Exceptional Value Research Articles

Biosensors and their widespread impact on human health

‘Give a man a biosensor, and you enable him to unlock a world of cost-effective solutions for research, diagnosis, and personalized healthcare’.Biosensors have emerged as a game-changer in the realms of research sciences and healthcare, offering exceptional value for money. The integration of biosensors into these fields holds immense promise, empowering researchers and medical practitioners to unlock intricate mysteries in food and water safety, human biology, and health assessment. These state-of-the-art technologies are a breath of fresh air, revolutionizing disease detection and tracking to unprecedented levels, elevating the ability to monitor the body's response. They have become the linchpin of numerous cost-effective, highly efficient, and streamlined medical devices prevalent in modern healthcare. By harnessing the sensitivity and specificity of biosensors, healthcare professionals can hit the nail on the head, identifying even the subtlest biomarkers and indicators of various ailments, and enabling timely intervention and treatment. The superior quality of these biosensors ensures unrivaled diagnostic accuracy, leading to more reliable and effective healthcare outcomes. In a nutshell, biosensors have raised the bar, making research, public safety, and tailored healthcare options a walk in the park, ultimately enhancing overall health and well-being.Biosensors offer immense potential in medical diagnostics due to their user-friendly nature, scalability, and efficient manufacturing. With intelligent wearable features, they facilitate seamless health monitoring for the elderly, bridging the gap between self-care and healthcare providers. This exchange of medical information reduces interference and hospital visits, opening avenues in wellness, fitness, and athletics for consumers and commercial entities.This paper explores the advancements in Biosensors technology and their promising benefits in medicine, focusing on cardiovascular diseases and using informative diagrams. It examines fourteen key applications of Biosensors in the medical field, highlighting the integration of biomedical devices, apps, firmware, and advanced algorithms. These developments pave the way for innovative medical therapies, real-time evidence-based insights, customized solutions, and informed guidance, shaping a bright future for healthcare.

Landscape planning as a tool for preserving the landscape values of the Dinara Nature Park

Abstract Dinara Mountain massif is recognized as an area of exceptional value, which led to its recent protection as a nature park, the second largest one in Croatia. The protected area includes the territory of two counties with four cities and nine municipalities, five of which are included with over 50% of the territory. The paper explains how landscape planning methods establish potential spatial conflicts and propose a compromise between landscape conservation and spatial development. The basic method was the identification and analysis of the landscape value models in 3 categories: natural, visual-experiential, and cultural-historical values. The creation of the models is preceded by a detailed analysis of all environmental components and the collection and creation of spatial data with the help of GIS tools. By overlapping the basic models, a cumulative model of the overall landscape values of the space is produced, which clearly shows which parts of the landscape have a higher value, regarding the protection requirements. The final value model is overlapped with the planned interventions and a clear overview of the vulnerability and possible conflict zones in the area is obtained. The paper describes the impact of individual interventions on the landscape qualities that need to be preserved and suggests guidelines for spatial plan corrections for the purpose of minimal impact on existing values, thus helping to prevent unwanted changes.

High Performance Air‐Processed Organic Photovoltaic Modules (≈55 cm2) with an Efficiency of >17.50% by Employing Halogen‐Free Solvent Processed Polymer Donors

AbstractOrganic photovoltaic (OPV) sub‐modules shall be feasible for production using halogen‐free solvents in air,high power conversion efficiencies (PCEs), and long‐term stability, which are challenging requirements. To achieve this goal, air‐processed OPVs are fabricated by employing a synthesized set of π‐conjugated polymers, NAP‐T‐SiBTZ (P1) and NAP‐TT‐SiBTZ (P2), with thiophene and thienothiophene π‐spacers, respectively. P1 and P2 incorporated ternary OPVs show excellent PCEs and are used to produce small‐area, sub‐module air‐processed devices using o‐xylene as the solvent. Interestingly, P2‐added ternary devices has remarkable PCEs of 17.62% (PM6:P2:Y7) and 17.96% (PM6:P2:L8‐BO), which is the highest reported for air‐processed OPVs. . Notably, P2‐associated ternary blends exhibit a nano‐morphology, increased charge carrier mobilities, exciton dissociation, and decreased non‐geminate recombination, which are deemed responsible for the enhanced PCEs observed. In addition, P2 demonstrates high efficiency for a thick‐film device (>300 nm), with a PCE of >16.50%. Notably, a 55 cm2 sub‐module produced by bar coating using o‐xylene in open air has a PCE of 13.88%. Additionally, P2‐containing devices demonstrate impressive thermal and photo‐stabilities. This study shows the potential of an OPV that may be used to produce low‐cost solar cell sub‐module at low cost with exceptional commercial value.

Exceptional values of entire functions of finite order in one of the variables

Let F(z,w) be a holomorphic function in Cn×C of finite order in w with n≥2. Let Ω be the set of points z∈Cn where F(z,w) is a non-constant function omitting a value π(z). Near a finite accumulation point z0 of Ω, we prove in the main result (Theorem 1) that Ω is a local analytic set and π(z) is holomorphic, and show the existence of a proper globally analytic set Δ of Cn such that either Ω⊂Δ or Ω=Cn∖Δ, being possible in the last case to also determine F(z,w) in terms of π(z). We apply this result to several problems. First, we extend a Theorem due to Nishino about exceptional values when near z0 dimension of Ω is n and assure the existence of a meromorphic function α(z) in Cn such that π(z)=α(z) except at points where α(z) has poles or F(z,w) is constant (also being F(z,w) a polynomial in w if α(z) is ∞). After, we prove that Ω is a local analytic set in Cn and the existence of a proper analytic subset E of Cn such that Ω⊂E or Ω=Cn∖E. Finally, we generalize a Lelong-Gruman Theorem about the set of points z where π(z)=0.

ПРОБЛЕМА БИОЛОГИЧЕСКОГО И СОЦИАЛЬНОГО В ОТЕЧЕСТВЕННОЙ ФИЛОСОФИИ ВТОРОЙ ПОЛОВИНЫ XX — НАЧАЛА XXI ВЕКА: ИСТОРИКО-ФИЛОСОФСКИЙ АНАЛИТИЧЕСКИЙ ОБЗОР

The article shows a historical retrospective of ideas in the domestic philosophical thought of the second half of the 20th century — the beginning of the 21st century, aff ecting the problem of the interaction of biological and social aspects of human development, as well as epistemological and ontological reasons for their appearance. The fi rst historical and philosophical analysis of the problem of the relationship between the biological and the social in the works of Russian thinkers of the second half of the 20th century — the beginning of the 21st century is presented. As part of the analytical review, modern scientifi c publications were tested for the fi rst time in their historical relationship with previous scientifi c research. All this is the exceptional value and novelty of this scientific research. As a result of the analysis, it can be assumed that the presented problems were noted in the works of domestic researchers and, perhaps, their fi rst thoughts arose even earlier than those of foreign scientists; in domestic thought, the problem of interaction between biological and social was interpreted as “coevolution”, and in the works of foreign researchers it was designated as “the theory of gene-cultural evolution”. Consideration of the problem made it possible to reveal that the hypothesis of gene-cultural relationship was the initial stage in the formation and development of such a scientifi c and academic direction as sociobiology, and which is determined by its kind of inherent immanence. All this also determines the exceptional value and novelty of the presented scientific analysis. The study of the laws of heredity and the basic principles of evolution of both humanity and all animal and plant organisms of the ecosystem actualizes the importance of developing a methodology with which it becomes possible to know the natural mechanisms of interdependence and complementarity that are happening around us, to comprehend the technology of the emergence and development of life from the position of philosophy.

Protein Hydrolysate from Underutilized Legumes: Unleashing the Potential for Future Functional Foods.

Proteins play a vital role in human development, growth, and overall health. Traditionally, animal-derived proteins were considered the primary source of dietary protein. However, in recent years, there has been a remarkable shift in dietary consumption patterns, with a growing preference for plant-based protein sources. This shift has resulted in a significant increase in the production of plant proteins in the food sector. Consequently, there has been a surge in research exploring various plant sources, particularly wild, and underutilized legumes such as Canavalia, Psophocarpus, Cajanus, Lablab, Phaseolus, and Vigna, due to their exceptional nutraceutical value. This review presents the latest insights into innovative approaches used to extract proteins from underutilized legumes. Furthermore, it highlights the purification of protein hydrolysate using Fast Protein Liquid Chromatography. This review also covers the characterization of purified peptides, including their molecular weight, amino acid composition, and the creation of three-dimensional models based on amino acid sequences. The potential of underutilized legume protein hydrolysates as functional ingredients in the food industry is a key focus of this review. By incorporating these protein sources into food production, we can foster sustainable and healthy practices while minimizing environmental impact. The investigation of underutilized legumes offers exciting possibilities for future research and development in this area, further enhancing the utilization of plant-based protein sources.

Biodiversity, Ecology and Distribution of Mediterranean Charophytes in Southern Italy.

Charophytes are amongst the most endangered primary producers in freshwater and coastal ecosystems. In spite of the extensive research on the group and its ecological and conservational relevance, scarce information is available on Mediterranean environments, especially rivers and small water reservoirs, where charophytes face challenging summer droughts and changes in hydrological regimes, as well as pervasive anthropogenic pressures. This research aimed, through repeated field observations, detailed analyses of population traits, and extensive characterization of the colonized environments, to foster an understanding of the distribution, biodiversity, and ecology of charophytes in an area of exceptional environmental value and that is still uninvestigated in relation to its charophyte flora, the southern Campania region (Italy). Overall, 17 populations were discovered, belonging to 4 taxa of the Chara genus: C. globularis, C. gymnophylla, C. vulgaris, and C. vulgaris var. papillata, reduced to 12 populations and to the first 3 taxa by the end of the study. The species occupied different ecological niches and colonized environments such as rivers and small ponds, with environment-dependent morphotypes. The occurrence of few taxa with a wide distribution, often forming ephemeral populations, suggests ongoing constraints on charophyte biodiversity in the area, favoring opportunistic species that are able to benefit from temporary refugia.

Cleaning Radio Frequency Interference in Pulsar-Folded Data Based on the Conditional Random Fields with an Adaptive Prior

Radio astronomy observations are frequently impacted by radio frequency interference (RFI). We propose a novel method, named 2σCRF, for cleaning RFI in the folded data of pulsar observations, utilizing a Bayesian-based model called conditional random fields (CRFs). This algorithm minimizes the “energy” of every pixel given an initial label. The standard deviations (i.e., rms values) of the folded pulsar data are utilized as pixels for all subintegrations and channels. Non-RFI data without obvious interference is treated as “background noise,” while RFI-affected data have different classes due to their exceptional rms values. This initial labeling can be automated and is adaptive to the actual data. The CRF algorithm optimizes the label category for each pixel of the image with the prior initial labels. We demonstrate the efficacy of the proposed method on pulsar folded data obtained from Five-hundred-meter Aperture Spherical radio Telescope observations. It can effectively recognize and tag various categories of RFIs, including broadband or narrowband, constant or instantaneous, and even weak RFIs that are unrecognizable in some pixels but picked out based on their neighborhoods. The results are comparable to those obtained via manual labeling but without the need for human intervention, saving time and effort.

PSO-based image encryption scheme using modular integrated logistic exponential map

Image encryption (IE) has been essential for internet-based storing and transferring in recent years. Effective chaotic systems play a crucial role in IE schemes which widely depend on chaotic maps and keys. However, the existing chaotic maps suffer from low performance and narrow chaotic ranges, and they utilize casual key generation approaches rather than optimum keys. In this study, an IE scheme based on key optimization using particle swarm optimization (PSO) algorithm and a novel modular integrated logistic exponential (MILE) map is presented. The chaotic performance of the MILE map is validated across a comparison with the existing maps thorough measurements such as LE, SE, 0–1 Test, and PE with means values of 12.0000, 2.1866, 0.9981, and 0.9998, respectively. The key is optimized on a small part of the image to be encrypted instead of the whole image that is employed by the existing works. That is why the IE scheme is faster than those works. Afterward, the PSO-based IE scheme is undergone reliable crypto-analyses and attacks. It is also verified through a comparison with the existing IE schemes. The proposed IE scheme is the best owing to having exceptional mean values of key sensitivity 99.6117, variance 893.12, χ2 222.4057, information entropy 7.9994, NPCR 99.6090, and UACI 33.4662. Thanks to the MILE map, the PSO-based IE scheme demonstrates excellent numerical and visual encryption results.

Stand Up to Stand Out: Natural Dietary Polyphenols Curcumin, Resveratrol, and Gossypol as Potential Therapeutic Candidates against Severe Acute Respiratory Syndrome Coronavirus 2 Infection.

The COVID-19 pandemic has stimulated collaborative drug discovery efforts in academia and the industry with the aim of developing therapies and vaccines that target SARS-CoV-2. Several novel therapies have been approved and deployed in the last three years. However, their clinical application has revealed limitations due to the rapid emergence of viral variants. Therefore, the development of next-generation SARS-CoV-2 therapeutic agents with a high potency and safety profile remains a high priority for global health. Increasing awareness of the "back to nature" approach for improving human health has prompted renewed interest in natural products, especially dietary polyphenols, as an additional therapeutic strategy to treat SARS-CoV-2 patients, owing to its good safety profile, exceptional nutritional value, health-promoting benefits (including potential antiviral properties), affordability, and availability. Herein, we describe the biological properties and pleiotropic molecular mechanisms of dietary polyphenols curcumin, resveratrol, and gossypol as inhibitors against SARS-CoV-2 and its variants as observed in in vitro and in vivo studies. Based on the advantages and disadvantages of dietary polyphenols and to obtain maximal benefits, several strategies such as nanotechnology (e.g., curcumin-incorporated nanofibrous membranes with antibacterial-antiviral ability), lead optimization (e.g., a methylated analog of curcumin), combination therapies (e.g., a specific combination of plant extracts and micronutrients), and broad-spectrum activities (e.g., gossypol broadly inhibits coronaviruses) have also been emphasized as positive factors in the facilitation of anti-SARS-CoV-2 drug development to support effective long-term pandemic management and control.

Wadi Al-Hitan or ‘Valley of Whales’ – an Eocene World Heritage Site in the Western Desert of Egypt

Abstract Wadi Al-Hitan is a UNESCO World Heritage Site in the Western Desert of Egypt famous for the fossils it has produced, including some of the most complete skulls and skeletons of Eocene whales known anywhere in the world. Most are from the early part of the late Eocene epoch of Earth history and lived in the Tethys Sea some 38 to 36 million years before present. Basilosaurus isis and Dorudon atrox are the most abundant and best known of the archaic whales. Sea cow skeletons are present as well, as are bones or teeth of a bird, crocodiles, turtles, bony fishes, and a diversity of sharks and rays. The fossils are preserved in sedimentary strata that accumulated as sea level rose and fell, producing a succession of nearshore marine environments. Rocks exposed at the surface, now dissected by erosion, enable study of stratigraphic sequences and their architecture. Taken together, the fossil-and-strata geoheritage of Wadi Al-Hitan has exceptional value for science and for public education.

Breaking Barriers in Kidney Disease Detection: Leveraging Intelligent Deep Learning and Artificial Gorilla Troops Optimizer for Accurate Prediction

In this groundbreaking study, we propose an innovative approach to tackle the formidable task of early detectionand accurate prediction of kidney diseases. By harnessing the potential of a comprehensive healthcare dataset andleveraging a machine learning model originally developed for kidney disease diagnosis, our methodology integratesintelligent feature selection techniques. These techniques, including heuristic based feature selection andevolutionary gravitational search-based feature selection (EGS-FS), allow us to identify the most informative featuresfor accurate prediction. Classification is performed using our newly designed Intelligent Deep Learning basedClassifier, which is further optimized using the cutting-edge Artificial Gorilla Troops Optimizer algorithm. Toassess the performance of our proposed model, we conduct a thorough evaluation and comparison against existingmethods using a range of statistical measures. Remarkably, our experimental results on the widely recognizedChronic Kidney Disease dataset showcase an exceptional accuracy value of 99%. This research not only contributesto the advancement of kidney disease prediction but also provides invaluable insights for efficient patientmanagement. By embracing this novel approach, clinicians can make informed decisions and revolutionize the fieldof kidney disease detection and treatment.

Endoscopic diagnosis and treatment of superficial non-ampullary duodenal epithelial tumors: A review.

The surface of the small bowel mucosa is covered more than any other section of the digestive canal; however, the overall prevalence of small bowel tumors of the whole gastrointestinal tract is evidently low. Owing to the improvement in endoscopic techniques, the prevalence of small bowel tumors has increased across multiple countries, which is mainly due to an increase in duodenal tumors. Superficial non-ampullary duodenal epithelial tumors (SNADETs) are defined as tumors originating from the non-ampullary region in the duodenum that share similarities and discrepancies with their gastric and colorectal counterparts in the pathogenesis and clinicopathologic characteristics. To date, white light endoscopy (WLE) remains the cornerstone of endoscopic diagnosis for SNADETs. Besides, narrow-band imaging (NBI) techniques and magnifying endoscopy (ME) have been widely used in the clinic and endorsed by multiple guidelines and consensuses for SNADETs' evaluation. Confocal laser endomicroscopy (CLE), endocytoscopy (ECS), and artificial intelligence (AI) are also up-and-coming methods, showing an exceptional value in the diagnosis of SNADETs. Similar to the endoscopic treatment for colorectal polyps, the choices for SNADETs mainly include cold snare polypectomy (CSP), endoscopic mucosal resection (EMR), endoscopic submucosal dissection (ESD), and laparoscopic endoscopic cooperative surgery (LECS). However, owing to the narrow lumen, rich vascularity, weak muscle layer, abundant Brunner's gland, and the hardship of endoscope control, the duodenum ranks as one of the most dangerous operating areas in the digestive tract. Therefore, endoscopists must anticipate the difficulties in endoscopic maneuverability, remain aware of the increased risk of complications, and then select the appropriate treatment according to the advantages and disadvantages of each method.

Mechanical and electronic properties of new stable transition metal carbonitrides

Using first-principles calculations, we conducted a comprehensive investigation into the crystal structures, mechanical properties, and electronic structures of Ta4CN3 and Ta2CN compounds through the substitution of Ta atoms with transition metals (Cr, Hf, Mo, Nb, Ti, V, W, and Zr). Sixteen stoichiometric TM4CN3 and TM2CN (TM = Cr, Hf, Mo, Nb, Ti, V, W, and Zr) systems were examined using elastic constants, thermal stability and phonon dispersion calculations. As a result, we identified eight stable structures. Subsequently, we systematically examined the mechanical properties of these eight stable phases. The calculated mechanical properties revealed exceptional hardness values for both Cr2CN and Mo2CN compounds, reaching 21.5 GPa and 21.4 GPa, respectively. The calculation of the electronic properties unequivocally established the metallic nature of these structures. These findings open up new avenues for the development of innovative materials with enhanced properties and performance.

Research on indoor positioning method based on LoRa-improved fingerprint localization algorithm

Traditional fingerprint localization algorithms need help with low localization accuracy, large data volumes, and device dependence. This paper proposes a LoRa-based improved fingerprint localization algorithm-particle swarm optimization-random forest-fingerprint localization for indoor localization. The first improvement step involves creating a new exceptional fingerprint value (referred to as RSSI-RANGE) by adding the Time of Flight ranging value (referred to as RANGE) to the Received Signal Strength Indication (RSSI) value and weighting them together. The second improvement step involves preprocessing the fingerprint data to eliminate gross errors using Gaussian and median filtering. After noise reduction, the particle swarm optimization algorithm is used to optimize the hyper parameters of the random forest algorithm, and the best RSSI-RANGE value is obtained using the random forest algorithm. The Kriging method is then used for interpolation to establish an offline fingerprint database, and the final online recognition and localization are performed. Experimental results demonstrate that the first improvement step improves localization accuracy by 53–57% in different experimental scenarios, while the second improves localization accuracy by 25–31%. When both steps are combined, the localization accuracy is improved by 58–63%. The effectiveness of this method is demonstrated through experiments.

Exploring the Biodiversity and Conservation Value of Alpine Grasslands in the Bucegi Massif, Romanian Carpathians

The Carpathian region harbors a wide range of threatened species, making it an area of exceptional conservation value. In the Alpine belt, grasslands cover the entire region and this study aims to describe the communities in the Bucegi Massif of the Romanian Carpathians and highlight their importance for conservation. The Braun–Blanquet approach was used to record floristic data from 47 phytosociological surveys, identifying a total of 235 plant species from 40 different families, including 30 threatened species. Canonical correspondence analysis was used to analyze the data, revealing that the distribution of vegetation is mainly influenced by elevation, slope and vegetation cover. Dominant grass species in these communities include Nardus stricta, Festuca violacea, Kobresia myosuroides, Festuca amethystina, Festuca airoides, Sesleria rigida, Festuca versicolor and Festuca carpatica. The alpine and boreal siliceous grasslands of the Carpathian Mountains, identified by Natura 2000 codes 6150, 6130 and 6170, host a wide range of plant species of significant conservation value. The higher altitude grasslands, especially, have outstanding plant species richness. We argue that although the habitats have been grazed, significant parts of the area are still in good ecological condition, having many typical natural features.

One-Pot Sol-Gel Synthesis of Highly Insulative Hybrid P(AAm-CO-AAc)-Silica Aerogels with Improved Mechanical and Thermal Properties.

Silica aerogels and their derivatives have outstanding thermal properties with exceptional values in the thermal insulation industry. However, their brittle nature restricts their large-scale commercialization. Thus, enhancing their mechanical strength without affecting their thermal insulating properties is essential. Therefore, for the first time, highly thermally stable poly(acrylamide-co-acrylic acid) partial sodium salt is used as a reinforcing polymer to synthesize hybrid P(AAm-CO-AAc)-silica aerogels via epoxy ring-opening polymerization in the present study. Functional groups in P(AAm-CO-AAc) partial sodium salts, such as CONH2 and COOH, acted as nucleophiles for the epoxy ring-opening reaction with (3-glycidyloxypropyl)trimethoxysilane, which resulted in a seven-fold enhancement in mechanical strength compared to that of pristine silica aerogel while maintaining thermal conductivity at less than 30.6 mW/mK and porosity of more than 93.68%. Moreover, the hybrid P(AAm-CO-AAc)-silica aerogel demonstrated improved thermal stability up to 343 °C, owing to the synergetic effect between the P(AAm-CO-AAc) and the silica aerogel, corresponding to the thermal stability and strong covalent bonding among them. These excellent results illustrate that this new synthetic approach for producing hybrid P(AAm-CO-AAc)-silica aerogels is useful for enhancing the mechanical strength of pristine silica aerogel without impairing its thermal insulating property and shows potential as an industrial heat insulation material.

EVCA Classifier: A MCMC-Based Classifier for Analyzing High-Dimensional Big Data

In this work, we introduce an innovative Markov Chain Monte Carlo (MCMC) classifier, a synergistic combination of Bayesian machine learning and Apache Spark, highlighting the novel use of this methodology in the spectrum of big data management and environmental analysis. By employing a large dataset of air pollutant concentrations in Madrid from 2001 to 2018, we developed a Bayesian Logistic Regression model, capable of accurately classifying the Air Quality Index (AQI) as safe or hazardous. This mathematical formulation adeptly synthesizes prior beliefs and observed data into robust posterior distributions, enabling superior management of overfitting, enhancing the predictive accuracy, and demonstrating a scalable approach for large-scale data processing. Notably, the proposed model achieved a maximum accuracy of 87.91% and an exceptional recall value of 99.58% at a decision threshold of 0.505, reflecting its proficiency in accurately identifying true negatives and mitigating misclassification, even though it slightly underperformed in comparison to the traditional Frequentist Logistic Regression in terms of accuracy and the AUC score. Ultimately, this research underscores the efficacy of Bayesian machine learning for big data management and environmental analysis, while signifying the pivotal role of the first-ever MCMC Classifier and Apache Spark in dealing with the challenges posed by large datasets and high-dimensional data with broader implications not only in sectors such as statistics, mathematics, physics but also in practical, real-world applications.

Uniqueness theorems of the Hahn difference operator of entire function with a Picard exceptional value

Abstract Let f f be a transcendental entire function of finite order with a Picard exceptional value β ∈ C \beta \in {\mathbb{C}} , q ∈ C \ { 0 , 1 } q\in {\mathbb{C}}\setminus \left\{0,1\right\} and c c are complex constants. The authors prove that D q , c f ( z ) − a f ( z ) − a = a a − β , \frac{{{\mathfrak{D}}}_{q,c}f\left(z)-a}{f\left(z)-a}=\frac{a}{a-\beta }, if D q , c f ( z ) {{\mathfrak{D}}}_{q,c}f\left(z) and f ( z ) f\left(z) share value a ( ≠ β ) a\left(\ne \beta ) CM, where D q , c f ( z ) = f ( q z + c ) − f ( z ) ( q − 1 ) z + c {{\mathfrak{D}}}_{q,c}f\left(z)=\frac{f\left(qz+c)-f\left(z)}{\left(q-1)z+c} is the Hahn difference operator. This result generalizes the related results of Zongxuan Chen [On the difference counterpart of Brück’s conjecture, Acta Math. Sci. 34B (2014), 653–659].

PSYCHOGENIC FACTORS AS A CAUSE OF DEPRESSIVE DISORDERS IN THE ELDERLY

Individuals with particular qualities or characteristics are predisposed to develop cognitive impairment non dementia. The mainaim to study socio-demographic factors and personality traits, that contribute tothe development of depressive disorders in elderly patients with cognitive deficits. The purpose of the article was to show that it is important to investigate the socio-psychological factors that can worsen the course of cognitive impairment, which already comes with age.During the research an observational study involving 111 patients from Kazakhstan with cognitive deficits was conducted: 59 patients, whose depressive disorders had been identified by clinical and psychopathological research using the Hamilton Depression Rating Scale (HRSD) and 52 patients for whom psychogenic depressive disorders had not been defined. The recruitment of the study participants was carried out in the state nursing home for the elderly and disabled. The study included male and female individuals aged 60-74 years with different national and ethnic backgrounds, with mild to moderate cognitive impairment, who wished to participate in the study and signed an informed consent to participate in the study.This study uncovered the primary factors that increase the chances of individuals with cognitive impairments developing a depressive disorder. These factors include personal attributes of the patients, such as their sex, whether they are married or not, and the quality of their relationship with their family members. In addition, personality traits from before their cognitive decline, such as a tendency for isolation, insecurity, and passivity, were also significant.This study could have an exceptional value for medicine and psychological workers on the whole and in Republic of Kazakhstan in particular to prevent depressive diseases were mentioned.