expected to be fully achieved when local government planning processes recognize the importance of and provides resources for the participation of meteorological services and community forecasting experts to help refine plans on a seasonal basis, and for systems for dissemination of advisories. Key words: Climate Change, Disaster Risk Reduction, Adaptation, Pastoralism, Ethiopia,

Today, issues related to people’s mental health and well-being have been described as a challenge for society, globally as well as in Sweden. This calls for new approaches to mental health promotion. The aim was to evaluate the adequacy of the content and structure, describing experiences of study circles as a means of supporting participants' self-care and self-compassion. The overall design is a descriptive, QUAL+quan design, where quantitative and qualitative results are integrated. Five participants participated in a focus group interview, of whom four completed questionnaires. One individual interview was conducted with the study circle leader. Study circles can be an arena for mental health promotion as learning and sharing of experience contributes to a sense of coherence, as well as self-compassion and a genuine concern for one’s own and others' wellbeing, but not considered an alternative to psychiatric care, for those in need of professional services. Study circles can be a possible means to support self-care and thereby promote mental health in the general population, and a valuable contribution to public health. However, in addition to modifications of the content, further research is needed on the qualifications for study circle leaders as well as the dissemination of study circles.

This publication aims to create 4K UHD video sequences with a specific focus on smart cities and transport. The created database will be accessible to all experts in the field and can be further used for research purposes. All reference video sequences will be transcoded into different quality settings. The resolution, compression standard, and bitrate will be changed. The work will aim to find out the best combination of video parameters from the measured values. Thanks to the overall evaluation, we will ensure for each single video sequence the quality with the best experience for the providers that will offer the service. The database will also be available with its objective and subjective ratings, with dynamicity information characterized by spatial and temporal information. After, It will be possible to compare the results of the subjective evaluation of a selected sample of our respondents with the work of other researchers who can evaluate with a different sample of evaluators.

Solid waste management is a global issue that affects every individual living in the world directly or indirectly. Failing to manage the waste properly will cause harm on public health, our natural environment, and even our happiness and prosperity. When asked about the reason behind the failure of third time plastic ban in Bhutan, one of the Bhutanese said it’s because of the lack of cooperation from public to support the move of government. Clean Bhutan, a non-governmental organization (NGO) that aims Zero Waste Bhutan by 2030, has collected around 5,900 metric tons of waste from December 2014 to February 2018. This project will create some awareness regarding the management of waste at individual level when people come in contact with the family of the researcher. It also talks abot how waste management and biodiversity conservation can go side by side.

The fetal splenic artery pulsatility index (PI) is a parameter that reflects fetal well-being and has been used as a predictor of adverse pregnancy outcomes. The aim of this study was to investigate the predictive value of splenic artery PI in GDM class A1 cases for NICU admission. In this prospective case controlled study, only sixty single pregnancy cases diagnosed with gestational diabetes mellitus class A1 were evaluated. Fetal splenic artery Doppler parameters Peak systolic velocity (PSV), Pulsatility index (PI), Resistivity index (RI) and End diastolic velocity (EDV) were measured in all cases. The rate of re-quirement of need of neanoatal intensive care unit was noted. In cases with need fetal intensive care, fetal splenic PI index was found to be statistically significantly lower than in healthy cases without it (0.94 ± 0.29 vs. 1.70 ± 0.53, respectively, p

The teaching and learning environment have changed significantly over the last decade with the integration of an increasing amount of information and communication technologies, especially in distance education due to global pandemics, total lockdowns, isolation, and quarantine periods. The number of numerous virtual and non-virtual tools involved in educational processes is growing day by day. In terms of technological innovations used in learning environments, many researchers and educators are trying their best to bring more creative ideas into the technology- based learning environments. Although higher educational institutions have greatly benefited from the growing number of hardware and software resources in the educational process, they have also faced some setbacks that have harmed the learning processes. These technologies have in turn complicated some of the processes in the learning atmosphere. Therefore, we take Spanish teaching course as an example to present the challenging smarter teaching and learning systems that higher educational institutions face today.

Rhodotorula mucilaginosa fungaemia is rare and highly resistance to antifungal therapy. We herein report a case involving a 31-year-old male admitted after a high-velocity road traffic accident. He sustained a grade IV liver injury with right hepatic vein thrombosis, necessitated an urgent laparotomy. Post-operatively, repeated imaging of the abdomen revealed presence of biloma. Percutaneous subdiaphragmatic drainage was done but appeared ineffective, prompting second surgery for a urgent hemi-hepatectomy. Patient then was nursed in the intensive care unit (ICU) however during his stay in the ICU he became more sepsis evident by worsening ventilatory support and rise in septic parameters from the biochemistry parameters. Despite on intravenous piperacillin-tazobactam and fluconazole, his septic parameters did not improve and a full septic workup was done and was found to be positive for Rhodotorula mucilaginosa from the blood cultures. After discussion with the infectious disease physicians and clinical microbiologist, it was decided to initiate a course of intravenous meropenem and amphotericin B based on minimum inhibitory concentration (MIC) values, considering the patient's extended ICU stay and catheter use. Eventually, successful weaning off mechanical ventilation was discharge from ICU care. This case underscores the necessity of individualized approaches, combining timely imaging, appropriate drainage techniques, and tailored treatments to optimize outcomes for such intricate post-traumatic complications.

The brain's unique characteristics make it exceptionally susceptible to oxidative stress, which arises from an imbalance between reactive oxygen species (ROS) production, reactive nitrogen species (RNS) production, and antioxidant defense mechanisms. This review explores the factors contributing to the brain vascular tone vulnerability in the presence of oxidative damage, which can be of clinical interest in critically ill patients or presenting acute brain injuries. The brain's high metabolic rate and inefficient electron transport chain in mitochondria lead to significant ROS generation. Moreover, non-replicating neuronal cells and low repair capacity increase susceptibility to oxidative insult. ROS can influence cerebral vascular tone and permeability, with a potential impact on cerebral autoregulation. Different ROS species, including superoxide and hydrogen peroxide, exhibit vasodilatory or vasoconstrictive effects on cerebral blood vessels. Reactive nitrogen species (RNS), particularly NO and peroxynitrite, also exert vasoactive effects. This review further investigates the neuroprotective effects of antioxidants, including superoxide dismutase (SOD), vitamin C, vitamin E, and the glutathione redox system. Various studies suggest that these antioxidants could be used as adjunct therapies to protect the cerebral vascular tone during high oxidative stress conditions. Nevertheless, more extensive research is required to comprehensively grasp the relationship between oxidative stress and cerebrovascular tone, as well as to explore the potential benefits of antioxidants as adjunctive therapies in critical illnesses and acute brain injuries.

Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatmnet, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.

Neonatal sepsis is a leading cause of morbidity and mortality in neonates, particularly in low and middle-income countries. The emergence of antimicrobial resistance is a rapidly growing global problem. A significant proportion of the pathogens that commonly cause neonatal sepsis are resistant to multiple antibiotics. Therefore, for the empirical treatment of neonatal sepsis, the repurposing of older antibiotics that are effective against multidrug-resistant pathogens is being investigated. This review aims to provide an overview of current research and experience using the repurposed antibiotics, Colistin and Fosfomycin, for the empirical treatment of neonatal sepsis. Based on current knowledge, Colistin and Fosfomycin may be potentially helpful for the empirical treatment of sepsis in neonates due to their efficacy against a wide range of pathogens and acceptable safety profile.

The goal of the paper is to investigate the expected participation and mentality of smart citizens in smart cities. The key question is the role of the human factor in smart environments globally studied through a research corpus of the mainstream summaries, trend reports, white papers and visions of business – governmental – university research co- operations. Foremost, a short review of the changing scholarly trends is presented as a theoretical framework. Concerning its key ideas, the corpus based findings are recapped and analysed by content networks and the most referred city strategies. Besides, a critical approach reveal further required factors and risks to investigate. The ultimate goal is to understand how the smart city landscape is shaped by citizen-based strategies, open data, empowerment and responsibility. Accordingly, the paper closes with theoretical, practical and metaphor-based recommendations to support the business and political decision making, and also, the emerging scholarly trends in the context of upcoming technological-structural changes.

The objective of this study was to carry out the extraction and characterization of chitin and chitosan from the exoskeletons of the Farfantepenaeus californiensis species of shrimp, as well as its application in the formulation of polymeric films plasticized with glycerol used in the process of food packaging. The characterization of the extracted polymer was made by Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), and physicochemical analysis of chitin and chitosan, resulting in a theoretical performance of 29.33% and 16.46%, respectively. In terms of quality, it was determined that the extracted chitosan had a molecular weight of 1.10 x105 g/mol and a deacetylation degree of 80.23%, which classifies it as a low-molecular weight chitosan, suitable for food packaging. It was shown that the extracted chitosan is fit for the production of the films by using the casting method, presenting a tension resistance of 1 MPa. The Antibacterial analysis was carried out using Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), bacterial models commonly found in food. The results showed an effective inhibition capability of the polymeric materials. Overall, the results for both chitosan and the films indicate a high potential for application in the food packaging sector.

Synchronous electrical cardioversion is a relatively common procedure in the emergency department (ED), often performed for unstable supraventricular tachycardia (SVT) or unstable ventricular tachycardia (VT). However, it is also used for stable cases resistant to drug therapy, which carry a risk of deterioration. In addition to the inherent risks linked with procedural sedation, there is a possibility of malignant arrhythmias or bradycardia, which could potentially result in cardiac arrest following this procedure. Here, we present a case of complete heart block unresponsive to transcutaneous pacing and positive inotropic and chronotropic drugs for 90 minutes, resulting in multiple cardiac arrests. The repositioning of the transcutaneous cardio-stimulation electrodes, one of them placed in the left latero-sternal position and the other at the level of the apex, led to immediate stabilization of the patient. Extubation of the patient was performed the following day, while full recovery and discharge within 7 days, after insertion of a permanent pacemaker.

In 2020 the world experienced the threat of the COVID-19 epidemic, as seniors and chronic disease patients typically try to reduce their exercise and social activities to avoid increasing the risk of infection, which could lead to increased loneliness and even many diseases. This research combining golf croquet games with AIoT companion robots constructs a home-based intelligent exercise system thus uses the Technology Acceptance Model (TAM), deduces users’ intention to use this system based on perceived usefulness and perceived ease of use, and adds the needs of love and belonging, esteem, cognitive, aesthetic, and self-actualization in Maslow’s needs theory to conduct system needs analysis. The analysis results show that participants have a high level of acceptance of this system, believing that it is easy to learn and operate, can increase interaction with others, also found that this system can improve self-confirmation, satisfying the third for knowledge, feeling happy, and self-actualization needs are easier to fulfill. In the future, we shall collect and record the seniors in the process of use, so as to find out their health problems as soon as possible, expand their daily life through this exercise, and achieve the goal of happy living and health care.

Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.

Background: We assessed the pertinence of yearly updating the International Working Group on the Diabetic Foot (IWGDF) risk classification in people with diabetes by quantifying the changes in the risk group and its accuracy in identifying those developing an ulcer (DFU) in a primary care setting. Methods: In our retrospective cohort study, we included all people with diabetes with a foot as-sessment registry between January 2016 and December 2018 in the Baixo Alentejo Local Health Unit. Foot-related data was collected at baseline after one and two years. DFU and/or death until December 2019 were registered. The proportion of people changing their risk status each year was calculated. Accuracy measures of the IWGDF classification to predict DFU occurrence at one, two, and three years were calculated. Results: A total of 2097 people were followed for three years, during which 0.1% died, and 12.4% developed a DFU. After two years, 3.6% of the participants had progressed to a higher-risk group. The IWGDF classification presented specificity values superior to 90% and negative predictive values superior to 99%. Conclusion: Foot risk status can be safely updated every two years instead of yearly. The IWGDF classification can accurately identify those not at risk of DFU.

Background: Patients with Inflammatory Bowel Disease (IBD) believe that diet is a key factor in the pathogenesis of their disease and in worsening their symptoms; they often follow restrictive diets that can lead to malnutrition, anxiety, and stress. Recent studies have found an association between eating disorders and IBD, particularly anorexia nervosa and ARFID (Avoidant Restrictive Food Intake Disorder). None of these studies report the association with orthorexia nervosa, that is the obsession with healthy and natural foods. Aim: to assess the risk of orthorexia nervosa in patients with IBD. Methods: 158 consecutive subjects were recruited, including 113 patients diagnosed with IBD and 45 controls. The Donini standardized questionnaire ORTO-15 was administered to assess the risk of orthorexia; clinical and demographic data were also collected. Results: patients with IBD have a prevalence of orthorexia of 77%, significantly higher than the 47% observed in the control group. In patients with IBD, the risk of orthorexia is associated with lower BMI and history of previous surgery. Conclusion: Many patients with IBD develop orthorexia nervosa that impacts the patient's psychological and social sphere, exposes them to a high risk of nutritional deficiencies, and affects quality of life. Further high quality studies are warranted to evaluate the clinical impact of orthorexia and its correlation with clinical features and frank eating disorders.

Vaccination, particularly against pneumoccus and influenza, is a low-cost primary prevention, useful to avoid hard complications, particularly among frail older people. In this study, we aimed to assess the effect of a strategy for influenza and pneumococcal vaccination for outpatients, never practiced before, evaluating what could stimulate or demotivate vaccination among older people. This study was conducted during the 2023-2024 influenza season at the outpatient clinics of the Geriatrics section, University Hospital of Palermo (Italy). A total of 101 vaccinations were carried out. Half of the patients could be considered as pre-frail and about 20% frail, according to a comprehensive geriatric evaluation. Among participants, 46.3% received only vaccination against Pneumococcus and 26.8% only anti-influenza or the combination of anti-pneumococcus and anti-influenza. Compared with the previous seasons, a 19.5% increase of influenza and 90.2 % of pneumococcal vaccine uptake was observed. Side effects of vaccination were the main reason of the previous rejections, namely 74.8% for influenza and 29.5% for anti-pneumococcal vaccination. In conclusion, our study indicates how a new vaccination strategy in different settings is possible. Proposing influenza and pneumococcal vaccination for outpatients could be an effective instrument to improve immunization coverage that is still low among older people.

Vein blood sampling is one of the methods of mass blood sampling, and is an act of drawing blood from a vein for blood type discrimination, confirmation of various physiological indicators, disease diagnosis, and the like, and is the most commonly used blood sampling method. An important point in vein blood sampling is to search for the exact location of the vein and insert the blood sampling. This is influenced by the patient's obesity, skin and blood vessel conditions, and the experience of the clinical technologist, nurse, and resident who performs the blood sampling. It is required to perform blood sampling technique practice. However, due to many limitations of the practice room or laboratory, there is a problem of using only a limited environment and model for clinical practice. As a result, many medical educational institutions have situations in which only fragmentary clinical practices are performed, and it is difficult to per-form a large number of blood sampling skills practices, so they do not provide enough experience to understand the actual skill field. In this paper, we propose a method for developing a virtual reality-based vein blood sampling simulator to practice a large number of blood sampling techniques without restrictions. The proposed vein blood sampling simulator can operate a 3D model related to vein blood sampling using HMD Controller and Haptic in a virtual space for vein blood sampling practice by wearing an HMD (Head mounted display), and can perform vein blood sampling practice through interaction with the patient 3D model. there is. In addition, the effectiveness of the simulator developed for dental students was verified, and as a result of the verification, the potential of the proposed vein blood sampling simulator was confirmed.

Keywords: mHealth; peritoneal dialysis; functionality; usability

Many people in the USA have lost their lives or become addicted to opioids via prescription opioids given for chronic pain. The chronic pain epidemic has emerged due to a convergence of factors, including the medicalization of pain as well as injuries happening in the workplace, in the home, or during recreation. Opioid prescriptions rose precipitously from the late 1990’s through to approximately 2020, and despite public awareness, still are increasing. This review examines literature on integrative approaches to chronic pain as delineated by pain type. The review is organized by the most common causes of chronic pain, and randomized controlled trials, meta-analyses and systematic reviews are extracted for each cause of chronic pain. Several promising interventions may alleviate chronic pain of some causes, and some interventions may work across pain causes. Lowering inflammation through dietary or lifestyle regimens may be a general way of reducing pain. Pain can be alleviated through several adjunctive and integrative treatment approaches, which may serve to lower the need for opioid medication.