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Artificial Intelligence is a popular topic these days. It is believed that artificial intelligence will be the solution to all existing societal problems, reducing human effort while also improving human interpretation of the world around them. Artificial intelligence, within the realm of computer science, is focused on developing machines capable of emulating human thought processes, operations, and adaptability. At present, artificial intelligence finds utility across diverse domains, including but not limited to medicine, engineering, agriculture, self-driving vehicles, and aviation. Another emerging arena for AI deployment is in energy platforms. This paper delves into the applications of artificial intelligence and their significance in fostering sustainability across various trends. Consequently, it provides insights and analysis regarding the longterm viability of AI, encompassing environmental and economic development. Furthermore, it highlights the positive impact of AI applications on sustainable development, encompassing aspects such as fulfilling energy requirements, generating employment opportunities, and enhancing environmental preservation.

The problem of power regulation has been well studied and there exist number of approaches in regulating the power supply to multi storage buildings. Some of the method uses number of supply units present in each floor, and number of control units present in the floors of the building. However, they suffer to achieve higher performance in power regulation. To handle this issue, a Genetic Algorithm Based power regulation model (GAPRM) is presented in this paper. The model considers various factors like number of electric stoves, no of heater, no of iron, no of vacuum cleaner, no of television, no of hair dryer, no of refrigerator, no of electric kettle, no of microwave oven, no of washing machines and Voltage requirement of each towards power regulation. The model fetches the details of each storage level and identifies the set of all electric devices present in the floor. Using these details, the method converts them into feature vector. With the incoming voltage, the method applies genetic algorithm to identify maximum units can be triggered with the supply at each floor. The genetic algorithm generates number of mutations and for each mutation; the method computes the fitness value in the name of Smart Voltage Fitness (SVF) measure. Based on the value of SVF, the method populates a subset of population. For the selected population, the method computes Power Regulation Value (PRV). According to the value of PRV, specific voltage has been regulated to the specific storage. This will be iterated for each storage level of the building. The proposed method improves the performance of power regulation in smart multi storage building.

The globalized and fiercely competitive nature of the international market has expanded the range of demands across all sectors of the agri-food business. The dairy business needs to adjust to the prevailing market conditions by enhancing resource efficiency, adopting environmentally sustainable practices, promoting transparency, and ensuring security. The Internet of Things (IoT), Edge Computing (EC), and deep learning play pivotal roles in facilitating these advancements as they enable the digitization of various components within the value chain. Solutions that depend on human observation via visual inspections are susceptible to delayed detection and potential human mistakes and need more scalability. The growing herd numbers raise a significant worry due to the potential negative impact on cow health and welfare, particularly about extended or undiscovered lameness. This condition has severe consequences for cows, eventually leading to a decline in milk output on the farm. To address this issue, an Integrated Approach to Dairy Farming (IA-DF) has been developed, which utilizes sophisticated Artificial Intelligence (AI) and data analytics methodologies using mobile applications to continuously monitor livestock and promptly detect instances of lameness in cattle. Initially, the VGG16 model, pre-trained on the ImageNet dataset, was used as the underlying architecture to extract the sequence of feature vectors associated with each video. This approach was adopted to circumvent the limitations of conventional feature engineering methods, which tend to be both time-consuming and labor-intensive with deep learning-based classification algorithms. IA-DF can extract semantic details from historical data in both forward and backward directions, hence enabling precise identification of fundamental behaviors shown by dairy cows.

The agricultural sector serves as a fundamental cornerstone of the economies of numerous countries, necessitating technological advancements despite limited financial resources. The Internet of Things (IoT) presents a novel aspect within the field of soil health monitoring, which has significant implications for advancing smart agriculture and farming practices. Integrating conventional agricultural practices with cutting-edge technologies, such as the IoT and Wireless Sensor Networks (WSN), can foster Smart Agriculture (SA). This paper presents IoT Integration for Enhanced Turmeric Cultivation (IoT-ETmC) in the context of SA. The TurmFox IoT and Edge-to-Cloud (ETC) technology can analyze gathered data and send it to the user through internet connectivity. The work involves the implementation of TurmFox in experiments focused on turmeric cultivation. The results demonstrate a notable improvement in the quality of turmeric as a direct outcome of this intervention. The curcumin levels in the given product are notably higher, ranging from 4450 to 5450 mg per 120g. This paper also aims to demonstrate the intuitive configuration of sensor-to-actuator connections for implementing desired SA. The real-time data obtained from Turmfox provides information on the pH values, moisture levels, and temperature, allowing for observing dynamic variations in environmental conditions within the specified period. The pH level was 6.5 at 09:00, with a moisture content of 51 g/m3 and a temperature of 293 K.

Researchers are focusing on the development of bio-fillers through different approaches for dental purposes. The present work reports a relatively simple and faster chemical approach for synthesising composites using silica from waste rice husk along with PEGMA, PNIPAm, HEMA, APS, and MBA. The synthesis includes no harmful chemical reagents and focuses on the effective management of waste. The X-ray diffraction revealed the crystalline nature of composite and with an average crystallite size of 35 nm for the silica particles. The average particle size for silica was found in the range 200–230 nm from dynamic light scattering having a stable structure with −18.23 ± 0.5 mV as the zeta potential. The porosity was analysed using scanning electron microscopy and the stoichiometric composition by energy-dispersive spectroscopy with the pore size from 95–350 nm. Fourier transform infrared spectroscopy revealed the presence of different functional groups. The thermal stability of silica was analysed through Thermogravimetric analysis. The antibacterial activity of the composite against E.coli and S. aureus demonstrated its efficacy against bacterial growth inhibition. The porous composite can be effectively used as a filler material in potential biomedical applications including dental composite for sustained drug loading and release. [ABSTRACT FROM AUTHOR]

Ayn Rand’s philosophy of Objectivism sparked a lot of interest and inclination among her readers, and even inspired them to form the Atlas Society in the United States. Her bestselling books The Fountainhead, We the Living, and Atlas Shrugged made history under the banner of American literature and enjoyed tremendous economic success as well. This study analyzes her autobiography We the Living, where she describes the hardships that aristocrats and business people endured and the brutality of communists. She implied that those in positions of leadership ought to have compassion while using their authority. Otherwise, no law can bring equal rights or peace to the people. Ayn Rand claimed that the narrative edifices the dramatic characters and the conflict between the individual and the state. This article recounts the tale of We the Living and the author's own impressions [ABSTRACT FROM AUTHOR]

The purpose of writing this review is to provide an update on recent advances in pregnancy and lactation-associated osteoporosis (PLO) research and summarize the current evidence for specific treatments. PLO is a transient and rare form of osteoporosis that affects women of childbearing age during the third trimester of pregnancy and post-partum. Though the pathophysiology of the PLO is poorly understood, several case series, case studies, and fewer cohort studies are available highlighting the role of pregnancy and lactation apart from conventional risk factors in the progression of PLO. Approximately 300 research and review articles related to PLO have been read from 1996 to 2023 which include several case studies, case series, cohort studies, meta-analyses, and narrative reviews from PubMed, Embase, Scopus, Google Scholar, World Health Organization regional databases. Common clinical manifestations include lower back and hip pain and rarely vertebral compression fractures. During pregnancy and lactation, women undergo reversible changes in mineral homeostasis and skeletal metabolism. Increased calcium absorption and urinary excretion during pregnancy and increased bone resorption along with renal calcium reabsorption in lactation are the main maternal metabolic adaptations that support the skeletal growth and development of the fetus and newborn respectively. Management of the PLO depends upon proper diagnosis and prognosis using biochemical bone turnover markers and bone histomorphometry. Conventional methods include calcium and vitamin D supplementation, giving up breastfeeding, physiotherapy, supportive braces, and bed rest. Bisphosphonates, denosumab, and teriparatide are commonly prescribed medications, assuring the recovery of bone mineral density besides certain side effects. Considering the transient nature, and underreporting of the cases, treatment recommendations should be personalized based on the parity, duration of lactation, presence or absence of fractures, societal status, age, ethnicity, and race. [ABSTRACT FROM AUTHOR]

The current work reports the synthesis of mixed ligand‐metal complexes of (2‐(2‐oxoacenaphthylen‐1 ylidene)hydrazinecarbothioamide) (L1) and carbamothioyl‐2‐chloronicotinohydrazonic acid (L2) and imidazole (im), triphenylphosphine (PPh3) and pyridine (py) ligands with Cu (II), Zn (II), Fe (II) and Co (II) salts.The chacterization was done through FT‐IR, NMR, UV‐visible, and elemental analysis. The X‐ray diffraction (XRD) patterns of [Cu(L1)(im)2(SO4)] demonstrated the crystalline morphology. In the FT‐IR spectra of the ligands, the characteristic frequencies corresponding to ν(C=N) (imine) and ν(C=S) (thiocarbonyl) were observed around 1650 and 1130 cm−1 respectively, which shifted upon complexation. In the 1H NMR spectra, the peak due to the NH proton was observed downfield, whereas that of ‐NH2 was observed comparatively upfield owing to its secondary amine nature. The molecular docking was performed against transforming growth beta (tgf‐β), protease of SARS‐CoV‐2 (Mpro) and aspartate kinase (Asp) kinase.The molecular simulation with the docked complex of [Zn(L2)(py)2(SO4)] and [Zn(L1)(PPh3)2(SO4)] with Asp kinase showed significant stability during the simulation time of 100 ns. The relative free energies of the simulated complexes were calculated with the molecular mechanics Poison‐Boltmann surface area (MM‐PBSA). The complexes were tested against ten bacterial strains and showed enhanced antibacterial activity than the ligands. [Cu(L1)(im)2(SO4)], [Zn(L1)(im)2(SO4)], [Fe(L1)(im)2(SO4)] and [Co(L1)(im)2(Cl2)] were found to be active against E.coli. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The objective of this study was to evaluate the efficacy and safety of topical nanoemulsion (NE)-loaded cream and gel formulations of Hippophae rhamnoides L. (sea buckthorn [SBT]) fruit oil for wound healing. MATERIALS AND METHODS: The NE-loaded cream and gel formulations of H. rhamnoides L. (SBT) fruit oil (IPHRFH) were prepared and evaluated for their wound-healing activity on female Sprague-Dawley (SD) rats. They were further divided into groups (seven) and the wound-healing activity was determined by measuring the area of the wound on the wounding day and on the 0th, 4th, 8th, and 10th days. The acute dermal toxicity of the formulations was assessed by observing the erythema, edema, and body weight (BW) of the rats. RESULTS: The topical NE cream and gel formulations of H. rhamnoides L. (SBT) fruit oil showed significant wound-healing activity in female SD rats. The cream formulation of IPHRFH showed 78.96%, the gel showed 72.59% wound contraction on the 8th day, whereas the positive control soframycin (1% w/w framycetin) had 62.29% wound contraction on the 8th day. The formulations also showed a good acute dermal toxicity profile with no changes significantly affecting BW and dermal alterations. CONCLUSIONS: The results of this study indicate that topical NE-loaded cream and gel formulation of H. rhamnoides L. (SBT) fruit oil are safe and effective for wound healing. The formulations showed no signs of acute dermal toxicity in female SD rats. [ABSTRACT FROM AUTHOR]

Now days, with the fast improvement in internet technology and social media network, user give his/her feedback on many things and companies also asked to give feedback about their services as product or service delivered to the customers in the form of review. User’s review about product or service playing major role in improving services as user pleasure. In this paper we present a sentiment analysis model, which is constructed with the help of sentiment lexicon with association of attention-based Bidirectional Gated Recurrent Unit (BiGRU) and CNN (Convolutional Neural Network). For experimental work we used movie review dataset and matched the sentiment analysis results of the proposed method with the mutual sentiment analysis models (SVM, Naïve Bayes, Decision Tree, Random Forest, CNN, and BiGRU) on the dataset. The Experiment result shows that proposed model perform better than other classifiers. [ABSTRACT FROM AUTHOR]

The expeditious advancement and widespread implementation of intelligent urban infrastructure have yielded manifold advantages, albeit concurrently engendering novel security predicaments. Examining current patterns in the security of smart cities is paramount in comprehending nascent risks and formulating efficacious preventative measures. The present study suggests the utilization of Latent Semantic Analysis (LSA) as a means to scrutinize and reveal implicit semantic associations within a collection of textual materials pertaining to the security of smart cities. Through the process of gathering and pre-processing pertinent textual data, constructing a matrix that represents the frequency of terms within documents, and utilizing techniques that reduce the number of dimensions, Latent Semantic Analysis (LSA) has the ability to uncover concealed patterns and associations among concepts related to security. This study proposes five recommendations for future research that employ a topic modeling technique to investigate the often-explored subjects related to smart city security. This discovery provides additional evidence in favor of the proposition that a robust blockchain-driven framework is vital for the advancement of smart cities. Latent Semantic Analysis (LSA) offers important insights into the dynamic landscape of smart city security by employing several techniques such as pattern recognition, document or phrase clustering, and result visualization. Through the examination of patterns and developments, individuals in positions of political authority, urban planning, and security knowledge possess the ability to uphold their proficiency, render judicious choices substantiated by empirical data, and establish proactive strategies aimed at preserving the security, privacy, and sustainability of intelligent urban environments. [ABSTRACT FROM AUTHOR]

In the current digital epoch, the vast expanse of information has revolutionized the accessibility of knowledge and perspectives. Nevertheless, this information abundance has introduced challenges in navigating and comprehending the deluge of textual data. The surge in online news articles, research papers, reports, and diverse document genres has accentuated the necessity for proficient document summarization techniques. Traditional manual methods of summarization are time-intensive and influenced by subjective biases. In contrast, the synergy between Natural Language Processing (NLP) and machine learning has unlocked the potential for automated document summarization, promising efficient information consumption and informed decision-making. This research paper delves into the convergence of these factors. It is driven by the Longformer model's distinctive capability to manage extensive texts while retaining contextual coherence--a potential solution to the hurdle of large document summarization. By capitalizing on the Longformer's architecture, this study endeavors to exploit its prowess in generating cohesive summaries from lengthy source documents, thereby amplifying the accessibility of intricate information. [ABSTRACT FROM AUTHOR]

Background: Orthodontic treatment is a widely embraced intervention aimed at enhancing dental aesthetics and correcting malocclusions among adolescents. However, concerns persist regarding its potential impact on oral health, particularly on the development of dental caries. This study aimed to systematically investigate the relationship between orthodontic treatment and the incidence of new carious lesions among adolescents., Methods: A prospective cohort design involving adolescents aged 12-18 years was employed. A total of 82 patients met the inclusion criteria. In addition, an age-matched control group of 82 participants who did not undergo orthodontic treatment was included. The study included both a treatment group undergoing orthodontic treatment (braces or aligners) and an age-matched control group that did not undergo any orthodontic intervention. Demographic characteristics, orthodontic treatment details, and oral hygiene practices were documented at baseline and throughout the study period. Dental examinations at six-month intervals post-treatment were conducted to track the incidence and progression of carious lesions., Results: The demographic characteristics, baseline oral health status, orthodontic treatment details, and oral hygiene practices were comparable between the treatment and control groups. Post-orthodontic treatment assessment revealed a slightly higher incidence of new carious lesions in the treatment group (14.6%) than in the control group (9.8%), although this difference was not statistically significant (p = 0.15). Dental examinations at six-month intervals demonstrated a gradual increase in caries incidence over time in both groups, with no substantial disparities observed., Conclusions: This study provides a comprehensive examination of the relationship between orthodontic treatment and the incidence of new carious lesions among adolescents. While a trend towards higher caries incidence in the treatment group was observed, the difference was not statistically significant. These findings contribute to the existing body of knowledge and emphasize the need for ongoing research to guide clinical practice., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Chauhan et al.)